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 a broad-spectrum pipeline. PepSAVI-MS is a pipeline for the screening and identification of cationic bioactive peptides from natural product sources. This platform is adaptable to any natural product source of peptides and can test against diverse physiological targets, including bacteria, fungi, viruses, protozoans, and cancer cells for which there is a developed bioassay.
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.
Provides functions for calculating a variety of different molecular properties and amino acid residue-based peptide descriptors. ModlAMP is a Python package to ease the discovery and design of novel synthetic antimicrobial peptides (AMPs) via the amalgamation of sequence generation, descriptor calculation, machine learning, and data analysis in a single programming environment. Furthermore, it enables the in-silico generation of bespoke peptide libraries with desired properties.
Identifies antimicrobial peptides (AMPs). Antilicrobial Peptide Scanner employs a deep neural network (DNN) classifier with convolutional and recurrent layers. It supports high-throughput screening experiments where wet-laboratory researchers want to conduct systematic virtual screenings of peptide libraries. This tool allows the prediction of antibacterial activity against specific species of bacteria.
Determines antibacterial, antiviral and antifungal peptides. iAMPpred aligns the peptide sequences onto numeric feature vectors using combinations of compositional, PHYC and structural (STRL) features. It employs a support vector machine (SVM) to predict antimicrobial peptides (AMPs). This tool can estimate the probabilities with which a candidate peptide sequence can be classified into antiviral, antibacterial and antifungal categories.
A web server that facilitates the prediction and design of anti-biofilm peptides, that offer a promising avenue for the development of new standalone therapeutics or adjuvants acting synergistically with pre-existing antibiotics. dPABBs attempts to develop a prediction strategy for the identification and optimisation of such anti-biofilm peptides, offering a comprehensive platform that allows the user to check both peptides and protein fragments for potential anti-biofilm activity and provides features like simultaneous multi-model predictions and mutant generation.
Provides an antimicrobial peptides (AMP) classification method. AmPEP is based on the distribution descriptors involving the random forest (RF) algorithm. The prediction model is based on the distribution patterns of amino acid properties along the sequence. The feature set is composed of 105 distribution descriptors covering seven physicochemical properties of peptides: hydrophobicity, normalized van der Waals volume, polarity, polarizability, charge, secondary structure, and solvent accessibility.
Assists in leveraging the potential of family-based signatures for identification of novel antimicrobial peptides (AMPs). CAMPSign is a webserver for large-scale identification of AMP belonging to a particular AMP family. It utilizes family-specific signatures captured using patterns and hidden Markov models (HMMs) for identification of peptides belonging to a particular AMP family.
Builds and analyzes the generative potential of amino acid sequences. LSTM_peptides is a set of scripts composed of two main functions: (i) “SequenceHandler” reads the sequences and converts them into a one-hot vector encoding and (ii) “Model” creates and trains the model with additional features for cross-validation, plot training and validation loss. The application can be used in combination with predictive models, evaluating the quality of the generated sequences.
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.
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.
Assists users to perform species-specific antimicrobial peptide prediction in fishes. Antimicrobial Peptide Prediction Server for Fish is an application that can serve for prediction for N-terminus residues, C-terminus residues and full sequences. It simplifies the discovery rate of lead antimicrobial peptides (AMPs) molecules having potential wider applications in diverse area like fish and human health as substitute of: antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and packaged food.
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.
Provides a physicochemical properties calculator of multiple peptides. CALCAMPI is a web application that calculates molar mass, charge, hydrophobicity, Boman index, aliphatic index, isoelectric point and percentage of hydrophobic amino acids. This method can be useful in identifying potential antimicrobial peptides (AMPs) based on their primary structure. This web app is part of the InverPep database.
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