Bhageerath -H: A homology/ ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins
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RESEARCH
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Bhageerath-H: A homology/ab initio hybrid server for predicting tertiary structures of monomeric soluble proteins B Jayaram1,2,3*, Priyanka Dhingra1,2, Avinash Mishra2,3, Rahul Kaushik2,3, Goutam Mukherjee1,2, Ankita Singh2, Shashank Shekhar2 From Asia Pacific Bioinformatics Network (APBioNet) Thirteenth International Conference on Bioinformatics (InCoB2014) Sydney, Australia. 31 July - 2 August 2014
Abstract Background: The advent of human genome sequencing project has led to a spurt in the number of protein sequences in the databanks. Success of structure based drug discovery severely hinges on the availability of structures. Despite significant progresses in the area of experimental protein structure determination, the sequencestructure gap is continually widening. Data driven homology based computational methods have proved successful in predicting tertiary structures for sequences sharing medium to high sequence similarities. With dwindling similarities of query sequences, advanced homology/ ab initio hybrid approaches are being explored to solve structure prediction problem. Here we describe Bhageerath-H, a homology/ ab initio hybrid software/server for predicting protein tertiary structures with advancing drug design attempts as one of the goals. Results: Bhageerath-H web-server was validated on 75 CASP10 targets which showed TM-scores ≥0.5 in 91% of the cases and Ca RMSDs ≤5Å from the native in 58% of the targets, which is well above the CASP10 water mark. Comparison with some leading servers demonstrated the uniqueness of the hybrid methodology in effectively sampling conformational space, scoring best decoys and refining low resolution models to high and medium resolution. Conclusion: Bhageerath-H methodology is web enabled for the scientific community as a freely accessible web server. The methodology is fielded in the on-going CASP11 experiment.
Background “The native conformation of a protein is determined by the totality of interatomic interactions and hence, by the amino acid sequence, in a given environment” (Nobel Lecture, Christian B. Anfinsen, December 11, 1972). According to Anfinsen’s protein folding hypothesis, a protein’s native structure is determined by its amino acid sequence which drives protein into its minimum Gibbs energy state [1]. This hypothesis evolved as a basic tenet for protein structure prediction algorithms (PSPAs). However limited * Correspondence: [email protected] 1 Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi-110016, India Full list of author information is available at the end of the article
understanding of net balance of forces involved in protein folding creates deficiencies in various proposed PSPAs. One of the early efforts in solving protein folding problem was driven by thermodynamic calculations, which incorporate searching algorithms to investigate a conformation that corresponds to minimum free energy [2]. Here the large number of degrees of freedom of a protein gives rise to innumerable conf
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