Researchprotein energeticsvariant effect predictiondeep mutational scanningprotein llm
Biophysics Enhances Deep Learning Protein Variant Prediction
9.8
Relevance ScoreA team led by Jianhan Chen (Barethiya et al.) on March 25, 2026 reports that integrating biophysics-derived energetics into convolutional and graph neural networks improves extrapolation in protein sequence-to-function prediction. They show stability-derived energetic features boost positional and mutational extrapolation on deep mutational scanning datasets and that adding protein language model evolutionary embeddings further improves accuracy. The approach yields more reliable variant effect predictors for protein engineering and genetic variation prioritization.
Scoring Rationale
High methodological novelty, broad applicability, and reproducibility raise impact; limited to evaluated DMS datasets and protein types.
Sources
- Read OriginalOvercoming extrapolation challenges of deep learning by incorporating physics in protein sequence-function modelingjournals.plos.org
