This software targets MD simulations at the exascale and proposes a novel technique for in situ data analysis and indexing of MD trajectories. Our technique maps individual trajectories' substructures (i.e., alpha-helices, beta-strands) to metadata frame by frame. The metadata captures the conformational properties of the substructures. The ense…
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data
src
.gitignore
LICENSE.txt
README.md

README.md

#In-Situ-Protein-Analytics

This repository contains tools used to perform an eigenvalue-based, in-situ data analytics of protein trajectories following the approach detailed in:

T. Johnston, B. Zhang, A. Liwo, S. Crivelli, and M. Taufer.
 In-Situ Data Analytics and Indexing of Protein Trajectories. Journal of Computational Chemistry (JCC), 2017.

This software targets MD simulations at the exascale and proposes a novel technique for in situ data analysis and indexing of MD trajectories. Our technique maps individual trajectories' substructures (i.e., alpha-helices, beta-strands) to metadata frame by frame. The metadata captures the conformational properties of the substructures. The ensemble of metadata can be used for automatic, strategic analysis within a trajectory or across trajectories, without manually identify those portions of trajectories in which critical changes take place. We demonstrate our technique's effectiveness by applying it to 26.3k helices and 31.2k strands from 9,917 PDB proteins and by providing three empirical case studies.