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Citations per year

Number of citations per year for the bioinformatics software tool SSpred
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Tool usage distribution map

This map represents all the scientific publications referring to SSpred per scientific context
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Protocols

SSpred specifications

Information


Unique identifier OMICS_30873
Name SSpred
Interface Web user interface
Restrictions to use None
Input data A sequence.
Input format FASTA
Computer skills Basic
Stability Stable
Maintained Yes

Maintainer


  • person_outline Atmakuri Rao

Publication for SSpred

SSpred citations

 (8)
library_books

Genomic Insight into the Host–Endosymbiont Relationship of Endozoicomonas montiporae CL 33T with its Coral Host

2016
Front Microbiol
PMCID: 4781883
PMID: 27014194
DOI: 10.3389/fmicb.2016.00251

[…] cterial secretome was predicted using EffectiveT3 (set “gram-” for SignalIP, “type III effector prediction with animal set” for classification module and “selective” for cut-off; Arnold et al., ) and SSPred (set to use a Hybrid-II approach; Pundhir and Kumar, ). Coiled-coil prediction and homology modeling were performed using COILS (Lupas et al., ) and SWISS-MODEL (Biasini et al., ), respectively […]

library_books

Bacterial Endosymbiosis in a Chordate Host: Long Term Co Evolution and Conservation of Secondary Metabolism

2013
PLoS One
PMCID: 3851785
PMID: 24324632
DOI: 10.1371/journal.pone.0080822

[…] are thought to influence host gene expression . We found one ankyrin repeat protein (P857_417) in the genome of Ca. X. pacificiensis, which was predicted to be secreted through a type IV pathway with SSPred , suggesting that this protein is a secreted effector in infection.In some insect systems, the host is thought to tightly control the proliferation of intracellular symbionts . Limited division […]

library_books

Predicting beta turns in proteins using support vector machines with fractional polynomials

2013
Proteome Sci
PMCID: 3908855
PMID: 24565438
DOI: 10.1186/1477-5956-11-S1-S5

[…] servation scoring function, and secondary structure predicted with PSIPRED to compute the inputs for prediction of β-turns and γ-turns. Liu et al. combine SVM with PSS information obtained by using E-SSpred, a secondary protein structure prediction method. DEBT predicts β-turns and their types using information from multiple sequence alignments, PSSs, and predicted dihedral angles. Tang et al. con […]

library_books

Predicting β Turns in Protein Using Kernel Logistic Regression

2013
Biomed Res Int
PMCID: 3590576
PMID: 23509793
DOI: 10.1155/2013/870372

[…] ction accuracy Q total = 80.7%, Q predicted = 58.98%, Q observed = 65.25%, sensitivity = 85.34%, and MCC = 0.50. We note that the Q total of our method is 0.2% lower than the Q total of BTNpred and E-SSpred, but because β-turns account for approximately 25% of the globular protein residues, Q total is a poor measure by itself, as it is possible to achieve Q total of 75% by predicting all residues […]

call_split

Predicting Turns in Proteins with a Unified Model

2012
PLoS One
PMCID: 3492357
PMID: 23144872
DOI: 10.1371/journal.pone.0048389
call_split See protocol

[…] For a query, the PSI-BLAST, SPSSMPred , and a shape string predictor were launched simultaneously. Then, five type features, PSSM, SPSSM, predicted secondary structure (PSS), predicted shape string (SSPred), and shape string profile (SSProfile), were obtained. Finally, all these features, which were combined into a vector of 43 elements for each residue in a sequence, were treated as the input in […]

library_books

Biochemical and Bioinformatic Characterization of Type II Metacaspase Protein (TaeMCAII) from Wheat

2012
PMCID: 3881575
PMID: 24415839
DOI: 10.1007/s11105-012-0450-6

[…] rs (Kurowski and Bujnicki ). Secondary structure predictions were obtained using a Metaserver consensus, which is constructed based on 16 secondary structure predictions methods (pssfinder, netsurfp, sspred, sspro4, spine, cdm, psipred, fdm, ssp, jnet, sspal, soprano, sable, prof, nnssp and gor).Fig. 1 […]


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SSpred institution(s)
Division of Statistical Genetics, Indian Agricultural Statistics Research Institute, New Delhi, India; Centre for Agricultural Bioinformatics, Indian Agricultural Statistics Research Institute, New Delhi, India
SSpred funding source(s)
Supported by World Bank funded National Agricultural Innovation Project (NAIP), ICAR Grants NAIP/Comp-4/C4/C-30033/2008-09, 30(68)/2009/Bio Informatics/NAIP/ O&M and IASRI (ICAR).

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