ArticleInternational journal of molecular sciences2010
Binding ligand prediction for proteins using partial matching of local surface patches.
Article in International journal of molecular sciences, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.Frontiers in molecular biosciences · 2023Article
- PatchSearch: a web server for off-target protein identification.Nucleic acids research · 2019Article
- A Novel Geometry-Based Approach to Infer Protein Interface Similarity.Scientific reports · 2018Article
- Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.BMC bioinformatics · 2018Article
- Discovery of Nicotinamide Adenine Dinucleotide Binding Proteins in the Escherichia coli Proteome Using a Combined Energetic- and Structural-Bioinformatics-Based Approach.Journal of proteome research · 2017Article
- PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.Journal of chemical information and modeling · 2016Article
- PatchSurfers: Two methods for local molecular property-based binding ligand prediction.Methods (San Diego, Calif.) · 2016Article
- Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.Bioinformatics (Oxford, England) · 2015Article
- The PFP and ESG protein function prediction methods in 2014: effect of database updates and ensemble approaches.GigaScience · 2015Article
- PL-PatchSurfer: a novel molecular local surface-based method for exploring protein-ligand interactions.International journal of molecular sciences · 2014Article
- Mass spectrometry coupled experiments and protein structure modeling methods.International journal of molecular sciences · 2013Review
- Exhaustive comparison and classification of ligand-binding surfaces in proteins.Protein science : a publication of the Protein Society · 2013Article
- In-depth performance evaluation of PFP and ESG sequence-based function prediction methods in CAFA 2011 experiment.BMC bioinformatics · 2013Article
- Unleashing the power of meta-threading for evolution/structure-based function inference of proteins.Frontiers in genetics · 2013Article
- Local functional descriptors for surface comparison based binding prediction.BMC bioinformatics · 2012Article
- Evaluation of function predictions by PFP, ESG,and PSI-BLAST for moonlighting proteins.BMC proceedings · 2012Article
- Recognizing protein-ligand binding sites by global structural alignment and local geometry refinement.Structure (London, England : 1993) · 2012Article
- Structure- and sequence-based function prediction for non-homologous proteins.Journal of structural and functional genomics · 2012Article
- Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.Proteins · 2012Article
- Constructing patch-based ligand-binding pocket database for predicting function of proteins.BMC bioinformatics · 2012Article
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2 authors.
Funding
Abstract
Functional elucidation of uncharacterized protein structures is an important task in bioinformatics. We report our new approach for structure-based function prediction which captures local surface features of ligand binding pockets. Function of proteins, specifically, binding ligands of proteins, can be predicted by finding similar local surface regions of known proteins. To enable partial comparison of binding sites in proteins, a weighted bipartite matching algorithm is used to match pairs of surface patches. The surface patches are encoded with the 3D Zernike descriptors. Unlike the existing methods which compare global characteristics of the protein fold or the global pocket shape, the local surface patch method can find functional similarity between non-homologous proteins and binding pockets for flexible ligand molecules. The proposed method improves prediction results over global pocket shape-based method which was previously developed by our group.
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