Biophysics-based protein language models for protein engineering.

ABSTRACT: Protein language models trained on evolutionary data have emerged as powerful tools for predictive problems involving protein sequence, structure and function. However, these models overlook decades of research into biophysical factors governing protein function. We propose mutational effect transfer learning (METL), a protein language model framework that unites advanced machine learning and biophysical modeling. Using the METL framework, we pretrain transformer-based neural networks on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics. We fine-tune METL on experimental sequence-function data to harness these biophysical signals and apply them when predicting protein properties like thermostability, catalytic activity and f

SUBMITTER: Gelman S 

PROVIDER: S-EPMC12446067 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature