Practical Solutions for Protein Sequence Design
Reinforcement Learning and Language Models
Protein sequence design is critical for drug discovery. Traditional methods like evolutionary strategies and Monte-Carlo simulations often struggle to efficiently explore amino acid sequence space. However, reinforcement learning and language models offer promising solutions by learning mutation policies and scoring proteins based on biological metrics.
Researchers from various institutions propose using language models as reward functions for generating new protein sequences. To address computational intensity, they introduce an alternative approach where optimization is based on scores from a smaller proxy model fine-tuned alongside learning mutation policies. Their experiments demonstrate that RL-based approaches achieve favorable biological plausibility and sequence diversity results. They also provide an open-source implementation to advance research in protein sequence design.
Various methods, such as evolutionary algorithms, reinforcement learning, generative models, and Bayesian optimization, have been explored for designing biological sequences. In the realm of protein sequence design using RL, the task is modeled as a Markov Decision Process where sequences are mutated based on actions chosen by an RL policy. Rewards are determined by evaluating the structural similarity using either an expensive oracle model or a cheaper proxy model periodically fine-tuned with true scores from the oracle.
The research findings demonstrate the effectiveness of leveraging language models to develop mutation policies for protein sequence generation, showcasing deep RL algorithms as robust contenders in this field.
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