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modlAMP / MOlecular Design Laboratory’s AntiMicrobial Peptides
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.
dPABBs / Design Peptides Against Bacterial Biofilms
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.
LSTM_peptides / Long Short-Term Memory
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.
Antimicrobial Peptide Prediction Server for Fish
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.
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