Xiaogen Zhou

This paper introduces DiffEnsemble, a diffusion-based framework for modeling protein conformational ensembles, showing improved performance over existing methods.

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 DEMO-EMReF, a Transformer-based framework for refining cryo-EM density maps, improving resolution and atomic model accuracy.

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.

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

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

The paper reports cryo-EM structures of transcription-translation complexes (TTCs) at different stages of the translation cycle in E. coli, revealing structural changes and their functional implications.

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.

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

This paper introduces DEMO-EM, a method for assembling multi-domain protein structures from cryo-EM maps, achieving high accuracy and outperforming existing methods.

This study identifies RTL8 as a novel UBQLN2 interactor that promotes its nuclear translocation and subnuclear localization in protein quality control.

DeepUMQA improves protein model quality assessment by integrating Ultrafast Shape Recognition features with deep learning, outperforming existing methods on benchmark datasets.

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.

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

This paper introduces GDDfold, a distance-guided protein folding algorithm that outperforms existing methods on benchmark datasets.

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.

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

TripletRes predicts high-accuracy protein contact-maps from primary sequences using deep residual convolutional networks and coevolutionary matrices, outperforming existing methods on benchmark datasets.

The paper re-analyzes computational findings from two studies, concluding that snakes are not intermediate hosts of 2019-nCoV and that spike protein insertions are naturally evolved from bat coronaviruses.