Computational and Theoretical Characterization of Ligand-protein Binding Mechanism

Project Summary/Abstract
The overarching goal – Computationally model biomolecular binding, iteratively informed
by experiments, to fully understand molecular recognition and binding mechanisms, apply
hidden free energy barriers to modify inhibitors for preferred binding kinetics, and use
binding/unbinding free energy profiles to understand the role of waters and how and why
residues far from ligand binding site can contribute to mutation effects and ligand selectivity.
Non-covalent molecular recognition plays a crucial role in biology, chemistry and medicine.
Kinetic binding rate constants, together with equilibrium constants, affect the speed, efficacy,
and safety of non-covalent drugs and inform their design. In some cases, binding kinetics are
the major determinant of a drug’s in vivo efficacy. However, kinetic behavior is mainly governed
by transient unseen intermediates during ligand binding/unbinding processes, very difficult to
observe experimentally. Computer simulations offer an alternative solution, both for describing
and understanding experimentally unseen phenomena and to inform drug design.
Real molecular systems are complicated and flexible and call for new modeling tools and
theories to compute ligand binding/unbinding free energy profiles. Used in combination with
experiments, our new modeling approach integrates data and interprets experiments as a
precursor to designing molecules with preferred binding kinetics/affinities.
Guided by excellent results obtained during the previous funding period, three Specific Aims are
proposed: 1) Develop and apply methods to understand mechanisms and processes of
molecular recognition that provide a comprehensive picture and applications for drug design; 2):
Understand the binding/unbinding free energy profile from multiple pathways and investigate the
effects of waters and sidechain mutations during recognition; 3) Adapt and apply the new
methods to ligand binding specificity and kinetics to understand off-site kinase targets. The
approach is innovative in its focus on control of kinetic behavior, advanced methods to
realistically model free energy profiles and, based on this realism, expand on the classical view
of molecular recognition. The proposed research is significant because it comprehensively
models free energy profiles, kinetic behavior, detailed water effects, and mutations that may
confer drug resistance. Significant outcomes: New computational tools to realistically design
ligands with preferred binding kinetics, understand solvent and mutation effects, explain drug
selectivity.

Grant Number: 5R01GM109045-09
NIH Institute/Center: NIH
Principal Investigator: Chia-en Chang