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Research paper of Dr. Raghava's Group


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2022 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 Before 2002

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Papers of Year-2022 


  1. Arora, C., Kaur, D., & Raghava, G. P. S. (2022). Universal and cross-cancer prognostic biomarkers for predicting survival risk of cancer patients from expression profile of apoptotic pathway genes. Proteomics, 22(3), e2000311. https://doi.org/10.1002/pmic.202000311
  2. Dhall, A., Jain, S., Sharma, N., Naorem, L. D., Kaur, D., Patiyal, S., & Raghava, G. P. S. (2022). In silico tools and databases for designing cancer immunotherapy. Advances in Protein Chemistry and Structural Biology, 129, 1–50. https://doi.org/10.1016/bs.apcsb.2021.11.008
  3. Dhall, A., Patiyal, S., & Raghava, G. P. S. (2022). HLAncPred: A method for predicting promiscuous non-classical HLA binding sites (preprint). Briefings in Bioinformatics.
  4. Gupta, S., Sharma, N., Naorem, L. D., Jain, S., & Raghava, G. P. S. (2022). Collection, compilation and analysis of bacterial vaccines. Computers in Biology and Medicine, 149, 106030. https://doi.org/10.1016/j.compbiomed.2022.106030
  5. Jain, S., Dhall, A., Patiyal, S., & Raghava, G. P. S. (2022). IL13Pred: A method for predicting immunoregulatory cytokine IL-13 inducing peptides. Computers in Biology and Medicine, 143, 105297.
  6. Kumar, V., & Raghava, G. P. S. (2022). In Silico Design of Chemically Modified Cell-Penetrating Peptides. Methods in Molecular Biology (Clifton, N.J.), 2383, 63–71. https://doi.org/10.1007/978-1-0716-1752-6_4
  7. Pande, A., Patiyal, S., Lathwal, A., Arora, C., Kaur, D., Dhall, A., Raghava, G. P. S. (2022). Pfeature: A Tool for Computing Wide Range of Protein Features and Building Prediction Models. Journal of Computational Biology : A Journal of Computational Molecular Cell Biology. https://doi.org/10.1089/cmb.2022.0241
  8. Patiyal, S., Dhall, A., & Raghava, G. P. S. (2022). A deep learning-based method for the prediction of DNA interacting residues in a protein. Briefings in Bioinformatics, 23(5). https://doi.org/10.1093/bib/bbac322
  9. Patiyal, S., Dhall, A., & Raghava, G. P. S. (2022). Prediction of risk-associated genes and high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques. Biology Methods and Protocols.
  10. Sharma, N., Naorem, L. D., Jain, S., & Raghava, G. P. S. (2022). ToxinPred2: an improved method for predicting toxicity of proteins. Briefings in Bioinformatics, 23(5). https://doi.org/10.1093/bib/bbac174

Papers of Year-2021 


  1. Agrawal, P., Bhagat, D., Mahalwal, M., Sharma, N., & Raghava, G. P. S. (2021). AntiCP 2.0: an updated model for predicting anticancer peptides. Briefings in Bioinformatics, 22(3). https://doi.org/10.1093/bib/bbaa153
  2. Arora, C., Kaur, D., Naorem, L. D., & Raghava, G. P. S. (2021). Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway. PloS One, 16(11), e0259534. https://doi.org/10.1371/journal.pone.0259534
  3. Chaudhary, A., Bhalla, S., Patiyal, S., Raghava, G. P. S., & Sahni, G. (2021). FermFooDb: A database of bioactive peptides derived from fermented foods. Heliyon, 7(4), e06668. https://doi.org/https://doi.org/10.1016/j.heliyon.2021.e06668
  4. Dhall, A., Patiyal, S., Sharma, N., Devi, N. L., & Raghava, G. P. S. (2021). Computer-aided prediction of inhibitors against STAT3 for managing COVID-19 associated cytokine storm. Computers in Biology and Medicine, 137(October 2021), 104780.
  5. Dhall, A., Patiyal, S., Sharma, N., Usmani, S. S., & Raghava, G. P. S. (2021). Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19. Briefings in Bioinformatics, 22(2), 936–945. https://doi.org/10.1093/bib/bbaa259
  6. Dwivedi, V. D., Arya, A., Yadav, P., Kumar, R., Kumar, V., & Raghava, G. P. S. (2021). DenvInD: dengue virus inhibitors database for clinical and molecular research. Briefings in Bioinformatics, 22(3). https://doi.org/10.1093/bib/bbaa098
  7. Gabor, A., Tognetti, M., Driessen, A., Tanevski, J., Guo, B., Cao, W., … Saez-Rodriguez, J. (2021). Cell-to-cell and type-to-type heterogeneity of signaling networks: insights from the crowd. Molecular Systems Biology, 17(10), e10402. https://doi.org/10.15252/msb.202110402
  8. Kaur, D., Arora, C., & Raghava, G. P. S. (2021). Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer. Molecular Diagnosis & Therapy, 25(5), 629–646. https://doi.org/10.1007/s40291-021-00539-1
  9. Kaur, D., Patiyal, S., Arora, C., Singh, R., Lodhi, G., & Raghava, G. P. S. (2021). In silico tool for predicting, scanning and designing defensins. Frontiers in Immunology, 4817.
  10. Kaur, H., Kumar, R., Lathwal, A., & Raghava, G. P. S. (2021). Computational resources for identification of cancer biomarkers from omics data. Briefings in Functional Genomics, 20(4), 213–222. https://doi.org/10.1093/bfgp/elab021
  11. Kumar, V., Patiyal, S., Dhall, A., Sharma, N., & Raghava, G. P. (2021). B3Pred: A Random-Forest-Based Method for Predicting and Designing Blood–Brain Barrier Penetrating Peptides. Pharmaceutics . https://doi.org/10.3390/pharmaceutics13081237
  12. Kumar, V., Patiyal, S., Kumar, R., Sahai, S., Kaur, D., Lathwal, A., & Raghava, G. P. S. (2021). B3Pdb: an archive of blood–brain barrier-penetrating peptides. Brain Structure and Function, 226(8), 2489–2495.
  13. Lathwal, A., Kumar, R., & Raghava, G. P. S. (2021). In-silico identification of subunit vaccine candidates against lung cancer-associated oncogenic viruses. Computers in Biology and Medicine, 130, 104215. https://doi.org/10.1016/j.compbiomed.2021.104215
  14. Maryam, L., Usmani, S. S., & Raghava, G. P. S. (2021). Computational resources in the management of antibiotic resistance: Speeding up drug discovery. Drug Discovery Today. https://doi.org/10.1016/j.drudis.2021.04.016
  15. Mathur, D., Kaur, H., Dhall, A., Sharma, N., & Raghava, G. P. S. (2021). SAPdb: A database of short peptides and the corresponding nanostructures formed by self-assembly. Computers in Biology and Medicine, 133, 104391. https://doi.org/10.1016/j.compbiomed.2021.104391
  16. Sapra, D., Kaur, H., Dhall, A., & Raghava, G. P. S. (2021). ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer. Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, 28(12), 1248–1257. https://doi.org/10.1089/cmb.2021.0348
  17. Sharma, N., Patiyal, S., Dhall, A., Devi, N. L., & Raghava, G. P. S. (2021). ChAlPred: A web server for prediction of allergenicity of chemical compounds. Computers in Biology and Medicine, 136, 104746. https://doi.org/10.1016/j.compbiomed.2021.104746
  18. Sharma, N., Patiyal, S., Dhall, A., Pande, A., Arora, C., & Raghava, G. P. S. (2021). AlgPred 2.0: an improved method for predicting allergenic proteins and mapping of IgE epitopes. Briefings in Bioinformatics, 22(4), bbaa294.
  19. Sieberts, S. K., Schaff, J., Duda, M., Pataki, B. A., Sun, M., Snyder, P., Omberg, L. (2021). Crowdsourcing digital health measures to predict Parkinson’s disease severity: the Parkinson’s Disease Digital Biomarker DREAM Challenge. NPJ Digital Medicine, 4(1), 53. https://doi.org/10.1038/s41746-021-00414-7

Papers of Year-2020 


  1. Arora, C., Kaur, D., Lathwal, A., & Raghava, G. P. S. (2020). Risk prediction in cutaneous melanoma patients from their clinico- pathological features: superiority of clinical data over gene expression data. Heliyon, 6(8), e04811. https://doi.org/10.1016/j.heliyon.2020.e04811
  2. Bhalla, S., Kaur, H., Kaur, R., Sharma, S., & Raghava, G. P. S. (2020). Expression based biomarkers and models to classify early and late-stage samples of Papillary Thyroid Carcinoma. PloS One, 15(4), e0231629. https://doi.org/10.1371/journal.pone.0231629
  3. Dhall, A., Patiyal, S., Kaur, H., Bhalla, S., Arora, C., & Raghava, G. P. S. (2020). Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics. Frontiers in Genetics, 11. https://doi.org/10.3389/fgene.2020.00221
  4. Kaur, D., Arora, C., & Raghava, G. P. S. (2020). A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information. Frontiers in Immunology, 11. https://doi.org/10.3389/fimmu.2020.00071
  5. Kaur, H., Bhalla, S., Kaur, D., & Raghava, G. P. (2020). CancerLivER: a database of liver cancer gene expression resources and biomarkers. Database : The Journal of Biological Databases and Curation, 2020. https://doi.org/10.1093/database/baaa012
  6. Kumar, R., Lathwal, A., Kumar, V., Patiyal, S., Raghav, P. K., & Raghava, G. P. S. (2020). CancerEnD: A database of cancer associated enhancers. Genomics, 112(5), 3696–3702.
  7. Kumar, V., Kumar, R., Agrawal, P., Patiyal, S., & Raghava, G. P. S. (2020). A Method for Predicting Hemolytic Potency of Chemically Modified Peptides From Its Structure. Frontiers in Pharmacology, 11, 54. https://doi.org/10.3389/fphar.2020.00054
  8. Lathwal, A., Kumar, R., Arora, C., & Raghava, G. P. S. (2020). Identification of prognostic biomarkers for major subtypes of non-small- cell lung cancer using genomic and clinical data. Journal of Cancer Research and Clinical Oncology, 146(11), 2743–2752. https://doi.org/10.1007/s00432-020-03318-3
  9. Lathwal, A., Kumar, R., & Raghava, G. P. S. (2020). Computer-aided designing of oncolytic viruses for overcoming translational challenges of cancer immunotherapy. Drug Discovery Today, 25(7), 1198–1205. https://doi.org/10.1016/j.drudis.2020.04.008
  10. Lathwal, A., Kumar, R., & Raghava, G. P. S. (2020). OvirusTdb: A database of oncolytic viruses for the advancement of therapeutics in cancer. Virology, 548, 109–116. https://doi.org/10.1016/j.virol.2020.05.016
  11. Patiyal, S., Agrawal, P., Kumar, V., Dhall, A., Kumar, R., Mishra, G., & Raghava, G. P. S. (2020). NAGbinder: An approach for identifying N-acetylglucosamine interacting residues of a protein from its primary sequence. Protein Science, 29(1), 201–210. https://doi.org/10.1002/pro.3761
  12. Patiyal, S., Kaur, D., Kaur, H., Sharma, N., Dhall, A., Sahai, S., Raghava, G. P. S. (2020). A Web-Based Platform on Coronavirus Disease-19 to Maintain Predicted Diagnostic, Drug, and Vaccine Candidates. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy, 39(6), 204–216.
  13. Usmani, S. S., & Raghava, G. P. S. (2020). Potential Challenges for Coronavirus (SARS-CoV-2) Vaccines Under Trial. Frontiers in Immunology, 11, 2562. https://doi.org/10.3389/fimmu.2020.561851

Papers of Year-2019 


  1. Akhter, S., Kaur, H., Agrawal, P., & Raghava, G. P. S. (2019). RareLSD: a manually curated database of lysosomal enzymes associated with rare diseases. Database : The Journal of Biological Databases and Curation, 2019. https://doi.org/10.1093/database/baz112
  2. Agrawal, P., Kumar, S., Singh, A., Raghava, G. P. S., & Singh, I. K. (2019). NeuroPIpred: a tool to predict, design and scan insect neuropeptides. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-41538-x
  3. Agrawal, P., Mishra, G., & Raghava, G. P. S. (2019). SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence, 625806. https://doi.org/10.1101/625806
  4. Agrawal, P., Patiyal, S., Kumar, R., Kumar, V., Singh, H., Raghav, P. K., & Raghava, G. P. S. (2019). ccPDB 2.0: an updated version of datasets created and compiled from Protein Data Bank. Database, 2019.
  5. Agrawal, P., Singh, H., Srivastava, H. K., Singh, S., Kishore, G., & Raghava, G. P. S. (2019). Benchmarking of different molecular docking methods for protein-peptide docking. BMC Bioinformatics, 19(Suppl 13), 426. https://doi.org/10.1186/s12859-018-2449-y
  6. Ahmad, S., Gromiha, M. M., Raghava, G. P. S., Schonbach, C., & Ranganathan, S. (2019). APBioNet’s annual International Conference on Bioinformatics (InCoB) returns to India in 2018. BMC Genomics, 19(Suppl 9), 266. https://doi.org/10.1186/s12864-019-5582-8
  7. Bhalla, S., Kaur, H., Dhall, A., & Raghava, G. P. S. (2019). Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients. Scientific Reports, 9(1), 15790. https://doi.org/10.1038/s41598-019-52134-4
  8. Brown, P., & Zhou, Y. (2019). Large expert-curated database for benchmarking document similarity detection in biomedical literature search. Database : The Journal of Biological Databases and Curation, 2019. https://doi.org/10.1093/database/baz085
  9. Kaur, D., Patiyal, S., Sharma, N., Usmani, S. S., & Raghava, G. P. S. (2019). PRRDB 2.0: a comprehensive database of pattern-recognition receptors and their ligands. Database, 2019.
  10. Kaur, H., Bhalla, S., & Raghava, G. P. S. (2019). Classification of early and late stage liver hepatocellular carcinoma patients from their genomics and epigenomics profiles. PloS One, 14(9), e0221476. https://doi.org/10.1371/journal.pone.0221476
  11. Kaur, H., Dhall, A., Kumar, R., & Raghava, G. P. S. (2019). Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma Using Large-Scale Transcriptomics Data. Frontiers in Genetics, 10, 1306. https://doi.org/10.3389/fgene.2019.01306
  12. Kumar, R., Nagpal, G., Kumar, V., Usmani, S. S., Agrawal, P., & Raghava, G. P. S. (2019). HumCFS: a database of fragile sites in human chromosomes. BMC Genomics, 19(Suppl 9), 985. https://doi.org/10.1186/s12864-018-5330-5
  13. Kumar, R., Patiyal, S., Kumar, V., Nagpal, G., & Raghava, G. P. S. (2019). In silico analysis of gene expression change associated with copy number of enhancers in pancreatic adenocarcinoma. International Journal of Molecular Sciences, 20(14), 3582.
  14. Lathwal, A., Arora, C., & Raghava, G. P. S. (2019). Prediction of risk scores for colorectal cancer patients from the concentration of proteins involved in mitochondrial apoptotic pathway. PloS One, 14(9), e0217527. https://doi.org/10.1371/journal.pone.0217527
  15. Raghav, P. K., Kumar, R., Kumar, V., & Raghava, G. P. S. (2019). Docking-based approach for identification of mutations that disrupt binding between Bcl-2 and Bax proteins: Inducing apoptosis in cancer cells. Molecular Genetics & Genomic Medicine, 7(11), e910. https://doi.org/10.1002/mgg3.910
  16. Usmani, S. S., Agrawal, P., Sehgal, M., Patel, P. K., & Raghava, G. P. S. (2019). ImmunoSPdb: an archive of immunosuppressive peptides. Database : The Journal of Biological Databases and Curation, 2019. https://doi.org/10.1093/database/baz012

Papers of Year-2018 


  1. Agrawal, P., Bhalla, S., Chaudhary, K., Kumar, R., Sharma, M., & Raghava, G. P. S. (2018). In Silico Approach for Prediction of Antifungal Peptides. Frontiers in Microbiology, 9, 323. https://doi.org/10.3389/fmicb.2018.00323
  2. Agrawal, P., Raghav, P. K., Bhalla, S., Sharma, N., & Raghava, G. P. S. (2018). Overview of Free Software Developed for Designing Drugs Based on Protein-Small Molecules Interaction. Current Topics in Medicinal Chemistry, 18(13), 1146–1167. https://doi.org/10.2174/1568026618666180816155131
  3. Agrawal, P., & Raghava, G. P. S. (2018). Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure. Frontiers in Microbiology, 9, 2551. https://doi.org/10.3389/fmicb.2018.02551
  4. Kumar, V., Agrawal, P., Kumar, R., Bhalla, S., Usmani, S. S., Varshney, G. C., & Raghava, G. P. S. (2018). Prediction of cell-penetrating potential of modified peptides containing natural and chemically modified residues. Frontiers in Microbiology, 9(APR). https://doi.org/10.3389/fmicb.2018.00725
  5. Mathur, D., Mehta, A., Firmal, P., Bedi, G., Sood, C., Gautam, A., & Raghava, G. P. S. (2018). TopicalPdb: A database of topically delivered peptides. PloS One, 13(2), e0190134. https://doi.org/10.1371/journal.pone.0190134
  6. Mathur, D., Singh, S., Mehta, A., Agrawal, P., & Raghava, G. P. S. (2018). In silico approaches for predicting the half-life of natural and modified peptides in blood. PloS One, 13(6), e0196829. https://doi.org/10.1371/journal.pone.0196829
  7. Nagpal, G., Chaudhary, K., Agrawal, P., & Raghava, G. P. S. (2018). Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants. Journal of Translational Medicine, 16(1), 181. https://doi.org/10.1186/s12967-018-1560-1
  8. Nagpal, G., Usmani, S. S., & Raghava, G. P. S. (2018). A web resource for designing subunit vaccine against major pathogenic species of bacteria. Frontiers in Immunology, 9(OCT). https://doi.org/10.3389/fimmu.2018.02280
  9. Usmani, S. S., Bhalla, S., & Raghava, G. P. S. (2018). Prediction of Antitubercular Peptides From Sequence Information Using Ensemble Classifier and Hybrid Features. Frontiers in Pharmacology, 9, 954. https://doi.org/10.3389/fphar.2018.00954
  10. Usmani, S. S., Kumar, R., Bhalla, S., Kumar, V., & Raghava, G. P. S. (2018). In Silico Tools and Databases for Designing Peptide-Based Vaccine and Drugs. Advances in Protein Chemistry and Structural Biology, 112, 221–263. https://doi.org/10.1016/bs.apcsb.2018.01.006
  11. Usmani, S. S., Kumar, R., Kumar, V., Singh, S., & Raghava, G. P. S. (2018). AntiTbPdb: a knowledgebase of anti-tubercular peptides. Database : The Journal of Biological Databases and Curation, 2018. https://doi.org/10.1093/database/bay025

Papers of Year-2017 


  1. Keller et. al., (2017) Predicting human olfactory perception from chemical features of odor molecules. Science, eaal2014. 
  2. Gaur et al. (2017) Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis. Journal of Chemical Sciences 129(5) 515-531. 
  3. Baindara, P., Gautam, A., Raghava, G. P. S., and Korpole, S. (2017). Anticancer properties of a defensin like class IId bacteriocin Laterosporulin10. Sci. Rep. 7, 46541. doi: 10.1038/srep46541.
  4. Bhalla, S., Chaudhary, K., Kumar, R., Sehgal, M., Kaur, H., Sharma, S., et al. (2017). Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer. Sci. Rep. 7, 44997. doi: 10.1038/srep44997.
  5. Bhalla, S., Verma, R., Kaur, H., Kumar, R., Usmani, S. S., Sharma, S., et al. (2017). CancerPDF: A repository of cancer-associated peptidome found in human biofluids. Sci. Rep. 7, 1511. doi: 10.1038/s41598-017-01633-3.
  6. Dhanda, S. K., Usmani, S. S., Agrawal, P., Nagpal, G., Gautam, A., and Raghava, G. P. S. (2017). Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief. Bioinform. 18, 467–478. doi: 10.1093/bib/bbw025.
  7. Keller, A., Gerkin, R. C., Guan, Y., Dhurandhar, A., Turu, G., Szalai, B., et al. (2017). Predicting human olfactory perception from chemical features of odor molecules. Science 355, 820–826. doi: 10.1126/science.aal2014.
  8. Nagpal, G., Chaudhary, K., Dhanda, S. K., and Raghava, G. P. S. (2017). Computational Prediction of the Immunomodulatory Potential of RNA Sequences. Methods Mol. Biol. 1632, 75–90. doi: 10.1007/978-1-4939-7138-1_5.
  9. Nagpal, G., Usmani, S. S., Dhanda, S. K., Kaur, H., Singh, S., Sharma, M., et al. (2017). Computer-aided designing of immunosuppressive peptides based on IL-10 inducing potential. Sci. Rep. 7, 42851. doi: 10.1038/srep42851.
  10. Pahil, S., Taneja, N., Ansari, H. R., and Raghava, G. P. S. (2017). In silico analysis to identify vaccine candidates common to multiple serotypes of Shigella and evaluation of their immunogenicity. PLoS One 12, e0180505. doi: 10.1371/journal.pone.0180505.
  11. Usmani, S. S., Bedi, G., Samuel, J. S., Singh, S., Kalra, S., Kumar, P., et al. (2017). THPdb: Database of FDA-approved peptide and protein therapeutics. PLoS One 12, e0181748. doi: 10.1371/journal.pone.0181748.

Papers of Year-2016 


  1. Gupta S, Chaudhary K, Dhanda SK, Kumar R, Kumar S, Sehgal M, Nagpal G, Raghava GP. A Platform for Designing Genome-Based Personalized Immunotherapy or Vaccine against Cancer. PLoS One. 2016 Nov 10;11(11):e0166372.   
  2. Mathur D, Prakash S, Anand P, Kaur H, Agrawal P, Mehta A, Kumar R, Singh S, Raghava GP. PEPlife: A Repository of the Half-life of Peptides. Sci Rep. 2016 Nov 7;6:36617. doi: 10.1038/srep36617.   
  3. Gupta AK, Kaur K, Rajput A, Dhanda SK, Sehgal M, Khan MS, Monga I, Dar SA, Singh S, Nagpal G, Usmani SS, Thakur A, Kaur G, Sharma S, Bhardwaj A, Qureshi A, Raghava GP, Kumar M. ZikaVR: An Integrated Zika Virus Resource for Genomics, Proteomics, Phylogenetic and Therapeutic Analysis. Sci Rep. 2016 Sep 16;6:32713.  
  4.  Nupur LN, Vats A, Dhanda SK, Raghava GP, Pinnaka AK, Kumar A. ProCarDB: a database of bacterial carotenoids. BMC Microbiol. 2016 May 26;16:96.  
  5. Gautam A, Nanda JS, Samuel JS, Kumari M, Priyanka P, Bedi G, Nath SK, Mittal G, Khatri N, Raghava GP. Topical Delivery of Protein and Peptide Using Novel Cell Penetrating Peptide IMT-P8. Sci Rep. 2016 May 18;6:26278.  
  6. Dhanda SK, Chaudhary K, Gupta S, Brahmachari SK, Raghava GP. A web-based resource for designing therapeutics against Ebola Virus. Sci Rep. 2016 Apr 26;6:24782.  
  7. Dhanda SK, Vir P, Singla D, Gupta S, Kumar S, Raghava GP. A Web-Based Platform for Designing Vaccines against Existing and Emerging Strains of Mycobacterium tuberculosis. PLoS One. 2016 Apr 20;11(4):e0153771.  
  8. Gupta S, Chaudhary K, Kumar R, Gautam A, Nanda JS, Dhanda SK, Brahmachari SK, Raghava GP. Prioritization of anticancer drugs against a cancer using genomic features of cancer cells: A step towards personalized medicine. Sci Rep. 2016 Mar 31;6:23857.  
  9. Dhanda SK, Usmani SS, Agrawal P, Nagpal G, Gautam A, Raghava GPS. Novel in silico tools for designing peptide-based subunit vaccines and immunotherapeutics. Brief Bioinform. 2017 May 1;18(3):467-478.  
  10. Singh H, Srivastava HK, Raghava GP. A web server for analysis, comparison and prediction of protein ligand binding sites. Biol Direct. 2016 Mar 25;11(1):14.  
  11. Chaudhary K, Kumar R, Singh S, Tuknait A, Gautam A, Mathur D, Anand P, Varshney GC, Raghava GP. A Web Server and Mobile App for Computing Hemolytic Potency of Peptides. Sci Rep. 2016 Mar 8;6:22843.  
  12. Gautam A, Chaudhary K, Kumar R, Gupta S, Singh H, Raghava GP. Managing Drug Resistance in Cancer: Role of Cancer Informatics. Methods Mol Biol. 2016;1395:299-312.  
  13. Kumar R, Raghava GP. ApoCanD: Database of human apoptotic proteins in the context of cancer. Sci Rep. 2016 Feb 10;6:20797.  
  14. Chaudhary K, Nagpal G, Dhanda SK, Raghava GP. Prediction of Immunomodulatory potential of an RNA sequence for designing non-toxic siRNAs and RNA-based vaccine adjuvants. Sci Rep. 2016 Feb 10;6:20678.  
  15. Singh H, Kumar R, Singh S, Chaudhary K, Gautam A, Raghava GP. Prediction of anticancer molecules using hybrid model developed on molecules screened against NCI-60 cancer cell lines. BMC Cancer. 2016 Feb 9;16:77. doi: 10.1186/s12885-016-2082-y. PubMed PMID: 26860193; PubMed Central PMCID: PMC4748564.
  16. Randhawa HK, Gautam A, Sharma M, Bhatia R, Varshney GC, Raghava GP, Nandanwar H. Cell-penetrating peptide and antibiotic combination therapy: a potentialalternative to combat drug resistance in methicillin-resistant Staphylococcus aureus. Appl Microbiol Biotechnol. 2016 May;100(9):4073-83.
  17. Singh H, Raghava GP. BLAST-based structural annotation of protein residues using Protein Data Bank. Biol Direct. 2016 Jan 25;11(1):4. Singh Nanda J, Kumar R, Raghava GP. dbEM: A database of epigenetic modifiers curated from cancerous and normal genomes. Sci Rep. 2016 Jan 18;6:19340. 

Papers of Year-2015


  1. Agrawal P, Bhalla S, Usmani SS, Singh S, Chaudhary K, Raghava GP and Gautam A(2015). CPPsite 2.0: a repository of experimentally validated cell-penetrating peptides. Nucleic Acids Res: pii: gkv1266. [PDF]
  2. Singh S, Chaudhary K, Dhanda SK, Bhalla S, Usmani SS, Gautam A, Tuknait A, Agrawal P, Mathur D, Raghava GP(2015). SATPdb: a database of structurally annotated therapeutic peptides. Nucleic Acids Res: pii: gkv1114. [PDF]
  3. Nagpal G, Gupta S, Chaudhary K, Dhanda SK, Prakash S and Raghava GPS, (2015). VaccineDA: Prediction, design and genome-wide screening of oligodeoxynucleotide-based vaccine adjuvants. Scientific reports 5. [PDF]
  4. Kumar R, Chaudhary K, Chauhan JS, Nagpal G, Kumar R, Sharma M,and Raghava GPS, (2015). An in silico platform for predicting, screening and designing of antihypertensive peptides. Scientific reports 5. [PDF]
  5. Kumar R, Chauhan JS and Raghava GPS, (2015). In Silico Designing and Screening of Antagonists against Cancer Drug Target XIAP. Current cancer drug targets. [PDF]
  6. Singh H, Singh S, Raghava GPS, (2015). In silico platform for predicting and initiating beta-turns in a protein at desired locations. Proteins: Structure, Function, and Bioinformatics. 83(5):910-921. [PDF]
  7. Bhatia R, Gautam A, SK, Mehta D, Kumar V, Raghava GPS and Varshney GC (2015. Assessment of SYBR Green I Dye-Based Fluorescence Assay for Screening Antimalarial Activity of Cationic Peptides and DNA Intercalating Agents. Antimicrobial agents and chemotherapy. 59(5):2886-2889 [PDF]
  8. Panwar B and Raghava,GPS(2015). Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides. Genomics. 105(4):197-203 [PDF]
  9. Singh H, Singh S, Singla D, Agarwal SM and Raghava GPS, (2015). QSAR based model for discriminating EGFR inhibitors and non-inhibitors using Random forest. Biology direct. 10(1) [PDF]
  10. Dhar J, Chakrabarti P, Saini H, Raghava GPS, Kishore R (2015). Omega-Turn: A novel beta-turn mimic in globular proteins stabilized by main chain to side chain C- H...O interaction. Proteins: Structure, Function, and Bioinformatics. 83(2) 203-214 [PDF]
  11. Gautam A, Sharma M, Vir P, Chaudhary K, Kapoor P, Kumar R, Nath SK and Raghava GPS (2015). Identification and characterization of novel protein-derived arginine-rich cell-penetrating peptides. European Journal of Pharmaceutics and Biopharmaceutics. 89:93-10 [PDF]
  12. Kumar R, Chaudhary K, Sharma M, Nagpal G, Chauhan JS, Singh S, Gautam A, and Raghava GPS (2015). AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides. Nucleic acids research 43 (D1), D956-D962 [PDF]
  13. Singh H, Gupta S, Gautam A, and Raghava GPS (2015). Designing B-Cell Epitopes for Immunotherapy and Subunit Vaccines. Peptide Antibodies:Methods in Molecular Biology. 327-340. [PDF]
  14. Gautam A, Chaudhary K, Kumar R, and Raghava GPS (2015). Computer-Aided Virtual Screening and Designing of Cell-Penetrating Peptides. Cell-Penetrating Peptides: Methods in Molecular Biology, 59-69. [PDF]
  15. Gupta S, Kapoor P, Chaudhary K, Gautam A, Kumar R, and Raghava GPS (2015). Peptide Toxicity Prediction. 143-157. [PDF]

Papers of Year-2014


  1. Panwar B and Raghava, G. P. S. (2014) Prediction of uridine modifications in tRNA sequences.BMC Bioinformatics 2;15:326  [PDF]
  2. Tyagi A1, Tuknait A1, Anand P1, Gupta S1, Sharma M1, Mathur D1, Joshi A1, Singh S1, Gautam A and Raghava, G. P. S. (2014) CancerPPD: a database of anticancer peptides and proteins.Nucleic Acids Res 43(Database issue):D837-43  [PDF]
  3. Yadav IS, Singh H, Khan MI, Chaudhury A, Agarwal SM and Raghava, G. P. S. (2014) EGFRIndb: epidermal growth factor receptor inhibitor database.Anticancer Agents Med Chem14(7):928-35  [PDF]
  4. Singh H, Singh S and Raghava, G. P. S. (2014) Evaluation of protein dihedral angle prediction methods. PLoS One9(8):e105667  [PDF]
  5. Sharma A, Singla D, Rashid M and Raghava, G. P. S. (2014) Designing of peptides with desired half-life in intestine-like environment. BMC Bioinformatics,15:282.  [PDF]
  6. Chauhan JS, Dhanda SK, Singla D; Open Source Drug Discovery Consortium, Agarwal SM and Raghava, G. P. S. (2014) QSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFR. PLoS One 3;9(7):e101079  [PDF]
  7. Gautam A, Kapoor P, Chaudhary K, Kumar R; Open Source Drug Discovery Consortium Raghava, G. P. S. (2014) Tumor homing peptides as molecular probes for cancer therapeutics, diagnostics and theranostics. Curr Med Chem 2367-91  [PDF]
  8. Kumar R, Chaudhary K, Singla D, Gautam A and Raghava, G. P. S. (2014) Designing of promiscuous inhibitors against pancreatic cancer cell lines. Scientific Report 14;4:4668  [PDF]
  9. Ahmad S, Gupta S, Kumar R, Varshney GC and Raghava, G. P. S. (2014) Herceptin resistance database for understanding mechanism of resistance in breast cancer patients. Scientific Report 27;4:4483  [PDF]
  10. Nagpal G, Sharma M, Kumar S, Chaudhary K, Gupta S, Gautam A and Raghava, G. P. S. (2014) PCMdb: pancreatic cancer methylation database. Scientific Report 26;4:4197  [PDF]
  11. B Panwar, A Arora, and Raghava, G. P. S. (2014) Prediction and classification of ncRNAs using structural information.BMC genomics 15 (1), 127  [PDF]
  12. Mehta D, Anand P, Kumar V, Joshi A, Mathur D, Singh S, Tuknait A, Chaudhary K, Gautam SK, Gautam A, Varshney GC, Raghava, G. P. S.(2014) ParaPep:a web resource for experimentally validated antiparasitic peptide sequences and their structures. Database, bau051 [PDF]
  13. NK Mishra, D Singla, S Agarwal, and Raghava, G. P. S. (2014) ToxiPred: A Server for Prediction of Aqueous Toxicity of Small Chemical Molecules in T. Pyriformis. Journal of Translational Toxicology 1 (1), 21-27  [PDF]

Papers of Year-2013


  1. Vikram, S., Kumar, S., Vaidya, B., Pinnaka, A. K., and Raghava, G. P. S. (2013) Draft genome sequence of the 2-chloro-4-nitrophenol-degrading bacterium Arthrobacter sp. strain SJCon, Genome announcements 1.  [PDF]
  2. Tyagi, A., Kapoor, P., Kumar, R., Chaudhary, K., Gautam, A., and Raghava, G. P. S.(2013) In Silico Models for Designing and Discovering Novel Anticancer Peptides, Scientific reports 3.  [PDF]
  3. Singla, D., Tewari, R., Kumar, A., and Raghava, G. P. S.(2013) Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv), Chemistry Central Journal 7, 49.  [PDF]
  4. Singla, D., Dhanda, S. K., Chauhan, J. S., Bhardwaj, A., Brahmachari, S. K., and Raghava, G. P. S. (2013) Open Source Software and Web Services for Designing Therapeutic Molecules, Current topics in medicinal chemistry 13, 1172-1191.  [PDF]
  5. Singh, N. K., Kumar, S., Raghava, G. P. S.and Mayilraj, S. (2013) Draft genome sequence of Acinetobacter baumannii strain MSP4-16, Genome announcements 1.  [PDF]
  6. Singh, H., Ansari, H. R., and Raghava, G. P. S.(2013) Improved Method for Linear B-Cell Epitope Prediction Using Antigen’s Primary Sequence, PloS one 8, e62216.  [PDF]
  7. Sharma, A., Kapoor, P., Gautam, A., Chaudhary, K., Kumar, R., Chauhan, J. S., Tyagi, A., and Raghava, G. P. S.(2013) Computational approach for designing tumor homing peptides, Scientific reports 3  [PDF]
  8. Panwar, B., Gupta, S., and Raghava, G. P. S.(2013) Prediction of vitamin interacting residues in a vitamin binding protein using evolutionary information, BMC bioinformatics 14, 44.  [PDF]
  9. Mangal, M., Sagar, P., Singh, H.Raghava, G. P. S., and Agarwal, S. M. (2013) NPACT: Naturally Occurring Plant-based Anti-cancer Compound-Activity-Target database, Nucleic acids research 41, D1124-D1129.  [PDF]
  10. Kumar, S., Vikram, S., and Raghava, G. P. S.(2013) Genome Annotation of Burkholderia sp. SJ98 with Special Focus on Chemotaxis Genes, PloS one 8, e70624.  [PDF]
  11. Singh, S. V., Kumar, N., Singh, S. N., Bhattacharya, T., Sohal, J. S., Singh, P. K., Singh, A. V., Singh, B., Chaubey, K. K., Gupta, S., Sharma, N., Kumar, S., and Raghava, G. P. S. (2013) Genome Sequence of the "Indian Bison Type" Biotype of Mycobacterium avium subsp. paratuberculosis Strain S5, Genome Announc 1.
  12. Kumar, S., Kaur, N., Singh, N. K., Raghava, G. P. S. and Mayilraj, S. (2013) Draft genome sequence of Streptomyces gancidicus strain BKS 13-15, Genome announcements 1.  [PDF]
  13. Kumar, S., Kaur, C., Kimura, K., Takeo, M., Raghava, Raghava, G. P. S., and Mayilraj, S. (2013) Draft genome sequence of the type species of the genus Citrobacter, Citrobacter freundii MTCC 1658, Genome announcements 1.  [PDF]
  14. Kumar, R., Chaudhary, K., Gupta, S., Singh, H., Kumar, S., Gautam, A., Kapoor, P., and Raghava, G. P. S.(2013) CancerDR: Cancer Drug Resistance Database, Scientific reports 3.  [PDF]
  15. Gupta, S., Kapoor, P., Chaudhary, K., Gautam, A., Kumar, R., Raghava, G. P. S., and Consortium, O. S. D. D. (2013) In Silico Approach for Predicting Toxicity of Peptides and Proteins, PloS one 8, e73957  [PDF]
  16. Gupta, S., Ansari, H. R., Gautam, A., and Raghava, G. P. S.(2013) Identification of B-cell epitopes in an antigen for inducing specific class of antibodies, Biology direct 8, 27.  [PDF]
  17. Gautam, A., Chaudhary, K., Singh, S., Joshi, A., Anand, P., Tuknait, A., Mathur, D., Varshney, G. C., and Raghava, G. P. S.(2013) Hemolytik: a database of experimentally determined hemolytic and non-hemolytic peptides, Nucleic acids research, gkt1008  [PDF]
  18. Gautam, A., Chaudhary, K., Kumar, R., Sharma, A., Kapoor, P., Tyagi, A., and Raghava, G. P. S.(2013) In silico approaches for designing highly effective cell penetrating peptides, Journal of translational medicine 11, 74.  [PDF]
  19. Dhanda, S. K., Singla, D., Mondal, A. K., and Raghava, G. P. S.(2013) DrugMint: A webserver for predicting and designing of drug-like molecules, Biology direct 8, 28.  [PDF]
  20. Chauhan, J. S., Rao, A., and Raghava, G. P. S.(2013) In silico Platform for Prediction of N-, O-and C-Glycosites in Eukaryotic Protein Sequences, PloS one 8, e67008.  [PDF]
  21. Bhartiya, D., Pal, K., Ghosh, S., Kapoor, S., Jalali, S., Panwar, B., Jain, S., Sati, S., Sengupta, S., and Sachidanandan, C. (2013) lncRNome: a comprehensive knowledgebase of human long noncoding RNAs, Database: the journal of biological databases and curation 2013.
  22. Bala, M., Kumar, S., Raghava, G. P. S. and Mayilraj, S. (2013) Draft genome sequence of Rhodococcus ruber strain BKS 20-38, Genome announcements 1  [PDF]
  23. Bala, M., Kumar, S., Raghava, G. P. S., and Mayilraj, S. (2013) Draft genome sequence of Rhodococcus qingshengii strain BKS 20-40, Genome announcements 1.  [PDF]
  24. Ansari, H. R., and Raghava, G. P. S.(2013) In Silico Models for B-Cell Epitope Recognition and Signaling, In In Silico Models for Drug Discovery, pp 129-138, Humana Press.
  25. Ahmed, F., Kaundal, R., and Raghava, G. P. S.(2013) PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors, BMC bioinformatics 14, S9.
  26. Iquebal, M. A., Jaiswal, S., Dhanda, S. K., Arora, V., Dixit, S. P., Raghava, G. P. S.., Rai, A., and Kumar, D. (2013) Development of a model webserver for breed identification using microsatellite DNA marker, BMC Genet 14, 118.
  27. Kaur, N., Kumar, S., Bala, M., Raghava, G. P. S., and Mayilraj, S. (2013) Draft Genome Sequence of Amycolatopsis decaplanina Strain DSM 44594T, Genome Announc 1, e0013813.
  28. Gupta, S., Ansari, H. R., Gautam, A., and Raghava, G. P. S. (2013) Identification of B-cell epitopes in an antigen for inducing specific class of antibodies, Biol Direct 8, 27.
  29. Dhanda, S. K., Vir, P., and Raghava, G. P. S. (2013) Designing of interferon-gamma inducing MHC class-II binders, Biol Direct 8, 30.
  30. Ansari, H. R., and Raghava, G. P. S. (2013) In silico models for B-cell epitope recognition and signaling, Methods Mol Biol 993, 129-138.
  31. Kumar, R., and Raghava, G. P. S. (2013) Hybrid Approach for Predicting Coreceptor Used by HIV-1 from Its V3 Loop Amino Acid Sequence, PLoS One 8.

Papers of Year-2012


  1. Vikram, S., Pandey, J., Bhalla, N., Pandey, G., Ghosh, A., Khan, F., Jain, R. K., and Raghava, G. P. S.(2012) Branching of the p-nitrophenol (PNP) degradation pathway in burkholderia sp. Strain SJ98: Evidences from genetic characterization of PNP gene cluster, AMB Express 2, 1-10.  [PDF]
  2. Vikram, S., Kumar, S., Subramanian, S., and Raghava, G. P. S. (2012 ) Draft Genome Sequence of the Nitrophenol-Degrading Actinomycete Rhodococcus imtechensis RKJ300, Journal of bacteriology 194, 3543-3543.  [PDF]
  3. Chauhan, J.S., Bhat, A.H., Raghava, G. P. S. and Rao, A. (2012) GlycoPP: A webserver for prediction of N- and O-glycosites in prokaryotic protein sequences. Plos One
  4. Gautam A., Singh H., Tyagi A., Chaudhary K., Kumar R., Kapoor P. and Raghava, G. P. S. (2012) CPPsite: a curated database of cell penetrating peptides. Database (Oxford) 2012:bas015,10.1093/database/bas015  [PDF]
  5. Kumar, S., Vikram, S. and Raghava, G. P. S. (2012) Genome sequence of a nitroaromatic compound degrading bacteria Burkholderia sp. SJ98. Journal of Bacteriology 94(12):3286  [PDF]
  6. Kapoor, P., Singh,H., Gautam, H., Chaudhary, K., Kumar, R. and Raghava, G. P. S. (2012) TumorHoPe: A database of tumor homing peptides. PLoS ONE 7(4): e35187. doi:10.1371/journal.pone.0035187  [PDF]
  7. Aithal, A., Sharma, A., Srivastava, S., Raghava, G.P.S. and Varshney, G.C. (2012) PolysacDB: A comprehensive database of microbial polysaccharide antigens and their antibodies. Plos ONE 7(4):e34613  [PDF]
  8. Singh H., Chauhan, J.S., Gromiha, M. M., Consortium, OSDD Raghava, G. P. S. (2012) ccPDB: compilation and creation of data sets from Protein Data Bank. Nucleic Acids Res. 2012 Jan;40(Database issue):D486-9.  [PDF]
  9. Bhat, A.H., Mondal, H., Chauhan, J.S., Raghava, G. P. S. Methi, A. and Rao, A. (2012) ProGlycProt: a repository of experimentally characterized prokaryotic glycoproteins. Nucleic Acids Res. 2012 Jan;40(Database issue):D388-93  [PDF]

Papers of Year-2011


  1. Zhang et al., (2011) Machine learning competition in immunology - Prediction of HLA class I binding peptides. J Immunol Methods. 2011 Nov 30;374(1-2):1-4.
  2. Bhardwaj, A., Scaria, V., Raghava, G. P. S., Lynn AM, Chandra N, Banerjee S, Raghunandanan MV, Pandey V, Taneja B, Yadav J, Dash D, Bhattacharya J, Misra A, Kumar A, Ramachandran S, Thomas Z and Brahmachari SK. (2011) Open source drug discovery- A new paradigm of collaborative research in tuberculosis drug development. Tuberculosis (Edinb). 2011 Sep;91(5):479-86  [PDF]
  3. Panwar, B. and Raghava, G. P. S. (2011) Predicting sub-cellular localization of tRNA synthetases from their primary structures.  [PDF]
  4. Tyagi A, Ahmed F, Thakur N, Sharma A,Raghava, G. P. S. and Kumar, M. (2011) HIVsirDB: A database of HIV inhibiting siRNAs. PLoS ONE 6(10): e25917.  [PDF]
  5. Agarwal, S., Mishra, N.K., Singh, H. and Raghava, G. P. S. (2011) Identification of Mannose Interacting Residues using Local Composition Plos ONE 6(9): e24039  [PDF]
  6. Ahmed, F. and Raghava, G. P. S. (2011) Designing of Highly Effective Complementary and Mismatch siRNAs for Silencing a Gene Plos ONE 6(8): e23443.  [PDF]
  7. Kumar, R., Panwar, B., Chauhan, J.S. and Raghava, G. P. S. (2011) Analysis and prediction of cancerlectins using evolutionary and domain information BMC Research Notes 4:237.  [PDF]
  8. Singla, D., Anurag, M., Dash, D. and Raghava, G. P. S. (2011) A Web Server for Predicting Inhibitors against Bacterial Target GlmU Protein BMC Pharmacology 11:5  [PDF]
  9. Agarwal, S.M., Raghav, D., Singh, H. and Raghava, G. P. S. (2011) CCDB: a curated database of genes involved in Cervix Cancer Nucleic Acids Res, 39:D975-9.  [PDF]
  10. Kumar,M., Gromiha, M.M. and Raghava, G. P. S. (2011) SVM based prediction of RNA-binding proteins using binding residues and evolutionary information Journal Molecular Recognition, 24(2):303-13.  [PDF]

Papers of Year-2010


  1. Rashid, M. and Raghava, G. P. S. (2010) A simple approach for predicting protein-protein interactions. Current Protein & Peptide Science, 11(7):589-600.  [PDF]
  2. Ansari, H. R. and Raghava, G. P. S. (2010) Identification of conformational B-cell Epitopes in an antigen from its primary sequence Immunome Research 6:6  [PDF]
  3. Mishra, N. K. and Raghava, G. P. S. (2010) Prediction of specificity and cross-reactivity of kinase inhibitors. Letters In Drug Design & Discovery, 8:223-228.
  4. Pashov, A.D., Monzavi-Karbassi, B. and Raghava, G. P. S. Kieber-Emmons, T (2010) Bridging Innate and Adaptive Anti-Tumor Immunity Targeting Glycans 2010:354068.  [PDF]
  5. Panwar, B. and Raghava, G. P. S. (2010) Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains BMC Genomics 2010, 11:507.  [PDF]
  6. Mishra,N.K.,Agarwal,S. and Raghava, G. P. S. (2010) Prediction of Cytochrome P450 Isoforms responsible for Metabolizing a Drug Molecule BMC Pharmacology 2010 10:8.  [PDF]
  7. Chauhan, J. S., Mishra, N. K. and Raghava, G. P. S. (2010) Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information BMC Bioinformatics 2010, 11:301. $nbsp;[PDF]
  8. Ansari , H. R. and Raghava, G. P. S. (2010) Identification of NAD interacting residues in proteins. BMC Bioinformatics 11:160.  [PDF]
  9. Garg, A., Tewari, R. and Raghava, G. P. S. (2010) KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials. BMC Bioinformatics 11:125.  [PDF]
  10. Singla, D., Sharma, A., Kaur, J., Panwar, B. and Raghava, G. P. S. (2010) BIAdb: a curated database of benzylisoquinoline alkaloids. BMC Pharmacology 10:4.  [PDF]
  11. Ansari, H. R., Flower, D.R. and Raghava, G. P. S. (2010) AntigenDB: An immunoinformatics database of pathogen antigens. Nucleic Acids Res. 38: D847-D853.
  12. Lata, S., Mishra, N.K. and Raghava, G. P. S. (2010) AntiBP2: Improved version of antibacterial peptide prediction. BMC Bioinformatics 11:S19  [PDF]
  13. Mishra, N.K. and Raghava, G. P. S. (2010) Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information. BMC Bioinformatics 11:S48.  [PDF]
  14. Garg, A., Tewari, R. and Raghava, G. P. S. (2010) Virtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis. BMC Bioinformatics 11:S53  [PDF]
  15. Chaudhary N, Mahajan L, Madan T, Kumar A, Raghava, G. P. S., Katti SB, Haq W and Sarma PU. (2010) Prophylactic and Therapeutic Potential of Asp f1 Epitopes in Naiive and Sensitized BALB/c Mice. Immune Netw. 2009 Oct;9(5):179-91  [PDF]

Papers of Year-2009


  1. Chauhan,J.S., Mishra, N.K. and Raghava, G. P. S. (2009) Identification of ATP binding residues of a protein from its primary sequence. BMC Bioinformatics 10:434.  [PDF]
  2. Verma, R., Varshney, G.C. and Raghava, G. P. S. (2009) Prediction of Mitochondrial Proteins of Malaria Parasite using Split Amino Acid Composition and PSSM profile. Amino Acids (In Press).  [PDF]
  3.  Rashid, M., Singla, D., Sharma, A., Kumar, M. and Raghava, G. P. S. (2009) Hmrbase: a database of hormones and their receptors. BMC Genomics 10:307.  [PDF]
  4. Lata, S. and Raghava, G. P. S. (2009) Prediction and classification of chemokines and their receptors Protein Engineering, Design, and Selection 22:441-4.  [PDF]
  5. Ahmed, F., Kumar, M. and Raghava, G. P. S. (2009) Prediction of polyadenylation signals in human DNA sequences using nucleotide frequencies. In Silico Biology 9:0012  [PDF]
  6. Arora, P.K., Kumar, M., Chauhan, A. Raghava, G. P. S. and Jain, R.K. (2009) OxDBase: A database of oxygenases involved in biodegradation BMC Research Notes 2:67  [PDF]
  7. Lata,S., Bhasin, M. and Raghava, G. P. S. (2009)MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes. BMC Research Notes 2:61  [PDF]
  8. 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 9:2324-2342  [PDF]
  9. Ahmed, F., Ansari, H. R. and Raghava, G. P. S. (2009) Prediction of guide strand of microRNAs from its sequence and secondary structure. BMC Bioinformatics 10:105.  [PDF]
  10. Kumar, M. and Raghava, G. P. S. (2009) Prediction of Nuclear Proteins using SVM and HMM Models. BMC Bioinformatics 10:22 [PDF]

Papers of Year-2008


  1. Garg, A. and Raghava, G. P. S. (2008) ESLpred2: Improved Method for Predicting Subcellular Localization of Eukaryotic Proteins. BMC Bioinformatics 9:503.  [PDF]
  2. Raghava, G. P. S., Hwang, D.J. and Han, J.H. (2008) ECGpred: Correlation and Prediction of Gene Expression from Nucleotide Sequence. The Open Bioinformatics Journal 2:64-71  [PDF]
  3. Kalita MK, Nandal UK, Pattnaik A, Sivalingam A, Ramasamy G, Kumar M, Raghava, G. P. S. and Gupta, D. (2008) CyclinPred: a SVM-based method for predicting cyclin protein sequences. PLoS ONE 3(7):e2605.  [PDF]
  4. Kush, A. and Raghava, G. P. S. (2008) AC2DGel: Analysis and Comparison of 2D Gels Journal of Proteomics & Bioinformatics 1:43-46.  [PDF]
  5. Lata, S. and Raghava, G. P. S. (2008) PRRDB: A comprehensive database of Pattern-Recognition Receptors and their ligands. BMC Genomics 9:180.  [PDF]
  6. Sethi D, Garg A and Raghava, G. P. S. (2008) DPROT: Prediction of Disordered Proteins using Evolutionary Information. Amino Acids 35:599-605 [PDF]
  7. Verma R, Tiwari A, Kaur S, Varshney G.C. and Raghava, G. P. S. (2008) Identification of Proteins Secreted by Malaria Parasite into Erythrocyte using SVM and PSSM profiles. . BMC Bioinformatics 9:201  [PDF]
  8. Vivona, S., Gardy J.L., Ramachandran, S., Brinkman, F.S.L., Raghava, G. P. S., Flower, D.R. and Filippini, F. (2008) Computer aided biotechnology: from immunoinformatics to reverse vaccinology. Trends in Biotechnology 26:190  [PDF]
  9. Garg, A. and Raghava, G. P. S. (2008) A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search. In Silico Biology 8:12  [PDF]
  10. Kumar, M., Thakur, V. and Raghava, G. P. S. (2008) COPid: composition based protein identification. In Silico Biology 8:11  [PDF]
  11. Lata, S. and Raghava, G. P. S.(2008) CytoPred: a server for prediction and classification of cytokines. Protein Engineering, Design and Selection 21:279-82  [PDF]

Papers of Year-2007


  1. Kumar, M., Gromiha, M.M. and Raghava, G. P. S. (2007) Prediction of RNA binding sites in a protein using SVM and PSSM profile.Proteins: Structure, Function and Bioinformatics. 71:189-94  [PDF]
  2. Pashov A., Monzavi-Karbassi B., Raghava, G. P. S. and Kieber-Emmons, T. (2007) Peptide mimotopes as prototypic templates of broad-spectrum surrogates of carbohydrate antigens for cancer vaccination CRITICAL REVIEWS IN IMMUNOLOGY 27 (3): 247-270  [PDF]
  3. Muthukrishnan S., Garg A. and Raghava, G. P. S. (2007) OxyPred: Prediction and Classification of Oxygen-Binding Proteins.Genomics, Proteomics & Bioinformatics 5:250-2  [PDF]
  4. Saha, S. and Raghava, G. P. S. (2007) Prediction of allergenic proteins and mapping of IgE epitopes in allergens. Nature Protocols 10.1038/nprot.2007.505 (Online)
  5. Singh, H. and Raghava, G. P. S. (2007) Prediction and mapping of promiscuous MHC class II binders in an antigen sequence Nature Protocols 10.1038/nprot.2007.502 (Online)
  6. Lata, S., Sharma, B.K. and Raghava, G. P. S. (2007) Analysis and prediction of antibacterial peptides Nature Protocols 10.1038/nprot.2007.503 (Online)  [PDF]
  7. Kumar, M. , Verma, R. and Raghava, G. P. S. (2007) Mitpred2: An improved method for predicting mitochondrial proteins using SVM and HMM Nature Protocols 10.1038/nprot.2007.488 (Online)
  8.  Kumar M., Gromiha M.M. and Raghava, G. P. S. (2007) Identification of DNA-binding proteins using support vector machines and evolutionary profiles BMC Bioinformatics 8:463
  9. Kaur, H., Garg, A. and Raghava, G. P. S. (2007) PEPstr: A de novo method for tertiary structure prediction of small bioactive peptides. Protein Pept Lett. 14:626-30  [PDF]
  10.  Rashid M., Saha S. and Raghava, G. P. S. (2007) Support Vector Machine-based Method for Predicting Subcellular Localization of Mycobacterial Proteins Using Evolutionary Information and Motifs BMC Bioinformatics 8: 337  [PDF]
  11. Raghava, G. P. S. (2007) Prediction of subcellular localization of proteins using machine learning techniques and evolutionary information Amino Acids 33(3): X-XI
  12.  Lata, S., Sharma, B.K. and Raghava, G. P. S. (2007) Analysis and prediction of antibacterial peptides BMC Bioinformatics 2007, 8:263
  13. Mishra, N., Kumar, M. and Raghava, G. P. S. (2007) Support vector machine based method for predicting Glutathione S-transferases proteins. Protein Pept Lett. 6:575-80  [PDF]
  14. Vidyasagar et al. Raghava, G. P. S.  (2007) BioSuite: A comprehensive bioinformatics software package (A unique industry-academia collaboration). CURRENT SCIENCE 92 (1): 29-38  [PDF]
  15. Saha, S. and Raghava, G. P. S. (2007) Prediction of bacterial toxins. In Silico Biology 7: 0028  [PDF]
  16. Saha, S. and Raghava, G. P. S. (2007) Prediction of neurotoxins based on their function and source. In Silico Biology 7: 0025  [PDF]
  17. Saha, S., Zack, J., Singh, B. and Raghava, G. P. S. (2007) VGIchan: Prediction and classification of voltage-gated ion channels. Genomics Proteomics & Bioinformatics 4:253-8  [PDF]
  18. Greenbaum et al. Raghava, G. P. S. (2007) Towards a consensus on datasets and evaluation metrics for developing B cell epitope prediction tools. Journal Molecular Recognition 20:75-82  [PDF]
  19. Bhasin, M. and Raghava, G. P. S. (2007) A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes. J. Biosci. 32:31-42 [PDF]

Papers of Year-2006


  1. Kaundal, R., Kapoor, A.S. and Raghava, G. P. S. (2006) Machine learning techniques in disease forecasting: a case study on rice blast prediction. BMC Bioinformatics 7: 485  [PDF]
  2. 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. [PDF]
  3.  Raghava, G. P. S. and Barton, G.J. (2006) Quantification of the variation in percentage identity for protein sequence alignments. BMC Bioinformatics 7: 415 [PDF]
  4. Saha, S. and Raghava, G. P. S. (2006) AlgPred: Prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Research 34:W202-9. [PDF]
  5. Kim, J.K., Raghava, G. P. S., Bang, S. and Choi, S. (2006) Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machine Pattern Recognition Letters 27: 996-1001. [PDF]
  6. Saha, S. and Raghava, G. P. S. (2006) VICMpred: SVM-based method for the prediction of functional proteins of gram-negative bacteria using amino acid patterns and composition. Genomics Proteomics & Bioinformatics 4:42-7. [PDF]
  7. 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. [PDF]
  8. Kaur, H. and Raghava, G.P.S. (2006) Prediction of Cα-H-O and Cα-H-π interactions in proteins using recurrent neural network. In-Silico Biology 6:11
  9. Singh, M.K., Srivastava, S., Raghava, G. P. S. and Varshney, G.C. (2006) HaptenDB: A comprehensive database of haptens, carrier proteins and anti-hapten antibodies. Bioinformatics 22:253-5. [PDF]

Papers of Year-2005


  1. Saha, S., Bhasin, M. and Raghava, G. P. S. (2005) BCIPEP: A database of B-cell epitopes. BMC Genomics 6:79.
  2. Garg, A., Kaur, H. and Raghava, G. P. S. (2005) Real value prediction of Solvent accessibility in proteins using multiple sequence alignment and secondary structure. Proteins: Structure, Function, and Bioinformatics 61(2): 318-324.
  3. The Indian Genome Variation Consortium (2005) The Indian Genome Variation database (IGVdb): a project overview. Human Genetics, Sep 2005: 1-11. [PDF]
  4. Bhasin, M. and Raghava, G. P. S. (2005)  Pcleavage: A SVM based Method for Prediction of Consitutive and Immuno proteasome Cleavage Sites in Antigenic Sequences . Nucleic Acids Research 33:W202-7. [PDF]
  5. Kumar,M., Bhasin, M., Natt, N.K. and Raghava,G. P. S. (2005) BhairPred: A webserver for Prediction of Beta-hairpins in proteins from Multiple Alignment Information Using ANN and SVM Techniques . Nucleic Acids Research 33:W154-9. [PDF]
  6. Bhasin, M., Garg A., and Raghava, G. P. S. (2005)PSLpred: prediction of subcellular localization of bacterial proteins. Bioinformatics 21: 2522-4. [PDF]
  7. Raghava, G. P. S. and Han, J. H. (2005) Correlation and prediction of gene expression level from amino acid and dipeptide composition of its protein. BMC Bioinformatics 6:59. [PDF]
  8. Garg,A., Bhasin,M and Raghava, G. P. S. (2005) SVM-based method for subcellular localization of human proteins using amino acid compositions, their order and similarity search. Journal of Biological Chemistry 280:14427-32. [PDF]
  9. Saha, S., Bhasin, M. and Raghava, G. P. S. (2005)  Bcipep . Nucleic Acids Reasearch (Online) http://www3.oup.co.uk/nar/database/summary/642/ )
  10. Singh M.K., Srivastava S., Raghava, G. P. S. and Varshney G.C (2004) HaptenDB: A database of hapten molecules. Nucleic Acids Reasearch (Online; http://www3.oup.co.uk/nar/database/summary/682/ )
  11. Bhasin, M. and Raghava, G. P. S. (2005) MHCBN: A comprehensive database of MHC/TAP binders/non-binders and T-cell epitopes. Nucleic Acids Reasearch (Online; http://www3.oup.co.uk/nar/database/summary/702/ ).
  12. Issac, B. and Raghava, G. P. S. (2005) FASTA servers for sequence similarity search. Proteomics Handbook. Edited by John M. Walker and published by Humana press. Chapter 48: 503-526.
  13. Bhasin, M. and Raghava, G. P. S. (2005) GPCRsclass : A web tool for classification of amine type of G-protein coupled Receptors. Nucleic Acids Research 33:W143-7. [PDF]

Papers of Year-2004


  1. Bhasin, M. and Raghava, G. P. S. (2004) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22:3195-204.[PDF]
  2. Kaur,H. and Raghava, G. P. S. (2004) A neural network method for prediction of beta-turn types in proteins using evolutionary information. Bioinformatics 20:2751-8. [PDF]
  3. Kaur, H. and Raghava, G. P. S. (2004) Prediction of Alpha-turns in proteins using PSI-BLAST profiles and secondary structure information. Proteins: Structure, Function, and Genetics 55:83-90 [PDF]
  4. Bhasin, M. and Raghava, G. P. S. (2004) SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence. Bioinformatics 12:421. [PDF]
  5. Bhasin, M. and Raghava, G. P. S. (2004) Analysis and Prediction of affinity of TAP binding peptides using Cascade SVM. Protein Science 13:596-607.
  6. Sharma, D., Issac, B., Raghava, G. P. S. (2004) Ramaswamy, R. Spectral Repeat Finder (SRF): Identification of repetitive sequences using fourier transformation. Bioinformatics 20: 1405-1412. [PDF]
  7. Natt, N.K., Kaur, H. and Raghava, G. P. S. (2004) Prediction of Transmembrane regions of beta-barrel proteins using ANN and SVM based method.Proteins: Structure, Function, and Bioinformatics 56:11-8. [PDF]
  8. Bhasin, M. and Raghava, G. P. S. (2004) ESLpred: SVM Based Method for Subcellular Localization of Eukaryotic Proteins using Dipeptide Composition and PSI-BLAST. Nucleic Acids Reasearch 32:W414-9. [PDF]
  9. Kaur, H. and Raghava, G. P. S. (2004) Role of evolutionary information in prediction of aromatic-backbone NH interactions in proteins. FEBS Letters 564:47-57. [PDF]
  10. Bhasin, M. and Raghava, G. P. S. (2004) Classification of nuclear receptors based on amino acid composition and dipeptide composition. Journal of Biological Chemistry 279: 23262-6. [PDF]
  11. Bhasin, M. and Raghava, G. P. S. (2004) GPCRpred: An SVM Based Method for Prediction of families and subfamilies of G-protein coupled receptors Nucleic Acids Reasearch 32:W383-9. [PDF]
  12. Issac, B. and Raghava, G. P. S. (2004) EGPred: Prediction of Eukaryotic genes using ab initio methods after combining with sequence similarity approaches Genome Research 14:1756-66. [PDF]
  13. Saha, S. and Raghava, G. P. S. (2004) BcePred: Prediction of continuous B-Cell epitopes in antigenic sequences using physico-chemical properties. ICARIS 2004: 197-204

Papers of Year-2003


  1. Bhasin M., Singh H., Raghava, G. P. S. (2003) MHCBN: Update 2002. Nucleic Acid Res. (Online) [PDF]
  2. Kaur, H. and Raghava, G. P. S.(2003) Prediction of Beta-turns in proteins from multiple alignment using neural network. Protein Sci 12:627-34 [PDF]
  3. Bhasin, M., Singh, H. and Raghava, G. P. S. (2003) MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19: 665 [PDF]
  4. Singh, H. and Raghava, G. P. S. (2003) ProPred1: Prediction of promiscuous MHC class-I binding sites. Bioinformatics, 19: 1009-14 [PDF]
  5. Kaur, H. and Raghava, G. P. S. (2003) A neural network based method for prediction of gama-turns in proteins from multiple sequence alignment. Protein Science; 12:923-929. [PDF]
  6. Kaur, H. and Raghava, G. P. S. (2003) BTEVAL: A server for evaluation of beta-turn prediction methods. Journal of Bioinformatics and Computational Biology 1(3):495-504 [PDF]
  7. Raghava, G. P. S. Solanki, R.J., Soni, V. and Agrawal, P. (2003) Fingerprinting methods for phylogenetic classification and indentification of microorganisms based on variation in 16S rRNA gene sequences. Page 373-82, Chapter 44, BioComputing: Computer Tools for Biologists. Edited By Staurt M. Brown
  8. Sarin,J., Raghava, G. P. S. and Chakraborti, P. K. (2003) Intrinsic contributions of polar amino acid residues towards thermal stability of an ABC-ATPase of mesophilic origin. Protein Science 12:2118-2120 [PDF]
  9. Bhasin, M., and Raghava, G. P. S. (2003) Prediction of promiscuous and high affinity mutated MHC binders. Hybrid Hybridomics, 22(4):229-34.
  10. Raghava, G. P. S., Searle, S.M., Audley, P.C., Barber J.D. and Barton G.J. (2003) OXBench: Evaluation of protein multiple sequence alignment BMC Bioinformatics 4:47 [PDF]

Papers of Year-2002


  1. Issac, B., Singh H., Kaur, H. and Raghava, G. P. S. (2002) Locating probable genes using fourier transform. Bioinformatics 18:196-7[PDF]
  2. Bhasin M., Singh H., Raghava, G. P. S. (2002) MHCBN Nucleic Acid Res. ( Online)
  3. Kaur, H. and Raghava, G. P. S. (2002) BetaTPred: Prediction of Beta-turns in a protein using statistical algorithms. Bioinformatics 18:498-9 [PDF]
  4. Issac, B. and Raghava, G. P. S. (2002) GWFASTA: A server for FASTA search in Eukaryotic and Microbial genomes. Biotechniques 33:548-56 [PDF]
  5. Kaur, H. and Raghava, G. P. S.. (2002) An Evaluation of Beta-Turn Prediction Methods. Bioinformatics 18:1508-14 [PDF]
  6. Singh, H. and Raghava, G. P. S. (2002) Detection of Orientation of MHC Class II Binding Peptides Using Bioinformatics Tools Biotech Software and Internet Report, 3:146.

Papers Before 2002


  1. Raghava, G. P. S. (2001) PDSB: public domain software in biology. Biotech Software and Internet Report 2:154-156. [PDF]
  2. Raghava, G. P. S. and Agrewala, J. N. (2001) A web based method for computing endpoint titer and concentration of antibody/antigen. Biotech Software and Internet Report, 2:196-7. [PDF]
  3. Singh, H. and Raghava, G. P. S. (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17: 1236-7 [PDF]
  4. Raghava, G. P. S. (2001) PDWSB: public domain web servers in biology. Biotech Software and Internet Report, 2:152-3 [PDF]
  5. Raghava, G. P. S. (2001) A web server for computing size of DNA/Protein fragment using graphical method. Biotech Software and Internet Report, 2:198-200
  6. Raghava, G. P. S. et al., Fingerprinting methods for phylogenetic classification and indentification of microorganisms based on variation in 16S rRNA gene sequences. Biotechniques 29:108-115. [PDF]
  7. Raghava, G. P. S. Proclass: A computer program for predicting the protein structural classes. J. Biosciences 24, 176
  8. Nihalani, D., Raghava, G.P.S and Sahni, G (1997). Mapping of the plasminogen binding site of streptokinase with short synthetic peptides. Protein Science, 6:1284-92. [PDF]
  9. Raghava, G. P. S. (1995) DNAOPT : A computer program to aid optimization of gel conditions of DNA gel electrophoresis and SDS-PAGE. Biotechniques 18: 274-81. [PDF]
  10. Raghava, G. P. S., Goel, A., Singh, A. M., and Varshney, G. (1994) A simple microassay for computing the hemolytic potency of drugs. Biotechniques it 17: 1148-53. [PDF]
  11. Raghava, G. P. S.. (1994) Improved estimation of DNA fragment lengths from gel electrophoresis. Biotechniques 17: 100-104
  12. Raghava, G. P. S.. and Agrewala, J.N. (1994) Method for determining the affinity of monoclonal antibody using non-competitive ELISA : A computer program. Journal of Immunoassay 15: 115-128. [PDF]
  13. Raghava, G. P. S.. and Sahni, G. (1994) GMAP: a multipurpose computer program to aid synthetic gene design, cassette mutagenesis and introduction of potential restriction sites into DNA sequences. Biotechniques 16: 1116-1123. [PDF]
  14. Agrewala, J.N., Raghava, G. P. S., Mishra, G.C. (1993) Measurement and computation of murine interleukine-4 and interfron-gamma by exploiting the unique abilities of these lymphokines to induce the secretion of IgG1 and IgG2a. Journal of Immunoassay 14, 83-97.[PDF]
  15. Raghava, G. P. S., Joshi, A.K. and Agrewala, J.N. (1992) Calculation of antibody and antigen concentrations from ELISA data using a graphical method. J. Immunol. Methods 153, 263-264. [PDF]
  16. Tripathy, S.C., Balasubramanian, R., Raghava, G. P. S., Chatterjee, J.K. (1988) Microprocessor based active and reactive power measuremet. Journal of the Institution of Engineers (India): Electrical Engineering Division 69 pt 2 , 73-77.

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