N. Ahalawat

This paper uses machine learning to analyze molecular dynamics simulations of protein-ligand binding, identifying conformational states and allosteric sites involved in substrate recognition.

This paper quantifies conformational heterogeneity in substrate-free cytochrome P450cam using simulations and identifies a dynamic equilibrium between closed and open conformations, suggesting an induced-fit mechanism for substrate recognition.

The paper identifies non-affine displacements as key to resolving the free energy landscape of macromolecules using time-structured independent component analysis and Markov state models.

This study uses molecular dynamics simulations to reveal the substrate recognition mechanism in cytochrome P450cam, showing that substrate entry occurs through a single channel without requiring large-scale loop opening.

The paper identifies collective displacements in apo-proteins that correlate with binding pathways without requiring actual binding events.

This paper identifies and optimizes collective variables to map the conformational landscape of GB1 β-hairpin, revealing four metastable states and a novel partial helical state.

This study uses molecular dynamics simulations to reveal the atomistic mechanism of benzene binding to a solvent-inaccessible cavity in L99A T4L, showing that subtle helix movements enable rapid ligand access without large conformational changes.