# Research and deployment of binding-domain flexible MovableType (MTFlex) for free energy-based affinity prediction and crystallographic structure determination

> **NIH NIH R44** · QUANTUMBIO, INC. · 2021 · $513,208

## Abstract

The study of protein/ligand binding is one of the central problems in computational biology because of its
importance in understanding intermolecular interactions, and because of its practical payoff in drug discovery
efforts. The transformative impact accurate target/ligand structure can have in the design of next generation
medicines cannot be overstated. If we could routinely and accurately design molecules using these approaches
it would revolutionize drug discovery by winnowing out compounds with no activity while focusing more effort
and scrutiny on highly active compounds. Determining the structure of a small molecule (drug candidate or lead
compound) bound to a biological receptor (protein implicated in disease) is a necessary step in this approach to
drug discovery.
In this proposal we describe a novel method we call MovableType (MT), which addresses the protein ligand
binding and scoring problem using fundamental statistical mechanics combined with a novel way to generate
the ensemble of a ligand in a protein binding pocket. Via a rapid assembly of the necessary partition functions
we directly obtain binding free energies and the low free energy poses. Conceptually, the MT method is
analogous to block and type set printing, which allows us to efficiently evaluate partition functions describing
regions or systems of interest. In this approach we construct two databases that 1) describe the probability of
certain pairwise interactions as a function of r obtained from a knowledge base (Protein Databank (PDB) or the
Cambridge Structural Database (CSD)) and 2) the energetics of the pairwise interactions as a function of r
obtained from empirical potentials, which can be either derived from the probabilities or can utilize extant pairwise
potentials like AMBER. Overall, the MT method is a general one and can use a broad range of two-body potential
functions and can be extended to higher-order interactions if so desired. Recent work with the MT method has
led to the launch of three core product modules: MTScore (both endstate and ensemble binding affinity prediction),
MTDock (ligand placement), and MTCS (ligand conformational search). In this project, we will extend our MT
product line and deliver this methodology to X-ray crystallographers and computational chemists for use in
automated sidechain rotamer and target loop sampling within and around the active site, accurate binding affinity
prediction and minima selection, and crystallographic density matching and placement. This work will involve
development of a new, integrated tool for automated structure/model preparation, rotamer/loop selection,
rotamer/loop generation (“MTFlex proper”), loop/totamer minimization, and analysis. We will commercially deploy
the technology, which we will call MTFlex, construct graphical user interfaces for use in MOE, Phenix, and our
web-based cloud platform. Finally, this software will be used in real life structure-based drug discovery problems
with our pha...

## Key facts

- **NIH application ID:** 10093097
- **Project number:** 5R44GM134781-03
- **Recipient organization:** QUANTUMBIO, INC.
- **Principal Investigator:** Lance M Westerhoff
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $513,208
- **Award type:** 5
- **Project period:** 2019-08-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10093097

## Citation

> US National Institutes of Health, RePORTER application 10093097, Research and deployment of binding-domain flexible MovableType (MTFlex) for free energy-based affinity prediction and crystallographic structure determination (5R44GM134781-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10093097. Licensed CC0.

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