# New Methods for Large-scale Computer Simulation

> **NIH NIH R01** · BAYLOR COLLEGE OF MEDICINE · 2020 · $336,813

## Abstract

Project Summary
 To predict the three-dimensional structures of a protein solely from its primary sequence remains a
grand and elusive challenge in modern computational biology. Molecular dynamics simulation has a high
promise for predicting protein structures and folding pathways at molecular details. Recent advances in im-
proved computer hardware and enhanced sampling methods have made it possible to ab initio fold proteins of
larger size. The highlight of the improved computer hardware is Anton, a massively parallel special-purpose
supercomputer designed by D.E. Shaw Research. Anton successfully folded the D14A fast-folding mutant of
the 80-residue l-repressor, which was achieved at 49 microseconds (μs) in 643μs-long simulations. On the
other hand, the latest advance in enhanced sampling methods is represented by the single-copy continuous
simulated tempering (CST) method developed by the PI’s group. The group of Dr. Klaus Schulten incorpo-
rated the CST method into the NAMD package, which repeatedly folded the 80-residue l-repressor HG mutant
from a fully extended conformation to the native state at 0.5 and 4μs in 10μs-long simulations with Ca root-
mean-square deviations (Ca-RMSD) of 1.7 Å on a conventional computing platform. In marked contrast, a
complete folding of the same protein was NOT observed using Anton at multiple temperatures even in 100μs-
long simulations. This performance of CST in folding simulation has never been matched by any other sam-
pling method for similar purposes on conventional computing platforms. Most recently, to further enhance
sampling efficiencies in studying larger systems, the PI has developed a more powerful parallel CST (PCST)
method. Initial ab initio folding simulation of trp-cage clearly demonstrated that the efficiency of PCST in facili-
tating multiple folding and unfolding events was even drastically superior to that of CST. The PCST method
serves as a solid foundation for the proposed research in three Specific Aims: 1). Development of the PCST
method for enhanced sampling; 2). Design of advanced temperature-dependent restraint schemes for
targeted sampling; 3). Development of advanced blind model selection methods for efficient target se-
lection. Our in-depth preliminary studies demonstrate that these new methods clearly outperformed all exist-
ing methods and suggest a high promise of success for the proposed research. Ultimately, these powerful
new algorithms will provide urgently-needed tools for protein simulations, and offer an effective solution for
structural refinement in experimental X-ray crystallography and electron cryo-microscopy.

## Key facts

- **NIH application ID:** 9898413
- **Project number:** 5R01GM127628-03
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** JIANPENG MA
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $336,813
- **Award type:** 5
- **Project period:** 2018-06-01 → 2022-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898413

## Citation

> US National Institutes of Health, RePORTER application 9898413, New Methods for Large-scale Computer Simulation (5R01GM127628-03). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9898413. Licensed CC0.

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