Saikat Chakrabarti , Ph.D.

Principal Scientist & Deputy Head
Structural Biology & Bioinformatics

Research Interest

Our laboratory research interests include studying the structure, function and evolution of proteins involved in different diseases especially those mediated by pathogens or systemic diseases involving multiple pathways with the help of computational approaches complemented by experimental analyses. For this purpose we utilize network biology principles and sequence or structural biology approaches.

Systems biology of host-pathogen interactions
During the last few years we have assembled and curated the intra protein-protein interaction (PPI) networks of an important human pathogen, Plasmodium falciparum. Using network biology approaches we have identified certain key regulators in these PPI. Their importance was further evaluated with a novel network perturbation method developed for this purpose. Further, we are addressing the issues related to dynamic change of the whole interactome leading to transition of life cycle stage of the malarial parasite.
We have also investigated another human pathogen, Leishmania sp.  where we have initiated the compilation and analysis of whole protein interactome data of Leishmania sp. so as to study their protein interaction properties both at systems and molecular level. We are implementing bioinformatics and experimental tools providing powerful analytical approaches towards identification and characterization of the virulence factors of the parasite.

Systems biology of cancer
Similar approaches have also been utilized to identify key important interactions, proteins and pathways involved in different cancers utilising genomics, transcriptomics and proteomics data generated in collaborative laboratories. We have developed a computational systems biology approach to build a meta-interaction network of signalling, metabolic and regulatory pathways within cellular systems using text mining, network assembly and graph theory approaches to understand complex diseases like glioblastoma and cervical cell carcinoma. Our objective is to represent a holistic picture of cellular interactome by integrating different types of biological processes at the level of signalling, transcriptional regulation and metabolic networks. In this respect, we have developed a pathway assembly tool named PALM-IST (Pathway assembly from literature mining- an information search tool), a platform combining both text mining and data mining methodologies to generate meta-pathways from biomedical abstracts with an objective to identify key crosstalk and bottleneck proteins from the plethora of protein signalling network information.
Additionally, we are exploring metabolic reprogramming in cancer cells with a combination of network and systems biology approaches to understand the molecular mechanism of this metabolic switch. Briefly, since cancer cells develop in hypoxic and hypo-nutrient environments in contrast to normal cells, the tumor cells exhibit adaptive responses such as a change in cellular bioenergetics to cope with such a different microenvironment. This phenomenon is one of the hallmarks of cancer and is referred to as, metabolic reprogramming. Metabolic reprogramming in cancer cells forms an important avenue in cancer research since it is required for malignant transformation as well as tumor development processes like invasion and metastasis.


Application of sequence/structural biology approaches to understand host-pathogen interactions, complex diseases or metabolic disorders
We also utilise molecular dynamic simulations, molecular docking, molecular modelling and sequence analysis approaches to solve intricate biological problems. For instance, we have investigated the effect of cholesterol during leishmaniasis on human MHC-II protein embedded within a lipid bilayer membrane using molecular dynamic simulations. We have also explored the effect of different single site mutations on cytochrome P450B1 enzyme structure leading to the development of glaucoma in human. We have identified an internalin-A like class of virulence factors in Leishmania sp. with the help of rigorous sequence similarity assessment methodologies. Additionally, we have developed a method to identify bacterial small RNAs and their target genes and explored their role in pathogenicity. Further, we have utilised molecular modelling and structural analyses approach to understand ATGL regulation by COP1 in the context of metabolic disorder like diabetes.


Ramalingaswamy Fellow
Staff Scientist @ NCBI/NLM/NIH, 2009
Postdoctoral Fellow @ NCBI/NLM/NIH, 2004-2009
PhD @ NCBS, TIFR, Bangalore, India, 1999-2004

Patents & Publications

  • Roy NS, Debnath S, Chakraborty A, Chakraborty P, Bera I, Ghosh R, Ghoshal N, Chakrabarti S, Roy S. Enhanced basepair dynamics pre-disposes protein-assisted flips of key bases in DNA strand separation during transcription initiation. Phys Chem Chem Phys. 2018 Apr 4;20(14):9449-9459. doi: 10.1039/C8CP01119B.
  • Ahmad B, Banerjee A, Tiwari H, Jana S, Bose S, Chakrabarti S. Structural and functional characterization of the Vindoline biosynthesis pathway enzymes of Catharanthus roseus. J Mol Model. 2018 Feb 13;24(3):53. doi: 10.1007/s00894-018-3590-2.
  • Khatra H, Khan PP, Pattanayak S, Bhadra J, Rather B, Chakrabarti S, Saha T, Sinha S. Hedgehog Antagonist Pyrimidine-Indole Hybrid Molecule Inhibits Ciliogenesis through Microtubule Destabilisation. Chembiochem. 2018 Apr 4;19(7):723-735. doi: 10.1002/cbic.201700631. Epub 2018 Mar 2.
  • Nargis T, Kumar K, Ghosh AR, Sharma A, Rudra D, Sen D, Chakrabarti S, Mukhopadhyay S, Ganguly D, Chakrabarti P. KLK5 induces shedding of DPP4 from circulatory Th17 cells in type 2 diabetes. Mol Metab. 2017 Nov;6(11):1529-1539. doi: 10.1016/j.molmet.2017.09.004. Epub 2017 Sep 27.
  • Ayyub SA, Dobriyal D, Shah RA, Lahry K, Bhattacharyya M, Bhattacharyya S, Chakrabarti S, Varshney U. Coevolution of the translational machinery optimizes initiation with unusual initiator tRNAs and initiation codons in mycoplasmas. RNA Biol. 2018 Jan 2;15(1):70-80. doi: 10.1080/15476286.2017.1377879. Epub 2017 Sep 29.
  • Mandloi S, Chakrabarti S. Protein sites with more coevolutionary connections tend to evolve slower, while more variable protein families acquire higher coevolutionary connections. Version 2. F1000Res. 2017 Apr 10 [revised 2017 Jan 1];6:453. doi: 10.12688/f1000research.11251.2. eCollection 2017.
  • Banerjee P, Chakraborty A, Mondal RK, Khatun M, Datta S, Das K, Pandit P, Mukherjee S, Banerjee S, Ghosh S, Chakrabarti S, Chowdhury A, Datta S. HBV quasispecies composition in Lamivudine-failed chronic hepatitis B patients and its influence on virological response to Tenofovir-based rescue therapy. Sci Rep. 2017 Mar 17;7:44742. doi: 10.1038/srep44742.
  • Mukherjee R, Das A, Chakrabarti S, Chakrabarti O. Calcium dependent regulation of protein ubiquitination – Interplay between E3 ligases and calcium binding proteins. Biochim Biophys Acta. 2017 Jul;1864(7):1227-1235. doi: 10.1016/j.bbamcr.2017.03.001. Epub 2017 Mar 8. Review.
  • Bathula C, Ghosh S, Hati S, Tripathy S, Singh S, Chakrabarti S, Sen S. Bioisosteric modification of known fucosidase inhibitors to discover a novel inhibitor of a-L-fucosidase. RSC Adv. Impact Factor : 3.289
  • Roychowdhury A, Samadder S, Das P, Mandloi S, Addya S, Chakraborty C, Basu PS, Mondal R, Roy A, Chakrabarti S, Roychoudhury S, Panda CK. Integrative genomic and network analysis identified novel genes associated with the development of advanced cervical squamous cell carcinoma. Biochim Biophys Acta.  Impact Factor : 5.083
  • Mukherjee I, Chakraborty A, Chakrabarti S. Identification of internalin-A-like virulent proteins in Leishmania donovani. Parasit Vectors.  Impact Factor : 3.234
  • Ghosh M, Niyogi S, Bhattacharyya M, Adak M, Nayak DK, Chakrabarti S, Chakrabarti P. Ubiquitin Ligase COP1 Controls Hepatic Fat Metabolism by Targeting ATGL for Degradation. Diabetes. Impact Factor : 8.784
  • Banerjee A,Chakraborty S, Chakraborty A, Chakrabarti S , and Ray K, Functional and Structural Analyses of CYP1B1 Variants Linked to Congenital and Adult-Onset Glaucoma to Investigate the Molecular Basis of These Diseases.Plos One. Impact Factor; 3.234
  • Bhattacharyya D, Hazra S, Banerjee A, Datta R, Kumar D, Chakrabarti S, and Chattopadhyay S. (2016) Transcriptome-­wide identification and characterization of CAD isoforms specific for podophyllotoxin biosynthesis from Podophyllum hexandrum” .Plant Molecular Biology. Impact Factor; 4.257
  • Alam SK, Yadav VK, Bajaj S, Datta A, Dutta SK, Bhattacharyya M, Bhattacharya S, Debnath S, Roy S, Boardman LA, Smyrk TC, Molina JR, Chakrabarti S, Chowdhury S, Mukhopadhyay D, Roychoudhury S. (2016) DNA damage-induced ephrin-B2 reverse signaling promotes chemoresistance and drives EMT in colorectal carcinoma harboring mutant p53.Cell death and differenciation. Impact Factor; 8.385
  • Das MR, Bag AK, Saha S, Ghosh A, Dey SK, Das P, Mandal C, Ray S, Chakrabarti S, Ray M, Jana SS (2016) Molecular association of glucose-6-phosphate isomerase and pyruvate kinase M2 with glyceraldehyde-3-phosphate dehydrogenase in cancer cells.BMC CANCER.Impact Factor; 3.319
  • Ghosh RD, Ghuwalewala S, Das P, Mandloi S, Alam SK, Chakraborty J, Sarkar S, Chakrabarti S, Panda CK, Roychoudhury S. (2016) MicroRNA profiling of cisplatin-resistant oral squamous cell carcinoma cell lines enriched with cancer-stem-cell-like and epithelial-mesenchymal transition-type features. Scientific Reports.Impact Factor; 5.078
  • Roy K, Mandloi S, Chakrabarti S, Roy S. (2016) Cholesterol Corrects Altered Conformation of MHC-II Protein in Leishmania donovani Infected Macrophages: Implication in Therapy.PLoS Neglected Tropical Diseases.Impact Factor; 4.489
  • Jain CK, Pradhan BS, Banerjee S, Mondal NB, Majumder SS, Bhattacharyya M, Chakrabarti S, Roychoudhury S, Majumder HK (2015) Sulfonoquinovosyl diacylglyceride selectively targets acute lymphoblastic leukemia cells and exerts potent anti-leukemic effects in vivo. Scientific Reports.Impact Factor; 5.078
  • Chanda SD, Banerjee A, Nandi S, Chakrabarti S, Sarkar MC (2015) Cordycepin an Adenosine Analogue Executes Anti Rotaviral Effect by Stimulating Induction of Type I Interferon . J Virol Antivir Res 4:2.
  • Mandloi, S. and Chakrabarti, S. (2015) PALM-IST: Pathway Assembly from Literature Mining–an Information Search Tool. Scientific reports, 5, 10021
  • Bhattacharyya, M. and Chakrabarti, S. (2015) Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malaria journal, 14, 70.
  • Anshu, A., Mannan, M.A., Chakraborty, A., Chakrabarti, S. and Dey, M. (2015) A novel role for protein kinase Kin2 in regulating HAC1 mRNA translocation, splicing, and translation. Mol. Cell. Biol., 35, 199-210.
  • Jain, C. K. et al. (2015) Sulfonoquinovosyl diacylglyceride selectively targets acute lymphoblastic leukemia cells and exerts potent anti-leukemic effects in vivo. Sci. Rep. 5, 12082.
  • Theeya N, Ta A, Das S, Mandal RS, Chakrabarti O, Chakrabarti S, Ghosh AN, Das S. (2015). An inducible and secreted eukaryote-like serine/threonine kinase of Salmonella enterica serovar Typhi promotes intracellular survival and pathogenesis. Infect Immun 83:522–533.
  • Paul A, Samaddar S, Bhattacharya A, Banerjee A, Das A, Chakrabarti S, DasGupta M. (2014) Gatekeeper tyrosine phosphorylation is autoinhibitory for Symbiosis Receptor Kinase. Imapct factor: 3.470.
  • Anshu, A., Mannan, A., Chakraborty, A., Chakrabarti, S., Dey, M.(2014) A Novel Role for Protein Kinase Kin2 in Regulating HAC1 mRNA Translocation,Splicing and Translation. Mol Cell Biol.
  • Banerjee, A., Dey, S., Chakraborty, A., Datta, A., Basu, A.,Chakrabarti, S. and Datta, S. (2014) Binding mode analysis of a major T3SS translocator protein PopB with its chaperone PcrH from Pseudomonas aeruginosa. Proteins (inpress). Imapact factor: 2.921
  • Nayak, M.K., Agrawal, A.S., Bose, S., Naskar, S., Bhowmick, R.,Chakrabarti, S., Sarkar, S. and Chawla-Sarkar, M. (2014) Antiviral activity of baicalin against influenza virus H1N1-pdm09 is due to modulation of NS1-mediated cellular innate immune responses. J Antimicrob Chemother, 69, 1298-1310. Impact factor: 4.686
  • Chakraborty, A., Mukherjee, S., Chattopadhyay, R., Roy, S. and Chakrabarti, S. (2014) Conformational Adaptation in the E. coli Sigma 32 Protein in Response to Heat Shock. J Phys Chem B, 118, 4793-4802.
    Impact factor: 3.607
  • Chakraborty, A. and Chakrabarti, S. (2014) A survey on prediction of specificity-determining sites in proteins. Brief Bioinform. Impact factor: 5.298
  • Roy, K., Ghosh, M., Pal, T.K., Chakrabarti, S. and Roy, S. (2013) Cholesterol lowering drug may influence cellular immune response by altering MHC II function. J Lipid Res, 54, 3106-3115. Impact factor: 5.559
  • Mazumder, A., Bose, M., Chakraborty, A., Chakrabarti, S. and Bhattacharyya, S.N. (2013) A transient reversal of miRNA-mediated repression controls macrophage activation. EMBO Rep, 14, 1008-1016. Impact factor: 7.189
  • Debashree De, Piyali Datta Chakraborty, Jyotirmoy Mitra, Kanika Sharma, Somnath Mandal, Aneesha Das, Saikat Chakrabarti and Debasish Bhattacharyya(2013). Ubiquitin-like Protein from Human Placental Extract has Collagenase Activity. PLoS ONE  8(3) : e59585. Impact factor:4.092 (2011).
  • Goyal, Manish; Alam, Athar; Iqbal, Mohd; Dey, Sumanta; Bindu, Samik; Pal, Chinmay; Banerjee, Anindyajit; Chakrabarti, Saikat; Bandyopadhyay, Uday (2011). Identification and molecular characterization of an Alba-family protein from human malaria parasite Plasmodium falciparum. Nucleic Acid Res 40, 1174-90. Impact factor: 7.836 (2010).
  • Chakraborty,  Abhijit; Ghosh, Sudeshna; Chowdhary, Garisha; Maulik,Ujjwal; Chakrabarti, Saikat* (2012). DBETH: A Database of Bacterial Exotoxins for Human. Nucleic Acid Res 40, D615-20. Impact factor: 7.836(2010).
  • Chakraborty, Abhijit; Mandloi, Sapan; Lanczycki, Christopher; Panchenko, Anna; Chakrabarti, Saikat* (2012). SPEER-SERVER: A web server for prediction of protein specificity determining sites. Nucleic Acid Res 40, W242-248. Impact factor: 7.836 (2012).
  • Saikat Chakrabarti* and Anna R. Panchenko*. (2010). Structural and functional roles of coevolved sites in proteins. PLoS ONE. 5(1):e8591. Impact factor: 4.351 (2009).
  • Ganesan Pugalenthi, Tank K, P.N. Suganthan, Christopher J. Lanczycki and Saikat Chakrabarti*. (2009).Prediction of functionally important sites of proteins using neural network ensemble approach. Biochem Biophys Res Commun. 384, 155-159. Impact factor: 2.82 (2006).
  • Saikat Chakrabarti* and Anna Panchenko. (2009). Ensemble approach to predict specificity determinants: benchmarking and validation. BMC Bioinformatics 10, 207. Impact factor: 3.62 (2006).
  • Ganesan Pugalenthi, Tank K, P.N. Suganthan and Saikat Chakrabarti*. (2009). Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble. Bioinformatics, Nov 27 (Epub ahead of print). Impact factor: 4.894 (2006).
  • Saikat Chakrabarti* and Anna R. Panchenko*. (2009). Coevolution in defining the functional specificity.Proteins, 75, 231-240. Impact factor: 4.684 (2006).
  • Christopher J. Lanczycki and Saikat Chakrabarti* (2008). A tool for the prediction of functionally important sites in proteins using a library of functional templates. Bioinformation, 2, 279-283. Impact factor: NA.
  • Ganeshan Pugalenthi, P.N. Suganthan, R. Sowdhamini and Saikat Chakrabarti*. (2008). MegaMotifBase: a database of structural motifs in protein families and superfamilies. Nucleic Acids Research. 36 (database issue).Impact factor: 6.317 (2006).
  • Saikat Chakrabarti*, Stephen H. Bryant and Anna R. Panchenko (2007). Functional specificity lies within the properties and evolutionary changes of amino acids. J Mol Biol. 373(3):801-10. Impact factor: 4.890 (2006).
  • Ganesan Pugalenthi, P.N. Suganthan, R. Sowdhamini and Saikat Chakrabarti*. (2007). SMotif: A server for structural motifs in proteins. Bioinformatic. 23, 637-638. Impact factor: 4.894 (2006).
  • Saikat Chakrabarti* and Christopher J. Lanczycki. (2007). Analysis and Prediction of Functionally Important Sites in Proteins. Protein Science, 16, 4-13. Impact factor: 3.642 (2006).
  • Saikat Chakrabarti*, Christopher J. Lanczycki, Anna R. Panchenko,Teresa M. Przytycka, Paul A. Thiessen and Stephen H. Bryant. (2006). State of the art: refinement of multiple sequence alignments. BMC Bioinformatics, 7, 499. Impact factor: 3.62 (2006).
  • Chakrabarti, S., Manohari, G., Pugalenthi, G. and R. Sowdhamini. (2006). SSToSS – Sequence-Structural Templates of Single-member Superfamilies. In Silico Biology. 6, 0029. Impact factor: NA.
  • Chakrabarti, S., Lanczycki, CJ., Panchenko, AR., Przytycka, TM., Thiessen, PA and Bryant SH. (2006). Refining multiple sequence alignments with conserved core regions. Nucleic Acid Res. 34, 2598-606. Impact factor: 6.317(2006).
  • Bhadra R, Sandhya S, Abhinandan KR, Chakrabarti S, Sowdhamini R, Srinivasan N. (2006). Cascade PSI-BLAST web server: a remote homology search tool for relating protein domains. Nucleic Acids Res. 34:W143-6.Impact factor: 6.317 (2006).
  • S. Sandhya, S. Chakrabarti, K.R. Abhinandan, R. Sowdhamini and N.Srinivasan. (2005). Detection of remote similarities between proteins by cascading PSI-BLAST. Journal of Biomolecular Structure and Dynamics. 23(3):283-98. Impact factor: 1.299 (2006).
  • Chakrabarti, S., Prem, AA., Bhardwaj, N, and Sowdhamini R (2004). SCANMOT: search for protein homologues in sequence databases using simultaneous restraints of multiple motifs. Nucleic Acid Research, 33:W274-6. Impact factor: 6.317 (2006).
  • Chakrabarti, S., Bhardwaj, N., Prem, AA, and Sowdhamini R. (2004) Improvement of Alignment Accuracy Utilizing Sequentially Conserved Motifs. BMC Bioinformatics, 5 (1):167. Impact factor: 3.62 (2006).
  • Chakrabarti, S., Jaisurya, J., and Sowdhamini, R. (2004). Improvement of Comparative Modeling: application of spatial orientation of motifs as additional restraints. Journal of Molecular Modeling. 10, 69-75. Impact factor: 1.384 (2006).
  • Chakrabarti, S, and Sowdhamini, R. (2003) Regions of minimal structural variation among members of protein domain superfamilies: Application to remote homology detection and modeling using distant relationships.  FEBS Letters. 569, 31-6. Impact factor: 3.372 (2006).
  • Chakrabarti, S., Venkataramanan, K., and Sowdhamini, R. (2003) SMoS: A database of Structural Motifs of Superfamily. Protein Eng. 2003 Nov; 16, 791-3. Impact factor: 3.0 (2006).
  • Chakrabarti, S and Sowdhamini, R (2003) Functional sites and evolutionary connections of Acyl Homoserine Lactone synthases. Protein Eng. 16, 271-278. Impact factor: 3.0 (2006).