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

Identifieur interne : 001009 ( Main/Exploration ); précédent : 001008; suivant : 001010

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 : ISTEX:005424A3BD5FB9BC2210AC03CB15EB4C2855427E

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 (P ad). 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 P ad 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. Availability: SPAAN is freely available through ftp://203.195.151.45 Contact: ramu@igib.res.in

Url:
DOI: 10.1093/bioinformatics/bti028


Affiliations:


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<term>Adhesin</term>
<term>Adhesins, Bacterial (chemistry)</term>
<term>Adhesins, Bacterial (metabolism)</term>
<term>Algorithms</term>
<term>Animals</term>
<term>Bioinformatics</term>
<term>Humans</term>
<term>Models, Biological</term>
<term>Models, Chemical</term>
<term>Models, Statistical</term>
<term>Neural Networks, Computer</term>
<term>Neural network</term>
<term>Prediction</term>
<term>Protein</term>
<term>Protein Isoforms (chemistry)</term>
<term>Protein Isoforms (metabolism)</term>
<term>Sequence Alignment (methods)</term>
<term>Sequence Analysis, Protein (methods)</term>
<term>Sequence Homology, Amino Acid</term>
<term>Software</term>
<term>Structure-Activity Relationship</term>
</keywords>
<keywords scheme="KwdFr" xml:lang="fr">
<term>Adhésines bactériennes ()</term>
<term>Adhésines bactériennes (métabolisme)</term>
<term>Algorithmes</term>
<term>Alignement de séquences ()</term>
<term>Analyse de séquence de protéine ()</term>
<term>Animaux</term>
<term>Humains</term>
<term>Isoformes de protéines ()</term>
<term>Isoformes de protéines (métabolisme)</term>
<term>Logiciel</term>
<term>Modèles biologiques</term>
<term>Modèles chimiques</term>
<term>Modèles statistiques</term>
<term>Relation structure-activité</term>
<term>Similitude de séquences d'acides aminés</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="chemistry" xml:lang="en">
<term>Adhesins, Bacterial</term>
<term>Protein Isoforms</term>
</keywords>
<keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en">
<term>Adhesins, Bacterial</term>
<term>Protein Isoforms</term>
</keywords>
<keywords scheme="MESH" qualifier="methods" xml:lang="en">
<term>Sequence Alignment</term>
<term>Sequence Analysis, Protein</term>
</keywords>
<keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr">
<term>Adhésines bactériennes</term>
<term>Isoformes de protéines</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Adhésine</term>
<term>Bioinformatique</term>
<term>Logiciel</term>
<term>Protéine</term>
<term>Prédiction</term>
<term>Réseau neuronal</term>
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<keywords scheme="Teeft" xml:lang="en">
<term>Adhesin</term>
<term>Adhesins</term>
<term>Algorithms</term>
<term>Amino</term>
<term>Amino acid</term>
<term>Amino acid composition</term>
<term>Amino acid frequencies</term>
<term>Amino acids</term>
<term>Animals</term>
<term>Bacterial adherence</term>
<term>Best epoch</term>
<term>Beta helix motif</term>
<term>Broad range</term>
<term>Charge composition</term>
<term>Clustalw score</term>
<term>Complementary evidence</term>
<term>Compositional</term>
<term>Compositional properties</term>
<term>Dipeptide</term>
<term>Dipeptide frequencies</term>
<term>Experimental characterization</term>
<term>False positives</term>
<term>Fimh</term>
<term>Fimh precursor</term>
<term>Fimh proteins</term>
<term>Further details</term>
<term>General applicability</term>
<term>Genome</term>
<term>Genome scans</term>
<term>Humans</term>
<term>Hydrophobic composition</term>
<term>Hypothetical proteins</term>
<term>Individual modules</term>
<term>Input layer</term>
<term>Input vectors</term>
<term>Models, Biological</term>
<term>Models, Chemical</term>
<term>Models, Statistical</term>
<term>Module</term>
<term>Multiplet frequencies</term>
<term>Natl acad</term>
<term>Neural</term>
<term>Neural Networks, Computer</term>
<term>Neural network</term>
<term>Neural network architecture</term>
<term>Neural networks</term>
<term>Neuron</term>
<term>Novel adhesins</term>
<term>Nucleic acids</term>
<term>Order moment</term>
<term>Other members</term>
<term>Pathogen</term>
<term>Protein</term>
<term>Protein pairs</term>
<term>Protein sequence</term>
<term>Protein sequences</term>
<term>Pseudomonas aeruginosa</term>
<term>Sequence Homology, Amino Acid</term>
<term>Several adhesins</term>
<term>Several proteins</term>
<term>Software</term>
<term>Software program</term>
<term>Spaan</term>
<term>Spike glycoprotein</term>
<term>Structure-Activity Relationship</term>
<term>Synthetic peptide vaccine</term>
<term>Target value</term>
<term>Total number</term>
<term>Uropathogenic escherichia coli</term>
<term>Vaccine</term>
<term>Viral entry</term>
<term>Weight connections</term>
<term>Wide range</term>
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<term>Algorithmes</term>
<term>Alignement de séquences</term>
<term>Analyse de séquence de protéine</term>
<term>Animaux</term>
<term>Humains</term>
<term>Isoformes de protéines</term>
<term>Logiciel</term>
<term>Modèles biologiques</term>
<term>Modèles chimiques</term>
<term>Modèles statistiques</term>
<term>Relation structure-activité</term>
<term>Similitude de séquences d'acides aminés</term>
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<div type="abstract" xml:lang="en">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 (P ad). 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 P ad 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. Availability: SPAAN is freely available through ftp://203.195.151.45 Contact: ramu@igib.res.in</div>
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