Fast prediction and visualization of protein binding pockets with PASS
PASS (Putative Active Sites with Spheres) is a simple computational tool that uses geometry to characterize regions of buried volume in proteins and to identify positions likely to represent binding sites based upon the size, shape, and burial extent of these volumes. Its utility as a predictive too...
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Published in: | Journal of computer-aided molecular design Vol. 14; no. 4; pp. 383 - 401 |
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Main Authors: | , |
Format: | Journal Article |
Language: | English |
Published: |
Netherlands
Springer Nature B.V
01-05-2000
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Subjects: | |
Online Access: | Get full text |
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Summary: | PASS (Putative Active Sites with Spheres) is a simple computational tool that uses geometry to characterize regions of buried volume in proteins and to identify positions likely to represent binding sites based upon the size, shape, and burial extent of these volumes. Its utility as a predictive tool for binding site identification is tested by predicting known binding sites of proteins in the PDB using both complexed macromolecules and their corresponding apoprotein structures. The results indicate that PASS can serve as a front-end to fast docking. The main utility of PASS lies in the fact that it can analyze a moderate-size protein (approximately 30 kDa) in under 20 s, which makes it suitable for interactive molecular modeling, protein database analysis, and aggressive virtual screening efforts. As a modeling tool, PASS (i) rapidly identifies favorable regions of the protein surface, (ii) simplifies visualization of residues modulating binding in these regions, and (iii) provides a means of directly visualizing buried volume, which is often inferred indirectly from curvature in a surface representation. PASS produces output in the form of standard PDB files, which are suitable for any modeling package, and provides script files to simplify visualization in Cerius2, InsightII, MOE, Quanta, RasMol, and Sybyl. PASS is freely available to all. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0920-654X 1573-4951 |
DOI: | 10.1023/A:1008124202956 |