An artificial neural network approach to improving the correlation between protein energetics and the backbone structure

Computational approaches to modeling protein structures have made significant advances over the past decade. However, the current limitation in modeling protein structures is to produce protein structures consistently below the limit of 6 Å compared to their native structure. Therefore, improvement...

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Bibliographic Details
Published in:Proteomics (Weinheim) 2013-01, Vol.13 (2), p.230-238
Main Authors: Fawcett, Timothy M., Irausquin, Stephanie J., Simin, Mikhail, Valafar, Homayoun
Format: Article
Language:English
Subjects:
Artificial neural network
Bioinformatics
Computational Biology - methods
Hydrogen Bonding
Models, Chemical
Models, Molecular
Neural Networks (Computer)
Protein Conformation
Protein energetics
Protein structure prediction
Protein structure refinement
Proteins - chemistry
Sequence Analysis, Protein
Software
Thermodynamics
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Summary:Computational approaches to modeling protein structures have made significant advances over the past decade. However, the current limitation in modeling protein structures is to produce protein structures consistently below the limit of 6 Å compared to their native structure. Therefore, improvement of protein structures consistently below the 6 Å limit using simulation of biophysical forces is of significant interest. Current protein force fields such as those implemented in CHARMM, AMBER, and NAMD have been deemed complete, yet their use in ab initio approaches to protein structure determination has been unsuccessful. Here, we introduce a new approach in evaluation of protein structures based on analysis of energy profiles produced by the SCOPE software package. The latest version of SCOPE produces a hydrogen bond profile that is substantially more informative than a single hydrogen bond energy value. We demonstrate how analysis of SCOPE's energy profile by an artificial neural network shows a significant improvement compared to the traditional force‐based approaches to evaluation of structures. The artificial neural network based analysis of SCOPE's energy profile showed identification of structures to within the range of 1.5–3.0 Å of the native structure. These results have been obtained by testing structures in the same Homology, Topology, Architecture, or Class of the CATH family.
ISSN:1615-9853
1615-9861
DOI:10.1002/pmic.201200330