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SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks

Identifieur interne : 000283 ( PascalFrancis/Curation ); précédent : 000282; suivant : 000284

SPAAN: a software program for prediction of adhesins and adhesin-like proteins using neural networks

Auteurs : Gaurav Sachdeva [Inde] ; Kaushal Kumar [Inde] ; Preti Jain [Inde] ; Srinivasan Ramachandran [Inde]

Source :

RBID : Pascal:05-0173064

Descripteurs français

English descriptors

Abstract

Motivation: The adhesion of microbial pathogens to host cells is mediated by adhesins. Experimental methods used for characterizing adhesins are time-consuming and demand large resources. The availability of specialized software can rapidly aid experimenters in simplifying this problem. We have employed 105 compositional properties and artificial neural networks to develop SPAAN, which predicts the probability of a protein being an adhesin (Pad). Results: SPAAN had optimal sensitivity of 89% and specificity of 100% on a defined test set and could identify 97.4% of known adhesins at high Pad value from a wide range of bacteria. Furthermore, SPAAN facilitated improved annotation of several proteins as adhesins. Novel adhesins were identified in 17 pathogenic organisms causing diseases in humans and plants. In the severe acute respiratory syndrome (SARS) associated human corona virus, the spike glycoprotein and nsps (nsp2, nsp5, nsp6 and nsp7) were identified as having adhesin-like characteristics. These results offer new lead for rapid experimental testing.
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A11 01  1    @1 SACHDEVA (Gaurav)
A11 02  1    @1 KUMAR (Kaushal)
A11 03  1    @1 JAIN (Preti)
A11 04  1    @1 RAMACHANDRAN (Srinivasan)
A14 01      @1 G. N. Ramachandran Knowledge Center for Genome Informatics, Institute of Genomics and Integrative Biology, Mall Road @2 Delhi 110 007 @3 IND @Z 1 aut. @Z 2 aut. @Z 3 aut. @Z 4 aut.
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C01 01    ENG  @0 Motivation: The adhesion of microbial pathogens to host cells is mediated by adhesins. Experimental methods used for characterizing adhesins are time-consuming and demand large resources. The availability of specialized software can rapidly aid experimenters in simplifying this problem. We have employed 105 compositional properties and artificial neural networks to develop SPAAN, which predicts the probability of a protein being an adhesin (Pad). Results: SPAAN had optimal sensitivity of 89% and specificity of 100% on a defined test set and could identify 97.4% of known adhesins at high Pad value from a wide range of bacteria. Furthermore, SPAAN facilitated improved annotation of several proteins as adhesins. Novel adhesins were identified in 17 pathogenic organisms causing diseases in humans and plants. In the severe acute respiratory syndrome (SARS) associated human corona virus, the spike glycoprotein and nsps (nsp2, nsp5, nsp6 and nsp7) were identified as having adhesin-like characteristics. These results offer new lead for rapid experimental testing.
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C03 02  X  SPA  @0 Predicción @5 06
C03 03  X  FRE  @0 Adhésine @5 07
C03 03  X  ENG  @0 Adhesin @5 07
C03 03  X  SPA  @0 Adesina @5 07
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C03 06  X  ENG  @0 Bioinformatics @5 10
C03 06  X  SPA  @0 Bioinformática @5 10
N21       @1 115
N44 01      @1 OTO
N82       @1 OTO

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Pascal:05-0173064

Le document en format XML

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