The field of computational biology has witnessed tremendous change in the years gone by. In its initial infancy stage, computation biology was used to solve only smaller biological problems. However, with the advancement in the field, scientists started using computational biological techniques heavily for solving even complex problems like protein modeling. In present era, computational biology is dominated by bioinformatics where managing, analyzing and mining biological data is a major challenge. And one of the major challenges for any computer or bioinformatics professional is to understand need of biologist and develop user-friendly software.
In whole manual we have describe two GPSR packages. This manual has three major sections; first section is written for students working in the field bioinformatics particularly for software developers. This section describes I) commonly used major computational tools, frequently used for developing bioinformatics tools; ii) type of prediction methods and iii) procedure for evaluating of a newly developed method. Second section is written for users who wish to analyze the proteins. In this section, all small programs is described which are commonly used for building major software packages. Third section describes stanalone programs based on our servers/methods, important for users who want to run our methods on whole proteome. I wish all the best for our users.

These programs and the package are free softwares for academic users. Permission to use, copy, and modify any part of this software for educational, research and non-profit purposes is hereby granted but distribution to third-party is prohibited. They are distributed in the hope that they will be useful but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose. If you want to include this software in a commercial product, please contact to Dr. GPS Raghava at raghava@imtech.res.in .

Prediction at Protein Level: These methods are developed to predict overall function of charactestics of proteins. In these methods we used complet protein as input. Following are few examples.
Subcellular level prediction The cellular localization of a protein is one of the most fundamental properties of any protein due to cellular division of labour. The correct prediction of subcellular location can be a major breakthrough for functional prediction, since to perform a function, protein must be located in their native location, such as nucleus or mitochondria or outside the cell in case of secretory proteins. The native subcellular localization of a protein is one of the indicators of protein function.
Class level prediction in which user can predict belonging class of proteins, e.g., DNA binding protein or Non-binding protein.
Family level prediction In this class it is predict the protein family.
Prediction at Residue level in this class predict particular interacting/binding amino acid residues instead of full-length protein sequence.

Cross-validation is a statistical method for validating a predictive model. Subsets of the data are held out, to be used as validating sets, a model is fit to the remaining data (a training set) and used to predict for the validation set. Averaging the quality of the predictions across the validation sets yields an overall measure of prediction accuracy.