PDBcor: An automated correlation extraction calculator for multi-state protein structures

• Published in: Structure (London) Vol. 30; no. 4; pp. 646 - 652.e2
• Main Authors: Ashkinadze, Dzmitry, Klukowski, Piotr, Kadavath, Harindranath, Güntert, Peter, Riek, Roland
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 07.04.2022
• Subjects: Magnetic Resonance Spectroscopy - methods; Models, Molecular; Motion; multi-state protein structure; Nuclear Magnetic Resonance, Biomolecular - methods; Protein Conformation; protein correlations; protein structure; Proteins - chemistry; statistical analysis of protein structures; protein structure; statistical analysis of protein structures; protein correlations; multi-state protein structure
• ISSN: 0969-2126; 1878-4186
• DOI: 10.1016/j.str.2021.12.002

Allostery and correlated motion are key elements linking protein dynamics with the mechanisms of action of proteins. Here, we present PDBCor, an automated and unbiased method for the detection and analysis of correlated motions from experimental multi-state protein structures. It uses torsion angle and distance statistics and does not require any structure superposition. Clustering of protein conformers allows us to extract correlations in the form of mutual information based on information theory. With PDBcor, we elucidated correlated motion in the WW domain of PIN1, the protein GB3, and the enzyme cyclophilin, in line with reported findings. Correlations extracted with PDBcor can be utilized in subsequent assays including nuclear magnetic resonance (NMR) multi-state structure optimization and validation. As a guide for the interpretation of PDBcor results, we provide a series of protein structure ensembles that exhibit different levels of correlation, including non-correlated, locally correlated, and globally correlated ensembles. [Display omitted] •PDBcor algorithm extracts protein-correlated motion from protein ensembles•PDBcor is based on GMM clustering and information theory•High sensitivity to correlated motion comes from the use of protein distances•PDBcor is unbiased, as the structure superposition step is not required Ashkinadze et al. present an unbiased algorithm, PDBcor, for the extraction of protein-correlated motion from protein structural ensembles. Using clustering and mutual information, this algorithm is based on the statistical analysis of protein interresidual distances. The authors validate it on three model proteins with known structural correlations