title: A homology/ab initio hybrid algorithm for sampling near-native protein conformations.
abstract: One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near-native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath-H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM-score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath-H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath-H Strgen web tool which is made freely accessible for protein decoy generation (http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp).
authors.affiliation: Department of Chemistry, Indian Institute of Technology, Hauz Khas, New Delhi, 110016, India.
authors.forename: Priyanka
authors.initials: P
authors.lastname: Dhingra
authors.forename: Bhyravabhotla
authors.initials: B
authors.lastname: Jayaram
mesh_terms: D000465:Algorithms; D008958:Models, Molecular; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D011789:Quantum Theory
chemical_list: D011506:Proteins
keywords: ab initio modeling; fold recognition; homology modeling; loop modeling; protein folding; protein tertiary structure prediction