ArticlePloS one2012
Protein surface matching by combining local and global geometric information.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- FP-Zernike: An Open-source Structural Database Construction Toolkit for Fast Structure Retrieval.Genomics, proteomics & bioinformatics · 2024Article
- Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022Review
- Article
- FTIP: an accurate and efficient method for global protein surface comparison.Bioinformatics (Oxford, England) · 2020Article
- Precise parallel volumetric comparison of molecular surfaces and electrostatic isopotentials.Algorithms for molecular biology : AMB · 2020Article
- A Novel Geometry-Based Approach to Infer Protein Interface Similarity.Scientific reports · 2018Article
- eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models.PLoS computational biology · 2014Article
- Implementation of the Hungarian algorithm to account for ligand symmetry and similarity in structure-based design.Journal of chemical information and modeling · 2014Article
- Ligand binding site similarity identification based on chemical and geometric similarity.The protein journal · 2013Article
- Measuring the shapes of macromolecules - and why it matters.Computational and structural biotechnology journal · 2013Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Comparison of the binding sites of proteins is an effective means for predicting protein functions based on their structure information. Despite the importance of this problem and much research in the past, it is still very challenging to predict the binding ligands from the atomic structures of protein binding sites. Here, we designed a new algorithm, TIPSA (Triangulation-based Iterative-closest-point for Protein Surface Alignment), based on the iterative closest point (ICP) algorithm. TIPSA aims to find the maximum number of atoms that can be superposed between two protein binding sites, where any pair of superposed atoms has a distance smaller than a given threshold. The search starts from similar tetrahedra between two binding sites obtained from 3D Delaunay triangulation and uses the Hungarian algorithm to find additional matched atoms. We found that, due to the plasticity of protein binding sites, matching the rigid body of point clouds of protein binding sites is not adequate for satisfactory binding ligand prediction. We further incorporated global geometric information, the radius of gyration of binding site atoms, and used nearest neighbor classification for binding site prediction. Tested on benchmark data, our method achieved a performance comparable to the best methods in the literature, while simultaneously providing the common atom set and atom correspondences.
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Registered trials
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.