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ASTRO‐FOLD 2.0: An enhanced framework for protein structure prediction
Author(s) -
Subramani A.,
Wei Y.,
Floudas C. A.
Publication year - 2012
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.12669
Subject(s) - dihedral angle , protein structure prediction , travelling salesman problem , force field (fiction) , fold (higher order function) , cluster analysis , simulated annealing , global optimization , mathematical optimization , computer science , integer programming , algorithm , linear programming , mathematics , protein structure , artificial intelligence , chemistry , hydrogen bond , biochemistry , organic chemistry , molecule , programming language
The three‐dimensional (3‐D) structure prediction of proteins, given their amino acid sequence, is addressed using the first principles–based approach ASTRO‐FOLD 2.0. The key features presented are: (1) Secondary structure prediction using a novel optimization‐based consensus approach, (2) β‐sheet topology prediction using mixed‐integer linear optimization (MILP), (3) Residue‐to‐residue contact prediction using a high‐resolution distance‐dependent force field and MILP formulation, (4) Tight dihedral angle and distance bound generation for loop residues using dihedral angle clustering and non‐linear optimization (NLP), (5) 3‐D structure prediction using deterministic global optimization, stochastic conformational space annealing, and the full‐atomistic ECEPP/3 potential, (6) Near‐native structure selection using a traveling salesman problem‐based clustering approach, ICON, and (7) Improved bound generation using chemical shifts of subsets of heavy atoms, generated by SPARTA and CS23D. Computational results of ASTRO‐FOLD 2.0 on 47 blind targets of the recently concluded CASP9 experiment are presented. © 2011 American Institute of Chemical Engineers AIChE J, 2012