# Computational and Theoretical Characterization of Ligand-protein Binding Mechanism

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA RIVERSIDE · 2023 · $80,313

## Abstract

The overarching goal of this proposal is to computationally model biomolecular binding,
iteratively informed by experiments, to fully understand molecular recognition and binding
mechanisms. We will apply hidden free energy barriers to modify inhibitors for preferred binding
kinetics and use the free energy landscape 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 of ligand
binding/unbinding is mainly governed by transient unseen intermediates, which are 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.

## Key facts

- **NIH application ID:** 10811524
- **Project number:** 3R01GM109045-09S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA RIVERSIDE
- **Principal Investigator:** Chia-en Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $80,313
- **Award type:** 3
- **Project period:** 2014-09-30 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10811524

## Citation

> US National Institutes of Health, RePORTER application 10811524, Computational and Theoretical Characterization of Ligand-protein Binding Mechanism (3R01GM109045-09S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10811524. Licensed CC0.

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