Computational Approaches for Antibacterial Drug Discovery

  • Prachi SrivastavaEmail author
  • Neha Srivastava


With the emerging problem of antibacterial drug resistance and resurgence of disease, there is immediate need for the development of new and effective therapeutic interventions to combat pathogens. Traditional methods of drug discovery are very expensive and time consuming, and carry high error rates. Computational approaches, on the other hand, predict drug targets and therapeutic agents with fewer side effects (i.e., minimal disease resurgence) and reduce the time and cost for discovery. Thus, the computational approaches have become a crucial part of drug development, as they streamline processing and testing in a cost-effective manner. This chapter highlights the significance and progress of computational approaches in antibacterial drug discovery.


Anti-bacterial drug resistance Disease resurgence Disease pathogen Computational approaches Drug discovery 


  1. Bajic et al. (2016, March/April) In silico toxicology: Computational methods for the prediction of chemical toxicity (Vol. 6). WIREs Computational Molecular Science published by Wiley.Google Scholar
  2. Belén, A., Pavón, I., & Maiden, M. C. J. (2009). Multilocus sequence typing. Methods in Molecular Biology, 551, 129–140.CrossRefGoogle Scholar
  3. Borhani, D. W. (2012). The future of molecular dynamics simulations in drug discovery. Journal of Computer-Aided Molecular Design, 26, 15–26.CrossRefGoogle Scholar
  4. Brooks, B.R. (2009, July 30). CHARMM: The biomolecular simulation program. Journal of Computational Chemistry 30(10): 1545–1614.Google Scholar
  5. Chen, N. Y. (1977). The biological functions of low-frequency phonons. Scientia Sinica, 20, 447–457.Google Scholar
  6. Yi Chen et al. (2017). Whole genome and core genome multilocus sequence typing and single nucleotide 2 polymorphism analyses of Listeria monocytogenes associated with an outbreak linked to 3 cheese, United States, 2013. Applied and Environmental Microbiology.Google Scholar
  7. Cheng, F., Li, W., Liu, G., & Tang, Y. (2013). In silico ADMET prediction: Recent advances, current challenges and future trends. Current Topics in Medicinal Chemistry., 13(11), 1273.CrossRefGoogle Scholar
  8. Chou, K. C. (2004). Review: Structural bioinformatics and its impact to biomedical science. Current Medicinal Chemistry, 11, 2105–2134.CrossRefGoogle Scholar
  9. Cumming, J. G., Davis, A. M., Muresan, S., Haeberlein, M., & Chen, H. (2013). Chemical predictive modelling to improve compound quality. Nature Reviews Drug Discovery, 12, 948–962.CrossRefGoogle Scholar
  10. David E. Shaw (2006). Scalable algorithms for molecular dynamics simulations on commodity clusters. Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, November 11–17, 2006.Google Scholar
  11. de Kraker, M. E. A., Stewardson, A. J., & Harbarth, S. (2016, November). Will 10 million people die a year due to antimicrobial resistance by 2050? PLoS Medicine 13(11): e1002184.Google Scholar
  12. Dearden, J. C. (2003). In silico prediction of drug toxicity. Journal of Computer-Aided Molecular Design, 17(4), 119–127.CrossRefGoogle Scholar
  13. Del Tordello, E., Rappuoli, R., & Delany, I. (2017) Reverse vaccinology. Human vaccines. Academic press.Google Scholar
  14. Dubey, R. D., Chandraker, G., Sahu, P. K., Paroha, S., Sahu, D. K., Verma, S., Daharwal, S. J., & Reddy, S. L. N. P. (2011). Computer aided drug design: A review. Research Journal of Engineering and Technology, 2(3), 104–108.Google Scholar
  15. ECDC. (2016). Expert Opinion on Whole Genome Sequencing for Public Health Surveillance.Google Scholar
  16. Ekins, S., Spektor, A. C., Clark, A. M., Dole, K., & Bunin, B. A. (2017). Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB). Drug Discovery Today, 22(3), 555–565.CrossRefGoogle Scholar
  17. Frantisek, F., Christopher, G. J., & Smytha, W. F. (2007). A simple, fast hybrid pattern-matching algorithm. Journal of Discrete Algorithms, 5(4), 682–695.CrossRefGoogle Scholar
  18. Ghanem, M., Wang, L., Zhang, Y., Edwards, S., Lu, A., Ley, D., & El-Gazzar, M. (2018). Core genome multilocus sequence typing: A standardized approach for molecular typing of mycoplasma gallisepticum. Journal of Clinical Microbiology, 56(1), e01145–e01117.PubMedGoogle Scholar
  19. Hunter, S. B., Vauterin, P., Lambert-Fair, M. A., Van Duyne, M. S., Kubota, K., Graves, L., Wrigley, D., Barrett, T., & Ribot, E. (2005). Establishment of a universal size standard strain for use with the PulseNet standardized pulsed-field gel electrophoresis protocols: Converting the national databases to the new size standard. Journal of Clinical Microbiology, 43(3), 1045–1050. Scholar
  20. Ishibashi, et al. (2016). Structure-based drug discovery for prion disease using a novel binding simulation. EBioMedicine, 9, 238–249.CrossRefGoogle Scholar
  21. Jain et al. (2014). Homology modeling and molecular dynamics simulations of a protein serine/threonine phosphatase stp1 in Staphylococcus aureus N315: a potential drug target (pp. 592–599). Received 20 Dec 2013, Accepted 03 Mar 2014, Published online: 15 Apr 2014.Google Scholar
  22. Jolley, K. A., & Maiden, M. C. (2010). BIGSdb: Scalable analysis of bacterial genome variation at the population level. BMC Bioinformatics, 11, 595.CrossRefGoogle Scholar
  23. Jones, H., & Rowland, Y. K. (2013). Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT: Pharmacometrics & Systems Pharmacology, 2(8), e63.Google Scholar
  24. Karavadi et al. (2014a) A novel approach of inter strain docking in accelerating the process of lead identification in pneumonia. Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115.Google Scholar
  25. Karavadi, et al. (2014b). Homology modeling of polymerase and cps biosynthesis proteins in cgsp14 strain of streptococcus pneumonia and its ligand identification: An insilico approach. I. Asian Journal of Pharmaceutical and Clinical Research, 7(Suppl 2), 162–165.Google Scholar
  26. Khursheed, A. (2013). In silico development of broad spectrum antibacterial by targeting peptide deformylase.
  27. Kohl, et al. (2014). Whole-genome-based Mycobacterium tuberculosis surveillance: A standardized, portable, and expandable approach. Journal of Clinical Microbiology, 52(7), 2479–2486.CrossRefGoogle Scholar
  28. Kujawski, J., Bernard, M. K., Janusz, A., & Weronika, K. (2012). Prediction of log P: Alogps application in medicinal chemistry education. Journal of Chemical Education, 89(1), 64–67.CrossRefGoogle Scholar
  29. Lapidus, A., Antipov, D., Bankevich, A., Gurevich, A., Korobeynikov, A., Nurk, S., Prjibelski, A., Safonova, Y., Vasilinetc, I., & Pevzner, P. A. (2014) New Frontiers of Genome Assembly with SPAdes 3.0. (poster).Google Scholar
  30. Lipinski, C. A, Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001, March ). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews 46(1–3): 3–26. Scholar
  31. Malkhed et al. (2013) Identification of novel leads applying in silico studies for Mycobacterium multidrug resistant (MMR) protein (pp. 1889–1906). Received 01 Jan 2013, Accepted 04 Sep 2013, Published online: 14 Oct 2013.Google Scholar
  32. Mishra and Srivastava. (2017). Review on computational approaches for identification of new targets and compounds for fighting against Filariasis. The Open Bioactive Compounds Journal 5.Google Scholar
  33. Motin, V. L., & Torres, A. G.. (2009). Molecular approaches to bacterial vaccines (pp. 63–76). Vaccines, 2009, Academic press.Google Scholar
  34. Musyoka et al. (2016). Structure based docking and molecular dynamic studies of Plasmodial cysteine proteases against a South African natural compound and its analogs. Scientific Reports 6: 23690.Google Scholar
  35. Nadeem, et al. (2015). Synthesis, spectral characterization and in vitro antibacterial evaluation and Petra/Osiris/Molinspiration analyses of new Palladium (II) iodide complexes with thioamides. Alexandria Journal of Medicine, 52, 279–228.CrossRefGoogle Scholar
  36. Nastasa, et al. (2018). Antibacterial Evaluation and Virtual Screening of New Thiazolyl-Triazole Schiff Bases as Potential DNA-Gyrase Inhibitors. International Journal of Molecular Sciences, 19, 222.CrossRefGoogle Scholar
  37. Osman, K. M., Ali, M. M., Radwan, M. I., Kim, H. K. & Han, J. (2009, July 21). Comparative Proteomic Analysis on Salmonella Gallinarum and Salmonella Enteritidis Exploring Proteins That May Incorporate Host Adaptation in Poultry. Journal of Proteomics 72(5): 815–821. ISSN 1876-7737.Google Scholar
  38. Parrott, N., Hainzl, D., Scheubel, E., Krimmer, S., Boetsch, C., Guerini, E., & Martin-Facklam, M. (2014). Physiologically based absorption modelling to predict the impact of drug properties on pharmacokinetics of bitopertin. AAPS Journal, 16(5), 1077–1084.CrossRefGoogle Scholar
  39. Parthasarathi, R., & Dhawan, A.. (2018). In Silico Approaches for Predictive Toxicology. In Vitro Toxicology (pp. 91–109).
  40. Peter, E. (2010). Molecular structure input on the web. Journal of Cheminformatics, 2, 1.CrossRefGoogle Scholar
  41. Phillips, et al. (2005). Scalable molecular dynamics with NAMD. Journal of Computational Chemistry, 26, 1781–1802.CrossRefGoogle Scholar
  42. Powell, M. (2018, April 23). ECCMID18: QIAGEN announces European launch of platform for syndromic insights, QIAstat-Dx. Infectious Diseases Hub. Retrieved 28 June 2018.
  43. Ramaswamy, et al. (2017). Molecular rationale behind the differential substrate specificity of RND transporters AcrB and AcrD. Scientific Reports volume, 7.Google Scholar
  44. Rappuoli, R. et al. (2012, October). Developing vaccines in the era of genomics: a decade of reverse vaccinology. Clinical Microbiology and Infection 18 (Supplement 5).Google Scholar
  45. Salomon-Ferrer, R., Case, D. A., & Walker, R. C. (2013). An overview of the Amber biomolecular simulation package. WIREs Comput. Mol. Sci., 3, 198–210.CrossRefGoogle Scholar
  46. Sandhaus, et al. (2018). Discovery of novel bacterial topoisomerase I inhibitors by use of in silico docking and in vitro assays. Scientific Reports volume, 8, 1437.CrossRefGoogle Scholar
  47. Srivastava and Tiwari. (2017). Critical Role of Computer Simulations in Drug Discovery and Development. Current Topics in Medicinal Chemistry, 17.Google Scholar
  48. Tetko, I. V., & Bruneau, P. (2004). Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database. J Pharm Sci.Google Scholar
  49. The Protein Data Bank and the challenge of structural genomics. (2000). Nature. Structural Biology, 7(11), 957–959. Scholar
  50. Wasinger, V. C., Cordwell, S. J., Cerpa-Poljak, A., Yan, J. X., Gooley, A. A., Wilkins, M. R., Duncan, M. W., Harris, R., Williams, K. L. & Humphery-Smith, I. (1995, July). Progress with Gene-Product Mapping of the Mollicutes: Mycoplasma Genitalium. Electrophoresis 16(7): 1090–1094. ISSN 0173-0835.Google Scholar
  51. Zerbino, D. R. (2010). Using the Velvetde novo Assembler for Short-Read Sequencing Technologies. In Andreas D. Baxevanis (ed.), Using the Velvet de novo assembler for short-read sequencing technologies. pp. Unit 11.5. ISBN 0471250953.
  52. Zhou, G. P., Huang, R. B., & Troy, F. A. (2015). 3D structural conformation and functional domains of poly sialyltransferase st8sia iv required for polysialylation of neural cell adhesion molecules. PPL, 22, 137–148.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.AMITY Institute of Biotechnology, AMITY UniversityLucknowIndia
  2. 2.Dr. A.P.J. Abdul Kalam Technical UniversityLucknowIndia

Personalised recommendations