PRED AHCP

PredAHCP facilitates the prediction of Anti-Hepatitis C Peptides (AHCPs) for designing peptide-based therapeutics against Hepatitis C virus. It employs a Random Forest (RF) model with 92% accuracy to predict AHCPs. The following study can be accessed via https://doi.org/10.1101/2024.05.05.592323. The data and code files can be accessed via Github https://github.com/PredAHCP.

Running Prediction on Peptide Sequences: To classify a list of peptide sequences, paste the sequences in FASTA format in the box below and click "Predict". The feature computations are performed automatically.

Enter Sequence in FASTA Format:

A sequence should start with a greater than character (">"), immediately followed by a brief description on the same line.
The lines that follow represent peptides using single-letter amino acid notation, with each sequence containing between 5 and 36 amino acid residues.

Examples:

> Sample_peptide_1
ADLEVVAATFVLVA

>Sample_peptide_2
DDDEVVAATYVLVA

The sequence length should be between 5 and 36