Translational Bioinformatics

INDUSTRIAL BIOTECHNOLOGY  / Biofuels and Industrial Biotechnology

Research Interests

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.

Computational requirements

  • 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

Recent Publications

1. Singh N, Khan IA, Rashid S, Rashid S, Roy S, Kaushik K, Kumar A, Das P, Lalwani S, Gupta D et al: MicroRNA Signatures for Pancreatic Cancer and Chronic Pancreatitis: Expression Profiling by NGS. Pancreas 2024, 53(3):e260-e267.

2. Gupta V, Birla S, Varshney T, Somarajan BI, Gupta S, Gupta M, Panigrahi A, Singh A, Gupta D: In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence. Indian J Ophthalmol 2024, 72(3):339-346.

3. Birla S, Varshney T, Singh A, Sharma A, Panigrahi A, Gupta S, Gupta D, Gupta V: Machine learning-assisted prediction of trabeculectomy outcomes among patients of juvenile glaucoma by using 5-year follow-up data. Indian Journal of Ophthalmology 2024:10.4103/IJO.IJO_2009_4123.

4. Yadav S, Ahamad S, Gupta D, Mathur P: Lead optimization, pharmacophore development and scaffold design of protein kinase CK2 inhibitors as potential COVID-19 therapeutics. J Biomol Struct Dyn 2023, 41(5):1811-1827.

5. Tyagi N, Roy S, Vengadesan K, Gupta D: Multi-omics approach for identifying CNV-associated lncRNA signatures with prognostic value in prostate cancer. Non-coding RNA Research 2023.

6. Tyagi N, Mehla K, Gupta D: Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles. PLoS One 2023, 18(3):e0281637.

7. Swain SP, Ahamad S, Samarth N, Singh S, Gupta D, Kumar S: In silico studies of alkaloids and their derivatives against N-acetyltransferase EIS protein from Mycobacterium tuberculosis. Journal of Biomolecular Structure and Dynamics 2023:1-15.

8. Sharma A, Garg A, Ramana J, Gupta D: VirulentPred 2.0: an improved method for prediction of virulent proteins in bacterial pathogens. Protein Science 2023, n/a(n/a):e4808.

9. Roy S, Gupta D: Analysis of breast cancer next-generation sequencing datasets for identifying fusion genes responsible for the cancer progression. Informatics in Medicine Unlocked 2023:101306.

10. Joon HK, Thalor A, Gupta D: Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures. Comput Biol Med 2023, 165:107430.

11. Jameel E, Madhav H, Agrawal P, Raza MK, Ahmedi S, Rahman A, Shahid N, Shaheen K, Gajra CH, Khan A et al: Identification of new oxospiro chromane quinoline-carboxylate antimalarials that arrest parasite growth at ring stage. J Biomol Struct Dyn 2023:1-22.

12. Haque A, Khan MWA, Alenezi KM, Soury R, Khan MS, Ahamad S, Mushtaque M, Gupta D: Synthesis, Characterization, Antiglycation Evaluation, Molecular Docking, and ADMET Studies of 4-Thiazolidinone Derivatives. ACS Omega 2023.

13. Haque A, Alenezi KM, Khan MWA, Soury R, Khan MS, Ahamad S, Ahmad S, Gupta D: In silico evaluation of 4-thiazolidinone-based inhibitors against the receptor for advanced glycation end products (RAGE). J Biomol Struct Dyn 2023:1-12.

14. Gupta V, Birla S, Varshney T, Somarajan BI, Gupta S, Gupta M, Panigrahi A, Singh A, Gupta D: In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence. Indian Journal of Ophthalmology 2023.

15. Ahmad S, Gupta D, Ahmed T, Islam A: Designing of new tetrahydro-beta-carboline-based ABCG2 inhibitors using 3D-QSAR, molecular docking, and DFT tools. J Biomol Struct Dyn 2023:1-12.

16. Ahamad S, Hema K, Gupta D: Identification of Novel Tau-Tubulin Kinase 2 Inhibitors Using Computational Approaches. ACS Omega 2023.

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.