R. Najmanovich

Understanding and predicting ligand efficacy in the mu-opioid receptor through quantitative dynamical analysis of complex structures

This study predicts ligand efficacy in the mu-opioid receptor using dynamic analysis and machine learning, showing dynamics are critical for accurate predictions.

The DynaSig-ML Python package: automated learning of biomolecular dynamics-function relationships

The paper introduces DynaSig-ML, a Python package that learns dynamics-function relationships in biomolecules using sequence variants and the Elastic Network Contact Model.

Sequence-sensitive elastic network captures dynamical features necessary for miR-125a maturation

The paper introduces ENCoM, a sequence-sensitive NMA model for RNA dynamics, showing it captures functional dynamics beyond sequence in miR-125a maturation.

Computational analysis of the effect of SARS-CoV-2 variant Omicron Spike protein mutations on dynamics, ACE2 binding and propensity for immune escape

This paper evaluates how Omicron SARS-CoV-2 Spike protein mutations affect ACE2 binding and immune escape by analyzing structural dynamics and binding interactions.

Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants

This study models conformational state dynamics of SARS-CoV-2 Spike protein variants using normal-mode analysis, showing how mutations affect open-state occupancy and infectivity.