3D Protein Retrieval Based on Pocket Modeling and Matching
Date Issued
2005
Date
2005
Author(s)
Yeh, Jeng-Sheng
DOI
en-US
Abstract
ABSTRACT
A framework for matching the partial surface data of three-dimensional (3D) protein structures is proposed. We use this framework to build a retrieval system for 3D structure of proteins. With this system as a filter, suggestions for its functions or corresponding binding drugs can be provided with the known proteins of similar shapes in our database as a front-end filter to reduce the search space for more accurate search by other methods.
The pipeline of our system has three stages: pocket modeling, matching, and refinement. First we extract the possible binding pockets of proteins and model them since the binding pockets are the active sites in protein-protein or protein-ligand interaction. We use the “Sphere Coverage” method to retrieve the binding pockets, that is, we use a virtual sphere to first roll along the solvent-accessible surface, and then if there is more than 50 percent of space filled by atoms of proteins, it suggests that the virtual spheres should be nearby the concave parts of proteins.
Furthermore, after constructing the 3D models of binding pockets, we implemented two algorithms for matching: the multi-view Zernike moments and the spin images. The multi-view Zernike moments can match the global shape by visual similarity in many different viewing directions. The spin images can find local surface features in rotation invariant way with respect to a reference point and its corresponding surface normal. All those two rotation invariant methods can help our matching of 3D protein data. In our experiments, given an unknown 3D protein, by extracting and modeling its possible binding pockets, we can use the above two methods to retrieve similar proteins from the database.
Since our method can match two 3D proteins, their receptors and ligands in a reasonably short time as a preliminary filter, it will benefit biochemists and biologists with very useful information in function prediction, in terms of possible functional sites of unknown proteins or suggestions for drug binding.
First, a web-based 3D protein retrieval system is available for protein structure data including all PDB and FSSP database. In this system, we use a visual-based matching method to compare the protein structure from multiple viewpoints. It takes less than three seconds for each query with 90 percents accuracy on an average.
Secondly, for the more difficult problem of finding possible receptor sites and its corresponding inhibitors, our system has the preliminary results using a 2.4 G Hz Pentium IV PC that (a) within 70 minutes, a query receptor site can be used to retrieve possible proteins that also have similar receptor sites from 107 different proteins. (b) Within 17 minutes, a given receptor site used as a query can retrieve a possible inhibitor/ligand that may fit into this given receptor site, out of 20 possible inhibitors/ligands, where each receptor/ligand pair takes about 50 seconds to compute. The rate of precision for experiment (a) above with a database of 107 candidates is (i) 68% for the top rank retrieved results, and (ii) 95% for top five ranked retrieved results, that is, the correct answer is one of the top five candidates. For experiment (b) above, only case studies are done, and formal experiments need to be conducted yet.
Subjects
電腦圖學
三維模型檢索
蛋白質檢索
蛋白質功能區域
蛋白質功能口袋
生物圖學
多視角 Zernike moments
Spin-images
computer graphics
3D model retrieval
protein retrieval
protein function sites
minimal binding surface
bioinformatics
bio graphics
multi-view Zernike moments
spin-images
Type
thesis
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