Wei‐Mou Zheng

The paper introduces MultiStateFold, a framework that improves prediction of multiple protein conformations by integrating Parallel Tempering into a protein language model, achieving better performance than existing methods.

ProDESIGN-LE designs protein sequences efficiently by learning residue local environments, achieving high accuracy in structure prediction and experimental validation.

ProALIGN improves protein structure prediction by learning optimal sequence-template alignments using deep learning, outperforming existing methods on multiple datasets.

CopulaNet directly learns residue co-evolution from multiple sequence alignments to improve protein structure prediction accuracy and efficiency.