Presaging critical residues in protein interfaces-web server (PCRPi-W): a web server to chart hot spots in protein interfaces

It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called hot spot, contributes the most to the total binding energy, and thus its identification is an important and relevant question that has clear applications in drug discovery and prote...

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Bibliographic Details
Published in:PloS one 2010-08, Vol.5 (8), p.e12352
Main Authors: Segura Mora, Joan, Assi, Salam A, Fernandez-Fuentes, Narcis
Format: Article
Language:English
Subjects:
Acids
Bayesian analysis
Biochemistry/Bioinformatics
Biochemistry/Drug Discovery
Bioinformatics
Computation
Computational Biology
Computational Biology/Macromolecular Structure Analysis
Computer applications
Computer Graphics
Downloading
Drug development
Drug discovery
Energy (Physics)
Genomes
Hot spots
Humans
Identification
Interfaces
Internet
Mutation
Protein Binding
Protein interaction
Protein structure
Protein-protein interactions
Proteins
Proteins - chemistry
Proteins - metabolism
Residues
Servers
Servers (Computers)
Signal transduction
Software
User interfaces
User-Computer Interface
Websites
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Summary:It is well established that only a portion of residues that mediate protein-protein interactions (PPIs), the so-called hot spot, contributes the most to the total binding energy, and thus its identification is an important and relevant question that has clear applications in drug discovery and protein design. The experimental identification of hot spots is however a lengthy and costly process, and thus there is an interest in computational tools that can complement and guide experimental efforts. Here, we present Presaging Critical Residues in Protein interfaces-Web server (http://www.bioinsilico.org/PCRPi), a web server that implements a recently described and highly accurate computational tool designed to predict critical residues in protein interfaces: PCRPi. PRCPi depends on the integration of structural, energetic, and evolutionary-based measures by using Bayesian Networks (BNs). PCRPi-W has been designed to provide an easy and convenient access to the broad scientific community. Predictions are readily available for download or presented in a web page that includes among other information links to relevant files, sequence information, and a Jmol applet to visualize and analyze the predictions in the context of the protein structure.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0012352