arXiv subjects

Filippo Stocco

Publications and source records attributed to Filippo Stocco.

2 recordsLinked to original sources

arXiv2025

Steering Generative Models for Protein Design: Aligning and Conditioning Strategies

Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins are particularly attractive for such approaches given their abundant data and the versatility of their representations, ranging from sequences to structures and functions. This versatility has motivated the rapid development of generative models for protein design, enabling the generation of functional proteins and enzymes with unprecedented success. However, because these models mirror their training distribution, they tend to sample from its most probable modes, while low-probability regions, often encoding valuable properties, remain underexplored. To address this challenge, recent work has proposed strategies for steering generative models toward user-specified properties. In this review, we survey and categorize these strategies, distinguishing approaches that modify model parameters, such as reinforcement learning or supervised fine-tuning, from those that keep the model's parameters fixed, including conditional generation, retrieval-augmented strategies, Bayesian guidance, and tailored sampling methods. Together, these developments are beginning to enable the steering of generative models toward proteins with desired properties.

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arXiv2024

Reinforcement Learning Guides Generative Protein Language Models

Protein engineering can optimize molecules for biotechnology and therapeutics, but navigating the high-dimensional sequence landscape remains challenging. Protein language models (pLMs) have shown to to generate functional proteins far from natural sequences, yet their outputs tend to reflect prevalent properties in training data, limiting discovery of rare properties such as high catalytic activity or thermostability. Here, we introduce ProtRL, a reinforcement learning framework for pLMs that iteratively updates model parameters to maximize externally defined reward functions. Across diverse design tasks, ProtRL shifts generation toward specified objectives while maintaining sequence diversity. We demonstrate the optimization of target folds, bounded and continuous fitness predictors, and multi-objective optimization in binder design. As a proof of concept, we applied ProtRL to experimental feedback for the engineering of epidermal growth factor receptor binders. Testing fewer than 100 designed variants across the experimental campaign, ProtRL-guided optimization provided a final round in which 16 of 22 variants bound EGFR. The best variant showed a dissociation constant of 5.5 nM, representing a nine-fold improvement over wild-type EGF and higher affinity than previously reported EGF variants identified through substantially larger screening campaigns. Our code and models are publicly available at github.com/AI4PDLab/ProtRL

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