ESMCrystal : Enhancing Protein Crystallization Prediction Through Protein Embeddings

EasyChair Preprint 14542

Abstract

Protein crystallization is a critical yet challenging step in determining protein structures, crucial for advancing our understanding of biological mechanisms. This study introduces ESMCrystal, a novel approach leveraging protein embeddings derived from the advanced Meta ESMFold2 architecture to predict protein crystallization. By integrating transfer learning techniques, ESMCrystal models demonstrate enhanced predictive performance across various datasets, highlighting the potential of deep learning in structural biology. This research not only improves the predictability of protein crystallization but also sets the stage for broader applications of machine learning in understanding complex biological systems. The standalone source code and models, along with the inference server are available at https://huggingface.co/jaykmr/ESMCrystal t6 8M v1 and https://huggingface.co/jaykmr/ESMCrystal t12 35M v2.

Keyphrases: Bioinformatics, ESMCrystal models, ESMFold2, Protein crystallization, Protein structure analysis, machine learning, protein embeddings, structural biology

@booklet{EasyChair:14542,
  author    = {Jayanth Kumar and Kavya Jayakumar and Jayaprakash Sundararaj},
  title     = {ESMCrystal : Enhancing Protein Crystallization Prediction Through Protein Embeddings},
  howpublished = {EasyChair Preprint 14542},
  year      = {EasyChair, 2024}}