Submission of Project Proposal (SEED FUND PHASE -3(1902))

Structure Based Drug Design against MurE from Acinetobacter baumannii

Dr. Amit Kumar Singh (Principal Investigator) Associate Professor Department of Biotechnology School of Engineering and Technology

Dr. J Muthukumaran (Co-PI-1) Assistant Professor Department of Biotechnology School of Engineering and Technology Sharda University

Mr. Sudeep Varshney (Co-PI-2) Assistant Professor Department of Computer Science & Engineering School of Engineering and Technology Sharda University

01) Name, address and contact details of PI(s) Dr. Amit Kumar Singh Associate Professor Department of Biotechnology School of Engineering and Technology Sharda University Email: [email protected] Phone: +91-7071522393

02). Name, address and contact details of Co-PI(s)

Dr. J Muthukumaran (Co-PI-1) Assistant Professor Department of Biotechnology School of Engineering and Technology Sharda University Email: [email protected] Phone: +91-6379942564

Mr. Sudeep Varshney (Co-PI-2) Assistant Professor Department of Computer Science & Engineering School of Engineering and Technology Sharda University Email: [email protected] Phone: +91-9718342590

03). Title: Structure Based Drug Design against MurE from Acinetobacter baumannii

04). ABSTRACT

UDP-N-acetylmuramoyl-L-alanyl-D-glutamate–2,6-diaminopimelate ligase (MurE) initiates reaction by adding meso-diaminopimelic acid to the nucleotide precursor UDP-N- acetylmuramoyl-L-alanyl-D-glutamate, during the synthesis of murein in the cytoplasm. This enzyme is crucial for microorganisms including A. baumannii, and is non-homologous to mammals; therefore, it can be used as potential antibacterial drug target. The crystallographic structure of UDP-N-acetylmuramoyl-L-alanyl-D-glutamate–L-lysine ligase (MurE) from Staphylococcus aureus with UDP-MurNAc- Ala-Glu-Lys (4C13) will be used to model the tertiary structure of UDP-N-acetylmuramoyl-L-alanine-D-glutamate: meso diaminopimelate ligase (MurE) from A. baumannii. The evaluated structure of MurE will be aligned on the template and nitrogen-termini, central, and carbon-termini domains will be obtained (the first, second, and third domain, respectively). From the conserved region, the active site residues will be investigated. Consequently, disrupting the active site amino acid chain could hamper the usual role of MurE. The inhibitor will be screen using online resource database such as Zinc, Pubchem and Drug bank. The natural ligands will be screened to eliminate molecules with unwanted properties for drug- likeness on the basis of physicochemical properties. For satisfied the ADMET parameters we will use ADMETSAR tool. Out of these, best compounds that have the best binding energies and docking results will be selected for molecular dynamics simulations.

05. Literature Survey

Nosocomial ailments are being infected by a variety of microorganisms, including bacteria, fungi, and viruses. Ailments could constitute of different reservoirs and is transmitted either via direct contact with any infected person or health care professionals, infirmary assets, visitants and a divers number of environmental agents (Santajit & Indrawattana, 2016). There are many bacteria which are infectious in nature as well as multiple drug resistant (MDR) due to their thread structural establishment and presence of antibiotic resistance genes (Azizi et al., 2016). Because of the unequivocal antibiotic resistance in the pathogenic enteric bacteria, it has started to exert a severe impact on the current medical technologies and is testing the limits of doctors to cure the infected individuals and has consequently led to a rapid increment in the death rate and healthcare monetary value (Russo et al., 2016). From the pathogenic bacteria which have evolved or acquired multi-drug resistance to antibiotics, a group of pathogens collectively referred to as ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp). Among ESKAPE, A. baumannii is the greatest cause for hospital-acquired contagions and therefore, particularly significant in nosocomial milieu (Santajit & Indrawattana, 2016). According to the world health organization, A. baumannii has been identified as one of the three most important problems causing microorganism for human health due to multi-drug resistance. Therefore, it is very important to find out novel antibiotic target gene in the bacteria and determine its three-dimensional protein to help with the exploration of antimicrobial agents (Kaushik et al., 2013).

The bacterial cell wall is a polymeric network of murein, which protects it against the excess amount of water in its surrounding that causes cell burst. UDP-N-acetylmuramoyl-L- alanyl-D-glutamate--2,6-diaminopimelate ligase (MurE) initiates the reaction by adding meso- diaminopimelic acid to nucleotide precursor which is known as UDP-N-acetylmuramoyl-L-alanyl- D-glutamate, during the synthesis of murein in the cytoplasm. The bacterial cell wall forming enzyme (MurE) is made up of linear repeating segments joined together via peptide bridges. Consequently, MurE has a remarkable impact on the survival of the bacteria due to its role in cell wall synthesis and is a crucial target for drug design (Gordon et al., 2001). In Acinetobacter baumannii, this ligase is the product of the MurE genes, located in between 303,112 to 304,611 nucleotides and contain 499 amino acid sequence (GenBank: CU459141.1). The molecular weight and theoretical PI of the enzyme is 54.98 kDa and 5.48 respectively and it is formed in the cytoplasm as a solitary segment (Gasteiger et al., 2005). This biological catalyst has been described and refined in different microorganisms such as S. aurous, E. coli, B. subtilis, and S. pneumonia (Gordon et al., 2001; Ruane et al., 2013). In the presence and absence of the cofactor adenosine di-phosphate, the crystal structure of MurE in complex with the substrate is determined from Staphylococcus aureus. The overall three-dimensional arrangement, domain style, and alpha/beta content of MurE enzyme from E. coli and Mycobacterium tuberculosis have similar with the crystallographic structure of MurE from Staphylococcus aureus (Ruane et al., 2013). After the formation of UDP-MurNAc, UDP-N-acetylmuramoyl-L-alanine-D-glutamate:meso diaminopimelate ligase (MurE) sequentially catalyze the ligation and polymerization of the pentapeptide chain. During the formation of the amino acid chain, the MurE enzyme cleaves adenosine tri-phosphate into adenosine di-phosphate and inorganic phosphate. The enzymatic mechanism of Mur-ligase from different bacterial species are similar, in which phosphoryl group activates the free carboxylic group and adenosine tri-phosphate is converted into adenosine di- phosphate as a reaction intermediate (Murzin, 1996). As a result, the amino group and the carbonyl carbon of the acyl phosphate are nucleophilically attacked in a condensation reaction. This whole process is undergoing in the presence of MurE enzyme during the formation of murein in the cytoplasm (Moraes et al., 2015). The different investigations indicated that the Mur enzymes have communal features: (1) Due to the presence of a divalent cation, the reaction mechanism are same, (2) In the amino acid chain it has similar Mur-residues in common other than adenosine tri- phosphate binding consensus sequence as well as (3) They have similar three-dimensional structures with three functional domains (Barreteau et al., 2008). This enzyme is crucial for microorganisms including A. baumannii and is non-homologous to mammals, therefore, it can be used as potential antibacterial drug target.

06. Detailed methodology 6.1. Homology modeling Acinetobacter baumannii (strain AYE) MurE, amino acid sequence will be retrieved from UniProt (https://www.uniprot.org) and accessed on (https://www.uniprot.org/uniprot/A0A0R4J6I7) using its enzyme entry name (EC 6.3.2.13). The sequence relatedness will be explored against the Protein Data Bank (PDB) in NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi) using BLASTp (Basic Local Alignment Search Tool) (Altschul et al., 1997) package in order to identify analogous template structures. The BLASTp comparison of a protein query with protein databases will be done in any combination to find the best sequence matches. Clustal Omega was used for sequence alignment of the query (MurE) with the template (Chojnacki, Cowley, Lee, Foix, & Lopez, 2017). Crystallographic structure of UDP- N-acetylmuramoyl-L-alanyl-D-glutamate--L-lysine ligase (MurE) from Staphylococcus aureus with UDP-MurNAc- Ala-Glu-Lys (4C13) will be used to modeled the tertiary structure of the UDP-N-acetylmuramoyl-L-alanine-D-glutamate:meso diaminopimelate ligase (MurE) from A. baumannii. The template structure will be retrieved from NCBI (PDB) with better sequence identity, resolution, query coverage, and E-value. MurE model structure will be done through modeling program which is known as Modeler9.21 (Sali & Blundell, 1993). The technique starts with by align the MurE sequence with the analogous 3-dimensional arrangement of the template. The modeled structure quality will be measured on the basis of the sequence similarity of the template with the query using alignment score values. Finally, the modeled structure will be built by carrying all the information taken from the template through three-dimensional restraints (Thompson, Higgins, & Gibson, 1994).

6.2. Energy minimization and Model evaluation The best model with the least DOPE score will be nominated for different optimization, evaluation and modification servers to make a change according to the best orientation of atoms using one or more parameters such as YASARA Energy Minimization Server for refining the overall physical and chemical conformation as well as R-group accuracy by changing hydrophobic interaction arrangement and ions (Krieger et al., 2009), Chiron protein structure refinement server to diminishing the hydrophobic interaction repulsion energy (Ramachandran, Kota, Ding, & Dokholyan, 2011), modrefiner (high-resolution protein structure refinement) for comparison and refine of modeled structure with native related hydrogen bonds, main chain conformation and R- group location (Xu & Zhang, 2011) and 3Drefine (protein structure refinement server) for collective bond minimization (covalent and hydrogen) (Bhattacharya, Nowotny, Cao, & Cheng, 2016). The stereochemistry quality measurement including Phi (Φ) and Psi (ψ) dissemination, backbone covalent forces as well as non-bonded inter-atom distances will be determined using PROCHECK through the Ramachandran plot (Laskowski, MacArthur, Moss, & Thornton, 1993). Distinguishing properly and improperly determined sections of protein structures based on their typical atomic interaction by statistical methods using ERRAT (Colovos & Yeates, 1993). Evaluation of the relationship between 3-dimensional amino acid sequence with 1-dimensional, which is indicated by α helices, β pleated sheets, loop, water soluble and insoluble amino acid, and finally associating the results with appropriate structures using Verify_3D model evaluation server (Bowie, Luthy, & Eisenberg, 1991).

6.3. Virtual high-throughput screening (vHTS) and Molecular docking Virtual high-throughput screening is a technique for finding appropriate natural compounds that have the capability to bind against functional molecules from big natural product libraries (Isa, 2019). In this plan, vHTS will be used to investigate the natural compounds that interact with MurE to get efficient antimicrobial compounds. Almost all medical institutions have molecular libraries either artificial or ordinary natural ligands in an immediately used form, however only a few qualifies for additional examination. In order to try the selected compounds in an enormous number, the researcher uses an exclusively computerized procedure known as high-density screening (Isa, 2019). Compounds from Zinc catalog will be used in this study. Virtual screening in the contradiction of MurE will be done with the help of PyRx 0.8 tool (Dallakyan & Olson, 2015). In order to get diverse hit orientation and the smallest energy state, the whole ligand molecules must be arranged with PyRx prior to molecular docking. MurE will be changed to macromolecule using AutoDock4 as well as the ligands also changed from the SDF file to Pdbqt file format. An organized form of all the information concerning atom type or category, movement and particle ions will be in the Pdbqt file format. The AutoDock4 Graphical User Interface created the Pdbqt. Docking is a technique used to investigate the appropriate conformation of a single molecule to hit another, most of the time the bulky molecule are proteins and lighter molecules are ligands to form a steady complex. Empirical free energy scoring function will be used to find information on the desired conformation, this assisted to guess the firmness as well as the looseness of the protein-ligand association (Alhaji Isa, Majumdar, Haider, & Kandasamy, 2018). PyRx 0.8 tool (Vina Wizard) will be used for hitting of MurE using ligand libraries to evaluate the binding conformation of the protein-ligand complex. So, in this study, docking will be performed using AutoDock4 and AutoDock Vina Wizard in PyRx 0.8 tool (Dallakyan & Olson, 2015). AutoDock4 and AutoDock Vina Wizard uses Python as programming language (scripting language) for recording free-energy scoring function and regression analysis. Pymol (EDU) Molecular Graphics System, Version 2.3. Schrodinger, LLC (DeLano, 2002) and Ligplot+ v.2.1 tool (Laskowski & Swindells, 2011) will be used for visualization of the target- ligand complex.

6.4. Pharmacokinetic analysis The difficulties of various ligands to use as medication were failed to ADMET (absorption, distribution, metabolism, excretion, and toxicity) tests. As a result, the AdmetSAR tool (http://lmmd.ecust.edu.cn/admetsar2) (Cheng et al., 2012), and the DataWarrior tool (http://www.openmolecules.org/datawarrior/) (Lipinski, Lombardo, Dominy, & Feeney, 2001; Veber et al., 2002) will be used to check the ADME and harmful properties of the selected ligands.

6.5. Molecular dynamic (MD) simulation MD simulations will be carried out on MurE-ligand complexes via an AMBERTOOLS8 package of Molecular Dynamic (Case et al., 2015) at 300 K at the molecular mechanics level. Initially explicit hydrogens will be added to the MurE–ligand complex via protonate 3D and this is the initial step for this technique. Parameters of drug molecules will be generated using antechamber. The ff14SB force field for proteins and the gaff force field for drug molecules in amber will be used to construct topology as well as coordinate file. To mimic the biological state for the complex system TIP3PBOX water is added in the octahedral box of 10 Å. The system is neutralized with Na+ ions buffer solution. Minimizing of the complete system will be used to remove the structural artifact arose during the model construction process, which is done by minimizing the system with alternative 5000 steps of conjugate gradient and steepest descent. In addition, after the elimination of the restrained, the system minimized for another steps of SD and CG with extra 2500 steps. Langevin dynamics temperature regulation will be used to heat the complete system with the temperature of 0.0k to 300k for 100,000 steps. For electrostatics treatment periodic boundary conditions along with PME summation will be used during the course of the MD simulations. Without pressure control, the collision frequency also set at 1 ps. The production of MD simulation will be carried out at fix conditions of temperature and pressure i.e. 300 K temperature and 1 atmosphere pressure.

07. Likely Outcome

-Generating the protein structure as well as finding hot spot activities, can be used to submit to protein database.

-Discovering of new and novel drug-able target site to the indicated bacteria and recommendation of this drug target protein in order to promote pharmacological sectors.

-An identification of potential anti-bacterial lead candidates is really challenging and cumbersome process in terms of time, experimental cost and manpower.

-However, the present comprehensive computational analysis will be helpful and beneficial because which saves time and long-term experimental studies.

-The proposed molecules obtained from this analysis is beneficial to the multi-disciplinary scientific groups for further experimental investigation.

08. Time line (Number of months = 12) 0-3 months: Obtaining the structural details of MurE using Homology modeling and finding its active sites 4-6 months: High-throughput virtual screening against MurE model protein using natural products from Zinc Data bases.

7-9 months:

Docking of screened molecules from virtual screening and molecular dynamic simulation of free protein and protein complexed with ligands.

10-12 months: - Preparation of Report - Preparation of major project proposal - Communication of research article

09. Impact analysis

We believe that structure based virtual screening is preliminary investigation and identification of highly promising lead molecules for the inhibition of the protein and the growth of the indicated bacteria. The proposed lead molecules used as an input for wet-lab conformation and clinical trial.

Furthermore, the proposed methodology will be very useful to screen the natural product as candidates to inhibit MurE protein involved as a drug target in Acenitobactor baumannii.

10. Budget and justifications

In order to perform the protein-ligand docking and MD simulations we really depend on high computational facility. Since we are planning to run MD simulation of both free protein and protein-ligand complexes, we are proposing following work stations Number of work-station: 1 External hard disk: 1 TB (For backup and storage) Estimated budget: 1 to 2 Lakhs

Purpose:

-Homology modeling - Protein and protein-ligand docking - Molecular dynamic simulation (both protein and protein ligand complex)

11. Undertaking that (i) the PI(s) will carry out work as proposed within a maximum period of one year, (ii) submit detailed proposal in parallel to funding agencies within six months of sanction.

The Principal Investigator will carry out the proposed research work entitled “Structure Based Drug Design against MurE from Acinetobacter baumannii” within the maximum period of 1 years including the completion of all the analysis and submission of results in the form of manuscript to the high impact journals.

Based on the results obtained from this preliminary investigation, the Principal Investigator will plan to write the detailed project proposal (which include protein ligand docking, MD simulation and validation of in silico results through experimental studies) and will submit in to funding agencies for financial support.

12. Brief Bio-Data

CV

Name Amit Kumar Singh Present Associate Professor Institutional Department of Biotechnology Address Sharda University, Phone: +91-9810518772 Email: [email protected]

Educational Qualifications

S. Exam Board/ Univ. Year of Specialized % Marks Div./ No. Passing Subject (s) Grade

All Institute of Biophysics Medical Sciences, New 1. Ph.D. 2010 Passed Delhi (Feb)

2. M.Sc. CCS University Campus, 2002 Industrial 74.4 1st Biotechnology

➢ Assistant Professor Department of Biotechnology Previous Experience School of Engineering and Technology Sharda University, Greater Noida (29th July 2013 to 31st October 2018)

➢ Young Scientist Fellow in DST-Fast Track Project, Department of Biophysics All India Institute of Medical Sciences New Delhi, (1st December 2010 to 29th July 2013) ➢ Scientist II in Biomedical Informatics Center Indian Council for Medical Research (ICMR), Head Quarter, New Delhi (March 2010 to 30th November 2010) ➢ CSIR-Senior Research Fellowship Department of Biophysics, All India Institute of Medical Sciences, New Delhi (From 2007 to 2010) ➢ CSIR-Junior Research fellowship Department of Biophysics, All India Institute of Medical Sciences, New Delhi (From 2005-2007)

Professional ➢ INSA Young Scientist Medal Award, 2012 from Indian National Science Recognition, Academy (INSA), New Delhi awards, fellowships ➢ DST Young Scientist Fellowship, 2010 from Department of Science and Technology (DST), New Delhi

➢ Selected, Nominated and Supported by Indian National Sciences Academy (INSA) and Israel Academy of Sciences and Humanities to attend the training workshop on “Sharing the Start-up Experience” at the Israel Academy of Sciences and Humanities in Jerusalem, Israel from 25th Nov to 28th Nov 2013.

➢ Qualified CSIR-NET-JRF (Junior Research Fellow), June 2004

➢ Qualified UGC-NET-JRF (Junior Research Fellow), December, 2004

➢ Qualified CSIR-NET-LS (Lecturer ship), June 2002

➢ Qualified GATE examination 2005

➢ Qualified GATE examination 2004

➢ Qualified GATE examination 2003

➢ Received Best Oral Presentation Award at National Conference on “Development and Advancement in Conservation, Propagation and Sustainable Utilization of Medicinal Plants” that is to be held on 20-21 January 2017 at School of Biotechnology, Gautam Buddha University, Greater Noida, sponsored by the National Medicinal Plants Board, Government of India, Ministry of AYUSH.

➢ Won ‘Best Poster Award’ from NSC-36, Chennai, 2007 from National Seminar on Crystallography ➢ Got level 4 (top level) in annual assessment for teaching and research, Sharda University.

➢ Received appreciation letter for best teacher in Biotechnology Department, Sharda University on the basis of feedback of students. Field of ➢ Protein Chemistry Specialization ➢ Protein Purification ➢ Protein Crystallization ➢ Protein Structure Determination ➢ Rational Structure Based Drug Design

List of Articles: Publications 1. Singh, A.K., Singh, N., Sharma, S., Singh, S.B., Kaur, P., Bhushan, A., Srinivasan, A. and Singh, T.P. (2008) Crystal structure of lactoperoxidase at 2.4 Å resolution: J. Mol. Biol., 376, 1060-1075.

2. Singh, A.K., Singh, N., Sharma, S., Shin, K., Takase, M., Kaur, P., Srinivasan, A. and Singh, T.P. (2009) Inhibition of lactoperoxidase by its own catalytic product: crystal structure of the hypothiocyanate-inhibited bovine lactoperoxidase at 2.3 Å resolution: Biophys. J., 96, 646-654.

3. Sheikh, I.A., Singh, A.K., Singh, N., Sinha, M., Singh, S.B., Bhushan, A., Kaur, P., Srinivasan, A., Sharma, S. and Singh, T.P. (2009) Structural evidence of substrate specificity in mammalian peroxidases: structure of the thiocyanate complex with lactoperoxidase and its interactions at 2.4 Å resolution: J. Biol. Chem., 284, 14849-14856.

4. Singh, A.K., Singh, N., Sinha, M., Bhushan, A., Kaur, P., Srinivasan, A., Sharma, S. and Singh, T.P. (2009) Binding modes of aromatic ligands to mammalian heme peroxidases with associated functional implications: crystal structures of lactoperoxidase complexes with acetylsalicylic acid, salicylhydroxamic acid, and benzylhydroxamic acid: J. Biol. Chem., 284, 20311-20318.

5. Singh, A.K., Kumar, R.P., Pandey, N., Singh, N., Sinha, M., Bhushan, A., Kaur, P., Sharma, S. and Singh, T.P. (2010) Mode of binding of the tuberculosis prodrug isoniazid to peroxidases: Crystal structure of bovine lactoperoxidase with isoniazid at 2.7 Å resolution: J. Biol. Chem., 285, 1569-1576.

6. Singh, A.K., Singh, N., Tiwari, A., Sinha, M., Kushwaha, G.S., Kaur, P., Srinivasan, A., Sharma, S. and Singh, T.P. (2010) First structural evidence for the mode of diffusion of aromatic ligands and ligand-induced closure of the hydrophobic channel in heme peroxidases:J. Biol. Inorg. Chem., 15, 1099-1107.

7. Singh, A.K., Pandey, N., Sinha, M., Kaur, P., Sharma, S. and Singh, T. P. (2011) Structural evidence for the order of preference of inorganic substrates in mammalian heme peroxidases: crystal structure of the complex of lactoperoxidase with four inorganic substrates, SCN-, I-, Br- and Cl- :Int. J. Biochem. Mol. Biol. 2, 328-339.

8. Singh A.K., Smith, M.L., Yamini S., Ohlsson, P., Sinha, M., Kaur, P., Sharma, S.,Paul., A.K., Singh, T.P. and Paul, K.G. (2012) Bovine Carbonyl Lactoperoxidase Structure at 2.0Å Resolution and Infrared Spectra as a Function of pH: Protein J. 31(7), 598-608.

9. Sharma, S, Singh A.K., Kaushik, S., Sinha, M., Singh, R.P., Sharma, P., Sirohi, H., Kaur, P. and Singh, T.P. (2013). Lactoperoxidase: Structural Insights into the Function, Ligand Binding and Inhibition. Int. J. Biochem. Mol. Biol. 4(3), 108-128.

10. Singh, R.P., Singh A., Sirohi, H., Singh A.K., Kaur, P., Sharma, S. and Singh, T.P. (2016). Dual binding mode of antithyroid drug methimazole to mammalian heme peroxidases: structural determination of the lactoperoxidase–methimazole complex at 1.97Å resolution. FEBS openbio, 14;6(7):640-50

11. P. Kaur, N. Pandey, A. K. Singh, M. Sinha, S. Sharma and T. P. Singh. (2011). First structural evidence for the order of preference of inorganic substrates by lactoperoxidase. Acta Cryst. A67, (2011). C768-C769.

12. Singh, A.K., Pandey, N., Sinha., M., Sharma, S., and Singh, T.P. (2012) Mammalian Heme Peroxidases and Mycobacterium tuberculosis, Understanding Tuberculosis - Deciphering the Secret Life of the Bacilli, Dr. Pere-Joan Cardona (Ed.), INTECH Open Access Publisher, ISBN: 978- 953-307-946-2

13. Sirohi H. V., Singh. P.K., Iqbal, N., Sharma, P., Singh, A.K., Kaur, P., Sharma, S., and Singh, T. P. (2017) Design of anti-thyroid drugs: Binding studies and structure determination of the complex of lactoperoxidase with 2-mercaptoimidazole at 2.30 Å resolution. Proteins. 2017 Oct;85(10):1882-1890.

14. Muluneh G., Mulat M., Singh A.K, and Kibret, M (2018). Prevalence and risk factors of salmonella in slaughtered cattle and slaughter house environment at Dessie slaughterhouse, Ethiopia. International Journal of Research and Analytical Reviews, 5(4): 61-72.

15 Muluneh G., Mulat M., and Singh A.K, (2018). In-vitro activities of selected medicinal plant extracts against post-harvest pathogenic fungi. Journal of Emerging Technologies and Innovative Research, 5(11): 162-171.

16 Muluneh G., and Singh A.K (2018). Plant protein as anti-ailment agents. International Journal of Research and Analytical Reviews, 5(4): 51-60.

17. Muluneh G., and Singh A.K (2019). Carcass Salmonella and Its Drug Resistance. International Journal of Pathogen Research, 2(1): 1-21; Article no.IJPR.4663

Extramural Research Grants:

S. No. Research Grant Funding Agency Amount (Rs.)

1. DST young Scientist Fast Track Project DST, New Delhi 23,00000 (Competed)

2. Research Grant from INSA, New Delhi, (2016-2019) INSA, New Delhi 15,00000 (Competed)

Number of Thesis Supervised:

PhD PG (MSc/M.Tech) UG (B.Tech/BSc)

4 (Persuing) 8 12

Travel Grant/Support Received

S. No. Conference/Workshop Funding Agency Type of Support 15h ICCBM held in Hamburg, 1. Germany from Sep 15-21, 2014 DST, New Delhi Full Support

“Sharing the Start-up Experience” at 2. the Israel Academy of Sciences and INSA, New Delhi Full Support Humanities in Jerusalem, Israel from 25th Nov to 28th Nov 2013

3. 39th Asian Crystallographic DBT, New Delhi Full Support Association (AsCa) held in Tsukuba, Japan from November 25-27, 2006

Place: New Delhi (Dr. Amit Kumar Singh) Date : 06-11-2019

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