Minghua Hou

ProSiteHunter improves protein binding site prediction by integrating sequence-based features and a fine-tuned language model, achieving better performance than existing methods.

This paper introduces DeepSCFold, a method for improving protein complex structure prediction by using sequence-based deep learning to model inter-chain interactions, achieving higher accuracy than existing methods.

MultiSAAI predicts antibody-antigen interactions using a multi-scale deep learning framework, achieving high AUROC scores on benchmark datasets.

DeepAssembly2 improves protein complex structure prediction using domain-domain interactions and integrates new features and models to enhance accuracy.

This paper introduces M-SADA, an evolutionary algorithm for modeling multiple conformational states of multidomain proteins, showing improved performance over AlphaFold2 on benchmark datasets.

This paper introduces MultiSFold, a method for predicting multiple protein conformations using a multi-objective evolutionary algorithm, achieving higher accuracy than AlphaFold2 and RoseTTAFold on benchmark tests.

This paper introduces SADA, a deep learning-assisted method for assembling multi-domain protein structures, achieving higher accuracy than existing methods.

This paper introduces VFlib, a variable-length fragment library for de novo protein structure prediction, which improves global precision over existing methods.