# Mapping Protein Surfaces in Computational Drug Design

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $306,088

## Abstract

ABSTRACT: The greatest limitation for structure-based drug discovery (SBDD) is the need to neglect water
and protein flexibility in most modeling. Here, we outline simulation methods that overcome these limitations.
This proposal focuses on developing MixMD, our method for mixed-solvent molecular dynamics (MD). MixMD
identifies critical binding sub-sites on protein surfaces (hotspots). Proteins are simulated in a box of explicit
water with 5% small, organic probe cosolvents. The waters and probes sample the local environments along
the protein surface, and sites with high occupancy of probes are identified as hotspots. MixMD has superior
performance over other cosolvent MD methods like MacKerell’s SILCS and Barril’s MDmix. Other methods are
plagued by many spurious, misleading, “extra” sites that indistinguishable from real binding sites, which greatly
hinders prospective applications.
Our long-term goal is to improve SBDD by developing methods that more accurately model protein-ligand
binding. Our underlying hypotheses are 1) MixMD’s more complete description of the physics of binding yields
better hotspot predictions than traditional SBDD methods and 2) both qualitative and quantitative data from
MixMD can be used in SBDD.
This proposal outlines two areas for developing MixMD and increasing its impact on SBDD. Specific Aim 1
develops methods for calculating the free energies, entropies, and enthalpies of the hotspot probes.
Comparisons will be made between occupancy-based, energy-based, and kinetics-based methods for
calculating those key binding properties. Specific Aim 2 will address a series of key challenges in SBDD. First,
MixMD will be used to identify bridging water molecules in binding sites. Clearly, hotspot locations ascertain
displaceable water, but it is just as important to pinpoint required, bridging waters in binding sites. Second, the
accessibility of difficult, cryptic sites will be examined. While mapping the sites, we will determine whether
pocket opening and probe binding are sequential events where probes “capture” open states or concerted
events where probes “induce” open states by pushing against the malleable torsions of the cryptic pocket.
Lastly, MixMD data will be used to predict druggabilities of binding sites. The Non-Redundant set of Druggable
and Less Druggable binding sites (NRDLD) will be used to derive a druggability index based on number of
hotspots, their affinities, their proximities, and their degree of burial in the protein.

## Key facts

- **NIH application ID:** 9857028
- **Project number:** 5R01GM065372-12
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** HEATHER A CARLSON
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $306,088
- **Award type:** 5
- **Project period:** 2002-04-01 → 2022-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9857028

## Citation

> US National Institutes of Health, RePORTER application 9857028, Mapping Protein Surfaces in Computational Drug Design (5R01GM065372-12). Retrieved via AI Analytics 2026-06-10 from https://api.ai-analytics.org/grant/nih/9857028. Licensed CC0.

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