Open-Source Protein Language Models for Function Prediction and Protein Design
This work provides a more accessible platform for protein function prediction and design, particularly benefiting researchers in synthetic biology and environmental sustainability with limited computational resources, though it is incremental in nature.
The authors tackled the limited accessibility of protein language models (PLMs) due to high computational costs by integrating a PLM into the open-source DeepChem framework, achieving reasonable results on protein prediction benchmarks and exploring enzyme design for plastic degradation.
Protein language models (PLMs) have shown promise in improving the understanding of protein sequences, contributing to advances in areas such as function prediction and protein engineering. However, training these models from scratch requires significant computational resources, limiting their accessibility. To address this, we integrate a PLM into DeepChem, an open-source framework for computational biology and chemistry, to provide a more accessible platform for protein-related tasks. We evaluate the performance of the integrated model on various protein prediction tasks, showing that it achieves reasonable results across benchmarks. Additionally, we present an exploration of generating plastic-degrading enzyme candidates using the model's embeddings and latent space manipulation techniques. While the results suggest that further refinement is needed, this approach provides a foundation for future work in enzyme design. This study aims to facilitate the use of PLMs in research fields like synthetic biology and environmental sustainability, even for those with limited computational resources.