Xinyue Cui

TopoFlow generates protein conformational ensembles using evolutionary and structural information, improving benchmark metrics and aligning with experimental data.

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.

DeepAAAssembly improves antibody-antigen complex modeling by integrating dynamic interactions, outperforming AlphaFold3 on a benchmark set.

M-DeepAssembly2 predicts multiple conformations of multi-domain proteins using deep learning, improving accuracy over previous 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 MultiSFold, a method for predicting multiple protein conformations using a multi-objective evolutionary algorithm, achieving higher accuracy than AlphaFold2 and RoseTTAFold on benchmark tests.

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

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