Functional protein design and enhancement with ontology reinforcement iteration

Domain: biology
Published: 2026-03-19
Editorial outcome: major_revision

Abstract
The discrepancy between computational modeling and experimental performance remains a major challenge in protein engineering. We present ORI (Ontology Reinforcement Iteration), a scalable framework integrating ontology-conditioned decoding with reinforcement learning from experimental feedback (RLWF). ORI leverages structured ontologies as semantic prompts to impose multi-level constraints, enabling controllable and interpretable protein generation. A closed-loop iterative workflow—comprising generation, experimental measurement, and model updating—enables continuous optimization under real-world objectives. We demonstrate ORI’s practical applicability through diverse tasks, including enzymatic activity optimization, thermal stability enhancement, and multifunctional protein engineering. Using this framework, we engineer variants with substantial improvements over natural baselines, such as a lysozyme with 100-fold higher activity, a chitinase stable at 85 °C, and dual-function enzymes exhibiting both lysozyme and chitinase activities. These results establish ORI as a robust technical platform for efficient, multi-objective protein engineering in real-world experimental settings.

Introduction
Proteins are central to nearly all biological processes, including catalysis, signaling, transport, and structural organization1. The ability to engineer proteins with targeted functional, structural, and physicochemical properties is therefore a foundational challenge in molecular biology, biotechnology, and bioengineering2. Progress in this area has broad implications, ranging from basic biological research to applications in therapeutics, diagnostics, and industrial biocatalysis. However, achieving reliable functional protein engineering remains difficult due to the complex and nonlinear relationships between protein sequence, structure, and function.

Recent advances in artificial intelligence have substantially expanded the scope of computational protein engineering. Structure prediction models have enabled accurate inference of protein folds from sequence alone3–5, while generative models have demonstrated the ability to produce protein sequences conditioned on structural or functional constraints6. Contemporary approaches now span diverse technical paradigms. Diffusion-based models such as DPLM7and EvoDiff8 operate directly in sequence space to capture evolutionary constraints, whereas DiMA9 and MeMDLM10 leverage latent or discrete diffusion to improve controllability, enabling applications including membrane protein design. Score-based models such as ProteinSGM11 provide an alternative generative formulation, while large-scale protein language models—including ProGen312 and ESM-C13—demonstrate the benefits of scale and the integration of structural information. Additional frameworks, such as FusOn-pLM14, PepTune15, and AMP-Diffusion16, further illustrate the growing applicability of generative models to peptide and protein engineering tasks. Parallel developments in discrete flow matching methods—such as Dirichlet, Fisher, and Gumbel Softmax flow matching17—have enabled guided and conditional generation in discrete sequence spaces. Collectively, these approaches exemplify the prevailing paradigm of guided discrete sampling in generative biology.

Related progress has also been achieved in RNA generative modeling. Systems such as Rinalmo18 and EvoFlow-RNA19 achieve state-of-the-art performance in RNA sequence design and structure prediction, underscoring the generality of generative frameworks across biomolecular modalities. Together, these advances reflect a broader trend: generative models are increasingly expected not only to capture sequence–structure relationships, but also to support functional optimization under experimentally relevant constraints.

Despite these advances, substantial challenges remain in translating computational designs into experimentally functional proteins. Many existing methods perform well when strong structural priors or templates are available, such as in regular folds or well-characterized binding motifs. However, they often struggle in settings where functional differences arise from subtle sequence variations among similar structures, or where experimental objectives—such as enzymatic activity, stability, or expression—are sparse, noisy, or non-differentiable. As a result, protein engineering workflows frequently rely on resource-intensive trial-and-error experimentation, leading to low success rates and limited scalability.

To incorporate functional optimization signals more directly, several strategies have been explored. Guided sampling approaches, including diffusion and flow matching, offer stability and sample efficiency when the objective is differentiable or can be approximated. However, these methods are limited when the target objective is sparse, noisy, or non-differentiable—such as enzyme activity, expression levels, thermostability, or other experimental phenotypes. Reinforcement learning (RL) combined with experimental assays has emerged as an alternative: pioneering studies from Baker’s group and others have shown the feasibility of iterative design guided by experimental feedback, and follow-up frameworks (e.g., EVOLVEpro) have adopted similar principles in active learning or small-sample settings20,21. While RL is attractive for directly optimizing non-differentiable objectives, existing approaches often face issues of instability and low sample efficiency in the high-dimensional sequence space, and most prior implementations remain constrained to specific scaffolds or narrowly defined targets.

Building on these insights, we introduce ORI (Ontology Reinforcement Iteration), a general-purpose and scalable computational framework for functional protein engineering. ORI unifies ontology-conditioned decoding with reinforcement learning from experimental feedback (RLWF) in a closed-loop system. Structured ontologies are used as semantic prompts to encode functional, structural, and property-level constraints, enabling controllable and interpretable protein generation. Reinforcement learning allows direct optimization of sparse or non-differentiable experimental objectives, while an iterative workflow—comprising sequence generation, experimental measurement, reward assignment, ontology refinement, and model updating—enables the co-evolution of both the generative model and its functional representations. By integrating guided discrete sampling with closed-loop experimental optimization, ORI addresses key limitations of prior methods and supports robust, multi-objective protein engineering.

In this study, we evaluate ORI as a technical platform through multiple protein engineering tasks, including the optimization of enzymatic activity, thermal stability, and multifunctionality. Using this framework, we generated and experimentally validated hundreds of protein variants across diverse enzyme families. These results demonstrate that ORI enables efficient exploration and optimization of protein functional landscapes under real-world experimental constraints, highlighting its potential as a general framework for functional protein engineering.

Results
The Framework of ORI

ORI is a closed-loop computational framework for functional protein engineering, consisting of three tightly integrated components (Figure 1). First, ORI includes a protein generation agent (PGA), a self-evolving language model that controls sequence generation under user-specified functional and property constraints. Second, a function-aware protein generation model (PGM) produces candidate protein sequences by following structured ontology guidance provided by the agent. Third, a unified sequence model (USM) evaluates generated sequences with respect to predicted quality, structural consistency, and functional properties prior to experimental testing. ORI further incorporates reinforcement learning from experimental feedback, enabling iterative refinement of model parameters based on wet-lab assay results. Through this closed-loop optimization process, the framework continuously improves its ability to navigate protein functional landscapes and to support efficient, high-quality protein engineering across diverse experimental objectives.

Inspired by the Gorilla22 and Toolformer23, we developed the self-evolving PDA using a large language model (LLM) with 2 billion (2B) parameters, enabling it to effectively utilize both internal and external tools for generating high-quality proteins with desired biological function (Methods). Firstly, PDA takes the user's natural language description of protein function and transforms it into structured ontology prompts to generate proteins with PGM. These ontology prompts are tag terms that reflect species, structures, functions, and other essential features guiding the protein creation using the PGM. Following this, USM models predict the characteristics of the generated proteins, including thermostability, solubility, functionality, etc. Then, ORI filters out high-quality proteins that may match the user’s requirements. Users can select proteins from high-quality ones for further wet lab validation. In ORI, the process of filtering proteins based on predicted characteristics is called the quality control pipeline. After the wet lab assays, ORI incorporates feedback from these experiments to update itself using reinforcement learning, leading to improved protein design.

Beyond PDA, another essential component of ORI is the function-aware PGM with 3 billion (3B) parameters. PGM is an autoregressive large language model, which adopts a decoder-only architecture similar to that of LLaMA serial models 24. The primary obstacle in developing PGM is enabling the model to learn the associations between protein sequences, structures, and biological functions. This is a complex task, as over three billion years of evolution have produced a rich resource of functional proteins with highly diverse and complex protein sequences and structures. To tackle this issue, we built a unified protein ontology system that integrates protein structures, biological functions, and other characteristics using information from UniProt25, SCOPe26, and published literature27,28, etc. We used this protein ontology system to create a well-annotated representative protein dataset. Over 166 million representative protein sequences, identified following the UniRef90 rule29, are annotated with ontology terms encompassing species, structures, functions, thermostability, solubility, etc. We used this well-annotated dataset to train the protein generation model. Thereafter, we constructed a large protein generation model with 3B parameters and extensive model training. This thorough training of the model aimed to decipher the generalized rules that connect protein sequences, structures, and biological functions. With this, the PGM is now prepared to generate proteins with the guidance of PDA.

ORI employs the USM model to predict the characteristics of proteins generated by the PGM, evaluating whether the resulting protein satisfies the user's requirements (Figure 2A). We designed USM, a unified biological language model, to address the limitations of current models in handling both single-sequence (SS) and multiple-sequence alignment (MSA) data, and to provide a unified representation of various biomolecular forms, including DNA, RNA, and protein. USM model is featured by specially designed Transformer blocks, which include Column Attention, Fast Row Attention, and SwiGLU Activation (Supplementary Figure 1). This design allows the model to efficiently extract sparse features from multiple sequence alignment (MSA) data, leading to improved computational efficiency and parameter scalability. Furthermore, the USM model employs a dynamic masking and mixed-sequence pre-training method, which enhances the model's ability to learn from MSA data and improves its performance on various downstream tasks. Compared to previous models, USM is superior in computational efficiency (Supplementary Table 1) and accuracy (Supplementary Table 2). USM with 100 million parameters (USM 100M) enables 2,939 Giga Floating-point Operations Per Second (GFLOPS) in model training, over 100 times that of ESM230 with the same parameters (Supplementary Table 1). Moreover, USM 100M achieved comparable or better performance than ESM2 with 15 billion parameters (ESM 15B) in predicting protein characteristics using benchmark datasets, such as fluorescence31, stability32, antibiotic resistance33 and metal ion binding34 (Supplementary Table 2). In this study, the USM model is pre-trained using UniRef90 protein sequences in the unified MSA form. Thereafter, we fine-tuned the USM model to predict protein characteristics, such as thermostability, solubility, signal peptide types, etc. Using USM-predicted protein features, users can select candidate proteins for further investigation.

Wet lab experiments are vital for advancing protein generative models as they provide valuable biological insights into the quality of the generated proteins. Researchers analyze these experimental outcomes to develop heuristic methods that refine or optimize the models, aiming to enhance their performance. However, this approach heavily relies on the subjective expertise of individual researchers, leading to potential variability and limited reproducibility. Taking inspiration from the success of Reinforcement Learning from Human Feedback (RLHF), such as iterative Direct Preference Optimization (DPO), in improving language models35,36, we propose an automated framework called Reinforcement Learning from Wet-lab Feedback (RLWF), for iterative optimization of PDA and PGM models in ORI. RLWF leverages feedback from wet lab experiments, such as successful and failed expression of protein designs, to conduct iterative optimization of these models. To address the common issue of length bias in RLHF, where the optimized models tend to generate excessively long sequences, we propose the R-DPO loss function in RLWF. This loss function incorporates a length regularizer, ensuring that the optimized model generates protein sequences of appropriate lengths. RLWF systematically increases the likelihood of producing high-quality protein designs that possess the desired functionality and characteristics, streamlining the model improvement process and reducing reliance on subjective judgment.

Engineering Proteins with Enzyme-like Activity

To evaluate the capability of ORI in functional protein engineering, we apply the framework to proteins with enzyme-like activity. Enzymes represent a broad and well-characterized class of functional proteins, making them a suitable testbed for assessing the effectiveness and robustness of the ORI pipeline. Using ontology prompts provided by the protein design agent (PDA), the protein generation model (PGM) generates 10,000 candidate protein sequences. These candidates are subsequently evaluated using ORI’s quality control pipeline, which integrates functional domain identification, sequence and structural similarity analysis relative to known enzymes, and property prediction using the unified sequence model (USM) (Figure 2A).

Here, sequence identity and structural identity quantify similarity between generated proteins and their closest natural counterparts performing the same function. The generated proteins exhibit high overall similarity to known enzymes, with a median sequence identity of 0.96 (Figure 2B), reflecting the effective training and calibration of the PGM. To focus the analysis on engineered variants rather than exact reproductions, sequences identical to known proteins are excluded from downstream evaluation.

Notably, analysis of the remaining candidates reveals a weak correlation between sequence identity and structural identity (Pearson’s r = 0.072; Figure 2C). Proteins with relatively low sequence identity (approximately 0.5) can nonetheless maintain high structural similarity to known enzymes (TM-score > 0.8; Figure 2D). Conversely, among proteins with high sequence similarity (>0.8), two distinct structural regimes emerge (Figure 2E): one corresponding to highly conserved structures (TM-score ≈ 0.9), and another exhibiting substantially lower structural similarity (TM-score ≈ 0.4). These observations indicate that neither sequence similarity nor structural similarity alone is sufficient to assess the quality of engineered proteins. Accordingly, ORI’s quality control pipeline jointly incorporates both sequence- and structure-based metrics to identify reliable candidates for experimental validation.

In addition to structural evaluation, ORI employs USM models to perform in silico functional assessment. Functional domains serve as key indicators of enzymatic activity. In the raw generation output, 45.89% of candidates contain the desired functional domain. After filtering through the USM-based quality control pipeline, this proportion increases to 98.91% (Figure 2F), demonstrating the effectiveness of the USM models in functional feature prediction.

We further benchmark ORI and its core generative module, PGM, against recent state-of-the-art protein generation methods, including ProteinSGM11, ProGen312, and EVO237, representing structure-based, sequence-based, and DNA-based generative paradigms, respectively. For each method, 1,000 proteins are generated and evaluated using structural confidence and functional relevance metrics. ORI achieves a substantially higher proportion of proteins with strong structural confidence, as measured by pLDDT thresholds of 70 and 90 (Supplementary Figure 2), as well as a higher fraction of proteins containing the target functional domains (Supplementary Figure 3). Comparison between ORI and PGM further highlights the benefit of integrating PDA-guided generation with USM-based evaluation, underscoring the advantage of ORI’s unified framework for functional protein engineering.

Successful protein expression remains a critical step in translating computational designs into experimentally testable candidates38. From the proteins passing quality control, we randomly select 100 candidates with predicted lysozyme activity for experimental validation (Methods). Lysozyme (EC 3.2.1.17) catalyzes the hydrolysis of β-(1–4) glycosidic linkages in bacterial cell walls. The selected proteins are expressed in eukaryotic HEK293 cells, prokaryotic E. coli, and a cell-free expression system. Overall expression success rates are comparable to those reported for natural lysozymes in prior studies6,38. Expression levels vary across systems, with 16.19% of proteins exceeding 0.1 mg/ml in HEK293 cells (Figure 2G), compared to 40% and 50% in E. coli and the cell-free system, respectively. In addition to higher expression yields, the cell-free system provides a host-independent and highly reproducible environment that facilitates rapid, parallel evaluation of engineered proteins. Proteins exhibiting high expression in cellular systems consistently achieve high expression in the cell-free system, making it well suited for standardized assessment within ORI’s closed-loop experimental workflow. Accordingly, the cell-free system is used as the default expression platform in subsequent experiments.

Finally, we assess lysozyme activity for 10 randomly selected proteins expressed using the cell-free system (Methods). These proteins display sequence identities ranging from 0.53 to 0.82 relative to their closest natural homologs (Supplementary Table 3). Six proteins exhibit detectable lysozyme activity at pH values above 6, and two variants, TX-L2 and TX-L6, show higher activity than natural hen lysozyme (Figure 2H). At pH 7.5, TX-L6 reaches a median activity of 1,598 U/mg, approximately 2.7-fold higher than the natural enzyme under the same conditions. The protein sequence of TX-L6 is shown in Supplementary Table 4.

Closed-Loop Protein Engineering with Experimental Feedback

As the ORI training data do not include protein expression levels or enzymatic activity measurements, proteins generated by the initial ORI model exhibit expression and activity profiles comparable to those of natural proteins in wet-lab assays. This behavior is consistent with expectations for generative models trained primarily on sequence and structural information. We therefore investigated whether incorporating experimental feedback could systematically improve design outcomes within the ORI framework.

To this end, we collected protein expression and enzyme activity data from the initial wet-lab assays and used reinforcement learning from wet-lab feedback (RLWF) to update the ORI system, with optimization primarily focused on the protein generation module (PGM) (Figure 3A, Methods). Using the optimized model, we generated an additional set of 10,000 proteins under the same functional specifications as before.

In silico evaluation revealed a clear shift toward improved structural confidence in proteins generated by the optimized model. Specifically, the median pLDDT score increased from approximately 75 in the original model to around 85 after RLWF optimization (Figure 3B). As pLDDT is commonly used as a proxy for structural stability and expression robustness39, this shift suggests that experimental feedback enabled the model to preferentially generate sequences with improved structural integrity, which is expected to facilitate downstream expression and handling.

To assess whether these in silico improvements translated into experimental gains, we conducted wet-lab expression assays on 80 randomly selected lysozyme-like proteins, including 20 designs from the original model and 60 from the optimized model. Proteins were grouped by functional domain family—glucosaminidase, transglycosylase, and phage lysozyme—to control for family-dependent expression differences. SDS–PAGE analysis of proteins expressed in the cell-free system revealed consistently higher expression levels for designs generated by the optimized model across all three families (Figure 3C–E). Median expression levels increased from 0.061 to 0.168 mg/ml for glucosaminidase, from 0.099 to 0.170 mg/ml for transglycosylase, and from 0.109 to 0.207 mg/ml for phage lysozyme (Figure 3F). These results indicate that RLWF effectively improved expression-related properties across diverse enzyme families.

We next examined whether RLWF also enhanced enzymatic function. Twenty proteins with robust expression in the cell-free system were randomly selected for lysozyme activity assays, and their activities were compared against TX-L6, a high-performing design from the original model. Thirteen of the twenty proteins exhibited higher lysozyme activity than TX-L6, with the top-performing design, TX-RL15, showing a 66-fold increase relative to TX-L6 (Figure 3G). Given that TX-L6 itself exhibits higher activity than hen egg-white lysozyme, TX-RL15 corresponds to an overall improvement exceeding two orders of magnitude relative to the natural baseline.

To place these results in context, we further benchmarked ORI-generated lysozymes against T4 lysozyme (T4L), a well-characterized enzyme with high catalytic efficiency40. While T4L consistently outperformed hen lysozyme across pH conditions (Supplementary Figure 4), a substantial fraction of ORI-generated lysozymes exceeded T4L activity under the same assay conditions (Supplementary Figure 5). Together, these results demonstrate that experimental feedback can be effectively integrated into ORI to iteratively bias the generative model toward sequences with improved expression and enzymatic performance.

Engineering Thermostable Enzyme-like Proteins

Thermostability is a critical determinant of enzyme performance in industrial and biomaterial applications, where elevated temperatures can enhance reaction rates but also accelerate protein denaturation. Chitinase (EC 3.2.1.14), an enzyme widely used in chitin degradation, typically loses activity at temperatures near or below 60 °C, which limits its practical deployment41.

We investigated whether ORI could be used to design enzymes with tunable thermostability while retaining catalytic function. Ontology terms related to thermostability are included in the ORI training corpus (Figure 4A), allowing the model to associate sequence features with thermal resilience. Using PDA, we generated ontology-conditioned prompts and produced thousands of chitinase-like proteins. Thermostability was assessed using a USM model trained to predict melting temperature, which achieves a Pearson correlation of 0.818 between predicted and experimental values on an independent test set (Figure 4B).

When no thermostability constraint was applied, the predicted melting temperature distribution of ORI-generated proteins closely matched that of natural chitinases, with a median of 48.3 °C (Figure 4C). In contrast, conditioning generation on increasing target melting temperatures resulted in systematic upward shifts in predicted thermostability (Figure 4D), demonstrating controllable modulation of this property within the ORI framework.

We benchmarked ORI against ProteinSGM11, ProGen312, and EVO237 in generating thermostable proteins, and against ProteinMPNN39 and Rosetta42 in thermostability optimization. ORI consistently produced proteins with higher predicted melting temperatures while requiring substantially lower computational cost (Supplementary Figures 6 and 7), highlighting an advantageous trade-off between performance and efficiency.

For experimental validation, we selected 20 chitinase-like proteins with predicted melting temperatures above 60 °C. Eighteen were successfully expressed in the cell-free system, and nine exhibited measurable chitinase activity. All nine retained full activity after 3 minutes at 60 °C, and five retained over 90% activity after one hour. Further assays showed that three proteins maintained high activity at 70 °C, and one design, TX-SC2, retained full activity after prolonged incubation at 85 °C, with partial activity even at 95 °C (Figure 4E,F).

Comparisons with selected naturally thermostable chitinases, including OfChth, SmChiB, and SmChiC from Ostrinia furnacalis and Serratia marcescens43–45, show that, under the same experimental conditions, ORI-designed chitinases retain substantial enzymatic activity at 85 °C, whereas the tested natural enzymes exhibit little or no detectable activity (Supplementary Figure 8). These results demonstrate that ORI can extend the operational temperature range of functional chitinases while preserving catalytic activity.

Engineering Proteins with Multi-Enzyme Activity

We next evaluated whether ORI could be applied to the design of multifunctional enzymes. Lysozyme and chitinase share related catalytic mechanisms and structural motifs but typically exhibit limited cross-reactivity. Natural dual-function enzymes, such as Hevamine A, generally display reduced activity relative to specialized monofunctional enzymes, making simultaneous optimization of multiple activities a challenging design problem46.

During initial PGM training, both monofunctional and multifunctional enzymes were included (Figure 5A). RLWF was subsequently applied using experimental activity data from lysozyme and chitinase assays, enabling the model to associate sequence features with quantitative functional outcomes. Using PDA-conditioned prompts, we generated 10,000 candidate proteins with potential dual activity. Designs supported by three independent USM models with high confidence scores were retained, yielding 426 candidates for further analysis.

From these, 100 proteins were randomly selected for experimental validation. Sixty-five were successfully expressed in the cell-free system, of which 25 exhibited dual-enzyme activity (Figure 5B). Sequence analysis revealed that while overall sequence identity to natural enzymes was moderate, conservation was higher within catalytic regions, suggesting preservation of essential functional motifs alongside broader sequence diversification (Figure 5C).

Dual-activity proteins were further compared to Hevamine A. Functional-region sequence identities were substantially lower than those of monofunctional designs, indicating that ORI did not simply recapitulate known dual-function architectures but instead explored alternative combinations of catalytic features (Figure 5D).

Proteins were ranked using a weighted score integrating both activities. The top-ranked design, TX-ME, adopts a TIM-barrel fold common to both enzyme families (Figure 5E). In comparative assays, TX-ME outperformed both monofunctional enzymes (T4 lysozyme and GH18 chitinase) and the natural dual-function enzyme Hevamine A in both activities (Figure 5F), demonstrating that ORI can balance and enhance multiple functional objectives within a single protein scaffold.

Methods
Preparing Ontology Datasets for Model Training

Representative Protein Sequences: The UniRef90 and UniRef50 datasets were collected from the UniProt database29. The UniRef90 dataset containing 166 million representative protein sequences was created by clustering UniRef100 sequences that are 11 or more residues long, ensuring that each cluster contains sequences with at least 90% identity and 80% overlap with the longest sequence in the cluster. The UniRef100 sequences consolidate identical sequences and sub-fragments of 11 or more residues from any organism into a single entry, displaying a representative protein sequence along with the accession numbers of all merged entries and links to the corresponding UniProtKB and UniParc records. UniRef50 containing 59 million representative protein sequences is formed by clustering UniRef90 seed sequences that share at least 50% sequence identity and 80% overlap with the longest sequence in the cluster. We further annotated these datasets and used them for model training.

Protein Species and Function Annotation: The UniProtKB dataset was downloaded from the UniProt database29. UniProt Knowledgebase (UniProtKB) is a comprehensive repository of functional information on proteins, encompassing amino acid sequences, protein names or descriptions, taxonomic data, widely accepted biological ontologies, classifications, cross-references, and clear indications of annotation quality, including evidence attribution for both experimental and computational data. The UniProtKB dataset includes functional information for approximately 250 million proteins. For our purposes, we extracted taxonomic information, cellular component data, molecular function details, and biological process information from the UniProtKB dataset to annotate the representative protein sequences in the UniRef90 and UniRef50 datasets. The taxonomic lineage information was preserved from the superkingdom level down to the species level. Overall, 107,891 unique taxonomic terms were retained in the annotated UniRef90 and UniRef50 datasets. The functional annotation of proteins includes Gene Ontology (GO) terms and Enzyme Commission (EC) numbers. The annotated UniRef90 and UniRef50 datasets contain 30,391 unique GO function terms and 9,217 EC function terms.

Protein Structure Annotation: The protein structures were collected from PDB52, AlphaFold Protein Structure Database53, and ESM Metagenomic Atlas30. The PDB is an archive of experimentally determined 3D structural data for large biological molecules, such as proteins, DNA, and RNA, which is crucial for research and education in fundamental biology, health, energy, and biotechnology. AlphaFold Protein Structure Database and ESM Metagenomic Atlas archive of protein structure predicted by AlphaFold3 and ESMfold30, respectively. For annotating the UniRef90 and UniRef50 datasets, we used experimentally validated protein structures and high-quality predicted protein structures with pTM > 0.7 and pLDDT > 0.7. These protein structures were then classified into SCOPe Class, Fold, and Superfamily classification for further annotation. The Structural Classification of Proteins—extended (SCOPe) is a database that builds upon the SCOPe database, which is a manually curated hierarchy of domains from most proteins with known structures, organized according to structural and evolutionary relationships. The classification includes Classes based primarily on secondary structure content and organization, Folds grouping structurally similar superfamilies, and Superfamilies that connect protein families with common functional and structural features inferred from a common evolutionary ancestor.

Protein Characteristics Annotation: The protein characteristics data were collected from literature, including the thermal stability of 48,000 proteins across 13 species ranging from archaea to humans and covering melting temperatures of 30–90 °C27, 8926 proteins of high thermal stability from Archaea species living in an extreme high-temperature environment54, 58 689 soluble and 70 954 insoluble proteins28, 25693 signal peptides that control protein secretion and translocation in all living organisms55, and protein subcellular localizations collected from SwissProt56 and Human protein atlas57. These data were used to train USM models in the quality control pipeline.

Tokenizer Construction

We constructed a byte-pair encoding (BPE)58 tokenizer tailored for protein design, with a vocabulary size of 49,152. To support flexibility in encoding heterogeneous biological inputs, we reserved 200 token IDs as special tokens, which can be dynamically replaced at runtime to represent ontology terms, annotations, or experimental constraints. The reserved token set is drawn from a comprehensive replacement resource pool encompassing digits, Latin and Greek alphabets, punctuation (! " # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~), whitespace characters (space \t \n \r \x0b \x0c), custom tokens (<CLS>, <PAD>, <MASK>, <EOD>, <:>), and additional domain-specific symbols (£, ¥, «, », ±, ·). This design ensures broad coverage for encoding biological prompt phrases and precise parsing of specialized terminology, such as protein functional annotations and enzyme classification numbers (e.g., EC:1.1.1.181).

During tokenization, each protein sequence was segmented into BPE subword tokens, optimized to capture both short functional motifs (e.g., catalytic triads, signal peptides) and long repetitive patterns (e.g., coiled-coils, tandem repeats). Ontology inputs—including functional annotations, EC numbers, structural constraints, and protein family names—were tokenized separately using the special token pool and subsequently concatenated with the protein tokens to form unified input sequences. In total, this process produced over 60 billion tokens from the integrated protein sequence and annotation datasets, which were used to train the PGM model.

The Protein Design Agent (PDA)

The Protein Design Agent (PDA) serves as the orchestration layer that translates user requirements into ontology prompts and function calls to the Protein Generation Model (PGM). In our protein design agent (PDA), we use a module similar to Gorilla22 and Toolformer23, to convert users’ task requirements to function calls of our models.

Supervised Fine-tuning with LoRA: To achieve this goal, we perform low-rank parameter adaption (LoRA) 59 on the open-sourced large language model Gemma2-2B60. The model is fine-tuned for 1 epoch with a single pass of all data. The batch size is 32 and the learning rate is 1e-4. The rank for LoRA is 16.

To obtain the training data, we use GPT4 to generate input and output pairs for each function and characteristic by providing GPT4 with the input requirements and functionality description of each function and characteristic. The inputs are the user’s requirements and the outputs are the corresponding function and characteristic ontology prompts and model function calls. We also provide high-quality examples generated by human experts as examples to GPT4 for the generation. Finally, we collected 5000 pairs of data generated by GPT4 and used GPT4 to score these generated data based on the scope of requirements and the correctness of ontology prompts and function calls. We further validated the correctness of the data with human experts. We used the top 2000 scoring data pairs for training. In addition, to detect unsupported requirements, we extracted 200 input prompts from the Stanford Alpaca dataset and designated the corresponding outputs as “unsupported”.

Examples of training data:
{
"input": "Our goal is to design 10 lysozymes that can be expressed by yeast.",
"output": "**API call**: PGM(Enzyme=\"Lysozyme\", EC=\"3.2.1.17\", Species=\"Saccharomyces\")\n**num of samples**: 10"
},
{
"input": "The task is to construct a high-temperature stable protein that acts like chitinase in glycosyl hydrolases family 18.",
"output": "**API call**: PGM(Enzyme=\"Glucosaminidase\", EC=\"3.2.1.14\", Pfam=\"Glyco_hydro_18\", Structure=\"TIM beta/alpha-barrel\", Melting Temperature=\"90\")\n**num of samples**: 1"
},
{
"input": "Write a song lyric about birds",
"output": "Not Support"
}

The supervised training objective minimized the cross-entropy loss between predicted outputs and the ground-truth ontology/function-call pairs:
𝐿 = −∑𝑇 𝑙𝑜𝑔𝑃(𝑦 |𝑦 < 𝑡,𝑥)
𝑆𝐹𝑇 𝑡=1 𝑡
(1)
where x denotes the user requirement, y is the ground-truth token at step t, and T is the output sequence length.

Reinforcement Learning with PPO: After fine-tuning using low-rank parameter adaptation (LoRA), we perform Proximal policy optimization (PPO)61 to further finetune the PDA module to improve the quality of ontology prompts and function calls. Specifically, given each requirement from the 2000 training data pairs, we used the module to generate its ontology prompts and function calls and based on these ontology prompts and function calls, called PGM to generate protein sequences and USM to predict the protein function and characteristics. Then we assigned the reward for the PDA based on the quality of the generated sequences.

A reward function was defined as follows:
• Failure in function call or invalid sequence: reward = 0.
• Valid sequence generation: base reward = 1.
• Correct enzyme category (verified by USM classifier): +1 reward.
• Requirement-specific property satisfaction (e.g., thermal stability, verified by USM predictor): +1 reward.
Thus, the reward R∈[0,3] reflected the quality and correctness of PDA outputs. PPO updated the policy by maximizing the expected reward while constraining policy divergence:
𝐿 = 𝐸 [min⁡(𝑟 (𝜃)𝐴 ,𝑐𝑙𝑖𝑝(𝑟 (𝜃),1−𝜖,1+𝜖)𝐴 )]
𝑝𝑝𝑜 𝑡 𝑡 𝑡, 𝑡 𝑡,
(2)
where 𝑟 (𝜃) is the probability ratio between new and old policies and 𝐴 is the estimated advantage. After obtaining the rewards for the output of the PDA, we perform PPO to update the model.

Overall Training Objective: The PDA training proceeded in two stages:
1. Supervised fine-tuning (SFT) using cross-entropy loss to learn accurate mappings from user requirements to ontology/function-call sequences.
2. Reinforcement learning (RL) with PPO, optimizing PDA to maximize downstream task rewards by validating protein generation outcomes.
This combined loss formulation ensured that PDA not only reproduced correct prompts from supervised data but also generalized effectively to unseen requirements by aligning with task-specific biological rewards.

The Protein Generation Model (PGM)

To train the protein generation model (PGM), we adopt a prompt-based encoding schema. The sequence begins with ontology tokens that provide conditioning context, introduced at the first position of the sequence. The ontology context typically contains 5–30 tokens, depending on the granularity of the annotation (e.g., “enzyme class EC:1.1.1.181” or “IgG1 antibody heavy chain”). In contrast, protein sequences are substantially longer, ranging from 100 to 1000 tokens after BPE segmentation. The input structure follows the format:
<CLS> Ontology_Tokens <:> Protein_Tokens <EOD>
This design ensures that ontology context acts as a prefix, guiding generation while preserving autoregressive protein modeling. All tokens (protein and ontology) are mapped into a shared embedding space via a learnable embedding layer. Positional encodings are applied to preserve order. Ontology tokens, although distinct in semantics, share the embedding matrix with protein tokens to enable the transfer of contextual information. This joint representation allows the PGM model to attend simultaneously to biological prompts and sequence context during self-attention, enabling it to learn the statistical patterns and relationships among protein sequences, structures and biological functions.

PGM is an autoregressive large language model that shares similarities with the decoder-only architecture used in LLaMA serial models. PGM consists of embedding layers, multiple decoder blocks, and multilayer perceptron (MLP) layers. The embedding layer maps each token to a continuous vector representation, capturing its unique properties. The decoder blocks capture the sequential dependencies between tokens, allowing the model to learn the context and relationships between functions and protein sequences. The decoder block follows the standard transformer architecture62, applies RMSNorm for pre-normalization 63 and multi-head attentions, uses the SwiGLU activation function 64, and rotary positional embeddings 65. The MLP layers consist of a fully connected layer and a normalization layer, which are responsible for generating the final output tokens that represent the generated protein sequences. The 3B PGM model consists of 24 decoder blocks and 24 multi-head attentions, with embedding dimensions of 3072. For evaluation purposes, we also trained PGM models with parameter sizes of 1 billion (1B), 3 billion (3B), and 7 billion (7B). The annotated UniRef90 dataset and UniRef50 were utilized to train these models, respectively. After evaluation, we used the 3B PGM model as the default protein generation model in ORI.

The model is trained under a causal language modeling (CLM) objective, predicting the next token given the concatenated prefix–protein sequence. Formally, given ontology tokens O = (o , ... ,o ) and protein tokens P = (p ,...,p ), the training objective is:
1 m 1 n
𝐿 = ∑𝑛 𝑙𝑜𝑔𝑃(𝑝 |𝑂,𝑝 < 𝑡) (3)
𝑡=1 𝑡
This encourages the model to learn mappings from ontology context to sequence-level realizations, effectively grounding protein generation in structured biological knowledge.

In ORI, PGM primarily operates on ontology prompts generated by the Protein Design Agent (PDA). However, users can also interact directly with PGM using simplified prompts. For example:
• <Glucosaminidase><temperature90><:>
• <EC:3.1.1.101><temperature60><:>
Both prompt forms are correctly parsed by PGM, which then generates protein sequences aligned with the specified functional and physicochemical requirements.

The Unified Sequence Model (USM)

We proposed unified sequence model (USM) models to make fast and accurate predictions of protein characteristics. The architecture of the USM is similar to the conventional Encoder Only Transformer model, as shown in Supplementary Figure 1. The model takes a multiple sequence alignment (MSA) Token MT ∈ NM×L as input, where N represents the set of non-negative integers, M is the number of sequences (I1 for single sequence input), and L is the sequence length. The output is denoted as O ∈ RB×L×D, where R represents the set of real numbers, B is the batch size, and D is the dimension of the embedding. The first layer of the model is the MSA Embedding layer, which embeds the discrete MSA Tokens. The core of the USM model consists of multiple USM Transformer Blocks, which extract features from the MSA. The final output is generated by the language model (LM) head layer. The LM Head layer consists of RMSNorm and Linear layers, which transform the MSA features into language probability outputs. Compared to the MSA Transformer model66, the USM Transformer Block design is illustrated in Supplementary Figure 1. It consists of three parts: Column Attention, Fast Row Attention, and SwiGLU Activation. Assuming the MSA features h ∈ RB × row × col × D, where row represents the number of rows in the MSA and col represents the number of columns. The Row Attention is similar to the conventional Multihead Attention and has a computational complexity of O(hdLM2). The Column Attention can be simplified as a transposed form of Row Attention, with a computational complexity of O(hdML2), where h is the number of attention heads, d is the head dimension, M is the number of sequences in the MSA, and L is the sequence length.

Unlike the MSA Transformer Block, the USM Block starts with Column Attention. The USM Block consists of three major parts: Column Attention, Fast Row Attention, and SwiGLU Activation. The Column Attention consists of Transpose and Multi-head Attention layers. We proposed a method called Fast Row Attention (FRA) based on the assumption of redundancy along the Column direction in MSA data, which leads to sparsity in the MSA features h along the same direction. To achieve this, we apply Average Pooling along the Column direction, reducing the MSA features h to h_row ∈ RB×row. This operation speeds up the subsequent Multi-Attention by a factor of col, and the computational complexity of Fast Row Attention is O(hdL2). By leveraging Fast Row Attention, the USM model demonstrates significantly improved speed and the ability to handle larger parameter sizes. SwiGLU Activation is a type of MLP layer that consists of Linear and SiLU activation functions. Additionally, we enhance the model's ability to extrapolate long sequences by combining Multihead Attention with Rotary Position Embedding (ROPE).

When there is only one sequence in the MSA input, the USM can be considered as a single sequence (SS) model and the Column Attention degenerates into an MLP. When degenerating into a single sequence model, the USM is similar to the ESM model, highlighting its versatility. The USM model can be used as both an MSA model and an SS model.

The USM model is trained using the Masked Token prediction approach, similar to the conventional BERT model training 67. The existing Masked Token method typically uses a fixed masking ratio, such as 0.15, during model training. However, due to the large number of MSA sequences, the tokens along the Column direction are often similar. This results in lower training efficiency with smaller masking ratios. To address this, we propose a dynamic Mask Token method that combines two masking schemes:
Column Random Masking: This operation randomly masks entire columns. Since learning from column masking is more challenging, we use a smaller proportion, set to 5% in our approach.
Global Random Masking: This operation applies dynamic masking with varying proportions. It randomly masks individual tokens. The masking ratio decreases gradually during training, starting from 0.3 and decreasing to 0.15.

The objective function for training the USM model is defined as follows:
𝐿 = ∑ 𝑙𝑜𝑔𝑝(𝑥 |𝑥̃;𝜃) (4)
(𝑟,𝑐) 𝑟𝑐
Here, r and c represent the rows and columns of MSA Tokens, x represents the original MSA to be predicted, 𝑥̃ represents the masked MSA, and θ represents the model parameters. By combining Column Random Masking and Global Random Masking, the USM model can effectively train on MSA data with improved efficiency and adaptability to the unique characteristics of MSA sequences.

Iterative Optimization with Wet Lab Feedback

Wet lab experiments are crucial in developing protein generative models as they provide valuable biological insights into the functionalities of the generated proteins. These insights are essential for validating the accuracy and effectiveness of the models. However, it is often observed that only a subset of these proteins exhibit the desired functionalities and activities, while the majority do not show significant activity. To improve the performance of these models, researchers often analyze the outcomes of wet lab experiments and devise heuristic methods to refine or retrain the models. However, this approach heavily relies on the subjective expertise of individual researchers, which can introduce variability and limit reproducibility.

To address this challenge, we propose a framework that is named Reinforcement Learning from Wet-lab Feedback (RLWF). This framework utilizes feedback from wet lab experiments and selectively incorporates generated data to optimize model preference. Doing so systematically increases the likelihood of generating protein sequences with the targeted properties.

Reinforcement Learning from Human Feedback (RLHF) has been effectively employed to optimize the output of Large Language Models (LLMs), aligning the generated responses with user preferences68. In this approach, a dataset D containing preference pairs (x, yw, yl)—comprising a prompt x, a preferred response yw, and a less preferred response yl—is utilized. The model π is then optimized to favor yw over yl. An alternative to RLHF, Direct Preference Optimization (DPO)69, simplifies this by minimizing the loss of DPO, using a fixed reference model π : ref
𝜋 (𝑦𝑤|𝑥) 𝜋 (𝑦𝑙|𝑥)
𝐿 = Ε −𝑙𝑜𝑔𝜎(𝛽log 𝜃 −𝛽log 𝜃 )
𝐷𝑃𝑂 (𝑥,𝑦𝑤,𝑦𝑙)~𝐷 𝜋𝑟𝑒𝑓(𝑦𝑤|𝑥) 𝜋𝑟𝑒𝑓(𝑦𝑙|𝑥)
(5)
Here, σ denotes the sigmoid function, and β is a hyperparameter that regularizes the model’s deviation from the reference model. Iterative enhancements of RLHF, such as Iterative DPO 35,36,70, have been developed to iteratively refine the model. This method involves generating a set of new preference pairs using the current model, evaluating them via a reward model or human judgment, and optimizing the model on this preference dataset using DPO in each iteration. Our framework adopts this iterative approach. Initially, a series of protein sequences S is generated using the current model prompted by x. The first iteration uses a pre-trained model. Subsequent virtual screening identifies a subset of promising candidates, S , for wet-lab testing, with the remainder labeled as S . Post-testing, sequences that exhibit desired functionality are used as preferred responses Sw . The sequences in S with low reference model loss and low pLDDT scores are used as unpreferred responses Sl .

This selection is based on the premise that the model should generate more functionally expressive sequences, while avoiding those preferred by the current model yet less likely to exhibit desired functionality. To address the model’s tendency to generate overly long sequences—a known issue termed length bias in RLHF 70–72—we incorporate a length regularizer into the DPO loss as R-DPO loss:
𝐿 = Ε [−𝑙𝑜𝑔𝜎(𝛽(log 𝜋 𝜃 (𝑦𝑤|𝑥) −log 𝜋 𝜃 (𝑦𝑙|𝑥) )+𝛼(|𝑦𝑤|−|𝑦𝑙|))]
𝑅−𝐷𝑃𝑂 (𝑥,𝑦𝑤,𝑦𝑙)~𝐷 I 𝜋𝑟𝑒𝑓(𝑦𝑤|𝑥) 𝜋𝑟𝑒𝑓(𝑦𝑙|𝑥)
(6)
Here, |𝑦𝑤| and |𝑦𝑙| is the sequence length of yw and yl, and α is a hyperparameter that controls the strength of the length penalty. We set β = 0.1 and α = 0.05 in this study. The model is then optimized by minimizing the R-DPO loss 𝐿 .
𝑅−𝐷𝑃𝑂

Iterative enhancements of RLHF, such as Iterative DPO 35,36,70, have been developed to iteratively refine the model. This method involves generating a set of new preference pairs using the current model, evaluating them via a reward model or human judgment, and optimizing the model on this preference dataset using DPO in each iteration.

Our framework adopts this iterative approach to refine the model for a given prompt x, for example, “<Glucosaminidase><temperature90><:>”. In the first iteration, both the reference model and the current model are initialized with the pre-trained model.The reference model is fixed.

At the start of each iteration, the current model is used to generate protein sequences by repeatedly feeding it the prompt x. This process is repeated 10,000 times to obtain a dataset, S₀, comprising 10,000 sequences. Virtual screening is then performed to identify a subset of promising candidates, S₁ (typically ~100 sequences), for wet-lab validation. The remaining 9,900 sequences form the set S₂.

For the sequences in S₂, we estimate their perplexity using the reference model and select the top K sequences with the highest perplexity to form the set 𝑆 . We also evaluate their predicted local distance difference test (pLDDT) scores and select the K sequences with the lowest scores to form the set 𝑆 . The intersection of 𝑆 and 𝑆 is used as the set of unpreferred responses, Sl. The value of K is chosen such that the size of Sl is greater than 100; in practice, K is set to 500. The preferred responses set, Sw, is defined as S₁.

We then construct the training dataset D using all possible pairs of preferred (yw ∈ Sw) and unpreferred (yl ∈ Sl) responses. This selection is based on the premise that the model should generate more functionally expressive sequences, while avoiding those preferred by the current model yet less likely to exhibit desired functionality.

The model is fine-tuned using the R-DPO loss for one epoch over the dataset D. The updated model is subsequently used as the current model in the next iteration, which is used to generate new sequences for testing, following the same process to construct the preference dataset and optimize the model. The reference model in each iteration is a fixed copy of the model updated in the previous iteration. The model loss of the generated sequences is estimated by the auto-regressive loss of the reference model.

The process of preference dataset construction is summarized in Supplementary Algorithm 1 and illustrated in Supplementary Figure 9. The process of the proposed framework is summarized in Supplementary Algorithm 2.

Benchmark with Baseline Methods

To comprehensively evaluate ORI, we benchmarked it against a wide range of recent state-of-the-art models, spanning conditioned protein generation and thermostability optimization.

ProteinSGM: ProteinSGM is a score-based diffusion model that produces native-like protein structures11. We used the crystal structure of hen egg-white lysozyme (PDB ID: 132L) and T4 lysozyme (PDB ID: 216L) as a template, where specific regions were masked to provide partial structural information as input. ProteinSGM was then used to generate 1000 conditional 6D coordinates from the ProteinSGM model, including Cβ–Cβ distances, ω and θ dihedral angles, and ϕ planar angles. These coarse-grained 6D representations were then converted into full-atom protein backbones using Rosetta MinMover42, which performed constrained backbone minimization to ensure physically realistic geometries.

ProGen3: ProGen3 is an autoregressive protein language model designed for sequence generation12. We provided the sequences of hen egg-white lysozyme (PDB ID: 132L) and T4 lysozyme (PDB ID: 216L) as conditioning contexts. Using these templates, we employed the ProGen3-3B model to generate amino acid sequences in both N-to-C (forward) and C-to-N (reverse) directions, conditioned on the functional and sequence constraints. A total of 1000 protein sequences were sampled, ensuring coverage of diverse sequence space.

Evo2: Evo 2 is a large-scale biological foundation model trained on 9.3 trillion DNA bases that learns genome-wide biological features directly from DNA sequence, enabling accurate prediction of functional variants and controllable genome-scale sequence generation37. For Evo2-based conditional protein generation, we first designed functional prompts according to the model’s requirements, encoding structural and biochemical features of target proteins. Specifically, we constructed text-based prompts describing hen egg-white lysozyme (PDB ID: 132L) and T4 lysozyme (PDB ID: 216L), including their structural class, enzymatic activity, and functional attributes. Conditioned on these prompts, Evo2 was used to generate 1,000 DNA sequences for each protein, ensuring that the outputs satisfied the specified biochemical and functional constraints. The generated DNA sequences were subsequently translated into protein sequences, followed by filtering to remove incomplete or invalid coding regions before downstream evaluation.

ProteinMPNN: ProteinMPNN is a graph-based generative model for protein sequence design conditioned on backbone structures39. For thermostability optimization, we first predicted the backbone structure of each target protein using AlphaFold23, which served as the input scaffold. ProteinMPNN was then applied to redesign the amino acid sequences on this backbone. For each target, we generated 1000 redesigned variants and selected the top 100 candidates based on the predicted stability score. The redesigned sequences were further filtered by predicted folding quality (pLDDT > 70) to ensure structural plausibility.

Rosetta: Rosetta is a widely used computational platform for macromolecular modeling, design, and analysis, integrating physics-based energy functions with knowledge-based statistical potentials42. For Rosetta-based thermostability optimization, we used the Rosetta ddG and Rosetta FastDesign protocols to perform sequence redesign aimed at improving folding stability. Specifically, starting from the experimentally determined or AlphaFold2-predicted protein structure, iterative rounds of side-chain repacking and backbone minimization were performed, guided by the Rosetta energy function. Each design trajectory generated multiple variants, and 1000 candidate sequences were obtained for each protein. The designed sequences were ranked according to Rosetta’s energy scores, with the top 100 lowest-energy conformations selected as putative thermostable variants.

Evaluating the Similarity to Natural Proteins

To evaluate the similarity of PGM-generated proteins to natural ones, we employed several methods. Firstly, we used MMseqs273 to determine the sequence identity between the generated proteins and natural proteins. Additionally, we predicted the structure of the generated proteins using AlphaFold23, ESMFold30 and USMFold, while assessed the structural identity to natural proteins using Foldseek74. The final structural identity of the generated proteins was determined by calculating the median structural identity among the structures predicted by the three methods. For each generated protein, we compared it to a collection of natural proteins obtained from the UniProt database29. The highest value obtained from these comparisons was considered as the final sequence and structure identity of the generated protein. This comprehensive evaluation allowed us to assess the similarity of the generated proteins to their natural counterparts accurately.

Gene Synthesis and Vector Construction

Gene encoding the protein that was to be validated was synthesized by Sino Biological (Beijing, China). To construct the expression clones, the synthesized gene and the destination vectors were digested using specific restriction enzymes (NdeI and XhoI, New England Biolabs, Cat# R0111 and R0146) according to the manufacturer's protocol. The digested fragments were purified and then ligated using T4 DNA Ligase (Thermo Fisher Scientific, Cat# EL0011) at a molar ratio of 1:3 (vector to insert) for 2 hours at 25°C. The protein expression vector is pcDNA3.4 (Thermo Fisher Scientific, Cat# A14697) for eukaryotic HEK293 cells, pET-28a (Novagen, Cat# 69864) for prokaryotic E.coli cells, and pJL1 (Addgene, Plasmid #69496) for the cell-free expression system. To ensure the accuracy of the constructs, all vectors were verified by Sanger sequencing (Sino Biological, Beijing, China) using universal primers. Upon confirmation, high-purity plasmids were extracted and purified using the PureLink HQ 96 Plasmid DNA Purification Kit (Thermo Fisher Scientific, Cat# K210096) following the manufacturer's instructions.

Protein Expression in Eukaryotic HEK293 Cells

HEK293 cells (Thermo Fisher Scientific, Cat# A14527) were cultured in FreeStyle 293 Expression Medium (Thermo Fisher Scientific, Cat# 12338018). For small-scale screening, the recombinant pcDNA3.4 expression vectors were transfected into the cells seeded at a density of 1 × 10^6 cells/mL in a 24-well cell culture plate (700 μL per well). Transfection was performed using Linear Polyethylenimine (PEI, MW 25,000, Polysciences, Cat# 23966) with a DNA:PEI mass ratio of 1:3. Five days post-transfection, the culture supernatant was harvested by centrifugation at 1,000 × g for 10 minutes. For Western Blot analysis, samples were separated by SDS-PAGE and transferred onto a PVDF membrane (Millipore, Cat# IPVH00010). After blocking with 5% non-fat milk in TBST for 1 hour, the membrane was incubated with His-Tag Antibody (HRP) (GenScript, Cat# A00612) at a 1:5,000 dilution for 1 hour at room temperature. Protein bands were visualized using Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific, Cat# 32106) with a fixed exposure time. The relative expression levels were quantified based on band intensity using ImageJ software. The candidates with the highest expression levels were selected for scale-up in 6-well plates, with 3 mL of cells per well transfected under the same conditions. After 5 days of expression, the supernatant was collected and incubated with HisPur Ni-NTA Magnetic Beads (Thermo Fisher Scientific, Cat# 88831) at 4°C for 2 hours with gentle agitation. The beads were washed three times with Wash Buffer (50 mM NaH PO , 300 mM NaCl, 20 mM imidazole, pH 8.0). The target proteins were then eluted using Elution Buffer (50 mM NaH PO , 300 mM NaCl, 250 mM imidazole, pH 8.0). The purity and molecular weight of the eluted proteins were verified by SDS-PAGE using 4-20% Mini-PROTEAN TGX Precast Gels (Bio-Rad, Cat# 4561096) and stained with Coomassie Brilliant Blue R-250 (Sigma-Aldrich, Cat# B0149).

Protein Expression in Prokaryotic E.coli Cells

The pET-28a expression vectors were transformed into chemically competent E. coli BL21(DE3) cells (Invitrogen, Cat# C600003) via the heat-shock method. A single colony was inoculated into 5 mL of LB medium (Sigma-Aldrich, Cat# L3147) containing 50 μg/mL Kanamycin and grown overnight at 37°C. The culture was then diluted 1:100 into 50 mL of fresh LB medium. When the culture reached an OD of 0.6–0.8, protein expression was induced by adding IPTG (Sigma-Aldrich, Cat# I6758) to a final concentration of 0.5 mM, followed by overnight incubation at 18°C with shaking at 220 rpm. After induction, cells were harvested by centrifugation at 5,000 × g for 15 minutes at 4°C. The cell pellet was resuspended in Lysis Buffer (50 mM NaH PO , 300 mM NaCl, pH 7.4) and subjected to sonication on ice using a Branson Sonifier. The resulting lysate was clarified by centrifugation at 12,000 × g for 30 minutes at 4°C. The supernatant was then incubated with 125 μL of Ni-charged MagBeads (GenScript, Cat# L00295) for 1 hour at 4°C to selectively enrich the poly-His-tagged target protein. For purification, the beads were washed three times with a Wash Buffer (50 mM NaH PO , 300 mM NaCl, and 10 mM imidazole, pH 7.4). The target protein was eluted using an Elution Buffer (50 mM NaH PO , 300 mM NaCl, and 150 mM imidazole, pH 7.4). The expression level and purity of the eluted protein were determined using SDS-PAGE with 4-20% Mini-PROTEAN TGX Precast Gels (Bio-Rad, Cat# 4561096) and visualized via Coomassie Brilliant Blue staining.

Protein Expression in Cell-free Expression System

For cell-free protein synthesis (CFPS), the pJL1 expression vectors were added to the Cell-free Protein Expression Kit (Sino Biological, Cat# CFKIT01). The reaction was performed in a total volume of 100 μL in a 1.5 mL microcentrifuge tube. The mixture was incubated at 30°C for 3 hours on an orbital shaker at a constant speed of 800 rpm. Following incubation, the crude reaction mixture was centrifuged at 12,000 × g for 10 minutes at 4°C to remove insoluble debris and pellets. The CFPS supernatant was collected and the target His-tagged proteins were purified using HisPur Ni-NTA Magnetic Beads (Thermo Fisher Scientific, Cat# 88831). Between purification steps, samples underwent buffer exchange into Phosphate-Buffered Saline (PBS, pH 7.4) using Amicon Ultra-0.5 Centrifugal Filter Units (3 kDa MWCO, Millipore, Cat# UFC5003) to remove imidazole or other salts. The final purified protein was concentrated and exchanged into a storage buffer (PBS with 10% glycerol). The purity and concentration of the samples at each stage, as well as the final product, were assessed by SDS-PAGE and UV absorbance at OD using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Cat# ND-2000). Uncropped SDS–PAGE scans corresponding to Figures 3c–e are provided in the Source Data.

Lysozyme Enzyme Activity Assessment

After protein purification, a small amount of the purified protein is taken and diluted for the assessment of its enzymatic activity. The enzymatic activity is measured using a lysozyme assay kit (Thermo Fisher Scientific, Cat#E22013), with hen lysozyme (Sigma-Aldrich, Cat#L4919) used as a positive control. The lysozyme activity assay is conducted using Micrococcus lysodeikticus cell walls that are labeled to the extent that their fluorescence is quenched. Lysozyme action can alleviate this quenching effect, resulting in a significant increase in fluorescence that is directly proportional to the lysozyme activity. To perform the assay, 50 µL of a working suspension containing 50 µg/mL of DQ lysozyme substrate is added to each well of a microplate containing the diluted proteins. The reaction mixtures are then incubated for at least 30 minutes at 37°C, while being protected from light. Fluorescence intensity was measured using a Varioskan LUX Multimode Microplate Reader (Thermo Fisher Scientific, Cat# VLBL00D0) equipped with standard fluorescein filters (Excitation/Emission: 494/518 nm). Enzymatic activity is quantified in Units (U), which is defined as the amount of enzyme required to produce a change in the absorbance at 450 nm of 0.001 units per minute, using a suspension of Micrococcus lysodeikticus as the substrate.

The lysozyme antibacterial enzymatic activity is measured using the Nuclease Activity QC System (SinoBiological, Cat#NE001) by Sino Biological, Inc. Lysozyme digestion of bacteria leads to the release of bacterial nucleases. The measurement of nuclease activity in the supernatant allows for the evaluation of bacterial disruption, which is indicative of the lysozyme's antibacterial enzymatic activity. To perform the assay, Escherichia coli in the logarithmic growth phase is collected by centrifugation. The bacteria are then washed five times with PBS buffer until no nuclease activity is detectable in the supernatant. The bacteria are resuspended and diluted in a resuspension buffer (50 mM Tris-HCl, 5 mM MgCl2, 150 mM NaCl). Next, 100 μL of the bacterial suspension, 40 μL of the enzyme to be tested, and 4 μL of the nuclease fluorescent substrate are mixed together. The mixture is incubated at 37°C for 30 minutes, followed by the addition of a termination solution to stop the reaction. The resulting fluorescence (Ex/Em: 480/520 nm) was measured to quantify nuclease activity, reflecting the degree of bacterial lysis.

Chitinase Enzyme Activity Assessment

For the evaluation of chitinase-like activity, a small portion of the purified protein was diluted in assay buffer (200 mM sodium phosphate, pH 8.0). The reaction was initiated by mixing 20 µL of the diluted protein with 80 µL of the substrate solution, containing a final concentration of 125 µM 4-Methylumbelliferyl-beta-D-$N,N',N''-triacetylchitotrioside [Mu-(GlcNAc)3] (Sigma-Aldrich, Cat# M5639).The reaction mixtures were incubated in black 96-well microplates (Greiner Bio-One, Cat# 655076) at specified temperatures (e.g., 37°C or 85°C) for 3-6 hours in a humidified incubator to prevent evaporation. The fluorescence signal of the released product, 4-methylumbelliferone (4-MU), was measured using a Varioskan LUX Multimode Microplate Reader (Thermo Fisher Scientific, Cat# VLBL00D0) at an excitation wavelength of 372 nm and an emission wavelength of 445 nm. To quantify the enzymatic activity, a standard curve of 4-methylumbelliferone (Sigma-Aldrich, Cat# M1381) was prepared under identical buffer and temperature conditions. One unit of activity (Uc) was defined as the amount of enzyme required to release 1 nmol of 4-methylumbelliferone per minute under the assay conditions.

Naming Rules of ORI-engineered Proteins

Functional proteins that have been validated through wet lab assays are assigned names consisting of a prefix followed by a number. The prefix consists of two parts separated by a hyphen ("-"). The first part, "TX," represents Tencent and Unknown, respectively. The second part consists of abbreviations indicating the type of protein. "C" represents Chitinase, "L" represents Lysozyme, "S" represents thermostable proteins, "R" represents proteins refined by RLWF models, and "ME" represents proteins with multi-enzyme function. The number following the prefix is used to differentiate proteins within the same category.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "He et al present a new language-model based protein design method and validate it by designing lysozymes and\nchitinases. The paper claims that ORI enables de novo design, but there is no evidence of that in any section. Additionally,\nthe claims of designing better than natural enzymes have not been rigorously demonstrated. For these reasons, I consider\nthe paper to be misleading. There are other, perhaps less serious, problems with this paper, but I will focus only on these\ntwo as they make this paper unpublishable.\nDe novo design:\nDe novo design means designing \"from scratch\", without natural templates. This is not done in this paper. The designs are\nentirely within the natural sequence families. See Fig1. The median, represented by a dashed line, is at 96% sequence\nidentity. This is not de novo, but in addition, it's a very timid design. The authors conclude from this that their model is\nexcellent in designing validated proteins, but all it shows is that the model has memorised wild-type sequences. They then\ncut the designs at a threshold of 90%, but this too is not de novo. All their designs are >35% sequence identity to a natural\ntemplate that carries out the same reaction. There are other design methods that enable robust design within sequence\nfamilies, but they don't and can't claim that they are de novo design methods. The repeated claims on \"novelty\" and \"de\nnovo\" in this paper are simply unfounded.\nDesigning proteins that are improved relative to natural ones:\nThe authors report that the lysozyme designs exhibit a 100-fold improvement over hen egg white lysozyme (HEWL). This\nsounds impressive, but T4 lysozyme (T4L) is sold by Sigma and advertised as possessing 200-fold improved activity relative\nto HEWL. T4L is within the training set and their design may be based on T4L, but the authors don't compare their designs'\nactivities to it, only relative to the less efficient HEWL. This is, again, simply misleading.\nThe highlight of the paper is the design of a multifunctional lysozyme/chitinase, which they claim is \"entirely novel function\nthat have never existed before\". This sounds impressive, but lysozyme and chitinase exhibit highly related catalytic\nmechanisms and similar folds, and natural lysozymes and chitinases exhibit cross reactivities, albeit weaker than their\nnative activities. So this multifunctionality is entirely not novel and in fact exists in natural families.\nTo close, I reiterate that the paper suffers from other problems, but the three I briefly remarked on are sufficient to preclude\npublication.", "reviewer_2": "This is a very exciting and ambitious paper. The ORI system represents a creative integration of autoregressive language\nmodels (PGM), pre-trained function predictors (USM), and a reinforcement learning loop with wet-lab feedback (RLWF), all\nscaffolded around a structured ontology framework. The paper demonstrates the ability to generate proteins with useful\nproperties—enzyme activity, thermostability, and even multi-enzyme functionality—highlighted by experimental validation of\nseveral sequences that outperform natural enzymes like lysozyme and chitinase. Overall, and the authors make a\ncompelling case for ORI as a full-stack generative biology system.\nThat said, to me, there are several areas where the manuscript falls short—particularly in mathematical clarity,\nbenchmarking, and contextualization with respect to recent generative modeling frameworks. I really think this paper could\nbenefit from significantly more rigorous comparisons to state-of-the-art models, as well as stronger justification for many of\nthe modeling decisions made. Below are five major concerns:\n1. The mathematical formulation of the model is super vague and inconsistent throughout. The PGM model is described as a\ndecoder-only autoregressive Transformer (similar to LLaMA), but the training objective, tokenization schema, and prompt\nstructure are never clearly defined. On page 6 and Figure 1, we see prompts such as <Homo><Oxidoreductase>\n<EC:1.1.1.181>..., but how these are tokenized, embedded, and used as prefix context is never precisely described. Is the\nontology token fed at the first position only? How long are the ontology sequences relative to the generated protein tokens?\nMoreover, the RLWF formulation in Figure 3 and page 13 is highly condensed. The modified DPO loss used in training is\nonly given symbolically, and the preference dataset construction lacks clarity around sampling, control baselines, and\nreward shaping. I’d strongly recommend the authors formally define the loss functions and generative process in each\nphase, perhaps using equations in an appendix, as the current level of abstraction is insufficient for replication.\n2. The comparisons to other generative protein models are incomplete and dated. There is no discussion of recent\ngenerative models like DPLM, EvoDiff, DiMA, MeMDLM or AMP-Diffusion, all of which have shown strong performance in\ndifferent protein design tasks using diffusion models. Similarly, discrete flow matching models like Dirichlet Flow Matching,\nFisher Flow Matching, Gumbel Softmax Flow Matching are absent—yet these models enable guided, conditional generation\nin a way directly comparable to ORI's ontology-conditioned decoding -- these should all be cited. The manuscript suggests\nthat reinforcement learning is the key innovation, but in reality, most modern generative biology frameworks rely on guided\ndiscrete sampling rather than RL, due to the instability and sample inefficiency of reward-based learning in high-dimensional\nsequence space. The authors should at minimum cite these models and explain what advantages, if any, RLWF provides\nover guided discrete sampling. Otherwise, I cannot recommend acceptance if the authors don't acknowledge the state-of-\nthe-art in the field.\n3. From my analysis, the experiments lack clear baselines against either modern PLMs or modality-specific generative\nmodels. The only comparisons made are to ProteinMPNN, ESM2, and natural protein statistics? There is no head-to-head\nagainst recent structure-free models like ProteinSGM, ProGen3, PepTune -- all of which can also be used to condition\ngeneration on properties like stability, non-toxicity and other function. Further, for domain-specific baselines, tools like\nUniPTM (for PTM prediction), PTM-Mamba, for PTM-aware embedding, and DPAC (for DNA-protein design) offer directly\ncomparable pipelines. The authors argue that ORI integrates sequence, structure, and function into a single prompt, but they\nnever show why this yields better results than methods that integrate these features more explicitly (via joint contrastive\ntraining, or structure-conditioned diffusion). I suggest the authors benchmark ORI-generated sequences against outputs from\nthese domain-specific, particularly on shared tasks.\n4. The experimental benchmarks are promising but not comprehensive. This is a big claim, and so the authors should\nspend some time optimizing their work. Figures 2–5 show strong wet-lab validation on lysozyme and chitinase targets, but all\nresults are focused on enzymes with naturally high expression and well-behaved domains. Where are the harder targets?\nAll experiments are also single-round selections with no comparison to a baseline design tool (i.e., structure-based docking\nor traditional motif grafting). Even for thermostability optimization (Figure 4), the authors never compare ORI-designed\nproteins to sequences generated by ProteinMPNN or structure-based optimization with Rosetta. I’d recommend testing ORI\non harder problems, kike flexible loops, intrinsically disordered regions (IDRs), or viral glycoproteins—and benchmarking\nagainst sequence-only models or structure-aware diffusion tools.\n5. The literature review focuses almost entirely on highly cited or media-prominent papers (AlphaFold, ProteinMPNN,\nProGen) while omitting many state-of-the-art systems that define the actual technical frontier in generative biology. The\nauthors cite ProGen1 but not ProGen3 (which introduces functional conditioning), nor ESM-C (contact-aware language\nmodeling), nor FusOn-pLM (isoform-aware fusion modeling). The omission of Rinalmo and EvoFlow-RNA from the RNA\nsection is particularly surprising given their state-of-the-art performance. If the claim is that ORI is a general-purpose,\nscalable generative system, the authors need to show that it outperforms or meaningfully differs from these prior systems:\nand that it wasn’t just trained on more data with better engineering.\nIn summary, I believe ORI is an exciting and impactful system, but the authors need to be more rigorous in how they\ndescribe, benchmark, and situate it. Without tighter math, stronger comparisons, and more comprehensive experiments, it’s\nhard for me to evaluate what’s truly novel versus what’s just larger and better resourced.", "reviewer_3": "The manuscript by Bing He et al. introduces ORI (Ontology Reinforcement Iteration), an AI platform for protein design. The\nsystem takes as input feature requests from users that are interpreted by an agent generating candidates that are filtered and\nvalidated with in-vitro assays. Results are then used to reinforce the generative process until it meets the user requirements.\nThe manuscript presents a series of three applications illustrating the functionalities and use cases of ORI.\nOverall ORI is a valuable tool with promising performances. However, the current presentation has several shortcomings\nthat could prevent the readers to fully appreciate its potential.\n1) The background and literature review in the introduction is incomplete. The idea of using reinforcement learning and\nexperimental assays as feedback for molecular design is not new (e.g., work of Baker’s group). A comprehensive review of\nprevious contributions is mandatory to contextualize this work.\n2) My next concern follows the previous comment. It is not clear to me what is the main innovation of this work. It would be\nimportant to clarify the contribution. Unless I misunderstood it, it feels more like a software contribution. The ORI platform is a\npipeline integrating multiple tools. Although, each individual component is not conceptually new, their integration in a unified\nworkflow has value. If such case, it would be eventually interesting to expand the discussion about the integration/use in\nexperimental labs.\n3) Unless I missed it, the manuscript does not provide comparison or benchmark with other software and methods (it brings\nus back to my first comment). As such, it is difficult to fully assess the technical advances of ORI. The examples provide in\nthe results section are interesting but do not replace a complete and rigorous validation of the platform. A large-scale (and\nmore quantitative) experiment (In contrast to a specific use cases) would also provide more confidence in the generalization\nof ORI.\n4) The analysis of the case studies presented in the paper could also be refined. Looking at the global sequence and\nstructure similarity is not sufficient. Further investigations of the designed proteins would help to validate the impact of the\noutput. For instance, it is not clear if the samples have highly similar active sites or if it could be found in the training data. It\nwould be important to show that similar result could not be obtained with a classical fragment assembly option, and could\nalso help to rule out any leak of information (I am assuming it is ok but the manuscript could expand the discussion).\n5) In general, I would advise revising the style of writing. Currently, the text uses a lot of unnecessary superlatives that do not\nbring anything to the scientific discussion. Some terms should also be more carefully chosen (e.g., in Figure 1, ORI does not\n“understand” the needs, maybe “interpret” or another formulation would be preferable), and some statements be better\njustified (e.g., on page 4, the authors say that “ORI can predict the optimal sequence and structure” but optimal is a strong\nclaim that should be proved)."}, "author_response": "Reviewer #1 (Remarks to the Author):\nHe et al present a new language-model based protein design method and validate it by designing lysozymes and\nchitinases. The paper claims that ORI enables de novo design, but there is no evidence of that in any section.\nAdditionally, the claims of designing better than natural enzymes have not been rigorously demonstrated. For these\nreasons, I consider the paper to be misleading. There are other, perhaps less serious, problems with this paper, but I\nwill focus only on these two as they make this paper unpublishable.\nComment 1\nDe novo design:\nDe novo design means designing \"from scratch\", without natural templates. This is not done in this paper. The designs\nare entirely within the natural sequence families. See Fig1. The median, represented by a dashed line, is at 96%\nsequence identity. This is not de novo, but in addition, it's a very timid design. The authors conclude from this that\ntheir model is excellent in designing validated proteins, but all it shows is that the model has memorised wild-type\nsequences. They then cut the designs at a threshold of 90%, but this too is not de novo. All their designs are >35%\nsequence identity to a natural template that carries out the same reaction. There are other design methods that enable\nrobust design within sequence families, but they don't and can't claim that they are de novo design methods. The\nrepeated claims on \"novelty\" and \"de novo\" in this paper are simply unfounded.\nResponse: We appreciate the reviewer’s careful evaluation of our work and acknowledge the concern regarding the\nterminology used in describing our approach. We agree that the term \"de novo\" requires clarification, and we\napologize for any confusion caused by its use.\nIn this study, our method leverages existing natural protein families to generate novel enzyme variants, which\nrepresent an innovative approach within the bounds of these families. While the designs are indeed constrained by\nsequence similarity to natural templates, our method introduces substantial diversity in sequence space, resulting in\nvariants with distinct functional properties. However, we recognize that these designs are not strictly \"de novo\" in\nthe traditional sense of creating sequences from entirely unrelated templates.\nTo address this point, we revised the manuscript to clearly state that our approach involves the generation of novel\nvariants within known protein families, rather than true \"de novo\" design. We have revised the description to more\nclearly emphasize the novelty of our approach, particularly its capability to enhance sequence diversity and functional\noptimization via ontology-conditioned decoding and reinforcement learning informed by wet-lab feedback.\nSpecifically, we have removed the term “de novo” from both the manuscript title and the main text.\nWe hope these clarifications will address the reviewer’s concerns and provide a more accurate description of our\nmethodology. Thank you for pointing this out.\nRevised Title:\nFunctional Protein Design and Enhancement with ORI\nRevised Introduction:\nBuilding on these insights, we introduce ORI (Ontology Reinforcement Iteration), a general-purpose and scalable\nframework for generating functional proteins. ORI unifies ontology-conditioned decoding with reinforcement\nlearning with experimental feedback (RLWF) into a single system. Its innovations include: (1) using structured\nontologies as semantic prompts to inject functional, structural, and property-level constraints into the generative\nprocess, enabling controllable and interpretable sequence generation; (2) combining the controllability of guided\ndiscrete sampling with the ability of RL to directly optimize sparse or non-differentiable objectives; and (3)\nimplementing a closed-loop evolutionary cycle that involves generation, experimental measurement, feedback-driven\nreward assignment, ontology refinement, and model updating, thereby enabling the co-evolution of both the\ngenerative model and its ontology. In this way, ORI leverages the strengths of guided sampling and RL simultaneously,\naddressing limitations of prior methods and enabling robust, multi-objective protein engineering in real-world\nsettings.\nComment 2\nDesigning proteins that are improved relative to natural ones:\nThe authors report that the lysozyme designs exhibit a 100-fold improvement over hen egg white lysozyme (HEWL).\nThis sounds impressive, but T4 lysozyme (T4L) is sold by Sigma and advertised as possessing 200-fold improved\nactivity relative to HEWL. T4L is within the training set and their design may be based on T4L, but the authors don't\ncompare their designs' activities to it, only relative to the less efficient HEWL. This is, again, simply misleading.\nResponse: Thank you for your insightful comment, and we apologize for any potential misunderstanding. We would\nlike to clarify that TX-RL15, which exhibits a 100-fold improvement over hen egg white lysozyme (HEWL), belongs\nto the transglycosylase family rather than being derived from T4 lysozyme (T4L), based on sequence and structural\nsimilarity. As shown in Supplementary Data 2, TX-RL15 consists of 78 amino acids (AA), much shorter than the\ntypical T4L, which contains around 160 AA.\nTo address the concern raised by the reviewer, we have now benchmarked ORI-generated lysozymes and HEWL\nagainst a T4L enzyme (PDB ID: 216L), which is well-studied and widely recognized for its high catalytic activity1.\nTo ensure a fair comparison, all proteins in this benchmark were expressed using the same cell-free expression system.\nAs shown in Supplementary Figure 4, the cell-free system expressed T4L consistently exhibits higher enzyme activity\nthan HEWL across all pH environments, with fold changes that are in line with those reported in the literature(Blaber,\nZhang, and Matthews 1993; Matthews et al. 1981; Abouhmad et al. 2017). Furthermore, Supplementary Figure 5\nshows that 60% of the ORI-generated lysozymes outperform T4L in enzyme activity. Notably, the best performer,\nTX-RL15, exhibits a 34-fold increase in enzyme activity compared to T4L, both of which were expressed in the same\ncell-free system. These new results and corresponding figures have been included in the revised version of the\nmanuscript.\nWe hope this additional comparison clarifies the superior performance of our designs and provides a more accurate\ncontext for evaluating their activity. Thank you again for your valuable feedback.\nAdded Figures:\nSupplementary Figure 4. Comparison of the assayed enzyme activity of T4 lysozyme and hen egg-white lysozyme\nacross different pH levels.\nSupplementary Figure 5. Comparison of the enzyme activity between ORI-generated proteins and T4 lysozyme. The\ny-axis indicates the fold change in lysozyme activity of ORI-generated proteins relative to T4 lysozyme.\nAdded Results:\nSince T4 lysozyme (T4L) is known to have much higher lysozyme activity than hen lysozyme39, we further\nbenchmarked ORI-generated lysozymes against T4L. To this end, we expressed a classic T4L (PDB ID: 216L) in a\ncell-free system. As shown in Supplementary Figure 4, the expressed T4L consistently outperformed hen lysozyme\nacross all pH environments. Remarkably, Supplementary Figure 5 reveals that 60% of ORI-generated lysozymes\nsurpass T4L in activity, with TX-RL15 standing out as the best performer, showing a 34-fold increase relative to T4L.\nComment 3\nThe highlight of the paper is the design of a multifunctional lysozyme/chitinase, which they claim is \"entirely novel\nfunction that have never existed before\". This sounds impressive, but lysozyme and chitinase exhibit highly related\ncatalytic mechanisms and similar folds, and natural lysozymes and chitinases exhibit cross reactivities, albeit weaker\nthan their native activities. So this multifunctionality is entirely not novel and in fact exists in natural families.\nResponse: Thank you for your thoughtful comment. We sincerely apologize for the overstatement in our original\ndescription and greatly appreciate the opportunity to clarify this point. In response to your feedback, we have revised\nthe manuscript to better reflect the context of multifunctionality in our design. While we initially described the\nmultifunctional nature of the lysozyme/chitinase as \"entirely novel,\" we now acknowledge that lysozymes and\nchitinases share related catalytic mechanisms and structural folds, and exhibit cross-reactivity, though weaker than\ntheir native activities. Our work, therefore, does not represent a completely unprecedented function, but rather a\nrationally engineered enhancement of an existing, latent multifunctionality. However, it is important to note that our\ndual-function enzyme outperforms single-function enzymes in each activity, unlike natural bifunctional enzymes,\nwhich typically show a reduction in activity for one of their functions.\nTo illustrate this, we performed a comparative analysis of the enzyme activity of our dual-function protein TX-ME\nagainst Hevamine A (HA, PDB: LLO), the naturally occurring dual-function protein reported to exhibit the highest\nlysozyme and chitinase activities, as well as against monofunctional enzymes, including T4 lysozyme (T4L, PDB:\n216L) and Glycoside Hydrolase Family 18 chitinase (GH18C, PDB: 1HKK). All proteins were expressed using a\ncell-free system, and their respective enzyme activities were evaluated in the same wet lab assay to ensure consistency\nand comparability. TX-ME exhibited significantly higher lysozyme activity than T4L (relative activity = 2.227), and\ngreater chitinase activity than GH18C (relative activity = 1.314). This improvement is a key distinguishing feature\nof our design. In contrast, the natural dual-function enzyme HA showed substantially lower lysozyme activity\n(relative activity = 0.246) compared to T4L, and much weaker chitinase activity (relative activity = 0.203) compared\nto GH18C (Figure 5F).\nWe have updated the manuscript to show the new results, highlighting that our multifunctional enzyme represents an\nenhancement within the natural enzyme families, rather than a completely novel function. We hope this revision\naddresses your concerns and more accurately represents the novelty and significance of our work. Thank you again\nfor your valuable feedback.\nAdded Figures\nFigure 5 (f) Assayed lysozyme and chitinase activities of TX-ME compared with the natural dual-enzyme Hevamine\nA (HA, PDB: 1LLO) and monofunctional enzymes, including T4 lysozyme (T4L, PDB: 216L) and Glycoside\nHydrolase Family 18 chitinase (GH18C, PDB: 1HKK).\nAdded Results\nPart 1:\nAs previously mentioned, our work with ORI has demonstrated its capacity to design proteins with enhanced functions\nand properties. Building on this success, we sought to explore ORI’s potential further by designing a multifunctional\nenzyme capable of exhibiting both lysozyme (EC 3.2.1.17) and chitinase (EC 3.2.1.14) activities. Lysozyme, known\nfor its antimicrobial properties, is commonly used as a natural preservative in the food industry, while chitinase plays\na key role in the degradation of chitin, a major component of insect and crustacean exoskeletons. Interestingly, some\ntypes of lysozyme and chitinase share similar catalytic mechanisms and structural folds, such as the TIM barrel\n(alpha/beta barrel). However, natural lysozymes and chitinases exhibit only weak cross-reactivity, with each enzyme\nfunctioning primarily within its own specific domain. Notably, proteins like Hevamine A (HA), which exhibit both\nlysozyme and chitinase activities, show much weaker enzymatic functions than those with a single, dedicated activity.\nDesigning a multi-enzyme protein with both lysozyme and chitinase functionalities, and ensuring it performs better\nthan single-function counterparts, presents a significant challenge.\nPart 2:\nWe conducted a comparative analysis of the enzyme activity of TX-ME against the dual-function enzyme Hevamine\nA (HA) (PDB: LLO) and monofunctional enzymes, including T4 lysozyme (T4L) (PDB: 216L) and Glycoside\nHydrolase Family 18 chitinase (GH18C) (PDB: 1HKK). All proteins were expressed using a cell-free system, and\ntheir respective enzyme activities were evaluated in the same wet lab assay to ensure consistency and comparability.\nTX-ME exhibited significantly higher lysozyme activity than T4L (relative activity = 2.227), and greater chitinase\nactivity than GH18C (relative activity = 1.314). In contrast, the natural dual-function enzyme HA showed\nsubstantially lower lysozyme activity (relative activity = 0.246) compared to T4L, and much weaker chitinase activity\n(relative activity = 0.203) compared to GH18C (Figure 5 D and 5E). These results underscore the enhanced\nfunctionality of TX-ME, which outperforms both the monofunctional enzymes and the natural dual-function enzyme\nHA in terms of catalytic activity for both lysozyme and chitinase activities. The significantly higher enzyme activities\nobserved for TX-ME suggest that the multi-functional design of the protein not only retains but also enhances the\nindividual functionalities compared to their natural or engineered counterparts.\nReviewer #2 (Remarks to the Author):\nThis is a very exciting and ambitious paper. The ORI system represents a creative integration of autoregressive\nlanguage models (PGM), pre-trained function predictors (USM), and a reinforcement learning loop with wet-lab\nfeedback (RLWF), all scaffolded around a structured ontology framework. The paper demonstrates the ability to\ngenerate proteins with useful properties—enzyme activity, thermostability, and even multi-enzyme functionality—\nhighlighted by experimental validation of several sequences that outperform natural enzymes like lysozyme and\nchitinase. Overall, and the authors make a compelling case for ORI as a full-stack generative biology system.\nThat said, to me, there are several areas where the manuscript falls short—particularly in mathematical clarity,\nbenchmarking, and contextualization with respect to recent generative modeling frameworks. I really think this paper\ncould benefit from significantly more rigorous comparisons to state-of-the-art models, as well as stronger justification\nfor many of the modeling decisions made. Below are five major concerns:\nComment 1\n1. The mathematical formulation of the model is super vague and inconsistent throughout. The PGM model is\ndescribed as a decoder-only autoregressive Transformer (similar to LLaMA), but the training objective, tokenization\nschema, and prompt structure are never clearly defined. On page 6 and Figure 1, we see prompts such as\n<Homo><Oxidoreductase><EC:1.1.1.181>..., but how these are tokenized, embedded, and used as prefix context is\nnever precisely described. Is the ontology token fed at the first position only? How long are the ontology sequences\nrelative to the generated protein tokens? Moreover, the RLWF formulation in Figure 3 and page 13 is highly\ncondensed. The modified DPO loss used in training is only given symbolically, and the preference dataset\nconstruction lacks clarity around sampling, control baselines, and reward shaping. I’d strongly recommend the\nauthors formally define the loss functions and generative process in each phase, perhaps using equations in an\nappendix, as the current level of abstraction is insufficient for replication.\nResponse: We thank the reviewer for pointing out the lack of clarity in the mathematical formulation. In the revision,\nwe have explicitly defined the tokenization schema, ontology-conditioned prompts, and their relation to the generated\nprotein tokens. We also provided formal equations for the training objectives and generative process in each phase,\nincluding the modified DPO loss. Furthermore, we clarified the construction of the preference dataset, including\nsampling strategy, control baselines, and reward shaping. These additions, together with an appendix containing full\nmathematical definitions, ensure that the method is now clearly presented and fully replicable.\nAdded Methods:\nTokenizer Construction\nWe constructed a byte-pair encoding (BPE)54 tokenizer tailored for protein design, with a vocabulary size of 49,152.\nTo support flexibility in encoding heterogeneous biological inputs, we reserved 200 token IDs as special tokens, which\ncan be dynamically replaced at runtime to represent ontology terms, annotations, or experimental constraints. The\nreserved token set is drawn from a comprehensive replacement resource pool encompassing digits, Latin and Greek\nalphabets, punctuation (! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \\ ] ^ _ ` { | } ~), whitespace characters\n(space \\t \\n \\r \\x0b \\x0c), custom tokens (<CLS>, <PAD>, <MASK>, <EOD>, <:>), and additional domain-specific\nsymbols (£, ¥, «, », ±, ·). This design ensures broad coverage for encoding biological prompt phrases and precise\nparsing of specialized terminology, such as protein functional annotations and enzyme classification numbers (e.g.,\nEC:1.1.1.181).\nDuring tokenization, each protein sequence was segmented into BPE subword tokens, optimized to capture both short\nfunctional motifs (e.g., catalytic triads, signal peptides) and long repetitive patterns (e.g., coiled-coils, tandem\nrepeats). Ontology inputs—including functional annotations, EC numbers, structural constraints, and protein family\nnames—were tokenized separately using the special token pool and subsequently concatenated with the protein\ntokens to form unified input sequences. In total, this process produced over 60 billion tokens from the integrated\nprotein sequence and annotation datasets, which were used to train the PGM model.\nThe Protein Design Agent (PDA)\nThe Protein Design Agent (PDA) serves as the orchestration layer that translates user requirements into ontology\nprompts and function calls to the Protein Generation Model (PGM). In our protein design agent (PDA), we use a\nmodule similar to Gorilla22 and Toolformer23, to convert users’ task requirements to function calls of our models.\nSupervised Fine-tuning with LoRA: To achieve this goal, we perform low-rank parameter adaption (LoRA) 51 on\nthe open-sourced large language model Gemma2-2B52. The model is fine-tuned for 1 epoch with a single pass of all\ndata. The batch size is 32 and the learning rate is 1e-4. The rank for LoRA is 16.\nTo obtain the training data, we use GPT4 to generate input and output pairs for each function and characteristic by\nproviding GPT4 with the input requirements and functionality description of each function and characteristic. The\ninputs are the user’s requirements and the outputs are the corresponding function and characteristic ontology prompts\nand model function calls. We also provide high-quality examples generated by human experts as examples to GPT4\nfor the generation. Finally, we collected 5000 pairs of data generated by GPT4 and used GPT4 to score these\ngenerated data based on the scope of requirements and the correctness of ontology prompts and function calls. We\nfurther validated the correctness of the data with human experts. We used the top 2000 scoring data pairs for training.\nIn addition, to detect unsupported requirements, we extracted 200 input prompts from the Stanford Alpaca dataset\nand designated the corresponding outputs as “unsupported”.\nExamples of training data:\n{\n\"input\": \"Our goal is to design 10 lysozymes that can be expressed by yeast.\",\n\"output\": \"**API call**: PGM(Enzyme=\\\"Lysozyme\\\", EC=\\\"3.2.1.17\\\", Species=\\\"Saccharomyces\\\")\\n**num of\nsamples**: 10\"\n},\n{\n\"input\": \"The task is to construct a high-temperature stable protein that acts like chitinase in glycosyl hydrolases\nfamily 18.\",\n\"output\": \"**API call**: PGM(Enzyme=\\\"Glucosaminidase\\\", EC=\\\"3.2.1.14\\\", Pfam=\\\"Glyco_hydro_18\\\",\nStructure=\\\"TIM beta/alpha-barrel\\\", Melting Temperature=\\\"90\\\")\\n**num of samples**: 1\"\n},\n{\n\"input\": \"Write a song lyric about birds\",\n\"output\": \"Not Support\"\n}\nThe supervised training objective minimized the cross-entropy loss between predicted outputs and the ground-truth\nontology/function-call pairs:\n𝑇\n𝐿 =−∑𝑙𝑜𝑔𝑃(𝑦 |𝑦<𝑡,𝑥)\n𝑆𝐹𝑇 𝑡\n𝑡=1\nwhere x denotes the user requirement, y is the ground-truth token at step t, and T is the output sequence length.\nt\nReinforcement Learning with PPO: After fine-tuning using low-rank parameter adaptation (LoRA), we perform\nProximal policy optimization (PPO)53 to further finetune the PDA module to improve the quality of ontology prompts\nand function calls. Specifically, given each requirement from the 2000 training data pairs, we used the module to\ngenerate its ontology prompts and function calls and based on these ontology prompts and function calls, called\nPGM to generate protein sequences and USM to predict the protein function and characteristics. Then we assigned\nthe reward for the PDA based on the quality of the generated sequences.\nA reward function was defined as follows:\n• Failure in function call or invalid sequence: reward = 0.\n• Valid sequence generation: base reward = 1.\n• Correct enzyme category (verified by USM classifier): +1 reward.\n• Requirement-specific property satisfaction (e.g., thermal stability, verified by USM predictor): +1 reward.\nThus, the reward R∈[0,3] reflected the quality and correctness of PDA outputs. PPO updated the policy by\nmaximizing the expected reward while constraining policy divergence:\n𝐿 =𝐸 [𝑚𝑖𝑛⁡(𝑟(𝜃)𝐴 ,𝑐𝑙𝑖𝑝(𝑟(𝜃),1−𝜖,1+𝜖)𝐴 )]\n𝑝𝑝𝑜 𝑡 𝑡 𝑡, 𝑡 𝑡,\nwhere 𝑟(𝜃) is the probability ratio between new and old policies and 𝐴 is the estimated advantage.\n𝑡 𝑡,\nAfter obtaining the rewards for the output of the PDA, we perform PPO to update the model.\nOverall Training Objective: The PDA training proceeded in two stages:\n1. Supervised fine-tuning (SFT) using cross-entropy loss to learn accurate mappings from user requirements to\nontology/function-call sequences.\n2. Reinforcement learning (RL) with PPO, optimizing PDA to maximize downstream task rewards by validating\nprotein generation outcomes.\nThis combined loss formulation ensured that PDA not only reproduced correct prompts from supervised data but also\ngeneralized effectively to unseen requirements by aligning with task-specific biological rewards.\nThe Protein Generation Model (PGM)\nTo train the protein generation model (PGM), we adopt a prompt-based encoding schema. The sequence begins with\nontology tokens that provide conditioning context, introduced at the first position of the sequence. The ontology\ncontext typically contains 5–30 tokens, depending on the granularity of the annotation (e.g., “enzyme class\nEC:1.1.1.181” or “IgG1 antibody heavy chain”). In contrast, protein sequences are substantially longer, ranging\nfrom 100 to 1000 tokens after BPE segmentation. The input structure follows the format:\n<CLS> Ontology_Tokens <:> Protein_Tokens <EOD>\nThis design ensures that ontology context acts as a prefix, guiding generation while preserving autoregressive protein\nmodeling. All tokens (protein and ontology) are mapped into a shared embedding space via a learnable embedding\nlayer. Positional encodings are applied to preserve order. Ontology tokens, although distinct in semantics, share the\nembedding matrix with protein tokens to enable the transfer of contextual information. This joint representation\nallows the PGM model to attend simultaneously to biological prompts and sequence context during self-attention,\nenabling it to learn the statistical patterns and relationships among protein sequences, structures and biological\nfunctions.\nPGM is an autoregressive large language model that shares similarities with the decoder-only architecture used in\nLLaMA serial models. PGM consists of embedding layers, multiple decoder blocks, and multilayer perceptron (MLP)\nlayers. The embedding layer maps each token to a continuous vector representation, capturing its unique properties.\nThe decoder blocks capture the sequential dependencies between tokens, allowing the model to learn the context and\nrelationships between functions and protein sequences. The decoder block follows the standard transformer\narchitecture55, applies RMSNorm for pre-normalization 56 and multi-head attentions, uses the SwiGLU activation\nfunction 57, and rotary positional embeddings 58. The MLP layers consist of a fully connected layer and a\nnormalization layer, which are responsible for generating the final output tokens that represent the generated protein\nsequences. The 3B PGM model consists of 24 decoder blocks and 24 multi-head attentions, with embedding\ndimensions of 3072. For evaluation purposes, we also trained PGM models with parameter sizes of 1 billion (1B), 3\nbillion (3B) and 7 billion (7B). The annotated UniRef90 dataset and UniRef50 were utilized to train these models,\nrespectively. After evaluation, we used the 3B PGM model as the default protein generation model in ORI.\nThe model is trained under a causal language modeling (CLM) objective, predicting the next token given the\nconcatenated prefix–protein sequence. Formally, given ontology tokens O = (o , ... ,o ) and protein tokens P =\n1 m\n(p ,...,p ), the training objective is:\n1 n\n𝑛\n𝐿 =∑𝑙𝑜𝑔𝑃(𝑝 |𝑂,𝑝<𝑡)\n𝑡\n𝑡=1\nThis encourages the model to learn mappings from ontology context to sequence-level realizations, effectively\ngrounding protein generation in structured biological knowledge.\nIn ORI, PGM primarily operates on ontology prompts generated by the Protein Design Agent (PDA). However, users\ncan also interact directly with PGM using simplified prompts. For example:\n• <Glucosaminidase><temperature90><:>\n• <EC:3.1.1.101><temperature60><:>\nBoth prompt forms are correctly parsed by PGM, which then generates protein sequences aligned with the specified\nfunctional and physicochemical requirements.\nIterative Optimization with Wet Lab Feedback\nWet lab experiments are crucial in developing protein generative models as they provide valuable biological insights\ninto the functionalities of the generated proteins. These insights are essential for validating the accuracy and\neffectiveness of the models. However, it is often observed that only a subset of these proteins exhibit the desired\nfunctionalities and activities, while the majority do not show significant activity. To improve the performance of these\nmodels, researchers often analyze the outcomes of wet lab experiments and devise heuristic methods to refine or\nretrain the models. However, this approach heavily relies on the subjective expertise of individual researchers, which\ncan introduce variability and limit reproducibility.\nTo address this challenge, we propose a novel framework that is named Reinforcement Learning from Wet-lab\nFeedback (RLWF). This framework utilizes feedback from wet lab experiments and selectively incorporates generated\ndata to optimize model preference. Doing so systematically increases the likelihood of generating protein sequences\nwith the targeted properties. Reinforcement Learning from Human Feedback (RLHF) has been effectively employed\nto optimize the output of Large Language Models (LLMs), aligning the generated responses with user preferences61.\nIn this approach, a dataset D containing preference pairs (x, yw, yl)—comprising a prompt x, a preferred response yw,\nand a less preferred response yl—is utilized. The model π is then optimized to favor yw over yl. An alternative to\nθ\nRLHF, Direct Preference Optimization (DPO)62, simplifies this by minimizing the loss of DPO, using a fixed reference\nmodel π :\nref\n𝜋 (𝑦𝑤|𝑥) 𝜋 (𝑦𝑙|𝑥)\n𝜃 𝜃\n𝐿 =𝛦 −𝑙𝑜𝑔𝜎(𝛽𝑙𝑜𝑔 −𝛽𝑙𝑜𝑔 )\n𝐷𝑃𝑂 (𝑥,𝑦𝑤,𝑦𝑙)~𝐷 𝜋 (𝑦𝑤|𝑥) 𝜋 (𝑦𝑙|𝑥)\n𝑟𝑒𝑓 𝑟𝑒𝑓\nHere, σ denotes the sigmoid function, and β is a hyperparameter that regularizes the model’s deviation from the\nreference model. Iterative enhancements of RLHF, such as Iterative DPO 35,36,63, have been developed to iteratively\nrefine the model. This method involves generating a set of new preference pairs using the current model, evaluating\nthem via a reward model or human judgment, and optimizing the model on this preference dataset using DPO in each\niteration. Our framework adopts this iterative approach. Initially, a series of protein sequences S is generated using\n0\nthe current model prompted by x. The first iteration uses a pre-trained model. Subsequent virtual screening identifies\na subset of promising candidates, S , for wet-lab testing, with the remainder labeled as S . Post-testing, sequences\n1 2\nthat exhibit desired functionality are used as preferred responses Sw . The sequences in S with low reference model\n1 2\nloss and low pLDDT scores are used as unpreferred responses Sl .\navoiding those preferred by the current model yet less likely to exhibit desired functionality. To address the model’s\ntendency to generate overly long sequences—a known issue termed length bias in RLHF 63–65—we incorporate a\nlength regularizer into the DPO loss as R-DPO loss:\n𝜋 (𝑦𝑤|𝑥) 𝜋 (𝑦𝑙|𝑥)\n𝜃 𝜃\n𝐿 =𝛦 [−𝑙𝑜𝑔𝜎(𝛽(𝑙𝑜𝑔 −𝑙𝑜𝑔 )+𝛼(|𝑦𝑤|−|𝑦𝑙|))]\n𝑅−𝐷𝑃𝑂 (𝑥,𝑦𝑤,𝑦𝑙)~𝐷 𝜋 (𝑦𝑤|𝑥) 𝜋 (𝑦𝑙|𝑥)\n𝑟𝑒𝑓 𝑟𝑒𝑓\nHere, |𝑦𝑤| and |𝑦𝑙| is the sequence length of yw and yl, and α is a hyperparameter that controls the strength of\nthe length penalty. We set β = 0.1 and α = 0.05 in this study. The model is then optimized by minimizing the R-DPO\nloss 𝐿 .\n𝑅−𝐷𝑃𝑂\nIterative enhancements of RLHF, such as Iterative DPO 35,36,63, have been developed to iteratively refine the model.\nThis method involves generating a set of new preference pairs using the current model, evaluating them via a reward\nmodel or human judgment, and optimizing the model on this preference dataset using DPO in each iteration.\nOur framework adopts this iterative approach to refine the model for a given prompt x, for example,\n“<Glucosaminidase><temperature90><:>”. In the first iteration, both the reference model and the current model\nare initialized with the pre-trained model.The reference model is fixed.\nAt the start of each iteration, the current model is used to generate protein sequences by repeatedly feeding it the\nprompt x. This process is repeated 10,000 times to obtain a dataset, S₀, comprising 10,000 sequences. Virtual\nscreening is then performed to identify a subset of promising candidates, S₁ (typically ~100 sequences), for wet-lab\nvalidation. The remaining 9,900 sequences form the set S₂.\nFor the sequences in S₂, we estimate their perplexity using the reference model and select the top K sequences with\n𝑝𝑝𝑙𝑥\nthe highest perplexity to form the set 𝑆 . We also evaluate their predicted local distance difference test (pLDDT)\n2\n𝑝𝐿𝐷𝐷𝑇 𝑝𝑝𝑙𝑥\nscores and select the K sequences with the lowest scores to form the set 𝑆 . The intersection of 𝑆 and\n2 2\n𝑆 𝑝𝐿𝐷𝐷𝑇 is used as the set of unpreferred responses, Sl. The value of K is chosen such that the size of Sl is greater than\n2\n100; in practice, K is set to 500. The preferred responses set, Sw, is defined as S₁.\nWe then construct the training dataset D using all possible pairs of preferred (yw ∈ Sw) and unpreferred (yl ∈ Sl)\nresponses. This selection is based on the premise that the model should generate more functionally expressive\nsequences, while avoiding those preferred by the current model yet less likely to exhibit desired functionality.\nThe model is fine-tuned using the R-DPO loss for one epoch over the dataset D. The updated model is subsequently\nused as the current model in the next iteration, which is used to generate new sequences for testing, following the\nsame process to construct the preference dataset and optimize the model. The reference model in each iteration is a\nfixed copy of the model updated in the previous iteration. The model loss of the generated sequences is estimated by\nthe auto-regressive loss of the reference model.\nThe process of preference dataset construction is summarized in Supplementary Algorithm 1 and illustrated in\nSupplementary Figure 9. The process of the proposed framework is summarized in Supplementary Algorithm 2.\nComment 2\n2. The comparisons to other generative protein models are incomplete and dated. There is no discussion of recent\ngenerative models like DPLM, EvoDiff, DiMA, MeMDLM or AMP-Diffusion, all of which have shown strong\nperformance in different protein design tasks using diffusion models. Similarly, discrete flow matching models like\nDirichlet Flow Matching, Fisher Flow Matching, Gumbel Softmax Flow Matching are absent—yet these models\nenable guided, conditional generation in a way directly comparable to ORI's ontology-conditioned decoding -- these\nshould all be cited. The manuscript suggests that reinforcement learning is the key innovation, but in reality, most\nmodern generative biology frameworks rely on guided discrete sampling rather than RL, due to the instability and\nsample inefficiency of reward-based learning in high-dimensional sequence space. The authors should at minimum\ncite these models and explain what advantages, if any, RLWF provides over guided discrete sampling. Otherwise, I\ncannot recommend acceptance if the authors don't acknowledge the state-of-the-art in the field.\nResponse: We thank the reviewer for this insightful comment and fully agree that our initial version did not\nsufficiently acknowledge recent state-of-the-art generative frameworks. In the revised manuscript, we have\nsubstantially expanded the Introduction to include relevant diffusion-based protein generation models such as DPLM,\nEvoDiff, DiMA, MeMDLM, and AMP-Diffusion, as well as discrete flow matching models including Dirichlet Flow\nMatching, Fisher Flow Matching, and Gumbel Softmax Flow Matching. These works are now appropriately cited\nand discussed.\nTo avoid confusion, we explicitly acknowledge that ORI’s ontology-conditioned decoding constitutes a form of\nguided discrete sampling, comparable in spirit to these diffusion and flow-matching frameworks. We have revised\nthe text to reflect this conceptual alignment.\nAt the same time, we emphasize that ORI goes beyond guided sampling by incorporating reinforcement learning\nwith experimental feedback (RLWF). As the reviewer correctly notes, guided discrete sampling is often more stable\nand sample-efficient in high-dimensional sequence space. However, RLWF offers complementary advantages: (i) it\nallows optimization of non-differentiable or sparse functional objectives (e.g., thermostability, expression yield, wet-\nlab activity assays) that cannot be directly incorporated into score-matching or flow-based objectives; and (ii) it\nenables iterative improvement of the generative model by directly integrating experimental feedback loops, which is\nessential for practical deployment in protein engineering pipelines. We believe this complementary role of RLWF\nprovides ORI with additional robustness and adaptability beyond existing guided sampling frameworks.\nAdded Introduction:\nPart1:\nBuilding on these foundations, cutting-edge generative approaches now span multiple technical paradigms. Methods\nsuch as DPLM10and EvoDiff11 apply diffusion directly in sequence space to capture evolutionary constraints, while\nDiMA12 and MeMDLM13 leverage latent or discrete diffusion to improve controllability and enable applications like\nmembrane protein design. ProteinSGM14 extends score-based generative modeling to protein sequences, offering\nanother powerful diffusion paradigm.\nPart2:\nSpecialized frameworks like PepTune16 and AMP-Diffusion17 further highlight the potential of generative models in\ntherapeutic peptide design, optimizing multiple objectives and targeting antimicrobial activity.\nPart3:\nParallel to these, discrete flow matching frameworks—including Dirichlet Flow Matching, Fisher Flow Matching,\nand Gumbel Softmax Flow Matching—have emerged as promising alternatives, enabling guided and conditional\ngeneration in discrete sequence spaces20. Together, these approaches exemplify the prevailing paradigm of guided\ndiscrete sampling in modern generative biology.\nPart4:\nBuilding on these insights, we introduce ORI (Ontology Reinforcement Iteration), a general-purpose and scalable\nframework for generating functional proteins. ORI unifies ontology-conditioned decoding with reinforcement\nlearning with experimental feedback (RLWF) into a single system. Its innovations include: (1) using structured\nontologies as semantic prompts to inject functional, structural, and property-level constraints into the generative\nprocess, enabling controllable and interpretable sequence generation; (2) combining the controllability of guided\ndiscrete sampling with the ability of RL to directly optimize sparse or non-differentiable objectives; and (3)\nimplementing a closed-loop evolutionary cycle that involves generation, experimental measurement, feedback-driven\nreward assignment, ontology refinement, and model updating, thereby enabling the co-evolution of both the\ngenerative model and its ontology. In this way, ORI leverages the strengths of guided sampling and RL simultaneously,\naddressing limitations of prior methods and enabling robust, multi-objective protein engineering in real-world\nsettings.\nComment 3\n3. From my analysis, the experiments lack clear baselines against either modern PLMs or modality-specific\ngenerative models. The only comparisons made are to ProteinMPNN, ESM2, and natural protein statistics? There is\nno head-to-head against recent structure-free models like ProteinSGM, ProGen3, PepTune -- all of which can also be\nused to condition generation on properties like stability, non-toxicity and other function. Further, for domain-specific\nbaselines, tools like UniPTM (for PTM prediction), PTM-Mamba, for PTM-aware embedding, and DPAC (for DNA-\nprotein design) offer directly comparable pipelines. The authors argue that ORI integrates sequence, structure, and\nfunction into a single prompt, but they never show why this yields better results than methods that integrate these\nfeatures more explicitly (via joint contrastive training, or structure-conditioned diffusion). I suggest the authors\nbenchmark ORI-generated sequences against outputs from these domain-specific, particularly on shared tasks.\nResponse: We thank the reviewer for this important comment regarding baselines. Following the suggestion, we\nhave expanded our comparisons to include ProteinSGM, ProGen3, and EVO2, representing recent advances in\nstructure-based, sequence-based, and DNA-based generative models, respectively. We benchmarked these models\nalongside ORI on the central task of conditioned protein generation, assessing the generated proteins using structural\nconfidence, functional relevance metrics, and thermostability. The results are now presented in Supplementary\nFigures 2, 3, and 6. These additional baselines provide a more complete and transparent context for evaluating ORI’\ns performance relative to modern state-of-the-art methods.\nFor other models mentioned by the reviewer, including PepTune, UniPTM, PTM-Mamba, and DPAC, our current\nframework does not directly overlap with their task definitions or model scope. Therefore, we were unable to perform\nhead-to-head benchmarking against these methods. We fully agree, however, that these systems represent important\npoints of comparison. To address this, we have added a discussion of these domain-specific generative systems and\nexplicitly acknowledged the limitations of our current evaluation, noting that future extensions of ORI should include\nbenchmarking on PTM-aware and DNA–binding-protein design tasks.\nWe hope these revisions improve the fairness and completeness of the evaluation, while transparently discussing the\ncurrent limitations of our model.\nAdded Figures:\nSupplementary Figure 2. Comparison of ORI with PGM, ProGen3, ProteinSGM, and Evo2 in conditioned protein\ngeneration. The y-axis shows the proportion of generated proteins with pLDDT scores above 70 and 90, respectively.\nThe conditioning prompts used for generation include protein descriptions, structural information, and sequences.\nAlthough the prompt formats differ across methods, they convey equivalent semantic information. Statistical\nsignificance between ORI and each baseline is indicated by stars (*p < 0.05, **p < 0.01, ***p < 0.001, ****p <\n0.0001), calculated using the Wilcoxon test.\nSupplementary Figure 3. Comparison of ORI with PGM, ProGen3, ProteinSGM, and Evo2 in generating functional\nproteins. The y-axis shows the proportion of generated proteins with the desired functional domain. Conditioning\nprompts for generation were provided as descriptions, structure-based, or sequence-based cues for specific protein\nfunctions, depending on the requirements of each method. Although the prompt formats differ across methods, they\nconvey equivalent semantic information. Statistical significance between ORI and each baseline is indicated by stars\n(*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001), calculated using the Wilcoxon test.\nSupplementary Figure 6. Comparison of thermostable protein generation by ORI, ProGen3, ProteinSGM, and Evo2.\nThe y-axis represents the melting temperatures of proteins generated by each method. Statistical significance between\nORI and each baseline is indicated by stars (*p < 0.05, **p < 0.01, ***p < 0.001, **p < 0.0001), calculated using\nthe Wilcoxon test.\nAdded Results\nPart1:\nWe benchmarked the performance of ORI and its protein generation module, PGM, against the most recent state-of-\nthe-art (SOTA) protein generation models, including ProteinSGM14, ProGen315, and EVO241, which represent\nrecent advances in structure-based, sequence-based, and DNA-based generative modeling, respectively. For\nevaluation, we generated 1,000 proteins with each method and assessed their quality using structural confidence and\nfunctional relevance metrics. ORI achieved a markedly higher proportion of proteins with strong structural\nconfidence, as reflected by the percentage of sequences with pLDDT scores above 70 and 90 (Supplementary Fig. 2),\nand a greater proportion of proteins containing the desired functional domains (Supplementary Fig. 3). Moreover,\ndirect comparison between ORI and its core generative module PGM revealed substantial performance gains arising\nfrom the integration of PDA and USM. Together, these contributions highlight the significant advantage of ORI’s\nunified framework for protein design, clearly surpassing current generative models.\nPart2:\nWe benchmarked the performance of ORI, against ProteinSGM14, ProGen315, and EVO241, in the task of generating\nproteins with high thermostability. ORI consistently produced proteins with substantially higher melting temperatures,\ndemonstrating a clear advantage over these state-of-the-art generative models (Supplementary Figure 6).\nAdded Methods\nBenchmark with Baseline Methods\nTo comprehensively evaluate ORI, we benchmarked it against a wide range of recent state-of-the-art models,\nspanning conditioned protein generation and thermostability optimization.\nProteinSGM: ProteinSGM is a score-based diffusion model that produces native-like protein structures14. We used\nthe crystal structure of hen egg-white lysozyme (PDB ID: 132L) and T4 lysozyme (PDB ID: 216L) as a template,\nwhere specific regions were masked to provide partial structural information as input. ProteinSGM was then used to\ngenerate 1000 conditional 6D coordinates from the ProteinSGM model, including Cβ–Cβ distances, ω and θ dihedral\nangles, and ϕ planar angles. These coarse-grained 6D representations were then converted into full-atom protein\nbackbones using Rosetta MinMover46, which performed constrained backbone minimization to ensure physically\nrealistic geometries.\nProGen3: ProGen3 is an autoregressive protein language model designed for sequence generation15. We provided\nthe sequences of hen egg-white lysozyme (PDB ID: 132L) and T4 lysozyme (PDB ID: 216L) as conditioning contexts.\nUsing these templates, we employed the ProGen3-3B model to generate amino acid sequences in both N-to-C\n(forward) and C-to-N (reverse) directions, conditioned on the functional and sequence constraints. A total of 1000\nprotein sequences were sampled, ensuring coverage of diverse sequence space.\nEvo2: Evo 2 is a large-scale biological foundation model trained on 9.3 trillion DNA bases that learns genome-wide\nbiological features directly from DNA sequence, enabling accurate prediction of functional variants and controllable\ngenome-scale sequence generation41. For Evo2-based conditional protein generation, we first designed functional\nprompts according to the model’s requirements, encoding structural and biochemical features of target proteins.\nSpecifically, we constructed text-based prompts describing hen egg-white lysozyme (PDB ID: 132L) and T4 lysozyme\n(PDB ID: 216L), including their structural class, enzymatic activity, and functional attributes. Conditioned on these\nprompts, Evo2 was used to generate 1,000 DNA sequences for each protein, ensuring that the outputs satisfied the\nspecified biochemical and functional constraints. The generated DNA sequences were subsequently translated into\nprotein sequences, followed by filtering to remove incomplete or invalid coding regions before downstream evaluation.\nProteinMPNN: ProteinMPNN is a graph-based generative model for protein sequence design conditioned on\nbackbone structures43. For thermostability optimization, we first predicted the backbone structure of each target\nprotein using AlphaFold26, which served as the input scaffold. ProteinMPNN was then applied to redesign the amino\nacid sequences on this backbone. For each target, we generated 1000 redesigned variants and selected the top 100\ncandidates based on the predicted stability score. The redesigned sequences were further filtered by predicted folding\nquality (pLDDT > 70) to ensure structural plausibility.\nRosetta: Rosetta is a widely used computational platform for macromolecular modeling, design, and analysis,\nintegrating physics-based energy functions with knowledge-based statistical potentials46. For Rosetta-based\nthermostability optimization, we used the Rosetta ddG and Rosetta FastDesign protocols to perform sequence\nredesign aimed at improving folding stability. Specifically, starting from the experimentally determined or\nAlphaFold2-predicted protein structure, iterative rounds of side-chain repacking and backbone minimization were\nperformed, guided by the Rosetta energy function. Each design trajectory generated multiple variants, and 1000\ncandidate sequences were obtained for each protein. The designed sequences were ranked according to Rosetta’s\nenergy scores, with the top 100 lowest-energy conformations selected as putative thermostable variants.\nAdded Discussion:\nWhile ORI demonstrates strong performance across diverse protein design tasks, we recognize important limitations\nin its current scope. Domain-specific generative systems such as PepTune18 for peptide generation and optimization,\nUniPTM51 and PTM-Mamba52 for post-translational modification (PTM)–aware modeling, and DPAC53 for DNA–\nbinding-protein prediction and design adopt specialized architectures and training objectives that explicitly integrate\nfunctional constraints not yet captured by ORI. In addition, many challenging protein classes remain outside the\npresent design space. Proteins with highly flexible loops or intrinsically disordered regions, such as therapeutic\npeptides and antibodies, pose unique challenges due to their conformational heterogeneity and context-dependent\nactivity. Proteins with extensive post-translational modifications, including viral glycoproteins decorated with glycan\nshields, further complicate design because of their dynamic structures and roles in immune evasion. Moving forward,\nextending ORI to handle these more complex protein classes and systematically benchmarking it against specialized\ndomain-specific models will be a critical direction for development.\nComment 4\n4. The experimental benchmarks are promising but not comprehensive. This is a big claim, and so the authors should\nspend some time optimizing their work. Figures 2–5 show strong wet-lab validation on lysozyme and chitinase targets,\nbut all results are focused on enzymes with naturally high expression and well-behaved domains. Where are the\nharder targets? All experiments are also single-round selections with no comparison to a baseline design tool (i.e.,\nstructure-based docking or traditional motif grafting). Even for thermostability optimization (Figure 4), the authors\nnever compare ORI-designed proteins to sequences generated by ProteinMPNN or structure-based optimization with\nRosetta. I’d recommend testing ORI on harder problems, kike flexible loops, intrinsically disordered regions (IDRs),\nor viral glycoproteins—and benchmarking against sequence-only models or structure-aware diffusion tools.\nResponse: We thank the reviewer for this constructive comment. Following the suggestion, we have expanded our\nexperimental comparisons to include ProteinMPNN and Rosetta, and the results are presented in Supplementary\nFigure 7. We compared these methods with ORI in terms of both thermostability improvement and computational\nresource requirements. ORI demonstrated significantly superior performance over both approaches. In addition, in\nline with this comment and the earlier feedback, we further benchmarked ORI against sequence-only generative\nmodels such as ProGen3 and Evo2, as well as the structure-aware diffusion model ProteinSGM. These results provide\na more comprehensive evaluation of ORI’s relative strengths. With regard to the reviewer’s point about harder targets\nsuch as flexible loops, intrinsically disordered regions, and viral glycoproteins, we note that these currently fall\noutside the design scope of ORI. The present work focuses on functional protein design and optimization, with\nenzymes as the primary application domain. We agree that extending ORI to address more challenging protein classes\nwill be an important direction, and we have added a discussion of these limitations to the revised manuscript.\nAdded Figures\nSupplementary Figure 7. Comparison of protein thermostability optimization by ORI, ProteinMPNN, and Rosetta.\n(a) Boxplots of the melting temperatures of proteins optimized by each method. (b) Computational cost of each\nmethod for optimizing the proteins shown in panel (a). Computational cost is reported as the log₁₀ value of CPU\nhours, with GPU runtimes converted into equivalent CPU hours for consistency. Statistical significance between ORI\nand each baseline is indicated by stars (*p < 0.05, **p < 0.01, ***p < 0.001, **p < 0.0001), calculated using the\nWilcoxon test.\nAdded Results\nBeyond de novo generation, we further evaluated ORI’s capability in protein thermostability optimization by\ncomparing it with the classic design tools ProteinMPNN43, and Rosetta46. The proteins optimized by ORI exhibited\nsignificantly higher melting temperatures than those optimized by either baseline(Supplementary Figure 7).\nImportantly, ORI achieved this improvement with remarkable computational efficiency: its optimization required\nroughly 1/1000 of the computational cost of Rosetta and 1/10 of ProteinMPNN. Together, these results highlight ORI’s\nstrong ability to design and optimize thermostable proteins, combining superior performance with orders-of-\nmagnitude gains in efficiency.\nAdded Discussion\nWhile ORI demonstrates strong performance across diverse protein design tasks, we recognize important limitations\nin its current scope. Domain-specific generative systems such as PepTune18 for peptide generation and optimization,\nUniPTM51 and PTM-Mamba52 for post-translational modification (PTM)–aware modeling, and DPAC53 for DNA–\nbinding-protein prediction and design adopt specialized architectures and training objectives that explicitly integrate\nfunctional constraints not yet captured by ORI. In addition, many challenging protein classes remain outside the\npresent design space. Proteins with highly flexible loops or intrinsically disordered regions, such as therapeutic\npeptides and antibodies, pose unique challenges due to their conformational heterogeneity and context-dependent\nactivity. Proteins with extensive post-translational modifications, including viral glycoproteins decorated with glycan\nshields, further complicate design because of their dynamic structures and roles in immune evasion. Moving forward,\nextending ORI to handle these more complex protein classes and systematically benchmarking it against specialized\ndomain-specific models will be a critical direction for development.\nComment 5\n5. The literature review focuses almost entirely on highly cited or media-prominent papers (AlphaFold,\nProteinMPNN, ProGen) while omitting many state-of-the-art systems that define the actual technical frontier in\ngenerative biology. The authors cite ProGen1 but not ProGen3 (which introduces functional conditioning), nor ESM-\nC (contact-aware language modeling), nor FusOn-pLM (isoform-aware fusion modeling). The omission of Rinalmo\nand EvoFlow-RNA from the RNA section is particularly surprising given their state-of-the-art performance. If the\nclaim is that ORI is a general-purpose, scalable generative system, the authors need to show that it outperforms or\nmeaningfully differs from these prior systems: and that it wasn’t just trained on more data with better engineering.\nResponse: We thank the reviewer for this constructive comment. We agree that our original introduction placed too\nmuch emphasis on a small set of widely cited models (e.g., AlphaFold, ProteinMPNN, ProGen) and did not\nsufficiently cover the most recent state-of-the-art systems that define the current frontier in generative biology.\nFollowing the reviewer’s suggestion, we have substantially expanded the literature review in the Introduction and\nrevised the narrative to provide a more comprehensive and technically balanced overview.\nSpecifically, we have added the following modifications to the manuscript:\n• Protein generative models: We now cite ProGen3, which introduced functional conditioning for controllable\nprotein generation, and we discuss ESM-C (contact-aware modeling) and FusOn-pLM (isoform-aware\nfusion modeling) as extensions of protein language models to structural and isoform contexts.\n• RNA generative models: We added a dedicated passage citing Rinalmo and EvoFlow-RNA, emphasizing\ntheir state-of-the-art performance in RNA sequence–structure co-design, to reflect that advances in\ngenerative biology extend beyond proteins. We also revised the transition so that these works naturally lead\nback to the unique challenges of protein design, thereby motivating ORI.\n• Positioning of ORI: To directly address the concern that ORI might appear as “just trained on more data with\nbetter engineering,” we have clarified that ORI differs from these prior systems by combining ontology-\ngrounded conditioning with reinforcement learning with feedback (RLWF). This allows ORI to optimize\nnon-differentiable design objectives (e.g., stability, expression yield) and to iteratively incorporate wet-lab\nfeedback, which is not addressed by most existing frameworks.\nWe hope these changes resolve the reviewer’s concern by acknowledging and citing all relevant state-of-the-art\ngenerative biology models, and making clear how ORI meaningfully differs from them in scope and methodology.\nAdded Introduction:\nPart1:\nIn parallel, large-scale language models such as ProGen315 demonstrate the benefits of scaling for controllable and\nhigh-quality protein generation. ESM-C extended language modeling to explicitly incorporate structural contact\ninformation16, while FusOn-pLM enabled isoform-aware fusion modeling17. These developments broaden the\napplicability of protein language models for functional protein engineering.\nPart2:\nBeyond proteins, advances have also been made in RNA generative modeling. Systems such as Rinalmo21 and\nEvoFlow-RNA22 achieve state-of-the-art performance in RNA sequence design and structure prediction,\ndemonstrating the feasibility of applying generative frameworks to biomolecules beyond proteins.\nPart3:\nBuilding on these insights, we introduce ORI (Ontology Reinforcement Iteration), a general-purpose and scalable\nframework for generating functional proteins. ORI unifies ontology-conditioned decoding with reinforcement\nlearning with experimental feedback (RLWF) into a single system. Its innovations include: (1) using structured\nontologies as semantic prompts to inject functional, structural, and property-level constraints into the generative\nprocess, enabling controllable and interpretable sequence generation; (2) combining the controllability of guided\ndiscrete sampling with the ability of RL to directly optimize sparse or non-differentiable objectives; and (3)\nimplementing a closed-loop evolutionary cycle that involves generation, experimental measurement, feedback-driven\nreward assignment, ontology refinement, and model updating, thereby enabling the co-evolution of both the\ngenerative model and its ontology. In this way, ORI leverages the strengths of guided sampling and RL simultaneously,\naddressing limitations of prior methods and enabling robust, multi-objective protein engineering in real-world\nsettings.\nReviewer #3 (Remarks to the Author):\nThe manuscript by Bing He et al. introduces ORI (Ontology Reinforcement Iteration), an AI platform for protein\ndesign. The system takes as input feature requests from users that are interpreted by an agent generating candidates\nthat are filtered and validated with in-vitro assays. Results are then used to reinforce the generative process until it\nmeets the user requirements. The manuscript presents a series of three applications illustrating the functionalities and\nuse cases of ORI.\nOverall ORI is a valuable tool with promising performances. However, the current presentation has several\nshortcomings that could prevent the readers to fully appreciate its potential.\nComment 1\n1) The background and literature review in the introduction is incomplete. The idea of using reinforcement learning\nand experimental assays as feedback for molecular design is not new (e.g., work of Baker’s group). A comprehensive\nreview of previous contributions is mandatory to contextualize this work.\nResponse: We thank the reviewer for this important comment. In the revised manuscript, we have expanded the\nintroduction to include a more comprehensive review of prior contributions that combine reinforcement learning with\nexperimental assays for molecular design. In particular, we now discuss recent advances from Baker’s group and\nothers(e.g., EVOLVEpro), which pioneered reinforcement learning frameworks integrating wet-lab feedback for\noptimizing protein sequences. These studies established the feasibility of coupling in silico design with experimental\nvalidation, and we explicitly acknowledge them as foundational to this line of research.\nAdded Introduction:\nBuilding on these foundations, cutting-edge generative approaches now span multiple technical paradigms. Methods\nsuch as DPLM10and EvoDiff11 apply diffusion directly in sequence space to capture evolutionary constraints, while\nDiMA12 and MeMDLM13 leverage latent or discrete diffusion to improve controllability and enable applications like\nmembrane protein design. ProteinSGM14 extends score-based generative modeling to protein sequences, offering\nanother powerful diffusion paradigm. In parallel, large-scale language models such as ProGen315 demonstrate the\nbenefits of scaling for controllable and high-quality protein generation. ESM-C extended language modeling to\nexplicitly incorporate structural contact information16, while FusOn-pLM enabled isoform-aware fusion modeling17.\nThese developments broaden the applicability of protein language models for functional protein engineering.\nSpecialized frameworks like PepTune18 and AMP-Diffusion19 further highlight the potential of generative models in\ntherapeutic peptide design, optimizing multiple objectives and targeting antimicrobial activity. Parallel to these,\ndiscrete flow matching frameworks—including Dirichlet Flow Matching, Fisher Flow Matching, and Gumbel\nSoftmax Flow Matching—have emerged as promising alternatives, enabling guided and conditional generation in\ndiscrete sequence spaces20. Together, these approaches exemplify the prevailing paradigm of guided discrete\nsampling in modern generative biology.\nBeyond proteins, advances have also been made in RNA generative modeling. Systems such as Rinalmo21 and\nEvoFlow-RNA22 achieve state-of-the-art performance in RNA sequence design and structure prediction,\ndemonstrating the feasibility of applying generative frameworks to biomolecules beyond proteins. Collectively, these\ndevelopments indicate a clear trend: generative models are moving from merely capturing sequence–structure\nrelationships toward integrating optimization signals into the generation process, directly guiding functional\nimprovements.\nDespite rapid methodological advances, key challenges remain in functional protein design. Many existing models\nperform well when structural priors are available—such as in the design of regular folds or ligand-binding motifs—\nbut struggle when only subtle functional differences exist among highly similar structures, or when designing novel\nfunctions without appropriate templates. In such cases, structural priors may be incomplete or even misleading,\nreducing design efficiency and accuracy. Moreover, the mapping from sequence variation to folding, stability, and\nfunction is highly complex, leading to high experimental failure rates and resource-intensive trial-and-error\nworkflows.\nTo address these challenges, researchers have explored different strategies for integrating functional optimization\nsignals into generative models. Guided sampling approaches, including diffusion and flow matching, offer stability\nand sample efficiency when the objective is differentiable or can be approximated. However, these methods are limited\nwhen the target objective is sparse, noisy, or non-differentiable—such as enzyme activity, expression levels,\nthermostability, or other experimental phenotypes. Reinforcement learning (RL) combined with experimental assays\nhas emerged as an alternative: pioneering studies from Baker’s group and others have shown the feasibility of\niterative design guided by experimental feedback, and follow-up frameworks (e.g., EVOLVEpro) have adopted\nsimilar principles in active learning or small-sample settings23,24. While RL is attractive for directly optimizing non-\ndifferentiable objectives, existing approaches often face issues of instability and low sample efficiency in the high-\ndimensional sequence space, and most prior implementations remain constrained to specific scaffolds or narrowly\ndefined targets.\nComment 2\n2) My next concern follows the previous comment. It is not clear to me what is the main innovation of this work. It\nwould be important to clarify the contribution. Unless I misunderstood it, it feels more like a software contribution.\nThe ORI platform is a pipeline integrating multiple tools. Although, each individual component is not conceptually\nnew, their integration in a unified workflow has value. If such case, it would be eventually interesting to expand the\ndiscussion about the integration/use in experimental labs.\nResponse: We thank the reviewer for highlighting the need to clarify the main innovation of our work. We agree that\nthe individual components of ORI—protein language modeling, ontology-conditioned generation, and reinforcement\nlearning with experimental feedback—are conceptually established. The primary contribution of ORI, however, lies\nin the systematic integration of these components into a unified, self-evolving platform for functional protein design.\nThis integration allows the model to combine structured functional knowledge (via ontology prompts) with\nexperimental feedback in a closed-loop pipeline, enabling iterative improvement of protein sequences based on real-\nworld assays.\nTo clarify this contribution, we have revised the Introduction to explicitly state that ORI’s novelty arises from (1)\ncombining ontology-conditioned guided generation with reinforcement learning for functional optimization, (2)\nimplementing a closed-loop workflow that co-evolves both the generative model and the ontology based on\nexperimental feedback, and (3) providing a scalable and extensible platform that can be directly deployed in\nexperimental laboratory settings. We also expanded the discussion on ORI’s application in wet-lab workflows,\nhighlighting how the system reduces trial-and-error design cycles and enhances the efficiency, accuracy, and\ninterpretability of protein engineering experiments.\nAdded Introduction:\nBuilding on these insights, we introduce ORI (Ontology Reinforcement Iteration), a general-purpose and scalable\nframework for generating functional proteins. ORI unifies ontology-conditioned decoding with reinforcement\nlearning with experimental feedback (RLWF) into a single system. Its innovations include: (1) using structured\nontologies as semantic prompts to inject functional, structural, and property-level constraints into the generative\nprocess, enabling controllable and interpretable sequence generation; (2) combining the controllability of guided\ndiscrete sampling with the ability of RL to directly optimize sparse or non-differentiable objectives; and (3)\nimplementing a closed-loop evolutionary cycle that involves generation, experimental measurement, feedback-driven\nreward assignment, ontology refinement, and model updating, thereby enabling the co-evolution of both the\ngenerative model and its ontology. In this way, ORI leverages the strengths of guided sampling and RL simultaneously,\naddressing limitations of prior methods and enabling robust, multi-objective protein engineering in real-world\nsettings.\nComment 3\n3) Unless I missed it, the manuscript does not provide comparison or benchmark with other software and methods (it\nbrings us back to my first comment). As such, it is difficult to fully assess the technical advances of ORI. The\nexamples provide in the results section are interesting but do not replace a complete and rigorous validation of the\nplatform. A large-scale (and more quantitative) experiment (In contrast to a specific use cases) would also provide\nmore confidence in the generalization of ORI.\nResponse: We thank the reviewer for this thoughtful comment. In the revised manuscript, we first summarize the\ntechnical advances of ORI, emphasizing our novel ontology-conditioned decoding with reinforcement learning with\nexperimental feedback (RLWF) for functional protein generation.\nTo provide a more rigorous validation, we expanded our benchmarks to include a broad spectrum of state-of-the-art\nmethods. Specifically, we compared ORI with ProGen3, Evo2, and ProteinSGM, representing recent sequence-based,\nDNA-based, and structure-aware generative models. Beyond specific case studies, we conducted larger-scale\nexperiments generating 1,000 sequences per method under standardized functional prompts, thereby enabling a more\nquantitative comparison of generalization. These head-to-head evaluations are presented in Supplementary Figures\n2, 3, and 6, where we assessed model outputs in terms of structural confidence, functional relevance, and\nthermostability, providing a quantitative measure of performance and consistency in large-scale generation. In\naddition, we benchmarked ORI against ProteinMPNN and Rosetta, two widely used tools for protein sequence design\nand structure-based optimization. As shown in Supplementary Figure 7, ORI achieved substantially greater\nimprovements in thermostability while also reducing computational resource requirements. Collectively, these results\nacross Supplementary Figures 2, 3, 6, and 7 provide a comprehensive validation of ORI’s performance and establish\nits technical advances relative to existing platforms.\nAdded Introduction:\nBuilding on these insights, we introduce ORI (Ontology Reinforcement Iteration), a general-purpose and scalable\nframework for generating functional proteins. ORI unifies ontology-conditioned decoding with reinforcement\nlearning with experimental feedback (RLWF) into a single system. Its innovations include: (1) using structured\nontologies as semantic prompts to inject functional, structural, and property-level constraints into the generative\nprocess, enabling controllable and interpretable sequence generation; (2) combining the controllability of guided\ndiscrete sampling with the ability of RL to directly optimize sparse or non-differentiable objectives; and (3)\nimplementing a closed-loop evolutionary cycle that involves generation, experimental measurement, feedback-driven\nreward assignment, ontology refinement, and model updating, thereby enabling the co-evolution of both the\ngenerative model and its ontology. In this way, ORI leverages the strengths of guided sampling and RL simultaneously,\naddressing limitations of prior methods and enabling robust, multi-objective protein engineering in real-world\nsettings.\nAdded Figures:\nSupplementary Figure 2. Comparison of ORI with PGM, ProGen3, ProteinSGM, and Evo2 in conditioned protein\ngeneration. The y-axis shows the proportion of generated proteins with pLDDT scores above 70 and 90, respectively.\nThe conditioning prompts used for generation include protein descriptions, structural information, and sequences.\nAlthough the prompt formats differ across methods, they convey equivalent semantic information. Statistical\nsignificance between ORI and each baseline is indicated by stars (*p < 0.05, **p < 0.01, ***p < 0.001, ****p <\n0.0001), calculated using the Wilcoxon test.\nSupplementary Figure 3. Comparison of ORI with PGM, ProGen3, ProteinSGM, and Evo2 in generating functional\nproteins. The y-axis shows the proportion of generated proteins with the desired functional domain. Conditioning\nprompts for generation were provided as descriptions, structure-based, or sequence-based cues for specific protein\nfunctions, depending on the requirements of each method. Although the prompt formats differ across methods, they\nconvey equivalent semantic information. Statistical significance between ORI and each baseline is indicated by stars\n(*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001), calculated using the Wilcoxon test.\nSupplementary Figure 6. Comparison of thermostable protein generation by ORI, ProGen3, ProteinSGM, and Evo2.\nThe y-axis represents the melting temperatures of proteins generated by each method. Statistical significance between\nORI and each baseline is indicated by stars (*p < 0.05, **p < 0.01, ***p < 0.001, **p < 0.0001), calculated using\nthe Wilcoxon test.\nSupplementary Figure 7. Comparison of protein thermostability optimization by ORI, ProteinMPNN, and Rosetta.\n(a) Boxplots of the melting temperatures of proteins optimized by each method. (b) Computational cost of each\nmethod for optimizing the proteins shown in panel (a). Computational cost is reported as the log₁₀ value of CPU\nhours, with GPU runtimes converted into equivalent CPU hours for consistency. Statistical significance between ORI\nand each baseline is indicated by stars (*p < 0.05, **p < 0.01, ***p < 0.001, **p < 0.0001), calculated using the\nWilcoxon test.\nAdded Results\nPart1:\nWe benchmarked the performance of ORI and its protein generation module, PGM, against the most recent state-of-\nthe-art (SOTA) protein generation models, including ProteinSGM14, ProGen315, and EVO241, which represent\nrecent advances in structure-based, sequence-based, and DNA-based generative modeling, respectively. For\nevaluation, we generated 1,000 proteins with each method and assessed their quality using structural confidence and\nfunctional relevance metrics. ORI achieved a markedly higher proportion of proteins with strong structural\nconfidence, as reflected by the percentage of sequences with pLDDT scores above 70 and 90 (Supplementary Fig. 2),\nand a greater proportion of proteins containing the desired functional domains (Supplementary Fig. 3). Moreover,\ndirect comparison between ORI and its core generative module PGM revealed substantial performance gains arising\nfrom the integration of PDA and USM. Together, these contributions highlight the significant advantage of ORI’s\nunified framework for protein design, clearly surpassing current generative models.\nPart2:\nWe benchmarked the performance of ORI, against ProteinSGM14, ProGen315, and EVO241, in in the task of generating\nproteins with high thermostability. ORI consistently produced proteins with substantially higher melting temperatures,\ndemonstrating a clear advantage over these state-of-the-art generative models (Supplementary Figure 6). Beyond de\nnovo generation, we further evaluated ORI’s capability in protein thermostability optimization by comparing it with\nthe classic design tools ProteinMPNN43, and Rosetta46. The proteins optimized by ORI exhibited significantly higher\nmelting temperatures than those optimized by either baseline(Supplementary Figure 7). Importantly, ORI achieved\nthis improvement with remarkable computational efficiency: its optimization required roughly 1/1000 of the\ncomputational cost of Rosetta and 1/10 of ProteinMPNN. Together, these results highlight ORI’s strong ability to\ndesign and optimize thermostable proteins, combining superior performance with orders-of-magnitude gains in\nefficiency.\nComment 4\n4) The analysis of the case studies presented in the paper could also be refined. Looking at the global sequence and\nstructure similarity is not sufficient. Further investigations of the designed proteins would help to validate the impact\nof the output. For instance, it is not clear if the samples have highly similar active sites or if it could be found in the\ntraining data. It would be important to show that similar result could not be obtained with a classical fragment\nassembly option, and could also help to rule out any leak of information (I am assuming it is ok but the manuscript\ncould expand the discussion).\nResponse: We thank the reviewer for this insightful and constructive comment. Following your suggestion, and to\nfurther ensure the novelty and functional credibility of ORI-designed proteins, we performed an in-depth analysis of\nboth global sequence diversity and functional region conservation. As shown in the newly added Figures 5C and 5D,\nORI-generated proteins exhibit much higher conservation within functional regions containing catalytic residues than\nacross their global sequences. These results indicate that ORI effectively explores novel global sequence space while\npreserving essential catalytic motifs required for enzymatic activity, demonstrating its ability to balance sequence\ninnovation with functional preservation.\nImportantly, ORI does not rely on any template-based or fragment assembly strategies. Instead, it operates as a\ngenerative language model that learns continuous representations of sequence–structure–function relationships\nacross large-scale protein datasets. During generation, ORI samples directly from this learned latent space using\nontology-conditioned decoding, allowing it to synthesize sequences that are statistically consistent with functional\nconstraints but not reconstructed from known templates or fragments. This mechanism fundamentally differs from\nclassical fragment assembly methods, which recombine existing sequence or structure fragments and may thus\nreproduce training examples or local structural patterns.\nIn line with the reviewer’s suggestion, we have expanded the Discussion section of the revised manuscript to elaborate\non how ORI’s generation mechanism differs from classical fragment assembly approaches, and how it mitigates\npotential biases or implicit information reuse. We hope these additional explanations better clarify the conceptual\ndistinction and novelty of our approach.\nAdded Figures\nFigure 5 (c) Distribution of overall and functional-region sequence identities of ORI-generated mono-enzyme\nproteins relative to natural lysozyme and chitinase proteins. Dashed lines represent the median sequence identity\nvalues. (d) Distribution of overall and functional-region sequence identities of ORI-generated dual-enzyme proteins\nrelative to the natural dual-function enzyme Hevamine A (HA). Dashed lines represent the median sequence identity\nvalues.\nAdded Results\nAs shown in Figure 5C, mono-enzyme proteins exhibit low overall sequence identity to natural lysozyme and chitinase\nproteins, with median values of 0.525 and 0.559, respectively. Interestingly, when focusing on the functional regions\nthat harbor the active sites of catalysis, the median sequence identities increased markedly to 0.839 and 0.868 relative\nto natural lysozyme and chitinase proteins. This indicates that while ORI-generated proteins explore novel sequence\nspace at the global level, they preserve critical functional motifs essential for catalytic activity, thereby balancing\ndiversity with functional conservation. This may also explain why these proteins exhibited only mono-enzyme activity.\nWe further analyzed the ORI-designed proteins that successfully exhibited dual-enzyme activities by comparing them\nto Hevamine A (HA), a natural dual-function enzyme with both lysozyme and chitinase activity (Figure 5D). The\nmedian overall sequence identity to HA was comparable to that of mono-enzyme proteins relative to their natural\ncounterparts. In contrast, the median functional-region sequence identity was 0.603, markedly lower than that of\nmono-enzyme proteins in comparison with either natural enzyme. This suggests that ORI did not simply reproduce\nthe catalytic organization of HA or either parent enzyme family. Instead, it likely generated dual-enzyme proteins\nthrough the recombination and reorganization of distinct catalytic motifs from both lysozyme and chitinase families,\nleading to de novo architectures capable of supporting both activities.\nAdded Discussion\nUnlike classical fragment assembly or template-based modeling approaches, ORI generates protein sequences de\nnovo through a generative language model that captures continuous representations of the sequence–structure–\nfunction landscape. Rather than recombining known sequence or structural fragments, ORI learns the statistical\ndependencies between amino acid sequences, protein structures, and functional annotations within a unified latent\nspace. During generation, ontology-conditioned decoding guides the model to produce sequences that satisfy specific\nfunctional constraints while maintaining structural plausibility. This mechanism enables ORI to explore novel\nsequence space while preserving key catalytic motifs, without directly reconstructing or reusing information from the\ntraining data. Therefore, ORI fundamentally differs from fragment-based methods and mitigates the risk of implicit\ninformation leakage or template bias, offering a more generalizable and data-driven framework for functional protein\ndesign.\nComment 5\n5) In general, I would advise revising the style of writing. Currently, the text uses a lot of unnecessary superlatives\nthat do not bring anything to the scientific discussion. Some terms should also be more carefully chosen (e.g., in\nFigure 1, ORI does not “understand” the needs, maybe “interpret” or another formulation would be preferable), and\nsome statements be better justified (e.g., on page 4, the authors say that “ORI can predict the optimal sequence and\nstructure” but optimal is a strong claim that should be proved).\nResponse: We thank the reviewer for this valuable stylistic feedback. In the revised manuscript, we carefully\nreviewed the text to remove unnecessary superlatives and ensure that the language remains precise and scientifically\nrigorous. For example, in Figure 1, we replaced “understanding the needs” with “interpreting the requirements,”\nwhich more accurately reflects the system’s function. Similarly, on page 4, we removed the statement “ORI can\npredict the optimal sequence and structure. We also revised other sections of the manuscript to ensure careful word\nchoice and to avoid unnecessary superlatives."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate that the authors toned down some of the language on novelty and removed the language on de novo design.\nThe presentation, however, is still inflated. Although there is much justified excitement about the application of LLMs to\nprotein design, resulting designs behave much as homologues of existing ones, and some call this a strategy to generate\n\"synthetic homologs\" rather than bona fide improvements in activity. Here, the authors generate such homologs, then do\nexperiments, based on which they test a second-generation set of designs. But iterative strategies (directed evolution and\nAI/ML-assisted directed evolution) have been the main path of protein engineering for many years. If the authors focused on\nenzymes considered difficult that have led to failure when other methods were applied, the work would be of broad interest.\nBut the enzymes they chose have been extensively studied by previous LLM applications, have thousands of sequence\nhomologs and are robust to mutation. As such, the study does not provide a sense of a \"breakthrough\" as the authors claim,\nand I do not consider it sufficiently novel for Nature Communications.", "reviewer_2": "The authors have satisfactorily addressed all of my comments and I recommend acceptance. I commend the authors on a\ngreat work!", "reviewer_3": "Overall, the authors addressed my comments. The introduction is now providing a fairer presentation of previous\ncontributions, and the evaluation includes a benchmark with other methods. Although, it could be interesting to also compare\nand discuss the sequence diversity achieved by these other methods. I remain a bit unclear with the novelty of the\ncontribution, but I acknowledge I may have my own bias."}, "author_response": "Reviewer #1 (Remarks to the Author):\nI appreciate that the authors toned down some of the language on novelty and removed the language\non de novo design. The presentation, however, is still inflated. Although there is much justified\nexcitement about the application of LLMs to protein design, resulting designs behave much as\nhomologues of existing ones, and some call this a strategy to generate \"synthetic homologs\" rather\nthan bona fide improvements in activity. Here, the authors generate such homologs, then do\nexperiments, based on which they test a second-generation set of designs. But iterative strategies\n(directed evolution and AI/ML-assisted directed evolution) have been the main path of protein\nengineering for many years. If the authors focused on enzymes considered difficult that have led to\nfailure when other methods were applied, the work would be of broad interest. But the enzymes\nthey chose have been extensively studied by previous LLM applications, have thousands of\nsequence homologs and are robust to mutation. As such, the study does not provide a sense of a\n\"breakthrough\" as the authors claim, and I do not consider it sufficiently novel for Nature\nCommunications.\nResponse:\nWe thank the reviewer for the detailed and candid feedback. In response to these concerns, we have\nsubstantially revised the manuscript to further de-emphasize claims of novelty or de novo protein\ndesign, and instead focus on the technical advances of the computational framework itself, in line\nwith the editor’s guidance.\nWe agree that iterative optimization strategies, including directed evolution and AI/ML-assisted\nvariants, are well established in protein engineering. The contribution of this work is therefore not\npositioned as a conceptual replacement of these paradigms, but rather as a unified and scalable\ncomputational framework that integrates ontology-guided generation with reinforcement learning\nfrom experimental feedback in a closed loop. We have clarified this framing throughout the revised\nmanuscript.\nRegarding the choice of enzymes, our intent is not to claim unprecedented difficulty or biological\nnovelty, but to demonstrate the practical applicability, robustness, and efficiency of the framework\nunder realistic experimental settings. We have accordingly moderated the language around\n“breakthrough” and refocused the narrative on framework design, optimization behavior, and\nexperimental validation as a proof of technical advances and capabilities.\nReviewer #2 (Remarks to the Author):\nThe authors have satisfactorily addressed all of my comments and I recommend acceptance. I\ncommend the authors on a great work!\nResponse:\nWe thank the reviewer for the positive assessment and kind comments, and we are pleased that the\nrevised manuscript has addressed all concerns.\nReviewer #3 (Remarks to the Author):\nOverall, the authors addressed my comments. The introduction is now providing a fairer\npresentation of previous contributions, and the evaluation includes a benchmark with other methods.\nAlthough, it could be interesting to also compare and discuss the sequence diversity achieved by\nthese other methods. I remain a bit unclear with the novelty of the contribution, but I acknowledge\nI may have my own bias.\nResponse:\nWe thank the reviewer for the thoughtful and constructive feedback, and for acknowledging the\nimprovements made in the revised manuscript. We are pleased that the revised introduction provides\na fairer positioning of prior work and that the added benchmarks help clarify ORI’s performance\nrelative to existing methods. Regarding sequence diversity, we have added a paragraph in the\nDiscussion section compare and discuss our method with other methods. Regarding the novelty of\nour work, in alignment with this and the editor’s suggestions, we have shifted the focus of our\nmanuscript to emphasize the technical innovation of the ORI framework itself. We hope that the\nrevised presentation and additional discussion help better convey this contribution, while we\nappreciate the reviewer’s candid acknowledgment of potential differences in perspective.\nAdded Discussion:\nSequence diversity is an important consideration for generative protein models, and many existing\napproaches emphasize broad exploration of sequence space. ORI is instead designed for goal-\ndirected protein engineering, where generation is guided by explicit functional objectives and\niteratively refined through experimental feedback. Under this setting, functional constraints\nnaturally focus the search on a functionally enriched subset of sequence space rather than\nmaximizing global diversity. Accordingly, our evaluation prioritizes functional performance and\noptimization efficiency, which more directly reflect ORI’s intended use in practical protein\nengineering."}]

Decision letter