Periwal N, Arora P, Thakur A, Agrawal L, Goyal Y, Rathore AS, Anand HS, Kaur B, Sood V. Antiprotozoal peptide
prediction using machine learning with effective feature selection techniques. Heliyon. 2024 Aug 13;10(16):e36163.
doi: 10.1016/j.heliyon.2024.e36163. PMID:39247292; PMCID: PMC11380031
Welcome to APPred
Antiprotozoal peptides are the peptides which act against free living
and parasitic protozoa. These peptides have been isolated from a wide range
of organisms and span across a varied group of peptide family. These
peptides act against the target protozoa by inducing cell death via
multiple mechanisms. With the increasing problem of resistance drug,
antiprotozoal peptides can be considered as an alternative therapeutic
approach. Therefore, this tool is designed for prediction of
antiprotozoal peptides.
Some of the features of APPred are as follows:
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Predict : This module allows the user to predict whether the
given peptide(s) have antiprotozoal activity or not.
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Design : This module allows the user to generate all the
possible single mutants of a given peptide and then predict whether
the mutants have antiprotozoal activity or not.
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Protein Scan : This module allows the user to generate all
the possible overlapping peptides of a given protein sequence and
determine whether the generated peptides have antiprotozoal
activity or not.