1
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Nguyen PT, Harris BJ, Mateos DL, González AH, Murray AM, Yarov-Yarovoy V. Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold. Channels (Austin) 2024; 18:2325032. [PMID: 38445990 PMCID: PMC10936637 DOI: 10.1080/19336950.2024.2325032] [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: 09/05/2023] [Accepted: 01/14/2024] [Indexed: 03/07/2024] Open
Abstract
Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel - CaV1.1, and human voltage-gated potassium (KV) channel - KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.
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Affiliation(s)
- Phuong Tran Nguyen
- Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USA
| | - Brandon John Harris
- Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USA
- Biophysics Graduate Group, University of California School of Medicine, Davis, CA, USA
| | - Diego Lopez Mateos
- Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USA
- Biophysics Graduate Group, University of California School of Medicine, Davis, CA, USA
| | - Adriana Hernández González
- Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USA
- Biophysics Graduate Group, University of California School of Medicine, Davis, CA, USA
| | | | - Vladimir Yarov-Yarovoy
- Department of Physiology and Membrane Biology, University of California School of Medicine, Davis, CA, USA
- Department of Anesthesiology and Pain Medicine, University of California School of Medicine, Davis, CA, USA
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2
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Chen S, Zhang S, Fang X, Lin L, Zhao H, Yang Y. Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences. Nat Commun 2024; 15:8808. [PMID: 39394203 PMCID: PMC11470027 DOI: 10.1038/s41467-024-53116-5] [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: 03/14/2024] [Accepted: 10/02/2024] [Indexed: 10/13/2024] Open
Abstract
Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX .
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Affiliation(s)
- Sheng Chen
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Sen Zhang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - XiaoYu Fang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Liang Lin
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China
| | - Huiying Zhao
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
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3
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Jiko C, Li J, Moon Y, Tanaka Y, Gopalasingam CC, Shigematsu H, Chae PS, Kurisu G, Gerle C. NDT-C11 as a Viable Novel Detergent for Single Particle Cryo-EM. Chempluschem 2024; 89:e202400242. [PMID: 38881532 DOI: 10.1002/cplu.202400242] [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: 04/01/2024] [Revised: 05/31/2024] [Accepted: 06/17/2024] [Indexed: 06/18/2024]
Abstract
Single particle cryo electron microscopy (cryo-EM) is now the major method for the determination of integral membrane protein structure. For the success of a given project the type of membrane mimetic used for extraction from the native cell membrane, purification to homogeneity and finally cryo-grid vitrification is crucial. Although small molecule amphiphiles - detergents - are the most widely used membrane mimetic, specific tailoring of detergent structure for single particle cryo-EM is rare and the demand for effective detergents not satisfied. Here, we compare the popular detergent lauryl maltose-neopentyl glycol (LMNG) with the novel detergent neopentyl glycol-derived triglucoside-C11 (NDT-C11) in its behavior as free detergent and when bound to two types of multisubunit membrane protein complexes - cyanobacterial photosystem I (PSI) and mammalian F-ATP synthase. We conclude that NDT-C11 has high potential to become a very useful detergent for single particle cryo-EM of integral membrane proteins.
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Affiliation(s)
- Chimari Jiko
- Institute for Integrated Radiation and Nuclear Science, Kyoto University, Kumatori, Osaka, 590-0494, Japan
| | - Jiannan Li
- Institute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Youngsun Moon
- Department of Bionano Engineering, Hanyang University, Ansan, 155-88, South Korea
| | - Yoshito Tanaka
- Graduate School of Life Science, University of Hyogo, Kamigori, 678-1297, Japan
| | - Chai C Gopalasingam
- Life Science Research Infrastructure Group, RIKEN SPring-8 Center, Sayo, 679-5148, Japan
| | - Hideki Shigematsu
- Structural Biology Division, Japan Synchrotron Radiation Research Institute, SPring-8, Sayo, 679-5148, Japan
| | - Pil Seok Chae
- Department of Bionano Engineering, Hanyang University, Ansan, 155-88, South Korea
| | - Genji Kurisu
- Institute for Protein Research, Osaka University, Suita, Osaka, 565-0871, Japan
| | - Christoph Gerle
- Life Science Research Infrastructure Group, RIKEN SPring-8 Center, Sayo, 679-5148, Japan
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4
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Zhang Y, Yang C, Xiong Y, Xiao Y. 3dDNAscoreA: A scoring function for evaluation of DNA 3D structures. Biophys J 2024; 123:2696-2704. [PMID: 38409781 PMCID: PMC11393702 DOI: 10.1016/j.bpj.2024.02.018] [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: 11/21/2023] [Revised: 01/31/2024] [Accepted: 02/21/2024] [Indexed: 02/28/2024] Open
Abstract
DNA molecules are vital macromolecules that play a fundamental role in many cellular processes and have broad applications in medicine. For example, DNA aptamers have been rapidly developed for diagnosis, biosensors, and clinical therapy. Recently, we proposed a computational method of predicting DNA 3D structures, called 3dDNA. However, it lacks a scoring function to evaluate the predicted DNA 3D structures, and so they are not ranked for users. Here, we report a scoring function, 3dDNAscoreA, for evaluation of DNA 3D structures based on a deep learning model ARES for RNA 3D structure evaluation but using a new strategy for training. 3dDNAscoreA is benchmarked on two test sets to show its ability to rank DNA 3D structures and select the native and near-native structures.
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Affiliation(s)
- Yi Zhang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Chenxi Yang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yiduo Xiong
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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5
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Gucwa M, Bijak V, Zheng H, Murzyn K, Minor W. CheckMyMetal (CMM): validating metal-binding sites in X-ray and cryo-EM data. IUCRJ 2024; 11:871-877. [PMID: 39141478 PMCID: PMC11364027 DOI: 10.1107/s2052252524007073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/18/2024] [Indexed: 08/16/2024]
Abstract
Identifying and characterizing metal-binding sites (MBS) within macromolecular structures is imperative for elucidating their biological functions. CheckMyMetal (CMM) is a web based tool that facilitates the interactive validation of MBS in structures determined through X-ray crystallography and cryo-electron microscopy (cryo-EM). Recent updates to CMM have significantly enhanced its capability to efficiently handle large datasets generated from cryo-EM structural analyses. In this study, we address various challenges inherent in validating MBS within both X-ray and cryo-EM structures. Specifically, we examine the difficulties associated with accurately identifying metals and modeling their coordination environments by considering the ongoing reproducibility challenges in structural biology and the critical importance of well annotated, high-quality experimental data. CMM employs a sophisticated framework of rules rooted in the valence bond theory for MBS validation. We explore how CMM validation parameters correlate with the resolution of experimentally derived structures of macromolecules and their complexes. Additionally, we showcase the practical utility of CMM by analyzing a representative cryo-EM structure. Through a comprehensive examination of experimental data, we demonstrate the capability of CMM to advance MBS characterization and identify potential instances of metal misassignment.
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Affiliation(s)
- Michal Gucwa
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
- Doctoral School of Exact and Natural SciencesJagiellonian UniversityKrakowPoland
| | - Vanessa Bijak
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
| | - Heping Zheng
- Bioinformatics CenterHunan University College of BiologyChangshaHunan410082People’s Republic of China
| | - Krzysztof Murzyn
- Department of Computational Biophysics and BioinformaticsJagiellonian UniversityKrakowPoland
| | - Wladek Minor
- Department of Molecular Physiology and Biological PhysicsUniversity of VirginiaCharlottesville22908USA
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6
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Nandi P, DeVore K, Wang F, Li S, Walker JD, Truong TT, LaPorte MG, Wipf P, Schlager H, McCleerey J, Paquette W, Columbres RCA, Gan T, Poh YP, Fromme P, Flint AJ, Wolf M, Huryn DM, Chou TF, Chiu PL. Mechanism of allosteric inhibition of human p97/VCP ATPase and its disease mutant by triazole inhibitors. Commun Chem 2024; 7:177. [PMID: 39122922 PMCID: PMC11316111 DOI: 10.1038/s42004-024-01267-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 08/01/2024] [Indexed: 08/12/2024] Open
Abstract
Human p97 ATPase is crucial in various cellular processes, making it a target for inhibitors to treat cancers, neurological, and infectious diseases. Triazole allosteric p97 inhibitors have been demonstrated to match the efficacy of CB-5083, an ATP-competitive inhibitor, in cellular models. However, the mechanism is not well understood. This study systematically investigates the structures of new triazole inhibitors bound to both wild-type and disease mutant forms of p97 and measures their effects on function. These inhibitors bind at the interface of the D1 and D2 domains of each p97 subunit, shifting surrounding helices and altering the loop structures near the C-terminal α2 G helix to modulate domain-domain communications. A key structural moiety of the inhibitor affects the rotameric conformations of interacting side chains, indirectly modulating the N-terminal domain conformation in p97 R155H mutant. The differential effects of inhibitor binding to wild-type and mutant p97 provide insights into drug design with enhanced specificity, particularly for oncology applications.
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Affiliation(s)
- Purbasha Nandi
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, AZ, USA
- Biology Department, Brookhaven National Laboratory, Upton, NY, USA
| | - Kira DeVore
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, AZ, USA
| | - Feng Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Shan Li
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Joel D Walker
- University of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Thanh Tung Truong
- University of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
- Faculty of Pharmacy, Phenikaa University, Hanoi, Vietnam
| | - Matthew G LaPorte
- University of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter Wipf
- University of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - John McCleerey
- Curia Global, Albany, NY, USA
- Graduate School of Arts and Sciences, Boston University, Boston, MA, USA
| | | | - Rod Carlo A Columbres
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
| | - Taiping Gan
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Yu-Ping Poh
- Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Mechanism of Evolution, Arizona State University, Tempe, AZ, USA
| | - Petra Fromme
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, AZ, USA
| | - Andrew J Flint
- Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | - Donna M Huryn
- University of Pittsburgh Chemical Diversity Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pharmaceutical Sciences, University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
- Department of Chemistry, University of Pennsylvania, Philadelphia, PA, USA
| | - Tsui-Fen Chou
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
- Proteome Exploration Laboratory, Beckman Institute, California Institute of Technology, Pasadena, CA, USA.
| | - Po-Lin Chiu
- School of Molecular Sciences, Arizona State University, Tempe, AZ, USA.
- Biodesign Center for Applied Structural Discovery, Arizona State University, Tempe, AZ, USA.
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7
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Włodarski T, Streit JO, Mitropoulou A, Cabrita LD, Vendruscolo M, Christodoulou J. Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method. Sci Rep 2024; 14:18149. [PMID: 39103467 PMCID: PMC11300795 DOI: 10.1038/s41598-024-68468-7] [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: 02/21/2024] [Accepted: 07/24/2024] [Indexed: 08/07/2024] Open
Abstract
Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.
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Affiliation(s)
- Tomasz Włodarski
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK.
- Institute of Biochemistry and Biophysics, Polish Academy of Sciences, Pawinskiego 5a, 02-106, Warsaw, Poland.
| | - Julian O Streit
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Alkistis Mitropoulou
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Lisa D Cabrita
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, UK
| | - John Christodoulou
- Institute of Structural and Molecular Biology, University College London, Gower Street, London, WC1E 6BT, UK
- Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK
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8
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Zhu D, Cao W, Li J, Wu C, Cao D, Zhang X. Correction of preferred orientation-induced distortion in cryo-electron microscopy maps. SCIENCE ADVANCES 2024; 10:eadn0092. [PMID: 39058771 DOI: 10.1126/sciadv.adn0092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 06/06/2024] [Indexed: 07/28/2024]
Abstract
Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy and modifications to grids or to air-water interfaces. However, these approaches often require time-consuming experiments, and the effect was always protein dependent. Here, we developed a procedure containing removing misaligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct this distortion by recovering missing data using a purely computational algorithm. This procedure called signal-to-noise ratio iterative reconstruction method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.
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Affiliation(s)
- Dongjie Zhu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101 Beijing, China
| | - Weili Cao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Junxi Li
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Chunling Wu
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Duanfang Cao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
| | - Xinzheng Zhang
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 100101 Beijing, China
- University of Chinese Academy of Sciences, 100049 Beijing, China
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9
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Yadav S, Vinothkumar KR. Factors affecting macromolecule orientations in thin films formed in cryo-EM. Acta Crystallogr D Struct Biol 2024; 80:535-550. [PMID: 38935342 PMCID: PMC11220838 DOI: 10.1107/s2059798324005229] [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: 11/20/2023] [Accepted: 06/01/2024] [Indexed: 06/28/2024] Open
Abstract
The formation of a vitrified thin film embedded with randomly oriented macromolecules is an essential prerequisite for cryogenic sample electron microscopy. Most commonly, this is achieved using the plunge-freeze method first described nearly 40 years ago. Although this is a robust method, the behaviour of different macromolecules shows great variation upon freezing and often needs to be optimized to obtain an isotropic, high-resolution reconstruction. For a macromolecule in such a film, the probability of encountering the air-water interface in the time between blotting and freezing and adopting preferred orientations is very high. 3D reconstruction using preferentially oriented particles often leads to anisotropic and uninterpretable maps. Currently, there are no general solutions to this prevalent issue, but several approaches largely focusing on sample preparation with the use of additives and novel grid modifications have been attempted. In this study, the effect of physical and chemical factors on the orientations of macromolecules was investigated through an analysis of selected well studied macromolecules, and important parameters that determine the behaviour of proteins on cryo-EM grids were revealed. These insights highlight the nature of the interactions that cause preferred orientations and can be utilized to systematically address orientation bias for any given macromolecule and to provide a framework to design small-molecule additives to enhance sample stability and behaviour.
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Affiliation(s)
- Swati Yadav
- National Centre for Biological SciencesTata Institute of Fundamental ResearchGKVK Post, Bellary RoadBengaluru560 065India
| | - Kutti R. Vinothkumar
- National Centre for Biological SciencesTata Institute of Fundamental ResearchGKVK Post, Bellary RoadBengaluru560 065India
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10
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Wagner WJ, Gross ML. Using mass spectrometry-based methods to understand amyloid formation and inhibition of alpha-synuclein and amyloid beta. MASS SPECTROMETRY REVIEWS 2024; 43:782-825. [PMID: 36224716 PMCID: PMC10090239 DOI: 10.1002/mas.21814] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Amyloid fibrils, insoluble β-sheets structures that arise from protein misfolding, are associated with several neurodegenerative disorders. Many small molecules have been investigated to prevent amyloid fibrils from forming; however, there are currently no therapeutics to combat these diseases. Mass spectrometry (MS) is proving to be effective for studying the high order structure (HOS) of aggregating proteins and for determining structural changes accompanying protein-inhibitor interactions. When combined with native MS (nMS), gas-phase ion mobility, protein footprinting, and chemical cross-linking, MS can afford regional and sometimes amino acid spatial resolution of the aggregating protein. The spatial resolution is greater than typical low-resolution spectroscopic, calorimetric, and the traditional ThT fluorescence methods used in amyloid research today. High-resolution approaches can struggle when investigating protein aggregation, as the proteins exist as complex oligomeric mixtures of many sizes and several conformations or polymorphs. Thus, MS is positioned to complement both high- and low-resolution approaches to studying amyloid fibril formation and protein-inhibitor interactions. This review covers basics in MS paired with ion mobility, continuous hydrogen-deuterium exchange (continuous HDX), pulsed hydrogen-deuterium exchange (pulsed HDX), fast photochemical oxidation of proteins (FPOP) and other irreversible labeling methods, and chemical cross-linking. We then review the applications of these approaches to studying amyloid-prone proteins with a focus on amyloid beta and alpha-synuclein. Another focus is the determination of protein-inhibitor interactions. The expectation is that MS will bring new insights to amyloid formation and thereby play an important role to prevent their formation.
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Affiliation(s)
- Wesley J Wagner
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael L Gross
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri, USA
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11
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Wankowicz SA, Ravikumar A, Sharma S, Riley B, Raju A, Hogan DW, Flowers J, van den Bedem H, Keedy DA, Fraser JS. Automated multiconformer model building for X-ray crystallography and cryo-EM. eLife 2024; 12:RP90606. [PMID: 38904665 PMCID: PMC11192534 DOI: 10.7554/elife.90606] [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] [Indexed: 06/22/2024] Open
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
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Affiliation(s)
- Stephanie A Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
- Ph.D. Program in Biology, The Graduate Center, City University of New YorkNew YorkUnited States
| | - Blake Riley
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
| | - Daniel W Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
- Atomwise IncSan FranciscoUnited States
| | - Daniel A Keedy
- Structural Biology Initiative, CUNY Advanced Science Research CenterNew YorkUnited States
- Department of Chemistry and Biochemistry, City College of New YorkNew YorkUnited States
- Ph.D. Programs in Biochemistry, Biology and Chemistry, The Graduate Center, City University of New YorkNew YorkUnited States
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California, San FranciscoSan FranciscoUnited States
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12
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Zhao Q, Hong X, Wang Y, Zhang S, Ding Z, Meng X, Song Q, Hong Q, Jiang W, Shi X, Cai T, Cong Y. An immobilized antibody-based affinity grid strategy for on-grid purification of target proteins enables high-resolution cryo-EM. Commun Biol 2024; 7:715. [PMID: 38858498 PMCID: PMC11164986 DOI: 10.1038/s42003-024-06406-z] [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: 01/17/2024] [Accepted: 05/31/2024] [Indexed: 06/12/2024] Open
Abstract
In cryo-electron microscopy (cryo-EM), sample preparation poses a critical bottleneck, particularly for rare or fragile macromolecular assemblies and those suffering from denaturation and particle orientation distribution issues related to air-water interface. In this study, we develop and characterize an immobilized antibody-based affinity grid (IAAG) strategy based on the high-affinity PA tag/NZ-1 antibody epitope tag system. We employ Pyr-NHS as a linker to immobilize NZ-1 Fab on the graphene oxide or carbon-covered grid surface. Our results demonstrate that the IAAG grid effectively enriches PA-tagged target proteins and overcomes preferred orientation issues. Furthermore, we demonstrate the utility of our IAAG strategy for on-grid purification of low-abundance target complexes from cell lysates, enabling atomic resolution cryo-EM. This approach greatly streamlines the purification process, reduces the need for large quantities of biological samples, and addresses common challenges encountered in cryo-EM sample preparation. Collectively, our IAAG strategy provides an efficient and robust means for combined sample purification and vitrification, feasible for high-resolution cryo-EM. This approach holds potential for broader applicability in both cryo-EM and cryo-electron tomography (cryo-ET).
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Affiliation(s)
- Qiaoyu Zhao
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Xiaoyu Hong
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Yanxing Wang
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Shaoning Zhang
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 200050, Shanghai, China
| | - Zhanyu Ding
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Xueming Meng
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Qianqian Song
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Qin Hong
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China
| | - Wanying Jiang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China
| | - Xiangyi Shi
- Shanghai Nanoport, Thermo Fisher Scientific, Shanghai, China
| | - Tianxun Cai
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, 200050, Shanghai, China
| | - Yao Cong
- Key Laboratory of RNA Innovation, Science and Engineering, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, 200031, Shanghai, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
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13
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Velloso JPL, de Sá AGC, Pires DEV, Ascher DB. Engineering G protein-coupled receptors for stabilization. Protein Sci 2024; 33:e5000. [PMID: 38747401 PMCID: PMC11094779 DOI: 10.1002/pro.5000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.
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Affiliation(s)
- João Paulo L. Velloso
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Alex G. C. de Sá
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Douglas E. V. Pires
- School of Computing and Information SystemsThe University of MelbourneParkvilleVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
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14
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Dahlström KM, Salminen TA. Apprehensions and emerging solutions in ML-based protein structure prediction. Curr Opin Struct Biol 2024; 86:102819. [PMID: 38631107 DOI: 10.1016/j.sbi.2024.102819] [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: 01/19/2024] [Revised: 03/05/2024] [Accepted: 03/31/2024] [Indexed: 04/19/2024]
Abstract
The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnological, diagnostics, and therapeutic applications. Over the past five years, machine learning (ML)-based modeling has pushed protein structure prediction to the next level with AlphaFold in the front line, predicting the structure for hundreds of millions of proteins. Further advances recently report promising ML-based approaches for solving remaining challenges by incorporating functionally important metals, co-factors, post-translational modifications, structural dynamics, and interdomain and multimer interactions in the structure prediction process.
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Affiliation(s)
- Käthe M Dahlström
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland; InFLAMES Research Flagship Center, Åbo Akademi University, 20520 Turku, Finland
| | - Tiina A Salminen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland; InFLAMES Research Flagship Center, Åbo Akademi University, 20520 Turku, Finland.
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15
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Liu N, Wang HW. Graphene in cryo-EM specimen optimization. Curr Opin Struct Biol 2024; 86:102823. [PMID: 38688075 DOI: 10.1016/j.sbi.2024.102823] [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: 02/03/2024] [Revised: 03/16/2024] [Accepted: 04/06/2024] [Indexed: 05/02/2024]
Abstract
Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize cryo-EM specimen preparation. It improves macromolecule embedding in ice, reduces beam-induced motion, while imposing negligible background noise. Various types of graphene-coated cryo-EM grids were implemented to improve the robustness and efficiency of specimen preparation. Graphene functionalization by different means has been proved specifically useful in addressing challenges related to the air-water interface (AWI), such as preferential orientation and sample denaturation. Graphene sandwich specimen preparation sets a new direction to explore in cryo-EM analysis of biological specimens. In this review, we discuss the current challenges and future prospects of graphene application in cryo-EM analysis of macromolecules.
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Affiliation(s)
- Nan Liu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hong-Wei Wang
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China.
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16
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Carlino E, Taurino A, Hasa D, Bučar DK, Polentarutti M, Chinchilla LE, Calvino Gamez JJ. Direct Imaging of Radiation-Sensitive Organic Polymer-Based Nanocrystals at Sub-Ångström Resolution. NANOMATERIALS (BASEL, SWITZERLAND) 2024; 14:872. [PMID: 38786829 DOI: 10.3390/nano14100872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 05/10/2024] [Accepted: 05/14/2024] [Indexed: 05/25/2024]
Abstract
Seeing the atomic configuration of single organic nanoparticles at a sub-Å spatial resolution by transmission electron microscopy has been so far prevented by the high sensitivity of soft matter to radiation damage. This difficulty is related to the need to irradiate the particle with a total dose of a few electrons/Å2, not compatible with the electron beam density necessary to search the low-contrast nanoparticle, to control its drift, finely adjust the electron-optical conditions and particle orientation, and finally acquire an effective atomic-resolution image. On the other hand, the capability to study individual pristine nanoparticles, such as proteins, active pharmaceutical ingredients, and polymers, with peculiar sensitivity to the variation in the local structure, defects, and strain, would provide advancements in many fields, including materials science, medicine, biology, and pharmacology. Here, we report the direct sub-ångström-resolution imaging at room temperature of pristine unstained crystalline polymer-based nanoparticles. This result is obtained by combining low-dose in-line electron holography and phase-contrast imaging on state-of-the-art equipment, providing an effective tool for the quantitative sub-ångström imaging of soft matter.
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Affiliation(s)
- Elvio Carlino
- Istituto di Cristallografia del Consiglio Nazionale delle Ricerche (IC-CNR), 70126 Bari, Italy
| | - Antonietta Taurino
- Istituto per la Microelettronica e i Microsistemi del Consiglio Nazionale delle Ricerche (IMM-CNR), 73100 Lecce, Italy
| | - Dritan Hasa
- Department of Chemical and Pharmaceutical Sciences University of Trieste, 34127 Trieste, Italy
| | | | | | - Lidia E Chinchilla
- Departamento de Ciencia de los Materiales, Ingeniería Metalúrgica y Química Inorgánica, Facultad de Ciencias, Universidad de Cádiz, 11519 Puerto Real, Cádiz, Spain
| | - Josè J Calvino Gamez
- Departamento de Ciencia de los Materiales, Ingeniería Metalúrgica y Química Inorgánica, Facultad de Ciencias, Universidad de Cádiz, 11519 Puerto Real, Cádiz, Spain
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17
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Wankowicz SA, Ravikumar A, Sharma S, Riley BT, Raju A, Flowers J, Hogan D, van den Bedem H, Keedy DA, Fraser JS. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.28.546963. [PMID: 37425870 PMCID: PMC10327213 DOI: 10.1101/2023.06.28.546963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.
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Affiliation(s)
- Stephanie A. Wankowicz
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Ashraya Ravikumar
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Shivani Sharma
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Ph.D. Program in Biology, The Graduate Center – City University of New York, New York, NY 10016
| | - Blake T. Riley
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Akshay Raju
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
| | - Jessica Flowers
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Daniel Hogan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Henry van den Bedem
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
- Atomwise, Inc., San Francisco, CA, United States
| | - Daniel A. Keedy
- Structural Biology Initiative, CUNY Advanced Science Research Center, New York, NY 10031
- Department of Chemistry and Biochemistry, City College of New York, New York, NY 10031
- Ph.D. Programs in Biochemistry, Biology, and Chemistry, The Graduate Center – City University of New York, New York, NY 10016
| | - James S. Fraser
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
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18
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Strickland MR, Rau MJ, Summers B, Basore K, Wulf J, Jiang H, Chen Y, Ulrich JD, Randolph GJ, Zhang R, Fitzpatrick JAJ, Cashikar AG, Holtzman DM. Apolipoprotein E secreted by astrocytes forms antiparallel dimers in discoidal lipoproteins. Neuron 2024; 112:1100-1109.e5. [PMID: 38266643 PMCID: PMC10994765 DOI: 10.1016/j.neuron.2023.12.018] [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: 09/11/2023] [Revised: 11/24/2023] [Accepted: 12/22/2023] [Indexed: 01/26/2024]
Abstract
The Apolipoprotein E gene (APOE) is of great interest due to its role as a risk factor for late-onset Alzheimer's disease. ApoE is secreted by astrocytes in the central nervous system in high-density lipoprotein (HDL)-like lipoproteins. Structural models of lipidated ApoE of high resolution could aid in a mechanistic understanding of how ApoE functions in health and disease. Using monoclonal Fab and F(ab')2 fragments, we characterize the structure of lipidated ApoE on astrocyte-secreted lipoproteins. Our results provide support for the "double-belt" model of ApoE in nascent discoidal HDL-like lipoproteins, where two ApoE proteins wrap around the nanodisc in an antiparallel conformation. We further show that lipidated, recombinant ApoE accurately models astrocyte-secreted ApoE lipoproteins. Cryogenic electron microscopy of recombinant lipidated ApoE further supports ApoE adopting antiparallel dimers in nascent discoidal lipoproteins.
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Affiliation(s)
| | - Michael J Rau
- Washington University Center for Cellular Imaging, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Brock Summers
- Washington University Center for Cellular Imaging, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Katherine Basore
- Washington University Center for Cellular Imaging, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - John Wulf
- Washington University Center for Cellular Imaging, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Hong Jiang
- Department of Neurology, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Yun Chen
- Department of Neurology, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Department of Pathology and Immunology, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Jason D Ulrich
- Department of Neurology, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Hope Center for Neurological Disorders, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Knight Alzheimer's Disease Research Center, 4488 Forest Park Ave., St. Louis, MO 63108, USA
| | - Gwendalyn J Randolph
- Department of Pathology and Immunology, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Rui Zhang
- Department of Biochemistry and Molecular Biophysics, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - James A J Fitzpatrick
- Washington University Center for Cellular Imaging, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - Anil G Cashikar
- Hope Center for Neurological Disorders, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Department of Psychiatry, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Taylor Family institute for Innovative Psychiatric Research, 660 S. Euclid Ave., St. Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Hope Center for Neurological Disorders, 660 S. Euclid Ave., St. Louis, MO 63110, USA; Knight Alzheimer's Disease Research Center, 4488 Forest Park Ave., St. Louis, MO 63108, USA.
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19
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Cebi E, Lee J, Subramani VK, Bak N, Oh C, Kim KK. Cryo-electron microscopy-based drug design. Front Mol Biosci 2024; 11:1342179. [PMID: 38501110 PMCID: PMC10945328 DOI: 10.3389/fmolb.2024.1342179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 01/31/2024] [Indexed: 03/20/2024] Open
Abstract
Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.
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Affiliation(s)
| | | | | | | | - Changsuk Oh
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
| | - Kyeong Kyu Kim
- Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, Republic of Korea
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20
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Majumdar S, Chiu YT, Pickett JE, Roth BL. Illuminating the understudied GPCR-ome. Drug Discov Today 2024; 29:103848. [PMID: 38052317 DOI: 10.1016/j.drudis.2023.103848] [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: 10/03/2023] [Revised: 11/17/2023] [Accepted: 11/28/2023] [Indexed: 12/07/2023]
Abstract
G-protein-coupled receptors (GPCRs) are the target of >30% of approved drugs. Despite their popularity, many of the >800 human GPCRs remain understudied. The Illuminating the Druggable Genome (IDG) project has generated many tools leading to important insights into the function and druggability of these so-called 'dark' receptors. These tools include assays, such as PRESTO-TANGO and TRUPATH, billions of small molecules made available via the ZINC virtual library, solved orphan GPCR structures, GPCR knock-in mice, and more. Together, these tools are illuminating the remaining 'dark' GPCRs.
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Affiliation(s)
- Sreeparna Majumdar
- Department of Pharmacology, UNC Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Yi-Ting Chiu
- Department of Pharmacology, UNC Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Julie E Pickett
- Department of Pharmacology, UNC Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Bryan L Roth
- Department of Pharmacology, UNC Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA.
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21
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Wuyun Q, Chen Y, Shen Y, Cao Y, Hu G, Cui W, Gao J, Zheng W. Recent Progress of Protein Tertiary Structure Prediction. Molecules 2024; 29:832. [PMID: 38398585 PMCID: PMC10893003 DOI: 10.3390/molecules29040832] [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: 12/30/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.
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Affiliation(s)
- Qiqige Wuyun
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yihan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Yifeng Shen
- Faculty of Environment and Information Studies, Keio University, Fujisawa 252-0882, Kanagawa, Japan;
| | - Yang Cao
- College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Gang Hu
- NITFID, School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin 300071, China
| | - Wei Cui
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Jianzhao Gao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China;
| | - Wei Zheng
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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22
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Longden TA, Lederer WJ. Electro-metabolic signaling. J Gen Physiol 2024; 156:e202313451. [PMID: 38197953 PMCID: PMC10783436 DOI: 10.1085/jgp.202313451] [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: 07/26/2023] [Revised: 10/27/2023] [Accepted: 12/14/2023] [Indexed: 01/11/2024] Open
Abstract
Precise matching of energy substrate delivery to local metabolic needs is essential for the health and function of all tissues. Here, we outline a mechanistic framework for understanding this critical process, which we refer to as electro-metabolic signaling (EMS). All tissues exhibit changes in metabolism over varying spatiotemporal scales and have widely varying energetic needs and reserves. We propose that across tissues, common signatures of elevated metabolism or increases in energy substrate usage that exceed key local thresholds rapidly engage mechanisms that generate hyperpolarizing electrical signals in capillaries that then relax contractile elements throughout the vasculature to quickly adjust blood flow to meet changing needs. The attendant increase in energy substrate delivery serves to meet local metabolic requirements and thus avoids a mismatch in supply and demand and prevents metabolic stress. We discuss in detail key examples of EMS that our laboratories have discovered in the brain and the heart, and we outline potential further EMS mechanisms operating in tissues such as skeletal muscle, pancreas, and kidney. We suggest that the energy imbalance evoked by EMS uncoupling may be central to cellular dysfunction from which the hallmarks of aging and metabolic diseases emerge and may lead to generalized organ failure states-such as diverse flavors of heart failure and dementia. Understanding and manipulating EMS may be key to preventing or reversing these dysfunctions.
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Affiliation(s)
- Thomas A. Longden
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
- Laboratory of Neurovascular Interactions, Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - W. Jonathan Lederer
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
- Laboratory of Molecular Cardiology, Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, MD, USA
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23
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Ye S, Zhong K, Huang Y, Zhang G, Sun C, Jiang J. Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics. J Am Chem Soc 2024; 146:2663-2672. [PMID: 38240637 DOI: 10.1021/jacs.3c12258] [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: 02/01/2024]
Abstract
The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.
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Affiliation(s)
- Sheng Ye
- School of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, People's Republic of China
| | - Kai Zhong
- Zernike Institute for Advanced Materials, University of Groningen, Nijenborgh 4, Groningen 9747AG, Netherlands
| | - Yan Huang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guozhen Zhang
- Hefei National Research Center of Physical Sciences at the Microscale, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
| | - Changyin Sun
- School of Artificial Intelligence, Anhui University, Hefei, Anhui 230601, People's Republic of China
| | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, China
- Hefei National Laboratory, University of Science and Technology of China, Hefei 230088, China
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24
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Xu J, Gao X, Zheng L, Jia X, Xu K, Ma Y, Wei X, Liu N, Peng H, Wang HW. Graphene sandwich-based biological specimen preparation for cryo-EM analysis. Proc Natl Acad Sci U S A 2024; 121:e2309384121. [PMID: 38252835 PMCID: PMC10835136 DOI: 10.1073/pnas.2309384121] [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: 06/11/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
High-quality specimen preparation plays a crucial role in cryo-electron microscopy (cryo-EM) structural analysis. In this study, we have developed a reliable and convenient technique called the graphene sandwich method for preparing cryo-EM specimens. This method involves using two layers of graphene films that enclose macromolecules on both sides, allowing for an appropriate ice thickness for cryo-EM analysis. The graphene sandwich helps to mitigate beam-induced charging effect and reduce particle motion compared to specimens prepared using the traditional method with graphene support on only one side, therefore improving the cryo-EM data quality. These advancements may open new opportunities to expand the use of graphene in the field of biological electron microscopy.
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Affiliation(s)
- Jie Xu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing100084, China
- Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Xiaoyin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing100871, China
| | - Liming Zheng
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing100871, China
| | - Xia Jia
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing100084, China
- Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Kui Xu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing100084, China
- Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Yuwei Ma
- State Key Laboratory for Turbulence and Complex System, Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing100871, China
| | - Xiaoding Wei
- State Key Laboratory for Turbulence and Complex System, Department of Mechanics and Engineering Science, College of Engineering, Peking University, Beijing100871, China
| | - Nan Liu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Hailin Peng
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing100871, China
- Beijing Graphene Institute, Beijing100095, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
| | - Hong-Wei Wang
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing100084, China
- Tsinghua-Peking Center for Life Sciences, School of Life Sciences, Tsinghua University, Beijing100084, China
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25
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Liu J, Lu Y, Zhu L. A kinetic model for solving a combination optimization problem in ab-initio Cryo-EM 3D reconstruction. Brief Bioinform 2024; 25:bbad473. [PMID: 38261343 PMCID: PMC10805181 DOI: 10.1093/bib/bbad473] [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: 07/13/2023] [Revised: 10/22/2023] [Accepted: 11/28/2023] [Indexed: 01/24/2024] Open
Abstract
Cryo-Electron Microscopy (cryo-EM) is a widely used and effective method for determining the three-dimensional (3D) structure of biological molecules. For ab-initio Cryo-EM 3D reconstruction using single particle analysis (SPA), estimating the projection direction of the projection image is a crucial step. However, the existing SPA methods based on common lines are sensitive to noise. The error in common line detection will lead to a poor estimation of the projection directions and thus may greatly affect the final reconstruction results. To improve the reconstruction results, multiple candidate common lines are estimated for each pair of projection images. The key problem then becomes a combination optimization problem of selecting consistent common lines from multiple candidates. To solve the problem efficiently, a physics-inspired method based on a kinetic model is proposed in this work. More specifically, hypothetical attractive forces between each pair of candidate common lines are used to calculate a hypothetical torque exerted on each projection image in the 3D reconstruction space, and the rotation under the hypothetical torque is used to optimize the projection direction estimation of the projection image. This way, the consistent common lines along with the projection directions can be found directly without enumeration of all the combinations of the multiple candidate common lines. Compared with the traditional methods, the proposed method is shown to be able to produce more accurate 3D reconstruction results from high noise projection images. Besides the practical value, the proposed method also serves as a good reference for solving similar combinatorial optimization problems.
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Affiliation(s)
- Jiaxuan Liu
- School of Information Science and Engineering, Lanzhou
| | - Yonggang Lu
- School of Information Science and Engineering, Lanzhou
| | - Li Zhu
- School of Life Sciences, Lanzhou University
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26
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Peng CX, Liang F, Xia YH, Zhao KL, Hou MH, Zhang GJ. Recent Advances and Challenges in Protein Structure Prediction. J Chem Inf Model 2024; 64:76-95. [PMID: 38109487 DOI: 10.1021/acs.jcim.3c01324] [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: 12/20/2023]
Abstract
Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.
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Affiliation(s)
- Chun-Xiang Peng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Fang Liang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Yu-Hao Xia
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kai-Long Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Ming-Hua Hou
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Gui-Jun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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27
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Li J, Wang L, Zhu Z, Song C. Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction. J Chem Inf Model 2024; 64:301-315. [PMID: 38117138 PMCID: PMC10777399 DOI: 10.1021/acs.jcim.3c01381] [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: 08/29/2023] [Revised: 12/03/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023]
Abstract
The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.
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Affiliation(s)
- Jiaxuan Li
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Lei Wang
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Zefeng Zhu
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Chen Song
- Center
for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
- Peking-Tsinghua
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
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28
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Heo L, Feig M. One bead per residue can describe all-atom protein structures. Structure 2024; 32:97-111.e6. [PMID: 38000367 PMCID: PMC10872525 DOI: 10.1016/j.str.2023.10.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/16/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023]
Abstract
Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.
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Affiliation(s)
- Lim Heo
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Michael Feig
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
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29
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Deniaud A, Kabasakal BV, Bufton JC, Schaffitzel C. Sample Preparation for Electron Cryo-Microscopy of Macromolecular Machines. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2024; 3234:173-190. [PMID: 38507207 DOI: 10.1007/978-3-031-52193-5_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
High-resolution structure determination by electron cryo-microscopy underwent a step change in recent years. This now allows study of challenging samples which previously were inaccessible for structure determination, including membrane proteins. These developments shift the focus in the field to the next bottlenecks which are high-quality sample preparations. While the amounts of sample required for cryo-EM are relatively small, sample quality is the key challenge. Sample quality is influenced by the stability of complexes which depends on buffer composition, inherent flexibility of the sample, and the method of solubilization from the membrane for membrane proteins. It further depends on the choice of sample support, grid pre-treatment and cryo-grid freezing protocol. Here, we discuss various widely applicable approaches to improve sample quality for structural analysis by cryo-EM.
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Affiliation(s)
- Aurélien Deniaud
- Univ. Grenoble Alpes, CNRS, CEA, IRIG - Laboratoire de Chimie et Biologie des Métaux, Grenoble, France
| | - Burak V Kabasakal
- School of Biochemistry, University of Bristol, Bristol, UK
- Turkish Accelerator and Radiation Laboratory, Gölbaşı, Ankara, Türkiye
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30
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Guo Q, He B, Zhong Y, Jiao H, Ren Y, Wang Q, Ge Q, Gao Y, Liu X, Du Y, Hu H, Tao Y. A method for structure determination of GPCRs in various states. Nat Chem Biol 2024; 20:74-82. [PMID: 37580554 DOI: 10.1038/s41589-023-01389-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/28/2023] [Indexed: 08/16/2023]
Abstract
G-protein-coupled receptors (GPCRs) are a class of integral membrane proteins that detect environmental cues and trigger cellular responses. Deciphering the functional states of GPCRs induced by various ligands has been one of the primary goals in the field. Here we developed an effective universal method for GPCR cryo-electron microscopy structure determination without the need to prepare GPCR-signaling protein complexes. Using this method, we successfully solved the structures of the β2-adrenergic receptor (β2AR) bound to antagonistic and agonistic ligands and the adhesion GPCR ADGRL3 in the apo state. For β2AR, an intermediate state stabilized by the partial agonist was captured. For ADGRL3, the structure revealed that inactive ADGRL3 adopts a compact fold and that large unusual conformational changes on both the extracellular and intracellular sides are required for activation of adhesion GPCRs. We anticipate that this method will open a new avenue for understanding GPCR structure‒function relationships and drug development.
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Affiliation(s)
- Qiong Guo
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Binbin He
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yixuan Zhong
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Haizhan Jiao
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, China
| | - Yinhang Ren
- Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, Tsinghua-Peking Center for Life Sciences, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Qinggong Wang
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Qiangqiang Ge
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Yongxiang Gao
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Xiangyu Liu
- Beijing Frontier Research Center for Biological Structure, Beijing Advanced Innovation Center for Structural Biology, Tsinghua-Peking Center for Life Sciences, School of Pharmaceutical Sciences, Tsinghua University, Beijing, China
| | - Yang Du
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, China
| | - Hongli Hu
- Kobilka Institute of Innovative Drug Discovery, School of Medicine, The Chinese University of Hong Kong, Shenzhen, China.
| | - Yuyong Tao
- Department of Laboratory Medicine, The First Affiliated Hospital of USTC, MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, Hefei National Center for Cross-Disciplinary Sciences, Biomedical Sciences and Health Laboratory of Anhui Province, Center for Advanced Interdisciplinary Science and Biomedicine of IHM, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
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31
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Ng TK, Ji J, Liu Q, Yao Y, Wang WY, Cao Y, Chen CB, Lin JW, Dong G, Cen LP, Huang C, Zhang M. Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2. Biomolecules 2023; 14:14. [PMID: 38275755 PMCID: PMC10813463 DOI: 10.3390/biom14010014] [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: 09/13/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024] Open
Abstract
Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.
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Affiliation(s)
- Tsz Kin Ng
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jie Ji
- Network & Information Centre, Shantou University, Shantou 515041, China
| | - Qingping Liu
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Key Laboratory of Carbohydrate and Lipid Metabolism Research, College of Life Science and Technology, Dalian University, Dalian 116622, China
| | - Yao Yao
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Shantou University Medical College, Shantou 515041, China
| | - Wen-Ying Wang
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
- Shantou University Medical College, Shantou 515041, China
| | - Yingjie Cao
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Chong-Bo Chen
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Jian-Wei Lin
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Geng Dong
- Shantou University Medical College, Shantou 515041, China
| | - Ling-Ping Cen
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Chukai Huang
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
| | - Mingzhi Zhang
- Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong, Shantou 515041, China; (T.K.N.)
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32
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Mazal H, Wieser FF, Sandoghdar V. Insights into protein structure using cryogenic light microscopy. Biochem Soc Trans 2023; 51:2041-2059. [PMID: 38015555 PMCID: PMC10754291 DOI: 10.1042/bst20221246] [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: 08/26/2023] [Revised: 11/13/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023]
Abstract
Fluorescence microscopy has witnessed many clever innovations in the last two decades, leading to new methods such as structured illumination and super-resolution microscopies. The attainable resolution in biological samples is, however, ultimately limited by residual motion within the sample or in the microscope setup. Thus, such experiments are typically performed on chemically fixed samples. Cryogenic light microscopy (Cryo-LM) has been investigated as an alternative, drawing on various preservation techniques developed for cryogenic electron microscopy (Cryo-EM). Moreover, this approach offers a powerful platform for correlative microscopy. Another key advantage of Cryo-LM is the strong reduction in photobleaching at low temperatures, facilitating the collection of orders of magnitude more photons from a single fluorophore. This results in much higher localization precision, leading to Angstrom resolution. In this review, we discuss the general development and progress of Cryo-LM with an emphasis on its application in harnessing structural information on proteins and protein complexes.
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Affiliation(s)
- Hisham Mazal
- Max Planck Institute for the Science of Light, 91058 Erlangen, Germany
- Max-Planck-Zentrum für Physik und Medizin, 91058 Erlangen, Germany
| | - Franz-Ferdinand Wieser
- Max Planck Institute for the Science of Light, 91058 Erlangen, Germany
- Max-Planck-Zentrum für Physik und Medizin, 91058 Erlangen, Germany
- Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
| | - Vahid Sandoghdar
- Max Planck Institute for the Science of Light, 91058 Erlangen, Germany
- Max-Planck-Zentrum für Physik und Medizin, 91058 Erlangen, Germany
- Friedrich-Alexander University of Erlangen-Nürnberg, 91058 Erlangen, Germany
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33
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Mishra S. Emerging Trends in Cryo-EM-based Structural Studies of Neuropathological Amyloids. J Mol Biol 2023; 435:168361. [PMID: 37949311 DOI: 10.1016/j.jmb.2023.168361] [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: 09/29/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023]
Abstract
Tauopathies, synucleinopathies, Aβ amyloidosis, TDP-43 proteinopathies, and prion diseases- these neurodegenerative diseases have in common the formation of amyloid filaments rich in cross-β sheets. Cryo-electron microscopy now permits the visualization of amyloid assemblies at atomic resolution, ushering a wide range of structural studies on several of these poorly understood amyloidogenic proteins. Amyloids are polymorphic with minor modulations in reaction environment affecting the overall architecture of their assembly, making amyloids an extremely challenging venture for structure-based therapeutic intervention. In 2017, the first cryo-EM structure of tau filaments from an Alzheimer's disease-affected brain established that in vitro assemblies might not necessarily reflect the native amyloid fold. Since then, brain-derived amyloid structures for several proteins across many neurodegenerative diseases have uncovered the disease-relevant amyloid folds. It has now been shown for tauopathies, synucleinopathies and TDP-43 proteinopathies, that distinct amyloid folds of the same protein might be related to different diseases. Salient features of each of these brain-derived folds are discussed in detail. It was also recently observed that seeded aggregation does not necessarily replicate the brain-derived structural fold. Owing to high throughput structure determination, some of these native amyloid folds have also been successfully replicated in vitro. In vitro replication of disease-relevant filaments will aid development of imaging ligands and defibrillating drugs. Towards this direction, recent high-resolution structures of tau filaments with positron emission tomography tracers and a defibrillating drug are also discussed. This review summarizes and celebrates the recent advancements in structural understanding of neuropathological amyloid filaments using cryo-EM.
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Affiliation(s)
- Suman Mishra
- Molecular Biophysics Unit, Biological Sciences Division, Indian Institute of Science, Bengaluru 560 012, Karnataka, India.
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34
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Ji C, Wei J, Zhang L, Hou X, Tan J, Yuan Q, Tan W. Aptamer-Protein Interactions: From Regulation to Biomolecular Detection. Chem Rev 2023; 123:12471-12506. [PMID: 37931070 DOI: 10.1021/acs.chemrev.3c00377] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
Serving as the basis of cell life, interactions between nucleic acids and proteins play essential roles in fundamental cellular processes. Aptamers are unique single-stranded oligonucleotides generated by in vitro evolution methods, possessing the ability to interact with proteins specifically. Altering the structure of aptamers will largely modulate their interactions with proteins and further affect related cellular behaviors. Recently, with the in-depth research of aptamer-protein interactions, the analytical assays based on their interactions have been widely developed and become a powerful tool for biomolecular detection. There are some insightful reviews on aptamers applied in protein detection, while few systematic discussions are from the perspective of regulating aptamer-protein interactions. Herein, we comprehensively introduce the methods for regulating aptamer-protein interactions and elaborate on the detection techniques for analyzing aptamer-protein interactions. Additionally, this review provides a broad summary of analytical assays based on the regulation of aptamer-protein interactions for detecting biomolecules. Finally, we present our perspectives regarding the opportunities and challenges of analytical assays for biological analysis, aiming to provide guidance for disease mechanism research and drug discovery.
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Affiliation(s)
- Cailing Ji
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Junyuan Wei
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Lei Zhang
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Xinru Hou
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Jie Tan
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Quan Yuan
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
| | - Weihong Tan
- Molecular Science and Biomedicine Laboratory (MBL), State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, China
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310022, China
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35
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Braxton JR, Southworth DR. Structural insights of the p97/VCP AAA+ ATPase: How adapter interactions coordinate diverse cellular functionality. J Biol Chem 2023; 299:105182. [PMID: 37611827 PMCID: PMC10641518 DOI: 10.1016/j.jbc.2023.105182] [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: 05/30/2023] [Revised: 08/05/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023] Open
Abstract
p97/valosin-containing protein is an essential eukaryotic AAA+ ATPase with diverse functions including protein homeostasis, membrane remodeling, and chromatin regulation. Dysregulation of p97 function causes severe neurodegenerative disease and is associated with cancer, making this protein a significant therapeutic target. p97 extracts polypeptide substrates from macromolecular assemblies by hydrolysis-driven translocation through its central pore. Growing evidence indicates that this activity is highly coordinated by "adapter" partner proteins, of which more than 30 have been identified and are commonly described to facilitate translocation through substrate recruitment or modification. In so doing, these adapters enable critical p97-dependent functions such as extraction of misfolded proteins from the endoplasmic reticulum or mitochondria, and are likely the reason for the extreme functional diversity of p97 relative to other AAA+ translocases. Here, we review the known functions of adapter proteins and highlight recent structural and biochemical advances that have begun to reveal the diverse molecular bases for adapter-mediated regulation of p97 function. These studies suggest that the range of mechanisms by which p97 activity is controlled is vastly underexplored with significant advances possible for understanding p97 regulation by the most known adapters.
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Affiliation(s)
- Julian R Braxton
- Graduate Program in Chemistry and Chemical Biology, University of California, San Francisco, San Francisco, California, USA; Department of Biochemistry and Biophysics and Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, California, USA
| | - Daniel R Southworth
- Department of Biochemistry and Biophysics and Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, California, USA.
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36
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Luo Z, Ni F, Wang Q, Ma J. OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis. Nat Methods 2023; 20:1729-1738. [PMID: 37813988 PMCID: PMC10630141 DOI: 10.1038/s41592-023-02031-6] [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: 11/22/2022] [Accepted: 08/24/2023] [Indexed: 10/11/2023]
Abstract
Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder-decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD .
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Affiliation(s)
- Zhenwei Luo
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
- Shanghai AI Laboratory, Shanghai, China
| | - Fengyun Ni
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, China
| | - Qinghua Wang
- Center for Biomolecular Innovation, Harcam Biomedicines, Shanghai, China
| | - Jianpeng Ma
- Multiscale Research Institute of Complex Systems, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China.
- Shanghai AI Laboratory, Shanghai, China.
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37
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Fernández-Quintero ML, Pomarici ND, Fischer ALM, Hoerschinger VJ, Kroell KB, Riccabona JR, Kamenik AS, Loeffler JR, Ferguson JA, Perrett HR, Liedl KR, Han J, Ward AB. Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines. Antibodies (Basel) 2023; 12:67. [PMID: 37873864 PMCID: PMC10594513 DOI: 10.3390/antib12040067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/25/2023] Open
Abstract
Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.
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Affiliation(s)
- Monica L. Fernández-Quintero
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Nancy D. Pomarici
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna-Lena M. Fischer
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Valentin J. Hoerschinger
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Katharina B. Kroell
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Jakob R. Riccabona
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Anna S. Kamenik
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Johannes R. Loeffler
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - James A. Ferguson
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hailee R. Perrett
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Klaus R. Liedl
- Institute of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innrain 80/82, A-6020 Innsbruck, Austria
| | - Julianna Han
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Andrew B. Ward
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
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38
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Zhang L, Rao Z. Structural biology and inhibition of the Mtb cell wall glycoconjugates biosynthesis on the membrane. Curr Opin Struct Biol 2023; 82:102670. [PMID: 37542906 DOI: 10.1016/j.sbi.2023.102670] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 06/08/2023] [Accepted: 07/10/2023] [Indexed: 08/07/2023]
Abstract
Glycoconjugates are the dominant components of the Mycobacterium tuberculosis cell wall. These glycoconjugates are essential for the viability of Mtb and attribute to drug resistance and virulence during infection. The assembly and maturation of the cell wall largely relies on the Mtb plasma membrane. A significant number of membrane-bound glycosyltransferases (GTs) and transporters play pivotal roles in forming the complex glycoconjugates and are targeted by the first-line anti-TB drug and potent drug candidates. Here we summarize the latest structural biology of mycobacterial GTs and transporters, and describe the modes of action of drug and drug candidates that are of substantial clinical value in anti-TB chemotherapeutics.
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Affiliation(s)
- Lu Zhang
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Zihe Rao
- Laboratory of Structural Biology, Tsinghua University, Beijing 100084, China.
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39
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Singh Y, Hocky GM, Nolen BJ. Molecular dynamics simulations support a multistep pathway for activation of branched actin filament nucleation by Arp2/3 complex. J Biol Chem 2023; 299:105169. [PMID: 37595874 PMCID: PMC10514467 DOI: 10.1016/j.jbc.2023.105169] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 08/10/2023] [Accepted: 08/12/2023] [Indexed: 08/20/2023] Open
Abstract
Actin-related protein 2/3 complex (Arp2/3 complex) catalyzes the nucleation of branched actin filaments that push against membranes in processes like cellular motility and endocytosis. During activation by WASP proteins, the complex must bind WASP and engage the side of a pre-existing (mother) filament before a branched filament is nucleated. Recent high-resolution structures of activated Arp2/3 complex revealed two major sets of activating conformational changes. How these activating conformational changes are triggered by interactions of Arp2/3 complex with actin filaments and WASP remains unclear. Here we use a recent high-resolution structure of Arp2/3 complex at a branch junction to design all-atom molecular dynamics simulations that elucidate the pathway between the active and inactive states. We ran a total of ∼4.6 microseconds of both unbiased and steered all-atom molecular dynamics simulations starting from three different binding states, including Arp2/3 complex within a branch junction, bound only to a mother filament, and alone in solution. These simulations indicate that the contacts with the mother filament are mostly insensitive to the massive rigid body motion that moves Arp2 and Arp3 into a short pitch helical (filament-like) arrangement, suggesting actin filaments alone do not stimulate the short pitch conformational change. In contrast, contacts with the mother filament stabilize subunit flattening in Arp3, an intrasubunit change that converts Arp3 from a conformation that mimics an actin monomer to one that mimics a filamentous actin subunit. Our results support a multistep activation pathway that has important implications for understanding how WASP-mediated activation allows Arp2/3 complex to assemble force-producing actin networks.
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Affiliation(s)
| | - Glen M Hocky
- Department of Chemistry, New York University; Simons Center for Computational Physical Chemistry, New York University.
| | - Brad J Nolen
- Department of Chemistry and Biochemistry, Institute of Molecular Biology, University of Oregon.
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40
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Yao Y, Zhu Y, Zhu C. Geometric phase correction: A direct phase correction method to register low contrast noisy TEM images. Micron 2023; 172:103503. [PMID: 37419024 DOI: 10.1016/j.micron.2023.103503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/25/2023] [Accepted: 06/26/2023] [Indexed: 07/09/2023]
Abstract
A major challenge in the emerging field of low-dose electron microscopy lies in the development of drift correction algorithms against beam-induced specimen motion and compatible with highly noisy transmission electron microscopy (TEM) images. We report here a new drift correction method, namely geometric phase correlation (GPC), to correlate the specimen motion in real space by directly measuring the unwrapped geometric phase shift in the spatial frequency domain of the TEM image (especially from the intensive Bragg spots for crystalline materials) with sub-pixel precision. The GPC method outperforms cross-correlation-based methods in both accuracy of specimen motion prediction from highly noisy TEM movies and computational efficiency of drift calculation from abundant image frames, which holds great promise for diverse applications in low-dose TEM imaging of beam-sensitive materials, such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs).
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Affiliation(s)
- Yuan Yao
- Beijing National Laboratory of Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China.
| | - Yihan Zhu
- Center for Electron Microscopy, Institute for Frontier and Interdisciplinary Sciences, State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology and College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China.
| | - Chongzhi Zhu
- Center for Electron Microscopy, Institute for Frontier and Interdisciplinary Sciences, State Key Laboratory Breeding Base of Green Chemistry Synthesis Technology and College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, Zhejiang, China
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41
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Grassmann G, Di Rienzo L, Gosti G, Leonetti M, Ruocco G, Miotto M, Milanetti E. Electrostatic complementarity at the interface drives transient protein-protein interactions. Sci Rep 2023; 13:10207. [PMID: 37353566 PMCID: PMC10290103 DOI: 10.1038/s41598-023-37130-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/16/2023] [Indexed: 06/25/2023] Open
Abstract
Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.
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Affiliation(s)
- Greta Grassmann
- Department of Biochemical Sciences "Alessandro Rossi Fanelli", Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy
| | - Lorenzo Di Rienzo
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy
| | - Giorgio Gosti
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy
- Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185, Rome, Italy
| | - Marco Leonetti
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy
- Soft and Living Matter Laboratory, Institute of Nanotechnology, Consiglio Nazionale delle Ricerche, 00185, Rome, Italy
| | - Giancarlo Ruocco
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy
- Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy
| | - Mattia Miotto
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy.
| | - Edoardo Milanetti
- Center for Life Nano & Neuro Science, Istituto Italiano di Tecnologia, Viale Regina Elena 291, 00161, Rome, Italy.
- Department of Physics, Sapienza University of Rome, Piazzale Aldo Moro 5, 00185, Rome, Italy.
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42
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Matsumoto S, Ishida S, Terayama K, Okuno Y. Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations. Biophys Physicobiol 2023; 20:e200022. [PMID: 38496243 PMCID: PMC10941960 DOI: 10.2142/biophysico.bppb-v20.0022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/12/2023] [Indexed: 03/19/2024] Open
Abstract
Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.
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Affiliation(s)
| | - Shoichi Ishida
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Kanagawa 230-0045, Japan
| | - Kei Terayama
- Graduate School of Medical Life Science, Yokohama City University, Yokohama, Kanagawa 230-0045, Japan
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
| | - Yasuhshi Okuno
- Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- RIKEN Center for Computational Science, Kobe, Hyogo 650-0047, Japan
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43
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Chari A, Stark H. Prospects and Limitations of High-Resolution Single-Particle Cryo-Electron Microscopy. Annu Rev Biophys 2023; 52:391-411. [PMID: 37159297 DOI: 10.1146/annurev-biophys-111622-091300] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Single particle cryo-electron microscopy (cryo-EM) has matured into a robust method for the determination of biological macromolecule structures in the past decade, complementing X-ray crystallography and nuclear magnetic resonance. Constant methodological improvements in both cryo-EM hardware and image processing software continue to contribute to an exponential growth in the number of structures solved annually. In this review, we provide a historical view of the many steps that were required to make cryo-EM a successful method for the determination of high-resolution protein complex structures. We further discuss aspects of cryo-EM methodology that are the greatest pitfalls challenging successful structure determination to date. Lastly, we highlight and propose potential future developments that would improve the method even further in the near future.
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Affiliation(s)
- Ashwin Chari
- Research Group for Structural Biochemistry and Mechanisms, Max-Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Holger Stark
- Department of Structural Dynamics, Max-Planck Institute for Multidisciplinary Sciences, Göttingen, Germany;
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44
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Zhang D, Yan Y, Huang Y, Liu B, Zheng Q, Zhang J, Xia N. Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional Autoencoder and K-Means+. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:1509-1521. [PMID: 37015394 DOI: 10.1109/tmi.2022.3231626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.
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45
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Saotome K, Dudgeon D, Colotti K, Moore MJ, Jones J, Zhou Y, Rafique A, Yancopoulos GD, Murphy AJ, Lin JC, Olson WC, Franklin MC. Structural analysis of cancer-relevant TCR-CD3 and peptide-MHC complexes by cryoEM. Nat Commun 2023; 14:2401. [PMID: 37100770 PMCID: PMC10132440 DOI: 10.1038/s41467-023-37532-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 03/21/2023] [Indexed: 04/28/2023] Open
Abstract
The recognition of antigenic peptide-MHC (pMHC) molecules by T-cell receptors (TCR) initiates the T-cell mediated immune response. Structural characterization is key for understanding the specificity of TCR-pMHC interactions and informing the development of therapeutics. Despite the rapid rise of single particle cryoelectron microscopy (cryoEM), x-ray crystallography has remained the preferred method for structure determination of TCR-pMHC complexes. Here, we report cryoEM structures of two distinct full-length α/β TCR-CD3 complexes bound to their pMHC ligand, the cancer-testis antigen HLA-A2/MAGEA4 (230-239). We also determined cryoEM structures of pMHCs containing MAGEA4 (230-239) peptide and the closely related MAGEA8 (232-241) peptide in the absence of TCR, which provided a structural explanation for the MAGEA4 preference displayed by the TCRs. These findings provide insights into the TCR recognition of a clinically relevant cancer antigen and demonstrate the utility of cryoEM for high-resolution structural analysis of TCR-pMHC interactions.
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Affiliation(s)
- Kei Saotome
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, 10591, USA.
| | - Drew Dudgeon
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, 10591, USA
| | | | | | - Jennifer Jones
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, 10591, USA
| | - Yi Zhou
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, 10591, USA
| | | | | | | | - John C Lin
- Regeneron Pharmaceuticals, Inc., Tarrytown, NY, 10591, USA
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46
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Pham M, Yuan Y, Rana A, Osher S, Miao J. Accurate real space iterative reconstruction (RESIRE) algorithm for tomography. Sci Rep 2023; 13:5624. [PMID: 37024554 PMCID: PMC10079852 DOI: 10.1038/s41598-023-31124-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 03/07/2023] [Indexed: 04/08/2023] Open
Abstract
Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by the tolerable radiation dose and/or the geometric constraint on the tilt range, a main challenge in tomography is to achieve the best possible 3D reconstruction from a limited number of projections with noise. Over the years, a number of tomographic reconstruction methods have been developed including direct inversion, real-space, and Fourier-based iterative algorithms. Here, we report the development of a real-space iterative reconstruction (RESIRE) algorithm for accurate tomographic reconstruction. RESIRE iterates between the update of a reconstructed 3D object and the measured projections using a forward and back projection step. The forward projection step is implemented by the Fourier slice theorem or the Radon transform, and the back projection step by a linear transformation. Our numerical and experimental results demonstrate that RESIRE performs more accurate 3D reconstructions than other existing tomographic algorithms, when there are a limited number of projections with noise. Furthermore, RESIRE can be used to reconstruct the 3D structure of extended objects as demonstrated by the determination of the 3D atomic structure of an amorphous Ta thin film. We expect that RESIRE can be widely employed in the tomography applications in different fields. Finally, to make the method accessible to the general user community, the MATLAB source code of RESIRE and all the simulated and experimental data are available at https://zenodo.org/record/7273314 .
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Affiliation(s)
- Minh Pham
- Department of Mathematics, University of California, Los Angeles, CA, 90095, USA.
| | - Yakun Yuan
- Department of Physics and Astronomy, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA
- Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Arjun Rana
- Department of Physics and Astronomy, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA
| | - Stanley Osher
- Department of Mathematics, University of California, Los Angeles, CA, 90095, USA
| | - Jianwei Miao
- Department of Physics and Astronomy, California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA.
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47
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Cheng H, Zheng L, Liu N, Huang C, Xu J, Lu Y, Cui X, Xu K, Hou Y, Tang J, Zhang Z, Li J, Ni X, Chen Y, Peng H, Wang HW. Dual-Affinity Graphene Sheets for High-Resolution Cryo-Electron Microscopy. J Am Chem Soc 2023; 145:8073-8081. [PMID: 37011903 PMCID: PMC10103130 DOI: 10.1021/jacs.3c00659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
With the development of cryo-electron microscopy (cryo-EM), high-resolution structures of macromolecules can be reconstructed by the single particle method efficiently. However, challenges may still persist during the specimen preparation stage. Specifically, proteins tend to adsorb at the air-water interface and exhibit a preferred orientation in vitreous ice. To overcome these challenges, we have explored dual-affinity graphene (DAG) modified with two different affinity ligands as a supporting material for cryo-EM sample preparation. The ligands can bind to distinct sites on the corresponding tagged particles, which in turn generates various orientation distributions of particles and prevents the adsorption of protein particles onto the air-water interface. As expected, the DAG exhibited high binding specificity and affinity to target macromolecules, resulting in more balanced particle Euler angular distributions compared to single functionalized graphene on two different protein cases, including the SARS -CoV-2 spike glycoprotein. We anticipate that the DAG grids will enable facile and efficient three-dimensional (3D) reconstruction for cryo-EM structural determination, providing a robust and general technique for future studies.
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Affiliation(s)
- Hang Cheng
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
- Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Shuimu BioSciences Ltd., Beijing 102206, China
| | - Liming Zheng
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Nan Liu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Frontier Research Center for Biological Structures, Tsinghua University, Beijing 100084, China
| | - Congyuan Huang
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jie Xu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Ye Lu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xiaoya Cui
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Frontier Research Center for Biological Structures, Tsinghua University, Beijing 100084, China
| | - Kui Xu
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
| | - Yuan Hou
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing 100190, China
| | - Junchuan Tang
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Zhong Zhang
- CAS Key Laboratory of Nanosystem and Hierarchical Fabrication, National Center for Nanoscience and Technology, Beijing 100190, China
| | - Jing Li
- Shuimu BioSciences Ltd., Beijing 102206, China
| | - Xiaodan Ni
- Shuimu BioSciences Ltd., Beijing 102206, China
| | - Yanan Chen
- School of Materials Science and Engineering, Tianjin University, Tianjin 300072, China
| | - Hailin Peng
- Center for Nanochemistry, Beijing Science and Engineering Center for Nanocarbons, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
- Beijing Graphene Institute (BGI), Beijing 100095, China
| | - Hong-Wei Wang
- Ministry of Education Key Laboratory of Protein Sciences, Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Tsinghua-Peking Joint Center for Life Sciences, Tsinghua University, Beijing 100084, China
- Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing 100084, China
- Beijing Frontier Research Center for Biological Structures, Tsinghua University, Beijing 100084, China
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48
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Acebrón I, Campanero-Rhodes MA, Solís D, Menéndez M, García C, Lillo MP, Mancheño JM. Atomic crystal structure and sugar specificity of a β-trefoil lectin domain from the ectomycorrhizal basidiomycete Laccaria bicolor. Int J Biol Macromol 2023; 233:123507. [PMID: 36754262 DOI: 10.1016/j.ijbiomac.2023.123507] [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: 11/03/2022] [Revised: 01/10/2023] [Accepted: 01/29/2023] [Indexed: 02/10/2023]
Abstract
Lectins from fruiting bodies are a diverse group of sugar-binding proteins from mushrooms that face the biologically relevant challenge of discriminating self- from non-self carbohydrate structures, therefore providing a basis for an innate defence system. Such a system entails both detection and destruction of invaders and/or feeders, and in contrast to more complex organisms with immense immune systems, these two functions normally rely on multitasking lectins, namely, lectins with different functional modules. Here, we present a novel fungal lectin, LBL, from the basidiomycete Laccaria bicolor. Using a diverse set of biophysical techniques, we unveil the fine details of the sugar-binding specificity of the N-terminal β-trefoil of LBL (LBL152), whose structure has been determined at the highest resolution so far reported for such a fold. LBL152 binds complex poly-N-Acetyllactosamine polysaccharides and also robust LBL152 binding to Caenorhabditis elegans and Drosophila melanogaster cellular extracts was detected in microarray assays, with a seeming preference for the fruit fly adult and pupa stages over the larva stage. Prediction of the structure of the C-terminal part of LBL with AlphaFold reveals a tandem repeat of two structurally almost identical domains of around 110 amino acids each, despite sharing low sequence conservation.
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Affiliation(s)
- Iván Acebrón
- Department of Crystallography and Structural Biology, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain
| | - María Asunción Campanero-Rhodes
- Department of Biological Physical Chemistry, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain; CIBER of Respiratory Diseases Enfermedades Respiratorias (CIBERES), ISCIII, Avda. Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Dolores Solís
- Department of Biological Physical Chemistry, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain; CIBER of Respiratory Diseases Enfermedades Respiratorias (CIBERES), ISCIII, Avda. Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Margarita Menéndez
- Department of Biological Physical Chemistry, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain; CIBER of Respiratory Diseases Enfermedades Respiratorias (CIBERES), ISCIII, Avda. Monforte de Lemos 3-5, 28029 Madrid, Spain
| | - Carolina García
- Department of Biological Physical Chemistry, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain
| | - M Pilar Lillo
- Department of Biological Physical Chemistry, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain
| | - José M Mancheño
- Department of Crystallography and Structural Biology, Institute of Physical Chemistry Rocasolano, CSIC, Serrano 119, 28006 Madrid, Spain.
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49
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Ho MR, Wu YM, Lu YC, Ko TP, Wu KP. Cryo-EM reveals the structure and dynamics of a 723-residue malate synthase G. J Struct Biol 2023; 215:107958. [PMID: 36997036 DOI: 10.1016/j.jsb.2023.107958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/17/2023] [Accepted: 03/19/2023] [Indexed: 03/30/2023]
Abstract
Determination of sub-100 kilodalton (kDa) structures by cryo-electron microscopy (EM) is a longstanding but not straightforward goal. Here, we present a 2.9-Å cryo-EM structure of a 723-amino acid apo-form malate synthase G (MSG) from Escherichia coli. The cryo-EM structure of the 82-kDa MSG exhibits the same global folding as structures resolved by crystallography and nuclear magnetic resonance (NMR) spectroscopy, and the crystal and cryo-EM structures are indistinguishable. Analyses of MSG dynamics reveal consistent conformational flexibilities among the three experimental approaches, most notably that the α/β domain exhibits structural heterogeneity. We observed that sidechains of F453, L454, M629, and E630 residues involved in hosting the cofactor acetyl-CoA and substrate rotate differently between the cryo-EM apo-form and complex crystal structures. Our work demonstrates that the cryo-EM technique can be used to determine structures and conformational heterogeneity of sub-100 kDa biomolecules to a quality as high as that obtained from X-ray crystallography and NMR spectroscopy.
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50
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Large EE, Chapman MS. Adeno-associated virus receptor complexes and implications for adeno-associated virus immune neutralization. Front Microbiol 2023; 14:1116896. [PMID: 36846761 PMCID: PMC9950413 DOI: 10.3389/fmicb.2023.1116896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/20/2023] [Indexed: 02/12/2023] Open
Abstract
Adeno-associated viruses (AAV) are among the foremost vectors for in vivo gene therapy. A number of monoclonal antibodies against several serotypes of AAV have previously been prepared. Many are neutralizing, and the predominant mechanisms have been reported as the inhibition of binding to extracellular glycan receptors or interference with some post-entry step. The identification of a protein receptor and recent structural characterization of its interactions with AAV compel reconsideration of this tenet. AAVs can be divided into two families based on which domain of the receptor is strongly bound. Neighboring domains, unseen in the high-resolution electron microscopy structures have now been located by electron tomography, pointing away from the virus. The epitopes of neutralizing antibodies, previously characterized, are now compared to the distinct protein receptor footprints of the two families of AAV. Comparative structural analysis suggests that antibody interference with protein receptor binding might be the more prevalent mechanism than interference with glycan attachment. Limited competitive binding assays give some support to the hypothesis that inhibition of binding to the protein receptor has been an overlooked mechanism of neutralization. More extensive testing is warranted.
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Affiliation(s)
| | - Michael S. Chapman
- Department of Biochemistry, University of Missouri, Columbia, MO, United States
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