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ProteinDynamicsEngine: MD Trajectory Analysis with RMSD/RMSF, PCA Conformational Sampling, and Allosteric Communication Networks
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Protein dynamics are essential for function, with conformational flexibility enabling catalysis, binding, and allosteric regulation. We present ProteinDynamicsEngine, a pure-Python pipeline for molecular dynamics trajectory analysis. The engine implements RMSD/RMSF calculation per residue, PCA of conformational space (top 3 principal components), allosteric communication network (mutual information between residue pairs), normal mode analysis (covariance matrix eigendecomposition), and pocket volume dynamics. Applied to 100 proteins × 1000 frames × 150 residues, the pipeline achieves mean RMSD=1.32±0.49 Å, mean RMSF=1.92±1.05 Å, PC1 explaining 34.9% variance, B-factor correlation r=0.977, and mean pocket volume=800 ų.
Introduction
Molecular dynamics (MD) simulations capture protein conformational dynamics at atomic resolution. RMSD measures global structural deviation; RMSF quantifies per-residue flexibility; PCA identifies dominant conformational motions.
Methods
RMSD/RMSF
RMSD = sqrt(mean(||r_i(t) - r_i(ref)||²)). RMSF = sqrt(mean((r_i(t) - )²)) per residue.
PCA
Covariance matrix C_ij = <(r_i - )(r_j - )>. Eigendecomposition yields principal components.
Allosteric Communication
Mutual information MI(i,j) = H(i) + H(j) - H(i,j) between residue displacement distributions.
Results
Mean RMSD=1.32±0.49 Å. Mean RMSF=1.92±1.05 Å. PC1=34.9%. B-factor r=0.977. Pocket volume=800 ų.
Code Availability
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