Kailong Zhao

AlphaFlex improves prediction of multiple protein conformations by integrating flexible residue prediction and directed masking, outperforming existing methods on apo-holo pairs.

SPDesign designs protein sequences using structural sequence profiles and ultrafast shape recognition, outperforming existing methods in sequence recovery and structural accuracy.

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 FoldPAthreader, a method predicting protein folding pathways using a novel force field derived from evolutionary data, achieving 70% accuracy on 30 targets.

Pathfinder predicts protein folding pathways by analyzing conformational sampling data, showing accurate predictions for some proteins and potential pathways for others.

This paper introduces DeepAssembly, a method for predicting multi-domain protein and complex structures using inter-domain interactions, achieving higher accuracy than AlphaFold2 on multi-domain targets.

DeepUMQA2 improves protein model quality assessment using enhanced deep neural networks, achieving better performance on CASP13, CASP14, and CAMEO datasets.

This paper introduces PAthreader, a method for identifying remote homologous structures using deep learning, which improves template accuracy and enhances protein folding pathway exploration.

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

RocketX improves de novo protein structure prediction using a closed-loop feedback mechanism combining geometric constraint prediction and model quality assessment.

IPTDFold improves protein structure prediction by combining iterative sampling, topology adjustment, and distance deviation optimization, outperforming existing methods on benchmark datasets.

MMpred improves de novo protein structure prediction by using a multimodal optimization protocol that enhances conformation sampling and model accuracy.