Conformation-dependent library for backbone geometry in proteins

This library contains information on the variation of backbone bond angles and bond lengths as a function of the backbone dihedral angles phi and psi. For some parameters, there is a systematic variation with phi or psi or both. This study was initiated by P. Andrew Karplus at Oregon State University. We performed kernel regression analysis to produce smoothly varying regression values for bond lengths and bond angles on phi and psi.

You can download the conformation-dependent backbone geometry library in an uncompressed, text format or in a zip archive. The text format is both human- and computer-friendly. The library file has Quick-Start information in its header.

The publication of this work can be found at Conformation Dependence of Backbone Geometry in Proteins, Structure, 2009 and its supplemental figures here.

For more information and details please visit our collaborator's websites:
http://proteingeometry.sourceforge.net and http://pgd.science.oregonstate.edu.

Abstract:

Protein structure determination and predictive modeling have long been guided by the paradigm that the peptide backbone has a single, context-independent ideal geometry. Both quantum-mechanics calculations and empirical analyses have shown this is an incorrect simplification in that backbone covalent geometry actually varies systematically as a function of the phi and psi backbone dihedral angles. Here, we use a nonredundant set of ultrahigh-resolution protein structures to define these conformation-dependent variations. The trends have a rational, structural basis that can be explained by avoidance of atomic clashes or optimization of favorable electrostatic interactions. To facilitate adoption of this paradigm, we have created a conformation-dependent library of covalent bond lengths and bond angles and shown that it has improved accuracy over existing methods without any additional variables to optimize. Protein structures derived from crystallographic refinement and predictive modeling both stand to benefit from incorporation of the paradigm.

Center for New Methods for High-Resolution Comparative Modeling

This center is funded under grant P20 GM76222 from the National Institutes of Health under the National Institute for General Medical Sciences, as part of the Protein Structure Initiative (PSI2).

The investigators involved in this project are:

• Roland Dunbrack, Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia PA
• David Baker, Department of Biochemistry, University of Washington, Seattle WA
• Jay Ponder, Department of Bioehemistry, Washington University, St. Louis, MO
• Michael I. Jordan, Departments of Statistics and of Electrical Engineering and Computer Sciences, University of California, Berkeley CA
• Paul Tseng, Department of Mathematics, University of Washington, Seattle WA