# Entropy for End-Point and FFT-Based Binding Free Energy Calculations

> **NIH NIH R01** · ILLINOIS INSTITUTE OF TECHNOLOGY · 2020 · $330,599

## Abstract

Project Summary
Computers are often used to predict how tightly two molecules associate, their binding free energy. These
predictions are helpful for designing drugs, predicting the consequences of genetic variation, and understanding
how molecules interact to sustain life. Unfortunately, currently available methods are either fast or accurate,
but not both. In general, fast methods do a poor job accounting for entropy, which is an important part of the
free energy. The main objective of this project is to develop better ways to account for entropy in two popular
techniques for studying molecular interactions: “end-point” simulations of the bound complexes and their
unbound counterparts; and molecular docking based on the Fast Fourier Transform. Specifically, new ways to
analyze calculation results will be derived, implemented, assessed, and optimized. Additionally, the methods
will be combined with enhanced sampling techniques.
 Our new end-point and FFT-based methods will be assessed by their ability to reproduce benchmark results
from slower but more accurate computational methods, as well as experimental results. The benchmark dataset
will include protein-ligand complexes and protein-protein complexes with known binding affinities and crystal
structures, as well as protein-protein complexes for which the effect of missense mutations on binding have
been measured. We will also perform benchmark calculations on mutants of the tumor suppressor p53 that gain
the ability to activate new proteins and promote tumor growth. In addition to serving as benchmarks, these
calculations may provide mechanistic insight into how proteins bind various ligands and how p53 mutants gain
new binding partners.
 Our new methods will also be tested in recurring community challenges: the “Drug Design Data Resource”
(D3R) grand challenge to predict protein-ligand complex structures and affinities and the “Critical Assessment of
PRediction of Interactions” (CAPRI) challenge for protein-protein structure prediction. These blinded challenges
will allow for an unbiased comparison of our methods to those from other research groups.
 Finally, we will assess our methods in a drug discovery project. We will use established methods and our new
methods to virtually screen a chemical library against a pair of structurally similar bacterial metabolic enzymes.
One enzyme is relevant to active and the other to dormant bacteria. Compounds predicted to selectively bind
the bacterial (opposed to human) enzymes will be experimentally tested in biochemical assays. We anticipate
that our improved methods will be significantly more accurate than established approaches, advancing research
ranging from interactome prediction to drug discovery.

## Key facts

- **NIH application ID:** 9969445
- **Project number:** 5R01GM127712-03
- **Recipient organization:** ILLINOIS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** David Do Le Minh
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $330,599
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9969445

## Citation

> US National Institutes of Health, RePORTER application 9969445, Entropy for End-Point and FFT-Based Binding Free Energy Calculations (5R01GM127712-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9969445. Licensed CC0.

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