Chunxiang Peng

The paper predicts the structure and function of all proteins in uropathogenic E. coli UTI89 using computational methods and identifies novel host-microbe and microbe-phage interaction pathways.

StoPred predicts protein complex stoichiometry using protein language models and graph attention networks, achieving higher accuracy for both homomeric and heteromeric complexes than existing methods.

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 DeepIDDP, a deep learning method for inter-domain distance prediction, which improves domain assembly accuracy when integrated with SADA.

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

DomBpred predicts protein domain boundaries using sequence-based methods, outperforming existing approaches in accuracy and multi-domain protein handling.

The paper introduces MPDB, a multi-domain protein structure database, and a server with modules for filtering and detecting structural analogues to aid in multi-domain protein modeling.

This paper introduces SNfold, a sequential niche multimodal conformation sampling algorithm for protein structure prediction, which improves sampling efficiency and accuracy compared to existing methods.