Sudipto Saha

Sudipto Saha
Associate Professor

Previous appointments:

Postdoctoral Fellow at Indiana University, School of Informatics, Indianapolis, USA from 2007-2008

Postdoctoral Fellow at Case Western Reserve University, School of Medicine, Cleveland, USA from 2008-20012

Research interests:

My lab aims to improve lung disease diagnosis, prognosis, and treatment using bioinformatics and systems biology approaches.


Current Research Focus

Lung mitochondrial dysfunction and lung microbiome dysbiosis can cause chronic asthma and lung cancer. We are interested in dissecting the mitochondrial proteins and their networks/pathways in the disease mice models and identifying the critical targets for therapeutics. We are also interested in studying the lung microbiome, gut-lung axis, and antimicrobial drug-resistant gene mutation patterns in asthma and tuberculosis.  

Understand the interaction between the lung microbiome, its metabolites, and host innate immune cells in obstructive pulmonary diseases.   The study of the association between the lung microbiome and its metabolites and immune cells like macrophages and neutrophils shall shed light on a new direction in asthma pathogenesis and management. We plan to use asthmatic patients' sputum samples to generate proteomics/genomics/metabolomics data and perform integrative OMICS analysis for drug discovery. 






Contact:

Address: Biological Sciences
Unified Academic Campus
Bose Institute
EN-80, Sector V
Bidhan Nagar
Kolkata - 700 091, India
E-Mail: ssaha4[at]jcbose.ac.in
Phone: +91-33-25693216

Research:

Current ongoing research work   

1. Identification of biomarkers of dysfunctional mitochondria with impaired mitophagy in obstructive pulmonary diseases like asthma. Compile a list of mitochondrial markers associated with several diseases reported in the literature. Perform a meta-analysis of these biomarkers in the transcript level available in the public domain of the asthmatic mice model.     

2. Study the detrimental role of gut-lung microbial metabolites in obstructive pulmonary diseases.   We analyze the metagenomic data from the MDPD database developed in our lab and predict the gene families associated with microbial metabolites. These predicted metabolites will be used to perturb the alveolar macrophage and lung epithelial cell lines, and the pro-inflammatory cytokines will be measured.  


Current ongoing research projects:

R1. DBT- grant (Ref. BT/PR40174/BTIS/137/45/2022) as PI titled as Continuation of the existing Centre of Excellence in Bioinformatics and expanding it as a data center involving the newer direction of research to address the healthcare and environmental issues of national need – BIC at Bose Institute, Kolkata, from 2022-2025

R2. ICMR -grant (Ref. 2021-12937/F1) as PI titled as Epidemiological survey on tribal communities of Dinajpur District in North Bengal to develop a knowledgebase on disease predisposition for estimating disease etiology, from 2023-2026

Databases Developed (8):

  1. mitoPADdb: A database of mitochondrial proteins associated with diseases. Available at http://bicresources.jcbose.ac.in/ssaha4/mitopaddb/ 
  2. MDPD: A database for taxonomic profiling, similarity and differential analyses of gut and lung microbiomes of pulmonary diseases. Available at  http://bicresources.jcbose.ac.in/ssaha4/mdpd/
  3. BCSCdb: a database of biomarkers of cancer stem cells. Available at http://bicresources.jcbose.ac.in/ssaha4/bcscdb/
  4. DAAB-V2: Updated database of allergy and asthma biomarkers. Available at http://bicresources.jcbose.ac.in/ssaha4/daab-v2/
  5. DRAGdb: Used to survey drug resistance-associated gene mutations in Mycobacterium tuberculosis, ESKAPE and other bacterial species. Available at http://bicresources.jcbose.ac.in/ssaha4/drag/ 
  6. MYCbase: a database of functional sites and biochemical properties of Myc in both normal and cancer cells. Available at http://bicresources.jcbose.ac.in/ssaha4/mycbase/
  7. PSCRIdb: A database of regulatory interactions and networks of pluripotent stem cell lines. Available at http://bicresources.jcbose.ac.in/ssaha4/pscridb/
  8. DAAB: a manually curated database of allergy and asthma biomarkers. Available at http://bicresources.jcbose.ac.in/ssaha4/daab/
  9. LMPID: a manually curated database of linear motifs mediating protein-protein interactions. Available at http://bicresources.jcbose.ac.in/ssaha4/lmpid/

Online prediction servers (7):

  1. SDLDpred - Symptom-based Drugs of Lifestyle-related Diseases prediction. Available at http://bicresources.jcbose.ac.in/ssaha4/sdldpred
  2. MCDR-MTB: Multiclass Classification of Drug Resistance in MTB clinical isolates. Available at http://bicresources.jcbose.ac.in/ssaha4/mcdr-mtb/ 
  3. PulmoPred: Used for the classification of obstructive and non-obstructive pulmonary diseases based on spirometry using machine learning techniques. Available at http://bicresources.jcbose.ac.in/ssaha4/pulmopred/
  4. LHSPred: A web-based application for predicting lung health severity. Available at http://bicresources.jcbose.ac.in/ssaha4/lhspred/
  5. LMDIPred: A web server for predicting linear peptide sequences binding to SH3, WW and PDZ domains. Available at http://bicresources.jcbose.ac.in/ssaha4/lmdipred/
  6. PPIMpred: a web server for high-throughput screening of small molecules targeting protein-protein interaction. Available at http://bicresources.jcbose.ac.in/ssaha4/PPIMpred/
  7. PluriPred: A Web server for predicting proteins involved in pluripotent networks. Available at http://bicresources.jcbose.ac.in/ssaha4/pluripred/

Projects Completed:

R1: Systematic discovery of novel linear motifs mediating protein-protein interactions

Short linear motifs (LMs) are often present in disordered regions of proteins and are responsible for thousands of protein-protein interactions (PPIs). We have developed a relational database ( LMPID ) of linear motifs that mediate protein-protein interactions (PPIs). We have used these datasets and applied machine learning techniques to predict the linear motifs mediating Protein-Protein Interactions (PPIs) with SH3, WW, and PDZ domains ( LMDIPred web server).

R2: Systematic discovery of biomarkers of asthma caused by common environmental allergens using human plasma proteomics, cytokine profiling, and network biology - a systems approach to drug discovery

Study the integrated profile derived from plasma and cytokine assay among asthma patients. Our goal is to discover asthma-related biomarkers and identify active networks based on protein-protein interactions and pathways affected and altered in asthma patients.

R3. Systematic identification of regulatory networks in pluripotent cells integrating coding and noncoding world

Identify the active subnetworks in pluripotent stem cells by merging the DNA, protein, and regulatory noncoding RNA interactions.

R4: Identifying small chemical modulators of protein-protein interactions for drug discovery

Small chemical inhibitors are targeted in interface regions of protein-protein interactions (for e,g., IL33/ST2), for identifying novel drug(s). 

Publications:

A.   Publications in Peer-reviewed Journals (84)

84. Das J, Bhattacharjee S, Saha S. 2024, mitoPADdb: A database of mitochondrial proteins associated with diseases. Mitochondrion. 78:101927. doi: 10.1016/j.mito.2024.101927.

83. Bhattacharjee S, Saha B, Saha S. 2024, Symptom-based drug prediction of lifestyle-related chronic diseases using unsupervised machine learning techniques. Comput Biol Med. 174:108413. doi: 10.1016/j.compbiomed.2024.108413.

82. Naik L, Patel S, Kumar A, Ghosh A, Mishra A, Das M, Nayak DK, Saha S, Mishra A, Singh R, Behura A, Dhiman R. 2024, 4-(Benzyloxy)phenol-induced p53 exhibits antimycobacterial response triggering phagosome-lysosome fusion through ROS-dependent intracellular Ca2+ pathway in THP-1 cells. Microbiol Res. 282:127664. doi: 10.1016/j.micres.2024.127664.

81. Saikh SR, Mushtaque MA, Pramanick A, Prasad JK, Roy D, Saha S, Das SK. 2024, Fog caused distinct diversity of airborne bacterial communities enriched with pathogens over central Indo-Gangetic plain in India. Heliyon.10(4):e26370. doi: 10.1016/j.heliyon.2024.e26370.

80. Bagchi S, Sharma AK, Ghosh A, Saha S, Basu J, Kundu M. 2024, RegX3-dependent transcriptional activation of kdpDE and repression of rv0500A are linked to potassium homeostasis in Mycobacterium tuberculosis. FEBS J. 2024  doi: 10.1111/febs.17100.

79.    Sarkar D, Saha S, Krishnamoorthy J and Bhunia A. 2023, Application of singular value decomposition analysis: Insights into the complex mechanisms of amyloidogenesis. Biophys Chem.  306:107157. doi: 10.1016/j.bpc.2023.107157.
78.    Raja TV, Alex R, Singh U, Kumar S, Das AK, Sengar G, Singh AK, Ghosh A, Saha S, Mitra A. 2023, Genome-wide identification and annotation of SNPs for economically important traits in Frieswal™, newly evolved crossbred cattle of India. 3 Biotech. 13(9):310. doi: 10.1007/s13205-023-03701-0.
77.    Sarkar RK, Ghosh N, Sircar G, Saha S. 2023, Updates on Databases of Allergens and Allergen-Epitopes. Methods Mol Biol. 2673:151-165. doi: 10.1007/978-1-0716-3239-0_10.  
76.    Ghosh N, Sircar G, Saha S. 2023, Computational Vaccine Design for Common Allergens. Methods Mol Biol. 2673:505-513. doi: 10.1007/978-1-0716-3239-0_33.
75.    Pati S, Mukherjee S, Dutta S, Guin A, Roy D, Bose S, Paul S, Saha S, Bhattacharyya S, Datta P, Chakraborty J, Sarkar DK, Sa G. 2023, Tumor-Associated CD19+CD39- B Regulatory Cells Deregulate Class-Switch Recombination to Suppress Antibody Responses. Cancer Immunol Res. 11(3):364-380. doi: 10.1158/2326-6066.CIR-21-1073.
74.    Ghosh A, Saha S. 2022, Meta-analysis of sputum microbiome studies identifies airway disease-specific taxonomic and functional signatures. J Med Microbiol. 72(12). doi: 10.1099/jmm.0.001617.
73.    Saran N., Saha S. 2022, Survey of mycobacterial fluoroquinolone resistance protein conservon (mfp conservon) in Mycobacteriaceae and identification of its promoter activity. Gene Reports 29:101684 https://doi.org/10.1016/j.genrep.2022.101684
72.    Bhattacharjee S, Saha B, Bhattacharyya P, Saha S. 2022, Classification of obstructive and non-obstructive pulmonary diseases on the basis of spirometry using machine learning techniques.  Journal of Computational Science, Volume 63, 101768; https://doi.org/10.1016/j.jocs.2022.101768
71.    Gaur D, Kumar N, Ghosh A, Singh P, Kumar P, Guleria J, Kaur S, Malik N, Saha S, Nystrom T, Sharma D. 2022, Ydj1 interaction at nucleotide-binding-domain of yeast Ssa1 impacts Hsp90 collaboration and client maturation. PLoS Genet. 18(11):e1010442. doi: 10.1371/journal.pgen.1010442.
70. Sircar G, Ghosh N, Saha S. 2022, Designing Next-Generation Vaccines Against Common Pan-Allergens Using In Silico Approaches. Monoclon Antib Immunodiagn Immunother. 41(5):231-242. doi: 10.1089/mab.2021.0033.
69.  Firdous S, Ghosh A, Saha S. 2022, BCSCdb: a database of biomarkers of cancer stem cells. Database (Oxford). 2022:baac082. doi: 10.1093/database/baac082.
68. Chaudhary D, Singh A, Marzuki M, Ghosh A, Kidwai S, Gosain TP, Chawla K, Gupta SK, Agarwal N, Saha S, Kumar Y, Thakur KG, Singhal A, Singh R. 2022, Identification of small molecules targeting homoserine acetyl transferase from Mycobacterium tuberculosis and Staphylococcus aureus. Sci Rep. 12(1):13801. doi: 10.1038/s41598-022-16468-w.
67.  Mahatha AC, Banerjee SK, Ghosh A, Lata S, Saha S, Basu J, Kundu M. 2022, A systems approach to decipher a role of transcription factor RegX3 in the adaptation of Mycobacterium tuberculosis to hypoxic stress. Microbiology (Reading). 168(8). doi: 10.1099/mic.0.001229.
66. Bhattacharjee S, Saha B, Bhattacharyya P, Saha S. 2022, LHSPred: A web based application for predicting lung health severity. Biomed Signal Process Control.77:103745. doi: 10.1016/j.bspc.2022.103745.
65.  Majumdar S, Bhattacharjee S, Jana T, Saha S. 2021, DAAB-V2: Updated database of allergy and asthma biomarkers. Allergy. 76(12):3829-3832  https://doi.org/10.1111/all.15100  
64.  Bhowmik M, Biswas Sarkar M, Kanti Sarkar R, Dasgupta A, Saha S, Jana K, Sircar G, Gupta Bhattacharya S.  2021, Cloning and immunobiochemical analyses on recombinant chymopapain allergen Cari p 2 showing pollen-fruit cross-reaction. Mol Immunol.  137:42-51. doi: 10.1016/j.molimm.2021.06.010.
63.  Joardar N, Shit P, Halder S, Debnath U, Saha S, Misra AK, Jana K, Sinha Babu SP. 2021, Disruption of redox homeostasis with synchronized activation of apoptosis highlights the antifilarial efficacy of novel piperine derivatives: An in vitro mechanistic approach. Free Radic Biol Med. 169:343-360. doi:10.1016/j.freeradbiomed.2021.04.026.
62. Majumdar S, Verma R, Saha A, Bhattacharyya P, Maji P, Surjit M, Kundu M, Basu J, Saha S.  2021, Perspectives About Modulating Host Immune System in Targeting SARS-CoV-2 in India. Front Genet. 12:637362. doi: 10.3389/fgene.2021.637362.
61.  Roy D, Bose S, Pati S, Guin A, Banerjee K, Saha S, Singhal AK, Chakraborty J, Sarkar DK, Sa G. 2021, GFI1/HDAC1-axis differentially regulates immunosuppressive CD73 in human tumor-associated FOXP3+ Th17 and inflammation-linked Th17 cells. Eur J Immunol. 51(5):1206-1217. doi: 10.1002/eji.202048892.doi: 10.1002/eji.202048892.
60. Ghosh N, Sharma N, Saha I, Saha S. 2021, Genome-wide analysis of Indian SARS-CoV-2 genomes to identify T-cell and B-cell epitopes from conserved regions based on immunogenicity and antigenicity. Int Immunopharmacol. 91:107276. doi: 10.1016/j.intimp.2020.107276.
59.  Mishra A, Behura A, Kumar A, Ghosh A, Naik L, Mawatwal S, Mohanty SS, Mishra A, Saha S, Bhutia SK, Singh R, Dhiman R. 2021, Soybean lectin induces autophagy through P2RX7 dependent activation of NF-κB-ROS pathway to kill intracellular mycobacteria. Biochim Biophys Acta Gen Subj. 1865(2):129806. doi: 10.1016/j.bbagen.2020.129806.
58. Ghosh N, Sircar G, Asam C, Wolf M, Hauser M, Saha S, Ferreira F, Bhattacharya SG. 2020,  Purification and biochemical characterization of Hel a 6, a cross-reactive pectate lyase allergen from Sunflower (Helianthus annuus L.) pollen. Sci Rep. 10(1):20177. doi: 10.1038/s41598-020-77247-z
57. Mahatha AC, Mal S, Majumder D, Saha S, Ghosh A, Basu J, Kundu M. 2020, RegX3 Activates whiB3 Under Acid Stress and Subverts Lysosomal Trafficking of Mycobacterium tuberculosis in a WhiB3-Dependent Manner. Front Microbiol. 11:572433. doi: 10.3389/fmicb.2020.572433.
56. Ghosh A, N S, Saha S. 2020, Survey of drug resistance associated gene mutations in Mycobacterium tuberculosis, ESKAPE and other bacterial species. Sci Rep. 10(1):8957. doi: 10.1038/s41598-020-65766-8.
55. Saha S and Ewing RM. 2020, Editorial: Integrated Omics for Defining Interactomes, Front Physiol. 11:81. doi: 10.3389/fphys.2020.00081.
54. Banerjee K, Jana T, Ghosh Z, Saha S. 2020, PSCRIdb: A database of regulatory interactions and networks of pluripotent stem cell lines. J Biosci. 45:53. PMIID: 32345779
53.. Chakravorty D, Ghosh A, Saha S. 2020, Computational approach to target USP28 for regulating Myc. Comput Biol Chem. 85:107208. doi: 10.1016/j.compbiolchem.2020.107208.
52. Mohammed S, Vineetha NS, James S, Aparna JS, Babu Lankadasari M, Maeda T, Ghosh A, Saha S, Li QZ, Spiegel S, Harikumar KB. 2020, Regulatory role of SphK1 in TLR7/9-dependent type I interferon response and autoimmunity. FASEB J. 34(3):4329-4347. doi: 10.1096/fj.201902847R.
51. Banerjee SK, Lata S, Sharma AK, Bagchi S, Kumar M, Sahu SK, Sarkar D, Gupta P, Jana K, Gupta UD, Singh R, Saha S, Basu J, Kundu M. 2019, The sensor kinase MtrB of Mycobacterium tuberculosis regulates hypoxic survival and establishment of infection. J Biol Chem. 294(52):19862-19876. doi: 10.1074/jbc.RA119.009449.
50. Bhowmik M, Majumdar S, Dasgupta A, Gupta Bhattacharya S, Saha S. 2019, Pilot-Scale Study Of Human Plasma Proteomics Identifies ApoE And IL33 As Markers In Atopic Asthma. J Asthma Allergy. 12:273-283. doi: 10.2147/JAA.S211569. eCollection 2019.
49. Chakravorty D, Banerjee K, Mapder T, Saha S. 2019, In silico modeling of phosphorylation dependent and independent c-Myc degradation. BMC Bioinformatics. 20(1):230. doi: 10.1186/s12859-019-2846-x.
48. Sarkar D, Jana T, Saha S. 2018, LMDIPred: A web-server for prediction of linear peptide sequences binding to SH3, WW and PDZ domains. PLoS One. 13(7):e0200430. doi: 10.1371/journal.pone.0200430.
47. Subramani C, Nair VP, Anang S, Mandal SD, Pareek M, Kaushik N, Srivastava A, Saha S, Shalimar, Nayak B, Ranjith-Kumar CT, Surjit M. 2018, Host-Virus Protein Interaction Network Reveals the Involvement of Multiple Host Processes in the Life Cycle of Hepatitis E Virus. mSystems. 3(1). pii: e00135-17. doi:10.1128/mSystems.00135-17.
46. Majumdar S, Ghosh A, Saha S. 2018, Modulating Interleukins and their Receptors Interactions with Small Chemicals Using In Silico Approach for Asthma. Curr Top Med Chem. 18(13):1123-1134. doi: 10.2174/1568026618666180801092839.
45. Chatterjee A, Sharma AK, Mahatha AC, Banerjee SK, Kumar M, Saha S, Basu J, Kundu M. 2018,  Global mapping of MtrA-binding sites links MtrA to regulation of its targets in Mycobacterium tuberculosis. Microbiology.  164(1):99-110. doi: 10.1099/mic.0.000585.
44. Mawatwal S, Behura A, Ghosh A, Kidwai S, Mishra A, Deep A, Agarwal S, Saha S, Singh R, Dhiman R. 2017, Calcimycin mediates mycobacterial killing by inducing intracellular calcium-regulated autophagy in a P2RX7 dependent manner. Biochim Biophys Acta Gen Subj. 1861(12):3190-3200. doi: 10.1016/j.bbagen.2017.09.010.
43. Subramani E, Rameshbabu AP, Jothiramajayam M, Subramanian B, Chakravorty D, Bose G, Joshi M, Ray CD, Lodh I, Chattopadhyay R, Saha S, Mukherjee A, Dhara S, Chakravarty B, Chaudhury K. 2017, Mycobacterial heat shock protein 65 mediated metabolic shift in decidualization of human endometrial stromal cells. Sci Rep. 7(1):3942. doi: 10.1038/s41598-017-04024-w.
42.  Mustfa SA, Singh M, Suhail A, Mohapatra G, Verma S, Chakravorty D, Rana S, Rampal R, Dhar A, Saha S, Ahuja V, Srikanth CV. 2017, SUMOylation pathway alteration coupled with downregulation of SUMO E2 enzyme at mucosal epithelium modulates inflammation in inflammatory bowel disease. Open Biol. 7(6). pii: 170024. doi: 10.1098/rsob.170024.
41. Chakravorty D, Jana T, Das Mandal S, Seth A, Bhattacharya A, Saha S. 2017, MYCbase:  a database of functional sites and biochemical properties of Myc in both normal and cancer cells. BMC Bioinformatics. 18(1):224. doi: 10.1186/s12859-017-1652-6.
40. Jana T, Ghosh A, Das Mandal S, Banerjee R, Saha S. 2017, PPIMpred: a web server for high-throughput screening of small molecules targeting protein-protein interaction. R Soc Open Sci. 4(4):160501. doi: 10.1098/rsos.160501.
39. Mandal SD, Saha S. 2016, PluriPred: AWeb server for predicting proteins involved in pluripotent network. J Biosci. 41(4):743-750. doi: 10.1007/s12038-016-9649-2.
38. Sircar G, Jana K, Dasgupta A, Saha S, Gupta Bhattacharya S. 2016, Epitope Mapping of Rhi o 1 and Generation of a Hypoallergenic Variant: A CANDIDATE MOLECULE FOR FUNGAL ALLERGY VACCINES. J Biol Chem. 291(34):18016-29. doi: 10.1074/jbc.M116.732032.
37. Sarkar D, Patra P, Ghosh A, Saha S. 2016, Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins. PLoS One. 11(5):e0155911. doi: 10.1371/journal.pone.0155911.
36. Barman RK, Jana T, Das S, Saha S. 2015, Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study. PLoS One. 10(12):e0145648. doi: 10.1371/journal.pone.0145648.
35. Sircar G, Saha B, Mandal RS, Pandey N, Saha S, Gupta Bhattacharya S. 2015, Purification, Cloning and Immuno-Biochemical Characterization of a Fungal Aspartic Protease Allergen Rhi o 1 from the Airborne Mold Rhizopus oryzae. PLoS One.  10(12):e0144547. doi: 10.1371/journal.pone.0144547.
34. Sircar G, Saha B, Jana T, Dasgupta A, Gupta Bhattacharya S, Saha S. 2015, DAAB: a manually curated database of allergy and asthma biomarkers. Clin Exp Allergy. 45(7):1259-61. doi: 10.1111/cea.12569.
33. Sarkar D, Jana T, Saha S. 2015, LMPID: a manually curated database of linear motifs mediating protein-protein interactions. Database (Oxford). 2015. pii: bav014. doi: 10.1093/database/bav014.
32. Vukoti K, Yu X, Sheng Q, Saha S, Feng Z, Hsu AL, Miyagi M. 2015, Monitoring newly synthesized proteins over the adult life span of Caenorhabditis elegans. J Proteome Res. 14(3):1483-94. doi: 10.1021/acs.jproteome.5b00021.
31. Maity A, Majumdar S, Priya P, De P, Saha S, Ghosh Dastidar S. 2015, Adaptability in protein structures: structural dynamics and implications in ligand design. J Biomol Struct Dyn. 33(2):298-321. doi: 10.1080/07391102.2013.873002.
30. Barman RK, Saha S, Das S. 2014, Prediction of interactions between viral and host proteins using supervised machine learning methods. PLoS One. 9(11):e112034. doi: 10.1371/journal.pone.0112034.
29. Chakraborty J, Jana T, Saha S, Dutta TK. 2014, Ring-Hydroxylating Oxygenase database: a database of bacterial aromatic ring-hydroxylating oxygenases in the management of bioremediation and biocatalysis of aromatic compounds. Environ Microbiol Rep. 6(5):519-23. doi: 10.1111/1758-2229.12182
28. Chakraborty S, Deb A, Maji RK, Saha S, Ghosh Z. 2014,  LncRBase: an enriched resource for lncRNA information. PLoS One.  9(9):e108010. doi: 10.1371/journal.pone.0108010.
27. Arora G, Tiwari P, Mandal RS, Gupta A, Sharma D, Saha S, Singh R. 2014,  High throughput screen identifies small molecule inhibitors specific for Mycobacterium tuberculosis phosphoserine phosphatase. J Biol Chem. 289(36):25149-65. doi: 10.1074/jbc.M114.597682.
26. Dhal PK, Barman RK, Saha S, Das S. 2014, Dynamic modularity of host protein interaction networks in Salmonella Typhi infection. PLoS One. 9(8):e104911. doi: 10.1371/journal.pone.0104911.
25. Sarkar A, Maji RK, Saha S, Ghosh Z. 2014, piRNAQuest: searching the piRNAome for silencers. BMC Genomics. 15:555. doi: 10.1186/1471-2164-15-555.
24. Sircar G, Saha B, Bhattacharya SG, Saha S. 2014, Allergic asthma biomarkers using systems approaches. Front Genet.  4:308. doi: 10.3389/fgene.2013.00308.
23. Sircar G, Saha B, Bhattacharya SG, Saha S. 2014, In silico prediction of allergenic proteins. Methods Mol Biol. 1184:375-88. doi: 10.1007/978-1-4939-1115-8_21.
22. Sircar G, Sarkar D, Bhattacharya SG, Saha S. 2014, Allergen databases. Methods Mol Biol. 1184:165-81. doi: 10.1007/978-1-4939-1115-8_9.
21. Song J, Saha S, Gokulrangan G, Tesar PJ, Ewing RM. 2012, DNA and chromatin modification networks distinguish stem cell pluripotent ground states. Mol Cell Proteomics. 11(10):1036-47. doi: 10.1074/mcp.M111.011114.
20. Yuan Y, Kadiyala CS, Ching TT, Hakimi P, Saha S, Xu H, Yuan C, Mullangi V, Wang L, Fivenson E, Hanson RW, Ewing R, Hsu AL, Miyagi M, Feng Z. 2012, Enhanced energy metabolism contributes to the extended life span of calorie-restricted Caenorhabditis elegans. J Biol Chem. 287(37):31414-26. doi: 10.1074/jbc.M112.377275.
19. Saha S, Dazard JE, Xu H, Ewing RM. 2012, Computational framework for analysis of prey-prey associations in interaction proteomics identifies novel human protein-protein interactions and networks. J Proteome Res. 11(9):4476-87. doi: 10.1021/pr300227y.
18. Dazard JE, Saha S, Ewing RM. 2012, ROCS: a reproducibility index and confidence score for interaction proteomics studies. BMC Bioinformatics. 13:128.  doi: 10.1186/1471-2105-13-128.
17. Saha S, Roman T, Galante A, Koyutürk M, Ewing RM. 2012, Network-based approaches for extending the Wnt signalling pathway and identifying context-specific sub-networks. Int J Comput Biol Drug Des. 5(3-4):185-205. doi: 10.1504/IJCBDD.2012.049203.
16.  Dhiman R, Bandaru A, Barnes PF, Saha S, Tvinnereim A, Nayak RC, Paidipally P, Valluri VL, Rao LV, Vankayalapati R. 2011, c-Maf-dependent growth of Mycobacterium tuberculosis in a CD14(hi) subpopulation of monocyte-derived macrophages. J Immunol.  186(3):1638-45. doi: 10.4049/jimmunol.1003146.
15.  Saha S, Kaur P, Ewing RM. 2010, The bait compatibility index: computational bait selection for interaction proteomics experiments. J Proteome Res. 9(10):4972-81. doi: 10.1021/pr100267t.
14. Saha S, Harrison SH, Chen JY. 2009, Dissecting the human plasma proteome and inflammatory response biomarkers. Proteomics. 9(2):470-84. doi: 10.1002/pmic.200800507.
13. Saha S, Harrison SH, Shen C, Tang H, Radivojac P, Arnold RJ, Zhang X, Chen JY. 2008, HIP2: an online database of human plasma proteins from healthy individuals. BMC Med Genomics. 1:12. doi: 10.1186/1755-8794-1-12.
12.  Rashid M, Saha S, Raghava GP. 2007, Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary  information and motifs. BMC Bioinformatics. 8:337. doi: 10.1186/1471-2105-8-337.
11. Saha S, Raghava GP. 2007, BTXpred: prediction of bacterial toxins. In Silico Biol. 7(4-5):405-12. PMID: 18391233
10. Saha S, Raghava GP. 2007, Prediction of neurotoxins based on their function and source. In Silico Biol. 7(4-5):369-87. PMID: 18391230
9. Saha S, Raghava GP. 2007, Predicting virulence factors of immunological interest. Methods Mol Biol. 409:407-15. doi: 10.1007/978-1-60327-118-9_31.
8. Saha S, Raghava GP. 2007, Prediction methods for B-cell epitopes. Methods Mol Biol. 409:387-94. doi: 10.1007/978-1-60327-118-9_29.
7. Saha S, Raghava GP. 2007,  Searching and mapping of B-cell epitopes in Bcipep database. Methods Mol Biol. 409:113-24. doi: 10.1007/978-1-60327-118-9_7.
6. Saha S, Zack J, Singh B, Raghava GP. 2006, VGIchan: prediction and classification of voltage-gated ion channels. Genomics Proteomics Bioinformatics. 4(4):253-8. doi: 10.1016/S1672-0229(07)60006-0.
5. Saha S, Raghava GP. 2006, Prediction of continuous B-cell epitopes in an antigen using recurrent neural network. Proteins. 65(1):40-8. doi: 10.1002/prot.21078.
4.  Saha S, Raghava GP. 2006, AlgPred: prediction of allergenic proteins and mapping of IgE epitopes. Nucleic Acids Res. 34(Web Server issue):W202-9. doi: 10.1093/nar/gkl343.
3. Saha S, Raghava GP. 2006, VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics Proteomics Bioinformatics. 4(1):42-7. doi: 10.1016/S1672-0229(06)60015-6.
2.  Saha S, Bhasin M, Raghava GP. 2005, Bcipep: a database of B-cell epitopes. BMC Genomics. 6:79. doi: 10.1186/1471-2164-6-79.
1. DN Kamra, S Saha, N Bhatt, LC Chaudhary & N Agarwal. 2003, Effect of diet on enzyme profile, biochemical changes and in sacco degradability of feeds in the rumen of buffalo. Asian Australasian Journal Of Animal Sciences.  16 (3), 374-379; https://doi.org/10.5713/ajas.2003.374

B.   Scientific Reviews (2) :

2.    Sarkar D, Saha S. 2019, Machine-learning techniques for the prediction of protein-protein interactions. J Biosci. 44(4). pii: 104. PMID: 31502581.
1.    Mandal RS, Saha S, Das S. 2015, Metagenomic surveys of gut microbiota. Genomics Proteomics Bioinformatics. 13(3):148-58. doi: 10.1016/j.gpb.2015.02.005.

C.   Articles published in conferences (3):
3.    S. Bhattacharjee, B. Saha and S. Saha. 2023, "Prediction of Recurrence in Non Small Cell Lung Cancer Patients with Gene Expression Data Using Machine Learning Techniques," 2023 International Conference on Computer, Electrical & Communication Engineering (ICCECE), Kolkata, India, pp. 1-8, doi: 10.1109/ICCECE51049.2023.10085448.
2.    S Saha & R Ewing. 2011, Systematic discovery of condition specific Wnt signaling subnetworks, IEEE BIBM Integrative Data Analysis in Systems biology (IDASB) workshop, pp. 229-234,  Atlanta, GA, USA, DOI: 10.1109/BIBMW.2011.6112379.
1.    Saha, S; Raghava, GPS. 2004, BcePred: prediction of continuous B-cell epitopes in antigenic sequences using physico-chemical properties; International Conference on Artificial Immune Systems; Lexture Notes in Computer Science ; pp.197-204; Springer, Berlin, Heidelberg DOI: 10.1007/978-3-540-30220-9_16.

D.   Book Chapters (9):

9. D Roy, P Roy, S Saha. 2024, Multi-omics in Study of Lung Microbiome. In Book:  Multi-Omics Analysis of the Human Microbiome, Mani, I., Singh, V. (eds), pp 243-274, Springer, Singapore, DOI: 10.1007/978-981-97-1844-3_12

8.    Firdous, SK Srivastava, S Saha. 2022, Cancer Biomarkers in the Era of Systems Biology. In Book:  Systems Biomedicine Approaches in Cancer Research, pp 51-70, Springer, Singapore, DOI: 10.1007/978-981-19-1953-4_3

7.    S Bhattacharjee, A Ghosh, B Saha, S Saha. 2022, Machine Learning and Systems Biology in Genomics and Health In Book: Machine Learning in Genomics., pp. 69-90,  Springer, Singapore, DOI: 10.1007/978-981-16-5993-5_4
6.    S Bhattacharjee, B Saha, S Saha. 2022, Classification of Lung Diseases Using Machine Learning Techniques. In Book: Artificial Intelligence Technologies for Computational Biology, pp. 75-94, CRC Press. DOI: 10.1201/9781003246688-4
5.    Ghosh A, Firdous S and Saha S,  2021, Bioinformatics for Human Microbiome, In Book "Advances in Bioinformatics"  Vijai Singh and Ajay Kumar (Eds): Springer, pp. 333–350, DOI: 10.1007/978-981-33-6191-1_17
4.    Majumdar S and Saha S.  2019, Systems Immunology approach in understanding the association of allergy and cancer, In book: Systems and Synthetic Immunology, S. Singh (ed.), Springer Nature Singapore Pte Ltd.  pp. 53–72, DOI: 10.1007/978-981-15-3350-1_2
3.    Chakravorty D, Banerjee K and Saha S. 2018, Integrative Omics for Interactomes, In book: Synthetic Biology, S. Singh (ed.), Springer Nature Singapore Pte Ltd, pp. 39-49, DOI: 10.1007/978-981-10-8693-9_3
2.    Sarkar D & Saha S. 2016, Computational Proteomics, In book: Systems Biology Application in Synthetic Biology, S. Singh (ed.), Springer India, pp. 11-20, DOI: 10.1007/978-81-322-2809-7_2
1. Saha S. 2013, Systems Immunology, In book: Encyclopedia of Systems Biology, Editors: Dubitzky, W., Wolkenhauer, O., Yokota, H., Cho, K.-H. (Eds.), Springer, pp. 2073-2078 , https://doi.org/10.1007/978-1-4419-9863-7_114

E.   Authored Book (1):

1.    Sudipto Saha, Sreyashi Majumdar, Parthasarathi Bhattacharyya, 2023, Book Titled “Pulmonomics: Omics Approaches for Understanding Pulmonary Diseases”, Springer Nature Singapore Pte Ltd. pp 1-405  https://doi.org/10.1007/978-981-99-3505-5  This book contains 15 chapters.

About this book: This book comprehensively reviews various omics approaches like genomics, proteomics, transcriptomics, and metabolomics for understanding pulmonary disease at molecular and systems levels.  Each chapter presents the pathogenesis of major pulmonary diseases: obstructive diseases, restrictive diseases, vascular diseases, infectious diseases, and neoplastic carcinoma. The book chapters provide current information about the role of the microbiome and applications of medical imaging, bioinformatics tools, and databases for diagnosis and designing treatment strategies against complex lung diseases. Further, the book discusses omics technologies to identify biomarkers for the early diagnosis and prognosis of pulmonary diseases. Finally, the book elucidates the multi-omics approaches and data integration using mathematical modeling for insights into the disease etiology towards improving the prognostics and predictive accuracy of disease phenotypes.




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Recognition:

  • DBT Ramalingaswami Re-entry Fellow, 2012-2017
  • European Respiratory Society Member, 2018-2022
  • CSIR-UGC JRF/SRF, 2002-2006

Teaching:

Ph.D Course Work

B12: Bioinformatics

B24: Proteomics


M.Sc-Ph.D Integrated Course

Proteomics

Statistical tools

Genetics and Genomics

Students:

Image Name Designation Department Campus Contact number Email
profile image Dibakar Roy Junior Research Fellow Bioinformatics Centre Centenary droy7329@gmail.com
profile image Jagannath Das Junior Research Fellow Bioinformatics Centre Centenary jagannath.zoology@gmail.com
profile image Koushikee Ghosh Junior Research Fellow Biological Sciences Unified koushikeeghosh61@gmail.com
profile image Paramita Roy DST Inspire Junior Research Fellow Bioinformatics Centre Centenary royparamita95@gmail.com
profile image Shazia Firdous Junior Research Fellow Bioinformatics Centre Centenary sazfirdaus03@gmail.com
profile image Stuti Ghosh Junior Research Fellow Bioinformatics Centre Centenary stutighosh50@gmail.com

Former:

Debasree Sarkar, Ph.D. awarded, University of Calcutta, 2018 

Tanmoy Jana, Ph.D. awarded, Maulana Abul Kalam Azad University of Technology, West Bengal, 2019

Debangana Chakravorty, Ph.D. awarded, University of Calcutta, 2021

Sreyashi Majumdar, Ph.D. awarded, University of Calcutta, 2022

Saran N, Ph.D., awarded, University of Calcutta, 2023

Abhirupa Ghosh, Ph.D. awarded, University of Calcutta, 2023

Group News:

Papers/Books recently accepted from our group

  1. Sudipto Saha, Sreyashi Majumdar, Parthasarathi Bhattacharyya, 2023, Book Titled Pulmonomics: Omics Approaches for Understanding Pulmonary Diseases, Springer Nature Singapore Pte Ltd. pp 1-405 . This book contains 15 chapters.

  2. Ghosh A and Saha S. 2022, Meta-analysis of sputum microbiome studies identifies airway disease-specific taxonomic and functional signatures. Journal of Medical Microbiology,72(12) DOI: 10.1099/jmm.0.001617 

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April 5, 2024, Sudipto saha gave an invited talk at the National Conference on "Exploring the Bioresources of India to fight against Antimicrobial Resistance (AMR)" at Science City Kolkata on “Exploring microbial genes associated with antimicrobial resistance in the lung microbiome of respiratory disease patients.”

December 22, 2023, Abhirupa Ghosh was awarded a Ph.D. from the University of Calcutta.
November 13, 2023, Sudipto Saha gave an invited talk at the International Conference on Bioinformatics (InCoB 2023) in Brisbane, Australia, on “MCDR-MTB: Multiclass Classification of Drug Resistance in MTB clinical isolates using WGS data” on November 13, 2023.

September 20, 2023, Saran N was awarded a Ph.D. from the University of Calcutta.
June 27, 2023, Sudipto saha gave an invited talk at the Bioinformatics Workshop at Sikkim State Council of Science & Technology, Vigyan Bhawan, Gangtok, East Sikkim on “Study of Pulmonary diseases using bioinformatics and systems biology approaches.”
February 23, 2023, Sudipto Saha gave an invited talk at the National Symposium cum workshop on Mass Spectrometry and its applications at the Dept Of Biophysics, Molecular Biology and Bioinformatics, University of Calcutta on “ Application of Mass-spectrometry-based proteomics in the diagnosis and treatment of adult atopic asthma “.

December 18, 2022: Sudipto Saha gave an invited talk at the workshop under the "Accelerate Vigyan' scheme of SERB (Ministry of Science and Technology, Govt of India) on "Machine learning based approach for the identification of biomarkers and for drug discovery" held by the Department of Biotechnology, National Institute of Technology Durgapur entitled "Machine learning based classification of lung diseases using different types of biomarkers". 
December 2, 2022: Saran N submitted his Ph.D. thesis titled "The Role Of Mycobacterial Fluroquinolone Pentapaptide (Mfp) Proteins Conferring Drug Resistance in Mycobacteria".
October 31- November 2, 2022: Dibakar Roy presented a poster entitled “MDPD: Microbiome Database of Pulmonary Diseases” at the 4th IBSE International Symposium on "Microbiomes in Environment, Space and Human Health", held at  IIT Madras, India.
August 13, 2022: Sudipto Saha gave an invited talk at the webinar "Microbial Technology: Present and Future" held by the Deptt. of Microbiology of Vidyasagar University on "The role of Lung Microbiome in Airway Diseases".
August 5, 2022, Sreyashi Majumdar was awarded a Ph.D.  from the University of Calcutta.
March 9, 2021: Debangana Chakravorty was awarded a Ph.D. from the University of Calcutta.
February 11-15, 2020: Saran N presented a poster entitled “Genome sequencing of Fluoroquinolone resistant M.smegmatis identifies key mutations in few genes associated with drug resistance” in India|EMBO Symposium on “Mycobacterial heterogeneity and host tissue tropism”, held at National Institute of Immunology(NII) and International Centre for Genetic Engineering and Biotechnology (ICGEB), New Delhi, India.
December 2, 2019: Debangana Chakravorty submitted her Ph.D. thesis titled "Bioinformatics based knowledge map of c-MYC regulatory networks and modulators".

November 21, 2019: Tanmoy Jana was awarded a Ph.D. from Maulana Abul Kalam Azad University of Technology, West Bengal.

November 9-12, 2019: Abhirupa Ghosh presented a poster entitled “Towards identifying drug-resistant gene-mutation signatures in lung microbiome of antibiotic exposed individual” in India|EMBO symposium on “Human microbiome: Resistance and Disease”, held at National Institute of Biomedical Genomics (NIBMG), Kalyani, India.
November 9-12, 2019: Sudipto Saha presented a poster entitled “Meta-analysis for identifying the relationship of Human Gut-Lung Microbiome to the lung function of Asthma using GLMdb” in India|EMBO symposium on “Human microbiome: Resistance and Disease”, held at National Institute of Biomedical Genomics (NIBMG), Kalyani, India.
September 20-21, 2019: Shazia Firdous attended the “1st TCGA conference and workshop in India” held at IISER Pune and presented a poster entitled “Biomarkers of Cancer Stem Cells (BCSCs) database”.
September 10-12, 2019:  Abhirupa Ghosh presented a poster entitled “Prediction of drug resistance in MTB using machine learning algorithms” and won the best poster bronze award in InCoB 2019 held at Universitas YARSI, Jakarta, Indonesia.
September 10-12, 2019: Debangana Chakravorty gave a short talk on “Computational approach to target USP28 for regulating Myc” in InCoB 2019 held at Universitas YARSI, Jakarta, Indonesia.   
June 19, 2019: Sudipto Saha gave an invited talk at the National Workshop and Hands-on Training on Computational Biology with Modern Tools, MAKAUT, WB on "Big data in Network Biology".
March 25-26, 2019: Sudipto Saha gave an invited talk at the  National Conference on Emerging Trends in Disease Model systems, organized by NCCS Pune on  “Systematic discovery of biomarkers and drug targets of atopic asthma using proteomics approach “  
February 14, 2019: Sudipto Saha gave an invited talk at the Indian Institute of Technology Jodhpur on “Understanding lung diseases using bioinformatics and systems biology approaches“ 

January 26-28, 2019: Bishnupur visit by the lab members 

January 9, 2019: Debasree Sarkar was awarded a Ph.D. from the University of Calcutta. 

January 6, 2019: Debangana Chakravorty, Sreyashi Majumdar, Abhirupa Ghosh, and Sudipto Saha completed Kolkata 5K Marathon Race, organized by Kolkata Police Force.

December 27, 2018: Debasree Sarkar defended her Ph.D. thesis titled "Systematic Discovery Of Linear Motifs Mediating Protein-Protein Interactions".

December 13-15, 2018: Sudipto Saha gave an invited talk at VIBCON-2018, Dimapur, Nagaland on "Predicting small chemical modulators of protein-protein interactions for drug discovery in lung diseases".

November 19-23, 2018: Krishnendu Banerjee and Debasree Sarkar attended EMBL-EBI Workshop on "Analysis of genome-scale data from bulk and single-cell sequencing" held at the National Institute of Biomedical Genomics (NIBMG), Kalyani, India.

November 5-10, 2018: Debangana Chakravorty, attended EMBO Practical Course on "Computational analysis of protein-protein interactions: Sequences, networks, and diseases" held in Rome, Italy with the EMBO Travel Grant award.

October 11-14, 2018: Sreyashi Majumdar attended and presented a poster titled “ DAAB-V2: An updated version of Database of Allergy and Asthma Biomarkers with SNPs, Protein Interactors and Drug Information." at the Joint Congress of the Asia Pacific Association of Allergy, Asthma and Clinical Immunology & the Asia Pacific Association of Pediatric Allergy Respirology and Immunology (APAAACI & APAPARI 2018 ), 2018, Bangkok, Thailand.

September 8-9, 2018: Sreyashi Majumdar attended and presented a poster titled “Improving diagnosis, prognosis, and treatment of lung diseases using bioinformatics and systems biology approaches” in Pulmocon, 2018, Kolkata. She won the best poster award for the said poster.

August 2, 2018: A paper titled "Prediction of half-maximal inhibitory concentration (IC50) for small chemical modulators targeting protein-protein interaction using support vector machine" was accepted for oral presentation at International Conference on Bioinformatics (InCoB 2018), 2018, New Delhi

August 1, 2018: Shazia Firdous joined as Junior Research Fellow (UGC)

July 25, 2018: Debangana Chakravorty's poster abstract titled " FBXW7 and Skp2 mediated c-Myc degradation: An in silico approach" ' accepted at International Conference on Bioinformatics (InCoB 2018), 2018, New Delhi

July 25, 2018: Tanmoy Jana's poster abstract titled "PPIMdb: A database of small chemicals modulating (inhibiting) protein-protein interactions" accepted at the International Conference on Bioinformatics (InCoB 2018), 2018, New Delhi

July 17-21,2018: Abhirupa Ghosh attended NIBMG Summer School 2018 on Systems Biology at the National Institute of Biomedical Genomics (NIBMG), Kalyani, WB

February 25-27, 2018: Krishnendu Banerjee and Sudipto Saha attended and presented posters in India|EMBO symposia on "Big data in biomedicine", New Delhi

February 15, 2018: Sudipto Saha gave a lecture on the occasion of the Silver Jubilee of the Machine Intelligence Unit at the Indian Statistical Institute, Kolkata. The title of the talk was "Systematic discovery of novel linear motifs mediating protein-protein interactions"

January 18, 2018: Debasree Sarkar (SRF) submitted her Ph.D. thesis titled "Systematic discovery of linear motifs mediating protein-protein interactions"