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Maddhuri Venkata Subramaniya SR, Terashi G, Kihara D. Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling. Bioinformatics 2023; 39:btad494. [PMID: 37549063 PMCID: PMC10444963 DOI: 10.1093/bioinformatics/btad494] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 07/28/2023] [Accepted: 08/04/2023] [Indexed: 08/09/2023] Open
Abstract
MOTIVATION The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4.5 Å), improvement in the map quality facilitates structure modeling. RESULTS We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3-6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools. AVAILABILITY AND IMPLEMENTATION https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.
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Affiliation(s)
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, United States
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
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2
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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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3
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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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4
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Exploring cryo-electron microscopy with molecular dynamics. Biochem Soc Trans 2022; 50:569-581. [PMID: 35212361 DOI: 10.1042/bst20210485] [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: 12/10/2021] [Revised: 01/31/2022] [Accepted: 02/02/2022] [Indexed: 11/17/2022]
Abstract
Single particle analysis cryo-electron microscopy (EM) and molecular dynamics (MD) have been complimentary methods since cryo-EM was first applied to the field of structural biology. The relationship started by biasing structural models to fit low-resolution cryo-EM maps of large macromolecular complexes not amenable to crystallization. The connection between cryo-EM and MD evolved as cryo-EM maps improved in resolution, allowing advanced sampling algorithms to simultaneously refine backbone and sidechains. Moving beyond a single static snapshot, modern inferencing approaches integrate cryo-EM and MD to generate structural ensembles from cryo-EM map data or directly from the particle images themselves. We summarize the recent history of MD innovations in the area of cryo-EM modeling. The merits for the myriad of MD based cryo-EM modeling methods are discussed, as well as, the discoveries that were made possible by the integration of molecular modeling with cryo-EM. Lastly, current challenges and potential opportunities are reviewed.
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Shekhar M, Terashi G, Gupta C, Sarkar D, Debussche G, Sisco NJ, Nguyen J, Mondal A, Vant J, Fromme P, Van Horn WD, Tajkhorshid E, Kihara D, Dill K, Perez A, Singharoy A. CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps. MATTER 2021; 4:3195-3216. [PMID: 35874311 PMCID: PMC9302471 DOI: 10.1016/j.matt.2021.09.004] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 3-5 Å resolution with coarse-grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations, unseen by traditional methods, are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python GUI.
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Affiliation(s)
- Mrinal Shekhar
- Center for Biophysics and Quantitative Biology, Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
| | - Chitrak Gupta
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Daipayan Sarkar
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - Gaspard Debussche
- Department of Mathematics and Computer Sciences, Grenoble INP, 38000 Grenoble, France
| | - Nicholas J Sisco
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ 85281, USA
| | - Jonathan Nguyen
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Arup Mondal
- Chemistry Department, Quantum Theory Project, University of Florida, Gainesville, Florida, 32611, USA
| | - John Vant
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Petra Fromme
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
| | - Wade D Van Horn
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Virginia G. Piper Center for Personalized Diagnostics, Arizona State University, Tempe, AZ 85281, USA
| | - Emad Tajkhorshid
- Center for Biophysics and Quantitative Biology, Department of Biochemistry, NIH Center for Macromolecular Modeling and Bioinformatics, Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, 61801, USA
| | - Daisuke Kihara
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Ken Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States
| | - Alberto Perez
- Chemistry Department, Quantum Theory Project, University of Florida, Gainesville, Florida, 32611, USA
| | - Abhishek Singharoy
- The School of Molecular Sciences, Arizona State University, Tempe, AZ 85287, USA
- The Biodesign Institute Center for Structural Discovery, Arizona State University, Tempe, AZ 85281, USA
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Han X, Terashi G, Christoffer C, Chen S, Kihara D. VESPER: global and local cryo-EM map alignment using local density vectors. Nat Commun 2021; 12:2090. [PMID: 33828103 PMCID: PMC8027200 DOI: 10.1038/s41467-021-22401-y] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 03/12/2021] [Indexed: 11/16/2022] Open
Abstract
An increasing number of density maps of biological macromolecules have been determined by cryo-electron microscopy (cryo-EM) and stored in the public database, EMDB. To interpret the structural information contained in EM density maps, alignment of maps is an essential step for structure modeling, comparison of maps, and for database search. Here, we developed VESPER, which captures the similarity of underlying molecular structures embedded in density maps by taking local gradient directions into consideration. Compared to existing methods, VESPER achieved substantially more accurate global and local alignment of maps as well as database retrieval.
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Affiliation(s)
- Xusi Han
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | - Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, IN, USA
| | | | - Siyang Chen
- Department of Computer Science, 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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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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Hoh SW, Burnley T, Cowtan K. Current approaches for automated model building into cryo-EM maps using Buccaneer with CCP-EM. Acta Crystallogr D Struct Biol 2020; 76:531-541. [PMID: 32496215 PMCID: PMC7271950 DOI: 10.1107/s2059798320005513] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 04/20/2020] [Indexed: 11/11/2022] Open
Abstract
This work focuses on the use of the existing protein-model-building software Buccaneer to provide structural interpretation of electron cryo-microscopy (cryo-EM) maps. Originally developed for application to X-ray crystallography, the necessary steps to optimise the usage of Buccaneer with cryo-EM maps are shown. This approach has been applied to the data sets of 208 cryo-EM maps with resolutions of better than 4 Å. The results obtained also show an evident improvement in the sequencing step when the initial reference map and model used for crystallographic cases are replaced by a cryo-EM reference. All other necessary changes to settings in Buccaneer are implemented in the model-building pipeline from within the CCP-EM interface (as of version 1.4.0).
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Affiliation(s)
- Soon Wen Hoh
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
| | - Tom Burnley
- Scientific Computing Department, Science and Technology Facilities Council, Research Complex at Harwell, Didcot OX11 0FA, United Kingdom
| | - Kevin Cowtan
- York Structural Biology Laboratory, Department of Chemistry, University of York, York YO10 5DD, United Kingdom
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Terashi G, Kagaya Y, Kihara D. MAINMASTseg: Automated Map Segmentation Method for Cryo-EM Density Maps with Symmetry. J Chem Inf Model 2020; 60:2634-2643. [PMID: 32197044 DOI: 10.1021/acs.jcim.9b01110] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Genki Terashi
- Department of Biological Sciences, Purdue University, West Lafayette, Indiana 47907, United States
| | - Yuki Kagaya
- Graduate School of Information Sciences, Tohoku University, Aramaki Aza, Aoba 6-3-09, Aoba-Ku, Sendai, Miyagi 980-8579, 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
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10
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Alnabati E, Kihara D. Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps. Molecules 2019; 25:molecules25010082. [PMID: 31878333 PMCID: PMC6982917 DOI: 10.3390/molecules25010082] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 12/20/2019] [Accepted: 12/20/2019] [Indexed: 01/16/2023] Open
Abstract
Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.
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Affiliation(s)
- Eman Alnabati
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
| | - Daisuke Kihara
- Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
- Correspondence:
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Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nat Methods 2019; 16:911-917. [PMID: 31358979 PMCID: PMC6717539 DOI: 10.1038/s41592-019-0500-1] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2018] [Accepted: 06/24/2019] [Indexed: 02/05/2023]
Abstract
An increasing number of protein structures have been solved by cryo-electron microscopy (cryo-EM). Although structures determined at near-atomic resolution are now routinely reported, many density maps are still determined at an intermediate resolution, where extracting structure information is still a challenge. We have developed a computational method, Emap2sec, which identifies the secondary structures of proteins (α helices, β sheets, and other structures) in an EM map of 5 to 10 Å resolution. Emap2sec uses a 3D deep convolutional neural network to assign secondary structure to each grid point in an EM map. We tested Emap2sec on 6.0 and 10.0 Å resolution EM maps simulated from 34 structures, as well as on 43 maps determined experimentally at 5.0 to 9.5 Å resolution. Emap2sec was able to clearly identify the secondary structures in many maps tested, and showed substantially better performance than existing methods.
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12
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Affiliation(s)
- Catherine L Lawson
- Institute for Quantitative Biomedicine, Rutgers University, Piscataway, NJ 08854, USA.
| | - Wah Chiu
- Division of Cryo-EM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025, USA; Department of Bioengineering, Department of Microbiology and Immunology, James H. Clark Center, Stanford University, Stanford, CA 94305, USA.
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