Title: ProSpero: Active Learning for Robust Protein Design Beyond Wild-Type Neighborhoods
Venue: NeurIPS 2025
Accepted: yes
Keywords: protein design

active learning

robust exploration

fitness optimization
Abstract: Designing protein sequences of both high fitness and novelty is a challenging task in data-efficient protein engineering. Exploration beyond wild-type neighborhoods often leads to biologically implausible sequences or relies on surrogate models that lose fidelity in novel regions. Here, we propose ProSpero, an active learning framework in which a frozen pre-trained generative model is guided by a surrogate updated from oracle feedback. By integrating fitness-relevant residue selection with biologically-constrained Sequential Monte Carlo sampling, our approach enables exploration beyond wild-type neighborhoods while preserving biological plausibility. We show that our framework remains effective even when the surrogate is misspecified.
ProSpero consistently outperforms or matches existing methods across diverse protein engineering tasks, retrieving sequences of both high fitness and novelty.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nProSpero introduces an iterative protein design method that goes beyond recent work PEX – that relies on local search, using a pretrained generative model to explore beyond a protein wild-type neighborhood. The pretrained generative model is used as a prior to guide the exploration, whilst proposing biologically plausible and diverse candidates in each round. \n\nProSpero is evaluated in eight multi-round design campaigns, where ground truth scores are taken from the TAPE model, and the ensemble surrogate is fine-tuned in each round. The paper introduces two methodological contributions: a targeted masking strategy to identify high-value residues to mutate, and a constrained SMC sampling process.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper is well written and organised. It was an enjoyable read. The reported results are good relative to benchmark methods, plus reproducibility is high, with open source code provided for the main ProSpero method and all baselines. \nFurther, efforts have been made to generate biological meaningful sequences, via constrained search (using AA charge classes), and also evaluating the generated sequences via biological validity metrics based on physicochemical properties. \n\nWeaknesses: \nProblem formulation: As far as I understand, the goal of ProSpero is to design protein sequences with high fitness values across $N$ iterations. This feels like an optimisation objective, not an active learning one - where we are trying to improve a model. \nPlease could you untie or explain the difference between your proposed active learning formulation, and a Bayesian optimization objective? It would appear to me that you have an optimisation objective, unless I am missing something. \n\nOn Clarity: \nI am confused exactly what distribution your sampling algorithm targets, and further, under what assumptions ProSpero does indeed target that distribution. Although clearly an effective proxy, it is not obvious to me what distribution the importance weights imply. \nOn the underlying assumptions: I suspect the sampling scheme is closely related to the field of pseudo-marginal MCMC (see Andrieu et al. (2009)), wherein an unbiased estimate of the unnormalised posterior is used in place of the underlying unnormalised density; precise details of this connection to such work would be appreciated. \nGenerally, I would greatly appreciate more details/precision with respect to your SMC scheme.\n\nOn novelty: \nThe significance of the proposed Targeted Masking contribution appears limited. To me this feels like random AA permutation, plus a surrogate and acquisition function applied on top.  \n\nPlease elucidate the connections between [26] and your proposed method.\nTo my understanding, CloneBO performs a similar *iterative design of protein sequences as inference-time guidance of a pre-trained generative model*, and performs the guidance *via conditioning on high value sequences*. Is there a connection between the high value sequences used to guide the tSMC samples in CloneBO and your use of a surrogate model? \n\n[26] Alan Nawzad Amin, et. al. Bayesian optimization of antibodies informed by a generative model of evolving sequences. ICLR 2025.\n\nIt would be of benefit if the authors situate their guidance method relative to other generative guidance methods, such as classifier guidance.\n\nQUESTIONS:\nRAAs: In practice, what is the impact of using the RAA based on charge particles? Was an ablation performed where you simply sample from the full AA alphabet? \n\nWhat value of n_sub was used in practice? Is this HP ablated? i.e. how many positions do you mask and the length of $\\mathcal{I}^{(i)}$? and what effect does this have on the novelty and quality of generated sequences? What is the sensitivity of this HP? My intuition is that when set too high or low, the sequences will score poorly for novelty. How was the HP set? \n\nLine 147 defines $\\pi^(i)$  as the order agnostic indices, why is it concatenated in that specific order? What is the intuition for why one would want to sample from I_neg before I_pos, followed by I_netural? Did you ablate or experiment with other potential orderings?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nPROSPERO is method for black-box optimization of proteins that combines biological priors extracted from a pretrained evolutionary diffusion model (EvoDiff) and human knowledge (e.g. Restricted amino-acid classes based on biochemical properties), with a sampling approach that operates in a restricted exploration space observing the biologically plausible priors. The method is tested on a number of in silico benchmarks for black box biological sequence design and is shown to perform well on optimization and diversity metrics within these benchmarks against standard methods in the field.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\nThe paper’s core thesis and application domain are relevant and correct. It should be helpful to incorporate biological priors into exploration algorithms, especially when one seeks to explore variants farther from the wild-type. \n\nIn terms of in silico benchmarking the work is well done and covers the important classes of algorithms. It also includes reasonable diversity metrics, which the method seems to perform well on. \n\nThe method also seems to be robust to noise, which is highly valuable. \n\nThe study execution within the in silico space of datasets, metrics, methods and evaluation is strong. \n\nWeaknesses\n\nMy major concerns about the work is that positioning itself as a method for “far from wild type” design, makes it more necessary to show at least one empirical result. The standard has raised in the past 3-4 years on design algorithms to move beyond in silico optimization, and empirical validation is also relatively cheap and fast. See Gruver et al Protein Design with Guided Discrete Diffusion, NeurIPS 2023. It is very hard to evaluate methods that claim to improve OOD design without any real-world validation, which while correlated with in silico, does not show state of the art performance. While the approach is entirely plausible, whether the biological priors actually help in design is a question you’d only answer if you evaluate it with real biology. I think the improvement offered here (in optimization performance in silico) is insufficient to convince me it will be an advantage in the real world. For instance Damani et al Beyond the training set.. 2023 (https://arxiv.org/pdf/2311.05363), claim almost 100% of samples far from WT that score highly on a surrogate model similar to those are broken in real life. My experience is consistent with this view and I while I think the priors incorporated in this method may help this problem, there is no clear evidence it will. \n\nMore granularly, for instance, the AAV landscape from FLEXS that is used for evaluation is a synthetic landscape, not a real one. It is built from summing the effects of single residues (which were measured). This is a convex landscape with one peak that any hill climbing algorithm can eventually (if inefficiently solve). No real biology is this way. So showing that the algorithm can climb this hill is necessary but entirely insufficient in showing that it can design sequences far from the wild type. There is also no reason why the priors incorporated into the model should help in climbing this landscape, because it is only mildly connected to those biological rules. \n\nAs mentioned above the landscapes used here do not show strong distributional shifts.  Without directly addressing this point, the main claim of the paper on its ability to do optimization far from wild type is unconvincing. \n\nSecondarily most protein design today occurs within 2-4 active learning rounds. Improved performance in that regime is far more valuable than performance improvements over more than 7 rounds of improvement\n\nQUESTIONS:\nI suggest the authors develop the paper further by doing an in vitro experiment which can be done by an outside entity or collaborative lab (costs the same as 1000-10000 GPU hours), even for one empirical round. \n\nOther than the in vitro validation, the main challenge/remedy or contribution would be to find protein optimization landscapes that show real distribution shift, and non-smooth optimization surfaces. Unfortunately at this time FLEXS and similar in silico landscapes are too low of a bar given what we know (helpful as a first check but not sufficient). The authors could think more critically about how to construct such landscapes, for instance using the Alphafold metrics and if they can make a convincing case about distribution shift in these landscapes, that's both a strong contribution and changes my view of the paper's promise.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents PROSPERO, an active learning framework for protein sequence design that aims to generate sequences with high fitness, novelty, and biological plausibility. Its key innovations are: (1) iterative sequence design as inference-time guidance of a pre-trained generative model within an active learning loop that incorporates biological priors; (2) a targeted masking strategy that edits fitness-relevant residues while protecting critical sites; and (3) biologically constrained Sequential Monte Carlo (SMC) sampling, which restricts mutations based on explicit priors to improve discovery of high-fitness, novel sequences even when the surrogate model is imperfect. Experiments show that PROSPERO consistently outperforms or matches existing methods across diverse tasks, and is robust to surrogate model misspecification.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The PROSPERO framework is clearly presented, with comprehensive technical details and good structure. Key components—such as targeted masking based on alanine scanning and biologically-constrained SMC sampling—are introduced in detail to boost the reproducibility.\n\n2. The paper conducts a comprehensive experimental evaluation including eight diverse protein engineering tasks, comparing PROSPERO to ten strong baselines. The ablation is comprehensive, and the results consistently demonstrate the effectiveness of  the proposed approach with an emphasis on exploration.  \n\n3. By guiding a frozen generative model with an active learning-updated surrogate and introducing targeted, biologically-aware masking, PROSPERO advances protein design. The approach effectively addresses the trade-off between novelty and biological plausibility, with broad potential impact on protein engineering applications.\n\nWeaknesses\n\n1.  Some technical components—such as SMC unrolling and perplexity correction—are formally described but may lack intuitive explanations for readers less familiar with these methods. The comparison with all baselines is somewhat uneven, limiting contextual understanding.\n\n2.  While innovative in integrating various ideas, components like SMC and charge-based grouping are adapted from prior work, and experimental scope is limited to controlled scenarios. PROSPERO’s performance on more complex or out-of-distribution tasks remains untested.\n\nQUESTIONS:\nrefer to above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn this work a new active learning framework is proposed for the design of protein sequences. The proposed method explores functionally important residue sites instead of random sites, which are identified inspired by a technique used in the lab to perform mutagenesis. Additionally, a pre-trained generative model along with a surrogate function of the property is used to sample sequences having optimized properties, while maintaining charge-class similarity to the wild-type at functionally important residue sites. This allows for the generation of biologically plausible sequences in regions of the design space where surrogate predictions may be unreliable.\n\nSTRENGTHS AND WEAKNESSES:\nWhat I really liked about this work is basing the model design on how experimentalist scan for important sites in mutagenesis experiments.\n\nQUESTIONS:\n1) How many sites are masked in each benchmark and how is this number determined?\n\n2) In each active learning round, only the best sample/sequence is used for masking and proposal of new candidates. Isn't this limiting the exploration capability of the model? I understand that simplifying the algorithm by sticking to the best sequence is a good design if it is working, but have you performed any ablation study on the number of top sequences used for candidate generation?\n\n3) I find the description for \"Sampling from ... using Sequential Monte Carlo\", lines 144-167 pretty hard to follow. It would be nice to explain what the overall procedure should look like and why before getting into its steps.\n\n4) Is the extension beyond wild-type neighborhood only measured by the novelty metric (average hamming distance between top 100 candidates and a starting sequence (wild-type))? What is the average hamming distance between the starting wild-type sequence and the rest of the sequences in the initial dataset? Or what is the average distance between the top 100 and the initial dataset? \nThe reason I am asking about the distance from the initial dataset is because it is discussed that the model is robust to the surrogate misspecification/uncertainty, so I would like to know if there is a way to quantify the extent of exploration beyond surrogate comfort zone (initial dataset) for the benchmark in Table 1.\n\n5) If I understood it correctly, all experiments are performed with fixed initial datasets. So the variability in the performance only comes from the model stochasticity. Have you examined how the initial dataset affects the model performance? This matters when the size of the initial dataset is small. \nYour algorithm starts by taking the best sample from the initial dataset, so having a smaller initial dataset, with less coverage of the landscape, could make the optimization harder. Based on the information included in the Appendix, the initial datasets are quite large (3K, 5K, 6K, 7K, 10K, 15K) which is NOT the case in many practical settings. Have you examined the variability of your model's performance on smaller initial datasets (<< 1000, e.g., 200)?\nThe size of the initial dataset affects the quality of the trained surrogate model as well as the search in the design space depending on how the initial samples are distributed within the landscape, as discussed in prior work [1]. I do not expect you to add extra experiments on this but how do you expect your model to behave for small initial datasets?\n\n6) I liked the idea and reasoning for scoring residues by Alanine replacement. Did you try other amino acids as baselines? I understand that this is not backed by what experimentalists would do but computationally, one would like to know how significant the choice of Alanine is over the rest of the amino acids. \n\n7) Nice-to-haves: Missed citation for a recent generative-based approach [1] developed to address  practical challenges of protein design.\n\nReferences:\n\n[1] Ghaffari et al. Robust Model-Based Optimization for Challenging Fitness Landscapes. ICLR 2024.\nhttps://arxiv.org/pdf/2305.13650.\n\nLIMITATIONS:\nNA", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-11"}