A web-based prediction server for Anticancer peptides. AntiCP would be helpful in: (i) predicting minimum mutations required for improving anticancer potency; (ii) virtual screening of peptides for discovering novel anticancer peptides, and (iii) scanning natural proteins for identification of anticancer peptides.
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.
Identifies anti-cancer peptides (ACP) from protein sequences at a large scale. ACPred-FL is a high throughput sequence-based predictor that provides two modes: (1) the classification mode f identifying peptide sequences as ACPs or non-ACPs, and (2) the prediction mode providing users with the option of mining potential ACPs from protein sequences. The software can facilitate the characterization of their functional ACPs mechanisms and accelerate their applications in cancer therapy.
A web-server for identifying whether a peptide belongs to anticancer or non-anticancer purely based on its sequence information alone. iACP uses the wrapper-type feature selection technique to seek optimized g-gap dipeptide. The predicted results obtained by iACP via the jackknife test, 5-fold cross-validation test, and independent dataset test have indicated that the new predictor is indeed quite promising, or at the very least, able to play a complimentary role to the existing state-of-the art methods in this area.
Predicts and designs tumor homing peptides (THPs). TumorHPD is a web server that provides facility to predict THPs and allows to design analogues with better tumor homing abilities. The software generates all possible single substitution mutants of original peptide, then predicts whether mutants and original peptide is tumor homing or not and calculates support vector machine (SVM) score for each peptide, and important physicochemical properties (e.g. hydrophobicity, amphipathicity, etc.). Query can be submitted as peptide, protein and in batch mode.
Finds dipeptidyl peptidase 4 inhibitors (DPP4). SVMDLF is a web server for lead prediction, developed from in-silico models and constructed using support vector machine (SVM) methods, that can predict DPP4 inhibitors or non-inhibitors. Users can draw or submit the compound structure of their interest and obtain a as Z-score, which is a normalized value of the SVM decision score. It can be helpful for lead optimization and design of new DPP4 inhibitors.
Allows users to simulate the effect of peptide vaccination in cancer therapy. VaccImm provides a web platform allowing three kinds of analysis: prostate, kidney or a personalized simulation based on user-defined inputs. The application is able to model cancer immunotherapy based on cancer epitope sequences and major histocompatibility complex (MHC) genotypes. It includes a personal workspace for saving the simulations and an exchange forum.
Combines features calculated from the peptide sequence to predict anticancer peptides (ACPs). MLACP is based on machine-learning (ML) algorithms, support vector machine (SVM) and random forest (RF) method. It employs a combination of all composition- and property-based features to proceed. This tool aims to assist researchers working in the field of ACP therapeutics and biomedical research.
An anti-cancer peptide predictor to predict and design anti-cancer peptide effectively and reports the query protein to have apoptotic function or not. ACPP includes three different modes : (i) protein scan with apoptotic domain prediction; (ii) multiple peptide mode; and (iii) peptide mutation mode for prediction and design of anti-cancer peptides. The user friendly interface and comprehensive output make ACPP suitable for researchers in designing anti-cancer peptide.
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