1
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Isaksen I, Jana S, Payne CM, Bissaro B, Røhr ÅK. The rotamer of the second-sphere histidine in AA9 lytic polysaccharide monooxygenase is pH dependent. Biophys J 2024; 123:1139-1151. [PMID: 38571309 PMCID: PMC11079946 DOI: 10.1016/j.bpj.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/10/2024] [Accepted: 04/01/2024] [Indexed: 04/05/2024] Open
Abstract
Lytic polysaccharide monooxygenases (LPMOs) catalyze a reaction that is crucial for the biological decomposition of various biopolymers and for the industrial conversion of plant biomass. Despite the importance of LPMOs, the exact molecular-level nature of the reaction mechanism is still debated today. Here, we investigated the pH-dependent conformation of a second-sphere histidine (His) that we call the stacking histidine, which is conserved in fungal AA9 LPMOs and is speculated to assist catalysis in several of the LPMO reaction pathways. Using constant-pH and accelerated molecular dynamics simulations, we monitored the dynamics of the stacking His in different protonation states for both the resting Cu(II) and active Cu(I) forms of two fungal LPMOs. Consistent with experimental crystallographic and neutron diffraction data, our calculations suggest that the side chain of the protonated and positively charged form is rotated out of the active site toward the solvent. Importantly, only one of the possible neutral states of histidine (HIE state) is observed in the stacking orientation at neutral pH or when bound to cellulose. Our data predict that, in solution, the stacking His may act as a stabilizer (via hydrogen bonding) of the Cu(II)-superoxo complex after the LPMO-Cu(I) has reacted with O2 in solution, which, in fine, leads to H2O2 formation. Also, our data indicate that the HIE-stacking His is a poor acid/base catalyst when bound to the substrate and, in agreement with the literature, may play an important stabilizing role (via hydrogen bonding) during the peroxygenase catalysis. Our study reveals the pH titration midpoint values of the pH-dependent orientation of the stacking His should be considered when modeling and interpreting LPMO reactions, whether it be for classical LPMO kinetics or in industry-oriented enzymatic cocktails, and for understanding LPMO behavior in slightly acidic natural processes such as fungal wood decay.
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Affiliation(s)
- Ingvild Isaksen
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway
| | - Suvamay Jana
- Department of Chemical and Materials Engineering, University of Kentucky, Lexington, Kentucky
| | - Christina M Payne
- Department of Chemical and Materials Engineering, University of Kentucky, Lexington, Kentucky
| | - Bastien Bissaro
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway; INRAE, Aix Marseille University, UMR1163 Biodiversité et Biotechnologie Fongiques, Marseille, France.
| | - Åsmund K Røhr
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences (NMBU), Ås, Norway.
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2
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Menchon G, Maveyraud L, Czaplicki G. Molecular Dynamics as a Tool for Virtual Ligand Screening. Methods Mol Biol 2024; 2714:33-83. [PMID: 37676592 DOI: 10.1007/978-1-0716-3441-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or nucleic acid is known. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to modulate particular biomolecular interactions or biological activities, related to a disease process. The structure-based virtual ligand screening process primarily relies on docking methods which allow predicting the binding of a molecule to a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of full protein flexibility information, no solvation and ion effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions, and even in membrane-like environments, describing more precisely the temporal evolution of the biological complex and ranking these complexes with more accurate binding energy calculations. In this chapter, we describe the up-to-date MD, which plays the role of supporting tools in the virtual ligand screening (VS) process.Without a doubt, using docking in combination with MD is an attractive approach in structure-based drug discovery protocols nowadays. It has proved its efficiency through many examples in the literature and is a powerful method to significantly reduce the amount of required wet experimentations (Tarcsay et al, J Chem Inf Model 53:2990-2999, 2013; Barakat et al, PLoS One 7:e51329, 2012; De Vivo et al, J Med Chem 59:4035-4061, 2016; Durrant, McCammon, BMC Biol 9:71-79, 2011; Galeazzi, Curr Comput Aided Drug Des 5:225-240, 2009; Hospital et al, Adv Appl Bioinforma Chem 8:37-47, 2015; Jiang et al, Molecules 20:12769-12786, 2015; Kundu et al, J Mol Graph Model 61:160-174, 2015; Mirza et al, J Mol Graph Model 66:99-107, 2016; Moroy et al, Future Med Chem 7:2317-2331, 2015; Naresh et al, J Mol Graph Model 61:272-280, 2015; Nichols et al, J Chem Inf Model 51:1439-1446, 2011; Nichols et al, Methods Mol Biol 819:93-103, 2012; Okimoto et al, PLoS Comput Biol 5:e1000528, 2009; Rodriguez-Bussey et al, Biopolymers 105:35-42, 2016; Sliwoski et al, Pharmacol Rev 66:334-395, 2014).
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Affiliation(s)
- Grégory Menchon
- Inserm U1242, Oncogenesis, Stress and Signaling (OSS), Université de Rennes 1, Rennes, France
| | - Laurent Maveyraud
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France
| | - Georges Czaplicki
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, Université Toulouse III - Paul Sabatier (UT3), Toulouse, France.
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3
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Coskun D, Chen W, Clark AJ, Lu C, Harder ED, Wang L, Friesner RA, Miller EB. Reliable and Accurate Prediction of Single-Residue p Ka Values through Free Energy Perturbation Calculations. J Chem Theory Comput 2022; 18:7193-7204. [PMID: 36384001 DOI: 10.1021/acs.jctc.2c00954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Accurate prediction of the pKa's of protein residues is crucial to many applications in biological simulation and drug discovery. Here, we present the use of free energy perturbation (FEP) calculations for the prediction of single-protein residue pKa values. We begin with an initial set of 191 residues with experimentally determined pKa values. To isolate sampling limitations from force field inaccuracies, we develop an algorithm to classify residues whose environments are significantly affected by crystal packing effects. We then report an approach to identify buried histidines that require significant sampling beyond what is achieved in typical FEP calculations. We therefore define a clean data set not requiring algorithms capable of predicting major conformational changes on which other pKa prediction methods can be tested. On this data set, we report an RMSE of 0.76 pKa units for 35 ASP residues, 0.51 pKa units for 44 GLU residues, and 0.67 pKa units for 76 HIS residues.
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Affiliation(s)
- Dilek Coskun
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Wei Chen
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Anthony J Clark
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Chao Lu
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Edward D Harder
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Lingle Wang
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
| | - Richard A Friesner
- Department of Chemistry, Columbia University, 3000 Broadway, MC 3110, New York, New York10036, United States
| | - Edward B Miller
- Schrödinger, Inc., 1540 Broadway, New York, New York10036, United States
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4
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Electrostatics in Computational Biophysics and Its Implications for Disease Effects. Int J Mol Sci 2022; 23:ijms231810347. [PMID: 36142260 PMCID: PMC9499338 DOI: 10.3390/ijms231810347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 12/25/2022] Open
Abstract
This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.
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5
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MacKenzie DWS, Schaefer A, Steckner J, Leo CA, Naser D, Artikis E, Broom A, Ko T, Shah P, Ney MQ, Tran E, Smith MTJ, Fuglestad B, Wand AJ, Brooks CL, Meiering EM. A fine balance of hydrophobic-electrostatic communication pathways in a pH-switching protein. Proc Natl Acad Sci U S A 2022; 119:e2119686119. [PMID: 35737838 PMCID: PMC9245636 DOI: 10.1073/pnas.2119686119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/29/2022] [Indexed: 12/24/2022] Open
Abstract
Allostery is the phenomenon of coupling between distal binding sites in a protein. Such coupling is at the crux of protein function and regulation in a myriad of scenarios, yet determining the molecular mechanisms of coupling networks in proteins remains a major challenge. Here, we report mechanisms governing pH-dependent myristoyl switching in monomeric hisactophilin, whereby the myristoyl moves between a sequestered state, i.e., buried within the core of the protein, to an accessible state, in which the myristoyl has increased accessibility for membrane binding. Measurements of the pH and temperature dependence of amide chemical shifts reveal protein local structural stability and conformational heterogeneity that accompany switching. An analysis of these measurements using a thermodynamic cycle framework shows that myristoyl-proton coupling at the single-residue level exists in a fine balance and extends throughout the protein. Strikingly, small changes in the stereochemistry or size of core and surface hydrophobic residues by point mutations readily break, restore, or tune myristoyl switch energetics. Synthesizing the experimental results with those of molecular dynamics simulations illuminates atomistic details of coupling throughout the protein, featuring a large network of hydrophobic interactions that work in concert with key electrostatic interactions. The simulations were critical for discerning which of the many ionizable residues in hisactophilin are important for switching and identifying the contributions of nonnative interactions in switching. The strategy of using temperature-dependent NMR presented here offers a powerful, widely applicable way to elucidate the molecular mechanisms of allostery in proteins at high resolution.
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Affiliation(s)
| | - Anna Schaefer
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Julia Steckner
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Christopher A. Leo
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Dalia Naser
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Efrosini Artikis
- Department of Chemistry and Biophysics, University of Michigan, Ann Arbor, MI 48109
| | - Aron Broom
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Travis Ko
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Purnank Shah
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Mikaela Q. Ney
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Elisa Tran
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Martin T. J. Smith
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Brian Fuglestad
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - A. Joshua Wand
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Charles L. Brooks
- Department of Chemistry and Biophysics, University of Michigan, Ann Arbor, MI 48109
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6
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Taylor C, Schönberger N, Laníková A, Patzschke M, Drobot B, Žídek L, Lederer F. Investigation of the structure and dynamics of gallium binding to high-affinity peptides elucidated by multi-scale simulation, quantum chemistry, NMR and ITC. Phys Chem Chem Phys 2021; 23:8618-8632. [PMID: 33876023 DOI: 10.1039/d1cp00356a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Gallium (as Ga3+) is a Group IIIa metal and its recovery from wastewaters has become increasingly important for its reuse. The use of peptides for recycling offers a low-cost and environmentally-friendly option but the structural characteristics of peptides likely to bind Ga3+ are largely unknown. Multiple computational methods, coupled with experimental verification via NMR and Isothermal Calorimetry (ITC), were used to establish that Ga3+ binds with high affinity to peptide sequences and to elucidate the structural characteristics that contributed. It was demonstrated that peptide pre-organisation is key to Ga3+ binding and that a favourable binding position is necessarily governed by the size and shape of the electrostatic environment as much as individual electrostatic interactions with peptide residues themselves. Given favourable conditions, Ga3+ retrieved plausible binding positions involving both charged and uncharged residues that greatly increases the range of bonding possibilities with other peptide sequences and offers insights for binding other metals. The addition of pH buffer substantially improved the affinity of Ga3+ and a structural role for a buffer component was demonstrated.
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Affiliation(s)
- Corey Taylor
- Department of Chemistry of the f-elements, Institute of Resource Ecology (IRE), Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328, Dresden, Germany.
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7
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Huang Y, Henderson JA, Shen J. Continuous Constant pH Molecular Dynamics Simulations of Transmembrane Proteins. Methods Mol Biol 2021; 2302:275-287. [PMID: 33877633 PMCID: PMC8062021 DOI: 10.1007/978-1-0716-1394-8_15] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Many membrane channels, transporters, and receptors utilize a pH gradient or proton coupling to drive functionally relevant conformational transitions. Conventional molecular dynamics simulations employ fixed protonation states, thus neglecting the coupling between protonation and conformational equilibria. Here we describe the membrane-enabled hybrid-solvent continuous constant pH molecular dynamics method for capturing atomic details of proton-coupled conformational dynamics of transmembrane proteins. Example protocols from our recent application studies of proton channels and ion/substrate transporters are discussed.
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Affiliation(s)
- Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen, Fujian, China
| | | | - Jana Shen
- University of Maryland School of Pharmacy, Baltimore, MD, USA.
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8
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Schindler CEM, Baumann H, Blum A, Böse D, Buchstaller HP, Burgdorf L, Cappel D, Chekler E, Czodrowski P, Dorsch D, Eguida MKI, Follows B, Fuchß T, Grädler U, Gunera J, Johnson T, Jorand Lebrun C, Karra S, Klein M, Knehans T, Koetzner L, Krier M, Leiendecker M, Leuthner B, Li L, Mochalkin I, Musil D, Neagu C, Rippmann F, Schiemann K, Schulz R, Steinbrecher T, Tanzer EM, Unzue Lopez A, Viacava Follis A, Wegener A, Kuhn D. Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects. J Chem Inf Model 2020; 60:5457-5474. [PMID: 32813975 DOI: 10.1021/acs.jcim.0c00900] [Citation(s) in RCA: 122] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.
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Affiliation(s)
| | - Hannah Baumann
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Andreas Blum
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Dietrich Böse
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | | | - Lars Burgdorf
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | | | - Eugene Chekler
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Paul Czodrowski
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Dieter Dorsch
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | | | - Bruce Follows
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Thomas Fuchß
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Ulrich Grädler
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Jakub Gunera
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Theresa Johnson
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Catherine Jorand Lebrun
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Srinivasa Karra
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Markus Klein
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Tim Knehans
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Lisa Koetzner
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Mireille Krier
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | | | | | - Liwei Li
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Igor Mochalkin
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Djordje Musil
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Constantin Neagu
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | | | - Kai Schiemann
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Robert Schulz
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany.,Institute of Pharmacy, Freie Universität Berlin, Königin-Luise-Straße 2+4, 14195 Berlin, Germany
| | | | - Eva-Maria Tanzer
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | | | - Ariele Viacava Follis
- EMD Serono Research and Development Institute Inc., 45A Middlesex Turnpike, Billerica, Massachusetts 01821, United States
| | - Ansgar Wegener
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
| | - Daniel Kuhn
- Merck KGaA, Frankfurter Strasse 250, 64293 Darmstadt, Germany
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9
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Seritan S, Bannwarth C, Fales BS, Hohenstein EG, Isborn CM, Kokkila‐Schumacher SIL, Li X, Liu F, Luehr N, Snyder JW, Song C, Titov AV, Ufimtsev IS, Wang L, Martínez TJ. TeraChem
: A graphical processing unit
‐accelerated
electronic structure package for
large‐scale
ab initio molecular dynamics. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE 2020. [DOI: 10.1002/wcms.1494] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Stefan Seritan
- Department of Chemistry and the PULSE Institute Stanford University Stanford California USA
- SLAC National Accelerator Laboratory Menlo Park California USA
| | - Christoph Bannwarth
- Department of Chemistry and the PULSE Institute Stanford University Stanford California USA
- SLAC National Accelerator Laboratory Menlo Park California USA
| | - Bryan S. Fales
- Department of Chemistry and the PULSE Institute Stanford University Stanford California USA
- SLAC National Accelerator Laboratory Menlo Park California USA
| | - Edward G. Hohenstein
- Department of Chemistry and the PULSE Institute Stanford University Stanford California USA
- SLAC National Accelerator Laboratory Menlo Park California USA
| | - Christine M. Isborn
- Department of Chemistry University of California Merced Merced California USA
| | | | - Xin Li
- Division of Theoretical Chemistry and Biology, School of Engineering Sciences in Chemistry, Biotechnology and Health KTH Royal Institute of Technology Stockholm Sweden
| | - Fang Liu
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | | | | | - Chenchen Song
- Department of Physics University of California Berkeley Berkeley California USA
- Molecular Foundry Lawrence Berkeley National Laboratory Berkeley California USA
| | | | - Ivan S. Ufimtsev
- Department of Structural Biology Stanford University School of Medicine Stanford California USA
| | - Lee‐Ping Wang
- Department of Chemistry University of California Davis Davis California USA
| | - Todd J. Martínez
- Department of Chemistry and the PULSE Institute Stanford University Stanford California USA
- SLAC National Accelerator Laboratory Menlo Park California USA
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10
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Harris RC, Shen J. GPU-Accelerated Implementation of Continuous Constant pH Molecular Dynamics in Amber: p Ka Predictions with Single-pH Simulations. J Chem Inf Model 2019; 59:4821-4832. [PMID: 31661616 DOI: 10.1021/acs.jcim.9b00754] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present a GPU implementation of the continuous constant pH molecular dynamics (CpHMD) based on the most recent generalized Born implicit-solvent model in the pmemd engine of the Amber molecular dynamics package. To test the accuracy of the tool for rapid pKa predictions, a series of 2 ns single-pH simulations were performed for over 120 titratable residues in 10 benchmark proteins that were previously used to test the various continuous CpHMD methods. The calculated pKa's showed a root-mean-square deviation of 0.80 and correlation coefficient of 0.83 with respect to experiment. Also, 90% of the pKa's were converged with estimated errors below 0.1 pH units. Surprisingly, this level of accuracy is similar to our previous replica-exchange simulations with 2 ns per replica and an exchange attempt frequency of 2 ps-1 (Huang, Harris, and Shen J. Chem. Inf. Model. 2018 , 58 , 1372 - 1383 ). Interestingly, for the linked titration sites in two enzymes, although residue-specific protonation state sampling in the single-pH simulations was not converged within 2 ns, the protonation fraction of the linked residues appeared to be largely converged, and the experimental macroscopic pKa values were reproduced to within 1 pH unit. Comparison with replica-exchange simulations with different exchange attempt frequencies showed that the splitting between the two macroscopic pKa's is underestimated with frequent exchange attempts such as 2 ps-1, while single-pH simulations overestimate the splitting. The same trend is seen for the single-pH vs replica-exchange simulations of a hydrogen-bonded aspartyl dyad in a much larger protein. A 2 ns single-pH simulation of a 400-residue protein takes about 1 h on a single NVIDIA GeForce RTX 2080 graphics card, which is over 1000 times faster than a CpHMD run on a single CPU core of a high-performance computing cluster node. Thus, we envision that GPU-accelerated continuous CpHMD may be used in routine pKa predictions for a variety of applications, from assisting MD simulations with protonation state assignment to offering pH-dependent corrections of binding free energies and identifying reactive hot spots for covalent drug design.
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Affiliation(s)
- Robert C Harris
- Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States
| | - Jana Shen
- Department of Pharmaceutical Sciences , University of Maryland School of Pharmacy , Baltimore , Maryland 21201 , United States
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11
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Relative Contributions of Solubility and Mobility to the Stability of Amorphous Solid Dispersions of Poorly Soluble Drugs: A Molecular Dynamics Simulation Study. Pharmaceutics 2018; 10:pharmaceutics10030101. [PMID: 30037083 PMCID: PMC6161151 DOI: 10.3390/pharmaceutics10030101] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 07/13/2018] [Accepted: 07/18/2018] [Indexed: 11/29/2022] Open
Abstract
Amorphous solid dispersions are considered a promising formulation strategy for the oral delivery of poorly soluble drugs. The limiting factor for the applicability of this approach is the physical (in)stability of the amorphous phase in solid samples. Minimizing the risk of reduced shelf life for a new drug by establishing a suitable excipient/polymer-type from first principles would be desirable to accelerate formulation development. Here, we perform Molecular Dynamics simulations to determine properties of blends of eight different polymer–small molecule drug combinations for which stability data are available from a consistent set of literature data. We calculate thermodynamic factors (mixing energies) as well as mobilities (diffusion rates and roto-vibrational fluctuations). We find that either of the two factors, mobility and energetics, can determine the relative stability of the amorphous form for a given drug. Which factor is rate limiting depends on physico-chemical properties of the drug and the excipients/polymers. The methods outlined here can be readily employed for an in silico pre-screening of different excipients for a given drug to establish a qualitative ranking of the expected relative stabilities, thereby accelerating and streamlining formulation development.
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12
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Giese TJ, York DM. A GPU-Accelerated Parameter Interpolation Thermodynamic Integration Free Energy Method. J Chem Theory Comput 2018; 14:1564-1582. [PMID: 29357243 PMCID: PMC5849537 DOI: 10.1021/acs.jctc.7b01175] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
There has been a resurgence of interest in free energy methods motivated by the performance enhancements offered by molecular dynamics (MD) software written for specialized hardware, such as graphics processing units (GPUs). In this work, we exploit the properties of a parameter-interpolated thermodynamic integration (PI-TI) method to connect states by their molecular mechanical (MM) parameter values. This pathway is shown to be better behaved for Mg2+ → Ca2+ transformations than traditional linear alchemical pathways (with and without soft-core potentials). The PI-TI method has the practical advantage that no modification of the MD code is required to propagate the dynamics, and unlike with linear alchemical mixing, only one electrostatic evaluation is needed (e.g., single call to particle-mesh Ewald) leading to better performance. In the case of AMBER, this enables all the performance benefits of GPU-acceleration to be realized, in addition to unlocking the full spectrum of features available within the MD software, such as Hamiltonian replica exchange (HREM). The TI derivative evaluation can be accomplished efficiently in a post-processing step by reanalyzing the statistically independent trajectory frames in parallel for high throughput. We also show how one can evaluate the particle mesh Ewald contribution to the TI derivative evaluation without needing to perform two reciprocal space calculations. We apply the PI-TI method with HREM on GPUs in AMBER to predict p Ka values in double stranded RNA molecules and make comparison with experiments. Convergence to under 0.25 units for these systems required 100 ns or more of sampling per window and coupling of windows with HREM. We find that MM charges derived from ab initio QM/MM fragment calculations improve the agreement between calculation and experimental results.
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Affiliation(s)
- Timothy J. Giese
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
| | - Darrin M. York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research, and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854-8087, United States
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13
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Menchon G, Maveyraud L, Czaplicki G. Molecular Dynamics as a Tool for Virtual Ligand Screening. Methods Mol Biol 2018; 1762:145-178. [PMID: 29594772 DOI: 10.1007/978-1-4939-7756-7_9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Rational drug design is essential for new drugs to emerge, especially when the structure of a target protein or catalytic enzyme is known experimentally. To that purpose, high-throughput virtual ligand screening campaigns aim at discovering computationally new binding molecules or fragments to inhibit a particular protein interaction or biological activity. The virtual ligand screening process often relies on docking methods which allow predicting the binding of a molecule into a biological target structure with a correct conformation and the best possible affinity. The docking method itself is not sufficient as it suffers from several and crucial limitations (lack of protein flexibility information, no solvation effects, poor scoring functions, and unreliable molecular affinity estimation).At the interface of computer techniques and drug discovery, molecular dynamics (MD) allows introducing protein flexibility before or after a docking protocol, refining the structure of protein-drug complexes in the presence of water, ions and even in membrane-like environments, and ranking complexes with more accurate binding energy calculations. In this chapter we describe the up-to-date MD protocols that are mandatory supporting tools in the virtual ligand screening (VS) process. Using docking in combination with MD is one of the best computer-aided drug design protocols nowadays. It has proved its efficiency through many examples, described below.
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Affiliation(s)
- Grégory Menchon
- Laboratory of Biomolecular Research, Paul Scherrer Institute, Villigen PSI, Switzerland
| | - Laurent Maveyraud
- Institute of Pharmacology and Structural Biology, UMR 5089, University of Toulouse III, Toulouse, France
| | - Georges Czaplicki
- Institute of Pharmacology and Structural Biology, UMR 5089, University of Toulouse III, Toulouse, France.
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14
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Current trends in molecular modeling methods applied to the study of cyclodextrin complexes. J INCL PHENOM MACRO 2017. [DOI: 10.1007/s10847-017-0763-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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15
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Lee TS, Hu Y, Sherborne B, Guo Z, York DM. Toward Fast and Accurate Binding Affinity Prediction with pmemdGTI: An Efficient Implementation of GPU-Accelerated Thermodynamic Integration. J Chem Theory Comput 2017; 13:3077-3084. [PMID: 28618232 DOI: 10.1021/acs.jctc.7b00102] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
We report the implementation of the thermodynamic integration method on the pmemd module of the AMBER 16 package on GPUs (pmemdGTI). The pmemdGTI code typically delivers over 2 orders of magnitude of speed-up relative to a single CPU core for the calculation of ligand-protein binding affinities with no statistically significant numerical differences and thus provides a powerful new tool for drug discovery applications.
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Affiliation(s)
- Tai-Sung Lee
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University , Piscataway, New Jersey 08854, United States
| | - Yuan Hu
- Department of Chemistry, Modeling and Informatics, Merck Research Laboratories, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Brad Sherborne
- Department of Chemistry, Modeling and Informatics, Merck Research Laboratories, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Zhuyan Guo
- Department of Chemistry, Modeling and Informatics, Merck Research Laboratories, 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Darrin M York
- Laboratory for Biomolecular Simulation Research, Center for Integrative Proteomics Research and Department of Chemistry and Chemical Biology, Rutgers University , Piscataway, New Jersey 08854, United States
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16
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Ng C, Nandha Premnath P, Guvench O. Rigidity and flexibility in the tetrasaccharide linker of proteoglycans from atomic-resolution molecular simulation. J Comput Chem 2017; 38:1438-1446. [PMID: 28101951 DOI: 10.1002/jcc.24738] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2016] [Revised: 12/12/2016] [Accepted: 12/20/2016] [Indexed: 01/09/2023]
Abstract
Proteoglycans (PGs) are covalent conjugates between protein and carbohydrate (glycosaminoglycans). Certain classes of glycosaminoglycans such as chondroitin sulfate/dermatan sulfate and heparan sulfate utilize a specific tetrasaccharide linker for attachment to the protein component: GlcAβ1-3Galβ1-3Galβ1-4Xylβ1-O-Ser. Toward understanding the conformational preferences of this linker, the present work used all-atom explicit-solvent molecular dynamics (MD) simulations combined with Adaptive Biasing Force (ABF) sampling to determine high-resolution, high-precision conformational free energy maps ΔG(φ, ψ) for each glycosidic linkage between constituent disaccharides, including the variant where GlcA is substituted with IdoA. These linkages are characterized by single, predominant (> 97% occupancy), and broad (45° × 60° for ΔG(φ, ψ) < 1 kcal/mol) free-energy minima, while the Xyl-Ser linkage has two such minima similar in free-energy, and additional flexibility from the Ser sidechain dihedral. Conformational analysis of microsecond-scale standard MD on the complete tetrasaccharide-O-Ser conjugate is consistent with ABF data, suggesting (φ, ψ) probabilities are independent of the linker context, and that the tetrasaccharide acts as a relatively rigid unit whereas significant conformational heterogeneity exists with respect to rotation about bonds connecting Xyl to Ser. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Cathy Ng
- Department of Pharmaceutical Sciences, University of New England College of Pharmacy, 716 Stevens Avenue, Portland, Maine, 04103
| | - Padmavathy Nandha Premnath
- Department of Pharmaceutical Sciences, University of New England College of Pharmacy, 716 Stevens Avenue, Portland, Maine, 04103
| | - Olgun Guvench
- Department of Pharmaceutical Sciences, University of New England College of Pharmacy, 716 Stevens Avenue, Portland, Maine, 04103.,Graduate School of Biomedical Science and Engineering, University of Maine, 5775 Stodder Hall, Orono, Maine, 04469
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