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Mapping of ligand-b...
Mapping of ligand-binding cavities in proteins
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- Andersson, David C., 1978- (author)
- Umeå universitet,Kemiska institutionen,Computational Life Science Cluster (CLiC), KBC
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Chen, Brian Y (author)
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- Linusson Jonsson, Anna, 1970- (author)
- Umeå universitet,Kemiska institutionen,Computational Life Science Cluster (CLiC), KBC
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(creator_code:org_t)
- 2009-11-12
- 2010
- English.
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In: Proteins. - : John Wiley & Sons, Inc. - 0887-3585 .- 1097-0134. ; 78:6, s. 1408-1422
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Abstract
Subject headings
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- The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs.
Subject headings
- NATURVETENSKAP -- Kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences (hsv//eng)
Keyword
- protein cavity comparison
- physicochemical properties
- alignment independent
- SCREEN
- principal component analysis
- binding sites
- medicinal chemistry
- drug design
- PCA clustering tree
- bioinformatics
Publication and Content Type
- ref (subject category)
- art (subject category)
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