TY - JOUR
T1 - MESSA
T2 - MEta-Server for protein Sequence Analysis
AU - Cong, Qian
AU - Grishin, Nick V.
N1 - Funding Information:
The project is supported by National Institutes of Health (GM094575 to NVG), the Citrus Research and Development Foundation (10-018-314 to NVG) and the Welch Foundation (I-1505 to NVG). QC is a Howard Hughes Medical Institute International Student Research fellow. We thank Jeremy Semeiks, Lisa N. Kinch and Bong-Hyun Kim for inspiring discussions and technical support. We are grateful to Janusz M. Bujnicki for helpful suggestions to improve the work.
PY - 2012/10/2
Y1 - 2012/10/2
N2 - Background: Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study. Since reliable prediction is usually based on the consensus of many computer programs, meta-severs have been developed to fit such needs. Most meta-servers focus on one aspect of sequence analysis, while others incorporate more information, such as PredictProtein for local sequence feature predictions, SMART for domain architecture and sequence motif annotation, and GeneSilico for secondary and spatial structure prediction. However, as predictions of local sequence properties, three-dimensional structure and function are usually intertwined, it is beneficial to address them together.Results: We developed a MEta-Server for protein Sequence Analysis (MESSA) to facilitate comprehensive protein sequence analysis and gather structural and functional predictions for a protein of interest. For an input sequence, the server exploits a number of select tools to predict local sequence properties, such as secondary structure, structurally disordered regions, coiled coils, signal peptides and transmembrane helices; detect homologous proteins and assign the query to a protein family; identify three-dimensional structure templates and generate structure models; and provide predictive statements about the protein's function, including functional annotations, Gene Ontology terms, enzyme classification and possible functionally associated proteins. We tested MESSA on the proteome of Candidatus Liberibacter asiaticus. Manual curation shows that three-dimensional structure models generated by MESSA covered around 75% of all the residues in this proteome and the function of 80% of all proteins could be predicted.Availability: MESSA is free for non-commercial use at http://prodata.swmed.edu/MESSA/.
AB - Background: Computational sequence analysis, that is, prediction of local sequence properties, homologs, spatial structure and function from the sequence of a protein, offers an efficient way to obtain needed information about proteins under study. Since reliable prediction is usually based on the consensus of many computer programs, meta-severs have been developed to fit such needs. Most meta-servers focus on one aspect of sequence analysis, while others incorporate more information, such as PredictProtein for local sequence feature predictions, SMART for domain architecture and sequence motif annotation, and GeneSilico for secondary and spatial structure prediction. However, as predictions of local sequence properties, three-dimensional structure and function are usually intertwined, it is beneficial to address them together.Results: We developed a MEta-Server for protein Sequence Analysis (MESSA) to facilitate comprehensive protein sequence analysis and gather structural and functional predictions for a protein of interest. For an input sequence, the server exploits a number of select tools to predict local sequence properties, such as secondary structure, structurally disordered regions, coiled coils, signal peptides and transmembrane helices; detect homologous proteins and assign the query to a protein family; identify three-dimensional structure templates and generate structure models; and provide predictive statements about the protein's function, including functional annotations, Gene Ontology terms, enzyme classification and possible functionally associated proteins. We tested MESSA on the proteome of Candidatus Liberibacter asiaticus. Manual curation shows that three-dimensional structure models generated by MESSA covered around 75% of all the residues in this proteome and the function of 80% of all proteins could be predicted.Availability: MESSA is free for non-commercial use at http://prodata.swmed.edu/MESSA/.
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U2 - 10.1186/1741-7007-10-82
DO - 10.1186/1741-7007-10-82
M3 - Article
C2 - 23031578
AN - SCOPUS:84866790716
SN - 1741-7007
VL - 10
JO - BMC Biology
JF - BMC Biology
M1 - 82
ER -