Literature DB >> 16006526

Prediction of water and metal binding sites and their affinities by using the Fold-X force field.

Joost W H Schymkowitz1, Frederic Rousseau, Ivo C Martins, Jesper Ferkinghoff-Borg, Francois Stricher, Luis Serrano.   

Abstract

The empirical force field Fold-X was developed previously to allow rapid free energy calculations in proteins. Here, we present an enhanced version of the force field allowing prediction of the position of structural water molecules and metal ions, together called single atom ligands. Fold-X picks up 76% of water molecules found to interact with two or more polar atoms of proteins in high-resolution crystal structures and predicts their position to within 0.8 A on average. The prediction of metal ion-binding sites have success rates between 90% and 97% depending on the metal, with an overall standard deviation on the position of binding of 0.3-0.6 A. The following metals were included in the force field: Mg2+, Ca2+, Zn2+, Mn2+, and Cu2+. As a result, the current version of Fold-X can accurately decorate a protein structure with biologically important ions and water molecules. Additionally, the free energy of binding of Ca2+ and Zn2+ (i.e., the natural logarithm of the dissociation constant) and its dependence on ionic strength correlate reasonably well with the experimental data available in the literature, allowing one to discriminate between high- and low-affinity binding sites. Importantly, the accuracy of the energy prediction presented here is sufficient to efficiently discriminate between Mg2+, Ca2+, and Zn2+ binding.

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Year:  2005        PMID: 16006526      PMCID: PMC1177371          DOI: 10.1073/pnas.0501980102

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  40 in total

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9.  Structure of native and apo carbonic anhydrase II and structure of some of its anion-ligand complexes.

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  147 in total

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4.  Structural Modeling of GR Interactions with the SWI/SNF Chromatin Remodeling Complex and C/EBP.

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5.  Anthrax lethal factor protease inhibitors: synthesis, SAR, and structure-based 3D QSAR studies.

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Journal:  J Med Chem       Date:  2006-01-12       Impact factor: 7.446

6.  FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.

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7.  Robust recognition of zinc binding sites in proteins.

Authors:  Jessica C Ebert; Russ B Altman
Journal:  Protein Sci       Date:  2007-11-27       Impact factor: 6.725

Review 8.  Challenges in the computational design of proteins.

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9.  Modeling protein-peptide recognition based on classical quantitative structure-affinity relationship approach: implication for proteome-wide inference of peptide-mediated interactions.

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Journal:  Protein J       Date:  2013-10       Impact factor: 2.371

10.  Prediction of structures of zinc-binding proteins through explicit modeling of metal coordination geometry.

Authors:  Chu Wang; Robert Vernon; Oliver Lange; Michael Tyka; David Baker
Journal:  Protein Sci       Date:  2010-03       Impact factor: 6.725

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