Predicts the presence and location of signal peptide cleavage sites in amino acid sequences from different organisms: Gram-positive prokaryotes, Gram-negative prokaryotes, and eukaryotes. SignalP is a neural network–based method which can discriminate signal peptides from transmembrane regions. The software incorporates a prediction of cleavage sites and a signal peptide/non-signal peptide prediction based on a combination of several artificial neural networks.
A computer-based tool specific to caspase substrate prediction. CaSPredictor predicted successfully 81% (111/137) of the cleavage sites in experimentally verified caspase substrates not annotated in its internal data file. Its accuracy and confidence was estimated as 80% using ROC methodology. It is useful for preliminary screening of protein databanks for putative caspase targets and mapping potential cleavage sites and the strength of interactions in certain proteins experimentally demonstrated to be caspase substrates.
Permits in silico prediction of protease-specific cleavage sites within substrate sequences. PROSPERous employs logistic regression models that integrate various scoring functions based on the local sequence environments of cleavage sites. It allows users to submit and process up to 1 000 sequences per job submission. This tool offers several different types of scoring functions on its online platform.
Identifies signal peptides and cleavage sites in protein sequences. DeepSig is based on sequence labelling method and deep learning methods. Its prediction feature employs a deep neural network architecture and a probabilistic method that integrate the current biological knowledge of the signal peptide structure. This software is available as a web application and as a downloadable standalone version for large-scale prediction.
Assists users to predict protease-specific substrates and their cleavage sites. iProt-Sub is a web application that resolves the problem linked to the identification of probable protease-specific substrates and their detailed cleavage sites from the substrate sequence information. This tool is able to extract a wide range of sequence-derived structural, physicochemical and evolutionary information.
Predicts the calpain substrate cleavage sites from amino acid sequences. LabCaS is based on a decision fusion protocol. It is robust to the extreme imbalance in positive and negative samples in the training dataset. It was tested by employing leave-one-protein-out jack-knife cross-validation which takes one protein sequence out for testing while keeping the remaining protein sequences for training.
Permits to identify the cleavage sites of a query protein sequence by human immunodeficiency virus (HIV)-1 and HIV-2 proteases. HIVcleave accepts as input a protein sequence in which each constituent amino acid is represented by its single-letter code.
A set of computational tools for investigating protease specificity. The main PoPS program allows users to model and profile protease specificity and predict substrate cleavage. Other tools are available to search for substrates within proteomes (protein databases for organisms), and create simple matrix models of specificity from experimental data.
Predicts potential cleavage sites cleaved by proteases or chemicals in a given protein sequence. PeptideCutter returns the query sequence with the possible cleavage sites mapped on it and/or a table of cleavage site positions.
Dr. Yashwanth Subbannayya obtained his M.Sc. degree in Medical Biochemistry from Manipal University. He qualified the competitive CSIR-UGC National Eligibility Test and joined the Institute of Bioinformatics, Bangalore as a UGC Junior Research Fellow. As part of his Ph.D. work, he studied the molecular mechanisms of gastric cancer in clinical specimens using quantitative proteomic technologies. This study, the results of which were published in Cancer Biology and Therapy, yielded a novel therapeutic target for gastric cancer- CAMKK2. Further, he also studied the serum proteome of gastric cancer patients and developed assays for potential markers using the revolutionary multiple reaction monitoring approach. The results of this study were published in Journal of Proteomics. In addition to his research work, he also trained extensively in sample preparation for mass spectrometry, fractionation techniques and gained expertise in quantitative proteomic techniques and data analysis. In addition, he also trained extensively in various validation platforms including immunohistochemsitry, multiple reaction monitoring and Western blot. He has also worked as a curator for several biological databases including NetPath, Human Protein Reference Database (HPRD) and Breast cancer database. His work in various research projects have yielded him 23 publications either as lead author or co-author in peer reviewed journals. He is a reviewer for the journal Proteomics.
Dr. Yashwanth Subbannayya joined the YU-IOB Center for Systems Biology and Molecular Medicine in June, 2015. During the initial period, his job consisted of assisting other personnel of the university in the establishment of YU-IOB Center for Systems Biology and Molecular Medicine. He was also involved in training of Ph.D. students in biological aspects. After the establishment of the center, he trained in cell culture techniques and metabolomics analysis. At YU-IOB CSBMM, he is studying the molecular mechanisms in various cancers including oral cancer. In addition, he is studying the molecular mechanisms as well as the metabolic constituents of traditional medicine formulations using mass spectrometry technologies. In June 2016, he convened the national symposium “Genomics in clinical practice: Future of precision medicine” held at Yenepoya University on June 1 and 2, 2016. The resource persons included 16 individuals from various academic organizations as well as industry. The symposium was attended by 218 participants from 24 institutions around India. He is a member of the Scientific Review Board of Yenepoya Research Centre where he facilitates timely scientific review of research projects.