Finds putative bacteriocin open reading frames (ORFs) in a DNA sequence. BAGEL uses knowledge-based bacteriocin databases and motif databases to make the identification. It combines direct and indirect mining by looking at context genes. This tool integrates RNASeq data, promoter and terminator predictions. It can investigate the sequence of the surrounding region on the genome for genes that might encode proteins involved in biosynthesis, transport, regulation and/or immunity.
Provides the workflow used to obtain whole-genome sequence data of 340 sequence type (ST) 772 Staphylococcus aureus isolates (the Bengal Bay clone). bengal-bay allows users to reproduce core analyses, including parameter settings, cluster resource configurations and versioned software distributions. The workflow implements Anaconda virtual environments, including software distributed in the Bioconda channel and is executable through Snakemake.
A web app to find antimicrobial peptides as new infection therapeutics. MLAMP is developed using ML-SMOTE and grey pseudo amino acid composition. MLAMP obtains 0.4846 subset accuracy and 0.16 hamming loss. This prediction performance is better than iAMP-2L and CAMP. Users can submit a peptide sequence to the webserver and subsequently the webserver returns the predicted result in real time.
Assists in discovering antibacterial peptides. AntiBP is an application that was developed to combat the dreadful antibiotic resistant bacteria. This method assists the researchers in finding and in designing peptides-based antibiotics. This application not includes post-translational modifications and topological aspects. It allows mapping and searching of antibacterial in a protein sequence.
A web application for identifying antimicrobial peptides (AMPs) and their functional types. iAMP-2L is a multi-label classifier based on the pseudo amino acid composition (PseAAC) and fuzzy K-nearest neighbor (FKNN) algorithm, where the components of PseAAC were featured by incorporating five physicochemical properties.
A prediction tool for classification of antimicrobial peptides (AMPs). ClassAMP uses random forests (RFs) and support vector machines (SVMs) to predict the propensity of a protein sequence to have antibacterial, antifungal, or antiviral activity.
Achieves antimicrobial peptide predictions with enhanced reliability based on support vector machine (SVM) Light, showing an accuracy of 90% (polynomial model). CS-AMPPred is based on five sequence descriptors: indexes of (i) a-helix and (ii) loop formation; and averages of (iii) net charge, (iv) hydrophobicity and (v) flexibility. It can be helpful for revealing the antimicrobial activity from multifunctional peptides and for a prediction prior to synthesis of some predicted proteins in protein databases.