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ACPred-FL / Anti-Cancer peptide Predictor with Feature Learning
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.
iACP / Anticancer Peptides
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.
SVMDLF / SVM-based DPP4 Lead Finder
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.
ACPP / Anti-cancer peptide predictor
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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