No abstract

In this chapter, two prediction servers of linear B-cell epiotpes have been described; (i) BcePred, based on physico-chemical properties that include hydrophilicity, flexibility/mobility, accessibility, polarity, exposed surface, turns, and antigenicity and ii) ABCpred, based on recurrent neural network. Both of the servers assist in locating linear epitope regions in a protein.

The machine learning techniques are playing a vital role in the field of immunoinformatics. In the past, a number of methods have been developed for predicting major histocompatibility complex (MHC)-binding peptides using machine learning techniques. These methods allow predicting MHC-binding peptides with high accuracy. In this chapter, we describe two machine learning technique-based methods, nHLAPred and MHC2Pred, developed for predicting MHC binders for class I and class II alleles, respectively. nHLAPred is a web server developed for predicting binders for 67 MHC class I alleles. This sever has two methods: ANNPred and ComPred. ComPred allows predicting binders for 67 MHC class I alleles, using the combined method [artificial neural network (ANN) and quantitative matrix] for 30 alleles and quantitative matrix-based method for 37 alleles. ANNPred allows prediction of binders for only 30 alleles purely based on the ANN. MHC2Pred is a support vector machine (SVM)-based method for prediction of promiscuous binders for 42 MHC class II alleles.

The transporter associated with antigen processing (TAP) plays a crucial role in the transport of the peptide fragments of the proteolysed antigenic or self-altered proteins to the endoplasmic reticulum where the association between these peptides and the major histocompatibility complex (MHC) class I molecules takes place. Therefore, prediction of TAP-binding peptides is highly helpful in identifying the MHC class I-restricted T-cell epitopes and hence in the subunit vaccine designing. In this chapter, we describe a support vector machine (SVM)-based method TAPPred that allows users to predict TAP-binding affinity of peptides over web. The server allows user to predict TAP binders using a simple SVM model or cascade SVM model. The server also allows user to customize the display/output. It is freely available for academicians and noncommercial organization at the address http://www.imtech.res.in/raghava/tappred.

ccPDB (http://crdd.osdd.net/raghava/ccpdb/) is a database of data sets compiled from the literature and Protein Data Bank (PDB). First, we collected and compiled data sets from the literature used for developing bioinformatics methods to annotate the structure and function of proteins. Second, data sets were derived from the latest release of PDB using standard protocols. Third, we developed a powerful module for creating a wide range of customized data sets from the current release of PDB. This is a flexible module that allows users to create data sets using a simple six step procedure. In addition, a number of web services have been integrated in ccPDB, which include submission of jobs on PDB-based servers, annotation of protein structures and generation of patterns. This database maintains >30 types of data sets such as secondary structure, tight-turns, nucleotide interacting residues, metals interacting residues, DNA/RNA binding residues and so on.