Skip to main content

Computer-Aided Drug Design: An Update

  • Protocol
  • First Online:
Antibiotics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2601))

Abstract

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Blaskovich MAT (2020) Antibiotics special issue: challenges and opportunities in antibiotic discovery and development. ACS Infect Dis 6:1286–1288

    Article  CAS  Google Scholar 

  2. Ribeiro da Cunha B, Fonseca LP, Calado CRC (2019) Antibiotic discovery: where have we come from, where do we go? Antibiotics 8:45

    Article  PubMed  PubMed Central  Google Scholar 

  3. Yu W, Guvench O, MacKerell AD (2013) Computational approaches for the design of protein–protein interaction inhibitors. In: Zinzalla G (ed) Understanding and exploiting protein–protein interactions as drug targets. Future Science Ltd., London, pp 99–102

    Google Scholar 

  4. Yu W, MacKerell AD (2017) Computer-aided drug design method. In: Sass P (ed) Antibiotics methods and protocols. Methods in Molecular Biology. Springer Science+Business Media, New York, pp 85–106

    Chapter  Google Scholar 

  5. Krebs FS, Esque J, Stote RH (2019) A computational study of the molecular basis of antibiotic resistance in a DXR mutant. J Comput Aided Mol Des 33:927–940

    Article  PubMed  CAS  Google Scholar 

  6. Li J, Beuerman R, Verma CS (2020) Dissecting the molecular mechanism of colistin resistance in mcr-1 bacteria. J Chem Inf Model 60:4975–4984

    Article  PubMed  CAS  Google Scholar 

  7. Liu Y, Wang Y, Walsh TR, Yi L, Zhang R, Spencer J, Doi Y, Tian G, Dong B, Huang X, Yu L, Gu D, Ren H, Chen X, Lv L, He D, Zhou H, Liang Z, Liu J, Shen J (2016) Emergence of plasmid-mediated colistin resistance mechanism MCR-1 in animals and human beings in China: a microbiological and molecular biological study. Lancet Infect Dis 16:161–168

    Article  PubMed  Google Scholar 

  8. O’Neill MJ, Wilks A (2013) The P. aeruginosa Heme binding protein PhuS is a Heme oxygenase titratable regulator of Heme uptake. ACS Chem Biol 8:1794–1802

    Article  PubMed  PubMed Central  Google Scholar 

  9. Nguyen AT, O'Neill MJ, Watts AM, Robson CL, Lamont IL, Wilks A, Oglesby-Sherrouse AG (2014) Adaptation of iron homeostasis pathways by a Pseudomonas aeruginosa Pyoverdine mutant in the cystic fibrosis lung. J Bacteriol 196:2265–2276

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liang D, Robinson E, Hom K, Yu W, Nguyen N, Li Y, Zong Q, Wilks A, Xue F (2018) Structure-based design and biological evaluation of inhibitors of the pseudomonas aeruginosa heme oxygenase (pa-HemO). Bioorg Med Chem Lett 28:1024–1029

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Xu X, Godoy-Ruiz R, Adipietro KA, Peralta C, Ben-Hail D, Varney KM, Cook ME, Roth BM, Wilder PT, Cleveland T, Grishaev A, Neu HM, Michel SL, Yu W, Beckett D, Rustandi RR, Lancaster C, Loughney JW, Kristopeit A, Christanti S, Olson JW, MacKerell AD, Des Georges A, Pozharski E, Weber DJ (2020) Structure of the cell-binding component of the Clostridium difficile binary toxin reveals a di-heptamer macromolecular assembly. Proc Natl Acad Sci U S A 117:1049–1058

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Varney KM, Bonvin AMJJ, Pazgier M, Malin J, Yu W, Ateh E, Oashi T, Lu W, Huang J, Diepeveen-de Buin M, Bryant J, Breukink E, MacKerell AD, de Leeuw EPH (2013) Turning defense into offense: Defensin mimetics as novel antibiotics targeting lipid II. PLoS Pathog 9:e1003732

    Article  PubMed  PubMed Central  Google Scholar 

  13. Fletcher S, Yu W, Huang J, Kwasny SM, Chauhan J, Opperman TJ, MacKerell AD, de Leeuw EPH (2015) Structure-activity exploration of a small-molecule lipid II inhibitor. Drug Des Devel Ther 9:2383–2394

    PubMed  PubMed Central  CAS  Google Scholar 

  14. Chauhan J, Yu W, Cardinale S, Opperman TJ, MacKerell AD, Fletcher S, de Leeuw EPH (2020) Optimization of a Benzothiazole Indolene scaffold targeting bacterial cell wall assembly. Drug Des Devel Ther 14:567–574

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Tooke CL, Hinchliffe P, Bragginton EC, Colenso CK, Hirvonen VHA, Takebayashi Y, Spencer J (2019) β-Lactamases and β-lactamase inhibitors in the 21st century. J Mol Biol 431:3472–3500

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Guvench O, MacKerell AD (2009) Computational fragment-based binding site identification by ligand competitive saturation. PLoS Comput Biol 5:e1000435

    Article  PubMed  PubMed Central  Google Scholar 

  17. Raman EP, Yu W, Guvench O, MacKerell AD (2011) Reproducing crystal binding modes of ligand functional groups using site-identification by ligand competitive saturation (SILCS) simulations. J Chem Inf Model 51:877–896

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Raman EP, Yu W, Lakkaraju SK, MacKerell AD (2013) Inclusion of multiple fragment types in the site identification by ligand competitive saturation (SILCS) approach. J Chem Inf Model 53:3384–3398

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Parvaiz N, Ahmad F, Yu W, MacKerell AD, Azam SS (2021) Discovery of beta-lactamase CMY-10 inhibitors for combination therapy against multi-drug resistant Enterobacteriaceae. PLoS One 16:e0244967

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Faller C, Raman EP, MacKerell AD, Guvench O (2015) Site identification by ligand competitive saturation (SILCS) simulations for fragment-based drug design. In: Klon AE (ed) Fragment-based methods in drug discovery. Springer, New York, pp 75–87

    Google Scholar 

  21. Yu W, Lakkaraju S, Raman EP, MacKerell AD (2014) Site-identification by ligand competitive saturation (SILCS) assisted pharmacophore modeling. J Comput Aided Mol Des 28:491–507

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Yu W, Lakkaraju SK, Raman EP, Fang L, MacKerell AD (2015) Pharmacophore modeling using site-identification by ligand competitive saturation (SILCS) with multiple probe molecules. J Chem Inf Model 55:407–420

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Abel R, Wang L, Harder ED, Berne BJ, Friesner RA (2017) Advancing drug discovery through enhanced free energy calculations. Acc Chem Res 50:1625–1632

    Article  PubMed  CAS  Google Scholar 

  24. King E, Aitchison E, Li H, Luo R (2021) Recent developments in free energy calculations for drug discovery. Front Mol Biosci 8:712085

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Chen J, Wang X, Pang L, Zhang JZH, Zhu T (2019) Effect of mutations on binding of ligands to guanine riboswitch probed by free energy perturbation and molecular dynamics simulations. Nucleic Acids Res 47:6618–6631

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Fowler PW (2020) How quickly can we predict trimethoprim resistance using alchemical free energy methods? Interface Focus 10:20190141

    Article  PubMed  PubMed Central  Google Scholar 

  27. Vamathevan J, Clark D, Czodrowski P, Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M, Zhao S (2019) Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 18:463–477

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Jackson PC (2019) Introduction to artificial intelligence: third edition. Dover Publications Inc, Mineola, New York

    Google Scholar 

  29. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL (2018) Artificial intelligence in radiology. Nat Rev Cancer 18:500–510

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Manning CD (2015) Computational linguistics and deep learning. Comput Linguist 41:701–707

    Article  Google Scholar 

  31. Owens JD, Houston M, Luebke D, Green S, Stone JE, Phillips JC (2008) GPU computing. Proc IEEE 96:879–899

    Article  Google Scholar 

  32. Melo MCR, Maasch JRMA, de la Fuente-Nunez C (2021) Accelerating antibiotic discovery through artificial intelligence. Commun Biol 4:1050

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Anahtar MN, Yang JH, Kanjilal S (2021) Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J Clin Microbiol 59:e01260–e01220

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Hyun JC, Kavvas ES, Monk JM, Palsson BO (2020) Machine learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens. PLoS Comput Biol 16:e1007608

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackerman Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R, Collins JJ (2020) A deep learning approach to antibiotic discovery. Cell 180:688–702

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, Johnston SE, Vrcic A, Wong B, Khan M, Asiedu J, Narayan R, Mader CC, Subramanian A, Golub TR (2017) The drug repurposing hub: a next-generation drug library and information resource. Nat Med 23:405–408

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Towns J, Cockerill T, Dahan M, Foster I, Gaither K, Grimshaw A, Hazlewood V, Lathrop S, Lifka D, Peterson GD, Roskies R, Scott JR, Wilkins-Diehr N (2014) XSEDE: accelerating scientific discovery. Comput Sci Eng 16:62–74

    Article  Google Scholar 

  38. Kotas C, Naughton T, Imam N (2018) A comparison of Amazon Web Services and Microsoft Azure cloud platforms for high performance computing. 2018 IEEE International Conference on Consumer Electronics (ICCE), pp 1–4

    Google Scholar 

  39. Brooks BR, Brooks CL, Mackerell AD, Nilsson L, Petrella RJ, Roux B, Won Y, Archontis G, Bartels C, Boresch S, Caflisch A, Caves L, Cui Q, Dinner AR, Feig M, Fischer S, Gao J, Hodoscek M, Im W, Kuczera K, Lazaridis T, Ma J, Ovchinnikov V, Paci E, Pastor RW, Post CB, Pu JZ, Schaefer M, Tidor B, Venable RM, Woodcock HL, Wu X, Yang W, York DM, Karplus M (2009) CHARMM: the biomolecular simulation program. J Comput Chem 30:1545–1614

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. Van Der Spoel D, Lindahl E, Hess B, Groenhof G, Mark AE, Berendsen HJC (2005) GROMACS: fast flexible and free. J Comput Chem 26:1701–1718

    Article  PubMed  Google Scholar 

  41. Phillips JC, Hardy DJ, Maia JD, Stone JE, Ribeiro JV, Bernardi RC, Buch R, Fiorin G, Hénin J, Jiang W, McGreevy R (2020) Scalable molecular dynamics on CPU and GPU architectures with NAMD. J Chem Phys 153:044130

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Eastman P, Swails J, Chodera JD, McGibbon RT, Zhao Y, Beauchamp KA, Wang L, Simmonett AC, Harrigan MP, Stern CD, Wiewiora RP, Brooks BR, Pande VS (2017) OpenMM 7: rapid development of high performance algorithms for molecular dynamics. PLoS Comput Biol 13:e1005659

    Article  PubMed  PubMed Central  Google Scholar 

  43. Hynninen A, Crowley MF (2014) New faster CHARMM molecular dynamics engine. J Comput Chem 35:406–413

    Article  PubMed  CAS  Google Scholar 

  44. Kohnke B, Kutzner C, Grubmuller H (2020) A GPU-accelerated fast multipole method for GROMACS: performance and accuracy. J Chem Theory Comput 16:6938–6949

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Harvey MJ, Giupponi G, De Fabritiis G (2009) ACEMD: accelerating biomolecular dynamics in the microsecond time scale. J Chem Theory Comput 5:1632–1639

    Article  PubMed  CAS  Google Scholar 

  46. Bernstein FC, Koetzle TF, Williams GJB, Meyer EF Jr, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The protein data bank: a computer-based archival file for macromolecular structures. J Mol Biol 112:535–542

    Article  PubMed  CAS  Google Scholar 

  47. Renaud JP, Chari A, Ciferri C, Liu W, Remigy H, Stark H, Wiesmann C (2018) Cryo-EM in drug discovery: achievements limitations and prospects. Nat Rev Drug Discov 17:471–492

    Article  PubMed  CAS  Google Scholar 

  48. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, Millán C, Park H, Adams C, Glassman CR, DeGiovanni A, Pereira JH, Rodrigues AV, van Dijk AA, Ebrecht AC, Opperman DJ, Sagmeister T, Buhlheller C, Pavkov-Keller T, Rathinaswamy MK, Dalwadi U, Yip CK, Burke JE, Garcia KC, Grishin NV, Adams PD, Read RJ, Baker D (2021) Accurate prediction of protein structures and interactions using a three-track neural network. Science 373:871–876

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, Bridgland A, Cowie A, Meyer C, Laydon A, Velankar S, Kleywegt GJ, Bateman A, Evans R, Pritzel A, Figurnov M, Ronneberger O, Bates R, Kohl SAA, Potapenko A, Ballard AJ, Romera-Paredes B, Nikolov S, Jain R, Clancy E, Reiman D, Petersen S, Senior AW, Kavukcuoglu K, Birney E, Kohli P, Jumper J, Hassabis D (2021) Highly accurate protein structure prediction for the human proteome. Nature 596:590–596

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Varadi M, Anyango S, Deshpande M, Nair S, Natassia C, Yordanova G, Yuan D, Stroe O, Wood G, Laydon A, Žídek A, Green T, Tunyasuvunakool K, Petersen S, Jumper J, Clancy E, Green R, Vora A, Lutfi M, Figurnov M, Cowie A, Hobbs N, Kohli P, Kleywegt G, Birney E, Hassabis D, Velankar S (2022) AlphaFold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res 50:D439–D444

    Article  PubMed  CAS  Google Scholar 

  52. MacKerell AD, Bashford D, Bellott M, Dunbrack RL, Evanseck JD, Field MJ, Fischer S, Gao J, Guo H, Ha S, Joseph-McCarthy D, Kuchnir L, Kuczera K, Lau FTK, Mattos C, Michnick S, Ngo T, Nguyen DT, Prodhom B, Reiher WE, Roux B, Schlenkrich M, Smith JC, Stote R, Straub J, Watanabe M, Wiórkiewicz-Kuczera J, Yin D, Karplus M (1998) All-atom empirical potential for molecular modeling and dynamics studies of proteins. J Phys Chem B 102:3586–3616

    Article  PubMed  CAS  Google Scholar 

  53. Best RB, Zhu X, Shim J, Lopes PEM, Mittal J, Feig M, MacKerell AD (2012) Optimization of the additive CHARMM all-atom protein force field targeting improved sampling of the backbone ϕ ψ and side-chain χ1 and χ2 dihedral angles. J Chem Theory Comput 8:3257–3273

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Vanommeslaeghe K, Hatcher E, Acharya C, Kundu S, Zhong S, Shim J, Darian E, Guvench O, Lopes P, Vorobyov I, Mackerell AD (2010) CHARMM general force field: a force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. J Comput Chem 31:671–690

    PubMed  PubMed Central  CAS  Google Scholar 

  55. Yu W, He X, Vanommeslaeghe K, MacKerell AD (2012) Extension of the CHARMM general force field to sulfonyl-containing compounds and its utility in biomolecular simulations. J Comput Chem 33:2451–2468

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Cornell WD, Cieplak P, Bayly CI, Gould IR, Merz KM, Ferguson DM, Spellmeyer DC, Fox T, Caldwell JW, Kollman PA (1995) A second generation force field for the simulation of proteins, nucleic acids and organic molecules. J Am Chem Soc 117:5179–5197

    Article  CAS  Google Scholar 

  57. Wang J, Wolf RM, Caldwell JW, Kollman PA, Case DA (2004) Development and testing of a general amber force field. J Comput Chem 25:1157–1174

    Article  PubMed  CAS  Google Scholar 

  58. Vanommeslaeghe K, MacKerell AD (2012) Automation of the CHARMM General Force field (CGenFF) I: bond perception and atom typing. J Chem Infor Model 52:3144–3154

    Article  CAS  Google Scholar 

  59. Vanommeslaeghe K, Raman EP, MacKerell AD (2012) Automation of the CHARMM General Force field (CGenFF) II: assignment of bonded parameters and partial atomic charges. J Chem Infor Model 52:3155–3168

    Article  CAS  Google Scholar 

  60. Kumar A, Yoluk O, MacKerell AD (2019) FFParam: standalone package for CHARMM additive and Drude polarizable force field parametrization of small molecules. J Comput Chem 41:958–970

    Article  PubMed  PubMed Central  Google Scholar 

  61. Lopes PEM, Huang J, Shim J, Luo Y, Li H, Roux B, MacKerell AD (2013) Polarizable force field for peptides and proteins based on the classical Drude oscillator. J Chem Theory Comput 9:5430–5449

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Lemkul JA, Huang J, Roux B, MacKerell AD (2016) An empirical polarizable force field based on the classical Drude oscillator model: development history and recent applications. Chem Rev 116:4983–5013

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Ponder JW, Wu C, Ren P, Pande VS, Chodera JD, Schnieders MJ, Haque I, Mobley DL, Lambrecht DS, DiStasio RA, Head-Gordon M, Clark GNI, Johnson ME, Head-Gordon T (2010) Current status of the amoeba polarizable force field. J Phys Chem B 114:2549–2564

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  64. Huang J, Lopes PEM, Roux B, MacKerell AD (2014) Recent advances in polarizable force fields for macromolecules: microsecond simulations of proteins using the classical Drude oscillator model. J Phys Chem Lett 5:3144–3150

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Huang J, Lemkul JA, Eastman PK, MacKerell AD (2018) Molecular dynamics simulations using the drude polarizable force field on GPUs with OpenMM: implementation validation and benchmarks. J Comput Chem 39:1682–1689

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Mobley DL, Bannan CC, Rizzi A, Bayly CI, Chodera JD, Lim VT, Lim NM, Beauchamp KA, Slochower DR, Shirts MR, Gilson MK, Eastman PK (2018) Escaping atom types in force fields using direct chemical perception. J Chem Theory Comput 14:6076–6092

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  67. Qiu Y, Smith DGA, Boothroyd S, Jang H, Hahn DF, Wagner J, Bannan CC, Gokey T, Lim VT, Stern CD, Rizzi A, Tjanaka B, Tresadern G, Lucas X, Shirts MR, Gilson MK, Chodera JD, Bayly CI, Mobley DL, Wang LP (2021) Development and benchmarking of open force Field v1.0.0-the parsley small-molecule force field. J Chem Theory Comput 17:6262–6280

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Unke OT, Meuwly M (2019) PhysNet: a neural network for predicting energies, forces, dipole moments and partial charges. J Chem Theory Comput 15:3678–3693

    Article  PubMed  CAS  Google Scholar 

  69. Poltavsky I, Tkatchenko A (2021) Machine learning force fields: recent advances and remaining challenges. J Phys Chem Lett 12:6551–6564

    Article  PubMed  CAS  Google Scholar 

  70. Bender BJ, Gahbauer S, Luttens A, Lyu J, Webb CM, Stein RM, Fink EA, Balius TE, Carlsson J, Irwin JJ, Shoichet BK (2021) A practical guide to large-scale docking. Nat Protoc 16:4799–4832

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Schaller D, Šribar D, Noonan T, Deng L, Nguyen TN, Pach S, Machalz D, Bermudez M, Wolber G (2020) Next generation 3D pharmacophore modeling. Wiley Interdiscip Rev Comput Mol Sci 10:e1468

    Article  CAS  Google Scholar 

  72. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function efficient optimization and multithreading. J Comput Chem 31:455–461

    PubMed  PubMed Central  CAS  Google Scholar 

  73. Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins: Struct Funct Bioinf 52:609–623

    Article  CAS  Google Scholar 

  74. Pagadala NS, Syed K, Tuszynski J (2017) Software for molecular docking: a review. Biophys Rev 9:91–102

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  75. Wójcikowski M, Zielenkiewicz P, Siedlecki P (2015) Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J Cheminform 7:26

    Article  PubMed  PubMed Central  Google Scholar 

  76. Gorgulla C, Boeszoermenyi A, Wang ZF, Fischer PD, Coote PW, Padmanabha Das KM, Malets YS, Radchenko DS, Moroz YS, Scott DA, Fackeldey K, Hoffmann M, Iavniuk I, Wagner G, Arthanari H (2020) An open-source drug discovery platform enables ultra-large virtual screens. Nature 580:663–668

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Kochnev Y, Hellemann E, Cassidy KC, Durrant JD (2020) Webina: an open-source library and web app that runs AutoDock Vina entirely in the web browser. Bioinformatics 36:4513–4515

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  78. Murail S, de Vries SJ, Rey J, Moroy G, Tufféry P (2021) SeamDock: an interactive and collaborative online docking resource to assist small compound molecular docking. Front Mol Biosci 8:716466

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  79. Koes DR, Camacho CJ (2011) Pharmer: efficient and exact pharmacophore search. J Chem Inf Model 51:1307–1314

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Koes DR, Camacho CJ (2012) ZINCPharmer: pharmacophore search of the ZINC database. Nucleic Acids Res 40:W409–W414

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Sunseri J, Koes DR (2016) Pharmit: interactive exploration of chemical space. Nucleic Acids Res 44:W442–W448

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Irwin JJ, Tang KG, Young J, Dandarchuluun C, Wong BR, Khurelbaatar M, Moroz YS, Mayfield J, Sayle RA (2020) ZINC20-a free ultralarge-scale chemical database for ligand discovery. J Chem Inf Model 60:6065–6073

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  83. https://www.molport.com

  84. Grygorenko OO, Radchenko DS, Dziuba I, Chuprina A, Gubina KE, Moroz YS (2020) Generating multibillion chemical space of readily accessible screening compounds. iScience 23:101681

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  85. Boyd NK, Teng C, Frei CR (2021) Brief overview of approaches and challenges in new antibiotic development: a focus on drug repurposing. Front Cell Infect Microbiol 11:684515

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  86. Konreddy AK, Rani GU, Lee K, Choi Y (2019) Recent drug-repurposing-driven advances in the discovery of novel antibiotics. Curr Med Chem 26:5363–5388

    Article  PubMed  CAS  Google Scholar 

  87. Discovery Studio Modeling Environment, Dassault Systèmes BIOVIA., https://www.3ds.com/products-services/biovia/: San Diego

  88. Molecular Operating Environment (MOE), Chemical Computing Group Inc., https://www.chemcomp.com: Montreal

  89. OEChem, OpenEye Scientific Software, Inc. https://www.eyesopen.com: Santa Fe

  90. SILCS, SilcsBio, LLC. https://www.silcsbio.com: Baltimore

  91. PlayMolecule, Acellera Inc., https://www.acellera.com: Barcelona

  92. Muhammed MT, Aki-Yalcin E (2019) Homology modeling in drug discovery: overview current applications and future perspectives. Chem Biol Drug Des 93:12–20

    Article  PubMed  CAS  Google Scholar 

  93. Moore PB, Hendrickson WA, Henderson R, Brunger AT (2022) The protein-folding problem: not yet solved. Science 375:507

    Article  PubMed  Google Scholar 

  94. Hollingsworth SA, Dror RO (2018) Molecular dynamics simulation for all. Neuron 99:1129–1143

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Lamoureux G, Harder E, Vorobyov IV, Roux B, MacKerell AD (2006) A polarizable model of water for molecular dynamics simulations of biomolecules. Chem Phys Lett 418:245–249

    Article  CAS  Google Scholar 

  96. Yu W, Lopes PEM, Roux B, MacKerell AD (2013) Six-site polarizable model of water based on the classical Drude oscillator. J Chem Phys 138:034508

    Article  PubMed  PubMed Central  Google Scholar 

  97. Lin F, Huang J, Pandey P, Rupakheti C, Li J, Roux BT, MacKerell AD (2020) Further optimization and validation of the classical Drude polarizable protein force field. J Chem Theory Comput 16:3221–3239

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Shi Y, Xia Z, Zhang J, Best R, Wu C, Ponder JW, Ren P (2013) The polarizable atomic multipole-based AMOEBA force field for proteins. J Chem Theory Comput 9:4046–4063

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  99. Kunz AP, van Gunsteren WF (2009) Development of a nonlinear classical polarization model for liquid water and aqueous solutions: COS/D. J Phys Chem A 113:11570–11579

    Article  PubMed  CAS  Google Scholar 

  100. Visscher KM, Geerke DP (2020) Deriving a polarizable force field for biomolecular building blocks with minimal empirical calibration. J Phys Chem B 124:1628–1636

    PubMed  PubMed Central  CAS  Google Scholar 

  101. Donchev AG, Ozrin VD, Subbotin MV, Tarasov OV, Tarasov VI (2005) A quantum mechanical polarizable force field for biomolecular interactions. Proc Natl Acad Sci U S A 102:7829–7834

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  102. Goel H, Yu W, Ustach VD, Aytenfisu AH, Sun D, MacKerell AD (2020) Impact of electronic polarizability on protein-functional group interactions. Phys Chem Chem Phys 22:6848–6860

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  103. Jo S, Cheng X, Lee J, Kim S, Park SJ, Patel DS, Beaven AH, Lee KI, Rui H, Park S, Lee HS, Roux B, MacKerell AD, Klauda JB, Qi Y, Im W (2017) CHARMM-GUI 10 years for biomolecular modeling and simulation. J Comput Chem 38:1114–1124

    Article  PubMed  CAS  Google Scholar 

  104. Kognole A, Lee J, Park SJ, Jo S, Chatterjee P, Lemkul JA, Huang J, MacKerell AD, Im W (2022) CHARMM-GUI Drude prepper for molecular dynamics simulation using the classical Drude polarizable force field. J Comput Chem 43:359–375

    Article  PubMed  CAS  Google Scholar 

  105. Chowdhary J, Harder E, Lopes PE, Huang L, MacKerell AD, Roux B (2013) A polarizable force field of dipalmitoylphosphatidylcholine based on the classical Drude model for molecular dynamics simulations of lipids. J Phys Chem B 117:9142–9160

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  106. Lamoureux G, MacKerell AD, Roux B (2003) A simple polarizable model of water based on classical Drude oscillators. J Chem Phys 119:5185–5197

    Article  CAS  Google Scholar 

  107. Genheden S, Ryde U (2015) The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449–461

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  108. Ustach VD, Lakkaraju SK, Jo S, Yu W, Jiang W, MacKerell AD (2019) Optimization and evaluation of site-identification by ligand competitive saturation (SILCS) as a tool for target-based ligand optimization. J Chem Inf Model 59:3018–3035

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  109. Goel H, Hazel A, Ustach VD, Jo S, Yu W, MacKerell AD (2021) Rapid and accurate estimation of protein-ligand relative binding affinities using site-identification by ligand competitive saturation. Chem Sci 12:8844–8858

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Goel H, Hazel A, Yu W, Jo S, MacKerell AD (2022) Application of site-identification by ligand competitive saturation in computer-aided drug design. New J Chem 46:919–932

    Article  PubMed  CAS  Google Scholar 

  111. Lanning ME, Yu W, Yap JL, Chauhan J, Chen L, Whiting E, Pidugu LS, Atkinson T, Bailey H, Li W, Roth BM, Hynicka L, Chesko K, Toth EA, Shapiro P, MacKerell AD, Wilder PT, Fletcher S (2016) Structure-based design of N-substituted 1-hydroxy-4-sulfamoyl-2-naphthoates as selective inhibitors of the Mcl-1 oncoprotein. Eur J Med Chem 113:273–292

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  112. Young BD, Yu W, Rodríguez DJV, Varney KM, MacKerell AD, Weber DJ (2021) Specificity of molecular fragments binding to S100B versus S100A1 as identified by NMR and site identification by ligand competitive saturation (SILCS). Molecules 26:381

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  113. Broomhead NK, Soliman ME (2017) Can we rely on computational predictions to correctly identify ligand binding sites on novel protein drug targets? Assessment of binding site prediction methods and a protocol for validation of predicted binding sites. Cell Biochem Biophys 75:15–23

    Article  PubMed  CAS  Google Scholar 

  114. Shanina E, Kuhaudomlarp S, Lal K, Seeberger PH, Imberty A, Rademacher C (2022) Druggable allosteric sites in β-propeller lectins. Angew Chem Int Ed 61:e202109339

    Article  CAS  Google Scholar 

  115. MacKerell AD, Jo S, Lakkaraju SK, Lind C, Yu W (2020) Identification and characterization of fragment binding sites for allosteric ligand design using the site identification by ligand competitive saturation hotspots approach (SILCS-hotspots). Biochim Biophys Acta Gen Subj 1864:129519

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  116. O’Reilly M, Cleasby A, Davies TG, Hall RJ, Ludlow RF, Murray CW, Tisi D, Jhoti H (2019) Crystallographic screening using ultra-low-molecular-weight ligands to guide drug design. Drug Discov Today 24:1081–1086

    Article  PubMed  Google Scholar 

  117. Taylor RD, MacCoss M, Lawson AD (2014) Rings in drugs. J Med Chem 57:5845–5859

    Article  PubMed  CAS  Google Scholar 

  118. Ness S, Martin R, Kindler AM, Paetzel M, Gold M, Jensen SE, Jones JB, Strynadka NC (2000) Structure-based design guides the improved efficacy of deacylation transition state analogue inhibitors of TEM-1 beta-lactamase. Biochemistry 39:5312–5321

    Article  PubMed  CAS  Google Scholar 

  119. Horn JR, Shoichet BK (2004) Allosteric inhibition through core disruption. J Mol Biol 336:1283–1291

    Article  PubMed  CAS  Google Scholar 

  120. Trisciuzzi D, Nicolotti O, Miteva MA, Villoutreix BO (2019) Analysis of solvent-exposed and buried co-crystallized ligands: a case study to support the design of novel protein-protein interaction inhibitors. Drug Discov Today 24:551–559

    Article  PubMed  CAS  Google Scholar 

  121. Mitternacht S (2016) FreeSASA: an open source C library for solvent accessible surface area calculations. F1000Res 5:189

    Article  PubMed  PubMed Central  Google Scholar 

  122. Delcour AH (2009) Outer membrane permeability and antibiotic resistance. Biochim Biophys Acta 1794:808–816

    Article  PubMed  CAS  Google Scholar 

  123. May KL, Grabowicz M (2018) The bacterial outer membrane is an evolving antibiotic barrier. Proc Natl Acad Sci U S A 115:8852–8854

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  124. Bennion BJ, Be NA, McNerney MW, Lao V, Carlson EM, Valdez CA, Malfatti MA, Enright HA, Nguyen TH, Lightstone FC, Carpenter TS (2017) Predicting a drug’s membrane permeability: a computational model validated with in vitro permeability assay data. J Phys Chem B 121:5228–5237

    Article  PubMed  CAS  Google Scholar 

  125. Marrink S, Berendsen HJC (1994) Simulation of water transport through a lipid membrane. J Phys Chem 98:4155–4168

    Article  CAS  Google Scholar 

  126. Lind C, Pandey P, Pastor RW, MacKerell AD (2021) Functional group distributions partition coefficients and resistance factors in lipid bilayers using site identification by ligand competitive saturation. J Chem Theory Comput 17:3188–3202

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  127. Gao Y, Lee J, Widmalm G, Im W (2020) Modeling and simulation of bacterial outer membranes with lipopolysaccharides and enterobacterial common antigen. J Phys Chem B 124:5948–5956

    Article  PubMed  CAS  Google Scholar 

  128. Kansy M, Senner F, Gubernator K (1998) Physicochemical high throughput screening: parallel artificial membrane permeation assay in the description of passive absorption processes. J Med Chem 41:1007–1010

    Article  PubMed  CAS  Google Scholar 

  129. Lee J, Patel DS, Ståhle J, Park SJ, Kern NR, Kim S, Lee J, Cheng X, Valvano MA, Holst O, Knirel YA, Qi Y, Jo S, Klauda JB, Widmalm G, Im W (2019) CHARMM-GUI membrane builder for complex biological membrane simulations with glycolipids and lipoglycans. J Chem Theory Comput 15:775–786

    Article  PubMed  CAS  Google Scholar 

  130. Carro L (2018) Protein-protein interactions in bacteria: a promising and challenging avenue towards the discovery of new antibiotics. Beilstein J Org Chem 14:2881–2896

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  131. Cossar PJ, Lewis PJ, McCluskey A (2020) Protein-protein interactions as antibiotic targets: a medicinal chemistry perspective. Med Res Rev 40:469–494

    Article  PubMed  CAS  Google Scholar 

  132. Kahan R, Worm DJ, de Castro GV, Ng S, Barnard A (2021) Modulators of protein-protein interactions as antimicrobial agents. RSC Chem Biol 2:387–409

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  133. Huang S (2014) Search strategies and evaluation in protein–protein docking: principles advances and challenges. Drug Discov Today 19:1081–1096

    Article  PubMed  CAS  Google Scholar 

  134. Yu W, Jo S, Lakkaraju SK, Weber DJ, MacKerell AD (2019) Exploring protein-protein interactions using the site-identification by ligand competitive saturation methodology. Proteins: Struct Funct Bioinf 87:289–301

    Article  CAS  Google Scholar 

  135. Solernou A, Fernandez-Recio J (2010) Protein docking by rotation-based uniform sampling (RotBUS) with fast computing of intermolecular contact distance and residue desolvation. BMC Bioinformatics 11:352

    Article  PubMed  PubMed Central  Google Scholar 

  136. Gaile GL, Burt JE (1980) Directional statistics. Concepts and techniques in modern geography, 25th edn. Geo Books, Norwich

    Google Scholar 

  137. Challener C (2018) Fighting bacterial resistance with biologics. Pharm Technol 42:36–37

    Google Scholar 

  138. Kollef MH, Betthauser KD (2021) Monoclonal antibodies as antibacterial therapies: thinking outside of the box. Lancet Infect Dis 21:1201–1202

    Article  PubMed  Google Scholar 

  139. Zurawski DV, McLendon MK (2020) Monoclonal antibodies as an antibacterial approach against bacterial pathogens. Antibiotics (Basel) 9:155

    Article  PubMed  CAS  Google Scholar 

  140. Watson A, Li H, Ma B, Weiss R, Bendayan D, Abramovitz L, Ben-Shalom N, Mor M, Pinko E, Bar Oz M, Wang Z, Du F, Lu Y, Rybniker J, Dahan R, Huang H, Barkan D, Xiang Y, Javid B, Freund NT (2021) Human antibodies targeting a mycobacterium transporter protein mediate protection against tuberculosis. Nat Commun 12:602

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Shire SJ (2009) Formulation and manufacturability of biologics. Curr Opin Biotechnol 20:708–714

    Article  PubMed  CAS  Google Scholar 

  142. Kamerzell TJ, Esfandiary R, Joshi SB, Middaugh CR, Volkin DB (2011) Protein-excipient interactions: mechanisms and biophysical characterization applied to protein formulation development. Adv Drug Deliv Rev 63:1118–1159

    Article  PubMed  CAS  Google Scholar 

  143. Jo S, Xu A, Curtis JE, Somani S, MacKerell AD (2020) Computational characterization of antibody-excipient interactions for rational excipient selection using the site identification by ligand competitive saturation-biologics approach. Mol Pharm 17:4323–4333

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  144. Somani S, Jo S, Thirumangalathu R, Rodrigues D, Tanenbaum LM, Amin K, MacKerell AD, Thakkar SV (2021) Toward biotherapeutics formulation composition engineering using site-identification by ligand competitive saturation (SILCS). J Pharm Sci 110:1103–1110

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work was supported by NIH grants R35GM131710 (AM), GM129327 (DW), AI152397 (DW), the University of Maryland Center for Biomolecular Therapeutics (CBT), the Samuel Waxman Cancer Research Foundation, and the Computer-Aided Drug Design (CADD) Center at the University of Maryland, Baltimore.

Conflict of Interest

A.D.M. is co-founder and CSO of SilcsBio LLC.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Wenbo Yu or Alexander D. MacKerell Jr .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Yu, W., Weber, D.J., MacKerell, A.D. (2023). Computer-Aided Drug Design: An Update. In: Sass, P. (eds) Antibiotics. Methods in Molecular Biology, vol 2601. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2855-3_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2855-3_7

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2854-6

  • Online ISBN: 978-1-0716-2855-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics