Curriculam Vitae of Gajendra P.S. Raghava
(1) Name in full:
Raghava, Gajendra Pal Singh
(2) Date of Birth:
25th May 1963
(4) Field of Specialization:
Senior Principal Scientist
Dr G P S Raghava, Head Bioinformatics Centre, Institute of Microbial Technology, Sector-39A, Chandigarh, India
(7) Academic career and professional attainments:
|Ph.D.||IMTECH/Panjab Univ. Chandigarh||1996||--|
|M.Tech.||Indian Institute of Technology, Delhi||1986||7.41 CGPA|
|M.Sc.||Meerut University, Meerut||1984||68.9%|
|B.Sc.||Meerut University, Meerut||1982||66.4%|
(8) National Awards/Recognition
- Fellow of National Academy of Sciences, India
- Fellow of Indian Academy of Sciences, Banglore
- National Bioscience Award for Carrier Development 2006, by DBT
- Shanti Swarup Bhatnagar Award 2008 in Biological Sciences
- Thomson Reuters Research Excellence - India Research Front Awards 2009
- NASI-Reliance Industries Platinum Jubilee Award (2009)
- JC Bose National Fellowship, 2010-15 by Department of Science and Technology, India
- Lakshmipat Singhania-IIM Lucknow National Leadership Awards 2011 (Young Leader in category of Science and Technology)
- One paper listed in top 70 highly cited papers (ranked 18) published by CSIR scientists in last 70 years
(9) Intellectual property etc.:
More than 70 Copyrights on software developed at Raghava’s group.
(10) Major Scientific Contribution:
Raghava has made major contribution field of bioinformatics particularly in the field of computer-aided epitope/peptide based vaccines. His papers have been cited more than 5000 times. One of his methods, Propred (Bioinformatics 2002; 18:196-7) has been cited nearly 400 times.
Potential Drug Targets:
He has developed a number of novel tools for anotating genomes and proteomes for the possible identification of disease associated genes and proteins.
Computer-Aided Vaccine Design:
- Annotation at nucleotide level: He has developed highly accurate methods for predicting genes and spectral repeats in genomes. These methods have been published in the top journals in the field (e.g. Genome Research; Bioinformatics).
- Annotation at protein level: He has described strategies for predicting and classifying the important functions of proteins. These include i) GPCRpred: Prediction of G-protein coupled receptor; ii) NRpred: method for nuclear receptors; iii) PSLpred/ESLpred/HSLpred for subcellular localization of proteins; iv) VICMpred/BTXpred for bacterial toxins and virulent proteins and v) Mitpred for mitochondrial proteins.
- Structure Prediction: He has developed a number of methods for predicting secondary, super-secondary and tertiary structure of proteins. His secondary structure prediction method was ranked within the top 5 methods in the world, according to the community wide competitions like CASP, CAFASP and EVA. Moreover his are the only servers from Asia that participates and compete successfully in most of the international competitions.
Raghava has been working in the field of immunoinformatics for last 10 years in order to understand the immune system with help of computer. The major aim has been to identify potential subunit vaccine candidates.
- Databases of Epitopes: A number of databases have been developed by his group that include; I) MHCBN: consisting 25000 MHC/TAP binders and T-cell epitopes; ii) BCIPEP have 3500 experimentally annotated B-cell epitopes and iii) HaptenDB have Haptens (antigenic but not immunogenic).
- Prediction of helper T-cell Epitopes: In this area he has developed the following major programs: I) Propred: Prediction of promiscuous binders for 51 MHC class II alleles using virtual matrices; ii) HLADR4pred: Prediction of HLA-DR4 binder using a highly accurate method and iii) MOT: Prediction of MHC class II binders using matrix optimization technique (MOT).
- CTL Epitopes: For this he has adopted a comprehensive approach, which includes methods for each component involved in endogenous antigen processing; I) Propred1: Prediction of promiscuous binders for 47 MHC class I alleles using virtual matrices; ii) nHLApred: Highly accurate prediction method for 67 MHC class I alleles; iii) MMBpred: Searching a potential vaccine candidate by introducing mutations at selected positions in the antigenic sequence and iv) TAPpred: prediction of T-cell epitopes (Protein Science, 2004, 13:596-607). He has developed a direct method for predicting CTL epitopes with an accuracy of around 75%. This allows discrimination between the MHC binder T-cell epitopes and the MHC binder non-epitopes (Vaccine, 2004, 22:3195-204).
- B-Cell Epitopes: Raghava used for the first time machine learning based method for predicting B-cell epitope and increase accuracy from 58 to 67%. National Institute of Allergy and Infectious Diseases (NIH, USA) organize a workshop September 7-8, 2006 in Bethesda; and invite Raghava to present his epitope prediction tools and to take part in the discussion on the progress in the field of epitope prediction (J Mol Recognit. 2007, 75-82). Recently method has been developed first time for predicting conformational B-cell epitopes.
Raghava is a highly original and dedicated bioinformatician, who has developed methods that provide extremely useful research-based service to scientific community in the field of life sciences. He has published more than 150 papers in highly reputed journals and these are highly cited by scientific community (total citations ~ 5300)
. In last five years his papers got around 3700 citations. First time his group developed public domain web servers in the field of chemo/pharmaco-informatics. He got Thomson Reuters Research Excellence - India Research Front Awards 2009
because two of his papers ranked in the category of highly cited papers in the world. These papers are regularly used by the biologists for designing epitope based vaccines. Broadly, he made major contribution in following three areas- vaccine-informatics, drug-informatics and genome/proteome annotations. In addition to bioinformatics, his group discovered novel cell penetrating, anti-cancer, anti-bacterial and tumor homing peptides using bioinformatics appropach and validated these peptides using experimental techniques. His group is responsible for creating important primary databases in the field of biology/chemistry. Recently his group developed OSDDlinux
(Operating System for Drug Discovery) a platform for drug discovery that integrate most of open source software used for drug discovery (http://osddlinux.osdd.net/