INDUSTRIAL BIOTECHNOLOGY / Biofuels and Industrial Biotechnology
Translational bioinformatics, structural biology, structural immunology, systems biology, molecular modeling, in-silico screening and non-coding RNAs, MD simulations.
Description of Research
The laboratory focuses on application of computational biology to develop tools for translational bioinformatics. The specific areas of interests of the laboratory include:
- development and application of artificial intelligence based methods to address complex biological data classification problems;
- computer aided 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;
- applications for Systems Biology for translational bioinformatics;
- computational and statistical analysis of NGS (Next Generation Sequence) data and;
- development of biological databases and algorithms.
The Group exploits Artificial Intelligence (AI) for solving various bioinformatics and cheminformatics research problems. The group has developed several web servers for sequence based prediction protein families, members of which do not have obvious sequence similarities, conserved motifs and domains. Recently we have developed servers for:- Plant viral suppressors (server: VSupPred), Cyclins (server: CyclinPred), Lipocalins (LipocalinPred), CDK inhibitor proteins (CDKIPred), virulent proteins (VirulentPred) and Fungal Adhesions (FaaPred). The group has recently developed standalone tools for new generation high throughput data analysis, for example miRMOD is a tool for determination of modified miRNAs by analysis of sRNA NGS data and its effect on targets. A Random forest algorithm for prediction of cancer stage in the most common renal cancer (ccRCC), based on patient RNAseq data, has been developed in the laboratory. The group is developing a proteomics tool for analysis of volatome of TB patients in collaboration with Translational Health Group at ICGEB. The group is collaborating with “Omics of Algae” group at ICGEB to perform NGS and metabolomics of algal genomes of importance for Biofuel production. The databases and tools developed by the group are freely available as webservers at https://bioinfo.icgeb.res.in.
- Develop tools for translational bioinformatics.
- Development and application of artificial intelligence based methods to address complex biological data classification problems;
- Computer aided 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;
- Applications for Systems Biology for translational bioinformatics;
- Computational and statistical analysis of NGS (Next Generation Sequence) data and;
- Development of biological databases and algorithms.
- Perform sequence assembly of genomes of importance for Biofuel production
- Perform FBA analysis for metabolic engineering of industrially importance microbes and algae.
- Perform variant analysis of human genome.
- Perform proteomics analysis
Kaushik, A., Shakir Ali, S., Gupta, D. 2017. Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways. Sci. Rep. 7, 40450; doi:10.1038/srep40450 (2017).
Zeeshan M., Kaur I., Joy J., Saini E., Paul G., Kaushik A., Dabral S., Mohmmed A., Gupta D., Malhotra P. 2017. Proteomic Identification and Analysis of Arginine-Methylated Proteins of Plasmodium falciparum at Asexual Blood Stages. J Proteome Res. Jan 3. doi: 10.1021/acs.jproteome.5b01052 [Epub ahead of print]
Kaur I., Zeeshan M., Saini E., et al. Widespread occurrence of lysine methylation in Plasmodium falciparum proteins at asexual blood stages. Sci Rep . 2016;6. doi:10.1038/srep35432.
Kumar R., Gupta D. Identification of CYP1B1-specific candidate inhibitors using combination of in silico screening, integrated knowledge-based filtering, and molecular dynamics simulations. Chem Biol Drug Des . 2016. doi:10.1111/cbdd.12803.
Kaushik, A., Bhatia, Y., Ali, S., Gupta, D. 2015. Gene network rewiring to study melanoma stage progression and elements essential for driving melanoma. PLOS ONE. 10(11):e0142443. doi: 10.1371/journal.pone.0142443.
Kaushik, A., Saraf, S., Mukherjee, S.K., Gupta, D. 2015. miRMOD: A tool for identification and analysis of 5′ and 3′ miRNA modifications in Next Generation Sequencing small RNA data. PeerJ. 3:e1332; DOI 10.7717/peerj.1332.
Saraf, A., Sanan-Mishra, N., Gursanscky, N.R., Carroll, B.J., Gupta, D., Mukherjee, S.K. 2015. 3′ and 5′ microRNA-end post-biogenesis modifications in plant transcriptomes: evidences from small RNA next generation sequencing data analysis. BBRC 467:892-9.
Jagga Z. and Gupta D. 2015. Machine learning for biomarker identification in cancer research- developments towards its clinical application. Future Medicine. 12:371-387.
Tajedin L., Anwar M., Gupta D, and Tuteja R. 2015. Comparative insight into nucleotide excision repair components of Plasmodium falciparum. DNA repair 28:60-72
Pandey R, Mohmmed A, Pierrot C, Khalife, J, Malhotra P and Gupta D. 2014. Genome wide in silico analysis of Plasmodium falciparum phosphatome. BMC Genomics. 15:1024
Bioinformatics Web servers
https://bioinfo.icgeb.res.in/faap/ Prediction method for fungal adhesins
https://bioinfo.icgeb.res.in/protvirdb/ a database of protozoan virulent proteins
https://bioinfo.icgeb.res.in/lipocalinpred/ Prediction method for Lipocalins
https://bioinfo.icgeb.res.in/codes/model.html Homology modelling of P. falciparum proteins.
https://bioinfo.icgeb.res.in/repeats ProtRepeatsDB database of amino acid repeats in genomes
https://bioinfo.icgeb.res.in/virulent SVM based prediction method for predicting virulent proteins.
https://bioinfo.icgeb.res.in/cyclinpred SVM based prediction method for predicting cyclin sequences.