Research Groups

Mammalian Biology: Structural and Computational Biology

Research Interests and Description

Staff Research Scientist: Dinesh Gupta, PhD

Group Leader: Amit Sharma

Group Members

Research Interests

Computational Biology, cyclins, virulent proteins, molecular modelling, in-silico screening, miRNA.

Description of Research

The laboratory focuses on the development and application of computational biology tools to address research problems in the post-genomic era. The specific areas of interests of the laboratory include:

• development and application of artificial intelligence based methods to address;
• classification problems in complex biological data;
• computational drug designing; including molecular modeling, molecular dynamics, homology modeling, virtual screening and cheminformatics for lead identification;
• application of comparative genomics methods to identify novel drug targets and gene expression regulatory elements like miRNAs and transcription factors;
• computational and statistical analysis of NGS (Next-generation sequence) data, and development of biological databases.

The Group extensively exploited artificial intelligence based techniques for solution of various bioinformatics and cheminformatics research problems. It has developed several web servers for sequence based prediction for important protein families, members of which do not have obvious sequence similarities, conserved motifs and domains- this includes Cyclins (server: CyclinPred), Lipocalins (LipocalinPred), CDK inhibitor proteins (CDKIPred), virulent proteins (VirulentPred) and Fungal Adhesions (FaaPred).
The group has extended the use of SVMs (Support Vector Machine, an Artificial Intelligence based method), and molecular modeling methods to develop target oriented focused library of compounds active against novel P. falciparum PfHslV and 20S proteasome, the newly identified drug targets against the parasite. The laboratory has recently developed SVM based cheminformatics method to predict proliferation inhibitors of P. falciparum. The group actively collaborates with other groups at ICGEB to validate predictions. Some of the previously accomplished projects of the lab include development of protein sequence databases including- ProtRepeatsDB: a database of different types of protein repeats sequences in genomes, ProtvirDB: a database of protozoan virulent protein sequences. Recently initiated projects include development of methods and computational pipeline for genome wide identification of small non-coding RNAs in P. falciparum and plant genomes.

Recent Publications

Ramana, J., Gupta, D. 2010. FaaPred: A SVM-Based Prediction Method for Fungal Adhesins and Adhesin-Like Proteins. PLoS One 5, e9695 PubMed link

Ramana, J., Gupta, D. 2010. Machine Learning Methods for Prediction of CDK-Inhibitors. PLoS One 5, e13357Ramana, J., Gupta, D. 2009. LipocalinPred: a SVM-based method for prediction of lipocalins. BMC Bioinformatics 10, 445 PubMed link

Ramana, J., Gupta, D. 2009. LipocalinPred: a SVM-based method for prediction of lipocalins. BMC Bioinformatics 10, 445 PubMed link

Ramana, J., Gupta, D. 2009. ProtVirDB: A database of protozoan virulent proteins. Bioinformatics 25, 1568-1569 PubMed link

Subramaniam, S., Mohmmed, A., Gupta, D. 2009. Molecular modeling studies of the interaction between plasmodium falciparum HslU and HslV subunits. J Biomol Struct Dyn 26, 473-479 PubMed link

Garg, A., Gupta, D. 2008. VirulentPred: A SVM based prediction method for virulent proteins in bacterial pathogens. BMC Bioinformatics 9, 62 PubMed link

Bioinformatics Web servers

http://bioinfo.icgeb.res.in/faap/ Prediction method for fungal adhesins
http://bioinfo.icgeb.res.in/protvirdb/ a database of protozoan virulent proteins
http://bioinfo.icgeb.res.in/lipocalinpred/ Prediction method for Lipocalins
http://bioinfo.icgeb.res.in/codes/model.html Homology modelling of P. falciparum proteins.
http://bioinfo.icgeb.res.in/repeats ProtRepeatsDB database of amino acid repeats in genomes
http://bioinfo.icgeb.res.in/virulent SVM based prediction method for predicting virulent proteins.
http://bioinfo.icgeb.res.in/cyclinpred SVM based prediction method for predicting cyclin sequences.