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An Automated Method for the Correction of Unsubstantiated Ramachandran Outliers in Protein Structures
Author(s) -
Smith Connor R.,
Alaniz Jacob A.,
West Korbin H. J.,
Weiss Charles J.,
Novak Walter R.P.
Publication year - 2017
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.31.1_supplement.761.19
Subject(s) - ramachandran plot , outlier , protein data bank , dihedral angle , computer science , python (programming language) , protein data bank (rcsb pdb) , protein structure , software , anomaly detection , data mining , chemistry , artificial intelligence , programming language , biochemistry , hydrogen bond , organic chemistry , molecule
Ramachandran outliers are amino acid residues with phi and psi dihedral angles that result in energetically unfavorable conformations. While some of these conformations are supported by the data and provide key information regarding protein structure and functionality, it is currently not known how many of these outliers are actually supported by the data and how many are simply errors in the structural model. Unfortunately, in the Protein Data Bank, there exists a large number of protein structures that contain an excessive number of these outliers (>0.2%). We have developed an algorithm capable of the automated correction of unsubstantiated Ramachandran outliers. This program was built with the Python programming language using the capabilities of the protein determination software PHENIX for outlier detection and refinement. Support or Funding Information This research is supported by the Wabash College Haines Biochemistry Fund.