ArticleBioinformatics (Oxford, England)2015
Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.Journal of cheminformatics · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Exploring neural networks to uncover information-richer features for protein interaction prediction.European biophysics journal : EBJ · 2026Article
- Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.Journal of chemical information and modeling · 2025Article
- Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?Combinatorial chemistry & high throughput screening · 2025Review
- Systematic computational strategies for identifying protein targets and lead discovery.RSC medicinal chemistry · 2024Review
- ProteinMAE: masked autoencoder for protein surface self-supervised learning.Bioinformatics (Oxford, England) · 2023Article
- Electrostatic complementarity at the interface drives transient protein-protein interactions.Scientific reports · 2023Article
- Surface ID: a geometry-aware system for protein molecular surface comparison.Bioinformatics (Oxford, England) · 2023Article
- Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches.Frontiers in molecular biosciences · 2023Article
- Classification of Protein-Binding Sites Using a Spherical Convolutional Neural Network.Journal of chemical information and modeling · 2022Article
- In Silico Methods for Identification of Potential Active Sites of Therapeutic Targets.Molecules (Basel, Switzerland) · 2022Review
- ContactPFP: Protein function prediction using predicted contact information.Frontiers in bioinformatics · 2022Article
- Real-time structure search and structure classification for AlphaFold protein models.Communications biology · 2022Article
- A novel computational strategy for defining the minimal protein molecular surface representation.PloS one · 2022Article
- 2D Zernike polynomial expansion: Finding the protein-protein binding regions.Computational and structural biotechnology journal · 2021Article
- Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning.Nature methods · 2020Article
- Exploring the computational methods for protein-ligand binding site prediction.Computational and structural biotechnology journal · 2020Review
- Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.Journal of computer-aided molecular design · 2019Article
- Three-Dimensional Krawtchouk Descriptors for Protein Local Surface Shape Comparison.Pattern recognition · 2019Article
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3 authors.
Funding
Abstract
motivationLigand 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.
resultsWe 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.