All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds
Author
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-08
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Recent advances in understanding protein folding have benefitted from coarse-grained representations of protein structures.
Empirical energy functions derived from these techniques occasionally succeed in distinguishing native structures from their corresponding ensembles of nonnative folds or decoys which display varying degrees of structural dissimilarity to the native proteins.
Here we utilized atomic coordinates of single protein chains, comprising a large diverse training set, to develop and evaluate twelve all-atom four-body statistical potentials obtained by exploring alternative values for a pair of inherent parameters.
Delaunay tessellation was performed on the atomic coordinates of each protein to objectively identify all quadruplets of interacting atoms, and atomic potentials were generated via statistical analysis of the data and implementation of the inverted Boltzmann principle.
Our potentials were evaluated using benchmarking datasets from Decoys-‘R’-Us, and comparisons were made with twelve other physics- and knowledge-based potentials.
Ranking 3rd, our best potential tied CHARMM19 and surpassed AMBER force field potentials.
We illustrate how a generalized version of our potential can be used to empirically calculate binding energies for target-ligand complexes, using HIV-1 protease-inhibitor complexes for a practical application.
The combined results suggest an accurate and efficient atomic four-body statistical potential for protein structure prediction and assessment.
American Psychological Association (APA)
Masso, Majid. 2017. All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds. BioMed Research International،Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1137749
Modern Language Association (MLA)
Masso, Majid. All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds. BioMed Research International No. 2017 (2017), pp.1-17.
https://search.emarefa.net/detail/BIM-1137749
American Medical Association (AMA)
Masso, Majid. All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-17.
https://search.emarefa.net/detail/BIM-1137749
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1137749