Enhanced Sampling in Molecular Dynamics Simulations: How Many MD Snapshots can be Needed to Reproduce the Biological Behavior?
Jazyk angličtina Země Nizozemsko Médium print
Typ dokumentu časopisecké články, přehledy
PubMed
38258786
DOI
10.2174/0113895575250433231103063707
PII: MRMC-EPUB-137628
Knihovny.cz E-zdroje
- Klíčová slova
- Medicinal chemistry, OWSCA, computational chemistry., conformational choice, molecular dynamics, snapshots,
- MeSH
- algoritmy MeSH
- farmaceutická chemie MeSH
- kvantová teorie MeSH
- lidé MeSH
- proteinkinasy aktivované AMP metabolismus chemie MeSH
- simulace molekulární dynamiky * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- přehledy MeSH
- Názvy látek
- proteinkinasy aktivované AMP MeSH
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.
Zobrazit více v PubMed
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 PubMed DOI
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 PubMed DOI
Wess G.; Urmann M.; Sickenberger B.; Medicinal chemistry: Challenges and opportunities. Angew Chem Int Ed 2001,40(18),3341-3350 PubMed DOI
Faruk Khan M.O.; Deimling M.J.; Philip A.; Medicinal chemistry and the pharmacy curriculum. Am J Pharm Educ 2011,75(8),161 PubMed DOI
Kornberg A.; The two cultures: Chemistry and biology. Biochemistry 1987,26(22),6888-6891 PubMed DOI
Lodish H.; Berk A.; Matsudaira P.; Kaiser C.A.; Krieger M.; Scott M.P.; Zipursky L.; Darnell J.; Molecular cell biology, 5th 2004
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 PubMed DOI
Bishop E.; Bradshaw T.D.; Autophagy modulation: A prudent approach in cancer treatment? Cancer Chemother Pharmacol 2018,82(6),913-922 PubMed DOI
Dikic I.; Elazar Z.; Mechanism and medical implications of mammalian autophagy. Nat Rev Mol Cell Biol 2018,19(6),349-364 PubMed DOI
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 PubMed DOI
Katara P.; Computational approaches for drug target identificationComputer-Aided Drug Design 2020,163-185 DOI
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 PubMed DOI
Alder B.J.; Wainwright T.E.; Studies in molecular dynamics. I. General method. J Chem Phys 1959,31(2),459-466 DOI
Şterbuleac D.; Molecular dynamics: A powerful tool for studying the medicinal chemistry of ion channel modulators. RSC Med Chem 2021,12(9),1503-1518 PubMed DOI
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 PubMed DOI
Kostal J.; Computational Chemistry in Predictive Toxicology: status quo et quo vadis? Advances in Molecular Toxicology 2016,139-186
Childers M.C.; Daggett V.; Insights from molecular dynamics simulations for computational protein design. Mol Syst Des Eng 2017,2(1),9-33 PubMed DOI
Hollingsworth S.A.; Dror R.O.; Molecular dynamics simulation for all. Neuron 2018,99(6),1129-1143 PubMed DOI
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 DOI
Carter J.W.; Tascini A.S.; Seddon J.M.; Bresme F.; Molecular dynamics computer simulations of biological systems. Computational Tools for Chemical Biology 2017,39-68 DOI
MacKerell A.D.; Atomistic models and force fields. Computational biochemistry and biophysics 2001,19-50 DOI
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 DOI
Salsbury F.R.; Molecular dynamics simulations of protein dynamics and their relevance to drug discovery. Curr Opin Pharmacol 2010,10(6),738-744 PubMed DOI
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 PubMed DOI
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 PubMed DOI
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 PubMed DOI
Lin X.; Li X.; Lin X.; A review on applications of computational methods in drug screening and design. Molecules 2020,25(6),1375 PubMed DOI
Sulimov V.B.; Kutov D.C.; Sulimov A.V.; Advances in docking. Curr Med Chem 2020,26(42),7555-7580 PubMed DOI
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 PubMed DOI
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 DOI
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 DOI
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 DOI
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 DOI
Gao R.X.; Yan R.; Wavelet packet transformWavelets; Gao, RX Yan, R, Eds 2011,69-81 DOI
Misiti M.; Misiti Y.; Oppenheim G.; Poggi J.M.; Wavelets and their Applications 2013
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 DOI
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 PubMed DOI
Case D.A.; Cheatham T.E.; Darden T.; Gohlke H.; Luo R.; Merz K.M.; Onufriev A.; Simmerling C.; Wang B.; Woods R.J.; The Amber biomolecular simulation programs. J Comput Chem 2005,26(16),1668-1688 PubMed DOI
Case D.A.; Aktulga H.M.; Belfon K.; Ben-Shalom I.Y.; Berryman J.T.; Brozell S.R.; Cerutti D.S.; Cheatham T.E.; 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 2022
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 PubMed DOI
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 PubMed DOI
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 PubMed DOI
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 PubMed DOI