article Open AccessTop 10% cited
In Silico Approach for Predicting Toxicity of Peptides and Proteins
PLoS ONE · 2013 · Vol. 8(9) · pp. e73957–e73957
Sudheer Gupta(Institute of Microbial Technology)Pallavi Kapoor(Institute of Microbial Technology)Kumardeep Chaudhary(Institute of Microbial Technology)Ankur Gautam(Institute of Microbial Technology)Rahul Kumar(Institute of Microbial Technology)Gajendra P. S. Raghava✉(Institute of Microbial Technology)
Abstract
ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).
vaccines and immunoinformatics approachesMachine Learning in BioinformaticsBiochemical and Structural CharacterizationIn silicoPeptideComputational biologyToxicityDipeptideSmall moleculeQuantitative structure–activity relationshipBiologyChemistryBioinformatics
MeSH terms
AnimalsArtificial IntelligenceComputer SimulationHumansModels, MolecularMolecular Sequence DataPeptidesSensitivity and SpecificitySoftwareInternetSequence Analysis, ProteinAmino Acid MotifsDatabases, Protein
Funding
- Council of Scientific and Industrial Research, India
- Open Source Drug Discovery
Citations
1,941
FWCI
5.53
field-weighted impact
References
34
Percentile
97%
vs. same field & year
Citations per year
References
Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network
Proteins Structure Function and Bioinformatics · 2006 · 1,709 citations
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