  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
language model. 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.
