Srinivasan Ramachandran

Dr. Srinivasan Ramachandran


Research Focus & Metrics

Academic Career Length

24Years (2003-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 September 2004 in Bioinformatics
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Journal Article
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GSGaurav Sachdeva
Gaurav Sachdeva
KKKaushal Kumar
Kaushal Kumar
PJPreti Jain
Preti Jain
SRSrinivasan Ramachandran
Srinivasan Ramachandran
Motivation: The adhesion of microbial pathogens to host cells is mediated by adhesins. Experimental methods used for characterizing adhesins are time-consuming and demand large resources. The availability of specialized software can rapidly aid experimenters in simplifying this problem. We have employed 105 compositional properties and artificial neural networks to develop SPAAN, which predicts the probability of a protein being an adhesin (Pad). Results: SPAAN had optimal sensitivity of 89% and specificity of 100% on a defined test set and could identify 97.4% of known adhesins at high Pad value from a wide range of bacteria. Furthermore, SPAAN facilitated improved annotation of several proteins as adhesins. Novel adhesins were identified in 17 pathogenic organisms causing diseases in humans and plants. In the severe acute respiratory syndrome (SARS) associated human corona virus, the spike glycoprotein and nsps (nsp2, nsp5, nsp6 and nsp7) were identified as having adhesin-like characteristics. These results offer new lead for rapid experimental testing. Availability: SPAAN is freely available through ftp://203.195.151.45 Contact:ramu@igib.res.in

1 March 2021 in Computers in Biology and Medicine
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Journal Article
Closed access
MSManoj Kumar Singh
Manoj Kumar Singh
AMAhmed Mobeen
Ahmed Mobeen
ACAmit Chandra
Amit Chandra
SJSweta Joshi
Sweta Joshi
SRSrinivasan Ramachandran
Srinivasan Ramachandran

15 April 2011 in BMC Genomics
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Journal Article
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RCRupanjali Chaudhuri
Rupanjali Chaudhuri
FAFaraz Alam Ansari
Faraz Alam Ansari
MRMuthukurussi Varieth Raghunandanan
Muthukurussi Varieth Raghunandanan
SRSrinivasan Ramachandran
Srinivasan Ramachandran
The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.

31 July 2017 in Scientific Reports
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Journal Article
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JRJyoti Rani
Jyoti Rani
IMInna Mittal
Inna Mittal
APAtreyi Pramanik
Atreyi Pramanik
NSNamita Singh
Namita Singh
NDNamita Dube
Namita Dube
SRSrinivasan Ramachandran
Srinivasan Ramachandran
We performed integrative analysis of genes associated with type 2 Diabetes Mellitus (T2DM) associated complications by automated text mining with manual curation and also gene expression analysis from Gene Expression Omnibus. They were analysed for pathogenic or protective role, trends, interaction with risk factors, Gene Ontology enrichment and tissue wise differential expression. The database T2DiACoD houses 650 genes, and 34 microRNAs associated with T2DM complications. Seven genesAGER, TNFRSF11B, CRK, PON1, ADIPOQ, CRPandNOS3are associated with all 5 complications. Several genes are studied in multiple years in all complications with high proportion in cardiovascular (75.8%) and atherosclerosis (51.3%). T2DM Patients’ skeletal muscle tissues showed high fold change in differentially expressed genes. Among the differentially expressed genes,VEGFAis associated with several complications of T2DM. A few genesACE2,ADCYAP1,HDAC4,NCF1,NFE2L2,OSM,SMAD1,TGFB1,BDNF,SYVN1,TXNIP,CD36,CYP2J2,NLRP3with details of protective role are catalogued. Obesity is clearly a dominant risk factor interacting with the genes of T2DM complications followed by inflammation, diet and stress to variable extents. This information emerging from the integrative approach used in this work could benefit further therapeutic approaches. The T2DiACoD is available atwww.http://t2diacod.igib.res.in/.

29 July 2013 in PLOS ONE
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SKSanjiv Kumar
Sanjiv Kumar
Bhanwar Lal PuniyaBhanwar Lal Puniya
Bhanwar Lal Puniya
SPShahila Parween
Shahila Parween
PNPradip Nahar
Pradip Nahar
SRSrinivasan Ramachandran
Srinivasan Ramachandran
Pathogenic bacteria interacting with eukaryotic host express adhesins on their surface. These adhesins aid in bacterial attachment to the host cell receptors during colonization. A few adhesins such as Heparin binding hemagglutinin adhesin (HBHA), Apa, Malate Synthase of M. tuberculosis have been identified using specific experimental interaction models based on the biological knowledge of the pathogen. In the present work, we carried out computational screening for adhesins of M. tuberculosis. We used an integrated computational approach using SPAAN for predicting adhesins, PSORTb, SubLoc and LocTree for extracellular localization, and BLAST for verifying non-similarity to human proteins. These steps are among the first of reverse vaccinology. Multiple claims and attacks from different algorithms were processed through argumentative approach. Additional filtration criteria included selection for proteins with low molecular weights and absence of literature reports. We examined binding potential of the selected proteins using an image based ELISA. The protein Rv2599 (membrane protein) binds to human fibronectin, laminin and collagen. Rv3717 (N-acetylmuramoyl-L-alanine amidase) and Rv0309 (L,D-transpeptidase) bind to fibronectin and laminin. We report Rv2599 (membrane protein), Rv0309 and Rv3717 as novel adhesins of M. tuberculosis H37Rv. Our results expand the number of known adhesins of M. tuberculosis and suggest their regulated expression in different stages.

20 January 2025 in 3 Biotech
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Journal Article
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AMAhmed Mobeen
Ahmed Mobeen
SJSweta Joshi
Sweta Joshi
FFFirdaus Fatima
Firdaus Fatima
ABAnasuya Bhargav
Anasuya Bhargav
YAYusra Arif
Yusra Arif
SRSrinivasan Ramachandran
Srinivasan Ramachandran