Biman Jana

Conformational Barrier-Driven Flux Redistribution across Competing Catalytic Pathways Unifies Diverse Enzyme Kinetic Regimes

This paper unifies diverse enzyme kinetic behaviors under a single conformational free-energy framework by showing how substrate availability and conformational barriers redistribute catalytic flux between pathways.

Local cooperative interactions reshape the folding transition in a one-dimensional spin-glass model

The paper uses a one-dimensional spin-glass model to show how local cooperative interactions affect protein folding stability and transition.

Transferable Collective Variable to accelerate Protein-Ligand (Un)Binding Transitions via Explainable Machine Learning and Intriguing Role of Ligand Solvation

This paper introduces an interpretable machine learning method to derive optimized collective variables for protein-ligand binding-unbinding transitions, achieving accurate free energy estimates and residence times with low computational cost.

A model of protein folding with multiple native states:Metamorphicity, Intrinsic disorderness and folding uponbinding of proteins

This paper presents a statistical mechanics model of protein folding that reproduces multiple protein behaviors, including single native state folding, metamorphicity, intrinsic disorder, and binding-induced folding.

Correlations among hydrogen bond fluctuations in the apo state are enough to reveal allosteric networks in proteins

This paper introduces a computational method to identify allosteric networks in proteins by analyzing hydrogen bond fluctuations in the apo state, demonstrating its effectiveness across three proteins.

Prediction of Enzyme function using interpretable optimized Ensemble learning framework

This paper introduces SOLVE, an interpretable ML framework for enzyme function prediction using primary sequence data, achieving high accuracy and identifying functional motifs.