Molecular Modeling of Penicillin Acylase Binding with a Penicillin Nucleus by High Performance Computing: Can Enzyme or its Mutants Possess β-lactamase Activity?

Authors

  • Evgeny M. Kirilin Lomonosov Moscow State University, Moscow, Russia https://orcid.org/0000-0003-4960-8925
  • Anna A. Bochkova Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Moscow, Russia
  • Nikolay V. Panin Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Moscow, Russia
  • Igor V. Pochinok Lomonosov Moscow State University, Research Computing Center, Moscow, Russia
  • Vytas Švedas Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Moscow, Russia https://orcid.org/0000-0002-1664-0307

DOI:

https://doi.org/10.14529/jsfi220206

Keywords:

moonlighting protein, penicillin acylase engineering, β-lactam antibiotics resistance, β-lactamase design, metadynamics

Abstract

High-performance computing has been used for molecular modeling of penicillin acylase interaction with a penicillin nucleus 6-aminopenicillanic acid (6-APA) to assess whether the wild-type enzyme or its mutants could possess β-lactamase activity. Applying parallel hybrid GPU/CPU computing technologies for metadynamics calculations with the PLUMED library in conjunction with AMBER software suite it has been shown that trace amounts of wild-type penicillin acylase6-APA complexes leading to a β-lactamase reaction can be formed. Higher β-lactamase activity can be observed in enzyme mutants by introducing charged residue in the substrate binding pocket and its proper positioning with respect to a catalytic nucleophile, including stabilization of the tetrahedral intermediate in the oxyanion hole. Thus, it has been shown that the certain mutations facilitate the orientation of the substrate required for the manifestation of β-lactamase activity in the penicillin acylase active center.

References

Barducci, A., Bussi, G., Parrinello, M.: Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method. Physical Review Letters 100(2), 020603 (Jan 2008). https://doi.org/10.1103/PhysRevLett.100.020603

Bonomi, M., Bussi, G., Camilloni, C., et al.: Promoting transparency and reproducibility in enhanced molecular simulations. Nature Methods 16(8), 670–673 (Aug 2019). https: //doi.org/10.1038/s41592-019-0506-8

Bruice, T.C.: A View at the Millennium: the Efficiency of Enzymatic Catalysis. Accounts of Chemical Research 35(3), 139–148 (Mar 2002). https://doi.org/10.1021/ar0001665

Case, D.A., Aktulga, H.M., Belfon, K., et al.: Amber 2018. University of California Press (2018), http://ambermd.org/doc12/Amber18.pdf

Drobot, V.V., Kirilin, E.M., Kopylov, K.E., Švedas, V.K.: PLUMED Plugin Integration into High Performance Pmemd Program for Enhanced Molecular Dynamics Simulations. Supercomputing Frontiers and Innovations 8(4), 94–99 (2021). https://doi.org/10.14529/jsfi210408

Gohlke, H., Klebe, G.: Approaches to the Description and Prediction of the Binding Affinity of Small-Molecule Ligands to Macromolecular Receptors. Angewandte Chemie International Edition 41(15), 2644–2676 (2002). https://doi.org/10.1002/1521-3773(20020802)41:15<2644::AID-ANIE2644>3.0.CO;2-O

Hult, K., Berglund, P.: Enzyme promiscuity: mechanism and applications. Trends in Biotechnology 25(5), 231–238 (May 2007). https://doi.org/10.1016/j.tibtech.2007.03.002

Izrailev, S., Stepaniants, S., Isralewitz, B., et al.: Steered Molecular Dynamics. In: Deuflhard, P., Hermans, J., Leimkuhler, B., et al. (eds.) Computational Molecular Dynamics: Challenges, Methods, Ideas. pp. 39–65. Lecture Notes in Computational Science and Engineering, Springer, Berlin, Heidelberg (1999). https://doi.org/10.1007/978-3-642-58360-5_2

Jurrus, E., Engel, D., Star, K., et al.: Improvements to the APBS biomolecular solvation software suite. Protein Science 27(1), 112–128 (2018). https://doi.org/10.1002/pro.3280

Khersonsky, O., Tawfik, D.S.: Enzyme promiscuity: a mechanistic and evolutionary perspective. Annual Review of Biochemistry 79, 471–505 (2010). https://doi.org/10.1146/annurev-biochem-030409-143718

Kollman, P.A., Massova, I., Reyes, C., et al.: Calculating Structures and Free Energies of Complex Molecules: Combining Molecular Mechanics and Continuum Models. Accounts of Chemical Research 33(12), 889–897 (Dec 2000). https://doi.org/10.1021/ar000033j

Kumar, S., Rosenberg, J.M., Bouzida, D., et al.: THE weighted histogram analysis method for free-energy calculations on biomolecules. I. The method. Journal of Computational Chemistry 13(8), 1011–1021 (1992). https://doi.org/10.1002/jcc.540130812

Leveson-Gower, R.B., Mayer, C., Roelfes, G.: The importance of catalytic promiscuity for enzyme design and evolution. Nature Reviews Chemistry 3(12), 687–705 (Dec 2019). https://doi.org/10.1038/s41570-019-0143-x

Limongelli, V., Bonomi, M., Parrinello, M.: Funnel metadynamics as accurate binding freeenergy method. Proceedings of the National Academy of Sciences 110(16), 6358–6363 (Apr 2013). https://doi.org/10.1073/pnas.1303186110

Livermore, D.M.: Beta-lactamase-mediated resistance and opportunities for its control. Journal of Antimicrobial Chemotherapy 41(suppl 4), 25–41 (Jun 1998). https://doi.org/10.1093/jac/41.suppl_4.25

Paterlini, M.G., Ferguson, D.M.: Constant temperature simulations using the Langevin equation with velocity Verlet integration. Chemical Physics 236(1), 243–252 (Sep 1998). https://doi.org/10.1016/S0301-0104(98)00214-6

Phillips, J.C., Hardy, D.J., Maia, J.D.C., et al.: Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of Chemical Physics 153(4), 044130 (Jul 2020). https://doi.org/10.1063/5.0014475

Raiteri, P., Laio, A., Gervasio, F.L., et al.: Efficient Reconstruction of Complex Free Energy Landscapes by Multiple Walkers Metadynamics. The Journal of Physical Chemistry B 110(8), 3533–3539 (Mar 2006). https://doi.org/10.1021/jp054359r

Sadiq, S.K., Coveney, P.V.: Computing the Role of Near Attack Conformations in an Enzyme-Catalyzed Nucleophilic Bimolecular Reaction. Journal of Chemical Theory and Computation 11(1), 316–324 (Jan 2015). https://doi.org/10.1021/ct5008845

Sakaguchi, K., Murao, S.: A Prcliminary Report on a New Enzyme, Penicillin-amidase. Journal of the agricultural chemical society of Japan 23(9), 411–411 (1950). https://doi.org/10.1271/nogeikagaku1924.23.411

Schmidt, M.W., Baldridge, K.K., Boatz, J.A., et al.: General atomic and molecular electronic structure system. Journal of Computational Chemistry 14(11), 1347–1363 (1993). https://doi.org/10.1002/jcc.540141112

Straatsma, T.P., McCammon, J.A.: Multiconfiguration thermodynamic integration. The Journal of Chemical Physics 95(2), 1175–1188 (Jul 1991). https://doi.org/10.1063/1.461148

Tribello, G.A., Bonomi, M., Branduardi, D., et al.: PLUMED 2: New feathers for an old bird. Computer Physics Communications 185(2), 604–613 (Feb 2014). https://doi.org/10.1016/j.cpc.2013.09.018

Vanquelef, E., Simon, S., Marquant, G., et al.: R.E.D. Server: a web service for deriving RESP and ESP charges and building force field libraries for new molecules and molecular fragments. Nucleic Acids Research 39(suppl 2), W511–W517 (Jul 2011). https://doi.org/10.1093/nar/gkr288

Voevodin, V.V., Antonov, A.S., Nikitenko, D.A., et al.: Supercomputer Lomonosov-2: Large Scale, Deep Monitoring and Fine Analytics for the User Community. Supercomputing Frontiers and Innovations 6(2), 4–11 (Jun 2019). https://doi.org/10.14529/jsfi190201

Zwanzig, R.W.: High-Temperature Equation of State by a Perturbation Method. I. Nonpolar Gases. The Journal of Chemical Physics 22(8), 1420–1426 (Aug 1954). https://doi.org/10.1063/1.1740409

Downloads

Published

2022-11-07

How to Cite

Kirilin, E. M., Bochkova, A. A., Panin, N. V., Pochinok, I. V., & Švedas, V. (2022). Molecular Modeling of Penicillin Acylase Binding with a Penicillin Nucleus by High Performance Computing: Can Enzyme or its Mutants Possess β-lactamase Activity?. Supercomputing Frontiers and Innovations, 9(2), 68–78. https://doi.org/10.14529/jsfi220206