Peng Tao

Angular Deviation Diffuser: A Transformer-Based Diffusion Model for Efficient Protein Conformational Ensemble Generation

This paper introduces a transformer-based diffusion model that generates protein conformational ensembles efficiently, showing alignment with MD simulations and broader conformational coverage.

Accurate Characterization of the Allosteric Energy Landscapes, Binding Hotspots and Long-Range Communications for KRAS Complexes with Effector Proteins : Integrative Approach Using Microsecond Molecular Dynamics, Deep Mutational Scanning of Binding Energetics and Allosteric Network Modeling

This study integrates molecular dynamics, mutational scanning, and network modeling to characterize KRAS-RAF1 binding hotspots, allostery, and conformational dynamics, revealing how oncogenic mutations alter binding affinity and signaling.

Prediction of Conformational Ensembles and Structural Effects of State-Switching Allosteric Mutants in the Protein Kinases Using Comparative Analysis of AlphaFold2 Adaptations with Sequence Masking and Shallow Subsampling

This study adapts AlphaFold2 to predict conformational ensembles of allosteric ABL kinase mutants, showing that sequence masking and subsampling improve prediction of structural state shifts caused by mutations.

Predicting Functional Conformational Ensembles and Binding Mechanisms of Convergent Evolution for SARS-CoV-2 Spike Omicron Variants Using AlphaFold2 Sequence Scanning Adaptations and Molecular Dynamics Simulations

This study uses AlphaFold2 and molecular dynamics to predict conformational ensembles and binding mechanisms of SARS-CoV-2 Omicron variants, showing agreement with experimental data.

Interpretable Atomistic Prediction and Functional Analysis of Conformational Ensembles and Allosteric States in Protein Kinases Using AlphaFold2 Adaptation with Randomized Sequence Scanning and Local Frustration Profiling

This study adapts AlphaFold2 to predict conformational ensembles and allosteric states of ABL kinase using sequence scanning and frustration profiling, revealing limitations in detecting low-populated inactive states.

SSnet: A Deep Learning Approach for Protein-Ligand Interaction Prediction

SSnet predicts protein-ligand interactions using secondary structure features, outperforming existing models and revealing key structural elements for ligand binding.