Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space (2405.18986v1)

Abstract: Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industries. However, protein optimization using computational methods remains challenging, especially when starting from low-fitness sequences. We propose LatProtRL, an optimization method to efficiently traverse a latent space learned by an encoder-decoder leveraging a large protein LLM. To escape local optima, our optimization is modeled as a Markov decision process using reinforcement learning acting directly in latent space. We evaluate our approach on two important fitness optimization tasks, demonstrating its ability to achieve comparable or superior fitness over baseline methods. Our findings and in vitro evaluation show that the generated sequences can reach high-fitness regions, suggesting a substantial potential of LatProtRL in lab-in-the-loop scenarios.

• The paper introduces LatProtRL, a novel reinforcement learning framework that formulates protein optimization as a Markov Decision Process in latent space.
• It employs an encoder-decoder model with pre-trained protein representations to efficiently explore vast combinatorial sequence spaces and enable multi-mutation optimizations.
• Results on GFP and AAV benchmarks demonstrate that LatProtRL outperforms traditional evolutionary and Bayesian methods by achieving superior protein fitness.

Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space

The paper "Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space" presents a novel approach to protein optimization, a critical task with significant applications in biotechnology and therapeutics. The challenge tackled in this research is optimizing protein sequences to enhance desired functionalities, typically initiated from low-fitness sequences. This issue is compounded by the vast combinatorial space of potential protein sequences, making exhaustive experimentation impractical.

Methodology

The authors introduce a method named LatProtRL, which leverages reinforcement learning (RL) to navigate the latent space of protein sequences. LatProtRL's central innovation lies in its formulation of protein optimization as a Markov Decision Process (MDP), enabling the exploitation of RL techniques to escape local optima within the protein fitness landscape. This approach significantly differs from traditional sequence-based optimizations by mapping protein sequences into a latent representation space learned by an encoder-decoder model. The encoder-decoder model is based on large-scale pre-trained protein LLMs that generate compact, informative representations of protein sequences.

Key Components:

• State Representation: Protein sequences are encoded into a low-dimensional latent space. This compression allows the RL agent to focus on meaningful mutations rather than exploring an unmanageably large sequence space.
• Action Space: Instead of direct sequence mutations, actions are modeled as perturbations in the latent space. This enables the exploration of variants requiring multiple simultaneous mutations, facilitating more efficient traversal of the fitness landscape.
• Optimization Loop: The method employs an on-policy RL algorithm, Proximal Policy Optimization (PPO), to train the policy that perturbs the latent representation, simulating evolutionary steps to maximize protein fitness.

Evaluation

LatProtRL's efficacy is demonstrated on two well-studied proteins: the Green Fluorescent Protein (GFP) and the adeno-associated virus (AAV) capsid proteins. These proteins serve as benchmarks due to the availability of extensive experimental data. The results show that LatProtRL can achieve fitness levels comparable or superior to baseline methods, including evolutionary strategies and Bayesian optimization techniques.

• Numerical Results: In the GFP medium and hard tasks, LatProtRL significantly outperforms other methods in generating high-fitness sequences, with demonstrated ability to reach high-fitness regions that are proximal to experimentally validated high-function sequences.

Theoretical and Practical Implications

The research offers a compelling argument for using latent space exploration over traditional sequence-based methods in protein design. The decoupling of representation learning and optimization provides scalability and adaptability, which can be crucial for other biological systems with large, rugged fitness landscapes.

Theoretical Implications:

• The latent space approach aligns with the increasing interest in representation learning, emphasizing the separation of feature extraction from downstream tasks, which could inspire similar methodologies in other domains of computational biology and beyond.

Practical Implications:

• LatProtRL can facilitate the targeted evolution of proteins, offering a substantial potential in applications like enzyme engineering, drug design, and synthetic biology where lab-in-the-loop scenarios are becoming increasingly critical.

Future Directions

Future work could explore integrating structural prediction feedback (e.g., from tools like AlphaFold) into the RL loop, potentially improving the biological plausibility of proposed designs. Further enhancements to the encoder-decoder architecture could also be explored to boost decoding accuracy, addressing current limitations when dealing with longer protein sequences or those with insertions/deletions.

Overall, this paper provides an innovative approach to protein sequence optimization that efficiently navigates complex fitness landscapes, representing a meaningful step in computational biology applications aimed at harnessing evolutionary strategies through machine learning techniques.