Literature DB >> 20861025

The binding site distance test score: a robust method for the assessment of predicted protein binding sites.

Daniel B Roche1, Stuart J Tetchner, Liam J McGuffin.   

Abstract

MOTIVATION: We propose a novel method for scoring the accuracy of protein binding site predictions-the Binding-site Distance Test (BDT) score. Recently, the Matthews Correlation Coefficient (MCC) has been used to evaluate binding site predictions, both by developers of new methods and by the assessors for the community-wide prediction experiment-CASP8. While being a rigorous scoring method, the MCC does not take into account the actual 3D location of the predicted residues from the observed binding site. Thus, an incorrectly predicted site that is nevertheless close to the observed binding site will obtain an identical score to the same number of non-binding residues predicted at random. The MCC is somewhat affected by the subjectivity of determining observed binding residues and the ambiguity of choosing distance cutoffs. By contrast the BDT method produces continuous scores ranging between 0 and 1, relating to the distance between the predicted and observed residues. Residues predicted close to the binding site will score higher than those more distant, providing a better reflection of the true accuracy of predictions. The CASP8 function predictions were evaluated using both the MCC and BDT methods and the scores were compared. The BDT was found to strongly correlate with the MCC scores while also being less susceptible to the subjectivity of defining binding residues. We therefore suggest that this new simple score is a potentially more robust method for future evaluations of protein-ligand binding site predictions. AVAILABILITY: http://www.reading.ac.uk/bioinf/downloads/.

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Year:  2010        PMID: 20861025     DOI: 10.1093/bioinformatics/btq543

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  11 in total

1.  Assessment of ligand binding site predictions in CASP10.

Authors:  Tiziano Gallo Cassarino; Lorenza Bordoli; Torsten Schwede
Journal:  Proteins       Date:  2014-02

2.  Assessment of ligand-binding residue predictions in CASP9.

Authors:  Tobias Schmidt; Jürgen Haas; Tiziano Gallo Cassarino; Torsten Schwede
Journal:  Proteins       Date:  2011-10-11

3.  Assessment of ligand binding residue predictions in CASP8.

Authors:  Gonzalo López; Iakes Ezkurdia; Michael L Tress
Journal:  Proteins       Date:  2009

4.  The FunFOLD2 server for the prediction of protein-ligand interactions.

Authors:  Daniel B Roche; Maria T Buenavista; Liam J McGuffin
Journal:  Nucleic Acids Res       Date:  2013-06-12       Impact factor: 16.971

5.  FunFOLD: an improved automated method for the prediction of ligand binding residues using 3D models of proteins.

Authors:  Daniel B Roche; Stuart J Tetchner; Liam J McGuffin
Journal:  BMC Bioinformatics       Date:  2011-05-16       Impact factor: 3.307

6.  The IntFOLD server: an integrated web resource for protein fold recognition, 3D model quality assessment, intrinsic disorder prediction, domain prediction and ligand binding site prediction.

Authors:  Daniel B Roche; Maria T Buenavista; Stuart J Tetchner; Liam J McGuffin
Journal:  Nucleic Acids Res       Date:  2011-03-31       Impact factor: 16.971

7.  FunFOLDQA: a quality assessment tool for protein-ligand binding site residue predictions.

Authors:  Daniel B Roche; Maria T Buenavista; Liam J McGuffin
Journal:  PLoS One       Date:  2012-05-30       Impact factor: 3.240

8.  IntFOLD: an integrated server for modelling protein structures and functions from amino acid sequences.

Authors:  Liam J McGuffin; Jennifer D Atkins; Bajuna R Salehe; Ahmad N Shuid; Daniel B Roche
Journal:  Nucleic Acids Res       Date:  2015-03-27       Impact factor: 16.971

Review 9.  Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

Authors:  Daniel Barry Roche; Danielle Allison Brackenridge; Liam James McGuffin
Journal:  Int J Mol Sci       Date:  2015-12-15       Impact factor: 5.923

10.  Predicting Ligand Binding Sites on Protein Surfaces by 3-Dimensional Probability Density Distributions of Interacting Atoms.

Authors:  Jhih-Wei Jian; Pavadai Elumalai; Thejkiran Pitti; Chih Yuan Wu; Keng-Chang Tsai; Jeng-Yih Chang; Hung-Pin Peng; An-Suei Yang
Journal:  PLoS One       Date:  2016-08-11       Impact factor: 3.240

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