Literature DB >> 9806937

Protein sidechain conformer prediction: a test of the energy function.

R J Petrella1, T Lazaridis, M Karplus.   

Abstract

BACKGROUND: Homology modeling is an important technique for making use of the rapidly increasing number of protein sequences in the absence of structural information. The major problems in such modeling, once the alignment has been made, concern the positions of loops and the orientations of sidechains. Although progress has been made in recent years for sidechain prediction, current methods appear to have a limit on the order of 70% in their accuracy. It is important to have an understanding of this limitation, which for energy-based methods could arise from inaccuracies of the potential function.
RESULTS: A test of the CHARMM function for sidechain prediction was performed. To eliminate the multiple-residue search problem, the minimum energy positions of individual sidechains in ten proteins were calculated in the presence of all other sidechains in their crystal orientations. This test provides a necessary condition that any energy function useful for sidechain placement must satisfy. For chi1 x chi2 rotations, the accuracies were 77.4% and 89.5%, respectively, and in the presence of crystal waters were 86.5% and 94.9%, respectively. If there was an error, the crystal structure usually corresponded to an alternative local minimum on the calculated energy map. Prediction accuracy correlated with the size of the energy gap between primary and secondary minima.
CONCLUSIONS: The results indicate that the errors in current sidechain prediction schemes cannot be attributed to the potential energy function per se. The test used here establishes a necessary condition that any proposed energy-based sidechain prediction method, as well as many statistically based methods, must satisfy.

Mesh:

Substances:

Year:  1998        PMID: 9806937     DOI: 10.1016/S1359-0278(98)00050-9

Source DB:  PubMed          Journal:  Fold Des        ISSN: 1359-0278


  15 in total

1.  Exploring steric constraints on protein mutations using MAGE/PROBE.

Authors:  J M Word; R C Bateman; B K Presley; S C Lovell; D C Richardson
Journal:  Protein Sci       Date:  2000-11       Impact factor: 6.725

2.  Side-chain modeling with an optimized scoring function.

Authors:  Shide Liang; Nick V Grishin
Journal:  Protein Sci       Date:  2002-02       Impact factor: 6.725

3.  Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library.

Authors:  Ronald W Peterson; P Leslie Dutton; A Joshua Wand
Journal:  Protein Sci       Date:  2004-03       Impact factor: 6.725

4.  Configurational-bias sampling technique for predicting side-chain conformations in proteins.

Authors:  Tushar Jain; David S Cerutti; J Andrew McCammon
Journal:  Protein Sci       Date:  2006-09       Impact factor: 6.725

5.  Modeling mutations in protein structures.

Authors:  Eric Feyfant; Andrej Sali; András Fiser
Journal:  Protein Sci       Date:  2007-09       Impact factor: 6.725

Review 6.  CHARMM: the biomolecular simulation program.

Authors:  B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus
Journal:  J Comput Chem       Date:  2009-07-30       Impact factor: 3.376

7.  Adapting Poisson-Boltzmann to the self-consistent mean field theory: application to protein side-chain modeling.

Authors:  Patrice Koehl; Henri Orland; Marc Delarue
Journal:  J Chem Phys       Date:  2011-08-07       Impact factor: 3.488

8.  Scientific benchmarks for guiding macromolecular energy function improvement.

Authors:  Andrew Leaver-Fay; Matthew J O'Meara; Mike Tyka; Ron Jacak; Yifan Song; Elizabeth H Kellogg; James Thompson; Ian W Davis; Roland A Pache; Sergey Lyskov; Jeffrey J Gray; Tanja Kortemme; Jane S Richardson; James J Havranek; Jack Snoeyink; David Baker; Brian Kuhlman
Journal:  Methods Enzymol       Date:  2013       Impact factor: 1.600

9.  Using quantum mechanics to improve estimates of amino acid side chain rotamer energies.

Authors:  P Douglas Renfrew; Glenn L Butterfoss; Brian Kuhlman
Journal:  Proteins       Date:  2008-06

10.  The human cytomegalovirus UL44 C clamp wraps around DNA.

Authors:  Gloria Komazin-Meredith; Robert J Petrella; Webster L Santos; David J Filman; James M Hogle; Gregory L Verdine; Martin Karplus; Donald M Coen
Journal:  Structure       Date:  2008-08-06       Impact factor: 5.006

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