Computational Alchemy Using Accelerated GPU Calculations: Fine Structural Tuning Inhibitors of L,D-transpeptidase 2 from Mycobacterium tuberculosis

Semen M. Baldin; Vsevolod O. Shegolev; Vytas K. Švedas

doi:10.14529/jsfi220305

Authors

Semen M. Baldin Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Moscow, Russia https://orcid.org/0000-0001-5116-9373
Vsevolod O. Shegolev Lomonosov Moscow State University, Faculty of Chemistry, Moscow, Russia https://orcid.org/0000-0002-8913-6697
Vytas K. 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/jsfi220305

Keywords:

computational alchemy, thermodynamic integration, protein—ligand binding, binding free energy, drug design, L,D-transpeptidase

Abstract

The computational alchemy is based on equilibrium methods of molecular dynamics (MD), including thermodynamic integration (TI) or free energy perturbation (FEP). It makes it possible to evaluate the energy characteristics of a subtile alchemical transformation of the prototype compound structures in order to modulate their functional properties, what can be used searching for new analogues of enzyme inhibitors. In recent years, the method is gaining new opportunities due to high-performance computing as different MD engines got the ability to perform TI or FEP calculations using GPU acceleration. We have tested the method on two experimentally studied enzyme-inhibitor systems: penicillin acylase from E. coli (PaEc) and Influenza A virus neuraminidase (NaIAv) and performed computational alchemy calculations for pairs of their known inhibitors. Thus, the validated methodology was then applied to estimate binding of L,D-transpeptidase 2 from M. tuberculosis (Ldt_Mt2) to the structural analogs of inhibitors previously discovered in our group. We have found that the performance of computational alchemy predictions is highly dependent on properly chosen simulation parameters and at optimal performance the difference between the calculated and experimentally determined relative binding energies is less than 1 kcal/mol. Thus, for optimal application to novel enzyme—inhibitor or receptor—ligand systems, we recommend prior adaptation of this method, based on validation of force field combinations as well as water models, to experimentally determined binding data of structurally related inhibitors/ligands to the molecular target of interest, if available, and then to proceed to the study of new compounds. High-performance computational alchemy can be used as a useful integrative part of the methodology searching for new enzyme inhibitors or receptor ligands and optimizing their structure at drug design.

References

Bayly, C., Cieplak, P., Cornell, W., Kollman, P.: A well-behaved electrostatic potential based method using charge restraints for deriving atomic charges: the RESP model. J Phys Chem 97, 10269–10280 (1993). https://doi.org/10.1021/j100142a004

Beutler, T.C., Mark, A.E., van Schaik, R.C., et al.: Avoiding singularities and numerical instabilities in free energy calculations based on molecular simulations. Chemical Physics Letters 222(6), 529–539 (Jun 1994). https://doi.org/10.1016/0009-2614(94)00397-1

Billones, J.B., Carrillo, M.C.O., Organo, V.G., et al.: Toward antituberculosis drugs: In silico screening of synthetic compounds against Mycobacterium tuberculosis L,D-transpeptidase Drug design, development and therapy 10, 1147–1157 (2016). https://doi.org/10.2147/DDDT.S97043

Case, D.A., Cheatham III, T.E., Darden, T., et al.: The amber biomolecular simulation programs. Journal of computational chemistry 26(16), 1668–1688 (2005). https://doi.org/10.1002/jcc.20290

Cordillot, M., Dubée, V., Triboulet, S., et al.: In vitro cross-linking of Mycobacterium tuberculosis peptidoglycan by L,D-transpeptidases and inactivation of these enzymes by carbapenems. Antimicrobial agents and chemotherapy 57(12), 5940–5945 (Dec 2013). https://doi.org/10.1128/AAC.01663-13

Cornell, W.D., Cieplak, P., Bayly, C.I., et al.: A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules. Journal of the American Chemical Society 117(19), 5179–5197 (May 1995). https://doi.org/10.1021/ja00124a002

He, X., Liu, S., Lee, T.S., et al.: Fast, accurate, and reliable protocols for routine calculations of protein–ligand binding affinities in drug design projects using AMBER GPU-TI with ff14SB/GAFF. ACS omega 5(9), 4611–4619 (2020). https://doi.org/10.1021/acsomega.9b04233

Izadi, S., Anandakrishnan, R., Onufriev, A.V.: Building water models: a different approach. The journal of physical chemistry letters 5(21), 3863–3871 (2014). https://doi.org/10.1021/jz501780a

Lee, T.S., Cerutti, D.S., Mermelstein, D., et al.: GPU-accelerated molecular dynamics and free energy methods in Amber18: performance enhancements and new features. Journal of chemical information and modeling 58(10), 2043–2050 (2018). https://doi.org/10.1021/acs.jcim.8b00462

Lee, T.S., Hu, Y., Sherborne, B., et al.: Toward fast and accurate binding affinity prediction with pmemdGTI: an efficient implementation of GPU-accelerated thermodynamic integration. Journal of chemical theory and computation 13(7), 3077–3084 (2017). https://doi.org/10.1021/acs.jctc.7b00102

Lehnert, R., Pletz, M., Reuss, A., Schaberg, T.: Antiviral medications in seasonal and pandemic influenza: A systematic review. Deutsches Ärzteblatt International 113(47), 799–807 (2016). https://doi.org/10.3238/arztebl.2016.0799

Lin, Y.H., Wessén, J., Pal, T., et al.: Numerical Techniques for Applications of Analytical Theories to Sequence-Dependent Phase Separations of Intrinsically Disordered Proteins. In: Phase-Separated Biomolecular Condensates: Methods and Protocols, pp. 51–94. Springer US, New York, NY (2023). https://doi.org/10.1007/978-1-0716-2663-4_3

Ma, Z., Lienhardt, C., McIlleron, H., et al.: Global tuberculosis drug development pipeline: The need and the reality. The lancet 375(9731), 2100–2109 (2010). https://doi.org/10.1016/S0140-6736(10)60359-9

Maier, J.A., Martinez, C., Kasavajhala, K., et al.: ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. Journal of chemical theory and computation 11(8), 3696–3713 (2015). https://doi.org/10.1021/acs.jctc.5b00255

Martelli, G., Pessatti, T.B., Steiner, E.M., et al.: N-Thio-β-lactams targeting L,Dtranspeptidase-2, with activity against drug-resistant strains of Mycobacterium tuberculosis. Cell Chemical Biology 28(9), 1321–1332.e5 (2021). https://doi.org/10.1016/j.chembiol.2021.03.008

Rullas, J., Dhar, N., McKinney, J.D., et al.: Combinations of β-Lactam Antibiotics Currently in Clinical Trials Are Efficacious in a DHP-I-Deficient Mouse Model of Tuberculosis Infection. Antimicrobial agents and chemotherapy 59(8), 4997–4999 (Aug 2015). https://doi.org/10.1128/AAC.01063-15

Solapure, S., Dinesh, N., Shandil, R., et al.: In vitro and in vivo efficacy of β-lactams against replicating and slowly growing/nonreplicating Mycobacterium tuberculosis. Antimicrobial agents and chemotherapy 57(6), 2506–2510 (Jun 2013). https://doi.org/10.1128/AAC.00023-13

Steinbrecher, T., Mobley, D.L., Case, D.A.: Nonlinear scaling schemes for Lennard-Jones interactions in free energy calculations. The Journal of chemical physics 127(21), 214108 (2007). https://doi.org/10.1063/1.2799191

Tian, C., Kasavajhala, K., Belfon, K.A.A., et al.: ff19SB: Amino-Acid-Specific Protein Backbone Parameters Trained against Quantum Mechanics Energy Surfaces in Solution. Journal of Chemical Theory and Computation 16(1), 528–552 (Jan 2020). https://doi.org/10.1021/acs.jctc.9b00591

Tian, C., Kasavajhala, K., Belfon, K.A., et al.: ff19SB: Amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. Journal of chemical theory and computation 16(1), 528–552 (2019). https://doi.org/10.1021/acs.jctc.9b00591

Tuckerman, M.E., Martyna, G.J.: Understanding Modern Molecular Dynamics: Techniques and Applications. The Journal of Physical Chemistry B 104(2), 159–178 (Jan 2000). https://doi.org/10.1021/jp992433y

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 (2019). https://doi.org/10.14529/jsfi190201

Von Itzstein, M.: The war against influenza: discovery and development of sialidase inhibitors. Nature reviews Drug discovery 6(12), 967–974 (2007). https://doi.org/10.1038/nrd2400

Von Itzstein, M., Wu, W.Y., Kok, G.B., et al.: Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363(6428), 418–423 (1993). https://doi.org/10.1038/363418a0

Wang, J., Wolf, R.M., Caldwell, J.W., et al.: Development and testing of a general amber force field. Journal of Computational Chemistry 25(9), 1157–1174 (Jul 2004). https://doi.org/10.1002/jcc.20035

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