Literature DB >> 8740358

Knowledge-based potentials for protein folding: what can we learn from known protein structures?

A Godzik1.   

Abstract

Empirical potentials capture the essence of regularities seen in protein structures and can be used in simulations and predictions of protein structure or function. Derivations of such potentials require comparisons to be made between experimentally derived protein structures and theoretically constructed reference states.

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Year:  1996        PMID： 8740358     DOI： 10.1016/s0969-2126(96)00041-x

Source DB:  PubMed          Journal:  Structure        ISSN： 0969-2126            Impact factor:   5.006


  14 in total

1.  Pair potentials for protein folding: choice of reference states and sensitivity of predicted native states to variations in the interaction schemes.

Authors:  M R Betancourt; D Thirumalai
Journal:  Protein Sci       Date:  1999-02       Impact factor: 6.725

2.  The diterpenoid alkaloid noroxoaconitine is a Mapkap kinase 5 (MK5/PRAK) inhibitor.

Authors:  Sergiy Kostenko; Mahmud Tareq Hassan Khan; Ingebrigt Sylte; Ugo Moens
Journal:  Cell Mol Life Sci       Date:  2010-07-17       Impact factor: 9.261

3.  DARS (Decoys As the Reference State) potentials for protein-protein docking.

Authors:  Gwo-Yu Chuang; Dima Kozakov; Ryan Brenke; Stephen R Comeau; Sandor Vajda
Journal:  Biophys J       Date:  2008-08-01       Impact factor: 4.033

4.  How do potentials derived from structural databases relate to "true" potentials?

Authors:  L Zhang; J Skolnick
Journal:  Protein Sci       Date:  1998-01       Impact factor: 6.725

5.  Derivation and testing of pair potentials for protein folding. When is the quasichemical approximation correct?

Authors:  J Skolnick; L Jaroszewski; A Kolinski; A Godzik
Journal:  Protein Sci       Date:  1997-03       Impact factor: 6.725

6.  Improving predicted protein loop structure ranking using a Pareto-optimality consensus method.

Authors:  Yaohang Li; Ionel Rata; See-wing Chiu; Eric Jakobsson
Journal:  BMC Struct Biol       Date:  2010-07-20

7.  In-silico homology modeling of three isoforms of insect defensins from the dengue vector mosquito, Aedes aegypti (Linn., 1762).

Authors:  K J Dhananjeyan; R Sivaperumal; R Paramasivan; V Thenmozhi; B K Tyagi
Journal:  J Mol Model       Date:  2008-12-16       Impact factor: 1.810

8.  Atomic hydration potentials using a Monte Carlo Reference State (MCRS) for protein solvation modeling.

Authors:  Sergei V Rakhmanov; Vsevolod J Makeev
Journal:  BMC Struct Biol       Date:  2007-03-30

Review 9.  Finding the needle in the haystack: towards solving the protein-folding problem computationally.

Authors:  Bian Li; Michaela Fooksa; Sten Heinze; Jens Meiler
Journal:  Crit Rev Biochem Mol Biol       Date:  2017-10-04       Impact factor: 8.250

10.  Random coil to globular thermal response of a protein (H3.1) with three knowledge-based coarse-grained potentials.

Authors:  Ras B Pandey; Barry L Farmer
Journal:  PLoS One       Date:  2012-11-14       Impact factor: 3.240
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