http://www.cnr.it/ontology/cnr/individuo/prodotto/ID269857
Identification and analysis of conserved pockets on protein surfaces (Articolo in rivista)
- Type
- Label
- Identification and analysis of conserved pockets on protein surfaces (Articolo in rivista) (literal)
- Anno
- 2013-01-01T00:00:00+01:00 (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
- 10.1186/1471-2105-14-S7-S9 (literal)
- Alternative label
Marco Cammisa1,2, Antonella Correra1,2, Giuseppina Andreotti2, Maria Vittoria Cubellis1,3* (2013)
Identification and analysis of conserved pockets on protein surfaces
in BMC bioinformatics
(literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
- Marco Cammisa1,2, Antonella Correra1,2, Giuseppina Andreotti2, Maria Vittoria Cubellis1,3* (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
- Rivista
- Note
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
- 1Department of Biology, University \"Federico II\", Via Cinthia, 80126, Naples,
Italy. 2Istituto di Chimica Biomolecolare -CNR, Comprensorio Olivetti, 80078,
Pozzuoli, Italy. 3Istituto di Biostrutture e Bioimmagini-CNR, Via
Mezzocannone, 80134, Napoli, Italy. (literal)
- Titolo
- Identification and analysis of conserved pockets on protein surfaces (literal)
- Abstract
- Abstract
Background: The interaction between proteins and ligands occurs at pockets that are often lined by conserved
amino acids. These pockets can represent the targets for low molecular weight drugs. In order to make the
research for new medicines as productive as possible, it is necessary to exploit \"in silico\" techniques, high
throughput and fragment-based screenings that require the identification of druggable pockets on the surface of
proteins, which may or may not correspond to active sites.
Results: We developed a tool to evaluate the conservation of each pocket detected on the protein surface by
CastP. This tool was named DrosteP because it recursively searches for optimal input sequences to be used to
calculate conservation. DrosteP uses a descriptor of statistical significance, Poisson p-value, as a target to optimize
the choice of input sequences. To benchmark DrosteP we used monomeric or homodimer human proteins with
known 3D-structure whose active site had been annotated in UniProt. DrosteP is able to detect the active site with
high accuracy because in 81% of the cases it coincides with the most conserved pocket. Comparing DrosteP with
analogous programs is difficult because the outputs are different. Nonetheless we could assess the efficacy of the
recursive algorithm in the identification of active site pockets by calculating conservation with the same input
sequences used by other programs.
We analyzed the amino-acid composition of conserved pockets identified by DrosteP and we found that it differs
significantly from the amino-acid composition of non conserved pockets.
Conclusions: Several methods for predicting ligand binding sites on protein surfaces, that combine 3D-structure
and evolutionary sequence conservation, have been proposed. Any method relying on conservation mainly
depends on the choice of the input sequences. DrosteP chooses how deeply distant homologs must be collected
to evaluate conservation and thus optimizes the identification of active site pockets. Moreover it recognizes
conserved pockets other than those coinciding with the sites annotated in UniProt that might represent useful
druggable sites. The distinctive amino-acid composition of conserved pockets provides useful hints on the
fundamental principles underlying protein-ligand interaction.
Availability: http://www.icb.cnr.it/project/drosteppy/ (literal)
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- Autore CNR
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