1
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Shen M, Huang Y, Cai Z, Cherny VV, DeCoursey TE, Shen J. Interior pH-sensing residue of human voltage-gated proton channel H v1 is histidine 168. Biophys J 2024:S0006-3495(24)00486-7. [PMID: 39054673 DOI: 10.1016/j.bpj.2024.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 07/07/2024] [Accepted: 07/22/2024] [Indexed: 07/27/2024] Open
Abstract
The molecular mechanisms governing the human voltage-gated proton channel hHv1 remain elusive. Here, we used membrane-enabled hybrid-solvent continuous constant pH molecular dynamics (CpHMD) simulations with pH replica exchange to further evaluate the structural models of hHv1 in the closed (hyperpolarized) and open (depolarized) states recently obtained with MD simulations and explore potential pH-sensing residues. The CpHMD titration at a set of symmetric pH conditions revealed three residues that can gain or lose protons upon channel depolarization. Among them, residue H168 at the intracellular end of the S3 helix switches from the deprotonated to the protonated state and its protonation is correlated with the increased tilting of the S3 helix during the transition from the closed to the open state. Thus, the simulation data suggest H168 as an interior pH sensor, in support of a recent finding based on electrophysiological experiments of Hv1 mutants. We propose that protonation of H168 acts as a key that unlocks the closed channel configuration by increasing the flexibility of the S2-S3 linker, which increases the tilt angle of S3 and enhances the mobility of the S4 helix, thus promoting channel opening. Our work represents an important step toward deciphering the pH-dependent gating mechanism of hHv1.
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Affiliation(s)
- Mingzhe Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland
| | - Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen, Fujian Province, China.
| | - Zhitao Cai
- College of Computer Engineering, Jimei University, Xiamen, Fujian Province, China
| | - Vladimir V Cherny
- Department of Physiology & Biophysics, Rush University Medical Center, Chicago, Illinois
| | - Thomas E DeCoursey
- Department of Physiology & Biophysics, Rush University Medical Center, Chicago, Illinois
| | - Jana Shen
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland.
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2
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Hussein A, Fan S, Lopez-Redondo M, Kenney I, Zhang X, Beckstein O, Stokes DL. Energy coupling and stoichiometry of Zn 2+/H + antiport by the prokaryotic cation diffusion facilitator YiiP. eLife 2023; 12:RP87167. [PMID: 37906094 PMCID: PMC10617992 DOI: 10.7554/elife.87167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023] Open
Abstract
YiiP from Shewanella oneidensis is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.
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Affiliation(s)
- Adel Hussein
- Department of Biochemistry and Molecular Pharmacology, NYU School of MedicineNew YorkUnited States
| | - Shujie Fan
- Department of Physics, Arizona State UniversityTempeUnited States
| | - Maria Lopez-Redondo
- Department of Biochemistry and Molecular Pharmacology, NYU School of MedicineNew YorkUnited States
| | - Ian Kenney
- Department of Physics, Arizona State UniversityTempeUnited States
| | - Xihui Zhang
- Department of Biochemistry and Molecular Pharmacology, NYU School of MedicineNew YorkUnited States
| | - Oliver Beckstein
- Department of Physics, Arizona State UniversityTempeUnited States
| | - David L Stokes
- Department of Biochemistry and Molecular Pharmacology, NYU School of MedicineNew YorkUnited States
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3
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Ancona N, Bastola A, Alexov E. PKAD-2: New entries and expansion of functionalities of the database of experimentally measured pKa's of proteins. JOURNAL OF COMPUTATIONAL BIOPHYSICS AND CHEMISTRY 2023; 22:515-524. [PMID: 37520074 PMCID: PMC10373500 DOI: 10.1142/s2737416523500230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
Almost all biological reactions are pH dependent and understanding the origin of pH dependence requires knowledge of the pKa's of ionizable groups. Here we report a new edition of PKAD, the PKAD-2, which is a database of experimentally measured pKa's of proteins, both wild type and mutant proteins. The new additions include 117 wild type and 54 mutant pKa values, resulting in total 1742 experimentally measured pKa's. The new edition of PKAD-2 includes 8 new wild type and 12 new mutant proteins, resulting in total of 220 proteins. This new edition incorporates a visual 3D image of the highlighted residue of interest within the corresponding protein or protein complex. Hydrogen bonds were identified, counted, and implemented as a search feature. Other new search features include the number of neighboring residues <4A from the heaviest atom of the side chain of a given amino acid. Here, we present PKAD-2 with the intention to continuously incorporate novel features and current data with the goal to be used as benchmark for computational methods.
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Affiliation(s)
- Nicolas Ancona
- Department of Biological Sciences, College of Science, Clemson University, 105 Sikes Hall, Address, Clemson, SC 29634, United States of America
| | - Ananta Bastola
- School of Computing, College of Engineering, Computing and Applied Sciences, Clemson University, 105 Sikes Hall, SC 29634, United States of America
| | - Emil Alexov
- Department of Physics, College of Science, Clemson University, 105 Sikes Hall, Address, Clemson, SC 29634, United States of America
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4
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Hussein A, Fan S, Lopez-Redondo M, Kenney I, Zhang X, Beckstein O, Stokes DL. Energy Coupling and Stoichiometry of Zn 2+/H + Antiport by the Cation Diffusion Facilitator YiiP. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.23.529644. [PMID: 36865113 PMCID: PMC9980050 DOI: 10.1101/2023.02.23.529644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Abstract
YiiP is a prokaryotic Zn2+/H+ antiporter that serves as a model for the Cation Diffusion Facilitator (CDF) superfamily, members of which are generally responsible for homeostasis of transition metal ions. Previous studies of YiiP as well as related CDF transporters have established a homodimeric architecture and the presence of three distinct Zn2+ binding sites named A, B, and C. In this study, we use cryo-EM, microscale thermophoresis and molecular dynamics simulations to address the structural and functional roles of individual sites as well as the interplay between Zn2+ binding and protonation. Structural studies indicate that site C in the cytoplasmic domain is primarily responsible for stabilizing the dimer and that site B at the cytoplasmic membrane surface controls the structural transition from an inward facing conformation to an occluded conformation. Binding data show that intramembrane site A, which is directly responsible for transport, has a dramatic pH dependence consistent with coupling to the proton motive force. A comprehensive thermodynamic model encompassing Zn2+ binding and protonation states of individual residues indicates a transport stoichiometry of 1 Zn2+ to 2-3 H+ depending on the external pH. This stoichiometry would be favorable in a physiological context, allowing the cell to use the proton gradient as well as the membrane potential to drive the export of Zn2+.
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Affiliation(s)
- Adel Hussein
- Dept. of Cell Biology, NYU School of Medicine, New York, NY 10016 USA
| | - Shujie Fan
- Dept. of Physics, Arizona State University, Tempe AZ
| | | | - Ian Kenney
- Dept. of Physics, Arizona State University, Tempe AZ
| | - Xihui Zhang
- Dept. of Cell Biology, NYU School of Medicine, New York, NY 10016 USA
| | | | - David L Stokes
- Dept. of Cell Biology, NYU School of Medicine, New York, NY 10016 USA
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5
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Cai Z, Liu T, Lin Q, He J, Lei X, Luo F, Huang Y. Basis for Accurate Protein p Ka Prediction with Machine Learning. J Chem Inf Model 2023; 63:2936-2947. [PMID: 37146199 DOI: 10.1021/acs.jcim.3c00254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pKa's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pKa prediction is crucial. Here we present a theoretical pKa data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pKa orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pKa prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pKa predictor and thus can be applied immediately to, for example, pKa database construction, protein design, drug discovery, and so on.
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Affiliation(s)
- Zhitao Cai
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Tengzi Liu
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Qiaoling Lin
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Jiahao He
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Xiaowei Lei
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Fangfang Luo
- College of Computer Engineering, Jimei University, Xiamen 361021, China
| | - Yandong Huang
- College of Computer Engineering, Jimei University, Xiamen 361021, China
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6
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Bignucolo O, Chipot C, Kellenberger S, Roux B. Galvani Offset Potential and Constant-pH Simulations of Membrane Proteins. J Phys Chem B 2022; 126:6868-6877. [PMID: 36049129 PMCID: PMC9483922 DOI: 10.1021/acs.jpcb.2c04593] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/17/2022] [Indexed: 02/01/2023]
Abstract
A central problem in computational biophysics is the treatment of titratable residues in molecular dynamics simulations of large biological macromolecular systems. Conventional simulation methods ascribe a fixed ionization state to titratable residues in accordance with their pKa and the pH of the system, assuming that an effective average model will be able to capture the predominant behavior of the system. While this assumption may be justifiable in many cases, it is certainly limited, and it is important to design alternative methodologies allowing a more realistic treatment. Constant-pH simulation methods provide powerful approaches to handle titratable residues more realistically by allowing the ionization state to vary statistically during the simulation. Extending the molecular mechanical (MM) potential energy function to a family of potential functions accounting for different ionization states, constant-pH simulations are designed to sample all accessible configurations and ionization states, properly weighted according to their Boltzmann factor. Because protonation and deprotonation events correspond to a change in the total charge, difficulties arise when the long-range Coulomb interaction is treated on the basis of an idealized infinite simulation model and periodic boundary conditions with particle-mesh Ewald lattice sums. Charging free-energy calculations performed under these conditions in aqueous solution depend on the Galvani potential of the bulk water phase. This has important implications for the equilibrium and nonequilibrium constant-pH simulation methods grounded in the relative free-energy difference corresponding to the protonated and unprotonated residues. Here, the effect of the Galvani potential is clarified, and a simple practical solution is introduced to address this issue in constant-pH simulations of the acid-sensing ion channel (ASIC).
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Affiliation(s)
- Olivier Bignucolo
- Department
of Biomedical Sciences, University of Lausanne, 1015 Lausanne, Switzerland
- SIB
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Christophe Chipot
- Department
of Biochemistry and Molecular Biology, The
University of Chicago, Chicago, Illinois 60637, United States
- Laboratoire
International Associé Centre National de la Recherche Scientifique
et University of Illinois at Urbana−Champaign, Unité
Mixte de Recherche n◦7019, Université
de Lorraine, B.P. 70239, 54506 Cedex Vandœuvre-lès-Nancy, France
- Department
of Physics, University of Illinois at Urbana−Champaign, Urbana, Illinois 61820, United States
| | - Stephan Kellenberger
- Department
of Biomedical Sciences, University of Lausanne, 1015 Lausanne, Switzerland
| | - Benoît Roux
- Department
of Biochemistry and Molecular Biology, The
University of Chicago, Chicago, Illinois 60637, United States
- Department
of Chemistry, The University of Chicago, 5735 S. Ellis Ave., Chicago, Illinois 60637, United States
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7
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Henderson JA, Liu R, Harris JA, Huang Y, de Oliveira VM, Shen J. A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0]. LIVING JOURNAL OF COMPUTATIONAL MOLECULAR SCIENCE 2022; 4:1563. [PMID: 36776714 PMCID: PMC9910290 DOI: 10.33011/livecoms.4.1.1563] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.
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Affiliation(s)
| | - Ruibin Liu
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Yandong Huang
- University of Maryland School of Pharmacy, Baltimore, MD
| | | | - Jana Shen
- University of Maryland School of Pharmacy, Baltimore, MD
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8
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Mahinthichaichan P, Vo QN, Ellis CR, Shen J. Kinetics and Mechanism of Fentanyl Dissociation from the μ-Opioid Receptor. JACS AU 2021; 1:2208-2215. [PMID: 34977892 PMCID: PMC8715493 DOI: 10.1021/jacsau.1c00341] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Indexed: 06/14/2023]
Abstract
Driven by illicit fentanyl, opioid related deaths have reached the highest level in 2020. Currently, an opioid overdose is resuscitated by the use of naloxone, which competitively binds and antagonizes the μ-opioid receptor (mOR). Thus, knowledge of the residence times of opioids at mOR and the unbinding mechanisms is valuable for assessing the effectiveness of naloxone. In the present study, we calculate the fentanyl-mOR dissociation time and elucidate the mechanism by applying an enhanced sampling molecular dynamics (MD) technique. Two sets of metadynamics simulations with different initial structures were performed while accounting for the protonation state of the conserved H2976.52, which has been suggested to modulate the ligand-mOR affinity and binding mode. Surprisingly, with the Nδ-protonated H2976.52, fentanyl can descend as much as 10 Å below the level of the conserved D1473.32 before escaping the receptor and has a calculated residence time τ of 38 s. In contrast, with the Nϵ- and doubly protonated H2976.52, the calculated τ are 2.6 and 0.9 s, respectively. Analysis suggests that formation of the piperidine-Hid297 hydrogen bond strengthens the hydrophobic contacts with the transmembrane helix (TM) 6, allowing fentanyl to explore a deep pocket. Considering the experimental τ of ∼4 min for fentanyl and the role of TM6 in mOR activation, the deep insertion mechanism may be biologically relevant. The work paves the way for large-scale computational predictions of opioid dissociation rates to inform evaluation of strategies for opioid overdose reversal. The profound role of the histidine protonation state found here may shift the paradigm in computational studies of ligand-receptor kinetics.
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Affiliation(s)
- Paween Mahinthichaichan
- Division
of Applied Regulatory Science, Office of Clinical Pharmacology, Office
of Translational Sciences, Center for Drug
Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Quynh N. Vo
- Division
of Applied Regulatory Science, Office of Clinical Pharmacology, Office
of Translational Sciences, Center for Drug
Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
| | - Christopher R. Ellis
- Division
of Applied Regulatory Science, Office of Clinical Pharmacology, Office
of Translational Sciences, Center for Drug
Evaluation and Research, United States Food and Drug Administration, Silver Spring, Maryland 20993, United States
| | - Jana Shen
- Department
of Pharmaceutical Sciences, University of
Maryland School of Pharmacy, Baltimore, Maryland 21201, United States
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9
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Cai Z, Luo F, Wang Y, Li E, Huang Y. Protein p K a Prediction with Machine Learning. ACS OMEGA 2021; 6:34823-34831. [PMID: 34963965 PMCID: PMC8697405 DOI: 10.1021/acsomega.1c05440] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/24/2021] [Indexed: 05/23/2023]
Abstract
Protein pK a prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pK a predictor DeepKa, which is trained and validated with the pK a values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang Y.J. Chem. Inf. Model.2018, 58, 1372-1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pK a predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pK a predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction.
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