Literature DB >> 11924737

Ab initio prediction of helical segments in polypeptides.

J L Klepeis1, C A Floudas.   

Abstract

An ab initio method has been developed to predict helix formation for polypeptides. The approach relies on the systematic analysis of overlapping oligopeptides to determine the helical propensity for individual residues. Detailed atomistic level modeling, including entropic contributions, and solvation/ionization energies calculated through the solution of the Poisson-Boltzmann equation, is utilized. The calculation of probabilities for helix formation is based on the generation of ensembles of low energy conformers. The approach, which is easily amenable to parallelization, is shown to perform very well for several benchmark polypeptide systems, including the bovine pancreatic trypsin inhibitor, the immunoglobulin binding domain of protein G, the chymotrypsin inhibitor 2, the R69 N-terminal domain of phage 434 repressor, and the wheat germ agglutinin.

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Year:  2002        PMID: 11924737     DOI: 10.1002/jcc.10002

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  14 in total

1.  Hybrid global optimization algorithms for protein structure prediction: alternating hybrids.

Authors:  J L Klepeis; M J Pieja; C A Floudas
Journal:  Biophys J       Date:  2003-02       Impact factor: 4.033

2.  ASTRO-FOLD: a combinatorial and global optimization framework for Ab initio prediction of three-dimensional structures of proteins from the amino acid sequence.

Authors:  J L Klepeis; C A Floudas
Journal:  Biophys J       Date:  2003-10       Impact factor: 4.033

3.  Molecular Simulations Find Stable Structures in Fragments of Protein G.

Authors:  Tjaša Urbič; Tomaž Urbič; Franc Avbelj; Ken A Dill
Journal:  Acta Chim Slov       Date:  2008-01-26       Impact factor: 1.735

4.  New compstatin variants through two de novo protein design frameworks.

Authors:  M L Bellows; H K Fung; M S Taylor; C A Floudas; A López de Victoria; D Morikis
Journal:  Biophys J       Date:  2010-05-19       Impact factor: 4.033

5.  Protein WISDOM: a workbench for in silico de novo design of biomolecules.

Authors:  James Smadbeck; Meghan B Peterson; George A Khoury; Martin S Taylor; Christodoulos A Floudas
Journal:  J Vis Exp       Date:  2013-07-25       Impact factor: 1.355

6.  An improved hybrid global optimization method for protein tertiary structure prediction.

Authors:  Scott R McAllister; Christodoulos A Floudas
Journal:  Comput Optim Appl       Date:  2010-03-01       Impact factor: 2.167

7.  Selecting high quality protein structures from diverse conformational ensembles.

Authors:  Ashwin Subramani; Peter A DiMaggio; Christodoulos A Floudas
Journal:  Biophys J       Date:  2009-09-16       Impact factor: 4.033

Review 8.  Computational methods for de novo protein design and its applications to the human immunodeficiency virus 1, purine nucleoside phosphorylase, ubiquitin specific protease 7, and histone demethylases.

Authors:  M L Bellows; C A Floudas
Journal:  Curr Drug Targets       Date:  2010-03       Impact factor: 3.465

9.  De novo peptide design with C3a receptor agonist and antagonist activities: theoretical predictions and experimental validation.

Authors:  Meghan L Bellows-Peterson; Ho Ki Fung; Christodoulos A Floudas; Chris A Kieslich; Li Zhang; Dimitrios Morikis; Kathryn J Wareham; Peter N Monk; Owen A Hawksworth; Trent M Woodruff
Journal:  J Med Chem       Date:  2012-04-20       Impact factor: 7.446

10.  ASTRO-FOLD 2.0: an Enhanced Framework for Protein Structure Prediction.

Authors:  A Subramani; Y Wei; C A Floudas
Journal:  AIChE J       Date:  2011-05-31       Impact factor: 3.993

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