# Computer-Aided Drug Design Targeting Protein Phosphorylation

> **NIH NIH R15** · UNIVERSITY OF MISSOURI-ST. LOUIS · 2022 · $469,500

## Abstract

As part of the long-term goal to develop and apply computational methods to aid the design of drugs targeting
protein kinases and related proteins, this research focuses on the development and application of the
ensemble docking method, and on the study of drug-binding kinetics.
Protein kinases continue to be the main targets for drug discovery in this research. The approval of about 60
inhibitors of protein kinases as drugs, mainly for treating cancer, has demonstrated protein kinases as
important drug targets. As over 500 protein kinases are present in human and many mutants are driving
diseases, many more drugs can be developed by targeting protein kinases.
Specific Aim 1 continues to develop and apply the ensemble docking method to drug discovery. Aim 1a tests
the hypothesis that scores, or their derivatives, from ensemble docking could predict whether lung cancer
patients carrying disease-driving mutants of protein kinases are responsive to approved drugs. Aim 1b
continues to validate the use of machine learning to improving ensemble docking. The validation will include all
the proteins in the Directory of Useful Decoys-Enhanced developed for evaluating the performance of docking
methods. Ensemble docking/machine learning models for these proteins will be made available to other
scientists through the web server EDock-ML. Scientists can submit a compound to EDock-ML and receive the
probability that the compound to be active. Aim 1c identifies new drug leads for the protein kinase c-MET with
the aid of EDock-ML.
Specific Aim 2 continues to test a combination of simulation methods for rapidly identifying compounds with
therapeutically useful drug-binding kinetics, using more experimental data that are becoming available. It uses
steered molecular dynamics (SMD) simulation for fast initial screening of chemical libraries, followed by
evaluating the most promising subset by expensive but more rigorous methods, including the umbrella
sampling technique, the Markov State Model, and the milestoning method. As it is still challenging to calculate
absolute dissociation/association rate from molecular simulations, using several methods employing different
approximations will help to draw robust and unbiased conclusions. After validation, the trajectories from the
simulation will be used to decipher the molecular mechanisms of drug dissociation from protein kinases,
including the examination of the generality of a two-step dissociation mechanism that has already been
identified. Understanding the molecular mechanisms can give hint on the design of drugs with therapeutically
useful drug-binding kinetics.
The projects are designed to be performed by undergraduates. Senior scientists will work alongside the
students often so that projects with higher impact can be included.

## Key facts

- **NIH application ID:** 10436417
- **Project number:** 2R15CA224033-02
- **Recipient organization:** UNIVERSITY OF MISSOURI-ST. LOUIS
- **Principal Investigator:** Chung F. Wong
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $469,500
- **Award type:** 2
- **Project period:** 2019-02-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10436417

## Citation

> US National Institutes of Health, RePORTER application 10436417, Computer-Aided Drug Design Targeting Protein Phosphorylation (2R15CA224033-02). Retrieved via AI Analytics 2026-06-12 from https://api.ai-analytics.org/grant/nih/10436417. Licensed CC0.

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