CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models
Authors: Junbo Yin
Chao Zha
Wenjia He
Chencheng Xu
Xin Gao
Venue: ICML 2025 poster (2025)
Keywords: function
protein design
diffusion model
multi-objective
Primary area: applications->health_medicine
TL;DR: A diffusion model capable of designing functional proteins comparable to natural proteins.

Abstract: Existing PLMs generate protein sequences based on a single-condition constraint from a specific modality, struggling to simultaneously satisfy multiple constraints across different modalities. In this work, we introduce CFP-GEN, a novel diffusion language model for Combinatorial Functional Protein GENeration. CFP-GEN facilitates the de novo protein design by integrating multimodal conditions with functional, sequence, and structural constraints. Specifically, an Annotation-Guided Feature Modulation (AGFM) module is introduced to dynamically adjust the protein feature distribution based on composable functional annotations, e.g., GO terms, IPR domains and EC numbers. Meanwhile, the ResidueControlled Functional Encoding (RCFE) module captures residue-wise interaction to ensure more precise control. Additionally, off-the-shelf 3D structure encoders can be seamlessly integrated to impose geometric constraints. We demonstrate that CFP-GEN enables high-throughput generation of novel proteins with functionality comparable to natural proteins, while achieving a high success rate in designing multifunctional proteins.

CFP-GEN:
Combinatorial Functional Protein Generation via Diffusion Language Models
Junbo Yin 123
Chao Zha 123
Wenjia He 123 Chencheng Xu 123
Xin Gao 1
Abstract
Condstooing
UC20001227
PLMM
OUTOTOEL
Function
Structure
Existing PLMs generate protein sequences based single-condition constraint from spe- cific modality, struggling to simultancously sat- isfy multiple constraints across different modal- ities. In this work, we introduce CFP-GEN, a novel diffusion language model for Combinatorial Functional Protein GENeration. CFP-GEN fa- cilitates the de novo protein design by integrat- ing multimodal conditions with functional, se- quence, and structural constraints. Specifically, an Annotation-Guided Feature Modulation (AGFM) module is introduced to dynamically adjust the protein feature distribution based on composable functional annotations, e.g., GO terms, IPR do- mains and EC numbers. Meanwhile, the Residue- Controlled Functional Encoding (RCFE) module captures residue-wise interaction ensure more precise control. Additionally, off-the-shelf 3D structure encoders can be seamlessly integrated to impose geometric constraints. We demonstrate that CFP-GEN enables tigh-throughput genera- tion of novel proteins with functionality compa- rable natural proteins, while achieving high success in designing multifunctional proteins.
Sp
Protein Dedign via Functional Annotations
XXOX GDVAEXXX
RMA FILDA
Sequence
X00X XF WWW.XX/EGOOGMMTEEEOOOOO
Prosein Design Cairded by Sequence Moeifs
Sp
PIMM
FLMM
APOSTINN
MON-UP FOD LODOOOL EVRRONAANR
Protein Dedign with Contraints
PLMs Single-Conditinn Constraint
(biPLMs with Multi-Conditinn Constraint
Figure 1. Motivation CFP-GEN. (a) Previous PLMs typ- ically generate proteins based on single-modality conditioning, considering only individual functional constraints. (b) In contrast, CFP-GEN incorporates multiple conditions from diverse modali- fies-function, sequence and structure-to impose comprehensive functional constraints, thereby leading to optimized proteins.
proteins. The growing capabilities of large-scale protein language models (PLMs) offer tremendous potential for dis- covering novel proteins that do not exist in nature would require millions of years of evolution to emerge (Hayes et al., 2025a; Madani et al., 2023; Alamdari et al., 2023).
Most current PLMs are primarily designed for unconditional protein generation (Lin et al., 2023; Ren et al., 2024; Qu et al., 2024), limiting their efficiency in addressing com- plex real-world challenges. In contrast, controllable protein generation (Listov et al., 2024; Ferruz & Höcker, 2022; Madani al., 2023), which tailors proteins meet specific biological functions by explicitly defined conditions, holds significant promise for advancing practical applications.
1. Introduction
De novo protein design (Watson et al., 2023; Krishna al., 2024; Dauparas et al., 2022; Wang et al., 2024; Lisanza et al., 2024) has emerged as a powerful strategy for numerous biotechnological applications, including drug development, enzyme engineering, and the creation novel therapeutic
Biologically meaningful proteins often simultaneously sat- isfy multiple functional constraints across diverse modali- ties, e.g., functional annotation, sequence or structure. As shown in Fig. 1, previous PLMs typically generate protein candidates based on single-condition input from spe- cific modality, and thus have to rely on iterative filtering or multi-step optimization to meet constraints across multiple modalities (Goverde et al., 2024). Moreover, this pipeline often struggles to satisfy all desired functionalities and be- comes impractical when available data is limited. Therefore,
Code and data available at http:://gihbbb.co//yjjjuuoooooo 'Computer Science Program, Computer, Electrical and Mathe- matical Sciences and Engineering Division, King Abdullah Uni- versity of Science and Technology (KAUST). Thuwal 23955- 6900, Kingdom of Saudi Arabia "Center of Excellence for Smart Health (KCSH), KAUST 'Center Excellence for Generative Al, KAUST. Correspondence to: Xin Gao <xin.gao @ saust.edu.sa.>.
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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CFP-Gen
developing an advanced protein generative model capable of simultaneously handling multiple function constraints within unified model would significantly streamline the multi-objective optimization in protein engineering.
level. It captures epistasis and interactions within se- quences, enabling the generation of novel yet func- tional proteins.
We thoroughly evaluate CFP-GEN across various tasks, in- cluding functional sequence generation, functional protein inverse folding, and multi-objective protein design. CFP- GEN achieves exceptional function performance, e.g. im- proving ESM3 by 30% in Fi --ccor,, as demonstrated by lead- ing function predictors. Additionally, it improves Amino Acid Recovery (AAR) of DPLM by 9% in inverse fold- ing. Notably, CFP-GEN demonstrates remarkable success rate in designing multi-functional proteins (e.g., enzymes exhibiting multiple catalytic activities). We anticipate CFP- GEN to become valuable and practical computational tool for addressing biomedical and biotechnological challenges.
In this work, we introduce CFP-GEN, a novel large- scale diffusion language model specifically developed for Combinatorial Functional Protein GENeration. CFP- GEN iteratively denoises the input protein sequence while simultaneously accounting for various functional conditions, including functional annotations, desired sequence motifs and 3D atomic coordinates of reference proteins. During inference, composable annotation tags (e.g., GO terms, IPR domains and EC numbers) are encoded as one-hot embed- dings and injected to the model through an Annotation- Guided Feature Modulation (AGFM) module. AGFM dy- namically modulates the normalized feature distributions of the noised protein sequence by leveraging all available an- notation tags. Compared to classifier-guiie -uided diffusion, this approach enables joint training to ensure strict alignment between functions and sequences, while also allowing for the flexible combinations of various functional annotations. Additionally, certain amino acid residues (i.e., sequence motif) critical in real-world applications, as they often determine the desired functional properties. To cater to this need, a Residue- Conttollled Functional Encoding (RCFE) module is introduced to explicitly encode these functional domains. RCFE employs an ESM-like transformer- based controller to effectively capture epistasis and evolutionary relationships among residues. As a result, this approach has the potential generate novel protein sequences with opti- mized functional sites. Moreover, CFP-GEN supports direct optimization of user-provided proteins by encoding their 3D backbone atomic coordinates as conditional features using off-the-shelf structure encoders. By jointly incorpo- rating functional constraints across diverse modalities, our approach ensures that the inverse folding process preserves structural accuracy while simultancously satisfying func- tional requirements. This provides a significant advantage in tasks that demand multi-objective optimization.
2. Related Work
PLMs have become indispensable tools in protein science, widely applied to both discriminative and generative tasks, ESM2 (Lin et al., 2023) is a pioncering approach that uti- lizes scalable language models to uncover patterns in pro- tein sequences across evolutionary space. By optimizing a masked modeling objective, ESM2 learns rich sequence rep- resentations, significantly improving performance in various discriminative tasks, including structure prediction (Back et al., 2021) and functional classification (Kulmanov et al., 2018). Meanwhile, ProtGPT2 (Ferruz et al., 2022) performs unconditional sequence generation, akin to natural language sentence generation. Moving toward controllable sequence generation, ProGen (Nijkamp et al., 2023) augments the language model with protein family tags, enabling the gen- eration of sequences within specific functional constraints. ProteoGAN (Kucera et al., 2022) incorporates GO terms for sequence generation, providing detailed in silico validations of its output sequences. Later, EvoDiff (Alamdari et al., 2023) introduces a discrete diffusion model instead of the masked language modeling objective. This enables condi- tional protein generation, such as sequence inpainting and MSA-guided generation. Further, ZymCTRL. (Munsamy et al., 2022) specializes in generating enzymes by condi- tioning on EC numbers, successfully designing sequences with catalytic activity. Most recently, DPLM (Wang et al., 2024) is proposed as a new foundation model that not only supports representation learning for discriminative tasks but also enables conditional generation guided by secondary structure classifications and sequence or structure motifs.
Our contributions can be summarized as follows:
A novel generative model, CFP-GEN, is developed for de novo design of functional proteins. CFP-GEN ad- dresses multiple functional conditions across various modalities simultaneously, offering more promising starting points compared to previous PLMs limited by single-condition inputs.
An Imnotation-Guider Feature Modulation (AGFM) is introduced to enable highly-comtrollable protein gen- eration by incorporating commonly used functional annotations in a composable and flexible manner.
All of the above PLMs are fundamentally limited to single- modality conditioning, i.e., generating sequences based on one functional constraint at a time. This reduces the flexi- bility and success rate of generated proteins when multiple functional constraints are required. Although the recently published ESM3 (Hayes et al., 2025b) supports multimodal
A
Functional Encoder (RCFE) is proposed to encode functional domains at the residue
2

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CFP-Gen
Functional Protein Design with Multi-Modal Conditioning
GO TERM
BOOXXXLA
MEHUPH RORLDD
00:3051267
(b) Sequence Molif
OSEONA BAKANLLAGA
G0:3043118
S
ALTINALAFION FOO
BNLTLVESPFOBBLL.OO
Encoder
Decoder
IPR DOMAIN
ESMFoldd
IPR039966
SAKEARRIONNIDAA ASRMPSI LAN SOSTEPALARLLEENA AARLREKKES
EC NUMBER
1,31,76
(c) Backbone Structure
XT
21:10/
XXXXXXXXXXXX (3) Noised Sequence
Functional Annotations
Sequences
Structures
Residue-Controlle Functional Encoding (RCFE)
Amnotation-Guided Feature Modulation (AGFM)
S
Z LINE
ESM Block
Modttte
Modited
ESM Block
Structure
Enscdder
GO
BC
ML
ESM
11 A
Annotation
Eibeedding
Z:10 Litear
Ze0 Liberr
Zero Linear
Raddbons Seructars
Wappe-Moor Norm S0
Salf- Attention ol
DeNorm
Layur SourNNor Norm
GD
Lagert
Scale
FFN
Sale
Moditeed ES Block
Modited ES Block
Modiffed ESM Block
EC
1
Nobed Socuannce Imtteditng
Nosed
Embedkiing
Figure 2. Pipeline of CFP-GEN model. Functional conditions from diverse modalities, combined with the noised sequence, are iteratively processed by the model to generate desired proteins. Within each modified ESM block. AGFM adaptively adjusts the noised sequence embedding based on combinations of various functional annotations. Furthermore, sequence motifs and backbone atomic coordinates are embedded by RCFE and structure encoder, respectively, providing precise and flexible guidance for the generation process.
inputs, defines function using a limited vocabulary, re- stricting its broader applicability. In contrast, our model incorporates more diverse functions. This ensures more precise control and leads functionally superior proteins.
acid category from the set V of 20 standard types. The cate- gorical distribution Cat(x; (x; p) models the sequence x, with p = (P1. P2. ................................ PL) as the collection of probability vec- tors. Each p: - (pi. ,.. Ps.Ivi) specifies the categor- ical distribution of the i-th residue in the sequence, i.e., Pi. represents the probability of selecting amino acid category c, always ensuring that Evev Pi,e 1, E LJ).
3. Methodology
We begin by introducing the essential concepts of CFP- GEN in $3.1. Next, we present the AGFM module in $3.2. Following that, we detail the RCFE module in $3.3, which designed to encode desired sequence motifs. Finally, we demonstrate how CFP-GEN smoothly incorporates fune- tional conditions across diverse modalities in $3.4.
Forward Process with Discrete Diffusion. We apply dis- crete diffusion to gradually corrupt the raw sequence x() over i (I,...,2 T} time steps by transitioning each amino acid token towards a stationary noise distribution. The tionary noise distribution is parameterized by fixed prob- ability vector Qnoise and can be expressed as: (x(+)) Cat(x((): qnnise) Following DPLM, Gnaise (x(0) satis- fies: Inoise (x) xf [X] and otherwise, where [X] refers to the absorbing state (c.g., <mask> ). Once a token transitions into this absorbing state, it will remain un- changed in all subsequent diffusion steps. This ensures that the forward process eventually leads all amino acid tokens to <mask>, unifying the principles behind masked language models (Kenton & Toutanova, 2019) and autoregressive lan- guage models (Ferruz et al., 2022). Mathematically, the forward transition process is given by:
3.1. Preliminaries of CFP-GEN
Diffusion models are widely recognized for their capabil- ity in de novo protein design. CFP-GEN is built upon the leading diffusion protein language model DPLM (Wang et al., 2024) to leverage its well-pretrained parameters at- tained from evolutionary-scale datasets. Concretely, discrete diffusion mechanism (Austin et al., 2021) employed to model the protein sequence distribution the amino acid category level. Let x q(x) denote a protein sequence of length L, represented as (I1, (I1, L,), where each E {0. 13V1 is a one-hot vector indicating an amino
g(x(1) | x((11)) = Cat( x(t-1)Q.).
(1)
3

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CFP-Gen
where Q₁ is the transition matrix at step 1. Each row of Q2 is
the prediction of highly functional proteins and how its composable nature empowers CFP-GEN to achieve multi- objective protein design.
probability vector defined as: Q+ F (1 3 Quoise: where / is the identity matrix and Br E [0, is the noise schedule. Due to the Markov property, the overall transition from (0) to x(() can be expressed as follows:
3.2. Annotation-Guided Conditioning by AGFM
((x(1) (0) (0) Cat(x! :p- == = (xx + +11 are (ooiee,
Functional annotations are both highly informative and rep- resentative, they are rigorously curated by biological experts to capture essential properties of proteins. In this work, we consider three commonly used annotations, ie., GO terms (gen, 2021), IPR numbers (Hunter et al., 2009), and EC numbers (Schomburg et al., 2004), to guide the generation process. Typically, each type of annotation char- acterizes the protein's profile from different perspective: GO captures the molecular functions, IPR defines the func- tional domains, and EC describes the related catalytic pro- cesses. Unlike ESM3 (Hayes et al., 2025b) relying only on a restricted vocabulary mapping to represent IPR annota- tions, we adopt a more tactful approach to combine diverse functions. While three types of annotations are presented as examples, our paradigm is extensible other forms of annotations, such Pfam (Bateman al., 2000).
(2) where a2 IT:=1 3g represents the accumulated effect of the noise schedule over steps. As t + T, 04 - O, ensuring that the sequence x( converges to the stationary noise Quoise at time step T.
Reverse Denoising with Composable Conditions. To en- able the generation of protein sequences with desired func- tionalities, we incorporate multimodal conditions into the discrete diffusion framework during the reverse process, illustrated in Fig. 2. These include OD annotation tags Canno- ID sequence motifs Cseq. and 3D structures Car, encoded by fAGEM: fRCFE, and fovrT, respectively. The details of these networks will be introduced in the following sections, where GVPT indicates the Tra-Traasfrrmer (Hsu et al., 2022) for encoding 3D backbone atomic coordinates.
Existing PLMs often encode a single type of annotation. For instance, ZymCTRL (Munsamy et al., 2022) designs enzymes conditioned solely on EC numbers, while Pro- teoGAN (Kucera et al., 2022) operates only with GO terms. Although ProGen (Madani et al., 2023) supports multiple annotation tags, it requires extensive fine-tuning with suffi- cient homologous sequences to ensure performance, which limits its application when training samples are scarce. By contrast, CFP-GEN is more flexible and versatile by ad- dressing multiple annotations simultaneously, without the need of further fine-tuning. By leveraging the complemen- tary characteristics of these high-quality annotations, we also enable a more comprehensive description of protein.
The reverse process reconstructs the sequence by iteratively denoising x(") back to x("), using the predicted (0)) at each step, which is derived from the KL divergence, i.e. DKL [x(x(t-"] x(1),
x || px(x(t-1) ). Thus, the
reverse step is expressed as follows:
1= x(())= = Σ ((x(1-1) x0)) (x )p (0) |x(1) c),
((0)
(3)
where c E (Canno: Gseq= Car) represents any combinations of conditions, depending on their availability. To predict (0)), the model incorporates throughout the network:
Pa (x (0) |x()), c) = Softmax (h'ima c)),
As shown in Fig. 2, an Annotation-Guider Feature Modula- tion (AGFM) module is integrated into each modified ESM block (Lin et al., 2023), where each block has been well pre- trained by DPLM. CFP-GEN is achieved by training only the annotation embedding layers and a single MLP layer. For each type of annotation (e.g.. GO, IPR, EC), we maintain dedicated embedding layer. Each annotation is mapped to same-dimensional vector representation through its respective embedding layer. This flexible design ensures that annotations from different sources can be directly summed together. The process is formally represented as: Xanno = fAGFM (x, CGo, CIPR, CFC), where x, Xanno E LxD denotes the input feature and the modulated feature output by AGFM within one block, based on the denoised sequence
h(x(0))) .c) = AFRCEE
Canmu), Crey): fovrr (Cstr) (4)
where W is the final output layer, A is a cross-attention layer and h (x)))), e) represents the core of CFP-GEN, sequentially integrating modules such as fAGPM- fRCFE and faver-
Optimization of CFP-GEN. The training objective to optimize the predicted (0)) against the raw sequence (0) using a weighted cross-entropy loss:
c E=E (xto))
(())
Σ ISISL
bi(t) log (x (0) (())))))))
(5)
(()) at time step t. Concretely, the sum of embeddings is passed through an MLP layer to regress three parameters, i.e., 7, 7,3, a = F(Cgo + CIPR + CgC), where 7, B and a are the scaling, shifting and gating factors, respectively. The
Here, (1) adjusts the influence of each diffusion time step f,
(t) determines the contribution of each position i, and 7(c) controls the strength of the condition c. In the following sections, we demonstrate how the inclusion of e facilitates
4

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CFP-Gen
MLP layer F follows specialized initialization strategy: the weights predicting Y and fI are initialized to zeros, en- suring that conditional information is gradually injected into x("), while the weights predicting a are initialized to ones, allowing for effective gating from the beginning. The scale y and shift /3 parameters modulate the distribution of the noised feature x before both the self-attention (SA) and feed- forward network (FFN) layers within each ESM block, akin B.
( of eam need to be updated, denoted as Esgg Owa)) This design preserves the powerful representation capabilities of the main branch that has been fully trained, while enabling the other branch to dynamically encode sequence motifs. Formally, we first pad the sequence motif with <mask> and project it into the same latent space as the denoised feature x E B within one block, which is denoted as Gsoq E R In this way, the workflow of RCFE to handle the sequence-level condition can be represented follows:
to feature de-normalization process: Xeat = Y Additionally, the gating factor a operates on the feature distribution X.m output by the SA and FFN layers through the following formulation: Xout = a O Xou + X(a
Xieq = Eesm (x; + (Ereq (xeee((++=20 (x+ i iin) Q Oaut).
(6)
where (in(; iin) denotes a zero-initialized linear layer ap-
Through the collaboration of diverse annotations, AGFM effectively adjusts the intermediate representations during diffusion, resulting in higher-quality proteins compared to previous approaches relying on single-condition control.
plied to Eseq. Note that this layer is only utilized in the first block of the conditional branch. Similarly, F-ul o-u)) is
another zero-initializze línear layer. Rero-initialization en- ables the gradual incorporation of meaningful information from the sequence motif condition while ensuring the stabil- ity of the main blocks. Finally, Xseq represents the updated feature from a main block, enriched with conditional infor- mation at both the annotation and sequence motif levels. It is worth mentioning that, in image generation models, typi- cally only the encoder part of U-Net used as the trainable branch. Similarly, we leverage only the first half blocks of the ESM2 model to encode sequence motifs, ensuring an efficient yet expressive representation.
3.3. Sequence-Guided Conditioning with RCFE
While AGFM has provided effective control over protein generation with annotations, certain applications require finer-grained guidance, e.g., biological experts often focus on functional sequence fragments (referred to as sequence motifs) to ensure that the generated proteins retain desired functionality associated with these motifs. Thus, we propose the Desidue-Controllee Functional Encoder (RCFE) with an additional network branch to handle this sequence-level condition. The core of RCFE lies in inferring the complete sequence while preserving even optimizing the specified sequence motifs, akin to the sequence inpainting task intro- duced by DPLM (Wang et al., 2024) and EvoDiff (Alamdari et al., 2023). However, unlike these methods, which rely on fixed amino acids throughout generation, RCFE dynam- ically updates the specified motifs during inference. This dynamic adjustment enables the model to emulate evolution- ary processes, offering the potential to discover improved sequence motifs with enhanced functional properties.
Consequently, the sequence-level condition serves as a strong complement to the annotation condition, lead- ing to enhanced controllability over the generated sequences compared using single condition modality alone.
3.4. Multimodal Conditioning within CFP-GEN
The fundamental principle in protein science that function, sequence, and structure are inherently interdependent. This intricate relationship reveals the importance of jointly mod- eling these modalities to achieve accurate protein design.
To this end, we consider practical scenario where biologi- cal experts have obtained the backbone structures of interest (e.g., from RFDiffusion (Watson et al., 2023)) and aim to determine the functional sequence that folds into this struc- ture, problem known as the inverse folding task. In the traditional paradigm, the goal is to generate sequence that correctly folds into given backbone structure. However, when additional functional constraints Canno are required, this task can be extended to functional protein inverse fold- ing or the inverse function task to generate a sequence x* that maximizes sequence recovery while simultaneously optimizing the functionality recovery matching Canno:
Specifically, /RCFE in Eq.4 consists of two branches of mod- ified ESM blocks, along with acro-initialized linear layers to jointly handle sequence-level and annotation-Ieve informa- tion. As shown in 22g.2, the bottom gray blocks represent the main branch, which processes the noised sequence and annotation tags, while the upper orange blocks represent the second branch, composed of trainable copies of the main branch, for tackling sequence-level conditioning. Inspired by ControlNet (Zhang et al., 2023), RCFE adapts this dual- branch design to transformer-base based ESM blocks, making it well-suited for protein sequence generation.
Let (: eeam) denote a modified ESM block in the main branch, which has been enhanced by AGFM to integrate the functional annotations. The parameters O in the main branch blocks remain frozen during training RCFE, while Owg in another branch blocks using a trainable copy
x" arg max p(x Cannos Cseq: Car) + Siune (x, Camno),
where Stine is a scoring function that evaluates how well the generated sequence x aligns with the desired functional properties. More discussion on Sunc can be found in $4.2.
5

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CFP-GGn
Table Function evaluation the generated sequences using diverse function predictors, e.g., DeepGO-SE (Kulmanov et al., 2024)
for GO terms, InterProScan (Blum et al., 2021) for IPR domains, and CLEAN (Yu et al.. 2023) for EC numbers, The best result in each column is highlighted bold, while the second best is underlined (excluding the positive control).
Models
Supported Condtions
MRR
MMD1
MMD-G. mic. Fit
mac. Fit
AUPR
AUCT
Evaluating the Protein Functionality via predicted GO Terms
Positive Control
0.939
0.000
0.000
0.543
0.522
0.402
0.775
Negative Control
0.017
0.215
0.125
0.205
0.020
0.025
0.501
DPLM (Wang al., 2024)
Sequence Motif, Structure
0.134
0.189
0.109
0.332
0.189
0.109
0.581
ProteoGAN (Kucera et al., 2022)
GO Term
0.277
0.095
0.055
0.376
0.093
0.121
0.510
ProGin2 (Nijkamp al., 2023)
GO Term, Sequence Motif
0.545
0.109
0.064
0.414
0.355
0.240
0.663
CFP-GEN (w/ GO)
GO, IPR, EC, Soq.. Strue.
0.601
0.112
0.060
0.429
0.370
0.245
0.674
CFP-GES (w/ GO and IPR)
GO, IPR, EC, Seq.. Strue.
0.779
0.066
0.039
0.496
0.458
0.339
0.732
CFP-GEN (w/ Motif)
GO, IPR, FC, Seq.. Strue.
0.839
0.046
0.028
0.504
0.492
0.370
0.762
CFP-GEN (w/ GO, IPR and Motif)
GO, IPR, EC, Seq., Strue.
0.870
0.036
0.022
0.532
0.550
0.435
0.795
Evaluating the Protein Functionality via predicted IPR Domains.
Positive Control
1.000
0.000
0.000
1.000
1.000
1.000
1.000
Negutive Control
0,016
0.224
0.131
0.003
0.000
0.004
0,500
DPLM (Wang al., 2024)
Sequence Motif, Siructure
0.053
0.192
0.112
0,548
0,422
0.369
0.695
ESM3 (Hayes al., 2025b)
IPR Domain, Seq. Struc.
0.148
0.101
0.060
0.690
0,565
0.502
0.762
ProGen2 (Nijkump
2023)
GO Term. Sequence Motif
0,281
0.112
0,067
0.712
0,584
0,536
0.772
CFP-GEN (w/ IPR)
GO. IPR, EC. Seq.. Strue.
0.332
0.094
0.056
0.882
0.826
0.782
0.899
CFP-GEN (w/ GO and IPR)
GO, IPR, EC, Seq.. Struc.
0.386
0.078
0.047
0,909
0,858
0.824
0.922
CFP-GEN (w/ Motif)
GO. IPR, EC, Seq.. Strue.
0.583
0.056
0.035
0.937
0,927
0.891
0.970
CFP-GEN (w/ GO, IPR and Motif)
GO, IPR, EC, Seq.. Strue.
0.654
0.046
0.030
0,983
0.975
0.965
0.992
Evaluating the Protein Functionality via predicted EC Numbers.
Positive Control
1.000
0.000
0.000
0.943
0.927
0.895
0.952
Negative Control
0.012
0.222
0.129
0.036
0.028
0.016
0,510
DPLM (Wang ct al., 2024)
Sequence Motif, Structure
0.211
0.189
0.109
0.241
0,186
0.127
0.572
ProGen2 (Nijkamp et al., 2023)
GO Term, Sequence Motif
0.530
0.127
0.075
0.519
0.387
0.318
0.661
ZymCTRL (Munsamy et al., 2022)
EC Number
0.562
0.044
0.026
0.901
0.774
0.743
0.876
CFP-GRNN (w/ EC)
GO. IPR, FC, Seq. Struc.
0.567
0.141
0.082
0.722
0.673
0.559
0.786
CFP-GEN (w/ EC, GO and IPR)
GO, IPR, EC, Seq. Struc.
0.774
0.103
0.060
0.780
0.743
0.644
0.831
CFP-GEN (w/ EC and Motif)
GO, IPR, BC, Seq.. Struc.
0.898
0.049
0.029
0.937
0,920
0,883
0.946
CFP-GEN (w/ EC, GO, IPR and Motif)
GO. IPR, EC, Soq.. Struc.
0.924
0,041
0.024
0.942
0.925
0.892
0.951
To achieve the objective in Eq. 7, we employ a structure encoder favet (i.e., GVP-Transformer) to embed the back- bone atom coordinates of the protein: Car = fovrr(caa (Csu). Subsequently, Car is injected into the final ESM block of the main branch through a cross-attention layer, following the strategy of DPLM. In our implementation, we found that the pretrained cross-attention layer from DPLM could be directly utilized without the need of further training, sug- gesting that more functional adapters can be incorporated. The denoised features, having already incorporated both Canno and Caeq. further interact with Catr to enable more precise and functionally coherent protein design.
4. Experiments
4.1. Experimental Setup
Datasets. To collect high-quality data for training CFP- GEN, we employ expert-curated functional annotations from SwissProt (UniProtKB) (Consortium, 2019), Inter- Pro (Hunter et al., 2009), and CARE (Yang et al., 2024) databases. Two datasets were constructed for general pro- tein design and enzyme design, respectively. For the general protein dataset, we include 103,939 protein sequences cov- ering 375 GO terms and 1,154 IPR domains. For the enzyme design dataset, we intersected SwissProt with the CARE dataset, yielding 139,551 enzyme sequences annotated with 661 EC numbers (4-level EC annotations). Additionally, the PDB (Berman et al., 2000) and AFDB (Jumper et al., 2021) databases were exploited to provide backbone atomic coordinates, ensuring structural constraints are incorporated into the dataset. Additional details regarding the datasets can be found in the Appendix $A.
Our functional protein inverse folding reduces the search space from a vast sequence space to sequences that are both structurally viable and functionally relevant. Compared to traditional inverse folding, which rely only on structural con- straints, our approach incorporates functional information as an additional constraint. This leads to higher sequence re- covery rates. We demonstrate significant improvements for both the zero-shot and supervised fine-tuning (SFT) versions of CFP-GEN with structural adapter in $4.3.
Implementation Details. Our model is built upon the pre- trained DPLMM650M and trained in two progressive stages.
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CFP-Gen
Structure
Function
Structure
Function
Table Evaluation of functional protein inverse folding. CFP- GEN achieves significantly higher AAR and improved functional- ity compared other inverse folding methods.
G00046916
G0:0033857
GD0004000
60-0026692
GD8046872
GD10034300
GD9016491
6000076405
Models
AAR
MRR
F Fux
scTM
pI.DDTT
GO
IPR006140
Positive Control
100.00
0.939
0.574
0.910
86.12
IPR036371
IP8003730
IPR006 108
ProtcinMPNN (Dauparas al., 2022)
45.28
0.133
0.902
IPR011324
IPRO29045
IPR008BE7
ESMIIF (Iss et al., 2022)
$7.39
0.495
0.551
0.895
83.49
IC24421
EC1-1.1.35
L.M-ESSIN (Zheng et al., 2023)
63.81
0.730
0.549
0.898
EC24228
EC-42:117
DPLM (Wang al., 2024)
66.94
0.721
0.552
0.883
85,33
Q92HU9
EC3.544
B4TCA8
ECS.1.23
CFP- JEP-GEN (Zero- Shott w/ GO and IPR)
72.05
0.866
0.571
0.887
83,28
TM-score: PLDOT: 885
TM-scorw 0.956 PIDDT. 82.8
CFP-GEN (SFT, GO and IPR)
76.39
0.882
0.581
0.889
83.53
Figure 3. Examples of multi-catalytic enzymes. CFP-GEN gen- erates high-quality proteins (i.e., TM-score above 90) with mul- timodal conditions. The ground-trute structures from the AFDB database are in green, while the generated structures are red.
First, we train the AGFM module enable effective integra- tion of functional annotations. The pretrained parameters from the first stage are then used in the next stage, where we train the copy ESM blocks in RCFE, allowing the model to condition on sequence motifs, The optimization schedule is the same as unconditional DPLM. For the structural condi- tioning, we directly use the GVP-Transformet and structure adapter from DPLM, which are pretrained on the CATH (Sil- litoe al., 2021) database, without additional fine-tuning for CFP-GEN. During inference, users can specify any func- tional constraints and their combinations as conditioning signals. Further model implementation details can be found in the Appendix $D.
We conduct performance comparison against leading PLMs under varying condition guidance. Notably, most of these PLMs support only single-condition input, whereas CFP- GEN allows combinations of input conditions across dif- ferent modalities. Detailed implementation for sequence generation using each approach can be found in the Ap- pendix $E. Furthermore, we provide positive and negative controls to establish the upper and lower bounds these evaluation metrics. The positive control consists of the real sequences from UniProtKB, while the negative control is the unconditionally generated sequences by DPLM.
4.2. Benchmarking Protein Functional Performance
4.2.1. 1. EVALUATION OF GO TERMS
In this section, we evaluate the functional properties of the generated protein sequences. To achieve this, we split two validation datasets from the general protein dataset and en- zyme dataset introduced in ŠA. We evaluate GO and IPR functions on the general protein dataset and EC function on the enzyme design dataset. The functional annotations of each natural sequence are used as conditioning prompts for the PLMs, generating one sequence per prompt with vary- ing length to strictly evaluate nearest-neighbor performance. We assess the similarity between the generated and the real sequences using various metrics, such as Maximum Mean Discrepancy (MMD), including its Gaussian kemel vari- ant (MMD-G), and Mean Reciprocal Rank (MRR). Then, various function predictors (i.e., Siune in Eq. 7) are applied to assign functional labels and evaluate whether the gener- ated sequences exhibit functional consistency. Specifically, we use DeepGO-SE (Kulmanov et al., 2024) for GO label prediction, InterProScan (Blum et al., 2021) for homology- based annotation, and CLEAN (Yu et al., 2023) for catalytic function prediction. Since these predicted labels correspond to multi-labe classification problem, we adopt commonly used function prediction metrics (Kim et al., 2023) to quanti- tatively evaluate the alignment between predicted functions and the prompt functions (i.e., ground-trutte
As shown in Tab. 1, the sequences in the negative control exhibit little functional consistency with the prompt GO terms (macro Fi-score = 0.02), proving the necessity of conditional generation. While both DPLM and ProteoGAN generate some functional proteins, ProGen2 achieves im- proved function when conditioned on the first 30 residues. In contrast, CFP-GEN demonstrates superior performance across various conditioning levels. Firstly, the generated sequences closely match the distribution of real sequences, as evidenced by low MMD. Even when conditioned only on GO terms, the model attains satisfactory functional con- sistency. Further integrating IPR information enhances per- formance, yielding a micro Fi-score of 0.496 and macro F -scor of 0.458. To further assess the impact of sequence motifs, we extract sequence fragments from IPR annota- tions, selecting those whose descriptions best align with the corresponding GO functions (resulting in 10-30 residues). Notably, even when using only these residues as condi- tioning inputs, the model generates functional sequences, reinforcing the strong correlation between sequence and function. By combining sequence motifs and functional annotations, the designed sequences outperform real se- quences (positive control), achieving a macro F, -ccre of 0.550 vs. 0.522, macro AUPR of 0.435 vs. 0.402, and AUC of 0.795 vs. 0.775. These results highlight the effectiveness
such as micro F1 scoore macro Fi- Fcose, macro AUPR and macro AUC. Details about the evaluation metrics are in Appendix SB.
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Sucen
facer
Rondly Compuiten
N Shersit Comparionn
Fette
621
Figure 4. Evaluation of multi-catalytic enzyme design. Our generated proteins exhibit high designability, meanwhile achieving high success rate and functionality as validated by CLEAN.
Figure 5. Comparison of sequence novelty and diversity be- tween real and our designed proteins across 7 typical EC numbers from different enzyme families.
of CFP-GEN in designing functionally enriched proteins.
4.3. Functional Protein Inverse Folding
4.2.2. EVALUATION OF IPR DOMAINS
In Tab. 2, we evaluate the inverse folding task on the gen- eral protein dataset, where backbone structure information, combined with the GO and IPR annotations are provided as input. Several state-of ofte-of--thearr methods, including Pro- teinMPNN, ESM-IF, LM-DESIGN and DPLM trained on CATH are used for comparison. Notably, most of these methods--including CFP-GEN emppoy en- coder networks pretrained only UniProtKB. In particular, CFP-GEN (Zero-Shot) does not involve any fine-tuning of DPLM's structural adapter. For comparison, we also provide a supervised fine-tuned variant, CFP-GEN (SFT), in which the structural adapter is trained on our general protein dataset using backbone structure inputs. The gener- ated sequences are then folded using ESMFold (Lin et al., 2023) assess their designability. Overall, most methods achieve high F -scoee (used in DeepGO-SE (Kulmanov et al., 2024)), revealing the strong coupling between struc- ture and function. Moreover, LM-DESIGN and DPLM demonstrate superior AAR and MRR performance. CFP- GEN (SFT), guided by functional annotations, achieves sig- nificantly higher AAR (+9.45%) and MRR (+16.10%) com- pared to the baseline DPLM while maintaining comparable designability. This highlights the importance of incorporat- ing functional annotations to guide the design process.
The evaluation results based on IPR domains provide further evidence of the superior performance of CFP-GEN. Since running InterProScan large-scale sequences is compu- tationally expensive, we perform the evaluation on uni- formly downsampled subset, using one-tenth of the GO validation set. We also implement the recently published ESM3, which supports IPR domain site input. Since ESM3 a masked prediction model, we provide an additional 30 residue from the real sequences as prompts. Similar residue prompts also given to DPLM and ProGen2 for sequence inpainting, ProGen2 demonstrates improved functionality. However, CFP-GEN demonstrates strong performance in terms of MRR and micro/macro Fi-score without requiring any sequence-leve prompts. When further incorporating sequence motifs, it achieves functionality comparable to that of natural proteins, i.e., an macro Fi F--cee of 0.975.
4.2.3. EVALUATION OF EC NUMBERS
In this section, we mainly compare CFP-GEN with ZymC- TRL. Please note that some EC numbers are not included in ZymCTRL and we thus evaluate it on a subset, where ZymCTRL achieves slightly higher results. Additionally, we provide sequence segments derived from IPR annota- tions as imput prompts to DPLM and ProGen2 conduct the sequence inpainting task for further comparison. As ex- pected, the natural sequences in the positive control exhibit strong enzymatic activity, validating CLEAN as reliable enzyme function predictor. CFP-GEN, when conditioned only on EC numbers, generates sequences with high cat- alytic activity. When further incorporating GO and IPR annotations, we observe significant improvements in both distribution-level metrics (i.e,, improving 21% in MRR) and macro F1 Fscrc (i.e., improving 7%),, highlighting the impor- tance of integrating comprehensive functional annotations. Finally, combining both functional labels and sequence mo- tifs achieves the highest functional scores, approaching that of natural ones (i.e., 0.925 vs. 0.924 in macro Fi Fcooc)r.). These results further prove the effectiveness of CFP-GEN.
4.4. Multi-objective Protein Design
We further demonstrate that using combinations of mul- timodal conditions enables the design of multi-functionall proteins. Specifically, we consider six multi muuli-catallytc en- zymes as design targets, each annotated at least three EC numbers. For each enzyme, we construct three homologous sequences as validation set. Some generated proteins are visualized in Fig. 3 to enhance interpretability, More intro- ductions of these enzymes can be found in the Appendix $F. We fine-tune CFP-GEN on the enzyme design dataset, where the homologous enzymes with same annotations are held out from the training set. We then compare our model against ZymCTRL by generating three sequences per en- zyme, and the results are illustrated in Fig. 4. In particular,
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compute the average success rate across all generated proteins, where design is considered successful if all as- signed EC numbers are present in the predicted labels, while also meeting the structural criteria of TM-score and PLDDT above 70. According to success rate and macro Fi-score, CFP-GEN significantly outperforms ZymCTRL, highlight- ing the advantages of multimodal conditioning.
sign, where it consistently outperforms existing approaches.
Although CFP-GEN represents an early attempt to jointly integrate multiple modalities for conditional protein gener- ation, it has several limitations. First, it currently supports only a subset of GO, IPR, and EC labels, limiting its gener- alizability to broader functional categories. Second, beyond commonly used functional annotations, richer conditional inputs-such as physicochemical properties (e.g., hydropho- bicity, charge, polarity) -re also essential to meet the re- quirements of bio-
4.5. Analysis of Novelty and Diversity
A crucial aspect of de novo protein design is the ability to generate novel and diverse proteins that do not exist in na- ture. To examine whether our model generates truly new proteins, we select diverse EC numbers from different top- level categories: Oxidoreductases, Transferases, Hydrolases, Lyases, Isomerases, Ligases, and Translocases. For each class, CFP-GEN generates 30 sequences conditioned only on the EC number. We then compare with 30 real proteins from the enzyme validation set with the corresponding EC number. Novelty is computed by measuring how different (i.e., sequence identity after alignment) each generated se- quence is from its most similar real protein in the training set, while diversity is computed by capturing how different the generated sequences are from the overall training set. To en- sure both metrics are interpretable in the same direction, we subtract the scores from (i.e., higher is better). The results are shown in Fig. 5. We observe that DEP--EENOO consistently achieves higher sequence novelty across all 7 EC numbers, demonstrating its strong potential for de novo protein design beyond simply replicating known sequences. Moreover, the generated sequences exhibit high intra-class diversity in 5 out of the 7 EC categories. These results suggest that the model has learned a more generalized representation, rather than overfitting to training examples. Additional analysis of CFP-GEN is presented in Appendix $C.
applications. Moreover, sequence-structure co-design should be incorporated to en- able end-to-end protein design. Future work will focus on scaling up the training datasets, enriching the scope of func- tional annotations, and further advancing unified models for more scalable and function-aware protein generation.
Impact Statement
This work introduces a multimodal conditional protein de- sign framework, enabling precise control over generated proteins by integrating diverse functional constraints. This approach has broad potential applications in enzyme en- gineering, drug discovery, and synthetic biology. We also provide comprehensive benchmark of existing controllable PLMs, offering valuable insights for researchers utilizing these computational tools.
From an ethical standpoint, our model is trained entirely on publicly available datasets (e.g., UniProtKB), ensuring reproducibility and transparency. However, potential biases may arise from unequal representation across protein fam- ilies and functions, as public datasets often reflect human- curated biases. Addressing these imbalances requires con- tinuous expansion of training data and careful validation on diverse proteins. Biosecurity risks must also be carefully considered. The ability of generative models to design novel proteins raises dual-use concerns, particularly in the con- text of biomedical applications. The safe use of biological generative models requires risk assessment, regulatory com- pliance and safeguards to prevent misuse while ensuring ethical and beneficial applications.
5. Conclusion and Future Work
This work introduces novel PLM, CFP-GEN, for func- tional protein design with a multimodal conditioning mech- anism. The core of CFP-GEN comprises three modules, each addressing functional annotations, sequence motifs, and backbone structures, respectively. Specifically, Innotation-Guicee Feature Modulation (AGFM) module is designed to flexibly and smoothly incorporate compos- able functional tags, such as GO terms, IPR domains, and Functional Encoding (RCFE) module introduced to encode crucial sequence motifs, facilitating scenarios where functional annotations are unavailable. Finally, a structure adapter is integrated into CFP-GEN without requiring additional training, ensur- ing coherence between function and structure. We evaluate CFP-GEN on three tasks: functional sequence generation, functional inverse folding, and multi-functional protein de-
A Acknowledgements
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No REI/1/5234-01- 01, RE1/1/5414-01-01 REI/1/5229-01--1 REI/1/5404-011 01, REI///5992-01-01, URFF/144663-01-01, Center of Ex-
EC numbers. A
cellence for Smart Health (KCSH), under award number 5932, and Center of Excellence for Generative AI, under award number 5940. For computer time, this research used Ibex managed by the Supercomputing Core Laboratory KAUST in Thuwal, Saudi Arabia.
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Supplementary Material of CFP-GEN
A. Dataset Curation
Existing PLMs for protein generation often focus on either unconditional sequence generation or single-condition controllable generation. For instance, ProteoGAN (Kucera et al., 2022) utilizes GO terms for functional control, ESM3 (Hayes et al., 2025b) leverages IPR domain information, and ZymCTRL (Munsamy et al., 2022) conditions on EC numbers for enzyme design. However, relying on single-condition constraints makes these datasets unsuitable for training a multimodal, multi-condition controllable PLM like CFP-GEN, necessitating the curation of new datasets for functional protein design.
A protein is usually defined by three different modalities, i.e., function, sequence, and structure. These three aspects are generally considered to be highly interdependent and mutually constraining, as a protein's 's sequence determines its structural fold, while both sequence and structure influence its functional properties. Given this intrinsic coupling, we annotate each protein from all three perspectives ensure a comprehensive representation. Specifically, we construct two specialized datasets to facilitate functional protein design: (1) A general protein design dataset with GO terms, IPR domains, functional sequence segments (motifs) and protein structures. (2) An enzyme design dataset specifically curated for generating catalytically active proteins by further adding EC number annotations.
A.1. The General Protein Dataset.
Gene Ontology (GO) terms provide structured and comprehensive vocabulary for annotating proteins with respect to their molecular functions, biological processes, and cellular components, thereby offering proper framework for understanding protein roles in various biological contexts. In this work, we focus primarily on molecular function annotations, which offer a more direct and interpretable characterization of each protein's 's activity. In addition to GO annotations, we incorporate InterPro (IPR) numbers, which categorize proteins based on conserved domains and families, facilitating the identification of functional and structural relationships across diverse protein sequences. Combining GO and IPR labels allows for a deeper understanding of proteins from both global and local perspectives and thus facilitating the protein generation process.
To build the general protein dataset, we collect protein sequences from the manually curated Swiss-Prot subset of UniPro- IKB (Consortium, 2019), ensuring high annotation reliability. To maintain balanced class distributions, we implement a filtering strategy that retains only GO and IPR terms with at least 100 annotated protein sequences, as suggested by ProteoGAN, to mitigate the impact of long-tail categories. Although this inevitably excludes some sequences from the training set, has demonstrated the model's capability to generalize across commonly used functional labels. Besides, our method primarily focuses on integrating different modalities as conditioning signals, enabling more comprehensive protein function modeling. We leave the addressing of long-tal maig data challenges in large-scale diffusion models to future iterations.
The final dataset contains 103,939 protein sequences covering 375 GO terms and 1,154 IPR domains. The distribution of GO and IPR annotations in our dataset is illustrated in Fig. 6. As shown in the GO annotation distribution, 30.6% of GO categories contain 100-200 protein sequences, while only 6.4% have more than 1,000 sequences. Similarly, in the IPR annotation distribution, 35.4% of IPR categories consist of 100-200 sequences, whereas only 2.0% contain more than 1,000 sequences. Despite the remaining long-tail distribution in the dataset, we observe that the model generalizes well across different categories, as indicated by the macro F1 score in Tab. 1.
Additionally, for each IPR number, we extract motif information from the annotated domain boundary positions. During both training and inference, we select the IPR domain most relevant to the given protein's 's GO annotations (if multiple domains are present, we use their intersection) to define the functional sequence segments. These segments (motifs) are then used as sequence-levar conditioning inputs to enhance the model's 's ability to capture functionally relevant patterns. Furthermore, for each retained protein sequence, we retrieve its corresponding structural information from PDB and AlphaFold DB (AFDB) databases. Specifically, we extract the backbone atomic coordinates of 'N', 'CA', 'C', and 'O' and used them as structure condition. Our analysis reveals that the backbone structure plays crucial role in determining protein function, proving the importance of incorporating structural features into PLMs.
To evaluate GO function, we construct validation set by selecting 30 sequences per GO/PPR label, resulting in a subset of 8,309 sequences. The training dataset is then formed by holding out these sequences to ensure an unbiased evaluation. For IPR function assignment, we further perform a 10-fold uniform downsampling of the GO validation set, yielding 831 sequences, to alleviate the computational overhead associated with InterProScan.
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GO Distribution by Sequence Count
IPR Distribution by Sequence Count
EC Distribution by Sequence Count
100-200
100-200
0-100
35.
3B 87%
21.9%
400-500
0.2%
500-1000
21.4%
12.1%
13.0%
400-500
300-400
400-500
300-400
200-300
Figure 6. Categorical distribution of functional annotations in the two datasets. Most functional labels contain fewer than 500 sequences, highlighting the long-tail distribution issue. However, CFP-GEN exhibits strong generalization across these functions.
A.2. The Enzyme Design Dataset.
Enzymes, as critically important class of proteins with wide applications in biotechnology and industrial catalysis, requiring a specialized dataset for high-quality enzyme design. EC numbers (Enzyme Commission numbers) classify enzymes based on their catalytic activities. Linking protein sequences to specific biochemical reactions facilitates better understanding of enzyme function. To construct a robust enzyme dataset, we leverage CARE (Yang et al., 2024), newly curated enzyme dataset comprising 185,995 sequences, each annotated with a level-4 EC number for precise functional clustering. To ensure compatibility with GO and IPR annotations, extract the subset of CARE that overlaps with Swiss-Pros yielding dataset of 139,551 sequences spanning 661 EC labels. The distribution of EC categories is illustrated in Fig. 6.
In line with our approach for the General Protein Dataset, we also integrate sequence motifss from IPR annotations and structural information from PDB/AFDB databases. To enable rigorous evaluation, we construct a validation set by sampling 30 sequences per EC label, resulting in a high-quality evaluation set of 16,187 sequences, while the remaining data is allocated the training set. Besides, cutti-ca enzymes in Fig. 4 are also held out from the training set ensure fair evaluation. As demonstrated in Tab. 1, our model achieves catalytic function comparable natural enzymes this large-scale validation set, assessed by the widely used CLEAN tool.
B. Evaluation Metrics
We conduct a comprehensive evaluation of the generated proteins across multiple levels to assess their quality rigorously:
(1) Sequence-level evaluation: We measure the similarity between the generated sequences and natural protein sequences. Additionally, we analyze the inter-group distances by clustering sequences based on their functional labels, ensuring that functionally related sequences exhibit appropriate distribution.
(2) Function- evaluation: We assess the functional consistency between the functions of generated sequences (i.e., estimated by SOTA function predictors) and their input prompts (i.e., derived from natural sequences). This is evaluated through multi-label classification metrics, ensuring that the predicted functions align with their prompt functions.
(3) Structure- evaluation: We evaluate the structural consistency the generated sequences by predicting their structures using ESMFold and comparing them with their corresponding PDB/AFDB structures. This ensures that the generated proteins not only maintain sequence-level fidelity but also exhibit high designability.
B.I. Sequence Evaluation Metrics
Maximum Mean Discrepancy (MMD): Maximum Mean Diserepancy (MMD) measures the distribution difference between generated protein sequences P and their corresponding natural sequences S. Due to its efficiency and high expressive capacity, we adopt normalized Spectrum Mapping to obtain sequence distribution, as suggested by ProteoGAN. This method computes k-mer occurrences within a sequence, providing a sufficiently rich representation to effectively distinguish protein properties. Given kernel function k(.,)). -). MMD is computed as:
MM P) Ss [k((8, s')] + Epp'' kk(,, p')] 2 2E [k((),) ((s.))
(8)
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CFP-Gen
where 8, S are real sequences, and p: P are generated sequences. A lower MMD value indicates better alignment between the generated and real distributions. For linear kernel, the MMD computation simplifies to direct Euclidean distance between the mean embeddings of the two distributions:
m
n
MMDrinear (S, P) = =llus - -PPll where, ug
86
Σ
Ps
m
Y1 j=l
represent the mean embeddings of the real and generated sequences, respectively, and
|| denotes the Euclidean norm.
Gaussian Kernel MMD (MMD-G): For an RBF (Gaussian) kernel, the MMD computation incorporates the radial basis function (RBF) to capture higher-order distributional differences:
MMD 2 Gaussian (S, P)
m Σ ks), +
Σ n² i
1
k(pr,p))
2 Σ k(s..p)) Pj)
(10)
mn
where the Gaussian kernel function is defined as:
k(x,y) exp (-7 - y)(
(11)
with y typically determined via median heuristic approach to adaptively select appropriate kernel bandwidth, ie.,
1
= median [r; - myle,); 7 = 2a²
(12)
where a is the median all pairwise Euclidean distances between embeddings from both groups.
By considering both the linear kernel MMD, which captures global differences, and the Gaussian kernel MMD, which captures higher-order distributional variations, ensure robust evaluation of the similarity between real and generated protein distributions.
Mean Reciprocal Rank (MRR): Mean Reciprocal Rank (MRR) evaluates how well the generated sequences P match the real sequences S in a function-aware manner based on linear MMD) distance.
We define S = {Se)ae
set of real protein sequences grouped by their function label where C' is the total number of function labels (i.e., GO/PP//C annotations), Similarly, let /' = {Po]e= be the corresponding generated protein sequences for each functional group. The goal is to assess whether the generated sequences for each function closely match the real ones. The MRR metric defined as:
M)
(13)
ranks (MMD(S.... P.))
c=1
where (S,, P.)) represents the ranking position of the MMD score between the real sequences S, and generated sequences P.. Specifically, for each function label pair (c, c'), we compute MMD(S,, Pe)). measuring the distributional difference between the real sequences of function and the generated sequences of function c'. The ranking of MMD(S., P.) is then determined by sorting these MMD values across all function pairs.
MRR ensures that the evaluation considers not only how well each function's generated sequences match their corresponding real sequences but also their relative distinction from other functional groups. A higher MRR indicates better functional alignment between the real and generated distributions. When MRR (S, P) = 1, it signifies that for each functional class, the generated sequences are the closest in distribution to their corresponding real sequences, demonstrating high-quality protein generation.
B.2. Function Evaluation Metrics
Macro Fccore:: Macro Fisccore computes the per-class Fisccosss, treating all classes equally, which is denoted as:
macro F, 1 C
2. Precision, Recall,
(14)
C 1=1 Precision; + Recall;
where C the number of classes, and Precision,, Recall; are the precision and recall for class i. This metric is particularly useful for evaluating performance across imbalanced datasets, as it ensures that smaller classes contribute equally to the final score.
14

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CFP-Gen
Miero Fi-score: In contrast, micro Fi Scooe aggregates the counts of true positives, false positives, and false negatives across all classes before computing single Fi -score::
miero Precision micro Recall
micro F
(15)
micro Precision + micro Recall
where micro precision and micro recall are computed as:
EiSTR. TP. i= FP.:
EE TP:
micro Precision
micro Recall
(16)
(TP, FN,)
where TP;, FP; and FN; represent true positives, false positives, and false negatives for class i, respectively. Unlike
macro -scor,, miero -core gives more weight to high-frequency classes.
Macro AUPR (Area Under the Precision-Recct Curve): Macro AUPR computes the area under the precision-recal
curve for each class and averages them:
macro AUPR 1 Σ AUPR, i=1
(17)
where AUPR; is the precision precision-recall area for class i. This metric is particularly valuable in scenarios with highly imbalanced datasets, where precision-recall trade-off's are more informative than ROC-based metrics.
Macro AUC (Area Under the Receiver Operating Characteristic Curve): Macro AUC measures classification performance by averaging the AUC values across all classes:
macro AUC
C Σ
AUC,
(18)
1=1
where AUC; is the ROC-AUC for class i. Higher values indicate better model discrimination between positive and negative instances.
F score: In Tab. 1, evaluate function scores using standard multi-label metrics, with confidence thresholds of 0.1 for GO and 0.01 for EC. To account for the hierarchical structure of GO functions, ancestor GO terms are also incorporated in evaluation. Additionally, in Tab. 2, to align with DeepGO-SE, we also consider F max -core computed at the sequence level. Given a confidence threshold r (ranging from 0.1 to 1.0 in increments of 0.01), we define: P, The number of correctly predicted GO terms present the ground truth set of each sequence. FP,; The number of predicted GO terms that do exist the ground truth of each sequence. FN,; The number of ground truth GO terms that not predicted. Using these, Precision, and Recall, threshold
calculated as:
TP,
TPP
Precision, TP, + FP,' Recall, = TP, + FN,
(19)
The F Score is then obtained by maximizing the Fi-score over range of confidence thresholds:
Precision, Recall,
F Fmax max
(20)
Precision, Recall,
Unlike macro -score, which averages per-class performance, Fmax focuses on per-sequence predictions.
B.3. Structure Evaluation Metrics
Amino Acid Recovery (AAR): AAR measures how well generated protein sequence x' matches a reference sequence x, given the protein structures as model inputs. Let x' = (#1 22y,... s't)) and x (æ1; "2....,..... .L) denote the generated sequence and the reference sequence of length L. AAR is then defined as:
6(1), x.) = f if if = 24
AAR
(21)
otherwise
The function 8(2{, x4) is the indicator function. higher AAR value indicates greater sequence fidelity to the reference, meaning the generated sequence closely resembles the original target sequence the residue level.
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CFP-Gen
Genérated Sequence Embeddlings
Real Sequence Embeddingg
60
00:0000757
00:014155
o0p
00:001776
00:0054100
10-
00:0141955
0000913760
00:0944055
60:0151991
G2:0163744
0O:0043790
-10
-10
-15
-15
-10
-35
(a) Embeddings of generated sequences based on different GO functions. (b) E Embeddings of real sequences based on different GO functions.
Figure 7. UMAP visualization of sequence embeddings conditioned on GO functions. The generated sequences from CFP-GEN ex- hibit a highly similar distribution to natural sequences, demonstrating the strong expressiveness and functional consistency of our approach.
TM-score (scTMM) scTM globally evaluates the structural similarity between a generated protein sequence x' and a reference protein sequence x, considering the backbone structure Car conditioning factor. Let F(x) and F(z') be the 3D structures predicted for sequences and x' respectively (using ESMFold in case), then scT' is defined as:
scTM TM-score(P((2)) F
F(x) Car)
(22)
Higher scT' scores indicate better structural consistency under backbone structure guidance.
Predicted Local Distance Difference Test (pL.DTT): PLDDT quantifies the confidence of a predicted protein structure by assessing the local agreement of residue positions. Given a predicted structure F(z') with L residues, the pLDDT score is computed as:
PLDDT I. E PLDDT
(23)
where PLDDT, is the per-residue confidence score of x; E {-1, .g...., PLDDT score indicates greater reliability in the predicted structure.
2/ derived from the folding model. A higher
C. Extensive Analysis of CFP-GEN
C.1. Analysis of Generated Protein Embeddings
To evaluate the alignment between generated and real protein sequences, we compare their embedding distributions across 20 randomly selected GO terms, with 30 sequences per GO term for both real and generated data. The embeddings are obtained using Spectrum Mapping, a highly expressive sequence representation method, and visualized via UMAP for dimensionality reduction. As shown in Fig. 7, the distributions of generated sequences closely align with those of real sequences, with each functional category maintaining consistent spatial position across both plots. The inter-cluster distances remain well-preserved, indicating that proteins with different functions occupy distinct embedding spaces, while the intra-class compactness suggests that functional specificity is effectively captured. Additionally, the model generalizes well across diverse functional spaces, preserving structured and biologically meaningful separations. Mínor overlaps in some central regions suggest that certain sequences may exhibit multiple GO functions, reflecting the inherent complexity of protein function annotation. The overall results demonstrate that CFP-GEN leverages Spectrum Mapping embeddings to effectively preserve the functional landscape of protein sequences, ensuring both functional coherence and class separability in the learned representation space.
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CFP-Gen
0.870
0.85
0:839
0.845
0.06
0.854
0.80
0.84
0.833
0.82
MRR
0.80
MRR
0.779
0.70
0.65
0.607
0.76
0.743
(8) GO:0.5, IPR:0 5
0.60
0.572
(A) ESM Block Number
(O) G0:0.7. IPR:0.7
(8) ESM Block Number
[D] 60:0.5. IFR.0.5. Motit0.3
0.55
(C) ESM Block Number 12
0.72
0
(PR-0.5. Modih00.5
0.511
0.50
6
Experiments
Experiments
(a) Model performance with varying control intensity of each functional condition.
(b) Model performance with varying numbers of copy ESM blocks in RCFE module.
Figure Hyperparameter Analysis CFP-GEN. examine the impact control intensity and the number of copy ESM blocks.
C.2. Mitigation of Mode Collapse.
One common issue with PLMs is mode collapse in the generated sequences. To evaluate whether CFP-GEN alleviates this problem, we analyzed the frequency of repeated n-gram patterns (n 2, 3, 4, 5, 6) in sequences generated under GO-conditioned prompts, as reported in Tab. 1. Sequences generated by DPLM are used as reference, and real protein sequences from the validation set serve as a positive control.
As shown Tab. CFP-GEN produces a similar number of 2-grams to real proteins, while significantly reducing the number of longer repetitive n-grams, especially 4-gram 6-gram patterns. Notably, the more functional conditions (e.g.. GO, IPR, Motif) are provided, the fewer repetitive patterns appear in the output, indicating better sequence quality and reduced mode collapse. These results provide strong evidence that CFP-GEN effectively alleviates the mode collapse issue observed in mainstream PLMs.
Table 3. Comparison of r-gram counts across different methods.
Method
2-gram
3-gram
4-gram
5-gram
6-gram
Positive Control
404
164
0
DPLM
315
462
104
46
26
CFP-GEN (w/ GO)
363
351
15
CFP-GEN (w/ GO and IPR)
365
332
CFP-GEN (w/ GO, IPR and Motif)
377
336
D. Hyperparameter Details
As introduced in the main paper, unless otherwise specified, most of the learning strategies and hyperparameters of the diffusion model remain consistent with those of DPLM. The batch size is set to million tokens, and training is conducted on 8 NVIDIA A 100 GPUs for around 72 hours of each stage. The Adam optimizer is employed with a maximum learning rate of 0.00004. During inference, we allow the model to perform 100 sampling steps, following the DPLM conditional generation, with sequence length varying from 200 400. The total model size CFP-GEN 1.48B parameters, excluding the GVP-transformer structure encoder.
During training, adjust ((c) in Eq. 5 by applying dropout to each conditioning input. The probability of randomly dropping out each condition and its corresponding impact model performance is shown in Fig. 8 (a). MRR is used to present the performance as we found this metric is more robust and expressive. The results indicate that setting the dropout probability to 0.5 for each condition achieves the best performance. Additionally, we investigate the optimal number of ESM block copies in the RCFE module. We compare the model performance conditioning on only the sequence motifs. As shown Fig. (b), setting the number of blocks to 16 achieves the best trade-off between performance and model complexity.
17

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CFP-Gen
E. Implementation of Existing PLMs
For ProGen2, we provide the model with GO terms along with the first 30 residues of the real sequence as prompts. When evaluating IPR/EC functions, only the first residues are used, as ProGen2 does not support EC/IPR annotations.
For ProteoGAN, directly input the GO terms of the real sequence. However, since ProteoGAN only supports 50 predefined GO terms, for GO terms not included in ProteoGAN's vocabulary, we attempt to map them to their closest ancestor terms that are supported. If no suitable ancestor is found, the sequence is ignored.
For discrete diffusion models such as DPLM, we provide the model with functional motifs (30 residues) and task it with performing sequence inpainting to reconstruct the missing residues.
For ESM3, we input both the IPR domain descriptions and their start-end positions, along with 30 residues initialize the sequence generation process.
For ZymCTRL, only single EC number per sequence is provided, as the model does not support multi-label inputs. If an EC number is not supported by ZymCTRL, we exclude the sequence from evaluation to ensure fair comparison.
F. Introduction of Multi-catalytic Enzymes
-/sc-euuclo SurE (EC Numbers: 3.1.3,5,, 3.6.1.11): This nucleotidase has broad substrate specificity, dephosphorylating various ribo- and deoxyribonucleoside s-mmonphosphatts and ribonucleoside s'-monophosphates, with the highest affinity for 3' 3'-AMP. It also hydrolyzes polyphosphate (exopolyphosphatase activity), preferentially targeting short-chain-length substrates (P20-25). This enzyme is potentially involved in the regulation of dNTP and NTP pools, as well as the turnover of 3' mononucleotides, which are produced by various intracellular RNases (T1, T2, and F) during RNA degradation.
Fatty Acid Oxidation Complex Subunit Alpha (EC Numbers: 1.1.1.35, 4.2.1.17, 5.1.2,3, This enzyme plays crucial role in the fatty acid 3-oxidation cycle, enabling both aerobic and anaerobic degradation of long-chain fatty acids. It catalyzes the conversion of enoyl-CoA to 3-oxoacyl-CoA via 1-3-hydroxyacyl-Com and can also process D-3-hydrroyyccyl-oo and cis-3-enoyl-CoA as substrates. This pathway is fundamental for cellular energy production and lipid metabolism.
Geranylgeranyl Pyrophosphate Synthase (EC Numbers: 2.5.1.1, 2.5.1.10, 2.5.1.29): This enzyme catalyzes the synthesis of geranylgeranyl pyrophosphate (GGPP) from farnesyl pyrophosphate and isopentenyl pyrophosphate, initiating the janthitremane biosynthesis pathway. It participates in multiple steps involving prenylation, oxidation, and cyclization, ultimately leading to the formation of complex indole diterpenes such as paspaline and shearinine These compounds exhibit diverse bioactivities.
Siroheme Synthase (EC Numbers: 1.3.1.76, 2.1.1.107, 4.99.1.4): This multifunctional enzyme catalyzes a series of -SeM)edependent
key reactions in the siroheme biosynthesis pathway. It first facilitates the methylation of uroporphyrinogen III at C-2 and C-7, forming precomrin-2 via precorrin-1. 1. It then catalyzes the NAD- dependent ring dehydrogenation of precorrin-2 to produce sirohydrochlorin. Finally, it promotes the ferrochelation of sirohydrochlorin, leading to the formation of siroheme, an essential cofactor in sulfite and nitrite reductases.
Putative Fatty Acid Oxidation Complex Trifunctional Enzyme (EC Numbers: 1.1.1.35, 4.2.1.17, This enzyme exhibits multiple catalytic activities involved in fatty acid metabolism, including 3-hydroxyacyl-Cood dehydrogenase activity, (elta())ddelaa()-eenool-oo
isomerase activity, and enoyl-CoA hydratase activity. It also possesses NAD*- binding capacity and plays a role in fatty acid catabolic processes. This enzyme is essential in the 3-oxidation pathway, facilitating the conversion of fatty acids into energy.
Purine Nucleoside Phosphorylase RC0672 (EC Numbers: 2.4.2.1, 2.4.2.28, 3.5.4.4): This purine nucleoside enzyme catalyzes the phosphorolysis of adenosine and inosine nucleosides, producing D-ribose 1-phosphate and the respective free bases, adenine and hypoxamthine. It also catalyzes the phosphorolysis of S-methyl-5 -meettyl=5-thioadenosiii -hioadennsiin into adenine and mnethyl-5-thio-----ee purine metabolism.
1-phosphate. Additionally, it exhibits adenosine deaminase activity, contributing to
18

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CFP-Gen
G. Visualization of Generated Proteins
In Fig. 9, we present additional visualizations generated proteins produced by CFP-GEN under multimodal conditions, specifically GO terms and backbone atomic coordinates. The structures are predicted using ESMFold, applied to the generated sequences. The reference PDB/AFDB structures, from which the backbone constraints are derived, are shown in green, while the structures of the designed proteins shown in red. The real functions of the reference proteins labeled in red, whereas the predicted functions of the generated proteins, inferred using DeepGO-SE, are labeled in gray.
Several key observations can be drawn from these results. First, most generated proteins exhibit high structural fidelity, as indicated by TM-scores and pLDDT values exceeding 90, demonstrating that CFP-GEN can effectively design structurally stable proteins. Second, the majority of prompted functions are successfully recovered. Since DeepGO-SE also predicts ancestor GO terms, the predicted functions often include more terms than the input prompt. Here, we display only those with confidence score greater than 0.3, further confirming GEN' ability design functionally relevant proteins. Third, some failure cases were also identified. For instance, UniProt ID Q9FYJ2 contains long, flexible loop regions extending far from the core fold. These regions are typically associated with intrinsically disordered regions (IDRs), which lack single well-defined conformation. As result, the predicted structure exhibits high deviations in loop regions compared to the ground truth structure. Similarly, UniProt ID Q9PS08 corresponds to an extremely short protein (around 30 residues), which poses significant challenges for both generative and folding models, as such short scquences are underrepresented in large protein datasets. Consequently, these proteins exhibit low TM-scores, highlighting the inherent difficulties designing ultra-short protein sequences. These findings demonstrate the strengths and limitations of CFP-GEN in functional protein generation, emphasizing its effectiveness in generating proteins with both figh-fidelity structures and functional relevance while also highlighting areas for future improvements to enhance model generalization.
19

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CFP-Gen
Structure
GT Function
Predicted Function
Structure
GT Function
Predicted
Function
'GO:0097159
'G0:0036094'
'GO:0043167'
'GO:0000166"
GO:0003824'
GO:0016491
'GO:0004764'
'GO:0050661'
GOO1900363'
60:1901265
'O:00055488'
'GO:003374'' 'GO:0008113'
'G::0066671
GO:0016667
GO:0003824'
'O:0033344''
60:0016499
'GO:00081 13'
GO:001 6614
60:0016611
GO0000 1287
60:0050661
A8MEE1
'GO0000464
B714T7
'G000052215
TM-score: 0.984 PLDDT: 95.1
TM-score: 0.988 PLDDT: 90.0
'GO:0022890'
'GO:0008324'
1GO:0055318
1O:0043167'
'O000150055
'GO:0055488'
'GO:0068873'
GO:0036222
'O:0022855
6O:0016740'
'GO:005529
GGO00032218'
GO:0047429'
GO:0036221'
GO:0003822
GO:0016772'
6O:0036218'
'GO:0015297
50:0
'OO00054991
GO:0022804
'O:00152297
6O:0046872'
'GO:0015081
GO:0047429'
'O::0028533'
60:0043169
'GO:0015291'
GO:0000267
GO:0015078
GO:00152999
P58630
Q5E6G4
GO:0005451
TM-score 0.980 PLDDT: 94.9
TM-score 0.962 PLDDT: 92.7 92.7
GO:0005 139
'GO:0005488'
'GO:0066740'
'GO:0033224'
'O:00087557'
'GO:0064330'
GO:0005448
GO:00081688
GO:0003824
'GO:0003676'
GO:0016741
'GO:0004477'
'O000004777
GO:0016430'
GO:0003676'
'GO:0004486
GO:0016-44 6491
60:01406400
CO
'GO:006611
60:0140098
60:0004488
GO:0006173
'GO:0016616
60:0008175'
60:0140102'
60:0008170
60:0008649
C6UBI3
Q74GN1
TM-score 0.937 PLDDT: 90.4
TM-score 0.984 PLDDT: 91.9
'GO:0097159'
'GO:0005488'
'GO:1901363'
'GO:0003677'
'GO:0005488'
'GO:0003676'
'GO:0055515'
GO:0003697'
'GO:0003690'
'GO:001 5459 GO:0090729
"OO0005559
'GO:0033565'
'G0:009877''
'GO:0033723'
'G::090729
'OO0003372''
'O000907299'
'O:0003697'
Q9FYJ2
Q9PS08
TM-score 0.295 PLDDT: 72.3 72.
TM-score 0.176 PLDDT: 65.7
Figure 9. Examples of generated functional proteins. We compare the generated proteins and their predicted functions with corre- sponding PDB/AFDB structures and functions. These results demonstrate the effectiveness and functional consistency of CFP-GEN.
20


Reviews:
[{"review_id": "fkbb7EpR3v", "reviewer": "Reviewer_1Xgk", "summary": "This paper proposes a novel protein language model, CFP-GEN, which leverages discrete diffusion generation to design functional proteins. The key innovation lies in incorporating annotated protein labels, such as Gene Ontology (GO) terms, InterPro (IPR) domains, and Enzyme Commission (EC) numbers, during diffusion training, similar to classifier-free guidance in diffusion models. Additionally, CFP-GEN allows conditioning on protein structure. Comprehensive experiments demonstrate that CFP-GEN outperforms previous protein sequence generative models in generating proteins with accurate GO terms, IPR domains, and EC numbers.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### **Reviewer 1Xgk**\n\nThanks so much for acknowledging the novelty of our work and for providing thoughtful and constructive comments. We provide clarifications to your concerns below, which we will incorporate into the final version. Please let us know if you have any further valuable comments or suggestions.\n\n---\n\n**Q1.  The novelty and diversity of the generated proteins.**\n\n**A1.** Thank you for your insightful advice. To examine whether our model generate truly de novo proteins, we selected 7 diverse EC numbers from different top-level categories: Oxidoreductases, Transferases, Hydrolases, Lyases, Isomerases, Ligases, and Translocases. For each EC number, CFP-GEN generates 30 sequences **conditioned only on the EC number**. We then compared these generated sequences with 30 real proteins from the enzyme validation set with the corresponding EC number. Novelty is computed by measuring how different each generated sequence is from its **most similar real protein** in the training set, while diversity is computed by capturing how different the generated sequences are from the **overall training set**. To ensure both metrics are interpretable in the same direction, we subtract the scores from 1 (i.e., **higher is better**).\n\n*R1-Table 1. Novelty comparison between real and designed proteins.*\n\n| Method | EC:1.5.1.5 | EC:2.7.11.1 | EC:3.6.4.13 | EC:4.2.1.33 | EC:5.2.1.8 | EC:6.1.1.20 | EC:7.1.2.2 |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Real Proteins | 0.254 | 0.234 | 0.334 | 0.252 | 0.296 | 0.215 | 0.221 |\n| CFP-GEN | 0.379 | 0.390 | 0.412 | 0.303 | 0.302 | 0.265 | 0.449 |\n\n*R1-Table 2. D*iversity *comparison between real and designed proteins.*\n\n| Method | EC:1.5.1.5 | EC:2.7.11.1 | EC:3.6.4.13 | EC:4.2.1.33 | EC:5.2.1.8 | EC:6.1.1.20 | EC:7.1.2.2 |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n| Real Proteins | 0.698 | 0.764 | 0.676 | 0.654 | 0.745 | 0.612 | 0.589 |\n| CFP-GEN | 0.748 | 0.766 | 0.725 | 0.565 | 0.714 | 0.677 | 0.760 |\n\nWe observe that CFP-GEN consistently achieves higher sequence novelty across all 7 EC numbers, demonstrating its strong potential for ***de novo* protein design beyond simply replicating known sequences**. Moreover, the generated sequences exhibit **high intra-class diversity** in 5 out of the 7 EC categories. These results suggest that the model has learned a more generalized representation of functional proteins, rather than overfitting to training examples, and effectively avoids mode collapse. We will update the final version to reflect these discussions.\n\n---\n\n**Q2.  Examples of conserved regions with mutations.**\n\n**A2.**  Thank you for raising this important point. We selected two representative case studies (e.g., EC: 1.5.1.5 and EC: 4.2.1.33) and performed **sequence alignments between CFP-GEN-generated sequences and known proteins from the same EC class**. We observed that CFP-GEN introduces **mutations at specific positions within conserved regions**, rather than simply copying them. The alignment results are presented below:\n\n---\n\n```jsx\nCFP-GEN(EC:1.5.1.5): ...GTPVFVHAGPFANINHGANS...   \nReal Proteins:       ...GTPAFVHGGPFANIAHGNSS...    \n\t\t\t\t     ...GTPLVVHAGPFANIAHGNSS...    \n\t\t\t\t     ...GTPVFVHAGPFANIAHGNSS...  \n\t\t\t\t     ...GTPVLVHAGPFANIAHGNSS...\n\t\t\t\t           ↑↑  ↑      ↑  ↑↑\n```\n\n```jsx\nCFP-GEN(EC:4.2.1.33): ...MTIVCGDSHTSTHGAFGALA...  \nReal Proteins:        ...MTVVCGDSHTSTHGAFGCLA...\n\t\t\t\t      ...MTIACGDSHTSTHGAFGAIA...\n\t\t\t\t      ...TTIVCGDSHTSTHGAFGALA...\n\t\t\t\t      ...MTIACGDSHTSTHGAFGNIA...\n\t\t\t\t         ↑ ↑↑             ↑↑      \n```\n\n---\n\nThe mutated positions are indicated **by arrows**. In the first case, the CFP-GEN-designed sequence preserves the core motif (e.g., **GTP…GPFANI**), while introducing mutations such as A→V, L→F, or A→N at semi-conserved sites. In the second case, mutations (e.g., T→M, V→I, C→A) occur at non-critical positions, while maintaining key motifs (**CGDSHTSTHGAFG**), supporting the model’s ability to retain functional cores while exploring novel sequence variations. This discussion and the corresponding examples will be included in the revised supplementary material. Thank you!", "claims_and_evidence": "The authors state that previous PLMs typically generate protein candidates based on a single-condition input from a specific modality. However, providing more references here would strengthen the argument by situating CFP-GEN more clearly within the broader landscape of protein generation models.\n\nThe remaining claims, such as CFP-GEN’s ability to design multi-functional proteins and its improved performance in inverse folding, are well-supported by experimental evidence. The reported results in Tables 1 and 2 demonstrate that CFP-GEN outperforms previous models in functional protein generation, structural fidelity, and multi-objective optimization, validating the effectiveness of its multimodal conditioning approach.", "methods_and_evaluation_criteria": "The method is based on the diffusion protein language model DPLM, embedding all annotations into the diffusion conditioning module.\nThe authors also propose the Annotation-Guided Feature Modulation (AGFM) module, which effectively adjusts the intermediate representations, and the Residue-Controlled Functional Encoder (RCFE), which enhances controllability over the generated sequences compared to previous approaches.\n\nThe authors evaluate the model on a protein sequence generation task using different annotations as prompts. For evaluation, they use DeepGO-SE for predicting Gene Ontology (GO) terms, InterProScan for homology-based annotation, and CLEAN for catalytic function prediction. Comprehensive experiments demonstrate that CFP-GEN, by leveraging annotation-based conditioning, outperforms other protein language models in generating functionally relevant sequences.", "theoretical_claims": "The paper does not focus on formal theoretical claims.", "experimental_designs_or_analyses": "As mention above, the authors evaluate CFP-GEN on a protein sequence generation task using different annotations as prompts. For evaluation, they utilize DeepGO-SE for predicting Gene Ontology (GO) terms, InterProScan for homology-based annotation, and CLEAN for catalytic function prediction. The experimental results demonstrate that CFP-GEN, by incorporating annotation-based conditioning, outperforms other protein language models in generating functionally relevant sequences.\n\nAdditionally, the authors assess the model on the inverse folding task, showing that incorporating more conditioning information reduces the uncertainty in sequence generation and leads to a higher sequence recovery rate. This highlights CFP-GEN’s ability to generate sequences that are both structurally and functionally consistent.", "relation_to_broader_scientific_literature": "This paper presents a novel and creative approach to protein sequence generation by incorporating protein label annotations into a diffusion-based language model. Given the increasing importance of functional protein design in biotechnology and drug discovery, this approach has the potential to influence future research and applications in protein engineering.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "Strengths:\nThe authors successfully integrate the three most common annotation labels—GO terms, IPR domains, and EC numbers—into a single protein language model. This multimodal conditioning approach makes protein design more controllable, allowing for more precise functional protein generation compared to previous single-condition models.\n\nWeaknesses:\nAlthough the authors conduct comprehensive experiments demonstrating that conditioning on annotations leads to the generation of functionally relevant proteins, it is unclear whether the model truly generates de novo proteins or primarily memorizes sequences from the training database. Instead of relying on extensive conditioning to generate highly specific proteins that closely resemble known sequences, the authors could explore using fewer conditions to assess whether the model can still generate novel yet functional proteins. This would provide stronger evidence of the model’s ability to innovate beyond known protein sequences.", "other_comments_or_suggestions": "line 355 natural sequence -> protein sequence", "questions_for_authors": "As mention above, one of the most exciting aspects of protein design is creating novel proteins that do not exist in nature but can perform specific functions. In this paper, the proposed method demonstrates that the generated sequences closely match the properties of the given condition labels. However, have you tested CFP-GEN’s ability to generate truly de novo proteins? For instance, if conditioned only on a specific EC number, can CFP-GEN generate sequences with novel structural motifs rather than sequences closely resembling known proteins? Additionally, have you analyzed the diversity of the generated sequences and structures? Do they all contain conserved regions, or does CFP-GEN exhibit variation in its outputs?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CHylcDsZJB", "reviewer": "Reviewer_JkEt", "summary": "This paper introduces CFP-GEN, a diffusion-based language model for combinatorial functional protein generation that integrates multimodal constraints. The proposed Annotation-Guided Feature Modulation and Residue-Controlled Functional Encoding modules enable flexible conditioning across diverse modalities. The model demonstrates superior performance in functional sequence generation, inverse folding, and multi-objective protein design.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### **Reviewer JkEt**\n\nWe appreciate your thoughtful comments and have addressed your concerns in detail below. The corresponding clarifications and expanded discussion will be reflected in the final version. We welcome any valuable suggestions you may have.\n\n---\n\n**Q1.** **Generalizability on rare functional annotations.**\n\n**A1**. To evaluate the generalizability of CFP-GEN on long-tail functions, we constructed an **expanded dataset** by relaxing the filtering threshold: we included all GO terms with **≥30 sequences in SwissProt**, resulting in a total of **726 GO terms** (a significant increase from 375 in the original dataset).\n\nThis updated dataset exhibits a **typical long-tail distribution**:\n\n- The **head 20%** of GO terms (146 classes) cover **72.7%** of the sequences,\n- The **tail 20%** (145 classes) cover only **2.4%** of the sequences.\n\nWe report generation results conditioned on GO/IPR labels, stratified by class frequency:\n\n| **Frequency Segment** | **#GO** | **% Seq.**  | **MRR↑** | **MMD↓** | **MMD-G↓** | **mic. F1↑** | **mac. F1↑** | **AUPR↑** | **AUC↑** |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| Head (Top 20%) | 146 | 72.7% | 0.725 | 0.072 | 0.043 | 0.598 | 0.529 | 0.409 | 0.763 |\n| Medium (60%) | 435 | 24.9% | 0.702 | 0.074 | 0.044 | 0.601 | 0.497 | 0.370 | 0.745 |\n| Tail (Bottom 20%) | 145 | 2.4% | 0.687 | 0.114 | 0.072 | 0.565 | 0.496 | 0.362 | 0.744 |\n\nThese results demonstrate that **CFP-GEN maintains robust performance even in tail categories**. While a slight performance drop is observed in the tail segment (e.g., MRR of 0.687 *vs.* 0.725 in head), the overall scores remain strong. This suggests that **CFP-GEN has learned generalizable design principles** that extend beyond well-represented functions, enabling it to generate proteins even for underrepresented functional categories. These discussions will be added to the supplementary material. Thanks!\n\n---\n\n**Q2.** **Structure-conditioned generation with fine-tuned weight.**\n\n**A2**. Thank you for the insightful comment. To address this, we conducted additional experiments to compare **frozen vs. fine-tuned structure encoder and cross-attention layers** within CFP-GEN.\n\n| **Setting** | **AAR ↑** | **MRR ↑** | **Fmax ↑** | **scTM ↑** | **pLDDT ↑** |\n| --- | --- | --- | --- | --- | --- |\n| CFP-GEN (Frozen) | 73.53 | 0.875 | 0.575 | 0.888 | 83.48 |\n| CFP-GEN(Finetuned) | 76.39 | 0.882 | 0.581 | 0.889 | 83.53 |\n\nAs shown, fine-tuning further improves performance, while the frozen variant already performs strongly—especially in low-resource settings with limited structural labels. These results highlight CFP-GEN’s flexibility, allowing full fine-tuning when data is sufficient. We will include these results in Table 2 of the manuscript for clarification. Thank you!\n\n---\n\n**Q3.** **CFP-GEN's relation to other inverse folding and multimodal PLM works.**\n\n**A3.**  \n\n- ***Multimodal PLMs:*** As discussed in the related work section, concurrent efforts such as DPLM support both sequence and structure modalities. However, in practice, inference is performed using **only one modality at a time, without effective multimodal fusion**. Furthermore, DPLM does not support function labels. While **ESM-3** enables multimodal inputs, its function relies on a limited set of **free-text keywords**, which cannot adequately capture complex functions (GO/IPR/EC descriptions). In contrast, CFP-GEN supports true multimodal fusion of sequence, structure, and functional labels, and demonstrates **superior performance over both DPLM and ESM-3 in original Table 1.**\n- ***Inverse Folding:*** Existing works, such as ProteinMPNN and ESM-IF, generate sequences **based only on protein backbone structures**. By comparison, CFP-GEN introduces **a new design paradigm that incorporates functional labels as additional input**. This enables function-aware inverse folding, improving the AAR, as shown in Table 2. We believe this is a practical and meaningful extension to traditional inverse folding.\n\nThese discussions will be added to the revised related work section.\n\n---\n\n**Q4.** **Failed examples of the generated sequences.**\n\n**A4**. Since CFP-GEN partially builds upon DPLM, it occasionally inherits the **mode collapse issues observed in DPLM**, leading to highly repetitive segments. As discussed in **Q1 in** **Reviewer s6DK**, we demonstrated that **CFP-GEN largely mitigates** this. However, we acknowledge that mode collapse can still occur sometimes:\n\n```jsx\nUniprotID: Q56217\n...GLALLLLLLLLLLLLPLPPPPPPPPPPPPPPPPPP...\n\nUniprotID: D4GWC8\n...KEMKEAEEAEAEAEKKAEAEAEKKAEAEAEKKAEEKKEE...\n```\n\nThis type of failure can be **mitigated by introducing more diverse conditions and adjusting diffusion hyperparameters**. We also plan to incorporate reinforcement learning feedback to further reduce such failure modes in future work. Thanks!", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "Yes. While the paper’s empirical focus is reasonable, a brief theoretical discussion on why AGFM and RCFE improve convergence or functional control would add value.", "experimental_designs_or_analyses": "The authors should conduct additional analysis of failure cases to provide insights into the model's limitations.", "relation_to_broader_scientific_literature": "Expanding the discussion of CFP-GEN's relation to inverse folding techniques and highlighting distinctions from concurrent multimodal PLM designs would improve the paper's clarity.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "**Strengths:**\n1. ​The integration of multimodal constraints into a unified framework addresses a critical gap in controllable protein generation. The composable conditioning mechanism (AGFM/RCFE) is a meaningful advance over single-modality approaches.\n​2. Comprehensive experiments across three tasks (functional generation, inverse folding, multi-functional design) validate the method’s superiority. The use of state-of-the-art function predictors and structural metrics strengthens credibility.\n3. High success rates in designing multi-functional enzymes and improved sequence recovery in inverse folding suggest tangible applications in enzyme engineering and drug discovery. \n\n**Weaknesses:**\n1. ​The dataset filters out low-frequency functional annotations (e.g., GO/IPR terms with <100 sequences), potentially limiting generalizability to rare functions. While results on held-out validation sets are strong, long-tail performance remains unverified.\n2. The use of a frozen, off-the-shelf structure encoder (GVP-Transformer) without fine-tuning may restrict structural optimization. While the authors claim that the pretrained cross-attention layer from DPLM can be used directly without fine-tuning, this assertion lacks experimental validation. Ablation studies on structure-conditioned generation are lacking.", "other_comments_or_suggestions": "N/A.", "questions_for_authors": "Can you provide examples of generated sequences that failed to achieve desired functional properties to better understand the model’s limitations?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "24s5ptRxSR", "reviewer": "Reviewer_p2fD", "summary": "This paper presents CFP-GEN, a large-scale diffusion language model developed for Combinatorial Functional Protein Generation under multiple constraints from diverse modalities. CFP-GEN facilitates de novo protein design by jointly incorporating functional, sequence, and structural constraints. It employs an iterative denoising process to refine protein sequences while conditioning on various functional annotations (such as GO terms, IPR domains, and EC numbers), sequence motifs, and 3D structural features.\n\nTo achieve this, the model introduces two key modules:\n(1) Annotation-Guided Feature Modulation (AGFM), which dynamically adjusts sequence representations based on composable functional annotations, and\n(2) Residue-Controlled Functional Encoding (RCFE), which explicitly encodes critical residues and captures residue interactions and evolutionary relationships.\n\nAdditionally, CFP-GEN supports the integration of 3D structural constraints through off-the-shelf structure encoders. Experimental results show that CFP-GEN can generate novel proteins with functionality comparable to natural proteins and achieves a high success rate in designing multifunctional proteins.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### **Reviewer p2fD**\n\nWe sincerely thank the reviewer for the positive feedback. We have carefully addressed the concerns below with new analyses and additional experiments, which will be incorporated into the final version. We welcome any further suggestions.\n\n---\n\n**Q1. In-depth analysis of performance gain.**\n\n**A1.** We appreciate the reviewer’s insightful concern. Here, we provide evidence that the performance gains are not a result of memorizing known sequences:\n\n- Novelty and Diversity (plese see **Q1 in Reviewer 1Xgk**):\n    \n    Our generated sequences exhibit much higher novelty and diversity compared to real proteins. This suggests that CFP-GEN does not simply replicate patterns of real proteins, but instead learns to **generate truly novel sequences**.\n    \n- Mutation Analysis (plese see **Q2 in Reviewer 1Xgk**):\n    \n    We observe plausible mutations even within conserved regions. These mutations tend to preserve functionally critical motifs while introducing reasonable variation, learning **generalizable design principles**.\n    \n- Secondary Structure Distribution (plese see **Q3 in Reviewer s6DK**):\n    \n    CFP-GEN tends to transform non-functional and flexible **coil regions** into functional **alpha helices and beta strands**. It reflects a **function-oriented redesign strategy.**\n    \nTaken together, these findings indicate that CFP-GEN’s improvements stem from its ability to learn function-guided design principles, rather than overfitting to the training data.\n\n---\n\n**Q2.** **Discussion on annotation-guided generation with ProGen2 and ZymCTRL.**\n\n**A2.** We agree that existing autoregressive (AR)-based PLMs such as ProGen2 and ZymCTRL can potentially support more annotations. However, we found that **diffusion models offer several advantages**: **1.** AR models generate strictly left-to-right, limiting their flexibility for tasks like motif scaffolding, which require conditioning at specific positions. In contrast, diffusion models **allow arbitrary conditioning and precise position control**, essential for realistic protein design. **2.** Diffusion models are more readily sequence representations to **discriminative tasks**, while AR-based models are primarily designed for generation-only tasks. **3.** Aligning multimodal prompts into a unified token space is challenging for AR models. Our diffusion framework supports **flexible multimodal fusion**, enabling separate encoding and seamless integration of each modality.\n\nSince ProGen2 lacks public training code, we extended ZymCTRL by enlarging its vocabulary to include GO and IPR classes, but this did not improve performance. We attribute this to inconsistent annotation formats: for instance, *EC:xxxx* is split into multiple tokens in ZymCTRL instead of a single semantic unit, making it hard to integrate with *GO:xxxx* or *IPR:xxxx*, which are typically treated as discrete categories. \n\nIn contrast, CFP-GEN treats each EC/GO/IPR label as an independent class, and integrates them through **learned embeddings with additive fusion**. We believe more work is needed to enable effective prompt engineering for AR models across diverse annotations, and we welcome future comparisons as such approaches become available. We appreciate the reviewer’s understanding.\n\n---\n\n**Q3.** **Inverse folding with additional functional labels .**\n\n**A3.** Since none of the existing inverse folding methods support functional labels (e.g., GO terms) as input, we implemented a heuristic baseline (**DPLM+DeepGO**). Specifically, we used DPLM to generate 20 candidate sequences per backbone, then applied DeepGO to predict GO terms and selected **the sequence with the highest overlap with the labeled GO**:\n\n| **Methods** | **AAR** | **MRR** | **Fmax** | **scTM** | **pLDDT** |\n| --- | --- | --- | --- | --- | --- |\n| DPLM | 66.94 | 0.721 | 0.552 | 0.883 | 85.33 |\n| DPLM+DeepGO | 67.29 | 0.726 | 0.559 | 0.886 | 85.49 |\n| CFP-GEN (w/ GO) | 72.05 | 0.866 | 0.571 | 0.887 | 83.28 |\n\nThe marginal gains of this pipeline suggest that functional labels are hard to integrate into existing inverse folding models, motivating our development of CFP-GEN, **an end-to-end solution that jointly reasons over structure and function.** We will include this discussion in the final version. Thank you!\n\n---\n\n**Q4.** **The contributions of AGFM and RCFE.**\n\n**A4**. Sorry for this confusion. The ablation studies are actually presented in the **GO-conditioned generation results in Table 1 of the manuscript:**\n\n- **CFP-GEN (w/ GO and IPR)** corresponds to using **AGFM only**, with an MRR of **0.779**.\n- **CFP-GEN (w/ Motif)** corresponds to using **RCFE only**, with an MRR of **0.839**.\n- **CFP-GEN (w/ GO, IPR and Motif)** combines **AGFM and RCFE**, achieving the best performance with an MRR of **0.870**.\n\nThese results indicate that both modules independently contribute to performance, and their combination yields additive benefits. We will make this more clear in the final version. Thanks!", "claims_and_evidence": "See Experimental Designs Or Analyses", "methods_and_evaluation_criteria": "Yes, the proposed method is well designed for the task of protein generation.", "theoretical_claims": "Not applicable, as there is no proof or theoretical claims.", "experimental_designs_or_analyses": "The paper claims that CFP-GEN enables combinatorial functional protein generation under multiple constraints from diverse modalities through the introduction of the AGFM and RCFE modules. However, the experimental evidence provided does not fully support these claims.\n\nIn the Benchmarking Protein Functional Performance experiments, the influence of training data is not sufficiently controlled, leaving open the possibility that performance gains may stem from data memorization rather than the effective use of composable functional annotations. Furthermore, existing models such as ProGen2 and ZymCTRL could, in principle, incorporate multiple annotation types via prompt engineering by extending their vocabularies. The absence of comparative experiments with such baselines raises concerns about whether AGFM provides a meaningful advantage for handling multimodal constraints.\n\nSimilarly, in the Functional Protein Inverse Folding experiments, CFP-GEN utilizes additional functional labels during generation. Since these labels are not equally available to baseline methods, this experimental setup does not provide clear evidence isolating the effectiveness of RCFE in controlling functional sites or capturing residue-level interactions.\n\nFurthermore, the paper lacks ablation studies on the model architecture, which are necessary to isolate the contributions of AGFM and RCFE. Without these analyses, it remains unclear how much each component contributes to the overall performance.", "relation_to_broader_scientific_literature": "The proposed CFP-GEN method is built upon prior diffusion protein language models, and extends these models by dynamically adjusting representations.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Please refer to the Summary", "other_comments_or_suggestions": "I strongly advise the authors to conduct additional experiments to make the paper stronger.", "questions_for_authors": "No.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "kFoKgX9vEx", "reviewer": "Reviewer_s6DK", "summary": "The paper proposes introducing multiple conditions into the protein sequence design process based on DPLM using a method similar to ControlNet. It achieves the integration of various conditions through the designed RCFE and AGFM modules. The performance on protein design tasks with multiple conditions shows a significant improvement compared to baseline models.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### **Reviewer s6DK**\n\nWe appreciate your recognition of the novelty and strong performance of our method. Your questions raise important points, and we provide detailed clarifications and new quantitative results below. We would be happy to receive any additional constructive feedback.\n\n---\n\n**Q1.** **Mitigation of sequence pattern collapse over DPLM.**\n\n**A1.** Thank you for careful review. To assess whether CFP-GEN mitigates the mode collapse issue observed in DPLM, we analyzed the frequency of repeated ***n*-gram patterns** (*n* = 2, 3, 4, 5, 6) in the generated sequences from the GO-conditioned generation results in original Table 1. Real protein sequences from the validation set were used as a **positive control**.\n\n| **Methods** | **2-gram** | **3-gram** | **4-gram** | **5-gram** | **6-gram** |\n| --- | --- | --- | --- | --- | --- |\n| Positive Control | 404 | 164 | 0 | 0 | 0 |\n| DPLM | 315 | 462 | 104 | 46 | 26 |\n| CFP-GEN (*w/* GO) | 363 | 351 | 15 | 9 | 8 |\n| CFP-GEN (*w/* GO and IPR) | 365 | 332 | 9 | 5 | 4 |\n| CFP-GEN (*w/* GO, IPR and Motif) | 377 | 336 | 4 | 1 | 1 |\n\nAs shown, CFP-GEN produces a similar number of 2-grams to real proteins, while **significantly reducing the number of longer repetitive n-grams, especially 4-gram to 6-gram patterns.** Notably, the more functional conditions (e.g., GO, IPR, Motif) are provided, the fewer repetitive patterns appear in the output, indicating better sequence quality and reduced mode collapse. These results provide strong evidence that CFP-GEN effectively alleviates the mode collapse issue observed in DPLM. Theses discussions will be included in the revised supplementary material. Thanks!\n\n---\n\n**Q2. More visualization results given motif as constraints.**\n\n**A2.** Thank you for the helpful suggestion. We agree that visualizing protein structures designed under specific motif constraints can provide valuable insights into the model's design choices under different functional conditions. However, due to the limitations of the rebuttal format, we are unable to include full visualizations here. We will provide these results in the revised supplementary material. We appreciate your understanding.\n\n---\n\n**Q3.**  **Discussion on scTM and pLDDT in Table2.**\n\n**A3.** Thank you for your careful and insightful observation. We have conducted an in-depth analysis to better understand the structural performance gap between CFP-GEN and DPLM baseline model. \n\nWe found that the slight drop in scTM and pLDDT scores is mainly due to its tendency to **generate more novel structural segments**. Specifically, we analyzed the distribution and transformation of secondary structure elements—namely alpha helices (H), beta strands (E), and coils (C)—within the designed proteins in original Table 2.\n\nUsing structural alignments between the designed and real target proteins, we categorized the transitions of secondary structure elements. For example, H→H represents a correctly preserved alpha helix, while C→H indicates a region originally a coil being redesigned as a helix. The results are shown below (format: **local average pLDDT / number of secondary structure**):\n\n| **Method** | **H→H** | **E→E** | **C→C** | **C→H** | **C→E** |\n| --- | --- | --- | --- | --- | --- |\n| DPLM | 91.34 / 758,185 | 94.10 / 307,397 | 86.13 / 584,587 | 84.03 / 85,871 | 92.42 / 61,861 |\n| CFP-GEN | 90.02 / 763,497 | 92.50 / 309,725 | 83.46 / 581,072 | 82.23 / 88,565 | 90.56 / 62,409 |\n| Δ Difference | –1.32 / +5,312 | –1.60 / +2,328 | –2.67 / –3,515 | –1.81 / +2,694 | –1.86 / +548 |\n\nWe observe that **CFP-GEN produces more H (helix) and E (strand) segments**, while the number of C (coil) regions is reduced. Many coil regions are transformed into more structured elements (C→H or C→E). This behavior reflects CFP-GEN’s design preference: **to replace non-functional, flexible regions (coils) with more functionally relevant secondary structures (helices and strands).**\n\nWhile this results in a slight drop in local confidence scores (e.g., pLDDT), likely due to the absence of global conformational energy optimization, it reflects a **function-oriented design strategy**. We consider this a **reasonable trade-off** in the context of novel functional protein generation, and view it as a promising direction for future improvement. In particular, we plan to explore energy-based reinforcement learning frameworks to further optimize this aspect. The above analysis and discussion will be included in the revised supplementary material. We appreciate the reviewer’s valuable observation.", "claims_and_evidence": "In my view, this paper is more focused on algorithmic improvements for a specific application, and therefore does not include many new claims. Instead, it represents a natural extension of existing models and methods. Overall, the claims in this paper are reasonable and supported.", "methods_and_evaluation_criteria": "The proposed methods in this paper and the Evaluation Criteria used during the assessment are reasonable. The core method of the paper is to integrate different condition information into DPLM using a ControlNet-like approach. Since ControlNet has already been thoroughly validated as an effective method for introducing generation conditions into diffusion models, this approach is reasonable. The Evaluation Criteria in this paper also follow the evaluation methods used in previous work.", "theoretical_claims": "This paper is more \"application-oriented,\" and therefore does not include many theoretical claims.", "experimental_designs_or_analyses": "The structure of this paper is clear and easy to follow. However, the experimental section needs further improvement, including additional analysis and more thorough discussion.Overall, the experimental design and analysis in this paper are reasonable, as the paper attempts to validate the impact of different conditions on model performance and demonstrates that the introduction of multiple conditions improves the generation performance of Diffusion Language Models. However, there are a few issues that need further validation:\n\n1. The DPLM model may encounter some mode collapse issues, such as generating sequences with many repeated segments. However, this paper does not discuss the impact of this issue, such as whether mode collapse is mitigated after introducing additional conditions. I believe this discussion is necessary.\n2. More case studies are needed. For example, visualizations of the model’s generated results when a motif is given as a constraint could further validate the model's ability to adhere to different types of conditions.\n3. The results in Table 2 need more explanation. CFP-GEN achieves excellent performance on three indicators, except for scTM and pLDDT, where there is a significant performance gap compared to the best baseline model. What is the reason for this phenomenon? Could it be due to overfitting to the data?", "relation_to_broader_scientific_literature": "This paper is an extension and elaboration of the existing DPLM (Diffusion Protein Language Model), incorporating methods such as condition integration. Compared to ESM3, this model supports more types of conditional inputs.", "essential_references_not_discussed": "The related work discussed in the paper is reasonable.", "other_strengths_and_weaknesses": "The structure of this paper is clear and easy to follow. However, the experimental section needs further improvement, including additional analysis and more thorough discussion.", "other_comments_or_suggestions": "I don't have other comments.", "questions_for_authors": "I don't have other questions.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper introduces CFP-GEN, a novel protein language model that employs discrete diffusion generation for functional protein design. Its key innovation is the integration of annotated protein labels—such as Gene Ontology (GO) terms, InterPro (IPR) domains, and Enzyme Commission (EC) numbers—during diffusion training, analogous to classifier-free guidance in diffusion models. Additionally, CFP-GEN supports structure-conditioned generation. Extensive experiments demonstrate that CFP-GEN surpasses existing protein sequence generation models in producing proteins with accurate functional annotations.

The reviewers commend the paper for its clear writing and highlight that the unified multimodal constraint framework addresses a critical gap in controllable protein generation. Results in Tables 1 and 2 show that CFP-GEN outperforms prior methods in functional protein generation, structural fidelity, and multi-objective optimization, validating its multimodal conditioning approach. While some concerns were raised regarding additional experimental analyses, connections to inverse folding and multimodal PLM works, and the contributions of AGFM and RCFE, the reviewers were largely satisfied with the authors' clarifications during the discussion period.

Thus, I recommend acceptance.
Decision: Accept (poster)