Dataset Information

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

ABSTRACT:

Motivation

Ligand binding is a key aspect of the function of many proteins. Thus, binding ligand prediction provides important insight in understanding the biological function of proteins. Binding ligand prediction is also useful for drug design and examining potential drug side effects.

Results

We present a computational method named Patch-Surfer2.0, which predicts binding ligands for a protein pocket. By representing and comparing pockets at the level of small local surface patches that characterize physicochemical properties of the local regions, the method can identify binding pockets of the same ligand even if they do not share globally similar shapes. Properties of local patches are represented by an efficient mathematical representation, 3D Zernike Descriptor. Patch-Surfer2.0 has significant technical improvements over our previous prototype, which includes a new feature that captures approximate patch position with a geodesic distance histogram. Moreover, we constructed a large comprehensive database of ligand binding pockets that will be searched against by a query. The benchmark shows better performance of Patch-Surfer2.0 over existing methods.

Availability and implementation

http://kiharalab.org/patchsurfer2.0/ CONTACT: dkihara@purdue.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Zhu X

PROVIDER: S-EPMC4341070 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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Publications

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

Zhu Xiaolei X Xiong Yi Y Kihara Daisuke D

Bioinformatics (Oxford, England) 20141029 5

<h4>Motivation</h4>Ligand binding is a key aspect of the function of many proteins. Thus, binding ligand prediction provides important insight in understanding the biological function of proteins. Binding ligand prediction is also useful for drug design and examining potential drug side effects.<h4>Results</h4>We present a computational method named Patch-Surfer2.0, which predicts binding ligands for a protein pocket. By representing and comparing pockets at the level of small local surface patc ...[more]

PMID: 25359888

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Project description:BACKGROUND: The accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9. RESULTS: The FunFOLD method shows a significant improvement over the best available servers and is shown to be competitive with the top manual prediction groups that were tested at CASP8. The FunFOLD method is also competitive with both the top server and manual methods tested at CASP9. When tested using common subsets of targets, the predictions from FunFOLD are shown to achieve a significantly higher mean Matthews Correlation Coefficient (MCC) scores and Binding-site Distance Test (BDT) scores than all server methods that were tested at CASP8. Testing on the CASP9 set showed no statistically significant separation in performance between FunFOLD and the other top server groups tested. CONCLUSIONS: The FunFOLD software is freely available as both a standalone package and a prediction server, providing competitive ligand binding site residue predictions for expert and non-expert users alike. The software provides a new fully automated approach for structure based function prediction using 3D models of proteins.

Dataset Information

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

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