تقرير
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
| العنوان: | Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space |
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| المؤلفون: | Lee, Minji, Vecchietti, Luiz Felipe, Jung, Hyunkyu, Ro, Hyun Joo, Cha, Meeyoung, Kim, Ho Min |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Quantitative Biology - Biomolecules, Quantitative Biology - Quantitative Methods |
| الوصف: | 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. Comment: ICML 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2405.18986 |
| رقم الأكسشن: | edsarx.2405.18986 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |