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Gao J, Tong M, Lee C, Gaertig J, Legal T, Bui KH. DomainFit: Identification of protein domains in cryo-EM maps at intermediate resolution using AlphaFold2-predicted models. Structure 2024:S0969-2126(24)00143-6. [PMID: 38754431 DOI: 10.1016/j.str.2024.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/18/2024] [Accepted: 04/19/2024] [Indexed: 05/18/2024]
Abstract
Cryoelectron microscopy (cryo-EM) has revolutionized the structural determination of macromolecular complexes. With the paradigm shift to structure determination of highly complex endogenous macromolecular complexes ex vivo and in situ structural biology, there are an increasing number of structures of native complexes. These complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because side chains cannot be visualized reliably. Here, we present DomainFit, a program for semi-automated domain-level protein identification from cryo-EM maps, particularly at resolutions lower than 4 Å. By fitting domains from AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the domains and protein candidates forming the density. Using DomainFit, we identified two microtubule inner proteins, one of which contains a CCDC81 domain and is exclusively localized in the proximal region of the doublet microtubule in Tetrahymena thermophila.
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Affiliation(s)
- Jerry Gao
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada
| | - Maxwell Tong
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada
| | - Chinkyu Lee
- Department of Cellular Biology, University of Georgia, Athens 30602-2607, GA, USA
| | - Jacek Gaertig
- Department of Cellular Biology, University of Georgia, Athens 30602-2607, GA, USA
| | - Thibault Legal
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada.
| | - Khanh Huy Bui
- Department of Anatomy and Cell Biology, Faculty of Medicine and Health Sciences, McGill University, Montréal, QC H3A 0C7, Canada; Centre de recherche en biologie structurale, McGill University, Montréal, QC H3G 0B1, Canada.
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2
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Terashi G, Wang X, Prasad D, Nakamura T, Kihara D. DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nat Methods 2024; 21:122-131. [PMID: 38066344 DOI: 10.1038/s41592-023-02099-0] [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: 03/15/2023] [Accepted: 10/22/2023] [Indexed: 12/19/2023]
Abstract
Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods.
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Affiliation(s)
- Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Devashish Prasad
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Tsukasa Nakamura
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
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3
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Terashi G, Wang X, Prasad D, Nakamura T, Zhu H, Kihara D. Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.19.563151. [PMID: 37904978 PMCID: PMC10614963 DOI: 10.1101/2023.10.19.563151] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM maps has generally improved, there are still many cases where tracing protein main-chains is difficult, even in maps determined at a near atomic resolution. Here, we have developed a protein structure modeling method, called DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, since Alphafold2 demonstrates high accuracy in protein structure prediction, we have integrated complementary strengths of de novo density tracing using deep learning with Alphafold2's structure modeling to achieve even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign chain identity to the structure models of homo-multimers.
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Affiliation(s)
- Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Devashish Prasad
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Tsukasa Nakamura
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Han Zhu
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana, 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, Indiana, 47907, USA
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4
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Park J, Joung I, Joo K, Lee J. Application of conformational space annealing to the protein structure modeling using cryo-EM maps. J Comput Chem 2023; 44:2332-2346. [PMID: 37585026 DOI: 10.1002/jcc.27200] [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: 12/08/2022] [Revised: 04/26/2023] [Accepted: 07/16/2023] [Indexed: 08/17/2023]
Abstract
Conformational space annealing (CSA), a global optimization method, has been applied to various protein structure modeling tasks. In this paper, we applied CSA to the cryo-EM structure modeling task by combining the python subroutine of CSA (PyCSA) and the fast relax (FastRelax) protocol of PyRosetta. Refinement of initial structures generated from two methods, rigid fitting of predicted structures to the Cryo-EM map and de novo protein modeling by tracing the Cryo-EM map, was performed by CSA. In the refinement of the rigid-fitted structures, the final models showed that CSA can generate reliable atomic structures of proteins, even when large movements of protein domains were required. In the de novo modeling case, although the overall structural qualities of the final models were rather dependent on the initial models, the final models generated by CSA showed improved MolProbity scores and cross-correlation coefficients to the maps. These results suggest that CSA can accomplish flexible fitting and refinement together by sampling diverse conformations effectively and thus can be utilized for cryo-EM structure modeling.
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Affiliation(s)
| | | | - Keehyoung Joo
- Center for Advanced Computations, Korea Institute for Advanced Study, Seoul, South Korea
| | - Jooyoung Lee
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, South Korea
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5
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Wang X, Terashi G, Kihara D. CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nat Methods 2023; 20:1739-1747. [PMID: 37783885 PMCID: PMC10841814 DOI: 10.1038/s41592-023-02032-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/24/2023] [Indexed: 10/04/2023]
Abstract
DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understanding the molecular mechanisms of their functions. Although an increasing number of nucleic acid structures and their complexes with proteins are determined by cryogenic electron microscopy (cryo-EM), structure modeling for DNA and RNA remains challenging particularly when the map is determined at a resolution coarser than atomic level. Moreover, computational methods for nucleic acid structure modeling are relatively scarce. Here, we present CryoREAD, a fully automated de novo DNA/RNA atomic structure modeling method using deep learning. CryoREAD identifies phosphate, sugar and base positions in a cryo-EM map using deep learning, which are traced and modeled into a three-dimensional structure. When tested on cryo-EM maps determined at 2.0 to 5.0 Å resolution, CryoREAD built substantially more accurate models than existing methods. We also applied the method to cryo-EM maps of biomolecular complexes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
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Affiliation(s)
- Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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6
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Blau C, Yvonnesdotter L, Lindahl E. Gentle and fast all-atom model refinement to cryo-EM densities via a maximum likelihood approach. PLoS Comput Biol 2023; 19:e1011255. [PMID: 37523411 PMCID: PMC10427019 DOI: 10.1371/journal.pcbi.1011255] [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: 09/30/2022] [Revised: 08/15/2023] [Accepted: 06/09/2023] [Indexed: 08/02/2023] Open
Abstract
Better detectors and automated data collection have generated a flood of high-resolution cryo-EM maps, which in turn has renewed interest in improving methods for determining structure models corresponding to these maps. However, automatically fitting atoms to densities becomes difficult as their resolution increases and the refinement potential has a vast number of local minima. In practice, the problem becomes even more complex when one also wants to achieve a balance between a good fit of atom positions to the map, while also establishing good stereochemistry or allowing protein secondary structure to change during fitting. Here, we present a solution to this challenge using a maximum likelihood approach by formulating the problem as identifying the structure most likely to have produced the observed density map. This allows us to derive new types of smooth refinement potential-based on relative entropy-in combination with a novel adaptive force scaling algorithm to allow balancing of force-field and density-based potentials. In a low-noise scenario, as expected from modern cryo-EM data, the relative-entropy based refinement potential outperforms alternatives, and the adaptive force scaling appears to aid all existing refinement potentials. The method is available as a component in the GROMACS molecular simulation toolkit.
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Affiliation(s)
- Christian Blau
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Linnea Yvonnesdotter
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
| | - Erik Lindahl
- Department of Applied Physics, Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm, Sweden
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7
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Monsen RC, Chua ED, Hopkins J, Chaires J, Trent J. Structure of a 28.5 kDa duplex-embedded G-quadruplex system resolved to 7.4 Å resolution with cryo-EM. Nucleic Acids Res 2023; 51:1943-1959. [PMID: 36715343 PMCID: PMC9976903 DOI: 10.1093/nar/gkad014] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 12/16/2022] [Accepted: 01/04/2023] [Indexed: 01/31/2023] Open
Abstract
Genomic regions with high guanine content can fold into non-B form DNA four-stranded structures known as G-quadruplexes (G4s). Extensive in vivo investigations have revealed that promoter G4s are transcriptional regulators. Little structural information exists for these G4s embedded within duplexes, their presumed genomic environment. Here, we report the 7.4 Å resolution structure and dynamics of a 28.5 kDa duplex-G4-duplex (DGD) model system using cryo-EM, molecular dynamics, and small-angle X-ray scattering (SAXS) studies. The DGD cryo-EM refined model features a 53° bend induced by a stacked duplex-G4 interaction at the 5' G-tetrad interface with a persistently unstacked 3' duplex. The surrogate complement poly dT loop preferably stacks onto the 3' G-tetrad interface resulting in occlusion of both 5' and 3' tetrad interfaces. Structural analysis shows that the DGD model is quantifiably more druggable than the monomeric G4 structure alone and represents a new structural drug target. Our results illustrate how the integration of cryo-EM, MD, and SAXS can reveal complementary detailed static and dynamic structural information on DNA G4 systems.
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Affiliation(s)
- Robert C Monsen
- UofL Health Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA
| | - Eugene Y D Chua
- National Center for CryoEM Access and Training (NCCAT), Simons Electron Microscopy Center, New York Structural Biology Center, NY 10027, USA
| | - Jesse B Hopkins
- The Biophysics Collaborative Access Team (BioCAT), Department of Biological, Chemical, and Physical Sciences, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Jonathan B Chaires
- UofL Health Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA
- Department of Medicine, University of Louisville, Louisville, KY 40202, USA
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY 40202, USA
| | - John O Trent
- UofL Health Brown Cancer Center, University of Louisville, Louisville, KY 40202, USA
- Department of Medicine, University of Louisville, Louisville, KY 40202, USA
- Department of Biochemistry and Molecular Genetics, University of Louisville, Louisville, KY 40202, USA
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8
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Lee S, Seok C, Park H. Benchmarking applicability of medium-resolution cryo-EM protein structures for structure-based drug design. J Comput Chem 2023; 44:1360-1368. [PMID: 36847771 DOI: 10.1002/jcc.27091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 01/18/2023] [Accepted: 02/05/2023] [Indexed: 03/01/2023]
Abstract
Cryo-electron microscopy (cryo-EM) is gaining large attention for high-resolution protein structure determination in solutions. However, a very high percentage of cryo-EM structures correspond to resolutions of 3-5 Å, making the structures difficult to be used in in silico drug design. In this study, we analyze how useful cryo-EM protein structures are for in silico drug design by evaluating ligand docking accuracy. From realistic cross-docking scenarios using medium resolution (3-5 Å) cryo-EM structures and a popular docking tool Autodock-Vina, only 20% of docking succeeded, when the success rate doubles in the same kind of cross-docking but using high-resolution (<2 Å) crystal structures instead. We decipher the reason for failures by decomposing the contribution from resolution-dependent and independent factors. The heterogeneity in the protein side-chain and backbone conformations is identified as the major resolution-dependent factor causing docking difficulty from our analysis, while intrinsic receptor flexibility mainly comprises the resolution-independent factor. We demonstrate the flexibility implementation in current ligand docking tools is able to rescue only a portion of failures (10%), and the limited performance was majorly due to potential structural errors than conformational changes. Our work suggests the strong necessity of more robust method developments on ligand docking and EM modeling techniques in order to fully utilize cryo-EM structures for in silico drug design.
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Affiliation(s)
- Seho Lee
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea
| | - Chaok Seok
- Department of Chemistry, Seoul National University, Seoul, Republic of Korea.,Galux Inc., Seoul, Republic of Korea
| | - Hahnbeom Park
- Brain Science Institute, Korea Institute of Science and Technology, Seoul, Republic of Korea
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9
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Botifoll M, Pinto-Huguet I, Arbiol J. Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook. NANOSCALE HORIZONS 2022; 7:1427-1477. [PMID: 36239693 DOI: 10.1039/d2nh00377e] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.
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Affiliation(s)
- Marc Botifoll
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Ivan Pinto-Huguet
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
| | - Jordi Arbiol
- Catalan Institute of Nanoscience and Nanotechnology (ICN2), CSIC and BIST, Campus UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain.
- ICREA, Pg. Lluís Companys 23, 08010 Barcelona, Catalonia, Spain
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10
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Alnabati E, Esquivel-Rodriguez J, Terashi G, Kihara D. MarkovFit: Structure Fitting for Protein Complexes in Electron Microscopy Maps Using Markov Random Field. Front Mol Biosci 2022; 9:935411. [PMID: 35959463 PMCID: PMC9358042 DOI: 10.3389/fmolb.2022.935411] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
An increasing number of protein complex structures are determined by cryo-electron microscopy (cryo-EM). When individual protein structures have been determined and are available, an important task in structure modeling is to fit the individual structures into the density map. Here, we designed a method that fits the atomic structures of proteins in cryo-EM maps of medium to low resolutions using Markov random fields, which allows probabilistic evaluation of fitted models. The accuracy of our method, MarkovFit, performed better than existing methods on datasets of 31 simulated cryo-EM maps of resolution 10 Å , nine experimentally determined cryo-EM maps of resolution less than 4 Å , and 28 experimentally determined cryo-EM maps of resolution 6 to 20 Å .
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Affiliation(s)
- Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, United States
- Department of Biological Sciences, Purdue University, West Lafayette, IN, United States
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11
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Alnabati E, Terashi G, Kihara D. Protein Structural Modeling for Electron Microscopy Maps Using VESPER and MAINMAST. Curr Protoc 2022; 2:e494. [PMID: 35849043 PMCID: PMC9299282 DOI: 10.1002/cpz1.494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) and stored in the Electron Microscopy Data Bank (EMDB). To interpret determined cryo-EM maps, several methods have been developed that model the tertiary structure of biomolecules, particularly proteins. Here we show how to use two such methods, VESPER and MAINMAST, which were developed in our group. VESPER is a method mainly for two purposes: fitting protein structure models into an EM map and aligning two EM maps locally or globally to capture their similarity. VESPER represents each EM map as a set of vectors pointing toward denser points. By considering matching the directions of vectors, in general, VESPER aligns maps better than conventional methods that only consider local densities of maps. MAINMAST is a de novo protein modeling tool designed for EM maps with resolution of 3-5 Å or better. MAINMAST builds a protein main chain directly from a density map by tracing dense points in an EM map and connecting them using a tree-graph structure. This article describes how to use these two tools using three illustrative modeling examples. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Protein structure model fitting using VESPER Alternate Protocol: Atomic model fitting using VESPER web server Basic Protocol 2: Protein de novo modeling using MAINMAST.
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Affiliation(s)
- Eman Alnabati
- Department of Computer SciencePurdue UniversityWest LafayetteIndiana
| | - Genki Terashi
- Department of Biological SciencesPurdue UniversityWest LafayetteIndiana
| | - Daisuke Kihara
- Department of Computer SciencePurdue UniversityWest LafayetteIndiana
- Department of Biological SciencesPurdue UniversityWest LafayetteIndiana
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12
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Zhu Z, Deng Z, Wang Q, Wang Y, Zhang D, Xu R, Guo L, Wen H. Simulation and Machine Learning Methods for Ion-Channel Structure Determination, Mechanistic Studies and Drug Design. Front Pharmacol 2022; 13:939555. [PMID: 35837274 PMCID: PMC9275593 DOI: 10.3389/fphar.2022.939555] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/07/2022] [Indexed: 11/13/2022] Open
Abstract
Ion channels are expressed in almost all living cells, controlling the in-and-out communications, making them ideal drug targets, especially for central nervous system diseases. However, owing to their dynamic nature and the presence of a membrane environment, ion channels remain difficult targets for the past decades. Recent advancement in cryo-electron microscopy and computational methods has shed light on this issue. An explosion in high-resolution ion channel structures paved way for structure-based rational drug design and the state-of-the-art simulation and machine learning techniques dramatically improved the efficiency and effectiveness of computer-aided drug design. Here we present an overview of how simulation and machine learning-based methods fundamentally changed the ion channel-related drug design at different levels, as well as the emerging trends in the field.
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Affiliation(s)
- Zhengdan Zhu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- Beijing Institute of Big Data Research, Beijing, China
| | - Zhenfeng Deng
- DP Technology, Beijing, China
- School of Pharmaceutical Sciences, Peking University, Beijing, China
| | | | | | - Duo Zhang
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- DP Technology, Beijing, China
| | - Ruihan Xu
- DP Technology, Beijing, China
- National Engineering Research Center of Visual Technology, Peking University, Beijing, China
| | | | - Han Wen
- DP Technology, Beijing, China
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13
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3D reconstruction from cryo-EM projection images using two spherical embeddings. Commun Biol 2022; 5:304. [PMID: 35379919 PMCID: PMC8979997 DOI: 10.1038/s42003-022-03255-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 03/11/2022] [Indexed: 11/08/2022] Open
Abstract
Single-particle analysis (SPA) in cryo-electron microscopy has become a powerful tool for determining and studying the macromolecular structure at an atomic level. However, since the SPA problem is a non-convex optimization problem with enormous search space and there is high level of noise in the input images, the existing methods may produce biased or even wrong final models. In this work, to deal with the problem, consistent constraints from the input data are explored in an embedding space, a 3D spherical surface. More specifically, the orientation of a projection image is represented by two intersection points of the normal vector and the local X-axis vector of the projection image on the unit spherical surface. To determine the orientations of the projection images, the global consistency constraints of the relative orientations of all the projection images are satisfied by two spherical embeddings which estimate the normal vectors and the local X-axis vectors of the projection images respectively. Compared to the traditional methods, the proposed method is shown to be able to rectify the initial computation errors and produce a more accurate estimation of the projection angles, which results in a better final model reconstruction from the noisy image data. A 3D reconstruction method using two spherical embeddings to resolve projection angles of the cryo-EM images is shown to improve the initial model reconstruction for single-particle analysis.
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14
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Wood DM, Dobson RC, Horne CR. Using cryo-EM to uncover mechanisms of bacterial transcriptional regulation. Biochem Soc Trans 2021; 49:2711-2726. [PMID: 34854920 PMCID: PMC8786299 DOI: 10.1042/bst20210674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/10/2021] [Accepted: 11/15/2021] [Indexed: 11/17/2022]
Abstract
Transcription is the principal control point for bacterial gene expression, and it enables a global cellular response to an intracellular or environmental trigger. Transcriptional regulation is orchestrated by transcription factors, which activate or repress transcription of target genes by modulating the activity of RNA polymerase. Dissecting the nature and precise choreography of these interactions is essential for developing a molecular understanding of transcriptional regulation. While the contribution of X-ray crystallography has been invaluable, the 'resolution revolution' of cryo-electron microscopy has transformed our structural investigations, enabling large, dynamic and often transient transcription complexes to be resolved that in many cases had resisted crystallisation. In this review, we highlight the impact cryo-electron microscopy has had in gaining a deeper understanding of transcriptional regulation in bacteria. We also provide readers working within the field with an overview of the recent innovations available for cryo-electron microscopy sample preparation and image reconstruction of transcription complexes.
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Affiliation(s)
- David M. Wood
- Biomolecular Interaction Centre and School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
| | - Renwick C.J. Dobson
- Biomolecular Interaction Centre and School of Biological Sciences, University of Canterbury, Christchurch, New Zealand
- Bio21 Molecular Science and Biotechnology Institute, Department of Biochemistry and Pharmacology, University of Melbourne, Parkville, VIC, Australia
| | - Christopher R. Horne
- Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, VIC 3052, Australia
- Department of Medical Biology, University of Melbourne, Parkville, VIC 3052, Australia
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15
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He J, Huang SY. EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps. Brief Bioinform 2021; 22:bbab156. [PMID: 33954706 PMCID: PMC8574626 DOI: 10.1093/bib/bbab156] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 03/30/2021] [Accepted: 04/06/2021] [Indexed: 02/06/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) has become one of important experimental methods in structure determination. However, despite the rapid growth in the number of deposited cryo-EM maps motivated by advances in microscopy instruments and image processing algorithms, building accurate structure models for cryo-EM maps remains a challenge. Protein secondary structure information, which can be extracted from EM maps, is beneficial for cryo-EM structure modeling. Here, we present a novel secondary structure annotation framework for cryo-EM maps at both intermediate and high resolutions, named EMNUSS. EMNUSS adopts a three-dimensional (3D) nested U-net architecture to assign secondary structures for EM maps. Tested on three diverse datasets including simulated maps, middle resolution experimental maps, and high-resolution experimental maps, EMNUSS demonstrated its accuracy and robustness in identifying the secondary structures for cyro-EM maps of various resolutions. The EMNUSS program is freely available at http://huanglab.phys.hust.edu.cn/EMNUSS.
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Affiliation(s)
- Jiahua He
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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16
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Hassan M, Coutsias EA. Kinematic Reconstruction of Cyclic Peptides and Protein Backbones from Partial Data. J Chem Inf Model 2021; 61:4975-5000. [PMID: 34570494 PMCID: PMC10129052 DOI: 10.1021/acs.jcim.1c00453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We present an algorithm, QBKR (Quaternary Backbone Kinematic Reconstruction), a fast analytical method for an all-atom backbone reconstruction of proteins and linear or cyclic peptide chains from Cα coordinate traces. Unlike previous analytical methods for deriving all-atom representations from coarse-grained models that rely on canonical geometry with planar peptides in the trans conformation, our de novo kinematic model incorporates noncanonical, cis-trans, geometry naturally. Perturbations to this geometry can be effected with ease in our formulation, for example, to account for a continuous change from cis to trans geometry. A simple optimization of a spring-based objective function is employed for Cα-Cα distance variations that extend beyond the cis-trans limit. The kinematic construction produces a linked chain of peptide units, Cα-C-N-Cα, hinged at the Cα atoms spanning all possible planar and nonplanar peptide conformations. We have combined our method with a ring closure algorithm for the case of ring peptides and missing loops in a protein structure. Here, the reconstruction proceeding from both the N and C termini of the protein backbone (or in both directions from a starting position for rings) requires freedom in the position of one Cα atom (a capstone) to achieve a successful loop or ring closure. A salient feature of our reconstruction method is the ability to enrich conformational ensembles to produce alternative feasible conformations in which H-bond forming C-O or N-H pairs in the backbone can reverse orientations, thus addressing a well-known shortcoming in Cα-based RMSD structure comparison, wherein very close structures may lead to significantly different overall H-bond behavior. We apply the fixed Cα-based design to the reverse reconstruction from noisy Cryo-EM data, a posteriori to the optimization. Our method can be applied to speed up the process of an all-atom description from voluminous experimental data or subpar electron density maps.
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Affiliation(s)
- Mosavverul Hassan
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States
| | - Evangelos A Coutsias
- Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, New York 11794, United States.,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794-5252, United States
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17
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A Fast Image Alignment Approach for 2D Classification of Cryo-EM Images Using Spectral Clustering. Curr Issues Mol Biol 2021; 43:1652-1668. [PMID: 34698131 PMCID: PMC8928942 DOI: 10.3390/cimb43030117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/14/2021] [Accepted: 10/14/2021] [Indexed: 01/22/2023] Open
Abstract
Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-EM is an important procedure for producing high-quality initial 3D structures, where image alignment is a fundamental step. In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters of rotation angles and translational shifts between the two projection images, which can obtain subpixel and subangle accuracy. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed image alignment algorithm and a spectral clustering algorithm are used to compute class averages for single-particle 3D reconstruction. The proposed image alignment algorithm is firstly tested on a Lena image and two cryo-EM datasets. Results show that the proposed image alignment algorithm can estimate the alignment parameters accurately and efficiently. The proposed method is also used to reconstruct preliminary 3D structures from a simulated cryo-EM dataset and a real cryo-EM dataset and to compare them with RELION. Experimental results show that the proposed method can obtain more high-quality class averages than RELION and can obtain higher reconstruction resolution than RELION even without iteration.
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18
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Mori T, Terashi G, Matsuoka D, Kihara D, Sugita Y. Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps. J Chem Inf Model 2021; 61:3516-3528. [PMID: 34142833 PMCID: PMC9282639 DOI: 10.1021/acs.jcim.1c00230] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Structural modeling of proteins from cryo-electron microscopy (cryo-EM) density maps is one of the challenging issues in structural biology. De novo modeling combined with flexible fitting refinement (FFR) has been widely used to build a structure of new proteins. In de novo prediction, artificial conformations containing local structural errors such as chirality errors, cis peptide bonds, and ring penetrations are frequently generated and cannot be easily removed in the subsequent FFR. Moreover, refinement can be significantly suppressed due to the low mobility of atoms inside the protein. To overcome these problems, we propose an efficient scheme for FFR, in which the local structural errors are fixed first, followed by FFR using an iterative simulated annealing (SA) molecular dynamics protocol with the united atom (UA) model in an implicit solvent model; we call this scheme "SAUA-FFR". The best model is selected from multiple flexible fitting runs with various biasing force constants to reduce overfitting. We apply our scheme to the decoys obtained from MAINMAST and demonstrate an improvement of the best model of eight selected proteins in terms of the root-mean-square deviation, MolProbity score, and RWplus score compared to the original scheme of MAINMAST. Fixing the local structural errors can enhance the formation of secondary structures, and the UA model enables progressive refinement compared to the all-atom model owing to its high mobility in the implicit solvent. The SAUA-FFR scheme realizes efficient and accurate protein structure modeling from medium-resolution maps with less overfitting.
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Affiliation(s)
- Takaharu Mori
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, United States
| | - Daisuke Matsuoka
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, United States.,Department of Computer Science, Purdue University, West Lafayette, Indiana 47907, United States
| | - Yuji Sugita
- RIKEN Cluster for Pioneering Research, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.,RIKEN Center for Computational Science, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.,RIKEN Center for Biosystems Dynamics Research, 7-1-26 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
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19
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Pakhrin SC, Shrestha B, Adhikari B, KC DB. Deep Learning-Based Advances in Protein Structure Prediction. Int J Mol Sci 2021; 22:5553. [PMID: 34074028 PMCID: PMC8197379 DOI: 10.3390/ijms22115553] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/12/2021] [Accepted: 05/18/2021] [Indexed: 12/29/2022] Open
Abstract
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.
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Affiliation(s)
- Subash C. Pakhrin
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA;
| | - Bikash Shrestha
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Badri Adhikari
- Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA;
| | - Dukka B. KC
- Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA;
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20
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He J, Huang SY. Full-length de novo protein structure determination from cryo-EM maps using deep learning. Bioinformatics 2021; 37:3480-3490. [PMID: 33978686 DOI: 10.1093/bioinformatics/btab357] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 04/03/2021] [Accepted: 05/08/2021] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-EM maps. However, building accurate models for the EM maps at 3-5 Å resolution remains a challenging and time-consuming process. With the rapid growth of deposited EM maps, there is an increasing gap between the maps and reconstructed/modeled 3-dimensional (3D) structures. Therefore, automatic reconstruction of atomic-accuracy full-atomstructures fromEMmaps is pressingly needed. RESULTS We present a semi-automatic de novo structure determination method using a deep learningbased framework, named as DeepMM, which builds atomic-accuracy all-atom models from cryo-EM maps at near-atomic resolution. In our method, the main-chain and Cα positions as well as their amino acid and secondary structure types are predicted in the EM map using Densely Connected Convolutional Networks. DeepMM was extensively validated on 40 simulated maps at 5 Å resolution and 30 experimental maps at 2.6-4.8 Å resolution as well as an EMDB-wide data set of 2931 experimental maps at 2.6-4.9 Å resolution, and compared with state-of-the-art algorithms including RosettaES, MAINMAST, and Phenix. Overall, our DeepMM algorithm obtained a significant improvement over existing methods in terms of both accuracy and coverage in building full-length protein structures on all test sets, demonstrating the efficacy and general applicability of DeepMM. AVAILABILITY http://huanglab.phys.hust.edu.cn/DeepMM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiahua He
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
| | - Sheng-You Huang
- School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P. R. China
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21
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Wang X, Alnabati E, Aderinwale TW, Maddhuri Venkata Subramaniya SR, Terashi G, Kihara D. Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Nat Commun 2021; 12:2302. [PMID: 33863902 PMCID: PMC8052361 DOI: 10.1038/s41467-021-22577-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 03/19/2021] [Indexed: 12/21/2022] Open
Abstract
An increasing number of density maps of macromolecular structures, including proteins and DNA/RNA complexes, have been determined by cryo-electron microscopy (cryo-EM). Although lately maps at a near-atomic resolution are routinely reported, there are still substantial fractions of maps determined at intermediate or low resolutions, where extracting structure information is not trivial. Here, we report a new computational method, Emap2sec+, which identifies DNA or RNA as well as the secondary structures of proteins in cryo-EM maps of 5 to 10 Å resolution. Emap2sec+ employs the deep Residual convolutional neural network. Emap2sec+ assigns structural labels with associated probabilities at each voxel in a cryo-EM map, which will help structure modeling in an EM map. Emap2sec+ showed stable and high assignment accuracy for nucleotides in low resolution maps and improved performance for protein secondary structure assignments than its earlier version when tested on simulated and experimental maps.
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Affiliation(s)
- Xiao Wang
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | - Tunde W Aderinwale
- Department of Computer Science, Purdue University, West Lafayette, IN, USA
| | | | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN, USA.
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.
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22
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Amphipathic environments for determining the structure of membrane proteins by single-particle electron cryo-microscopy. Q Rev Biophys 2021; 54:e6. [PMID: 33785082 DOI: 10.1017/s0033583521000044] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the past decade, the structural biology of membrane proteins (MPs) has taken a new turn thanks to epoch-making technical progress in single-particle electron cryo-microscopy (cryo-EM) as well as to improvements in sample preparation. The present analysis provides an overview of the extent and modes of usage of the various types of surfactants for cryo-EM studies. Digitonin, dodecylmaltoside, protein-based nanodiscs, lauryl maltoside-neopentyl glycol, glyco-diosgenin, and amphipols (APols) are the most popular surfactants at the vitrification step. Surfactant exchange is frequently used between MP purification and grid preparation, requiring extensive optimization each time the study of a new MP is undertaken. The variety of both the surfactants and experimental approaches used over the past few years bears witness to the need to continue developing innovative surfactants and optimizing conditions for sample preparation. The possibilities offered by novel APols for EM applications are discussed.
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23
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Zhang Y, Krieger J, Mikulska-Ruminska K, Kaynak B, Sorzano COS, Carazo JM, Xing J, Bahar I. State-dependent sequential allostery exhibited by chaperonin TRiC/CCT revealed by network analysis of Cryo-EM maps. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 160:104-120. [PMID: 32866476 PMCID: PMC7914283 DOI: 10.1016/j.pbiomolbio.2020.08.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 06/25/2020] [Accepted: 08/16/2020] [Indexed: 12/17/2022]
Abstract
The eukaryotic chaperonin TRiC/CCT plays a major role in assisting the folding of many proteins through an ATP-driven allosteric cycle. Recent structures elucidated by cryo-electron microscopy provide a broad view of the conformations visited at various stages of the chaperonin cycle, including a sequential activation of its subunits in response to nucleotide binding. But we lack a thorough mechanistic understanding of the structure-based dynamics and communication properties that underlie the TRiC/CCT machinery. In this study, we present a computational methodology based on elastic network models adapted to cryo-EM density maps to gain a deeper understanding of the structure-encoded allosteric dynamics of this hexadecameric machine. We have analysed several structures of the chaperonin resolved in different states toward mapping its conformational landscape. Our study indicates that the overall architecture intrinsically favours cooperative movements that comply with the structural variabilities observed in experiments. Furthermore, the individual subunits CCT1-CCT8 exhibit state-dependent sequential events at different states of the allosteric cycle. For example, in the ATP-bound state, subunits CCT5 and CCT4 selectively initiate the lid closure motions favoured by the overall architecture; whereas in the apo form of the heteromer, the subunit CCT7 exhibits the highest predisposition to structural change. The changes then propagate through parallel fluxes of allosteric signals to neighbours on both rings. The predicted state-dependent mechanisms of sequential activation provide new insights into TRiC/CCT intra- and inter-ring signal transduction events.
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Affiliation(s)
- Yan Zhang
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - James Krieger
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - Karolina Mikulska-Ruminska
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - Burak Kaynak
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | | | - José-María Carazo
- Centro Nacional de Biotecnología (CSIC), Darwin, 3, 28049, Madrid, Spain
| | - Jianhua Xing
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, University of Pittsburgh, 800 Murdoch Building, 3420 Forbes Avenue, Pittsburgh, PA, 15261, USA.
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24
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Seffernick JT, Lindert S. Hybrid methods for combined experimental and computational determination of protein structure. J Chem Phys 2020; 153:240901. [PMID: 33380110 PMCID: PMC7773420 DOI: 10.1063/5.0026025] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 11/10/2020] [Indexed: 02/04/2023] Open
Abstract
Knowledge of protein structure is paramount to the understanding of biological function, developing new therapeutics, and making detailed mechanistic hypotheses. Therefore, methods to accurately elucidate three-dimensional structures of proteins are in high demand. While there are a few experimental techniques that can routinely provide high-resolution structures, such as x-ray crystallography, nuclear magnetic resonance (NMR), and cryo-EM, which have been developed to determine the structures of proteins, these techniques each have shortcomings and thus cannot be used in all cases. However, additionally, a large number of experimental techniques that provide some structural information, but not enough to assign atomic positions with high certainty have been developed. These methods offer sparse experimental data, which can also be noisy and inaccurate in some instances. In cases where it is not possible to determine the structure of a protein experimentally, computational structure prediction methods can be used as an alternative. Although computational methods can be performed without any experimental data in a large number of studies, inclusion of sparse experimental data into these prediction methods has yielded significant improvement. In this Perspective, we cover many of the successes of integrative modeling, computational modeling with experimental data, specifically for protein folding, protein-protein docking, and molecular dynamics simulations. We describe methods that incorporate sparse data from cryo-EM, NMR, mass spectrometry, electron paramagnetic resonance, small-angle x-ray scattering, Förster resonance energy transfer, and genetic sequence covariation. Finally, we highlight some of the major challenges in the field as well as possible future directions.
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Affiliation(s)
- Justin T. Seffernick
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, Ohio 43210, USA
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25
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Solares MJ, Jonaid GM, Luqiu WY, Liang Y, Evans MC, Dearnaley WJ, Sheng Z, Kelly DF. Microchip-Based Structure Determination of Disease-Relevant p53. Anal Chem 2020; 92:15558-15564. [PMID: 33124814 PMCID: PMC7816086 DOI: 10.1021/acs.analchem.0c03599] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The tumor suppressor protein TP53 (p53) plays a multifaceted role in all cells of the human body. Mutations in the TP53 gene are often involved in cancer induction and disease progression. Despite its important role in health and development, structural information for p53 remains incomplete. Here, we present a microchip-based technology to facilitate structural studies of p53 assemblies derived from human cancer cells. These devices do not introduce foreign sequences to the p53 gene and maintain naturally occurring post-translational modifications. Using cryo-electron microscopy, structures for the p53 monomer (∼50 kDa) and tetramer (∼200 kDa) were resolved to ∼4.8 and ∼7 Å, respectively. These structures revealed new insights for flexible regions of p53 along with biologically relevant ubiquitination sites. Collectively, the convergence of nanotechnology tools and structural imaging builds a strong framework to understand the oncogenic impact of p53 in human tissues.
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Affiliation(s)
- Maria J Solares
- Molecular, Cellular, and Integrative Biosciences Graduate Program, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Structural Oncology, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - G M Jonaid
- Bioinformatics and Genomics Graduate Program, Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Structural Oncology, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - William Y Luqiu
- Center for Structural Oncology, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina 27708, United States
| | - Yanping Liang
- Fralin Biomedical Research Institute at VTC, Roanoke, Virginia 24016, United States
| | - Madison C Evans
- Department of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - William J Dearnaley
- Department of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Structural Oncology, Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Zhi Sheng
- Fralin Biomedical Research Institute at VTC, Roanoke, Virginia 24016, United States
| | - Deborah F Kelly
- Department of Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania 16802, United States
- Center for Structural Oncology, Pennsylvania State University, University Park, Pennsylvania 16802, United States
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26
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Cárdenas R, Martínez-Seoane J, Amero C. Combining Experimental Data and Computational Methods for the Non-Computer Specialist. Molecules 2020; 25:E4783. [PMID: 33081072 PMCID: PMC7594097 DOI: 10.3390/molecules25204783] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 08/25/2020] [Accepted: 08/28/2020] [Indexed: 01/01/2023] Open
Abstract
Experimental methods are indispensable for the study of the function of biological macromolecules, not just as static structures, but as dynamic systems that change conformation, bind partners, perform reactions, and respond to different stimulus. However, providing a detailed structural interpretation of the results is often a very challenging task. While experimental and computational methods are often considered as two different and separate approaches, the power and utility of combining both is undeniable. The integration of the experimental data with computational techniques can assist and enrich the interpretation, providing new detailed molecular understanding of the systems. Here, we briefly describe the basic principles of how experimental data can be combined with computational methods to obtain insights into the molecular mechanism and expand the interpretation through the generation of detailed models.
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Affiliation(s)
| | | | - Carlos Amero
- Laboratorio de Bioquímica y Resonancia Magnética Nuclear, Centro de Investigaciones Químicas, Instituto de Investigación en Ciencias Básicas y Aplicadas, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico; (R.C.); (J.M.-S.)
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