# Markov State Model approaches for folding, binding and design

> **NIH NIH R01** · TEMPLE UNIV OF THE COMMONWEALTH · 2024 · $368,343

## Abstract

Project Summary
Understanding the conformational dynamics of proteins and their binding partners is crucial to predicting and
designing their function. Molecular simulations are suitable for this task, but remain challenging for ligand
binding systems where dissociation occurs on very slow time scales. We are developing new Markov state
model (MSM) approaches, which describe conformational dynamics as a network of transitions between
metastable states, to address this challenge. Multi-scale Markov models (MEMMs) offer a robust framework
for building variationally optimal models of dynamics on long time scales, from ensembles of short trajectories
sampled in biased thermodynamic ensembles, to predict ligand binding affinities, rates and mechanisms.
 During the coronavirus pandemic, our group used the distributed computing platform Folding@home (FAH)
to perform virtual screening of SARS-CoV-2 main protease inhibitors by utilizing expanded-ensemble (EE)
simulations, in which multiple alchemical intermediates can be sampled in a single simulation, to estimate
binding free energies. This has inspired us to combine EE and MSM methods that can leverage the power of
FAH to make fundamental advances in virtual screening and molecular design, in three specific aims:
 Our first aim is to improve EE methods for computing ligand binding free energies. In collaboration with the
Shirts Lab, we seek to understand and ameliorate convergence issues, and explore and unify related
approaches. We will investigate how well EE estimates of free energies of mutations can be used with MEMMs
to predict changes in protein folding stability and rates. Finally, we will work with the Karanicolas Lab to
determine the extent to which EE-calculated ABFEs on FAH can be used alongside advanced machine
learning classifiers to discover both active and potent inhibitors from structure-based virtual screening studies.
 Our second aim is to develop a combined metadynamics (metaD) + MEMM approach for modeling binding
reactions. We will develop and test two different strategies in which metaD is used to derive negative potentials
of mean force along binding reaction coordinates that can be used as bias potentials for constructing multi-
ensemble Markov models (MEMMs) of ligand binding. We will test these methods in toy binding systems, and
small ligands of L99A lysozyme. Finally, we will apply metaD+MEMMs to predict affinities, rates and
mechanisms of the macrolide natural product carolacton binding to FolD and its known drug-resistant mutants.
 Our third aim is to examine the extent to which solution-state preorganization determines binding affinity,
and whether simulation-based modeling can use this idea quantitatively for computational design. For a
corpus of 105 cyclic peptides with published affinities, EE+MSM approaches will test the validity of a two-step
conformational selection model. The results of this work will guide the design, testing and optimization of cyclic
peptide bind...

## Key facts

- **NIH application ID:** 10866507
- **Project number:** 5R01GM123296-08
- **Recipient organization:** TEMPLE UNIV OF THE COMMONWEALTH
- **Principal Investigator:** Vincent Voelz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $368,343
- **Award type:** 5
- **Project period:** 2017-05-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866507

## Citation

> US National Institutes of Health, RePORTER application 10866507, Markov State Model approaches for folding, binding and design (5R01GM123296-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10866507. Licensed CC0.

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