How to make the most of your masked language model for protein engineering
arXiv:2603.10302v1 Announce Type: new Abstract: A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing a flexible, effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro on actual antibody therapeutics campaigns. Firstly, we propose sampling with stochastic beam search, exploiting the fact that MLMs are remarkably efficient at evaluating the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation in terms of entire-sequence evaluation enables flexible guidance with multiple optimization objectives. Secondly, we report results from our extensive in vitro head-to-head evaluation for the antibody engineering setting. This reveals that choice of sampling method is at least as impactful as the model used, motiva
arXiv:2603.10302v1 Announce Type: new Abstract: A plethora of protein language models have been released in recent years. Yet comparatively little work has addressed how to best sample from them to optimize desired biological properties. We fill this gap by proposing a flexible, effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro on actual antibody therapeutics campaigns. Firstly, we propose sampling with stochastic beam search, exploiting the fact that MLMs are remarkably efficient at evaluating the pseudo-perplexity of the entire 1-edit neighborhood of a sequence. Reframing generation in terms of entire-sequence evaluation enables flexible guidance with multiple optimization objectives. Secondly, we report results from our extensive in vitro head-to-head evaluation for the antibody engineering setting. This reveals that choice of sampling method is at least as impactful as the model used, motivating future research into this under-explored area.
Executive Summary
This article presents a novel sampling method for masked language models (MLMs) in protein engineering, proposing the use of stochastic beam search to optimize desired biological properties. The authors systematically evaluate models and methods in silico and in vitro on actual antibody therapeutics campaigns, revealing that the choice of sampling method is at least as impactful as the model used. The findings highlight the importance of addressing the gap in optimizing desired biological properties, and motivate future research into the under-explored area of sampling methods. The proposed method demonstrates flexibility and effectiveness in evaluating pseudo-perplexity, enabling guidance with multiple optimization objectives. The article contributes to the growing field of protein engineering and has significant implications for the development of antibody therapeutics.
Key Points
- ▸ A novel sampling method for MLMs is proposed, utilizing stochastic beam search to optimize biological properties.
- ▸ The method is evaluated in silico and in vitro on actual antibody therapeutics campaigns, demonstrating effectiveness.
- ▸ Choice of sampling method is found to be at least as impactful as the model used, highlighting the importance of addressing this gap.
Merits
Strength of the proposed method
The stochastic beam search method enables flexible guidance with multiple optimization objectives, reframing generation in terms of entire-sequence evaluation. This flexibility enables the method to accommodate various biological properties and objectives.
Demerits
Limited assessment of existing methods
The article primarily focuses on the proposed method, with limited evaluation of existing methods in comparison. A more comprehensive assessment of existing methods would strengthen the article's contributions.
Small-scale in vitro evaluation
The in vitro evaluation is limited to a small-scale head-to-head comparison. A larger-scale evaluation would provide more robust insights into the proposed method's performance and generalizability.
Expert Commentary
The article presents a significant contribution to the field of protein engineering, highlighting the importance of addressing the gap in optimizing desired biological properties. The proposed method demonstrates flexibility and effectiveness in evaluating pseudo-perplexity, enabling guidance with multiple optimization objectives. While limitations exist, the findings have significant implications for the development of antibody therapeutics and motivate future research into the under-explored area of sampling methods. The article's comprehensive evaluation of MLMs and methods in silico and in vitro provides robust insights into the proposed method's performance and generalizability. As the field of protein engineering continues to evolve, the proposed method has the potential to accelerate the development of novel therapeutics and improve our understanding of protein structures and functions.
Recommendations
- ✓ Future research should focus on a more comprehensive evaluation of existing methods in comparison to the proposed method.
- ✓ Large-scale in vitro evaluation should be conducted to provide more robust insights into the proposed method's performance and generalizability.
