1
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Keller J, Fernández-Busnadiego R. In situ studies of membrane biology by cryo-electron tomography. Curr Opin Cell Biol 2024; 88:102363. [PMID: 38677049 DOI: 10.1016/j.ceb.2024.102363] [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: 02/01/2024] [Revised: 03/26/2024] [Accepted: 04/08/2024] [Indexed: 04/29/2024]
Abstract
Cryo-electron tomography (cryo-ET) allows high resolution 3D imaging of biological samples in near-native environments. Thus, cryo-ET has become the method of choice to analyze the unperturbed organization of cellular membranes. Here, we briefly discuss current cryo-ET workflows and their application to study membrane biology in situ, under basal and pathological conditions.
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Affiliation(s)
- Jenny Keller
- University Medical Center Göttingen, Institute for Neuropathology, Göttingen, 37077, Germany; Collaborative Research Center 1190 "Compartmental Gates and Contact Sites in Cells", University of Göttingen, Göttingen, Germany.
| | - Rubén Fernández-Busnadiego
- University Medical Center Göttingen, Institute for Neuropathology, Göttingen, 37077, Germany; Collaborative Research Center 1190 "Compartmental Gates and Contact Sites in Cells", University of Göttingen, Göttingen, Germany; Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, 37077, Germany; Faculty of Physics, University of Göttingen, Göttingen, 37077, Germany.
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2
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Abouelkheir M, Roy T, Krzyscik MA, Özdemir E, Hristova K. Investigations of membrane protein interactions in cells using fluorescence microscopy. Curr Opin Struct Biol 2024; 86:102816. [PMID: 38648680 PMCID: PMC11141325 DOI: 10.1016/j.sbi.2024.102816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/06/2024] [Accepted: 03/26/2024] [Indexed: 04/25/2024]
Abstract
The interactions between proteins in membranes govern many cellular functions. Our ability to probe for such interactions has greatly evolved in recent years due to the introduction of new fluorescence techniques. As a result, we currently have a choice of methods that can be used to assess the spatial distribution of a membrane protein, its association state, and the thermodynamic stability of the oligomers in the native milieu. These biophysical measurements have revealed new insights into important biological processes in cellular membranes.
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Affiliation(s)
- Mahmoud Abouelkheir
- Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore MD 21218, USA; Chemistry-Biology Interface Program, Johns Hopkins University, Baltimore MD 21218, USA
| | - Tanaya Roy
- Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore MD 21218, USA
| | - Mateusz A Krzyscik
- Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore MD 21218, USA
| | - Ece Özdemir
- Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore MD 21218, USA
| | - Kalina Hristova
- Department of Materials Science and Engineering and Institute for NanoBioTechnology, Johns Hopkins University, Baltimore MD 21218, USA; Chemistry-Biology Interface Program, Johns Hopkins University, Baltimore MD 21218, USA.
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3
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Li H, Sun X, Cui W, Xu M, Dong J, Ekundayo BE, Ni D, Rao Z, Guo L, Stahlberg H, Yuan S, Vogel H. Computational drug development for membrane protein targets. Nat Biotechnol 2024; 42:229-242. [PMID: 38361054 DOI: 10.1038/s41587-023-01987-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 09/13/2023] [Indexed: 02/17/2024]
Abstract
The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.
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Affiliation(s)
- Haijian Li
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Xiaolin Sun
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Wenqiang Cui
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Marc Xu
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Junlin Dong
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Babatunde Edukpe Ekundayo
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Dongchun Ni
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Zhili Rao
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Liwei Guo
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China
| | - Henning Stahlberg
- Laboratory of Biological Electron Microscopy, IPHYS, SB, EPFL and Department of Fundamental Microbiology, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.
| | - Shuguang Yuan
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
| | - Horst Vogel
- Center for Computer-Aided Drug Discovery, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology/Chinese Academy of Sciences (SIAT/CAS), Shenzhen, China.
- Institut des Sciences et Ingénierie Chimiques (ISIC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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4
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Kiani YS, Jabeen I. Challenges of Protein-Protein Docking of the Membrane Proteins. Methods Mol Biol 2024; 2780:203-255. [PMID: 38987471 DOI: 10.1007/978-1-0716-3985-6_12] [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: 07/12/2024]
Abstract
Despite the recent advances in the determination of high-resolution membrane protein (MP) structures, the structural and functional characterization of MPs remains extremely challenging, mainly due to the hydrophobic nature, low abundance, poor expression, purification, and crystallization difficulties associated with MPs. Whereby the major challenges/hurdles for MP structure determination are associated with the expression, purification, and crystallization procedures. Although there have been significant advances in the experimental determination of MP structures, only a limited number of MP structures (approximately less than 1% of all) are available in the Protein Data Bank (PDB). Therefore, the structures of a large number of MPs still remain unresolved, which leads to the availability of widely unplumbed structural and functional information related to MPs. As a result, recent developments in the drug discovery realm and the significant biological contemplation have led to the development of several novel, low-cost, and time-efficient computational methods that overcome the limitations of experimental approaches, supplement experiments, and provide alternatives for the characterization of MPs. Whereby the fine tuning and optimizations of these computational approaches remains an ongoing endeavor.Computational methods offer a potential way for the elucidation of structural features and the augmentation of currently available MP information. However, the use of computational modeling can be extremely challenging for MPs mainly due to insufficient knowledge of (or gaps in) atomic structures of MPs. Despite the availability of numerous in silico methods for 3D structure determination the applicability of these methods to MPs remains relatively low since all methods are not well-suited or adequate for MPs. However, sophisticated methods for MP structure predictions are constantly being developed and updated to integrate the modifications required for MPs. Currently, different computational methods for (1) MP structure prediction, (2) stability analysis of MPs through molecular dynamics simulations, (3) modeling of MP complexes through docking, (4) prediction of interactions between MPs, and (5) MP interactions with its soluble partner are extensively used. Towards this end, MP docking is widely used. It is notable that the MP docking methods yet few in number might show greater potential in terms of filling the knowledge gap. In this chapter, MP docking methods and associated challenges have been reviewed to improve the applicability, accuracy, and the ability to model macromolecular complexes.
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Affiliation(s)
- Yusra Sajid Kiani
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Ishrat Jabeen
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
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5
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Koukos PI, Dehghani-Ghahnaviyeh S, Velez-Vega C, Manchester J, Tieleman DP, Duca JS, Souza PCT, Cournia Z. Martini 3 Force Field Parameters for Protein Lipidation Post-Translational Modifications. J Chem Theory Comput 2023; 19:8901-8918. [PMID: 38019969 DOI: 10.1021/acs.jctc.3c00604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
Protein lipidations are vital co/post-translational modifications that tether lipid tails to specific protein amino acids, allowing them to anchor to biological membranes, switch their subcellular localization, and modulate association with other proteins. Such lipidations are thus crucial for multiple biological processes including signal transduction, protein trafficking, and membrane localization and are implicated in various diseases as well. Examples of lipid-anchored proteins include the Ras family of proteins that undergo farnesylation; actin and gelsolin that are myristoylated; phospholipase D that is palmitoylated; glycosylphosphatidylinositol-anchored proteins; and others. Here, we develop parameters for cysteine-targeting farnesylation, geranylgeranylation, and palmitoylation, as well as glycine-targeting myristoylation for the latest version of the Martini 3 coarse-grained force field. The parameters are developed using the CHARMM36m all-atom force field parameters as reference. The behavior of the coarse-grained models is consistent with that of the all-atom force field for all lipidations and reproduces key dynamical and structural features of lipid-anchored peptides, such as the solvent-accessible surface area, bilayer penetration depth, and representative conformations of the anchors. The parameters are also validated in simulations of the lipid-anchored peripheral membrane proteins Rheb and Arf1, after comparison with independent all-atom simulations. The parameters, along with mapping schemes for the popular martinize2 tool, are available for download at 10.5281/zenodo.7849262 and also as supporting information.
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Affiliation(s)
- Panagiotis I Koukos
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
| | - Sepehr Dehghani-Ghahnaviyeh
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Camilo Velez-Vega
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - John Manchester
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - D Peter Tieleman
- Department of Biological Sciences, University of Calgary, Calgary T2N 1N4 Alberta, Canada
- Centre for Molecular Simulation, University of Calgary, Calgary T2N 1N4 Alberta, Canada
| | - José S Duca
- Computer-Aided Drug Discovery, Global Discovery Chemistry, Novartis Institutes for BioMedical Research, 181 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States
| | - Paulo C T Souza
- Molecular Microbiology and Structural Biochemistry, (MMSB, UMR 5086), CNRS & University of Lyon, 69367 Lyon, France
- Laboratory of Biology and Modeling of the Cell, École Normale Supérieure de Lyon, Université Claude Bernard Lyon 1, CNRS UMR 5239 and Inserm U1293, 46 Allée d'Italie, 69364 Lyon, France
| | - Zoe Cournia
- Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou, 11527 Athens, Greece
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6
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Le J, Loo JA. Detection of Lipid-Bound Bacteriorhodopsin Trimer Complex Directly from Purple Membrane by Native Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2620-2624. [PMID: 37975648 PMCID: PMC10947533 DOI: 10.1021/jasms.3c00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2023]
Abstract
Native mass spectrometry (MS) was used to detect the membrane protein, bacteriorhodopsin (bR), in its 27 kDa monomeric form and trimeric assemblies directly from lipid-containing purple membranes (PMs) from the halophilic archaeon, Halobacterium salinarum. Trimer bR ion populations bound to lipid molecules were detected with n-octyl β-d-glucopyranoside as the solubilizing detergent; the use of octyl tetraethylene glycol monooctyl ether or n-dodecyl-β-d-maltopyranoside resulted in only detection of monomeric bR. The archaeal lipids phosphotidylglycerolphosphate methyl ester and 3-HSO3-Galp-β1,6-Manp-α1,2-Glcp-α1,1-sn-2,3-diphytanylglycerol were the only lipids in the PMs found to bind to bR, consistent with previous high-resolution structural studies. Removal of the lipids from the sample resulted in the detection of only the bR monomer, highlighting the importance of specific lipids for stabilizing the bR trimer. To the best of our knowledge, this is the first report of the detection of the bR trimer with resolved lipid-bound species by MS.
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Affiliation(s)
- Jessie Le
- Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, CA 90095 USA
| | - Joseph A. Loo
- Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, CA 90095 USA
- Department of Biological Chemistry, David Geffen School of Medicine at UCLA, University of California-Los Angeles, Los Angeles, CA 90095 USA
- Molecular Biology Institute, University of California-Los Angeles, Los Angeles, CA, 90095 USA
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7
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Hoang Trinh TK, Catalano C, Guo Y. Fabrication of membrane proteins in the form of native cell membrane nanoparticles using novel membrane active polymers. NANOSCALE ADVANCES 2023; 5:5932-5940. [PMID: 37881706 PMCID: PMC10597567 DOI: 10.1039/d3na00381g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/04/2023] [Indexed: 10/27/2023]
Abstract
Membrane proteins are a widespread class of bio-macromolecules responsible for numerous vital biological processes and serve as therapeutic targets for a vast array of contemporary medications. For membrane protein isolation and purification, detergents have historically been used. Despite this, detergents frequently result in protein instability. Consequently, their application was limited. Recent detergent-free approaches have been invented. Among these, styrene-maleic acid lipid particle (SMALP), diisobutylene-maleic acid lipid particle (DIBMALP), and native cell membrane nanoparticle (NCMN) systems are the most prevalent. The NCMN system intends to create a library of membrane-active polymers suitable for high-resolution structure determination of membrane protein. Design, synthesis, characterization, and comparative application evaluations of three novel classes of NCMN polymers, NCMNP13-x, NCMNP21-x, and NCMNP21b-x, are presented in this article. Although each NCMN polymer can solubilize distinct model membrane proteins and retain native lipids in NCMN particles, only the NCMNP21b-x family produces lipid-protein particles with ideal buffer compatibility and high homogeneity suitable for single-particle cryo-EM analysis. NCMNP21b-x polymers that generate high-quality NCMN particles are particularly desirable for membrane protein structural biology.
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Affiliation(s)
- Thi Kim Hoang Trinh
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond VA 23298 USA
- Institute for Structural Biology, Drug Discovery and Development, School of Pharmacy, Virginia Commonwealth University Richmond VA 23219 USA
| | - Claudio Catalano
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond VA 23298 USA
- Institute for Structural Biology, Drug Discovery and Development, School of Pharmacy, Virginia Commonwealth University Richmond VA 23219 USA
| | - Youzhong Guo
- Department of Medicinal Chemistry, School of Pharmacy, Virginia Commonwealth University Richmond VA 23298 USA
- Institute for Structural Biology, Drug Discovery and Development, School of Pharmacy, Virginia Commonwealth University Richmond VA 23219 USA
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8
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Campuzano IDG. A Research Journey: Over a Decade of Denaturing and Native-MS Analyses of Hydrophobic and Membrane Proteins in Amgen Therapeutic Discovery. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2413-2431. [PMID: 37643331 DOI: 10.1021/jasms.3c00175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Membrane proteins and associated complexes currently comprise the majority of therapeutic targets and remain among the most challenging classes of proteins for analytical characterization. Through long-term strategic collaborations forged between industrial and academic research groups, there has been tremendous progress in advancing membrane protein mass spectrometry (MS) analytical methods and their concomitant application to Amgen therapeutic project progression. Herein, I will describe a detailed and personal account of how electrospray ionization (ESI) native mass spectrometry (nMS), ion mobility-MS (IM-MS), reversed phase liquid chromatographic mass spectrometry (RPLC-MS), high-throughput solid phase extraction mass spectrometry, and matrix-assisted laser desorption ionization mass spectrometry methods were developed, optimized, and validated within Amgen Research, and importantly, how these analytical methods were applied for membrane and hydrophobic protein analyses and ultimately therapeutic project support and progression. Additionally, I will discuss all the highly important and productive collaborative efforts, both internal Amgen and external academic, which were key in generating the samples, methods, and associated data described herein. I will also describe some early and previously unpublished nano-ESI (nESI) native-MS data from Amgen Research and the highly productive University of California Los Angeles (UCLA) collaboration. I will also present previously unpublished examples of real-life Amgen biotherapeutic membrane protein projects that were supported by all the MS (and IM) analytical techniques described herein. I will start by describing the initial nESI nMS experiments performed at Amgen in 2011 on empty nanodisc molecules, using a quadrupole time-of-flight MS, and how these experiments progressed on to the 15 Tesla Fourier transform ion cyclotron resonance MS at UCLA. Then described are monomeric and multimeric membrane protein data acquired in both nESI nMS and tandem-MS modes, using multiple methods of ion activation, resulting in dramatic spectral simplification. Also described is how we investigated the far less established and less published subject, that is denaturing RPLC-MS analysis of membrane proteins, and how we developed a highly robust and reproducible RPLC-MS method capable of effective separation of membrane proteins differing in only the presence or absence of an N-terminal post translational modification. Also described is the evolution of the aforementioned RPLC-MS method into a high-throughput solid phase extraction MS method. Finally, I will give my opinion on key developments and how the area of nMS of membrane proteins needs to evolve to a state where it can be applied within the biopharmaceutical research environment for routine therapeutic project support.
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Affiliation(s)
- Iain D G Campuzano
- Amgen Research, Center for Research Acceleration by Digital Innovation, Molecular Analytics, Thousand Oaks, California 91320, United States
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9
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Farrelly MD, Zhai J, Tiong AYJ, van 't Hag L, Yu HH, Li J, Martin LL, Thang SH. Membrane interaction and selectivity of novel alternating cationic lipid-nanodisc assembling polymers. Biomater Sci 2023; 11:5955-5969. [PMID: 37477383 DOI: 10.1039/d3bm00477e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Synthetic polymer nanodiscs are self-assembled structures formed from amphipathic copolymers encapsulating membrane proteins and surrounding phospholipids into water soluble discs. These nanostructures have served as an analytical tool for the detergent free solubilisation and structural study of membrane proteins (MPs) in their native lipid environment. We established the polymer-lipid nanodisc forming ability of a novel class of amphipathic copolymer comprised of an alternating sequence of N-alkyl functionalised maleimide (AlkylM) of systematically varied hydrocarbon chain length, and cationic N-methyl-4-vinyl pyridinium iodide (MVP). Using a combination of physicochemical techniques, the solubilisation efficiency, size, structure and shape of DMPC lipid containing poly(MVP-co-AlkylM) nanodiscs were determined. Lipid solubilisation increased with AlkylM hydrocarbon chain length from methyl (MM), ethyl (EtM), n-propyl (PM), iso-butyl (IBM) through to n-butyl (BM) maleimide bearing polymers. More hydrophobic derivatives formed smaller sized nanodiscs and lipid ordering within poly(MVP-co-AlkylM) nanodiscs was affected by nanodisc size. In dye-release assays, shorter N-alkyl substituted polymers, particularly poly(MVP-co-EtM), exhibited low activities against eukaryotic mimetic POPC membrane and increased their liposome disruption as POPC : POPG membrane mixtures increased in their anionic POPG component, resembling the charge profile of bacterial membranes. These trends in membrane selectivity were transferred towards native cell systems in which gram-positive Staphylococcus aureus and gram-negative Acenobacter baumannii bacterial strains were relatively susceptible to disruption by hydrophobic n-butyl- and n-propyl-poly(MVP-co-AlkylM) derivatives compared to human red blood cells (HRBCs), with a more pronounced selectivity resulting from poly(MVP-co-PM). Such selective membrane interaction by less hydrophobic polymers provides a framework for polymer design towards applications including selective membrane component solubilisation, biosensing and antimicrobial development.
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Affiliation(s)
| | - Jiali Zhai
- School of Science, STEM College, RMIT University Melbourne, VIC 3000, Australia
| | - Alice Y J Tiong
- Department of Chemical and Biological Engineering, Monash University, Clayton, VIC 3800, Australia
| | - Leonie van 't Hag
- Department of Chemical and Biological Engineering, Monash University, Clayton, VIC 3800, Australia
| | - Heidi H Yu
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University Clayton, VIC 3800, Australia
| | - Jian Li
- Infection Program and Department of Microbiology, Biomedicine Discovery Institute, Monash University Clayton, VIC 3800, Australia
| | - Lisandra L Martin
- School of Chemistry, Monash University Clayton, VIC 3800, Australia.
| | - San H Thang
- School of Chemistry, Monash University Clayton, VIC 3800, Australia.
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10
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Manzer ZA, Selivanovitch E, Ostwalt AR, Daniel S. Membrane protein synthesis: no cells required. Trends Biochem Sci 2023; 48:642-654. [PMID: 37087310 DOI: 10.1016/j.tibs.2023.03.006] [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] [Received: 11/07/2022] [Revised: 02/20/2023] [Accepted: 03/22/2023] [Indexed: 04/24/2023]
Abstract
Despite advances in membrane protein (MP) structural biology and a growing interest in their applications, these proteins remain challenging to study. Progress has been hindered by the complex nature of MPs and innovative methods will be required to circumvent technical hurdles. Cell-free protein synthesis (CFPS) is a burgeoning technique for synthesizing MPs directly into a membrane environment using reconstituted components of the cellular transcription and translation machinery in vitro. We provide an overview of CFPS and how this technique can be applied to the synthesis and study of MPs. We highlight numerous strategies including synthesis methods and folding environments, each with advantages and limitations, to provide a survey of how CFPS techniques can advance the study of MPs.
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Affiliation(s)
- Zachary A Manzer
- R.F. School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Ekaterina Selivanovitch
- R.F. School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Alexis R Ostwalt
- R.F. School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Susan Daniel
- R.F. School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA.
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11
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Andersen CG, Bavnhøj L, Pedersen BP. May the proton motive force be with you: A plant transporter review. Curr Opin Struct Biol 2023; 79:102535. [PMID: 36796226 DOI: 10.1016/j.sbi.2023.102535] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 02/16/2023]
Abstract
As our ecosystems experience challenges associated with climate change, an improved understanding of the fundamental biochemical processes governing plant physiology is needed. Strikingly, current structural information on plant membrane transporters is severely limited compared to other kingdoms of life, with only 18 unique structures in total. To advance future breakthroughs and insight in plant cell molecular biology, structural knowledge of membrane transporters is indispensable. This review summarizes the current status of structural knowledge in the plant membrane transporter field. Plants utilize the proton motive force (PMF) to drive secondary active transport. We discuss the PMF, how it relates to secondary active transport and provide a classification of PMF driven secondary active transport, discussing recently published structures of symporters, antiporters, and uniporters from plants.
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Affiliation(s)
| | - Laust Bavnhøj
- Department of Molecular Biology and Genetics, Aarhus University, 8000, Aarhus C, Denmark. https://twitter.com/laustbavnhoej
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12
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Gao T, Zhao Y, Zhang L, Wang H. Secondary and Topological Structural Merge Prediction of Alpha-Helical Transmembrane Proteins Using a Hybrid Model Based on Hidden Markov and Long Short-Term Memory Neural Networks. Int J Mol Sci 2023; 24:ijms24065720. [PMID: 36982795 PMCID: PMC10057634 DOI: 10.3390/ijms24065720] [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: 02/07/2023] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023] Open
Abstract
Alpha-helical transmembrane proteins (αTMPs) play essential roles in drug targeting and disease treatments. Due to the challenges of using experimental methods to determine their structure, αTMPs have far fewer known structures than soluble proteins. The topology of transmembrane proteins (TMPs) can determine the spatial conformation relative to the membrane, while the secondary structure helps to identify their functional domain. They are highly correlated on αTMPs sequences, and achieving a merge prediction is instructive for further understanding the structure and function of αTMPs. In this study, we implemented a hybrid model combining Deep Learning Neural Networks (DNNs) with a Class Hidden Markov Model (CHMM), namely HDNNtopss. DNNs extract rich contextual features through stacked attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) networks and Convolutional Neural Networks (CNNs), and CHMM captures state-associative temporal features. The hybrid model not only reasonably considers the probability of the state path but also has a fitting and feature-extraction capability for deep learning, which enables flexible prediction and makes the resulting sequence more biologically meaningful. It outperforms current advanced merge-prediction methods with a Q4 of 0.779 and an MCC of 0.673 on the independent test dataset, which have practical, solid significance. In comparison to advanced prediction methods for topological and secondary structures, it achieves the highest topology prediction with a Q2 of 0.884, which has a strong comprehensive performance. At the same time, we implemented a joint training method, Co-HDNNtopss, and achieved a good performance to provide an important reference for similar hybrid-model training.
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Affiliation(s)
- Ting Gao
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130117, China; (T.G.); (Y.Z.)
| | - Yutong Zhao
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130117, China; (T.G.); (Y.Z.)
| | - Li Zhang
- School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China;
| | - Han Wang
- School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130117, China; (T.G.); (Y.Z.)
- Correspondence:
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13
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Pei J, Cong Q. AFTM: a database of transmembrane regions in the human proteome predicted by AlphaFold. Database (Oxford) 2023; 2023:7077442. [PMID: 36917599 PMCID: PMC10013729 DOI: 10.1093/database/baad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/31/2023] [Accepted: 02/10/2023] [Indexed: 03/16/2023]
Abstract
Transmembrane proteins (TMPs), with diverse cellular functions, are difficult targets for structural determination. Predictions of TMPs and the locations of transmembrane segments using computational methods could be unreliable due to the potential for false positives and false negatives and show inconsistencies across different programs. Recent advances in protein structure prediction methods have made it possible to identify TMPs and their membrane-spanning regions using high-quality structural models. We developed the AlphaFold Transmembrane proteins (AFTM) database of candidate human TMPs by identifying transmembrane regions in AlphaFold structural models of human proteins and their domains using the positioning of proteins in membranes, version 3 program, followed by automatic corrections inspired by manual analysis of the results. We compared our results to annotations from the UniProt database and the Human Transmembrane Proteome (HTP) database. While AFTM did not identify transmembrane regions in some single-pass TMPs, it identified more transmembrane regions for multipass TMPs than UniProt and HTP. AFTM also showed more consistent results with experimental structures, as benchmarked against the Protein Data Bank Transmembrane proteins (PDBTM) database. In addition, some proteins previously annotated as TMPs were suggested to be non-TMPs by AFTM. We report the results of AFTM together with those of UniProt, HTP, TmAlphaFold, PDBTM and Membranome in the online AFTM database compiled as a comprehensive resource of candidate human TMPs with structural models. Database URL http://conglab.swmed.edu/AFTM.
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Affiliation(s)
- Jimin Pei
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, 6001 Forest Park Rd, Dallas, TX 75390, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, 6001 Forest Park Rd, Dallas, TX 75390, USA
- Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, 6001 Forest Park Rd., Dallas, TX 75390, USA
| | - Qian Cong
- *Corresponding author: Tel: +001-2146457401; Fax: +001-2146481666;
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14
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Burley SK, Bhikadiya C, Bi C, Bittrich S, Chao H, Chen L, Craig PA, Crichlow GV, Dalenberg K, Duarte JM, Dutta S, Fayazi M, Feng Z, Flatt JW, Ganesan S, Ghosh S, Goodsell DS, Green RK, Guranovic V, Henry J, Hudson BP, Khokhriakov I, Lawson CL, Liang Y, Lowe R, Peisach E, Persikova I, Piehl DW, Rose Y, Sali A, Segura J, Sekharan M, Shao C, Vallat B, Voigt M, Webb B, Westbrook JD, Whetstone S, Young JY, Zalevsky A, Zardecki C. RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning. Nucleic Acids Res 2023; 51:D488-D508. [PMID: 36420884 PMCID: PMC9825554 DOI: 10.1093/nar/gkac1077] [Citation(s) in RCA: 187] [Impact Index Per Article: 187.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a 'living data resource.' Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.
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Affiliation(s)
- Stephen K Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Charmi Bhikadiya
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Chunxiao Bi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Sebastian Bittrich
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Henry Chao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Li Chen
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Paul A Craig
- School of Chemistry and Materials Science, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Gregg V Crichlow
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Kenneth Dalenberg
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Shuchismita Dutta
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Maryam Fayazi
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Sai Ganesan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sutapa Ghosh
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - David S Goodsell
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Rachel Kramer Green
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Vladimir Guranovic
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jeremy Henry
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Brian P Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Igor Khokhriakov
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Catherine L Lawson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yuhe Liang
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Irina Persikova
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ben Webb
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - John D Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Shamara Whetstone
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jasmine Y Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Arthur Zalevsky
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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15
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Jiang W, Lacroix J, Luo YL. Importance of molecular dynamics equilibrium protocol on protein-lipid interaction near channel pore. BIOPHYSICAL REPORTS 2022; 2:100080. [PMID: 36425669 PMCID: PMC9680783 DOI: 10.1016/j.bpr.2022.100080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Multiscale molecular dynamics simulations using Martini coarse-grained (CG) and all-atom (AA) force fields are commonly used in membrane protein studies. In particular, reverse mapping an equilibrated CG model to an AA model offers an efficient way for preparing large membrane protein systems with complex protein shapes and lipid compositions. Here, we report that this hybrid CG-equilibrium-AA-production protocol may artificially increase lipid density and decrease hydration in ion channel pores walled with transmembrane gaps. To understand the origin of this conundrum, we conducted replicas of CG, AA, and CG reverse-mapped AA simulations of the pore domain of the mechanosensitive Piezo1 channel in a nonconducting conformation. Lipid/water density analysis and free energy calculations reveal that the lack of initial pore hydration allows excessive lipids to enter the upper pore lumen through gaps between pore helices during CG simulation. Due to the mismatch between CG and AA lipid kinetics, these pore lipids remain trapped in the subsequent AA simulations, despite unfavorable binding free energy. We tested several CG equilibrium protocols and found that a protocol restraining the whole lipid produces pore hydration consistent with AA results, thus eliminating this artifact for further studies of lipid gating and protein-lipid interactions.
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Affiliation(s)
- Wenjuan Jiang
- College of Pharmacy, Western University of Health Sciences, Pomona, California
| | - Jerome Lacroix
- Graduate College of Biomedical Sciences, Western University of Health Sciences, Pomona, California
| | - Yun Lyna Luo
- College of Pharmacy, Western University of Health Sciences, Pomona, California
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16
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Lin L, Chen S, Xia W, Li T, Dong L, Zhang Y, Zhang Y, Li H, Zhao Y, Fu X. A new strategy: Inhibitory effect and mechanism of Cymbopogon martinii essential oil on Aspergillus flavus. FOOD BIOSCI 2022. [DOI: 10.1016/j.fbio.2022.102315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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17
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Raghuraman H, Böckmann A. Editorial: Structural dynamics of membrane proteins, Volume II. Front Mol Biosci 2022; 9:1037083. [PMID: 36310593 PMCID: PMC9608756 DOI: 10.3389/fmolb.2022.1037083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- H. Raghuraman
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata, India
- *Correspondence: H. Raghuraman, ; Anja Böckmann,
| | - Anja Böckmann
- Microbiologie Moléculaire et Biochimie Structurale (MMSB), UMR5086 CNRS, Lyon University, Lyon, France
- *Correspondence: H. Raghuraman, ; Anja Böckmann,
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18
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Burley SK, Berman HM, Duarte JM, Feng Z, Flatt JW, Hudson BP, Lowe R, Peisach E, Piehl DW, Rose Y, Sali A, Sekharan M, Shao C, Vallat B, Voigt M, Westbrook JD, Young JY, Zardecki C. Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students. Biomolecules 2022; 12:1425. [PMID: 36291635 PMCID: PMC9599165 DOI: 10.3390/biom12101425] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/23/2022] [Accepted: 09/26/2022] [Indexed: 11/18/2022] Open
Abstract
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
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Affiliation(s)
- Stephen K. Burley
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Helen M. Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Jose M. Duarte
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Zukang Feng
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Justin W. Flatt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brian P. Hudson
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Robert Lowe
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ezra Peisach
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Dennis W. Piehl
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Yana Rose
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Andrej Sali
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Department of Bioengineering and Therapeutic Sciences, Department of Pharmaceutical Chemistry, Quantitative Biosciences Institute, University of California San Francisco, San Francisco, CA 94158, USA
| | - Monica Sekharan
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Chenghua Shao
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Maria Voigt
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - John D. Westbrook
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
| | - Jasmine Y. Young
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Christine Zardecki
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
- Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
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19
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Thangaratnarajah C, Rheinberger J, Paulino C. Cryo-EM studies of membrane proteins at 200 keV. Curr Opin Struct Biol 2022; 76:102440. [PMID: 36029606 DOI: 10.1016/j.sbi.2022.102440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022]
Abstract
Single-particle cryogenic electron-microscopy (cryo-EM) has emerged as a powerful technique for the structural characterisation of membrane proteins, especially for targets previously thought to be intractable. Taking advantage of the latest hard- and software developments, high-resolution three-dimensional (3D) reconstructions of membrane proteins by cryo-EM has become routine, with 300-kV transmission electron microscopes (TEMs) being the current standard. The use of 200-kV cryo-TEMs is gaining increasingly prominence, showing the capabilities of reaching better than 2 Å resolution for soluble proteins and better than 3 Å resolution for membrane proteins. Here, we highlight the challenges working with membrane proteins and the impact of cryo-EM, and review the technical and practical benefits, achievements and limitations of imaging at lower electron acceleration voltages.
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Affiliation(s)
- Chancievan Thangaratnarajah
- University of Groningen, Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Electron Microscopy and Membrane Enzymology Group, Nijenborgh 4, 9747 AG, Groningen, Netherlands.
| | - Jan Rheinberger
- University of Groningen, Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Electron Microscopy and Membrane Enzymology Group, Nijenborgh 4, 9747 AG, Groningen, Netherlands. https://twitter.com/rheinbergerj
| | - Cristina Paulino
- University of Groningen, Faculty of Science and Engineering, Groningen Biomolecular Sciences and Biotechnology, Electron Microscopy and Membrane Enzymology Group, Nijenborgh 4, 9747 AG, Groningen, Netherlands.
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20
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Quispe C, Herrera-Bravo J, Khan K, Javed Z, Semwal P, Painuli S, Kamiloglu S, Martorell M, Calina D, Sharifi-Rad J. Therapeutic applications of curcumin nanomedicine formulations in cystic fibrosis. Prog Biomater 2022; 11:321-329. [PMID: 35904711 DOI: 10.1007/s40204-022-00198-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/17/2022] [Indexed: 12/12/2022] Open
Abstract
Medicinal applications of turmeric-derived curcumin have been known to mankind for long ages. Its potential in managing "cystic fibrosis" has also been evaluated. This autosomal recessive genetic disease is caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) which involves an impaired secretion of chloride ions and leads to hypersecretion of thick and sticky mucus and serious complications including airway obstruction, chronic lung infection, and inflammatory reactions. This narrative review aims to highlight the available evidence for the efficacy of curcumin nanoformulations in its potential treatment of cystic fibrosis. Recent research has shown that curcumin acts on the localized mutant CFTR ion channel at the plasma membrane. Preclinical studies have also shown that curcumin nanoformulations have promising effects in the treatment of cystic fibrosis. In this context, the purpose of this narrative review is to highlight the general bioactivity of curcumin, the types of formulations and related studies, thus opening new therapeutic perspectives for CF.
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Affiliation(s)
- Cristina Quispe
- Facultad de Ciencias de La Salud, Universidad Arturo Prat, Avda. Arturo Prat 2120, 1110939, Iquique, Chile
| | - Jesús Herrera-Bravo
- Departamento de Ciencias Básicas, Facultad de Ciencias, Universidad Santo Tomas, Bogotá, Chile.,Center of Molecular Biology and Pharmacogenetics, Scientific and Technological Bioresource Nucleus, Universidad de La Frontera, 4811230, Temuco, Chile
| | - Khushbukhat Khan
- Department of Healthcare Biotechnology, Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Sector H-12, Islamabad, 44000, Pakistan
| | - Zeeshan Javed
- Center for Applied Molecular Biology, University of the Punjab, Lahore, Pakistan
| | - Prabhakar Semwal
- Department of Biotechnology, Graphic Era University, Dehradun, India.,Uttarakhand State Council for Science and Technology, Dehradun, India
| | - Sakshi Painuli
- Department of Biotechnology, Graphic Era University, Dehradun, India
| | - Senem Kamiloglu
- Science and Technology Application and Research Center (BITUAM), Bursa Uludag University, 16059, Gorukle, Bursa, Turkey
| | - Miquel Martorell
- Department of Nutrition and Dietetics, Faculty of Pharmacy, and Centre for Healthy Living, University of Concepción, 4070386, Concepción, Chile.
| | - Daniela Calina
- Department of Clinical Pharmacy, University of Medicine and Pharmacy of Craiova, 200349, Craiova, Romania.
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21
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Chen Y, Wei E, Chen Y, He P, Wang R, Wang Q, Tang X, Zhang Y, Zhu F, Shen Z. Identification and subcellular localization analysis of membrane protein Ycf 1 in the microsporidian Nosema bombycis. PeerJ 2022; 10:e13530. [PMID: 35833014 PMCID: PMC9272817 DOI: 10.7717/peerj.13530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/11/2022] [Indexed: 01/22/2023] Open
Abstract
Microsporidia are obligate intracellular parasites that can infect a wide range of vertebrates and invertebrates including humans and insects, such as silkworm and bees. The microsporidium Nosema bombycis can cause pebrine in Bombyx mori, which is the most destructive disease in the sericulture industry. Although membrane proteins are involved in a wide range of cellular functions and part of many important metabolic pathways, there are rare reports about the membrane proteins of microsporidia up to now. We screened a putative membrane protein Ycf 1 from the midgut transcriptome of the N. bombycis-infected silkworm. Gene cloning and bioinformatics analysis showed that the Ycf 1 gene contains a complete open reading frame (ORF) of 969 bp in length encoding a 322 amino acid polypeptide that has one signal peptide and one transmembrane domain. Indirect immunofluorescence results showed that Ycf 1 protein is distributed on the plasma membrane. Expression pattern analysis showed that the Ycf 1 gene expressed in all developmental stages of N. bombycis. Knockdown of the Ycf 1 gene by RNAi effectively inhibited the proliferation of N. bombycis. These results indicated that Ycf 1 is a membrane protein and plays an important role in the life cycle of N. bombycis.
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Affiliation(s)
- Yong Chen
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Erjun Wei
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Ying Chen
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Ping He
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Runpeng Wang
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China
| | - Qiang Wang
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China,Chinese Academy of Agricultural Sciences, Institute of Sericulture, Zhenjiang, China
| | - Xudong Tang
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China,Chinese Academy of Agricultural Sciences, Institute of Sericulture, Zhenjiang, China
| | - Yiling Zhang
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China,Chinese Academy of Agricultural Sciences, Institute of Sericulture, Zhenjiang, China
| | - Feng Zhu
- Zaozhuang University, Zaozhuang, Shangdong, China
| | - Zhongyuan Shen
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China,Chinese Academy of Agricultural Sciences, Institute of Sericulture, Zhenjiang, China
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22
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Brahma R, Raghuraman H. Cost-effective Purification of Membrane Proteins using a Dual-detergent Strategy. Curr Protoc 2022; 2:e452. [PMID: 35714356 PMCID: PMC7614515 DOI: 10.1002/cpz1.452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Understanding the mechanisms of membrane protein function is critical for biomedical research and drug discovery as membrane proteins constitute ∼30% of the proteins encoded by the genomes of both lower and higher organisms and are targets for two-thirds of approved drugs worldwide. Significant progress has been made in engineering host expression systems for large-scale production of membrane proteins and in determining their three-dimensional high-resolution structures. Despite these efforts, the study of membrane proteins at the atomic level is challenging due to poor expression and extraction, low yields of functional protein, and the complexity and heterogeneity of source membranes. Structural and spectroscopic studies of any membrane protein require that the protein be extracted from its native membranes into a membrane-mimetic stable environment, which is often achieved by the use of detergents. Unfortunately, there is no magic detergent that can extract all membrane proteins and successful extraction often requires a thorough screen of detergents. Furthermore, membrane protein purification in general and the detergents used are very expensive, which puts a financial constraint on sophisticated membrane protein studies. To overcome this hurdle, a dual-detergent strategy has recently been developed and successfully applied to purify various classes of pure, stable, and functionally relevant membrane proteins in a cost-effective manner. This strategy uses an inexpensive detergent for solubilization of the desired protein from membranes and a second detergent during protein purification. In the Basic Protocol, we describe the dual-detergent strategy to significantly reduce the overall purification cost of a bacterial membrane protein using the magnesium ion channel MgtE as an example. Support Protocols are also provided for selecting a suitable E. coli strain for protein expression and the optimal detergent(s) for membrane protein solubilization. © 2022 Wiley Periodicals LLC. Basic Protocol: Expression, membrane solubilization, and cost-effective purification of MgtE Support Protocol 1: Selecting a suitable E. coli strain for optimal protein expression Support Protocol 2: Identification of suitable detergents for membrane protein solubilization.
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Affiliation(s)
- Rupasree Brahma
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Bidhannagar, Kolkata, India.,Homi Bhabha National Institute, Anushaktinagar, Mumbai, India
| | - H Raghuraman
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Bidhannagar, Kolkata, India.,Homi Bhabha National Institute, Anushaktinagar, Mumbai, India
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23
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Lakey JH, Paracini N, Clifton LA. Exploiting neutron scattering contrast variation in biological membrane studies. BIOPHYSICS REVIEWS 2022; 3:021307. [PMID: 38505417 PMCID: PMC10903484 DOI: 10.1063/5.0091372] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 05/03/2022] [Indexed: 03/21/2024]
Abstract
Biological membranes composed of lipids and proteins are central for the function of all cells and individual components, such as proteins, that are readily studied by a range of structural approaches, including x-ray crystallography and cryo-electron microscopy. However, the study of complex molecular mixtures within the biological membrane structure and dynamics requires techniques that can study nanometer thick molecular bilayers in an aqueous environment at ambient temperature and pressure. Neutron methods, including scattering and spectroscopic approaches, are useful since they can measure structure and dynamics while also being able to penetrate sample holders and cuvettes. The structural approaches, such as small angle neutron scattering and neutron reflectometry, detect scattering caused by the difference in neutron contrast (scattering length) between different molecular components such as lipids or proteins. Usually, the bigger the contrast, the clearer the structural data, and this review uses examples from our research to illustrate how contrast can be increased to allow the structures of individual membrane components to be resolved. Most often this relies upon the use of deuterium in place of hydrogen, but we also discuss the use of magnetic contrast and other elements with useful scattering length values.
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Affiliation(s)
- Jeremy H. Lakey
- Institute for Cell and Molecular Bioscience, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, United Kingdom
| | - Nicolò Paracini
- Biofilms Research Center for Biointerfaces, Malmö University, Per Albin Hanssons väg 35, 21432 Malmö, Sweden
| | - Luke A. Clifton
- ISIS Pulsed Neutron and Muon Source, STFC Rutherford Appleton Laboratory, Didcot OX11 0QX, United Kingdom
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24
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DOCKGROUND membrane protein-protein set. PLoS One 2022; 17:e0267531. [PMID: 35580077 PMCID: PMC9113569 DOI: 10.1371/journal.pone.0267531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/10/2022] [Indexed: 11/19/2022] Open
Abstract
Membrane proteins are significantly underrepresented in Protein Data Bank despite their essential role in cellular mechanisms and the major progress in experimental protein structure determination. Thus, computational approaches are especially valuable in the case of membrane proteins and their assemblies. The main focus in developing structure prediction techniques has been on soluble proteins, in part due to much greater availability of the structural data. Currently, structure prediction of protein complexes (protein docking) is a well-developed field of study. However, the generic protein docking approaches are not optimal for the membrane proteins because of the differences in physicochemical environment and the spatial constraints imposed by the membranes. Thus, docking of the membrane proteins requires specialized computational methods. Development and benchmarking of the membrane protein docking approaches has to be based on high-quality sets of membrane protein complexes. In this study we present a new dataset of 456 non-redundant alpha helical binary interfaces. The set is significantly larger and more representative than the previously developed sets. In the future, it will become the basis for the development of docking and scoring benchmarks, similar to the ones for soluble proteins in the Dockground resource http://dockground.compbio.ku.edu.
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25
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Laurents DV. AlphaFold 2 and NMR Spectroscopy: Partners to Understand Protein Structure, Dynamics and Function. Front Mol Biosci 2022; 9:906437. [PMID: 35655760 PMCID: PMC9152297 DOI: 10.3389/fmolb.2022.906437] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 04/25/2022] [Indexed: 11/29/2022] Open
Abstract
The artificial intelligence program AlphaFold 2 is revolutionizing the field of protein structure determination as it accurately predicts the 3D structure of two thirds of the human proteome. Its predictions can be used directly as structural models or indirectly as aids for experimental structure determination using X-ray crystallography, CryoEM or NMR spectroscopy. Nevertheless, AlphaFold 2 can neither afford insight into how proteins fold, nor can it determine protein stability or dynamics. Rare folds or minor alternative conformations are also not predicted by AlphaFold 2 and the program does not forecast the impact of post translational modifications, mutations or ligand binding. The remaining third of human proteome which is poorly predicted largely corresponds to intrinsically disordered regions of proteins. Key to regulation and signaling networks, these disordered regions often form biomolecular condensates or amyloids. Fortunately, the limitations of AlphaFold 2 are largely complemented by NMR spectroscopy. This experimental approach provides information on protein folding and dynamics as well as biomolecular condensates and amyloids and their modulation by experimental conditions, small molecules, post translational modifications, mutations, flanking sequence, interactions with other proteins, RNA and virus. Together, NMR spectroscopy and AlphaFold 2 can collaborate to advance our comprehension of proteins.
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26
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Yang M, Luo W, Zhang W, Wang H, Xue D, Wu Y, Zhao S, Zhao F, Zheng X, Tao H. Ugi Reaction Mediated Detergent Assembly for Membrane Protein Studies. Chem Asian J 2022; 17:e202200372. [PMID: 35575910 DOI: 10.1002/asia.202200372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/16/2022] [Indexed: 11/11/2022]
Abstract
Despite the continuous efforts, the current repertoire of detergents is still far from sufficient for the biophysics studies of membrane proteins (MPs). Toward the rapid expansion of detergent diversity, we herein report a new strategy based on Ugi reaction mediated modular assembly. Structural varieties, including hydrophobic tails and hydrophilic heads, could be conveniently introduced from the multiple reaction components. New detergents then were comprehensively evaluated in the physical properties and preliminarily screened by the thermal stabilization for a transporter MsbA and a spectrum of G protein-coupled receptors (GPCRs). For the glucagon-like peptide-1 receptor (GLP-1R), a class B GPCR, detergent M-23-M finally stood out in a second evaluation for the maintenance of homogeneity and was further illustrated its application in the improvement of NMR study. Besides the promising utility in the MP study, the current results exhibit intriguing structural-physical relationship that would allow the guidance in the tuning of detergent properties in the future.
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Affiliation(s)
- Meifang Yang
- University of South China, Department of Pharmacy, CHINA
| | - Weiling Luo
- ShanghaiTech University, iHuman Institute, CHINA
| | - Wei Zhang
- ShanghaiTech University, iHuman Institute, CHINA
| | - Huixia Wang
- ShanghaiTech University, iHuman Institute, CHINA
| | | | - Yiran Wu
- ShanghaiTech University, iHuman Institute, CHINA
| | - Suwen Zhao
- ShanghaiTech University, iHuman Institute, CHINA
| | - Fei Zhao
- ShanghaiTech University, iHuman Institute, 230 Haike Road, 201210, Shanghai, CHINA
| | - Xing Zheng
- University of South China, Department of Pharmacy, CHINA
| | - Houchao Tao
- Shanghai University of Traditional Chinese Medicine, Shanghai Frontiers Science Center of TCM Chemical Biology, Room 2421, Building 2, 1200 Cailun Road, 230032, Shanghai, CHINA
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27
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Maczuga NT, Tran ENH, Morona R. Topology of the Shigella flexneri Enterobacterial Common Antigen polymerase WzyE. MICROBIOLOGY (READING, ENGLAND) 2022; 168. [PMID: 35470793 DOI: 10.1099/mic.0.001183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Enterobacteriales have evolved a specialized outer membrane polysaccharide [Enterobacterial Common Antigen (ECA)] which allows them to persist in various environmental niches. Biosynthesis of ECA initiates on the cytoplasmic leaflet of the inner membrane (IM) where glycosyltransferases assemble ECA repeat units (RUs). Complete RUs are then translocated across the IM and assembled into polymers by ECA-specific homologues of the Wzy-dependent pathway. Consisting of the membrane proteins Wzx, Wzy and Wzz, the Wzy-dependent pathway is the most common polysaccharide biosynthetic pathway in Gram-negative bacteria where it is most notably involved in LPS O antigen (Oag) biosynthesis. As such, the majority of research directed towards these proteins has been orientated towards Oag biosynthetic homologues with little directed towards ECA homologues. Belonging to the Shape, Elongation, Division and Sporulation (SEDS) protein family, Wzy proteins are polymerases, and are characterized as possessing little or no peptide homology among homologues as well as being polytopic membrane proteins with functionally relevant residues within periplasmic loops, as defined by C-terminal reporter fusion topology mapping. Here, we present the first the first major study into the ECA polymerase WzyE. Multiple sequence alignments and topology mapping showed that WzyE is unlike WzyB proteins involved with Oag biosynthesis WzyE displays high peptide conservation across Enterobacteriales. In silico structures and reporter mapping allowed us to identify possible functionally conserved residues with WzyESF's periplasmic loops, which we showed were crucial for its function. This work provides novel insight into Wzy proteins and suggests that WzyE is an optimal model to investigate Wzy proteins and the Wzy-dependent pathway.
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Affiliation(s)
- Nicholas T Maczuga
- Department of Molecular and Biomedical Sciences, Research Centre for Infectious Diseases, School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Elizabeth N H Tran
- Department of Molecular and Biomedical Sciences, Research Centre for Infectious Diseases, School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Renato Morona
- Department of Molecular and Biomedical Sciences, Research Centre for Infectious Diseases, School of Biological Sciences, The University of Adelaide, Adelaide, SA, Australia
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28
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Brahma R, Raghuraman H. Measuring Membrane Penetration Depths and Conformational Changes in Membrane Peptides and Proteins. J Membr Biol 2022; 255:469-483. [PMID: 35274157 DOI: 10.1007/s00232-022-00224-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/23/2022] [Indexed: 10/18/2022]
Abstract
The structural organization and dynamic nature of the biomembrane components are important determinants for numerous cellular functions. Particularly, membrane proteins are critically important for various physiological functions and are important drug targets. The mechanistic insights on the complex functionality of membrane lipids and proteins can be elucidated by understanding the interplay between structure and dynamics. In this regard, membrane penetration depth represents an important parameter to obtain the precise depth of membrane-embedded molecules that often define the conformation and topology of membrane probes and proteins. In this review, we discuss about the widely used fluorescence quenching-based methods (parallax method, distribution analysis, and dual-quencher analysis) to accurately determine the membrane penetration depths of fluorescent probes that are either membrane-embedded or attached to lipids and proteins. Further, we also discuss a relatively novel fluorescence quenching method that utilizes tryptophan residue as the quencher, namely the tryptophan-induced quenching, which is sensitive to monitor small-scale conformational changes (short distances of < 15 Å) and useful in mapping distances in proteins. We have provided numerous examples for the benefit of readers to appreciate the importance and applicability of these simple yet powerful methods to study membrane proteins.
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Affiliation(s)
- Rupasree Brahma
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, A CI of Homi Bhabha National Institute, 1/AF, Bidhannagar, Kolkata, 700 064, India
| | - H Raghuraman
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, A CI of Homi Bhabha National Institute, 1/AF, Bidhannagar, Kolkata, 700 064, India.
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29
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Yang P, Ning K. How much metagenome data is needed for protein structure prediction: The advantages of targeted approach from the ecological and evolutionary perspectives. IMETA 2022; 1:e9. [PMID: 38867727 PMCID: PMC10989767 DOI: 10.1002/imt2.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 12/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2024]
Abstract
It has been proven that three-dimensional protein structures could be modeled by supplementing homologous sequences with metagenome sequences. Even though a large volume of metagenome data is utilized for such purposes, a significant proportion of proteins remain unsolved. In this review, we focus on identifying ecological and evolutionary patterns in metagenome data, decoding the complicated relationships of these patterns with protein structures, and investigating how these patterns can be effectively used to improve protein structure prediction. First, we proposed the metagenome utilization efficiency and marginal effect model to quantify the divergent distribution of homologous sequences for the protein family. Second, we proposed that the targeted approach effectively identifies homologous sequences from specified biomes compared with the untargeted approach's blind search. Finally, we determined the lower bound for metagenome data required for predicting all the protein structures in the Pfam database and showed that the present metagenome data is insufficient for this purpose. In summary, we discovered ecological and evolutionary patterns in the metagenome data that may be used to predict protein structures effectively. The targeted approach is promising in terms of effectively extracting homologous sequences and predicting protein structures using these patterns.
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Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐Imaging, Department of Bioinformatics and Systems BiologyCenter of AI Biology, College of Life Science and Technology, Huazhong University of Science and TechnologyWuhanHubeiChina
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30
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Kopf AH, Lijding O, Elenbaas BOW, Koorengevel MC, Dobruchowska JM, van Walree CA, Killian JA. Synthesis and Evaluation of a Library of Alternating Amphipathic Copolymers to Solubilize and Study Membrane Proteins. Biomacromolecules 2022; 23:743-759. [PMID: 34994549 PMCID: PMC8924871 DOI: 10.1021/acs.biomac.1c01166] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Amphipathic copolymers
such as poly(styrene-maleic acid) (SMA)
are promising tools for the facile extraction of membrane proteins
(MPs) into native nanodiscs. Here, we designed and synthesized a library
of well-defined alternating copolymers of SMA analogues in order to
elucidate polymer properties that are important for MP solubilization
and stability. MP extraction efficiency was determined using KcsA
from E. coli membranes, and general solubilization
efficiency was investigated via turbidimetry experiments on membranes
of E. coli, yeast mitochondria, and synthetic
lipids. Remarkably, halogenation of SMA copolymers dramatically improved
solubilization efficiency in all systems, while substituents on the
copolymer backbone improved resistance to Ca2+. Relevant
polymer properties were found to include hydrophobic balance, size
and positioning of substituents, rigidity, and electronic effects.
The library thus contributes to the rational design of copolymers
for the study of MPs.
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Affiliation(s)
- Adrian H Kopf
- Membrane Biochemistry and Biophysics, Bijvoet Center for Biomolecular Research, Institute of Biomembranes, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Odette Lijding
- Membrane Biochemistry and Biophysics, Bijvoet Center for Biomolecular Research, Institute of Biomembranes, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Barend O W Elenbaas
- Membrane Biochemistry and Biophysics, Bijvoet Center for Biomolecular Research, Institute of Biomembranes, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Martijn C Koorengevel
- Membrane Biochemistry and Biophysics, Bijvoet Center for Biomolecular Research, Institute of Biomembranes, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - Justyna M Dobruchowska
- Department of Chemical Biology and Drug Discovery, Utrecht Institute for Pharmaceutical Sciences, and Bijvoet Center for Biomolecular Research, Utrecht University, 3584 CH Utrecht, The Netherlands
| | - Cornelis A van Walree
- Membrane Biochemistry and Biophysics, Bijvoet Center for Biomolecular Research, Institute of Biomembranes, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
| | - J Antoinette Killian
- Membrane Biochemistry and Biophysics, Bijvoet Center for Biomolecular Research, Institute of Biomembranes, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
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31
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Josts I, Kehlenbeck DM, Nitsche J, Tidow H. Studying integral membrane protein by SANS using stealth reconstitution systems. Methods Enzymol 2022; 677:417-432. [DOI: 10.1016/bs.mie.2022.08.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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32
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Barret L, Schubeis T, Kugler V, Guyot L, Pintacuda G, Wagner R. Production and Preparation of Isotopically Labeled Human Membrane Proteins in Pichia pastoris for Fast-MAS-NMR Analyses. Methods Mol Biol 2022; 2507:201-221. [PMID: 35773584 DOI: 10.1007/978-1-0716-2368-8_11] [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: 06/15/2023]
Abstract
Membrane proteins (MPs) comprise about one-third of the human proteome, playing critical roles in many physiological processes and associated disorders. Consistently, they represent one of the largest classes of targets for the pharmaceutical industry. Their study at the molecular level is however particularly challenging, resulting in a severe lack of structural and dynamic information that is hindering their detailed functional characterization and the identification of novel potent drug candidates.Magic Angle Spinning (MAS) NMR is a reliable and efficient method for the determination of protein structures and dynamics and for the identification of ligand binding sites and equilibria. MAS-NMR is particularly well suited for MPs since they can be directly analysed in a native-like lipid bilayer environment but used to require aggravating large amounts of isotope enriched material. The frequent toxicity of human MP overexpression in bacterial cultures poses an additional hurdle, resulting in the need for alternative (and often more costly) expression systems. The recent development of very fast (up to 150 kHz) MAS probes has revolutionized the field of biomolecular solid-state NMR enabling higher spectral resolution with significant reduction of the required sample, rendering eukaryotic expression systems cost-effective.Here is presented a set of accessible procedures validated for the production and preparation of eukaryotic MPs for Fast-MAS 1H-detected NMR analysis. The methodology is illustrated with the human copper uptake protein hCTR1 recombinantly produced and 13C-15N uniformly labeled with the versatile and affordable Pichia pastoris system. Subsequent purification procedures allow the recovery of mg amounts that are then reconstituted into liposome formulations compatible with solid-state NMR handling and analysis.
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Affiliation(s)
- Lina Barret
- Biotechnology and Cell Signalling, IMPReSs Protein Facility, UMR7242 CNRS-University of Strasbourg, Illkirch, France
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs de Lyon (UMR 5082-CNRS, Université Claude Bernard Lyon 1, École Normale Supérieure Lyon), Université de Lyon, Villeurbanne, France
| | - Tobias Schubeis
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs de Lyon (UMR 5082-CNRS, Université Claude Bernard Lyon 1, École Normale Supérieure Lyon), Université de Lyon, Villeurbanne, France
| | - Valérie Kugler
- Biotechnology and Cell Signalling, IMPReSs Protein Facility, UMR7242 CNRS-University of Strasbourg, Illkirch, France
| | - Lucile Guyot
- Biotechnology and Cell Signalling, IMPReSs Protein Facility, UMR7242 CNRS-University of Strasbourg, Illkirch, France
- NovAliX, Illkirch, France
| | - Guido Pintacuda
- Centre de Résonance Magnétique Nucléaire à Très Hauts Champs de Lyon (UMR 5082-CNRS, Université Claude Bernard Lyon 1, École Normale Supérieure Lyon), Université de Lyon, Villeurbanne, France
| | - Renaud Wagner
- Biotechnology and Cell Signalling, IMPReSs Protein Facility, UMR7242 CNRS-University of Strasbourg, Illkirch, France.
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Egea PF. Mechanisms of Non-Vesicular Exchange of Lipids at Membrane Contact Sites: Of Shuttles, Tunnels and, Funnels. Front Cell Dev Biol 2021; 9:784367. [PMID: 34912813 PMCID: PMC8667587 DOI: 10.3389/fcell.2021.784367] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 11/08/2021] [Indexed: 11/13/2022] Open
Abstract
Eukaryotic cells are characterized by their exquisite compartmentalization resulting from a cornucopia of membrane-bound organelles. Each of these compartments hosts a flurry of biochemical reactions and supports biological functions such as genome storage, membrane protein and lipid biosynthesis/degradation and ATP synthesis, all essential to cellular life. Acting as hubs for the transfer of matter and signals between organelles and throughout the cell, membrane contacts sites (MCSs), sites of close apposition between membranes from different organelles, are essential to cellular homeostasis. One of the now well-acknowledged function of MCSs involves the non-vesicular trafficking of lipids; its characterization answered one long-standing question of eukaryotic cell biology revealing how some organelles receive and distribute their membrane lipids in absence of vesicular trafficking. The endoplasmic reticulum (ER) in synergy with the mitochondria, stands as the nexus for the biosynthesis and distribution of phospholipids (PLs) throughout the cell by contacting nearly all other organelle types. MCSs create and maintain lipid fluxes and gradients essential to the functional asymmetry and polarity of biological membranes throughout the cell. Membrane apposition is mediated by proteinaceous tethers some of which function as lipid transfer proteins (LTPs). We summarize here the current state of mechanistic knowledge of some of the major classes of LTPs and tethers based on the available atomic to near-atomic resolution structures of several "model" MCSs from yeast but also in Metazoans; we describe different models of lipid transfer at MCSs and analyze the determinants of their specificity and directionality. Each of these systems illustrate fundamental principles and mechanisms for the non-vesicular exchange of lipids between eukaryotic membrane-bound organelles essential to a wide range of cellular processes such as at PL biosynthesis and distribution, lipid storage, autophagy and organelle biogenesis.
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Affiliation(s)
- Pascal F Egea
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles, CA, United States
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Das A, Bysack A, Raghuraman H. Effectiveness of dual-detergent strategy using Triton X-100 in membrane protein purification. Biochem Biophys Res Commun 2021; 578:122-128. [PMID: 34560581 DOI: 10.1016/j.bbrc.2021.09.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 09/16/2021] [Indexed: 11/27/2022]
Abstract
Membrane solubilization by detergents is a critical step for successful membrane protein purification. Alkyl maltoside detergents such as DDM and DM are very expensive and are commonly used to produce most of the high-quality proteins in stable and functional form. Recently, dual-detergent strategy using inexpensive detergents for membrane solubilization step has been shown to be highly effective in purifying different classes of membrane proteins in a cost-effective manner. In this work, we have monitored the effectiveness of 'dual-detergent strategy' towards successful purification of the isolated voltage sensing domain (VSD) of KvAP and the inward rectifying K+ channel, KirBac1.1. We demonstrate that the inexpensive detergent Triton X-100 extracts the activated conformation of the KvAP-VSD well without compromising the structural integrity of the sensor, and also retains its proper structural dynamics. Importantly, the cost associated with solubilizing the KvAP sensor can be reduced by ∼2000 fold. To the best of our knowledge, our results constitute the first report characterizing the purification of KvAP voltage sensor using an inexpensive detergent. However, the dual-detergent strategy using Triton X-100 for membrane solubilization is not effective for the purification of inward rectifying K+ channel, KirBac1.1 even in presence of high salt concentration during solubilization. We propose that the dual-detergent strategy will be useful for extracting stable and functional proteins that are both DDM- and DM-extractable, but will be ineffective if the protein is only DM-extractable. The relevance of the effectiveness of dual-detergent strategy with respect to the hydrophobic thickness of proteins is discussed.
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Affiliation(s)
- Anindita Das
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute, 1/AF Bidhannagar, Kolkata, 700 064, India
| | - Arpan Bysack
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute, 1/AF Bidhannagar, Kolkata, 700 064, India
| | - H Raghuraman
- Crystallography and Molecular Biology Division, Saha Institute of Nuclear Physics, Homi Bhabha National Institute, 1/AF Bidhannagar, Kolkata, 700 064, India.
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Nin-Hill A, Mueller NPF, Molteni C, Rovira C, Alfonso-Prieto M. Photopharmacology of Ion Channels through the Light of the Computational Microscope. Int J Mol Sci 2021; 22:12072. [PMID: 34769504 PMCID: PMC8584574 DOI: 10.3390/ijms222112072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Revised: 10/31/2021] [Accepted: 11/02/2021] [Indexed: 12/13/2022] Open
Abstract
The optical control and investigation of neuronal activity can be achieved and carried out with photoswitchable ligands. Such compounds are designed in a modular fashion, combining a known ligand of the target protein and a photochromic group, as well as an additional electrophilic group for tethered ligands. Such a design strategy can be optimized by including structural data. In addition to experimental structures, computational methods (such as homology modeling, molecular docking, molecular dynamics and enhanced sampling techniques) can provide structural insights to guide photoswitch design and to understand the observed light-regulated effects. This review discusses the application of such structure-based computational methods to photoswitchable ligands targeting voltage- and ligand-gated ion channels. Structural mapping may help identify residues near the ligand binding pocket amenable for mutagenesis and covalent attachment. Modeling of the target protein in a complex with the photoswitchable ligand can shed light on the different activities of the two photoswitch isomers and the effect of site-directed mutations on photoswitch binding, as well as ion channel subtype selectivity. The examples presented here show how the integration of computational modeling with experimental data can greatly facilitate photoswitchable ligand design and optimization. Recent advances in structural biology, both experimental and computational, are expected to further strengthen this rational photopharmacology approach.
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Affiliation(s)
- Alba Nin-Hill
- Departament de Química Inorgànica i Orgànica (Secció de Química Orgànica) and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, 08028 Barcelona, Spain; (A.N.-H.); (C.R.)
| | - Nicolas Pierre Friedrich Mueller
- Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany;
- Faculty of Mathematics and Natural Sciences, Heinrich-Heine-University Düsseldorf, Universitätsstr. 1, 40225 Düsseldorf, Germany
| | - Carla Molteni
- Physics Department, King’s College London, London WC2R 2LS, UK;
| | - Carme Rovira
- Departament de Química Inorgànica i Orgànica (Secció de Química Orgànica) and Institut de Química Teòrica i Computacional (IQTCUB), Universitat de Barcelona, 08028 Barcelona, Spain; (A.N.-H.); (C.R.)
- Institució Catalana de Recerca i Estudis Avançats (ICREA), 08020 Barcelona, Spain
| | - Mercedes Alfonso-Prieto
- Institute for Advanced Simulations IAS-5 and Institute of Neuroscience and Medicine INM-9, Computational Biomedicine, Forschungszentrum Jülich, 52425 Jülich, Germany;
- Cécile and Oskar Vogt Institute for Brain Research, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
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Ugarte La Torre D, Takada S. Modeling lipid-protein interactions for coarse-grained lipid and Cα protein models. J Chem Phys 2021; 155:155101. [PMID: 34686048 DOI: 10.1063/5.0057278] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Biological membranes that play major roles in diverse functions are composed of numerous lipids and proteins, making them an important target for coarse-grained (CG) molecular dynamics (MD) simulations. Recently, we have developed the CG implicit solvent lipid force field (iSoLF) that has a resolution compatible with the widely used Cα protein representation [D. Ugarte La Torre and S. Takada, J. Chem. Phys. 153, 205101 (2020)]. In this study, we extended it and developed a lipid-protein interaction model that allows the combination of the iSoLF and the Cα protein force field, AICG2+. The hydrophobic-hydrophilic interaction is modeled as a modified Lennard-Jones potential in which parameters were tuned partly to reproduce the experimental transfer free energy and partly based on the free energy profile normal to the membrane surface from previous all-atom MD simulations. Then, the obtained lipid-protein interaction is tested for the configuration and placement of transmembrane proteins, water-soluble proteins, and peripheral proteins, showing good agreement with prior knowledge. The interaction is generally applicable and is implemented in the publicly available software, CafeMol.
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Affiliation(s)
- Diego Ugarte La Torre
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
| | - Shoji Takada
- Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto, Japan
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Principles and Methods in Computational Membrane Protein Design. J Mol Biol 2021; 433:167154. [PMID: 34271008 DOI: 10.1016/j.jmb.2021.167154] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/03/2021] [Accepted: 07/06/2021] [Indexed: 01/13/2023]
Abstract
After decades of progress in computational protein design, the design of proteins folding and functioning in lipid membranes appears today as the next frontier. Some notable successes in the de novo design of simplified model membrane protein systems have helped articulate fundamental principles of protein folding, architecture and interaction in the hydrophobic lipid environment. These principles are reviewed here, together with the computational methods and approaches that were used to identify them. We provide an overview of the methodological innovations in the generation of new protein structures and functions and in the development of membrane-specific energy functions. We highlight the opportunities offered by new machine learning approaches applied to protein design, and by new experimental characterization techniques applied to membrane proteins. Although membrane protein design is in its infancy, it appears more reachable than previously thought.
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Gushchin I, Aleksenko VA, Orekhov P, Goncharov IM, Nazarenko VV, Semenov O, Remeeva A, Gordeliy V. Nitrate- and Nitrite-Sensing Histidine Kinases: Function, Structure, and Natural Diversity. Int J Mol Sci 2021; 22:5933. [PMID: 34072989 PMCID: PMC8199190 DOI: 10.3390/ijms22115933] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 05/27/2021] [Accepted: 05/28/2021] [Indexed: 12/18/2022] Open
Abstract
Under anaerobic conditions, bacteria may utilize nitrates and nitrites as electron acceptors. Sensitivity to nitrous compounds is achieved via several mechanisms, some of which rely on sensor histidine kinases (HKs). The best studied nitrate- and nitrite-sensing HKs (NSHKs) are NarQ and NarX from Escherichia coli. Here, we review the function of NSHKs, analyze their natural diversity, and describe the available structural information. In particular, we show that around 6000 different NSHK sequences forming several distinct clusters may now be found in genomic databases, comprising mostly the genes from Beta- and Gammaproteobacteria as well as from Bacteroidetes and Chloroflexi, including those from anaerobic ammonia oxidation (annamox) communities. We show that the architecture of NSHKs is mostly conserved, although proteins from Bacteroidetes lack the HAMP and GAF-like domains yet sometimes have PAS. We reconcile the variation of NSHK sequences with atomistic models and pinpoint the structural elements important for signal transduction from the sensor domain to the catalytic module over the transmembrane and cytoplasmic regions spanning more than 200 Å.
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Affiliation(s)
- Ivan Gushchin
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
| | - Vladimir A. Aleksenko
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
| | - Philipp Orekhov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
- Faculty of Biology, M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Ivan M. Goncharov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
| | - Vera V. Nazarenko
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
| | - Oleg Semenov
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
| | - Alina Remeeva
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
| | - Valentin Gordeliy
- Research Center for Molecular Mechanisms of Aging and Age-Related Diseases, Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia; (V.A.A.); (P.O.); (I.M.G.); (V.V.N.); (O.S.); (A.R.)
- Institut de Biologie Structurale J.-P. Ebel, Université Grenoble Alpes-CEA-CNRS, 38000 Grenoble, France
- Institute of Biological Information Processing (IBI-7: Structural Biochemistry), Forschungszentrum Jülich, 52428 Jülich, Germany
- JuStruct: Jülich Center for Structural Biology, Forschungszentrum Jülich, 52428 Jülich, Germany
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Berman HM, Gierasch LM. How the Protein Data Bank changed biology: An introduction to the JBC Reviews thematic series, part 1. J Biol Chem 2021; 296:100608. [PMID: 33785358 PMCID: PMC8086130 DOI: 10.1016/j.jbc.2021.100608] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
This collection of articles celebrates the 50th anniversary of the Protein Data Bank (PDB), the single global digital archive of biological macromolecular structures. The impact of the PDB is immense; we have invited a number of top researchers in structural biology to illustrate its influence on an array of scientific fields. What emerges is a compelling picture of the synergism between the PDB and the explosive progress witnessed in many scientific areas. Availability of reliable, openly accessible, well-archived structural information has arguably had more impact on cell and molecular biology than even some of the enabling technologies such as PCR. We have seen the science move from a time when structural biologists contributed the lion’s share of the structures to the PDB and for discussion within their community to a time when any effort to achieve in-depth understanding of a biochemical or cell biological question demands an interdisciplinary approach built atop structural underpinnings.
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Affiliation(s)
- Helen M Berman
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Department of Biological Sciences and Bridge Institute, University of Southern California, Los Angeles, California, USA.
| | - Lila M Gierasch
- Departments of Biochemistry & Molecular Biology and Chemistry, University of Massachusetts, Amherst, Massachusetts, USA.
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