Back

Dynamics-aware Evolutionary Profiling Uncouples Structural Rigidity from Functional Motion to Enable Enhanced Variant Interpretation

Karagöl, T.; Karagöl, A.

2026-01-20 bioinformatics
10.64898/2026.01.16.699945 bioRxiv
Show abstract

Evolutionary conservation is a powerful part of mutational intolerance prediction, yet traditional pathogenicity metrics frequently conflate two distinct biophysical constraints: structural stability (rigidity) and functional mechanics (dynamics). We introduce Dynamics-Aware Evolutionary Profiling to resolve this ambiguity, integrating Molecular Dynamics with evolutionary conservation and coupling analysis across human/cross-species-proteome of 151 protein structures. By mathematically uncoupling biophysical forces, we define orthogonal metrics; the Rigid Conserved Score (RCS) for the structural scaffold, and the Dynamic Conserved Score (DCS) for flexible residues. Our analysis reveals a fundamental bifurcation in pathogenicity. RCS serves as a filter for lethal structural failure, isolating hydrophobic core residues whose mutation triggers unfolding. In contrast, DCS identified a rare population of residues that are evolutionarily highly-conserved but structurally mobile; these Dynamic-Conserved sites exhibit intermediate pathogenicity and are enriched in flexible hinge residues (Gly, Pro). Validation against 737 human variants from ClinVar demonstrates that DCS captures a distinct pathogenic mechanism regarding essential protein motion. Notably, DCS and RCS correctly flagged some pathogenic variants of NARS1 and PGK1 that were misclassified as benign or ambiguous by AlphaMissense. These results indicate that while the rigid core represents a stability bottleneck, DCS isolates functional sites likely driving allosteric regulation. We provide an open-access web interface (ADEPT) for these metrics. By isolating dynamic-conserved residues, this framework refines the interpretation of Variants of Uncertain Significance in dynamic regions and reveals tunable targets for rational drug design, moving beyond the static optimization of the folded state.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.