Epitope-based vaccine design, Genomes Annotation, Computational Biology.
Group is working in the field of computational biology since 1991. During last 20 years, a large number of methods (computer programs and web servers) have been developed to assist the researchers working in the field of life sciences. These methods can be divided broadly in following categories I) simple calculation programs, ii) epitope-based vaccine design, iii) Annotation of genomes/proteomes, iv) biological interactions of proteins and v) drug targets and inhibitors. Following is brief description of activities at our group. Simple calculation programs:Most of programs developed in initial phase (1991-2000) comes under this category. The aim of these programs was to assist the biologist in their day-to-day activity.
Epitope-based vaccine design: Since 1995, subunit vaccine design has become an integral part of vaccine design in which immunogenic region of protein is used instead of a complete complete as vaccine candidate. Therefore identification of immunologically active regions/epitopes recognized by T/B cells plays a crucial role in subunit vaccine design. Experimental methods for the identification of such regions include overlapping peptide synthesis, random cloning, and display libraries. Though accurate, these methods are both cost and labor-intensive. Therefore computation prediction of such sites based on sequence and structure feature of protein is of great value. In order to overcome some of the problems face by experimental biologist a project “Computer-aided Subunit Vaccine Design based on Epitope” was initiated (http://www.imtech.res.in/raghava/). The aim of this project was to develop better methods for predicting potential vaccine candidates.
Annotation of genomes/proteomes: Presently more than 1000 organisms have been sequenced or are in the advanced stage of sequencing. This has posed a major challenge for bioinformaticians to annotate these genomes for predicting the genes and the repeat regions. The protein sequence databases are also growing exponentially due to progress in sequence techniques. The major problem is functional annotation, as most of the proteins obtained from the genomes do not provide any information about the function of protein. Group is developing in-silico methods for annotating genomes and proteomes.
Genomes Annotation: We are actively working to analyze the data and driving rules for genome annotation[11,13,24,34]. Following are our major contributions. FTG is a method for locating probable protein coding region in prokaryotic genomes using Fast Fourier Transformation (FFT). EGPred is a similarity aided ab Initio method for predicting location and structure of genes in eukaryote genomes. SRF is a program to find spectral repeats using FFT. GWFASTA developed for genome wise sequence similarity search using FASTA.
Functional Annotation of Proteomes: It is difficult to predict function of a protein directly. Thus we developed method for predicting important class of proteins and proteins reside in specific location of a cell. We developed large number of program/web servers for predicting subcellular localization of proteins.
Biological interactions of proteins: Function of a protein depends its interaction with other proteins and ligands. Our group have developed method for predicting DNA binding proteins and DNA interacting regions in proteins. In addition methods have been developed for predicting RNA interacting regions in RNA binding proteins. Development of methods for predicting interaction of protein interaction with other proteins and peptides is in progress.
Drug targets and inhibitors: One of the important challenge for bioinformaticians to discover effective drugs in silico particularly against drug resistant strains of pathogens. In the post genome era, where thousand of genome are already sequenced, major challenge is to identify drug targets/antigens. Group is working last number of years to assist researchers working in the field of drug discovery.
Searching Drug Targets: Group group developed number of novel methods for classifying and predicting receptors (G-protein coupled receptor (GPCR), nuclear receptors), toxins and virulent proteins . These methods are playing and will play an important role in drug development as they allow prediction of important class of proteins like GPCR (more than 50% drugs in market are against GPCR). In addition methods have been developed for predicting secretory proteins like Pseapred for secretory proteins of malaria, SRTpred for non-classical secretory proteins.
Protein Structures Prediction: The prediction of structure of target/protein is one of major challenge in drug development. Group developed methods for predicting secondary structure (regular as well as irregular), super secondary structure (e.g. beta-hairpins, beta-barrels) and tertiary structure (ab initio methods for bioactive peptides).
RNA interference (RNAi): It is a system within living cells that helps to control which genes are active and how active they are. Two types of small RNA molecules – microRNA (miRNA) and small interfering RNA (siRNA) – are central to RNA interference. Our group is working to develop methods for understanding mechanism of RNAi.
Designing Inhibitor: Designing inhibitor against essential genes of unique pathway is major activity of group at present. Number of software for designing inhibitors is in pipeline.
- Kundal, R. and Raghava, G. P. S. (2009) RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information. Proteomics 9:2324-42.
- Saha, S. and Raghava, G. P. S. (2006) Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. PROTEINS: Structure, Function, and Bioinformatics 65:42-9.
- Saha, S. and Raghava, G. P. S. (2006) AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Research 34:W202-9.
- Kumar, M. , Verma, R. and Raghava, G. P. S. (2006) Prediction of mitochondrial proteins using support vector machine and hidden markov model . J. Biol. Chem. 281: 5357 - 5363.
- Bhasin, M., Garg A., and Raghava, G. P. S. (2005) A PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21: 2522-4.