Nishank Raisinghani

Quantitative Characterization and Prediction of the Binding Determinants and Immune Escape Hotspots for Groups of Broadly Neutralizing Antibodies Against Omicron Variants: Atomistic Modeling of the SARS-CoV-2 Spike Complexes with Antibodies

This study identifies immune escape hotspots and binding determinants for broadly neutralizing antibodies against Omicron variants using molecular dynamics and deep mutational scanning.

Assessing Structures and Conformational Ensembles of Apo and Holo Protein States Using Randomized Alanine Sequence Scanning Combined with Shallow Subsampling in AlphaFold2 : Insights and Lessons from Predictions of Functional Allosteric Conformations

This study evaluates AlphaFold2's ability to predict apo and holo protein conformations using randomized alanine masking and subsampling, finding it effective for minor changes but less so for large domain movements.

Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape

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.