1
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
2
|
Liu BH, Liu M, Radhakrishnan S, Jaladanki CK, Gao C, Tang JP, Kumari K, Go ML, Vu KAL, Seo HS, Song K, Tian X, Feng L, Tan JL, Bassal MA, Arthanari H, Qi J, Dhe-Paganon S, Fan H, Tenen DG, Chai L. Targeting transcription factors through an IMiD independent zinc finger domain. bioRxiv 2024:2024.01.03.574032. [PMID: 38260640 PMCID: PMC10802279 DOI: 10.1101/2024.01.03.574032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Immunomodulatory imide drugs (IMiDs) degrade specific C2H2 zinc finger degrons in transcription factors, making them effective against certain cancers. SALL4, a cancer driver, contains seven C2H2 zinc fingers in four clusters, including an IMiD degron in zinc finger cluster two (ZFC2). Surprisingly, IMiDs do not inhibit growth of SALL4 expressing cancer cells. To overcome this limit, we focused on a non-IMiD degron, SALL4 zinc finger cluster four (ZFC4). By combining AlphaFold and the ZFC4-DNA crystal structure, we identified a potential ZFC4 drug pocket. Utilizing an in silico docking algorithm and cell viability assays, we screened chemical libraries and discovered SH6, which selectively targets SALL4-expressing cancer cells. Mechanistic studies revealed that SH6 degrades SALL4 protein through the CUL4A/CRBN pathway, while deletion of ZFC4 abolished this activity. Moreover, SH6 led to significant 62% tumor growth inhibition of SALL4+ xenografts in vivo and demonstrated good bioavailability in pharmacokinetic studies. In summary, these studies represent a new approach for IMiD independent drug discovery targeting C2H2 transcription factors in cancer.
Collapse
|
3
|
Terwilliger TC, Liebschner D, Croll TI, Williams CJ, McCoy AJ, Poon BK, Afonine PV, Oeffner RD, Richardson JS, Read RJ, Adams PD. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat Methods 2024; 21:110-116. [PMID: 38036854 PMCID: PMC10776388 DOI: 10.1038/s41592-023-02087-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/11/2023] [Indexed: 12/02/2023]
Abstract
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.
Collapse
Affiliation(s)
- Thomas C Terwilliger
- New Mexico Consortium, Los Alamos, NM, USA.
- Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Dorothee Liebschner
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Tristan I Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Airlie J McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Billy K Poon
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Robert D Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
| |
Collapse
|
4
|
Brookes E, Rocco M, Vachette P, Trewhella J. AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank. J Appl Crystallogr 2023; 56:910-926. [PMID: 37555230 PMCID: PMC10405597 DOI: 10.1107/s1600576723005344] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 06/15/2023] [Indexed: 08/10/2023] Open
Abstract
By providing predicted protein structures from nearly all known protein sequences, the artificial intelligence program AlphaFold (AF) is having a major impact on structural biology. While a stunning accuracy has been achieved for many folding units, predicted unstructured regions and the arrangement of potentially flexible linkers connecting structured domains present challenges. Focusing on single-chain structures without prosthetic groups, an earlier comparison of features derived from small-angle X-ray scattering (SAXS) data taken from the Small-Angle Scattering Biological Data Bank (SASBDB) is extended to those calculated using the corresponding AF-predicted structures. Selected SASBDB entries were carefully examined to ensure that they represented data from monodisperse protein solutions and had sufficient statistical precision and q resolution for reliable structural evaluation. Three examples were identified where there is clear evidence that the single AF-predicted structure cannot account for the experimental SAXS data. Instead, excellent agreement is found with ensemble models generated by allowing for flexible linkers between high-confidence predicted structured domains. A pool of representative structures was generated using a Monte Carlo method that adjusts backbone dihedral allowed angles along potentially flexible regions. A fast ensemble modelling method was employed that optimizes the fit of pair distance distribution functions [P(r) versus r] and intensity profiles [I(q) versus q] computed from the pool to their experimental counterparts. These results highlight the complementarity between AF prediction, solution SAXS and molecular dynamics/conformational sampling for structural modelling of proteins having both structured and flexible regions.
Collapse
Affiliation(s)
- Emre Brookes
- Department of Chemistry and Biochemistry, University of Montana, 32 Campus Drive, Missoula, MT 59812, USA
| | - Mattia Rocco
- Proteomica e Spettrometria di Massa, IRCCS Ospedale Policlinico San Martino, Largo R. Benzi 10, Genova 16132, Italy
| | - Patrice Vachette
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Gif-sur-Yvette 91198, France
| | - Jill Trewhella
- School of Life and Environmental Sciences, The University of Sydney, NSW 2006, Australia
| |
Collapse
|
5
|
Brookes EH, Rocco M. Beyond the US-SOMO-AF database: a new website for hydrodynamic, structural, and circular dichroism calculations on user-supplied structures. Eur Biophys J 2023; 52:225-232. [PMID: 36853343 PMCID: PMC10460822 DOI: 10.1007/s00249-023-01636-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 02/07/2023] [Indexed: 06/13/2023]
Abstract
At the 25th International Analytical Ultracentrifugation Workshop and Symposium, we described the recent implementation of the UltraScan SOlution MOdeler AlphaFold (US-SOMO-AF) database, containing hydrodynamic, structural, CD calculations, and other ancillary information, performed on the entire AF v2 database of predicted protein structures, containing more than 1,000,000 entries. The scope of the US-SOMO-AF database was that of providing direct access to pre-calculated physicochemical parameters for rapid assessment against their experimentally determined counterparts to test the compatibility in solution of predicted AlphaFold structures. In the meantime, the AlphaFold consortium has extended its database of predicted structures to an astonishing > 200 million entries, making it quite impractical for their coverage in the US-SOMO-AF database. Therefore, we have created the US-SOMO-Web site, allowing the rapid calculations of all the properties, as present in the US-SOMO-AF database, on user-supplied PDB and mmCIF structures, as well as allowing direct processing of the latest AlphaFold models. Major features on the website are described, along with current limitations and potential future developments.
Collapse
Affiliation(s)
- Emre H Brookes
- Department of Chemistry and Biochemistry, University of Montana, Missoula, MT, 59812, USA.
| | - Mattia Rocco
- Retired, Proteomica e Spettrometria di Massa, IRCCS Ospedale Policlinico San Martino, Largo R. Benzi 10, 16132, Genova, Italy
| |
Collapse
|
6
|
Aithani L, Alcaide E, Bartunov S, Cooper CDO, Doré AS, Lane TJ, Maclean F, Rucktooa P, Shaw RA, Skerratt SE. Advancing structural biology through breakthroughs in AI. Curr Opin Struct Biol 2023; 80:102601. [PMID: 37182397 DOI: 10.1016/j.sbi.2023.102601] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 03/06/2023] [Accepted: 04/03/2023] [Indexed: 05/16/2023]
Abstract
The past century has witnessed an exponential increase in our atomic-level understanding of molecular and cellular mechanisms from a structural perspective, with multiple landmark achievements contributing to the field. This, coupled with recent and continuing breakthroughs in artificial intelligence methods such as AlphaFold2, and enhanced computational power, is enabling our understanding of protein structure and function at unprecedented levels of accuracy and predictivity. Here, we describe some of the major recent advances across these fields, and describe, as these technologies coalesce, the potential to utilise our enhanced knowledge of intricate cellular and molecular systems to discover novel therapeutics to alleviate human suffering.
Collapse
Affiliation(s)
- Laksh Aithani
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK.
| | - Eric Alcaide
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Sergey Bartunov
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Christopher D O Cooper
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Andrew S Doré
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Thomas J Lane
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Finlay Maclean
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Prakash Rucktooa
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK
| | - Robert A Shaw
- CHARM Therapeutics Ltd., The Stanley Building, 7 St. Pancras Square, London, N1C 4AG, UK
| | - Sarah E Skerratt
- CHARM Therapeutics Ltd., B900, Babraham Research Campus, Babraham, Cambridge, CB22 3AT, UK.
| |
Collapse
|
7
|
Gutnik D, Evseev P, Miroshnikov K, Shneider M. Using AlphaFold Predictions in Viral Research. Curr Issues Mol Biol 2023; 45:3705-3732. [PMID: 37185764 PMCID: PMC10136805 DOI: 10.3390/cimb45040240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023] Open
Abstract
Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.
Collapse
Affiliation(s)
- Daria Gutnik
- Limnological Institute of the Siberian Branch of the Russian Academy of Sciences, 3 Ulan-Batorskaya Str., 664033 Irkutsk, Russia
| | - Peter Evseev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| | - Konstantin Miroshnikov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| | - Mikhail Shneider
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| |
Collapse
|
8
|
Buel GR, Chen X, Myint W, Kayode O, Folimonova V, Cruz A, Skorupka KA, Matsuo H, Walters KJ. E6AP AZUL interaction with UBQLN1/2 in cells, condensates, and an AlphaFold-NMR integrated structure. Structure 2023; 31:395-410.e6. [PMID: 36827983 PMCID: PMC10081965 DOI: 10.1016/j.str.2023.01.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/13/2023] [Accepted: 01/30/2023] [Indexed: 02/25/2023]
Abstract
The E3 ligase E6AP/UBE3A has a dedicated binding site in the 26S proteasome provided by the RAZUL domain of substrate receptor hRpn10/S5a/PSMD4. Guided by RAZUL sequence similarity, we test and demonstrate here that the E6AP AZUL binds transiently to the UBA of proteasomal shuttle factor UBQLN1/2. Despite a weak binding affinity, E6AP AZUL is recruited to UBQLN2 biomolecular condensates in vitro and E6AP interacts with UBQLN1/2 in cellulo. Steady-state and transfer nuclear Overhauser effect (NOE) experiments indicate direct interaction of AZUL with UBQLN1 UBA. Intermolecular contacts identified by NOE spectroscopy (NOESY) data were combined with AlphaFold2-Multimer predictions to yield an AZUL:UBA model structure. We additionally identify an oligomerization domain directly adjacent to UBQLN1/2 UBA (UBA adjacent [UBAA]) that is α-helical and allosterically reconfigured by AZUL binding to UBA. These data lead to a model of E6AP recruitment to UBQLN1/2 by AZUL:UBA interaction and provide fundamental information on binding requirements for interactions in condensates and cells.
Collapse
Affiliation(s)
- Gwen R Buel
- Protein Processing Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Xiang Chen
- Protein Processing Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Wazo Myint
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Olumide Kayode
- Protein Processing Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Varvara Folimonova
- Protein Processing Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Anthony Cruz
- Protein Processing Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Katarzyna A Skorupka
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Hiroshi Matsuo
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Kylie J Walters
- Protein Processing Section, Center for Structural Biology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA.
| |
Collapse
|
9
|
Varadi M, Bordin N, Orengo C, Velankar S. The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Curr Opin Struct Biol 2023; 79:102543. [PMID: 36807079 DOI: 10.1016/j.sbi.2023.102543] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/21/2023]
Abstract
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
Collapse
Affiliation(s)
- Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK. https://twitter.com/nicolabordin
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| |
Collapse
|
10
|
Arduini A, Laprise F, Liang C. SARS-CoV-2 ORF8: A Rapidly Evolving Immune and Viral Modulator in COVID-19. Viruses 2023; 15:871. [PMID: 37112851 PMCID: PMC10141009 DOI: 10.3390/v15040871] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
The COVID-19 pandemic has resulted in upwards of 6.8 million deaths over the past three years, and the frequent emergence of variants continues to strain global health. Although vaccines have greatly helped mitigate disease severity, SARS-CoV-2 is likely to remain endemic, making it critical to understand its viral mechanisms contributing to pathogenesis and discover new antiviral therapeutics. To efficiently infect, this virus uses a diverse set of strategies to evade host immunity, accounting for its high pathogenicity and rapid spread throughout the COVID-19 pandemic. Behind some of these critical host evasion strategies is the accessory protein Open Reading Frame 8 (ORF8), which has gained recognition in SARS-CoV-2 pathogenesis due to its hypervariability, secretory property, and unique structure. This review discusses the current knowledge on SARS-CoV-2 ORF8 and proposes actualized functional models describing its pivotal roles in both viral replication and immune evasion. A better understanding of ORF8's interactions with host and viral factors is expected to reveal essential pathogenic strategies utilized by SARS-CoV-2 and inspire the development of novel therapeutics to improve COVID-19 disease outcomes.
Collapse
Affiliation(s)
- Ariana Arduini
- Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada; (A.A.); (F.L.)
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
| | - Frederique Laprise
- Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada; (A.A.); (F.L.)
- Department of Microbiology and Immunology, McGill University, Montreal, QC H3A 2B4, Canada
| | - Chen Liang
- Lady Davis Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada; (A.A.); (F.L.)
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
- Department of Microbiology and Immunology, McGill University, Montreal, QC H3A 2B4, Canada
| |
Collapse
|
11
|
Plonski AP, Reed SM. Assessing protein homology models with docking reproducibility. J Mol Graph Model 2023; 121:108430. [PMID: 36812741 DOI: 10.1016/j.jmgm.2023.108430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/12/2023]
Abstract
Results of the recent Critical Assessment of Protein Structure (CASP) competitions demonstrate that protein backbones can be predicted with very high accuracy. In particular, the artificial intelligence methods of AlphaFold 2 from DeepMind were able to produce structures that were similar enough to experimental structures that many described the problem of protein prediction solved. However, for such structures to be used for drug docking studies requires precision in the placement of side chain atoms as well. Here we built a library of 1334 small molecules and examined how reproducibly they bound to the same site on a protein using QuickVina-W, a branch of the program Autodock that is optimized for blind searches. We discovered that the higher the backbone quality of the homology model the greater the similarity between the small molecule docking to the experimental and modeled structures. Furthermore, we found that specific subsets of this library were particularly useful for identifying small differences between the best of the best modeled structures. Specifically, when the number of rotatable bonds in the small molecule increased, differences in binding sites became more apparent.
Collapse
|
12
|
Ren F, Ding X, Zheng M, Korzinkin M, Cai X, Zhu W, Mantsyzov A, Aliper A, Aladinskiy V, Cao Z, Kong S, Long X, Man Liu BH, Liu Y, Naumov V, Shneyderman A, Ozerov IV, Wang J, Pun FW, Polykovskiy DA, Sun C, Levitt M, Aspuru-Guzik A, Zhavoronkov A. AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem Sci 2023; 14:1443-1452. [PMID: 36794205 PMCID: PMC9906638 DOI: 10.1039/d2sc05709c] [Citation(s) in RCA: 49] [Impact Index Per Article: 49.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/05/2023] [Indexed: 01/11/2023] Open
Abstract
The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 μM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.
Collapse
Affiliation(s)
- Feng Ren
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Xiao Ding
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Min Zheng
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Mikhail Korzinkin
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Xin Cai
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Wei Zhu
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Alexey Mantsyzov
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Alex Aliper
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Vladimir Aladinskiy
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Zhongying Cao
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Shanshan Kong
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Xi Long
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Bonnie Hei Man Liu
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Yingtao Liu
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Vladimir Naumov
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Anastasia Shneyderman
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Ivan V Ozerov
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Ju Wang
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
| | - Frank W Pun
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Daniil A Polykovskiy
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| | - Chong Sun
- Department of Chemistry, Department of Computer Science, University of Toronto, Vector Institute for Artificial Intelligence, Canadian Institute for Advanced Research Toronto Ontario Canada
| | - Michael Levitt
- Department of Structural Biology, Stanford University Palo Alto CA USA
| | - Alán Aspuru-Guzik
- Department of Chemistry, Department of Computer Science, University of Toronto, Vector Institute for Artificial Intelligence, Canadian Institute for Advanced Research Toronto Ontario Canada
| | - Alex Zhavoronkov
- Insilico Medicine Shanghai Ltd Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road. Pudong New District Shanghai 201203 China
- Insilico Medicine Kong Kong Ltd Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok Hong Kong China
| |
Collapse
|
13
|
Chang L, Perez A. Ranking Peptide Binders by Affinity with AlphaFold. Angew Chem Int Ed Engl 2023; 62:e202213362. [PMID: 36542066 DOI: 10.1002/anie.202213362] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 12/20/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022]
Abstract
AlphaFold has revolutionized structural biology by predicting highly accurate structures of proteins and their complexes with peptides and other proteins. However, for protein-peptide systems, we are also interested in identifying the highest affinity binder among a set of candidate peptides. We present a novel competitive binding assay using AlphaFold to predict structures of the receptor in the presence of two peptides. For systems in which the individual structures of the peptides are well predicted, the assay captures the higher affinity binder in the bound state, and the other peptide in the unbound form with statistical significance. We test the application on six protein receptors for which we have experimental binding affinities to several peptides. We find that the assay is best suited for identifying medium to strong peptide binders that adopt stable secondary structures upon binding.
Collapse
Affiliation(s)
- Liwei Chang
- Department of Chemistry, University of Florida, Gainesville, FL, USA.,Quantum Theory Project, University of Florida, Gainesville, FL, USA
| | - Alberto Perez
- Department of Chemistry, University of Florida, Gainesville, FL, USA.,Quantum Theory Project, University of Florida, Gainesville, FL, USA
| |
Collapse
|
14
|
Liao B, Wang C, Li X, Man Y, Ruan H, Zhao Y. Genome-wide analysis of the Populus trichocarpa laccase gene family and functional identification of PtrLAC23. Front Plant Sci 2023; 13:1063813. [PMID: 36733583 PMCID: PMC9887407 DOI: 10.3389/fpls.2022.1063813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 12/21/2022] [Indexed: 06/18/2023]
Abstract
INTRODUCTION Biofuel is a kind of sustainable, renewable and environment friendly energy. Lignocellulose from the stems of woody plants is the main raw material for "second generation biofuels". Lignin content limits fermentation yield and is therefore a major obstacle in biofuel production. Plant laccase plays an important role in the final step of lignin formation, which provides a new strategy for us to obtain ideal biofuels by regulating the expression of laccase genes to directly gain the desired lignin content or change the composition of lignin. METHODS Multiple sequence alignment and phylogenetic analysis were used to classify PtrLAC genes; sequence features of PtrLACs were revealed by gene structure and motif composition analysis; gene duplication, interspecific collinearity and Ka/Ks analysis were conducted to identify ancient PtrLACs; expression levels of PtrLAC genes were measured by RNA-Seq data and qRT-PCR; domain analysis combine with cis-acting elements prediction together showed the potential function of PtrLACs. Furthermore, Alphafold2 was used to simulate laccase 3D structures, proLAC23::LAC23-eGFP transgenic Populus stem transects were applied to fluorescence observation. RESULTS A comprehensive analysis of the P. trichocarpa laccase gene (PtLAC) family was performed. Some ancient PtrLAC genes such as PtrLAC25, PtrLAC19 and PtrLAC41 were identified. Gene structure and distribution of conserved motifs clearly showed sequence characteristics of each PtrLAC. Combining published RNA-Seq data and qRT-PCR analysis, we revealed the expression pattern of PtrLAC gene family. Prediction results of cis-acting elements show that PtrLAC gene regulation was closely related to light. Through above analyses, we selected 5 laccases and used Alphafold2 to simulate protein 3D structures, results showed that PtrLAC23 may be closely related to the lignification. Fluorescence observation of proLAC23::LAC23-eGFP transgenic Populus stem transects and qRT-PCR results confirmed our hypothesis again. DISCUSSION In this study, we fully analyzed the Populus trichocarpa laccase gene family and identified key laccase genes related to lignification. These findings not only provide new insights into the characteristics and functions of Populus laccase, but also give a new understanding of the broad prospects of plant laccase in lignocellulosic biofuel production.
Collapse
Affiliation(s)
- Boyang Liao
- College of Biological Science and Technology, Beijing Forestry University, Beijing, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, China
- Institute of Environmental Biology and Life Support Technology, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Chencan Wang
- College of Biological Science and Technology, Beijing Forestry University, Beijing, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, China
| | - Xiaoxu Li
- College of Biological Science and Technology, Beijing Forestry University, Beijing, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, China
| | - Yi Man
- College of Biological Science and Technology, Beijing Forestry University, Beijing, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, China
| | - Hang Ruan
- School of Cyber Science and Technology, Beihang University, Beijing, China
| | - Yuanyuan Zhao
- College of Biological Science and Technology, Beijing Forestry University, Beijing, China
- National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Forestry University, Beijing, China
| |
Collapse
|
15
|
Pak MA, Markhieva KA, Novikova MS, Petrov DS, Vorobyev IS, Maksimova ES, Kondrashov FA, Ivankov DN. Using AlphaFold to predict the impact of single mutations on protein stability and function. PLoS One 2023; 18:e0282689. [PMID: 36928239 PMCID: PMC10019719 DOI: 10.1371/journal.pone.0282689] [Citation(s) in RCA: 58] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 02/21/2023] [Indexed: 03/17/2023] Open
Abstract
AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is "solved". However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding.
Collapse
Affiliation(s)
- Marina A. Pak
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Mariia S. Novikova
- Armand Hammer United World College of the American West, Montezuma, New Mexico, United Stated of America
| | - Dmitry S. Petrov
- Specialized Educational and Scientific Center of UrFU (SUNC UrFU), Ekaterinburg, Russia
| | - Ilya S. Vorobyev
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Fyodor A. Kondrashov
- Institute of Science and Technology Austria, Maria Gugging, Austria
- Evolutionary and Synthetic Biology Unit, Okinawa Institute of Science and Technology Graduate University, Onna, Okinawa, Japan
| | - Dmitry N. Ivankov
- Center of Life Sciences, Skolkovo Institute of Science and Technology, Moscow, Russia
- * E-mail:
| |
Collapse
|
16
|
Abstract
AlphaFold2 is a machine-learning based program that predicts a protein structure based on the amino acid sequence. In this article, we report on the current usages of this new tool and give examples from our work in the Coronavirus Structural Task Force. With its unprecedented accuracy, it can be utilized for the design of expression constructs, de novo protein design and the interpretation of Cryo-EM data with an atomic model. However, these methods are limited by their training data and are of limited use to predict conformational variability and fold flexibility; they also lack co-factors, post-translational modifications and multimeric complexes with oligonucleotides. They also are not always perfect in terms of chemical geometry. Nevertheless, machine learning-based fold prediction is a game changer for structural bioinformatics and experimentalists alike, with exciting developments ahead.
Collapse
Affiliation(s)
- Maximilian Edich
- Institute for Nanostructure and Solid State Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.
| | - David C Briggs
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Oliver Kippes
- Institute for Nanostructure and Solid State Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.
| | - Yunyun Gao
- Institute for Nanostructure and Solid State Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.
| | - Andrea Thorn
- Institute for Nanostructure and Solid State Physics, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany.
| |
Collapse
|
17
|
Ferrario E, Miggiano R, Rizzi M, Ferraris DM. The integration of AlphaFold-predicted and crystal structures of human trans-3-hydroxy-l-proline dehydratase reveals a regulatory catalytic mechanism. Comput Struct Biotechnol J 2022; 20:3874-3883. [PMID: 35891782 PMCID: PMC9309405 DOI: 10.1016/j.csbj.2022.07.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 07/13/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
Computational methods for protein structure prediction have made significant strides forward, as evidenced by the last development of the neural network AlphaFold, which outperformed the CASP14 competitors by consistently predicting the structure of target proteins. Here we show an integrated structural investigation that combines the AlphaFold and crystal structures of human trans-3-Hydroxy-l-proline dehydratase, an enzyme involved in hydroxyproline catabolism and whose structure had never been reported before, identifying a structural element, absent in the AlphaFold model but present in the crystal structure, that was subsequently proved to be functionally relevant. Although the AlphaFold model lacked information on protein oligomerization, the native dimer was reconstructed using template-based and ab initio computational approaches. Moreover, molecular phasing of the diffraction data using the AlphaFold model resulted in dimer reconstruction and straightforward structure solution. Our work adds to the integration of AlphaFold with experimental structural and functional data for protein analysis, crystallographic phasing and structure solution.
Collapse
Affiliation(s)
- Eugenio Ferrario
- University of Piemonte Orientale, Department of Pharmaceutical Sciences, Largo Donegani 2, 28100 Novara, Italy
| | - Riccardo Miggiano
- University of Piemonte Orientale, Department of Pharmaceutical Sciences, Largo Donegani 2, 28100 Novara, Italy
- IXTAL srl, Via Bovio 6, 28100 Novara, Italy
| | - Menico Rizzi
- University of Piemonte Orientale, Department of Pharmaceutical Sciences, Largo Donegani 2, 28100 Novara, Italy
| | - Davide M. Ferraris
- University of Piemonte Orientale, Department of Pharmaceutical Sciences, Largo Donegani 2, 28100 Novara, Italy
- IXTAL srl, Via Bovio 6, 28100 Novara, Italy
| |
Collapse
|
18
|
Gao K, Wang R, Chen J, Cheng L, Frishcosy J, Huzumi Y, Qiu Y, Schluckbier T, Wei X, Wei GW. Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2. Chem Rev 2022; 122:11287-11368. [PMID: 35594413 PMCID: PMC9159519 DOI: 10.1021/acs.chemrev.1c00965] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.
Collapse
Affiliation(s)
- Kaifu Gao
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Rui Wang
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Jiahui Chen
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Limei Cheng
- Clinical
Pharmacology and Pharmacometrics, Bristol
Myers Squibb, Princeton, New Jersey 08536, United States
| | - Jaclyn Frishcosy
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuta Huzumi
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Yuchi Qiu
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Tom Schluckbier
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Xiaoqi Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
| | - Guo-Wei Wei
- Department
of Mathematics, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Biochemistry and Molecular Biology, Michigan
State University, East Lansing, Michigan 48824, United States
| |
Collapse
|
19
|
Abstract
This paper presents a short summary of the protein folding problem, what it is and why it is significant. Introduces the CASP competition and how accuracy is measured. Looks at different approaches for solving the problem followed by a review of the current breakthroughs in the field introduced by AlphaFold 1 and AlphaFold 2.
Collapse
Affiliation(s)
- Ştefan-Bogdan Marcu
- Department of Computer Science, University College Cork, Cork, Ireland
- *Correspondence: Ştefan-Bogdan Marcu
| | - Sabin Tăbîrcă
- Department of Computer Science, University College Cork, Cork, Ireland
- Department of Informatics, Faculty of Informatics and Mathematics, Transilvania University, Brasov, Romania
| | - Mark Tangney
- Department of Computer Science, University College Cork, Cork, Ireland
| |
Collapse
|
20
|
Thorn A. Artificial intelligence in the experimental determination and prediction of macromolecular structures. Curr Opin Struct Biol 2022; 74:102368. [DOI: 10.1016/j.sbi.2022.102368] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 02/22/2022] [Accepted: 03/08/2022] [Indexed: 11/26/2022]
|
21
|
Weissenow K, Heinzinger M, Rost B. Protein language-model embeddings for fast, accurate, and alignment-free protein structure prediction. Structure 2022; 30:1169-1177.e4. [DOI: 10.1016/j.str.2022.05.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 02/25/2022] [Accepted: 04/29/2022] [Indexed: 01/27/2023]
|
22
|
Barbarin-Bocahu I, Graille M. The X-ray crystallography phase problem solved thanks to AlphaFold and RoseTTAFold models: a case-study report. Acta Crystallogr D Struct Biol 2022; 78:517-531. [PMID: 35362474 DOI: 10.1107/s2059798322002157] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 02/23/2022] [Indexed: 11/10/2022]
Abstract
The breakthrough recently made in protein structure prediction by deep-learning programs such as AlphaFold and RoseTTAFold will certainly revolutionize biology over the coming decades. The scientific community is only starting to appreciate the various applications, benefits and limitations of these protein models. Yet, after the first thrills due to this revolution, it is important to evaluate the impact of the proposed models and their overall quality to avoid the misinterpretation or overinterpretation of these models by biologists. One of the first applications of these models is in solving the `phase problem' encountered in X-ray crystallography in calculating electron-density maps from diffraction data. Indeed, the most frequently used technique to derive electron-density maps is molecular replacement. As this technique relies on knowledge of the structure of a protein that shares strong structural similarity with the studied protein, the availability of high-accuracy models is then definitely critical for successful structure solution. After the collection of a 2.45 Å resolution data set, we struggled for two years in trying to solve the crystal structure of a protein involved in the nonsense-mediated mRNA decay pathway, an mRNA quality-control pathway dedicated to the elimination of eukaryotic mRNAs harboring premature stop codons. We used different methods (isomorphous replacement, anomalous diffraction and molecular replacement) to determine this structure, but all failed until we straightforwardly succeeded thanks to both AlphaFold and RoseTTAFold models. Here, we describe how these new models helped us to solve this structure and conclude that in our case the AlphaFold model largely outcompetes the other models. We also discuss the importance of search-model generation for successful molecular replacement.
Collapse
|
23
|
McCoy AJ, Sammito MD, Read RJ. Implications of AlphaFold2 for crystallographic phasing by molecular replacement. Acta Crystallogr D Struct Biol 2022; 78:1-13. [PMID: 34981757 PMCID: PMC8725160 DOI: 10.1107/s2059798321012122] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 11/13/2021] [Indexed: 12/11/2022] Open
Abstract
The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models.
Collapse
Affiliation(s)
- Airlie J. McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Massimo D. Sammito
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| | - Randy J. Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Hills Road, Cambridge CB2 0XY, United Kingdom
| |
Collapse
|
24
|
Yang Q, Jian X, Syed AAS, Fahira A, Zheng C, Zhu Z, Wang K, Zhang J, Wen Y, Li Z, Pan D, Lu T, Wang Z, Shi Y. Structural Comparison and Drug Screening of Spike Proteins of Ten SARS-CoV-2 Variants. Research (Wash D C) 2022; 2022:9781758. [PMID: 35198984 PMCID: PMC8829538 DOI: 10.34133/2022/9781758] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/30/2021] [Indexed: 12/14/2022]
Abstract
SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) has evolved many variants with stronger infectivity and immune evasion than the original strain, including Alpha, Beta, Gamma, Delta, Epsilon, Kappa, Iota, Lambda, and 21H strains. Amino acid mutations are enriched in the spike protein of SARS-CoV-2, which plays a crucial role in cell infection. However, the impact of these mutations on protein structure and function is unclear. Understanding the pathophysiology and pandemic features of these SARS-CoV-2 variants requires knowledge of the spike protein structures. Here, we obtained the spike protein structures of 10 main globally endemic SARS-CoV-2 strains using AlphaFold2. The clustering analysis based on structural similarity revealed the unique features of the mainly pandemic SARS-CoV-2 Delta variants, indicating that structural clusters can reflect the current characteristics of the epidemic more accurately than those based on the protein sequence. The analysis of the binding affinities of ACE2-RBD, antibody-NTD, and antibody-RBD complexes in the different variants revealed that the recognition of antibodies against S1 NTD and RBD was decreased in the variants, especially the Delta variant compared with the original strain, which may induce the immune evasion of SARS-CoV-2 variants. Furthermore, by virtual screening the ZINC database against a high-accuracy predicted structure of Delta spike protein and experimental validation, we identified multiple compounds that target S1 NTD and RBD, which might contribute towards the development of clinical anti-SARS-CoV-2 medicines. Our findings provided a basic foundation for future in vitro and in vivo investigations that might speed up the development of potential therapies for the SARS-CoV-2 variants.
Collapse
Affiliation(s)
- Qiangzhen Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Xuemin Jian
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Ali Alamdar Shah Syed
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Aamir Fahira
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Chenxiang Zheng
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zijia Zhu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Ke Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Jinmai Zhang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Yanqin Wen
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zhiqiang Li
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao 266003, China
| | - Dun Pan
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Tingting Lu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai 200030, China
- Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao 266003, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
- Department of Psychiatry, First Teaching Hospital of Xinjiang Medical University, Urumqi 830046, China
| |
Collapse
|
25
|
Jovine L. Using machine learning to study protein-protein interactions: From the uromodulin polymer to egg zona pellucida filaments. Mol Reprod Dev 2021; 88:686-693. [PMID: 34590381 DOI: 10.1002/mrd.23538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022]
Abstract
Neural network-based models for protein structure prediction have recently reached near-experimental accuracy and are fast becoming a powerful tool in the arsenal of biologists. As suggested by initial studies using RoseTTAFold or the ColabFold implementation of AlphaFold2, a particularly interesting future development will be the optimization of these computational methods to also routinely yield high-confidence predictions of protein-protein interactions. Here I use AlphaFold2 and ColabFold to investigate the activation and polymerization of uromodulin (UMOD)/Tamm-Horsfall protein, a zona pellucida (ZP) module-containing protein whose precursor and filamentous structures have been previously determined experimentally by X-ray crystallography and cryo-EM, respectively. Despite having no knowledge of the UMOD polymer structure (coordinates for which were neither used for model training nor as template), AlphaFold2/ColabFold are able to recapitulate a crucial conformational change underlying UMOD polymerization, as well as the general organization of protein subunits within the resulting filament. This surprising result is achieved by simply deleting from the input sequence a stretch of residues that correspond to a polymerization-inhibiting C-terminal propeptide. By mimicking in silico the activating effect of propeptide dissociation triggered by site-specific proteolysis of the protein precursor, this example has implications for the assembly of egg coat proteins and the many other molecules that also contain a ZP module. Most importantly, it shows the potential of exploiting machine learning not only to accurately predict the structures of individual proteins or complexes, but also to carry out computational experiments replicating specific molecular events.
Collapse
Affiliation(s)
- Luca Jovine
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| |
Collapse
|
26
|
Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, Bridgland A, Cowie A, Meyer C, Laydon A, Velankar S, Kleywegt GJ, Bateman A, Evans R, Pritzel A, Figurnov M, Ronneberger O, Bates R, Kohl SAA, Potapenko A, Ballard AJ, Romera-Paredes B, Nikolov S, Jain R, Clancy E, Reiman D, Petersen S, Senior AW, Kavukcuoglu K, Birney E, Kohli P, Jumper J, Hassabis D. Highly accurate protein structure prediction for the human proteome. Nature 2021; 596:590-596. [PMID: 34293799 PMCID: PMC8387240 DOI: 10.1038/s41586-021-03828-1] [Citation(s) in RCA: 1287] [Impact Index Per Article: 429.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 07/16/2021] [Indexed: 02/07/2023]
Abstract
Protein structures can provide invaluable information, both for reasoning about biological processes and for enabling interventions such as structure-based drug development or targeted mutagenesis. After decades of effort, 17% of the total residues in human protein sequences are covered by an experimentally determined structure1. Here we markedly expand the structural coverage of the proteome by applying the state-of-the-art machine learning method, AlphaFold2, at a scale that covers almost the entire human proteome (98.5% of human proteins). The resulting dataset covers 58% of residues with a confident prediction, of which a subset (36% of all residues) have very high confidence. We introduce several metrics developed by building on the AlphaFold model and use them to interpret the dataset, identifying strong multi-domain predictions as well as regions that are likely to be disordered. Finally, we provide some case studies to illustrate how high-quality predictions could be used to generate biological hypotheses. We are making our predictions freely available to the community and anticipate that routine large-scale and high-accuracy structure prediction will become an important tool that will allow new questions to be addressed from a structural perspective.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Sameer Velankar
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Gerard J Kleywegt
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Alex Bateman
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | | | | | | |
Collapse
|
27
|
Masrati G, Landau M, Ben-Tal N, Lupas A, Kosloff M, Kosinski J. Integrative Structural Biology in the Era of Accurate Structure Prediction. J Mol Biol 2021; 433:167127. [PMID: 34224746 DOI: 10.1016/j.jmb.2021.167127] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/28/2021] [Accepted: 06/28/2021] [Indexed: 11/16/2022]
Abstract
Characterizing the three-dimensional structure of macromolecules is central to understanding their function. Traditionally, structures of proteins and their complexes have been determined using experimental techniques such as X-ray crystallography, NMR, or cryo-electron microscopy-applied individually or in an integrative manner. Meanwhile, however, computational methods for protein structure prediction have been improving their accuracy, gradually, then suddenly, with the breakthrough advance by AlphaFold2, whose models of monomeric proteins are often as accurate as experimental structures. This breakthrough foreshadows a new era of computational methods that can build accurate models for most monomeric proteins. Here, we envision how such accurate modeling methods can combine with experimental structural biology techniques, enhancing integrative structural biology. We highlight the challenges that arise when considering multiple structural conformations, protein complexes, and polymorphic assemblies. These challenges will motivate further developments, both in modeling programs and in methods to solve experimental structures, towards better and quicker investigation of structure-function relationships.
Collapse
Affiliation(s)
- Gal Masrati
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Meytal Landau
- Department of Biology, Technion-Israel Institute of Technology, Haifa 3200003, Israel; European Molecular Biology Laboratory (EMBL), Hamburg 22607, Germany
| | - Nir Ben-Tal
- Department of Biochemistry and Molecular Biology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Andrei Lupas
- Department of Protein Evolution, Max Planck Institute for Developmental Biology, 72076 Tübingen, Germany.
| | - Mickey Kosloff
- Department of Human Biology, Faculty of Natural Sciences, University of Haifa, 199 Aba Khoushy Ave., Mt. Carmel, 3498838 Haifa, Israel.
| | - Jan Kosinski
- European Molecular Biology Laboratory (EMBL), Hamburg 22607, Germany; Centre for Structural Systems Biology (CSSB), Hamburg 22607, Germany; Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117 Heidelberg, Germany.
| |
Collapse
|
28
|
Gupta M, Azumaya CM, Moritz M, Pourmal S, Diallo A, Merz GE, Jang G, Bouhaddou M, Fossati A, Brilot AF, Diwanji D, Hernandez E, Herrera N, Kratochvil HT, Lam VL, Li F, Li Y, Nguyen HC, Nowotny C, Owens TW, Peters JK, Rizo AN, Schulze-Gahmen U, Smith AM, Young ID, Yu Z, Asarnow D, Billesbølle C, Campbell MG, Chen J, Chen KH, Chio US, Dickinson MS, Doan L, Jin M, Kim K, Li J, Li YL, Linossi E, Liu Y, Lo M, Lopez J, Lopez KE, Mancino A, Moss FR, Paul MD, Pawar KI, Pelin A, Pospiech TH, Puchades C, Remesh SG, Safari M, Schaefer K, Sun M, Tabios MC, Thwin AC, Titus EW, Trenker R, Tse E, Tsui TKM, Wang F, Zhang K, Zhang Y, Zhao J, Zhou F, Zhou Y, Zuliani-Alvarez L, Agard DA, Cheng Y, Fraser JS, Jura N, Kortemme T, Manglik A, Southworth DR, Stroud RM, Swaney DL, Krogan NJ, Frost A, Rosenberg OS, Verba KA. CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes. Res Sq 2021:rs.3.rs-515215. [PMID: 34031651 PMCID: PMC8142659 DOI: 10.21203/rs.3.rs-515215/v1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a potential role of Nsp2 in linking viral transcription within the viral replication-transcription complexes (RTC) to the translation initiation of the viral message. Collectively, the structure reported here, combined with mutant interaction mapping, provides a foundation for functional studies of this evolutionary conserved coronavirus protein and may assist future drug design.
Collapse
Affiliation(s)
- Meghna Gupta
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Caleigh M. Azumaya
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Michelle Moritz
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Sergei Pourmal
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Amy Diallo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Gregory E. Merz
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Gwendolyn Jang
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Mehdi Bouhaddou
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Andrea Fossati
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Axel F. Brilot
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Devan Diwanji
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Evelyn Hernandez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Nadia Herrera
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Huong T. Kratochvil
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Victor L. Lam
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Fei Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yang Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Henry C. Nguyen
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Carlos Nowotny
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Tristan W. Owens
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Jessica K. Peters
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Alexandrea N. Rizo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Ursula Schulze-Gahmen
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Amber M. Smith
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Iris D. Young
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Zanlin Yu
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Daniel Asarnow
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Christian Billesbølle
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Melody G. Campbell
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Current affiliation: Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jen Chen
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kuei-Ho Chen
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Un Seng Chio
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Miles Sasha Dickinson
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Loan Doan
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Mingliang Jin
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kate Kim
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Junrui Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yen-Li Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Edmond Linossi
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yanxin Liu
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Megan Lo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Jocelyne Lopez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kyle E. Lopez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Adamo Mancino
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Frank R. Moss
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Michael D. Paul
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Komal Ishwar Pawar
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Adrian Pelin
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Thomas H. Pospiech
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Cristina Puchades
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Soumya Govinda Remesh
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Maliheh Safari
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kaitlin Schaefer
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Ming Sun
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Current affiliation: Beam Therapeutics, Cambridge, MA 02139, USA
| | - Mariano C Tabios
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Aye C. Thwin
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Erron W. Titus
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Raphael Trenker
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Eric Tse
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Tsz Kin Martin Tsui
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Feng Wang
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kaihua Zhang
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yang Zhang
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Jianhua Zhao
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Fengbo Zhou
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yuan Zhou
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Lorena Zuliani-Alvarez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | | | - David A Agard
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Yifan Cheng
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
- Howard Hughes Medical Institute, San Francisco, CA 94158, USA
| | - James S Fraser
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Natalia Jura
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
- The University of California, Berkeley–University of California, San Francisco Graduate Program in Bioengineering, University of California, San Francisco, CA 94158, USA
| | - Aashish Manglik
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Daniel R. Southworth
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Robert M Stroud
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Danielle L Swaney
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adam Frost
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Oren S Rosenberg
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Medicine, University of California, San Francisco, CA 94143, USA
| | - Kliment A Verba
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| |
Collapse
|
29
|
Gupta M, Azumaya CM, Moritz M, Pourmal S, Diallo A, Merz GE, Jang G, Bouhaddou M, Fossati A, Brilot AF, Diwanji D, Hernandez E, Herrera N, Kratochvil HT, Lam VL, Li F, Li Y, Nguyen HC, Nowotny C, Owens TW, Peters JK, Rizo AN, Schulze-Gahmen U, Smith AM, Young ID, Yu Z, Asarnow D, Billesbølle C, Campbell MG, Chen J, Chen KH, Chio US, Dickinson MS, Doan L, Jin M, Kim K, Li J, Li YL, Linossi E, Liu Y, Lo M, Lopez J, Lopez KE, Mancino A, Moss FR, Paul MD, Pawar KI, Pelin A, Pospiech TH, Puchades C, Remesh SG, Safari M, Schaefer K, Sun M, Tabios MC, Thwin AC, Titus EW, Trenker R, Tse E, Tsui TKM, Wang F, Zhang K, Zhang Y, Zhao J, Zhou F, Zhou Y, Zuliani-Alvarez L, Agard DA, Cheng Y, Fraser JS, Jura N, Kortemme T, Manglik A, Southworth DR, Stroud RM, Swaney DL, Krogan NJ, Frost A, Rosenberg OS, Verba KA. CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes. bioRxiv 2021:2021.05.10.443524. [PMID: 34013269 PMCID: PMC8132225 DOI: 10.1101/2021.05.10.443524] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The SARS-CoV-2 protein Nsp2 has been implicated in a wide range of viral processes, but its exact functions, and the structural basis of those functions, remain unknown. Here, we report an atomic model for full-length Nsp2 obtained by combining cryo-electron microscopy with deep learning-based structure prediction from AlphaFold2. The resulting structure reveals a highly-conserved zinc ion-binding site, suggesting a role for Nsp2 in RNA binding. Mapping emerging mutations from variants of SARS-CoV-2 on the resulting structure shows potential host-Nsp2 interaction regions. Using structural analysis together with affinity tagged purification mass spectrometry experiments, we identify Nsp2 mutants that are unable to interact with the actin-nucleation-promoting WASH protein complex or with GIGYF2, an inhibitor of translation initiation and modulator of ribosome-associated quality control. Our work suggests a potential role of Nsp2 in linking viral transcription within the viral replication-transcription complexes (RTC) to the translation initiation of the viral message. Collectively, the structure reported here, combined with mutant interaction mapping, provides a foundation for functional studies of this evolutionary conserved coronavirus protein and may assist future drug design.
Collapse
Affiliation(s)
- Meghna Gupta
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Caleigh M Azumaya
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Michelle Moritz
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Sergei Pourmal
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Amy Diallo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Gregory E Merz
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Gwendolyn Jang
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Mehdi Bouhaddou
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Andrea Fossati
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Axel F Brilot
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Devan Diwanji
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Evelyn Hernandez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Nadia Herrera
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Huong T Kratochvil
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Victor L Lam
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Fei Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yang Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Henry C Nguyen
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Carlos Nowotny
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Tristan W Owens
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Jessica K Peters
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Alexandrea N Rizo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Ursula Schulze-Gahmen
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Amber M Smith
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Iris D Young
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Zanlin Yu
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Daniel Asarnow
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Christian Billesbølle
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Melody G Campbell
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Current affiliation: Division of Basic Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jen Chen
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kuei-Ho Chen
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Un Seng Chio
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Miles Sasha Dickinson
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Loan Doan
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Mingliang Jin
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kate Kim
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Junrui Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yen-Li Li
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Edmond Linossi
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yanxin Liu
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Megan Lo
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Jocelyne Lopez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kyle E Lopez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Adamo Mancino
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Frank R Moss
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Michael D Paul
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Komal Ishwar Pawar
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Adrian Pelin
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Thomas H Pospiech
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Cristina Puchades
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Soumya Govinda Remesh
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Maliheh Safari
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kaitlin Schaefer
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Ming Sun
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Current affiliation: Beam Therapeutics, Cambridge, MA 02139, USA
| | - Mariano C Tabios
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Aye C Thwin
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Erron W Titus
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Raphael Trenker
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Eric Tse
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Tsz Kin Martin Tsui
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Feng Wang
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Kaihua Zhang
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yang Zhang
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Jianhua Zhao
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Fengbo Zhou
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
| | - Yuan Zhou
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Lorena Zuliani-Alvarez
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - David A Agard
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Yifan Cheng
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
- Howard Hughes Medical Institute, San Francisco, CA 94158, USA
| | - James S Fraser
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Natalia Jura
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Cardiovascular Research Institute, University of California, San Francisco, CA 94158, USA
| | - Tanja Kortemme
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA
- The University of California, Berkeley-University of California, San Francisco Graduate Program in Bioengineering, University of California, San Francisco, CA 94158, USA
| | - Aashish Manglik
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Daniel R Southworth
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Robert M Stroud
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
| | - Danielle L Swaney
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA 94158, USA
- J. David Gladstone Institutes, San Francisco, CA 94158, USA
- Department of Microbiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Adam Frost
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Oren S Rosenberg
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, CA 94158, USA
- Chan-Zuckerberg Biohub, San Francisco, CA 94158, USA
- Department of Medicine, University of California, San Francisco, CA 94143, USA
| | - Kliment A Verba
- QBI Coronavirus Research Group Structural Biology Consortium, University of California, San Francisco, CA 94158, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, CA 94158, USA
- QBI, University of California, San Francisco, CA 94158, USA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| |
Collapse
|
30
|
Flower TG, Hurley JH. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8. Protein Sci 2021; 30:728-734. [PMID: 33625752 PMCID: PMC7980513 DOI: 10.1002/pro.4050] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/21/2021] [Accepted: 02/22/2021] [Indexed: 12/01/2022]
Abstract
The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre-existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for MR. The ORF8 protein of SARS-CoV-2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all β-sheet fold and few orthologs. We previously determined experimentally the structure of ORF8 using the single anomalous dispersion (SAD) phasing method, having been unable to find an MR solution to the crystallographic phase problem. Following a report of an accurate prediction of the ORF8 structure, we assessed whether the predicted model would have succeeded as an MR search model. A phase problem solution was found, and the resulting structure was refined, yielding structural parameters equivalent to the original experimental solution.
Collapse
Affiliation(s)
- Thomas G. Flower
- Department of Molecular and Cell Biology and California Institute for Quantitative BiosciencesUniversity of CaliforniaBerkeleyCaliforniaUSA
| | - James H. Hurley
- Department of Molecular and Cell Biology and California Institute for Quantitative BiosciencesUniversity of CaliforniaBerkeleyCaliforniaUSA
| |
Collapse
|