Saikat Chakrabarti , Ph.D.

Senior 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

60. Biswas N, Kumar K, Bose S, Bera R and Chakrabarti S. Analysis of Pan-Omics Data in Human Interactome Network (APODHIN) Front Genet. 2020. Impact Factor :3.258

59. Das MR,Banerjee A, Sarkar S, Majumder J, Chakrabarti S. and Jana SS 
Amino-alcohol Bio-conjugate of Naproxen Exhibits Anti-inflammatory Activity through NF-κB Signaling Pathway. bioRxiv. 2020.

58. Bhattacharya M, De S, Chakrabarti S. 
Origin and Evolution of DNA methyltransferases (DNMT) along the tree of life: A multi-genome survey. bioRxiv. 2020.

57.Kumari N, Karmakar A, Chakrabarti S. and Kumar S 
Integrative computational approach revealed crucial genes associated with different stages of diabetic retinopathy. Front Genet. 2020. Impact Factor :3.258

56. Biswas N and Chakrabarti S. 
Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer. Front Oncol. 2020. Impact Factor :4.84

55. Sanga S, Ghosh A, Kumar K, Polavarapu K, Kumar VP, Vengalil S, Nashi S, Bardhan M, Arunachal G, Raju S, Gayathri N, Biswas NK, Chakrabarti S, Nalini A, Roy S, Acharya M.
Whole Exome Analyses of Congenital Muscular Dystrophy and Congenital Myopathy Patients from India Reveal a Wide Spectrum of Known and Novel Mutations. Eur J Neurol 2020 Impact Factor : 4.516

54. Kumar K , Chakraborty A and Chakrabarti S*
PresRAT: a server for identification of bacterial small-RNA sequences and their targets with probable binding region. RNA Biology 2020 Impact Factor : 5.35

53. Paul K, Chakraborty S, Mallick P, Bhattacharjee P, Pal TK, Chatterjee N and Chakrabarti S. 
Supercritical carbon dioxide extracts of small cardamom and yellow mustard seeds have fasting hypoglycemic effects: Diabetic rat, predictive iHOMA2 models and molecular docking study. British Journal of Nutrition. 2020 Impact Factor : 3.5

52. Das A, Biswas N, and Chakrabarti S. 
Leish-ExP: A Database Of Exclusive Proteins From Leishmania Parasite. Br. J Bio. Med. Res. 04, 03, 1253-1269. 2020. Impact Factor :4.7

51. Mookherjee D, Das S, Mukherjee R, Bera M, Jana SC, Chakrabarti S, Chakrabarti O. 
RETREG1/FAM134B mediated autophagosomal degradation of AMFR/GP78 and OPA1 -a dual organellar turnover mechanism. Autophagy. 2020 Impact Factor :11.13

50. Das S, Banerjee A, Kamran M, Ejazi SA, Asad M, Ali N, Chakrabarti S 
A chemical inhibitor of heat shock protein 78 (HSP78) from Leishmania donovani represents a potential anti-leishmanial drug candidate JBC. 2020 Impact Factor :4.106

49. Kaul Z, Mookherjee D, Das S, Chatterjee D, Chakrabarti S, Chakrabarti O. 
Loss of tumor susceptibility gene 101 (TSG101) perturbs endoplasmic reticulum structure and function.BBA Mol Cell Res. 2020 Impact Factor :4.739

48. Ganguly S, Mukherjee I, Chakrabarti S, Roy S, Sundar S, Chattopadhyay K and Bhattacharyya SN 
MicroRNA Exporter HuR Clears Internalized Pathogens by Promoting Pro-inflammatory Response in Infected Macrophages EMBO Molecular Medicine 2020. Impact factor: 10.293

47.Tania Luthra, VenkannaBanothu, Uma Adepally, Krishna Kumar , Swathi M, Chakrabarti S , Maddi SR, Sen S. 
Discovery of novel pyrido-pyrrolidine hybrid compounds as alphaglucosidase inhibitors and alternative agent for control of type 1 diabetes. European Journal of Medicinal Chemistry 2020. Impact factor: 4.816

46.Chakraborty J, Kanungo A, Mahata T, Kumar K, Sharma G, Pal R, Ahammed KS, Patra D, Majhi B, Chakrabarti S, Das S and Dutta S. 
Quinoxaline derivatives disrupt the base stacking of hepatitis C virus-internal ribosome entry site RNA: reduce translation and replication. ChemCommun 2019. Impact factor: 5.996

45. Chakraborty S, Paul K, Mallick P, Pradhan S, Das K, Chakrabarti S, Nandi DK, Bhattacharjee P 
Consortia of bioactives in supercritical carbon dioxide extracts of mustard and small cardamom seeds lower serum cholesterol levels in rats: new leads for hypocholesterolaemic supplements from spices. J Nutr Sci. 2019. Impact factor: *.*
doi: 10.1017/jns.2019.28

44. Chatterjee D, Kaul Z Das S, Sougrat R, Chakrabarti S, Chakrabarti O. 
Cytosolic aggregates in presence of non-translocated proteins perturb endoplasmic reticulum structure and dynamics. Traffic 2019. Impact factor: 4.4
doi: 10.1111/tra.12694

43. Bag AK, Mandloi S, Jarmalavicius S, Mondal S, Kumar K, Mandal C, Walden P, Chakrabarti S, Mandal C. 
Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma. PLos Computational Biology 2019. Impact factor: 4.428

42. Luthra T, Nayak AK, Bose S, Chakrabarti S, Gupta A, Sen S. 
Indole based antimalarial compounds targeting the melatonin pathway: Their design, synthesis and biological evaluation. European Journal of Medicinal Chemistry 2019. Impact factor: 4.816

41. Shadab M, Banerjee A, Sinha R, Das S, Asad M, Kamran M, Maji M, Deepthi M, Jha B, Kumar M, Tripathi A, Kumar B, Chakrabarti S, ALI N. 
RNA-seq revealed expression of many novel genes associated with Leishmania donovani persistence and clearance in the host macrophage. Front. Cell. Infect Microb 2019. Impact factor: 4.3

40. Mukherjee I, Roy S, Chakrabarti S
Identification of important effector proteins in the FOXJ1 transcriptional network associated with ciliogenesis and ciliary function. Front. Genet. 2019. Impact factor: 4.151

39. Mukherjee R, Bhattacharya A, Sau A, Basu S, Chakrabarti S, Chakrabarti O. 
Calmodulin regulates MGRN1-GP78 interaction mediated ubiqitin proteasomal degradation system. FASEB Journal. 2018. Impact factor: 5.498

38. Chauhan J, Dasgupta M, Luthra T, Awasthi A, Tripathi S, Banerjee A, Pausl S, Nag D, Chakrabarti S, Chakrabarti G, Sen S. 
Design, Synthesis and biological evaluation of antimitotic C2-aroyl/arylimino tryptamine derivatives that are also potent inhibitors of indoleamine-2,3-dioxygenase(IDO) European J. of Pharma Sc. 2018. Impact factor: 3.088

37. Palit S, Mukherjee S, Niyogi S, Banerjee A, Patra D, Chakraborty A, Chakrabarti S, Chakrabarti P, Dutta S. 
Quinolineglycomimetic conjugates reducing lipogenesis and lipid accumulation in hepatocytes. Chem Bio Chem 2018. Impact factor: 3.088

36. 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 ChemChem Phys 2018. Impact factor: 4.123

35. 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. of mol Mod 2018. Impact factor: 1.425

34. Khatra H, Khan P P, Pattanayak S, Bhadra J, Rather B, Chakrabarti S, Saha T, Sinha S 
Hedgehog antagonist pyrimidine-indole-hybrid molecule inhibits ciliogenesis through microtubule destabilization. ChemBioChem 2018. Impact factor: 3.088

33. Ayyub S A, Dobriyal D, Shah R A, Lahry K, Bhattacharyya M, Bhattacharyya S, ChakrabartiS &Varshney U 
Coevolution of the translational machinery optimizes initiation with unusual initiator tRNAs and initiation codons in mycoplasmas. RNA Biology 2017. Impact Factor: 5.3

32. 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. Molecular Metabolism 2017;6:453. Impact Factor: 6.799

31. Mandloi S. and Chakrabarti S. 
Protein sites with more coevolutionary connections tend to evolve slower, while more variable protein families acquire higher coevolutionary connections. F1000 Research 2017;6:453. Impact Factor: 1.13

30. 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, 7, 44742 Impact Factor : 5.228
doi: 10.1038/srep44742. PMID: 28303969

29. Mukherjee R, Das AChakrabarti S, Chakrabarti O. 
Calcium dependent regulation of protein ubiquitination – interplay between E3 ligases and calcium binding proteins. BBA - Molecular Cell Research., 2017. Impact Factor : 5.12
doi: 10.1016/j.bbamcr.2017.03.001 PMID: 28285986

28. 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., 2017, 7, 3563-3572. Impact Factor : 3.289
doi: 10.1039/C6RA24939F.

27. 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. BiochimBiophysActa. 2016;1861:2899-2911. Impact Factor : 5.083
doi: 10.1016/j.bbagen.2016.09.014. PMID: 27641506

26. Mukherjee I, Chakraborty A, Chakrabarti S
Identification of internalin-A-like virulent proteins in Leishmania donovani. Parasit Vectors. 2016;9(1):557. Impact Factor : 3.234
doi: 10.1186/s13071-016-1842-5. PMID: 27765050

25. 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. 2016. pii: db160506. Impact Factor : 8.784
doi: 10.2337/db16-0506. PMID:27658392

24. Bhattacharyya D, Hazra S, Banerjee A, Datta R, Kumar D, Chakrabarti S, and Chattopadhyay S. 
Transcriptome-wide identification and characterization of CAD isoforms specific for podophyllotoxin biosynthesis from Podophyllum hexandrum. Plant Mol Biol. 2016;92(1-2):1-23. Impact Factor: 3.905
doi: 10.1007/s11103-016-0492-5. PMID:27387305

23. Banerjee A,Chakraborty S, Chakraborty AChakrabarti 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. 2016;11(5):e0156252. Impact Factor: 3.057
doi: 10.1371/journal.pone.0156252. PMID: 27243976

22. 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. 
DNA damage-induced ephrin-B2 reverse signaling promotes chemoresistance and drives EMT in colorectal carcinoma harboring mutant p53. Cell Death and Differentiation. 2016;23(4):707-22. Impact Factor : 8.218
doi: 10.1038/cdd.2015.133. PMID: 26494468

21. Das MR, Bag AK, Saha S, Ghosh A, Dey SK, Das P, Mandal C, Ray S, Chakrabarti S, Ray M, Jana SS 
Molecular association of glucose-6-phosphate isomerase and pyruvate kinase M2 with glyceraldehyde-3-phosphate dehydrogenase in cancer cells. BMC Cancer. 2016;16:152. Impact Factor: 3.265
doi: 10.1186/s12885-016-2172-x. PMID: 26911935

20. Ghosh RD, Ghuwalewala S, Das P, Mandloi S, Alam SK, Chakraborty J, Sarkar S, Chakrabarti S, Panda CK, Roychoudhury S. 
MicroRNA profiling of cisplatin-resistant oral squamous cell carcinoma cell lines enriched with cancer-stem-cell-like and epithelial-mesenchymal transition-type features. Sci Rep. 2016;6:23932. Impact Factor : 5.228
doi: 10.1038/srep23932. PMID: 27045798

19. Roy K, Mandloi S, Chakrabarti S, Roy S. 
Cholesterol Corrects Altered Conformation of MHC-II Protein in Leishmania donovani Infected Macrophages: Implication in Therapy. PLoSNegl Trop Dis. 2016;10(5):e0004710. Impact Factor: 3.948
doi:10.1371/journal.pntd.0004710.PMID: 27214205

18. Jain CK, Pradhan BS, Banerjee S, Mondal NB, Majumder SS, Bhattacharyya M, Chakrabarti S, Roychoudhury S, Majumder HK. 
Sulfonoquinovosyldiacylglyceride selectively targets acute lymphoblastic leukemia cells and exerts potent anti-leukemic effects in vivo. Sci Rep. 2015;5:12082. Impact Factor : 5.228
doi: 10.1038/srep12082. PMID: 26189912

17. Chanda SD, Banerjee A, Nandi S, Chakrabarti S, Sarkar MC 
Cordycepin an Adenosine Analogue Executes Anti Rotaviral Effect by Stimulating Induction of Type I Interferon. J VirolAntivir Res 2015;4:2. Impact Factor : 0.8
16. Khan MW, Biswas D, Ghosh M, Mandloi S, Chakrabarti S and Chakrabarti P. 
mTORC2 controls cancer cell survival by modulating gluconeogenesis. Cell Death Discov. 2015;1:15016. Impact Factor : 4.114
doi: 10.1038/cddiscovery.2015.16 PMID:27551450

15. Mandloi S. and Chakrabarti S. 
PALM-IST: Pathway Assembly from Literature Mining--an Information Search Tool. Sci Rep. 2015;5:10021. Impact Factor: 5.228
doi:10.1038/srep10021 PMID: 25989388

14. Bhattacharyya M. and Chakrabarti S. 
Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies. Malaria journal. 2015;14:70. Impact Factor : 3.079
doi: 10.1186/s12936-015-0562-1. PMID:2587964

13. Anshu A, Mannan MA, Chakraborty AChakrabarti S. and Dey M. 
A novel role for protein kinase Kin2 in regulating HAC1 mRNA translocation, splicing, and translation. Mol. Cell. Biol. 2015;35(1):199-210. Impact Factor : 4.427
doi:10.1128/MCB.00981-14. PMID: 25348718

12. Theeya N, Ta A, Das S, Mandal RS, Chakrabarti O, Chakrabarti S, Ghosh AN, Das S. 
An inducible and secreted eukaryote-like serine/threonine kinase of Salmonella enterica serovar Typhi promotes intracellular survival and pathogenesis. Infect Immun. 2015;83(2):522-33. Impact Factor : 3.603
doi: 10.1128/IAI.02521-14. PMID: 25404028


11. Paul A, Samaddar S, Bhattacharya A, Banerjee A, Das A, Chakrabarti S, DasGupta M. Gatekeeper tyrosine phosphorylation is autoinhibitory for Symbiosis Receptor Kinase. FEBS Lett. 2014;588(17):2881-9. Impact Factor : 3.519
doi: 10.1016/j.febslet.2014.06.056. PMID: 24996184

10. Banerjee A, Dey S, Chakraborty A, Datta A, Basu A, Chakrabarti S and Datta S. 
Binding mode analysis of a major T3SS translocator protein PopB with its chaperone PcrH from Pseudomonas aeruginosa. Proteins. 2014;82(12):3273-85. Impact Factor : 2.499
doi: 10.1002/prot.24666. PMID: 25116453

9. Nayak MK, Agrawal AS, Bose S, Naskar S, Bhowmick R, Chakrabarti S, Sarkar S and Chawla-Sarkar M. 
Antiviral activity of baicalin against influenza virus H1N1-pdm09 is due to modulation of NS1-mediated cellular innate immune responses. J AntimicrobChemother. 2014;69(5):1298-310. Impact Factor : 4.919
doi: PMID: 24458510

8. Chakraborty A, Mukherjee S, Chattopadhyay R, Roy S and Chakrabarti S. 
Conformational Adaptation in the E. coli Sigma 32 Protein in Response to Heat Shock. J Phys Chem B. 2014;118(18):4793-802. Impact Factor : 3.187
doi: 10.1021/jp501272n PMID: 24766146

7. Chakraborty A and Chakrabarti S. 
A survey on prediction of specificity-determining sites in proteins. Brief Bioinform. 2014;16(1):71-88. Impact Factor : 8.399
doi: 10.1093/bib/bbt092 PMID: 24413183

6. Roy K, Ghosh M, Pal TKChakrabarti S and Roy S. 
Cholesterol lowering drug may influence cellular immune response by altering MHC II function. J Lipid Res. 2014;16(1):71-88. Impact Factor : 4.368
doi: 10.1194/jlr.M041954. PMID: 24038316

5. Mazumder A, Bose M, Chakraborty A, Chakrabarti S and Bhattacharyya SN. 
A transient reversal of miRNA-mediated repression controls macrophage activation. EMBO Rep. 2013;14(11):1008-16. Impact Factor : 7.739
doi: 10.1038/embor.2013.149. PMID: 24030283

4. De D, Datta Chakraborty P, Mitra J, Sharma K, Mandal S, Das A, Chakrabarti S, Bhattacharyya D. 
Ubiquitin-Like Protein from Human Placental Extract Exhibits Collagenase Activity. PLoS ONE. 2013;8(3):e59585. Impact factor : 3.057
doi: 10.1371/journal.pone.0059585 PMID: 23555718

3. Chakraborty A, Mandloi S, Lanczycki CJ, Panchenko AR, Chakrabarti S. 
SPEER-SERVER: a web server for prediction of protein specificity determining sites. Nucleic Acids Res. 2012;40(Web Server issue):W242-8. Impact Factor : 9.202
doi: 10.1093/nar/gks559. PMID: 22689646

2. Chakraborty A , Ghosh S, Chowdhary G, Maulik U, Chakrabarti S. 
DBETH: A Database of Bacterial Exotoxins for Human Nucleic Acids Res. 2012;40(Database issue):D615-20. Impact Factor : 9.202
doi: 10.1093/nar/gkr942 PMID: 22102573

1.Goyal M, Alam A, Iqbal MS, Dey S, Bindu S, Pal C, Banerjee A, Chakrabarti S, Bandyopadhyay U. 
Identification and molecular characterization of an Alba-family protein from human malaria parasite Plasmodium falciparum. Nucleic Acids Res. 2012;40(3):1174-90. Impact Factor : 9.202
doi: 10.1093/nar/gkr821 PMID: 22006844

Other publications...

1. Chakrabarti S and Panchenko AR. Structural and functional roles of coevolved sites in proteins; PLoS ONE. 2010;5(1):e8591; Impact Factor : 3.057

2. Pugalenthi G, Tank K, Suganthan PN, Lanczycki CJ and Chakrabarti S. Prediction of functionally important sites of proteins using neural network ensemble approach. BiochemBiophys Res Commun. 2009;384:155-159; Impact Factor : 2.371

3. Chakrabarti S and Panchenko AR. Ensemble approach to predict specificity determinants: benchmarking and validation. BMC Bioinformatics 2009;10:207; Impact Factor : 2.435

4. Pugalenthi G, Tank K, Suganthan PN and, ChakrabartiS.Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble. Bioinformatics 2009; (Epub ahead of print); Impact Factor : 5.766

5. Chakrabarti S and Panchenko AR. Coevolution in defining the functional specificity. Proteins 2009;75:231-240; Impact Factor : 2.499

6. Lanczycki CJ and Chakrabarti S. A tool for the prediction of functionally important sites in proteins using a library of functional templates. Bioinformation 2008; 2(7):279-83; Impact Factor : 0.80

7. Pugalenthi G, Suganthan PN, Sowdhamini R and Chakrabarti S. MegaMotifBase: a database of structural motifs in protein families and superfamilies. Nucleic Acids Research 2008;36 (database issue); Impact Factor : 9.202

8. Chakrabarti S, Bryant SH and Panchenko AR. Functional specificity lies within the properties and evolutionary changes of amino acids. J Mol Biol. 2007;373(3):801-10; Impact Factor : 4.517

9. Pugalenthi G, Suganthan PN, Sowdhamini R and Chakrabarti S. SMotif: A server for structural motifs in proteins. Bioinformatic 2007;23, 637-638; Impact Factor : 5.766

10. Chakrabarti S and Lanczycki CJ.Analysis and Prediction of Functionally Important Sites in Proteins. Protein Science 2007;16, 4-13; Impact Factor : 3.039

11. Chakrabarti S, Lanczycki CJ, Panchenko AR, Przytycka TM, Thiessen PA and Bryant SH. State of the art: refinement of multiple sequence alignments. BMC Bioinformatics 2006;7, 499; Impact Factor : 2.435

12. Chakrabarti S, Manohari G, Pugalenthi G and R. Sowdhamini.SSToSS - Sequence-Structural Templates of Single-member Superfamilies. InSilico Biology 2006;6, 0029; Impact Factor : 1.67

13. Chakrabarti S, LanczyckiCJ,Panchenko AR, Przytycka TM, Thiessen PA and Bryant SH. Refining multiple sequence alignments with conserved core regions. Nucleic Acid Res. 2006;34, 2598-606; Impact Factor : 9.202

14. Bhadra R, Sandhya S, Abhinandan KR, Chakrabarti S, Sowdhamini R, Srinivasan N. Cascade PSI-BLAST web server: a remote homology search tool for relating protein domains. Nucleic Acids Res. 2006;34:W143-6; Impact Factor : 9.202

15. Sandhya S, Chakrabarti S, Abhinandan KR, Sowdhamini R and Srinivasan N. Detection of remote similarities between proteins by cascading PSI-BLAST. Journal of Biomolecular Structure and Dynamics 2005;23(3):283-98; Impact Factor : 2.3

16. Chakrabarti S, Prem AA, Bhardwaj N, and Sowdhamini R. SCANMOT: search for protein homologues in sequence databases using simultaneous restraints of multiple motifs. Nucleic Acid Research 2004;33:W274-6; Impact Factor : 9.202

17. Chakrabarti S, Bhardwaj N, Prem AA, and Sowdhamini R. Improvement of Alignment Accuracy Utilizing Sequentially Conserved Motifs. BMC Bioinformatics 2004;5;(1):167; Impact Factor : 5.766

18. Chakrabarti S, Jaisurya J, and Sowdhamini R. Improvement of Comparative Modeling: application of spatial orientation of motifs as additional restraints. Journal of Molecular Modeling 2004;10, 69-75; Impact Factor : 1.438

19. Chakrabarti S and Sowdhamini R. Regions of minimal structural variation among members of protein domain superfamilies: Application to remote homology detection and modeling using distant relationships;FEBS Letters 2003;569, 31-6; Impact Factor : 3.519

20. Chakrabarti S, Venkataramanan K, and Sowdhamini R. SMoS: A database of Structural Motifs of Superfamily. Protein Eng 2003;16, 791-3; Impact Factor : 2.364

21. Chakrabarti S and Sowdhamini R. Functional sites and evolutionary connections of Acyl Homoserine Lactone synthases. Protein Eng 2003;16, 271-278; Impact Factor : 2.364