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ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
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MATLAB Workflow for MD Analysis: From Raw Signal Extraction (Center of Mass/Dipole) to Core Residue Identification via ACF Fitting

Authors: Kang, Hyeona; Lee, Sungu;

MATLAB Workflow for MD Analysis: From Raw Signal Extraction (Center of Mass/Dipole) to Core Residue Identification via ACF Fitting

Abstract

This repository provides a comprehensive MATLAB-based analytical framework for processing Molecular Dynamics (MD) trajectories. The workflow covers the entire pipeline, starting from raw signal extraction to the statistical identification of structural-dynamical hotspots (Core Residues). Key Features: 1. Signal Processing: Extracts raw Center of Mass (CoM) and Dipole moment information from MD trajectories. 2. Metric Calculation: Computes proximity ($u$) and alignment ($m$) metrics to quantify residue-target interactions. 3. Dynamic Analysis: Calculates Autocorrelation Functions (ACF) for each metric to observe time-dependent decay. 4. Adaptive Fitting: Employs a bi-exponential fitting protocol to extract fast and slow relaxation times ($\tau_{f}, \tau_{s}$). File Structure: Data Files (.mat): 4 initial files containing raw trajectory signals (Center of Mass and Dipole coordinates). Source Code (.m): upload_01_Data.m: Processes raw signals to calculate $u, m$ metrics and generates the corresponding ACFs. upload_02_ACF_Fitting.m: Performs the dual-metric adaptive fitting and to find final hotspots. How to Use: Place all .mat files and .m scripts in the same MATLAB working directory. Execute upload_01_Data.m: first to generate the metric-based dataset. Execute upload_02_ACF_Fitting.m to perform the dynamic analysis.

Related Organizations
Keywords

protein-ligand interaction, residue dynamics, molecular dynamics simulation, autocorrelation function

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average