Milestoning simulation of ligand dissociation from the glycogen synthase kinase 3β
Samith Rathnayake
Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, United States
Search for more papers by this authorBrajesh Narayan
School of Physics, University College Dublin, Belfield, Dublin, Ireland
Search for more papers by this authorRon Elber
Department of Chemistry, Oden Institute for Computational Engineering and Science, The University of Texas at Austin, Austin, Texas, United States
Search for more papers by this authorCorresponding Author
Chung F. Wong
Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, United States
Correspondence
Chung F. Wong, Department of Chemistry and Biochemistry, University of Missouri-St. Louis, 1 University Boulevard, St. Louis, MO 63121, United States.
Email: [email protected]
Search for more papers by this authorSamith Rathnayake
Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, United States
Search for more papers by this authorBrajesh Narayan
School of Physics, University College Dublin, Belfield, Dublin, Ireland
Search for more papers by this authorRon Elber
Department of Chemistry, Oden Institute for Computational Engineering and Science, The University of Texas at Austin, Austin, Texas, United States
Search for more papers by this authorCorresponding Author
Chung F. Wong
Department of Chemistry and Biochemistry, University of Missouri-St. Louis, St. Louis, Missouri, United States
Correspondence
Chung F. Wong, Department of Chemistry and Biochemistry, University of Missouri-St. Louis, 1 University Boulevard, St. Louis, MO 63121, United States.
Email: [email protected]
Search for more papers by this authorFunding information: National Institutes of Health, Grant/Award Numbers: GM59796, CA224033; Welch Foundation, Grant/Award Number: F-1896; Thomas Preston PhD Scholarship Fund; National Science Foundation, Grant/Award Number: CNS-1429294
Abstract
As drug-binding kinetics has become an important factor to be considered in modern drug discovery, this work evaluated the ability of the Milestoning method in computing the absolute dissociation rate of a ligand from the serine–threonine kinase, glycogen synthase kinase 3β, which is a target for designing drugs to treat diseases such as neurodegenerative disorders and diabetes. We found that the Milestoning method gave good agreement with experiment with modest computational costs. Although the time scale for dissociation lasted tens of seconds, the collective molecular dynamics simulations total less than 1μs. Computing the committor function helped to identify the transition states (TSs), in which the ligand moved substantially away from the binding pocket. The glycine-rich loop with a serine residue attaching to its tips was found to undergo large movement from the bound to the TSs and might play a role in controlling drug-dissociation kinetics.
Open Research
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Supporting Information
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REFERENCES
- 1Tummino PJ, Copeland RA. Residence time of receptor-ligand complexes and its effect on biological function. Biochemistry. 2008; 47(20): 5481-5492.
- 2Copeland RA, Pompliano DL, Meek TD. Drug-target residence time and its implications for lead optimization. Nat Rev Drug Discov. 2006; 5(9): 730-739.
- 3Zhang R, Monsma F. The importance of drug-target residence time. Curr Opin Drug Discov Devel. 2009; 12(4): 488-496.
- 4Swinney DC. The role of binding kinetics in therapeutically useful drug action. Curr Opin Drug Discov Devel. 2009; 12(1): 31-39.
- 5Swinney DC. Can binding kinetics translate to a clinically differentiated drug? From theory to practice. Lett Drug Des Discov. 2006; 3(8): 569-574.
- 6Swinney DC. Biochemical mechanisms of new molecular entities (NMEs) approved by United States FDA during 2001-2004: mechanisms leading to optimal efficacy and safety. Curr Top Med Chem. 2006; 6(5): 461-478.
- 7Swinney DC. Biochemical mechanisms of drug action: what does it take for success? Nat Rev Drug Discov. 2004; 3(9): 801-808.
- 8Lu H, Tonge PJ. Drug-target residence time: critical information for lead optimization. Curr Opin Chem Biol. 2010; 14(4): 467-474.
- 9Lipton SA. Paradigm shift in neuroprotection by NMDA receptor blockade: memantine and beyond. Nat Rev Drug Discov. 2006; 5(2): 160-170.
- 10Wu H, Pfarr DS, Tang Y, et al. Ultra-potent antibodies against respiratory syncytial virus: effects of binding kinetics and binding valence on viral neutralization. J Mol Biol. 2005; 350(1): 126-144.
- 11Goyal M, Rizzo M, Schumacher F, Wong CF. Beyond thermodynamics: drug binding kinetics could influence epidermal growth factor signaling. J Med Chem. 2009; 52(18): 5582-5585.
- 12Bairy S, Wong CF. Influence of kinetics of drug binding on EGFR signaling: a comparative study of three EGFR signaling pathway models. Proteins: Struct Funct Bioinform. 2011; 79(8): 2491-2504.
- 13Wong CF, Bairy S. Drug design for protein kinases and phosphatases: flexible-receptor docking, binding affinity and specificity, and drug-binding kinetics. Curr Pharm Des. 2013; 19(26): 4739-4754.
- 14Copeland RA. Evolution of the drug-target residence time model. Expert Opin Drug Discov. 2021; 16(12): 1441-1451.
- 15Folmer RHA. Drug target residence time: a misleading concept. Drug Discov Today. 2018; 23(1): 12-16.
- 16Vauquelin G. Link between a high kon for drug binding and a fast clinical action: to be or not to be? MedChemComm. 2018; 9(9): 1426-1438.
- 17Huang Z, Wong CF. A mining minima approach to exploring the docking pathways of p-nitrocatechol sulfate to YopH. Biophys J. 2007; 93(12): 4141-4150.
- 18Noé F, Rosta E. Markov models of molecular kinetics. J Chem Phys. 2019; 151(19):190401.
- 19Husic BE, Pande VS. Markov state models: from an art to a science. J Am Chem Soc. 2018; 140(7): 2386-2396.
- 20Ahn SH, Jagger BR, Amaro RE. Ranking of ligand binding kinetics using a weighted ensemble approach and comparison with a multiscale milestoning approach. J Chem Info Model. 2020; 60(11): 5340-5352.
- 21Zuckerman DM, Chong LT. Weighted ensemble simulation: review of methodology, applications, and software. Ann Rev Biophys. 2017; 46: 43-57.
- 22Dickson A, Brooks CL. WExplore: hierarchical exploration of high-dimensional spaces using the weighted ensemble algorithm. J Phys Chem B. 2014; 118(13): 3532-3542.
- 23Donyapour N, Roussey NM, Dickson A. REVO: resampling of ensembles by variation optimization. J Chem Phys. 2019; 150(24): 244112.
- 24Bolhuis PG, Chandler D, Dellago C, Geissler PL. Transition path sampling: throwing ropes over rough mountain passes, in the dark. Annu Rev Phys Chem. 2002; 53: 291-318.
- 25Mollica L, Decherchi S, Zia SR, Gaspari R, Cavalli A, Rocchia W. Kinetics of protein-ligand unbinding via smoothed potential molecular dynamics simulations. Sci Rep. 2015; 5:11539.
- 26Deb I, Frank AT. Accelerating rare dissociative processes in biomolecules using selectively scaled MD simulations. J Chem Theory Comput. 2019; 15(11): 5817-5828.
- 27Kokh DB, Amaral M, Bomke J, et al. Estimation of drug-target residence times by τ-random acceleration molecular dynamics simulations. J Chem Theory Comput. 2018; 14(7): 3859-3869.
- 28Elber R. A milestoning study of the kinetics of an allosteric transition: atomically detailed simulations of deoxy Scapharca hemoglobin. Biophys J. 2007; 92(9): L85-L87.
- 29Votapka LW, Jagger BR, Heyneman AL, Amaro RE. SEEKR: simulation enabled estimation of kinetic rates, a computational tool to estimate molecular kinetics and its application to trypsin-benzamidine binding. J Phys Chem B. 2017; 121(15): 3597-3606.
- 30Ray D, Stone SE, Andricioaei I. Markovian weighted ensemble milestoning (M-WEM): long-time kinetics from short trajectories. J Chem Theory Comput. 2022; 18(1): 79-95.
- 31Tang Z, Chen SH, Chang CEA. Transient states and barriers from molecular simulations and the Milestoning theory: kinetics in ligand-protein recognition and compound design. J Chem Theory Comput. 2020; 16: 1882-1895.
- 32Narayan B, Buchete NV, Elber R. Computer simulations of the dissociation mechanism of Gleevec from Abl kinase with Milestoning. J Phys Chem B. 2021; 125(22): 5706-5715.
- 33Arciniegas Ruiz SM, Eldar-Finkelman H. Glycogen synthase kinase-3 inhibitors: preclinical and clinical focus on CNS-A decade onward. Front Mol Neurosci. 2022; 14:792364.
- 34Pal D, Mukherjee S, Song IH, Nimse SB. GSK-3 inhibitors: a new class of drugs for Alzheimer's disease treatment. Curr Drug Targets. 2021; 22(15): 1725-1737.
- 35Demuro S, Di Martino RMC, Ortega JA, Cavalli A. GSK-3β, fyn, and DYRK1A: master regulators in neurodegenerative pathways. Int J Mol Sci. 2021; 22(16):9098.
- 36D'mello SR. When good kinases go rogue: GSK3, p38 MAPK and CDKs as therapeutic targets for Alzheimer's and Huntington's disease. Int J Mol Sci. 2021; 22(11): 5911.
- 37Pitasi CL, Liu J, Gausserès B, et al. Implication of glycogen synthase kinase 3 in diabetes-associated islet inflammation. J Endocrinol. 2020; 244(1): 133-148.
- 38Bala A, Roy S, Das D, et al. Role of glycogen synthase kinase-3 in the etiology of type 2 diabetes mellitus: a review. Curr Diabetes Rev. 2022; 18(3):e300721195147.
- 39Cai Z, Zhao Y, Zhao B. Roles of glycogen synthase kinase 3 in Alzheimer's disease. Curr Alzheimer Res. 2012; 9(7): 864-879.
- 40Uemura K, Kuzuya A, Shimozono Y, et al. GSK3beta activity modifies the localization and function of presenilin 1. J Biol Chem. 2007; 282(21): 15823-15832.
- 41Ly PT, Wu Y, Zou H, et al. Inhibition of GSK3β-mediated BACE1 expression reduces Alzheimer-associated phenotypes. J Clin Invest. 2013; 123(1): 224-235.
- 42Sun X, Sato S, Murayama O, et al. Lithium inhibits amyloid secretion in COS7 cells transfected with amyloid precursor protein C100. Neurosci Lett. 2002; 321(1–2): 61-64.
- 43Luo Y, Bolon B, Kahn S, et al. Mice deficient in BACE1, the Alzheimer's β-secretase, have normal phenotype and abolished β-amyloid generation. Nat Neurosci. 2001; 4(3): 231-232.
- 44Jackson GR, Wiedau-Pazos M, Sang TK, et al. Human wild-type tau interacts with wingless pathway components and produces neurofibrillary pathology in Drosophila. Neuron. 2002; 34(4): 509-519.
- 45Lovestone S, Boada M, Dubois B, et al. A phase II trial of tideglusib in Alzheimer's disease. J Alzheimers Dis. 2015; 45(1): 75-88.
- 46Bhat RV, Andersson U, Andersson S, Knerr L, Bauer U, Sundgren-Andersson AK. The conundrum of GSK3 inhibitors: is it the dawn of a new beginning? J Alzheimers Dis. 2018; 64(s1): S547-S554.
- 47Griebel G, Stemmelin J, Lopez-Grancha M, et al. The selective GSK3 inhibitor, SAR502250, displays neuroprotective activity and attenuates behavioral impairments in models of neuropsychiatric symptoms of Alzheimer's disease in rodents. Sci Rep. 2019; 9(1): 18045.
- 48Liang SH, Chen JM, Normandin MD, et al. Discovery of a highly selective glycogen synthase kinase-3 inhibitor (PF-04802367) that modulates tau phosphorylation in the brain: translation for PET neuroimaging. Angew Chem Int Ed Engl. 2016; 55(33): 9601-9605.
- 49Luo G, Chen L, Burton CR, et al. Discovery of isonicotinamides as highly selective, brain penetrable, and orally active glycogen synthase kinase-3 inhibitors. J Med Chem. 2016; 59(3): 1041-1051.
- 50Song Y, Kim HD, Lee MK, et al. Maysin and its flavonoid derivative from centipedegrass attenuates amyloid plaques by inducting humoral immune response with Th2 skewed cytokine response in the Tg (APPswe, PS1dE9) Alzheimer's mouse model. PLoS One. 2017; 12(1):e0169509.
- 51Narayan B, Fathizadeh A, Templeton C, et al. The transition between active and inactive conformations of Abl kinase studied by rock climbing and Milestoning. Biochim Biophys Acta - Gen Subj. 2020; 1864(4):129508.
- 52Burley SK, Bhikadiya C, Bi C, et al. RCSB protein data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res. 2021; 49(1): D437-D451.
- 53Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. Nucleic Acids Res. 2000; 28: 235-242.
- 54Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. J Mol Graph. 1996; 14(1): 33-38.
- 55Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983; 79(2): 926-935.
- 56Darden T, York D, Pedersen L. Particle mesh Ewald - an n.log(N) method for Ewald sums in large systems. J Chem Phys. 1993; 98(12): 10089-10092.
- 57Huang J, Rauscher S, Nawrocki G, et al. CHARMM36m: an improved force field for folded and intrinsically disordered proteins. Nat Methods. 2016; 14(1): 71-73.
- 58Isralewitz B, Baudry J, Gullingsrud J, Kosztin D, Schulten K. Steered molecular dynamics investigations of protein function. J Mol Graph Model. 2001; 19(1): 13-25.
- 59Stepaniants S, Izrailev S, Schulten K. Extraction of lipids from phospholipid membranes by steered molecular dynamics. J Mol Model. 1997; 3(12): 473-475.
- 60Gobbo D, Piretti V, Di Martino RMC, et al. Investigating drug-target residence time in kinases through enhanced sampling simulations. J Chem Theory Comput. 2019; 15(8): 4646-4659.