# Revealing pathways and kinetics of molecular recognition with advanced molecular simulation algorithms

> **NIH NIH R01** · MICHIGAN STATE UNIVERSITY · 2024 · $347,124

## Abstract

Project Summary
Biology at the nanoscale is driven by molecular recognition. Cells are lined with receptors that recognize a
myriad of biomolecules and transmit signals to the cell interior. This forms the basis of cell-cell communication
that allows multicellular organisms to thrive. An understanding of molecular recognition is tremendously
important for human health, as it underlies our immune system’s response to threats from viruses, bacteria and
cancer cells. Unfortunately, these recognition processes are often much more complicated than the canonical
“lock-and-key” paradigm. Many neuropeptides and peptide hormones are dozens of residues in length and can
exhibit tremendous structural plasticity. The study of molecular recognition in such dynamic systems is a
challenge, as only a limited understanding of the interactions can be gleaned from structural approaches alone.
 Computational molecular dynamics (MD) simulation allows us to view complex biomolecular systems in
atomic detail, allowing us to compute binding and unbinding rates, and to study their molecular determinants. A
well-known drawback of MD is the difficulty of exploring conformational landscapes without getting trapped in
local free energy minima. The PI (Dickson) has over a decade of experience developing new methods for
tackling this problem, which are able to calculate transition rates in complex systems. Particularly, the REVO
method (“Reweighting Ensembles by Variation Optimization”) has shown success on unbinding pathways of
drug-like ligands with mean first passage times up to 34 minutes, which is billions of times beyond the typical
reach of straightforward MD. This is done without applying biasing forces to the system or making any
assumptions about equilibrium conditions. REVO is essentially an evolutionary algorithm in trajectory space,
where a large group of trajectories are run in parallel, and outlier trajectories are identified using a
measurement of distance to other ensemble members. These outliers are “cloned”, which increases the
probability of seeing long-timescale events happen, even in short-time trajectories.
 The proposed work will augment this method with recent advances in the automatic detection of slow
collective variables (or “CVs”). The VAMPnet (“Variational Approach for Markov Processes”) approach will be
used to detect groups of CVs to use in the REVO distance calculation. A method for iteratively improving the
accuracy of rate calculations (“Asynchronous weighted ensemble”) is also proposed. Together these
developments will constitute a powerful tool for studying long-timescale events in complex biomolecular
systems that will be made freely available to other researchers. Through collaborations with experimental
partners, this method will be applied to reveal the molecular mechanisms of: i) peptide signaling for a class-B
GPCR (Aim 2), and ii) specific recognition of bacterial LPS by a phage tail-spike protein (Aim 3). These
mechanisms will infor...

## Key facts

- **NIH application ID:** 10876907
- **Project number:** 5R01GM130794-07
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Alexander Dickson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $347,124
- **Award type:** 5
- **Project period:** 2018-09-20 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10876907

## Citation

> US National Institutes of Health, RePORTER application 10876907, Revealing pathways and kinetics of molecular recognition with advanced molecular simulation algorithms (5R01GM130794-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10876907. Licensed CC0.

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