# Long Time Dynamics of Biomolecules

> **NIH NIH R01** · UNIVERSITY OF TEXAS AT AUSTIN · 2021 · $304,517

## Abstract

Proteins are essential for many cell functions including signaling, activation, generation
of mechanical energy, and processing of biochemical reactions. One major class of
proteins is that of enzymes: molecular machines that work in cycles to facilitate rapid
progression of chemical reactions. Computational studies and simulations are likely to
shed light on the operation of these biologically important molecules. However, actions
of enzymes are conducted on extended spatial and temporal scales that are a challenge
for computational studies. Theories and algorithms are developed in my group to
address these challenges. These technologies include algorithms for reaction path
calculations and the theory of Milestoning. On the application side, the focus of the next
grant cycle is on HIV RT. We will investigate critical steps in the machine cycle of the
enzyme HIV RT, which include the weak binding of the nucleotide to the protein surface,
the chemical step, byproduct release, and sliding of the protein along the DNA to free
space for a new substrate. On the computational and theoretical side, we are planning
three advances. In the first advance, we will introduce a novel algorithm for the
calculations of minimum free energy pathways. There are two advantages to the new
algorithm that we call “chain growth”. The first is that it works in a large space of coarse
variables. The second advantage is that the algorithm does not require an initial guess
for the path, and, therefore, the optimization is significantly easier than other algorithms
that we have used in the past. The second advance planned is the development of an
automated script that will run the Milestoning algorithm using essentially any Molecular
Dynamics software package. The MD packages will generate the short trajectories
required in the Milestoning analyses, and the script will provide the initial conditions,
analyze the short trajectories for convergence, and initiate new trajectories as needed. If
the simulation is converged the script will compute kinetic and thermodynamic
observables. The script will make it easier for researchers to use the Milestoning theory
and algorithm within the framework of their familiar MD program. The third advance will
compute an optimal reaction coordinate (iso-committor surfaces) from Milestoning
trajectories and will make it possible to further refine, analyze, and test the simulations.

## Key facts

- **NIH application ID:** 10146406
- **Project number:** 5R01GM059796-21
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Ron Elber
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $304,517
- **Award type:** 5
- **Project period:** 2000-03-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10146406

## Citation

> US National Institutes of Health, RePORTER application 10146406, Long Time Dynamics of Biomolecules (5R01GM059796-21). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10146406. Licensed CC0.

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