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Mini-Reviews in Medicinal Chemistry

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ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

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Mini-Review Article

Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?

Author(s): Camila A. Tavares, Taináh M.R. Santos, Mateus A. Gonçalves, Elaine F.F. da Cunha and Teodorico C. Ramalho*

Volume 24, Issue 11, 2024

Published on: 22 January, 2024

Page: [1063 - 1069] Pages: 7

DOI: 10.2174/0113895575250433231103063707

Price: $65

Abstract

Since its early days in the 19th century, medicinal chemistry has concentrated its efforts on the treatment of diseases, using tools from areas such as chemistry, pharmacology, and molecular biology. The understanding of biological mechanisms and signaling pathways is crucial information for the development of potential agents for the treatment of diseases mainly because they are such complex processes. Given the limitations that the experimental approach presents, computational chemistry is a valuable alternative for the study of these systems and their behavior. Thus, classical molecular dynamics, based on Newton's laws, is considered a technique of great accuracy, when appropriated force fields are used, and provides satisfactory contributions to the scientific community. However, as many configurations are generated in a large MD simulation, methods such as Statistical Inefficiency and Optimal Wavelet Signal Compression Algorithm are great tools that can reduce the number of subsequent QM calculations. Accordingly, this review aims to briefly discuss the importance and relevance of medicinal chemistry allied to computational chemistry as well as to present a case study where, through a molecular dynamics simulation of AMPK protein (50 ns) and explicit solvent (TIP3P model), a minimum number of snapshots necessary to describe the oscillation profile of the protein behavior was proposed. For this purpose, the RMSD calculation, together with the sophisticated OWSCA method was used to propose the minimum number of snapshots.

Keywords: Medicinal chemistry, molecular dynamics, snapshots, OWSCA, conformational choice, computational chemistry.

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[1]
Gioiello, A.; Piccinno, A.; Lozza, A.M.; Cerra, B. The medicinal chemistry in the era of machines and automation: Recent advances in continuous flow technology. J. Med. Chem., 2020, 63(13), 6624-6647.
[http://dx.doi.org/10.1021/acs.jmedchem.9b01956] [PMID: 32049517]
[2]
Perricone, U.; Gulotta, M.R.; Lombino, J.; Parrino, B.; Cascioferro, S.; Diana, P.; Cirrincione, G.; Padova, A. An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge. MedChemComm, 2018, 9(6), 920-936.
[http://dx.doi.org/10.1039/C8MD00166A] [PMID: 30108981]
[3]
Wess, G.; Urmann, M.; Sickenberger, B. Medicinal chemistry: Challenges and opportunities. Angew. Chem. Int. Ed., 2001, 40(18), 3341-3350.
[http://dx.doi.org/10.1002/1521-3773(20010917)40:18<3341:AID-ANIE3341>3.0.CO;2-D] [PMID: 11592134]
[4]
Faruk Khan, M.O.; Deimling, M.J.; Philip, A. Medicinal chemistry and the pharmacy curriculum. Am. J. Pharm. Educ., 2011, 75(8), 161.
[http://dx.doi.org/10.5688/ajpe758161] [PMID: 22102751]
[5]
Kornberg, A. The two cultures: Chemistry and biology. Biochemistry, 1987, 26(22), 6888-6891.
[http://dx.doi.org/10.1021/bi00396a002] [PMID: 3427050]
[6]
Lodish, H.; Berk, A.; Matsudaira, P.; Kaiser, C.A.; Krieger, M.; Scott, M.P.; Zipursky, L.; Darnell, J. Molecular cell biology, 5th; W. H. Freeman: San Francisco, 2004.
[7]
Kocak, M.; Ezazi Erdi, S.; Jorba, G.; Maestro, I.; Farrés, J.; Kirkin, V.; Martinez, A.; Pless, O. Targeting autophagy in disease: Eastablished and new strategies. Autophagy, 2022, 18(3), 473-495.
[http://dx.doi.org/10.1080/15548627.2021.1936359] [PMID: 34241570]
[8]
Bishop, E.; Bradshaw, T.D. Autophagy modulation: A prudent approach in cancer treatment? Cancer Chemother. Pharmacol., 2018, 82(6), 913-922.
[http://dx.doi.org/10.1007/s00280-018-3669-6] [PMID: 30182146]
[9]
Dikic, I.; Elazar, Z. Mechanism and medical implications of mammalian autophagy. Nat. Rev. Mol. Cell Biol., 2018, 19(6), 349-364.
[http://dx.doi.org/10.1038/s41580-018-0003-4] [PMID: 29618831]
[10]
Campbell, I.B.; Macdonald, S.J.F.; Procopiou, P.A. Medicinal chemistry in drug discovery in big pharma: Past, present and future. Drug Discov. Today, 2018, 23(2), 219-234.
[http://dx.doi.org/10.1016/j.drudis.2017.10.007] [PMID: 29031621]
[11]
Katara, P. Computational approaches for drug target identification.Computer-Aided Drug Design; Singh, D.B., Ed.; Springer: Singapore, 2020, pp. 163-185.
[http://dx.doi.org/10.1007/978-981-15-6815-2_8]
[12]
Wu, G.; Zhao, T.; Kang, D.; Zhang, J.; Song, Y.; Namasivayam, V.; Kongsted, J.; Pannecouque, C.; De Clercq, E.; Poongavanam, V.; Liu, X.; Zhan, P. Overview of recent strategic advances in medicinal chemistry. J. Med. Chem., 2019, 62(21), 9375-9414.
[http://dx.doi.org/10.1021/acs.jmedchem.9b00359] [PMID: 31050421]
[13]
Alder, B.J.; Wainwright, T.E. Studies in molecular dynamics. I. General method. J. Chem. Phys., 1959, 31(2), 459-466.
[http://dx.doi.org/10.1063/1.1730376]
[14]
Şterbuleac, D. Molecular dynamics: A powerful tool for studying the medicinal chemistry of ion channel modulators. RSC Med. Chem., 2021, 12(9), 1503-1518.
[http://dx.doi.org/10.1039/D1MD00140J] [PMID: 34671734]
[15]
De Vivo, M.; Masetti, M.; Bottegoni, G.; Cavalli, A. Role of molecular dynamics and related methods in drug discovery. J. Med. Chem., 2016, 59(9), 4035-4061.
[http://dx.doi.org/10.1021/acs.jmedchem.5b01684] [PMID: 26807648]
[16]
Kostal, J. Computational Chemistry in Predictive Toxicology: status quo et quo vadis? In: Advances in Molecular Toxicology; Elsevier, 2016; pp. 139-186.
[17]
Childers, M.C.; Daggett, V. Insights from molecular dynamics simulations for computational protein design. Mol. Syst. Des. Eng., 2017, 2(1), 9-33.
[http://dx.doi.org/10.1039/C6ME00083E] [PMID: 28239489]
[18]
Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron, 2018, 99(6), 1129-1143.
[http://dx.doi.org/10.1016/j.neuron.2018.08.011] [PMID: 30236283]
[19]
Huggins, D.J.; Biggin, P.C.; Dämgen, M.A.; Essex, J.W.; Harris, S.A.; Henchman, R.H.; Khalid, S.; Kuzmanic, A.; Laughton, C.A.; Michel, J.; Mulholland, A.J.; Rosta, E.; Sansom, M.S.P.; van der Kamp, M.W. Biomolecular simulations: From dynamics and mechanisms to computational assays of biological activity. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2019, 9(3), e1393.
[http://dx.doi.org/10.1002/wcms.1393]
[20]
Carter, J.W.; Tascini, A.S.; Seddon, J.M.; Bresme, F. Molecular dynamics computer simulations of biological systems. In: Computational Tools for Chemical Biology; Martín-Santamaría, S., Ed.; Royal Society of Chemistry: London, 2017; pp. 39-68.
[http://dx.doi.org/10.1039/9781788010139-00039]
[21]
MacKerell, A.D., Jr Atomistic models and force fields. In: Computational biochemistry and biophysics; Becker, O.M.; MacKerell, A.D., Jr; Roux, B.; Watanabe, M., Eds.; CRC Pres: Boca Raton, 2001; pp. 19-50.
[http://dx.doi.org/10.1201/9780203903827.ch2]
[22]
Martin, M.G. Comparison of the AMBER, CHARMM, COMPASS, GROMOS, OPLS, TraPPE and UFF force fields for prediction of vapor–liquid coexistence curves and liquid densities. Fluid Phase Equilib., 2006, 248(1), 50-55.
[http://dx.doi.org/10.1016/j.fluid.2006.07.014]
[23]
Salsbury, F.R., Jr Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Curr. Opin. Pharmacol., 2010, 10(6), 738-744.
[http://dx.doi.org/10.1016/j.coph.2010.09.016] [PMID: 20971684]
[24]
Ghahremanpour, M.M.; Tirado-Rives, J.; Deshmukh, M.; Ippolito, J.A.; Zhang, C.H.; Cabeza de Vaca, I.; Liosi, M.E.; Anderson, K.S.; Jorgensen, W.L. Identification of 14 known drugs as inhibitors of the main protease of SARS-CoV-2. ACS Med. Chem. Lett., 2020, 11(12), 2526-2533.
[http://dx.doi.org/10.1021/acsmedchemlett.0c00521] [PMID: 33324471]
[25]
Sharma, V.; Panwar, A.; Sharma, A.; Punj, V.; Saini, R.V.; Saini, A.K.; Sharma, A.K. A comparative molecular dynamic simulation study on potent ligands targeting mTOR/FRB domain for breast cancer therapy. Biotechnol. Appl. Biochem., 2022, 69(4), 1339-1347.
[http://dx.doi.org/10.1002/bab.2206] [PMID: 34056758]
[26]
Shukla, R.; Singh, T.R. Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer’s disease. J. Biomol. Struct. Dyn., 2020, 38(1), 248-262.
[http://dx.doi.org/10.1080/07391102.2019.1571947] [PMID: 30688165]
[27]
Lin, X.; Li, X.; Lin, X. A review on applications of computational methods in drug screening and design. Molecules, 2020, 25(6), 1375.
[http://dx.doi.org/10.3390/molecules25061375] [PMID: 32197324]
[28]
Sulimov, V.B.; Kutov, D.C.; Sulimov, A.V. Advances in docking. Curr. Med. Chem., 2020, 26(42), 7555-7580.
[http://dx.doi.org/10.2174/0929867325666180904115000] [PMID: 30182836]
[29]
Mancini, D.T.; Souza, E.F.; Caetano, M.S.; Ramalho, T.C. 99 Tc NMR as a promising technique for structural investigation of biomolecules: theoretical studies on the solvent and thermal effects of phenylbenzothiazole complex. Magn. Reson. Chem., 2014, 52(4), 129-137.
[http://dx.doi.org/10.1002/mrc.4043] [PMID: 24446055]
[30]
Coutinho, K.; Canuto, S.; Zerner, M.C. A monte carlo-quantum mechanics study of the solvatochromic shifts of the lowest transition of benzene. J. Chem. Phys., 2000, 112(22), 9874-9880.
[http://dx.doi.org/10.1063/1.481624]
[31]
Malaspina, T.; Coutinho, K.; Canuto, S. Ab initio calculation of hydrogen bonds in liquids: A sequential Monte Carlo quantum mechanics study of pyridine in water. J. Chem. Phys., 2002, 117(4), 1692-1699.
[http://dx.doi.org/10.1063/1.1485963]
[32]
Coutinho, K.; Canuto, S. Solvent effects in emission spectroscopy: A Monte Carlo quantum mechanics study of the n←π* shift of formaldehyde in water. J. Chem. Phys., 2000, 113(20), 9132-9139.
[http://dx.doi.org/10.1063/1.1320827]
[33]
Gonçalves, M.A.; Santos, L.S.; Prata, D.M.; Peixoto, F.C.; da Cunha, E.F.F.; Ramalho, T.C. Optimal wavelet signal compression as an efficient alternative to investigate molecular dynamics simulations: Application to thermal and solvent effects of MRI probes. Theor. Chem. Acc., 2017, 136(1), 15.
[http://dx.doi.org/10.1007/s00214-016-2037-z]
[34]
Gao, R.X.; Yan, R. Wavelet packet transform.Wavelets; Gao, R.X. Yan, R., Eds; Springer: New York, 2011, pp. 69-81.
[http://dx.doi.org/10.1007/978-1-4419-1545-0_5]
[35]
Misiti, M.; Misiti, Y.; Oppenheim, G.; Poggi, J.M. Wavelets and their Applications; John Wiley & Sons: London, 2013.
[36]
Gonçalves, M.A.; Santos, L.S.; Peixoto, F.C.; da Cunha, E.F.F.; Silva, T.C.; Ramalho, T.C. Comparing structure and dynamics of solvation of different iron oxide phases for enhanced magnetic resonance imaging. ChemistrySelect, 2017, 2(31), 10136-10142.
[http://dx.doi.org/10.1002/slct.201701705]
[37]
Pereira, B.T.L.; Gonçalves, M.A.; Mancini, D.T.; Kuca, K.; Ramalho, T.C. First attempts of the use of 195Pt NMR of phenylbenzothiazole complexes as spectroscopic technique for the cancer diagnosis. Molecules, 2019, 24(21), 3970.
[http://dx.doi.org/10.3390/molecules24213970] [PMID: 31684009]
[38]
Case, D.A.; Cheatham, T.E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688.
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[39]
Case, D.A.; Aktulga, H.M.; Belfon, K.; Ben-Shalom, I.Y.; Berryman, J.T.; Brozell, S.R.; Cerutti, D.S.; Cheatham, T.E., III; Cisneros, G.A.; Cruzeiro, V.W.D.; Darden, T.A.; Duke, R.E.; Giambasu, G.; Gilson, M.K.; Gohlke, H.; Goetz, A.W.; Harris, R.; Izadi, S.; Izmailov, S.A.; Kasavajhala, K.; Kaymak, M.C.; King, E.; Kovalenko, A.; Kurtzman, T.; Lee, T.S.; LeGrand, S.; Li, P.; Lin, C.; Liu, J.; Luchko, T.; Luo, R.; Machado, M.; Man, V.; Manathunga, M.; Merz, K.M.; Miao, Y.; Mikhailovskii, O.; Monard, G.; Nguyen, H.; O’Hearn, K.A.; Onufriev, A.; Pan, F.; Pantano, S.; Qi, R.; Rahnamoun, A.; Roe, D.R.; Roitberg, A.; Sagui, C.; Schott-Verdugo, S.; Shajan, A.; Shen, J.; Simmerling, C.L.; Skrynnikov, N.R.; Smith, J.; Swails, J.; Walker, R.C.; Wang, J.; Wang, J.; Wei, H.; Wolf, R.M.; Wu, X.; Xiong, Y.; Xue, Y.; York, D.M.; Zhao, S.; Kollman, P.A. Amber 2022; University of California: San Francisco, 2022.
[40]
Aledavood, E.; Forte, A.; Estarellas, C.; Javier Luque, F. Structural basis of the selective activation of enzyme isoforms: Allosteric response to activators of β1- and β2-containing AMPK complexes. Comput. Struct. Biotechnol. J., 2021, 19, 3394-3406.
[http://dx.doi.org/10.1016/j.csbj.2021.05.056] [PMID: 34194666]
[41]
Yan, Y.; Zhou, X.E.; Novick, S.J.; Shaw, S.J.; Li, Y.; Brunzelle, J.S.; Hitoshi, Y.; Griffin, P.R.; Xu, H.E.; Melcher, K. Structures of AMP-activated protein kinase bound to novel pharmacological activators in phosphorylated, non-phosphorylated, and nucleotide free states. J. Biol. Chem., 2019, 294(3), 953-967.
[http://dx.doi.org/10.1074/jbc.RA118.004883] [PMID: 30478170]
[42]
Roe, D.R.; Cheatham, T.E. III PTRAJ and CPPTRAJ: Software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput., 2013, 9(7), 3084-3095.
[http://dx.doi.org/10.1021/ct400341p] [PMID: 26583988]
[43]
Gonçalves, M.A.; Gonçalves, A.S.; Franca, T.C.C.; Santana, M.S.; da Cunha, E.F.F.; Ramalho, T.C. Improved protocol for the selection of structures from molecular dynamics of organic systems in solution: The value of investigating different wavelet families. J. Chem. Theory Comput., 2022, 18(10), 5810-5818.
[http://dx.doi.org/10.1021/acs.jctc.2c00593] [PMID: 36103405]

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