1
|
Kretsch RC, Xu L, Zheludev IN, Zhou X, Huang R, Nye G, Li S, Zhang K, Chiu W, Das R. Tertiary folds of the SL5 RNA from the 5' proximal region of SARS-CoV-2 and related coronaviruses. Proc Natl Acad Sci U S A 2024; 121:e2320493121. [PMID: 38427602 DOI: 10.1073/pnas.2320493121] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 01/05/2024] [Indexed: 03/03/2024] Open
Abstract
Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2), resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4 to 6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across these human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9 to 8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4 to 9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities and notable differences, with implications for potential protein-binding modes and therapeutic targets.
Collapse
Affiliation(s)
| | - Lily Xu
- Department of Microbiology and Immunology, Stanford University, Stanford, CA 94305
| | - Ivan N Zheludev
- Department of Biochemistry, Stanford University, Stanford, CA 94305
| | - Xueting Zhou
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA 94305
| | - Rui Huang
- Department of Biochemistry, Stanford University, Stanford, CA 94305
| | - Grace Nye
- Department of Biochemistry, Stanford University, Stanford, CA 94305
| | - Shanshan Li
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Kaiming Zhang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Wah Chiu
- Biophysics Program, Stanford University, Stanford, CA 94305
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA 94305
- CryoEM and Bioimaging Division, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA 94025
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA 94305
- Department of Biochemistry, Stanford University, Stanford, CA 94305
- HHMI, Stanford University, Stanford, CA 94305
| |
Collapse
|
2
|
He S, Huang R, Townley J, Kretsch RC, Karagianes TG, Cox DBT, Blair H, Penzar D, Vyaltsev V, Aristova E, Zinkevich A, Bakulin A, Sohn H, Krstevski D, Fukui T, Tatematsu F, Uchida Y, Jang D, Lee JS, Shieh R, Ma T, Martynov E, Shugaev MV, Bukhari HST, Fujikawa K, Onodera K, Henkel C, Ron S, Romano J, Nicol JJ, Nye GP, Wu Y, Choe C, Reade W, Participants E, Das R. Ribonanza: deep learning of RNA structure through dual crowdsourcing. bioRxiv 2024:2024.02.24.581671. [PMID: 38464325 PMCID: PMC10925082 DOI: 10.1101/2024.02.24.581671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Prediction of RNA structure from sequence remains an unsolved problem, and progress has been slowed by a paucity of experimental data. Here, we present Ribonanza, a dataset of chemical mapping measurements on two million diverse RNA sequences collected through Eterna and other crowdsourced initiatives. Ribonanza measurements enabled solicitation, training, and prospective evaluation of diverse deep neural networks through a Kaggle challenge, followed by distillation into a single, self-contained model called RibonanzaNet. When fine tuned on auxiliary datasets, RibonanzaNet achieves state-of-the-art performance in modeling experimental sequence dropout, RNA hydrolytic degradation, and RNA secondary structure, with implications for modeling RNA tertiary structure.
Collapse
Affiliation(s)
- Shujun He
- Department of Chemical Engineering, Texas A&M University, TX, USA
| | - Rui Huang
- Department of Biochemistry, Stanford CA, USA
| | | | | | | | - David B T Cox
- Department of Biochemistry, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
| | | | - Dmitry Penzar
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
| | - Valeriy Vyaltsev
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Elizaveta Aristova
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Arsenii Zinkevich
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Artemy Bakulin
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
| | - Hoyeol Sohn
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Daniel Krstevski
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | | | | | | | - Donghoon Jang
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
| | | | - Roger Shieh
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Tom Ma
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Eduard Martynov
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
| | - Maxim V Shugaev
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
| | | | | | | | | | - Shlomo Ron
- Department of Chemical Engineering, Texas A&M University, TX, USA
- Department of Biochemistry, Stanford CA, USA
- Eterna Massive Open Laboratory
- Biophysics Program, Stanford CA, USA
- Department of Medicine, Division of Hematology, and Department of Biochemistry, Stanford CA, USA
- Department of Mathematics, Stanford CA, USA
- AIRI, Moscow, Russia
- Vavilov Institute of General Genetics, Moscow 119991, Russia
- Institute of Translational Medicine, Pirogov Russian National Research Medical University, Moscow 117997, Russia
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Russian Federation
- GO Inc., Tokyo, Japan
- Department of Electrical and Computer Engineering, Inha University, Incheon, Republic of Korea
- DeltaX, Seoul, Republic of Korea
- Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russian Federation
- Department of Materials Science and Engineering, University of Virginia, Charlottesville, VA 22904-4745, USA
- Vergesense, CA
- DeNA, Tokyo, Japan
- NVIDIA, Tokyo, Japan
- NVIDIA, Munich
- Howard Hughes Medical Institute
- Department of Bioengineering, Stanford CA, USA
- Kaggle, San Francisco CA, USA
| | - Jonathan Romano
- Eterna Massive Open Laboratory
- Howard Hughes Medical Institute
| | | | - Grace P Nye
- Department of Biochemistry, Stanford CA, USA
| | - Yuan Wu
- Department of Biochemistry, Stanford CA, USA
- Howard Hughes Medical Institute
| | | | | | | | - Rhiju Das
- Department of Biochemistry, Stanford CA, USA
- Biophysics Program, Stanford CA, USA
- Howard Hughes Medical Institute
| |
Collapse
|
3
|
Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. Proteins 2023; 91:1747-1770. [PMID: 37876231 PMCID: PMC10841292 DOI: 10.1002/prot.26602] [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: 04/25/2023] [Revised: 08/21/2023] [Accepted: 09/07/2023] [Indexed: 10/26/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty-two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and x-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as noncanonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
Collapse
Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
| |
Collapse
|
4
|
Mulvaney T, Kretsch RC, Elliott L, Beton JG, Kryshtafovych A, Rigden DJ, Das R, Topf M. CASP15 cryo-EM protein and RNA targets: Refinement and analysis using experimental maps. Proteins 2023; 91:1935-1951. [PMID: 37994556 PMCID: PMC10697286 DOI: 10.1002/prot.26644] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/25/2023] [Accepted: 11/10/2023] [Indexed: 11/24/2023]
Abstract
CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, experimental structures by their nature are only models themselves-their construction involves a certain degree of subjectivity in interpreting density maps and translating them to atomic coordinates. Here, we directly utilized density maps to evaluate the predictions by employing a method for ranking the quality of protein chain predictions based on their fit into the experimental density. The fit-based ranking was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy, and occasionally even better than the reference structure in some regions of the model. Local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. Additionally, the top 118 predictions associated with 9 protein target reference structures were selected for automated refinement, in addition to the top 40 predictions for 11 RNA targets. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure. This refinement was successful despite large conformational changes often being required, showing that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryo-EM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors, and together with the lack of consensus amongst models in these regions suggests that modeling, in combination with model-fit to the density, holds the potential for identifying more flexible regions within the structure.
Collapse
Affiliation(s)
- Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Rachael C Kretsch
- Biophysics Program, Stanford University School of Medicine, California, USA
| | - Luc Elliott
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, Liverpool, UK
| | - Joseph G Beton
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
| | | | - Daniel J Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, Liverpool, UK
| | - Rhiju Das
- Biophysics Program, Stanford University School of Medicine, California, USA
- Department of Biochemistry, Stanford University School of Medicine, California, USA
- Howard Hughes Medical Institute, Stanford University, California, USA
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV), Hamburg, Germany
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| |
Collapse
|
5
|
Kretsch RC, Andersen ES, Bujnicki JM, Chiu W, Das R, Luo B, Masquida B, McRae EK, Schroeder GM, Su Z, Wedekind JE, Xu L, Zhang K, Zheludev IN, Moult J, Kryshtafovych A. RNA target highlights in CASP15: Evaluation of predicted models by structure providers. Proteins 2023; 91:1600-1615. [PMID: 37466021 PMCID: PMC10792523 DOI: 10.1002/prot.26550] [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: 04/27/2023] [Revised: 06/16/2023] [Accepted: 06/26/2023] [Indexed: 07/20/2023]
Abstract
The first RNA category of the Critical Assessment of Techniques for Structure Prediction competition was only made possible because of the scientists who provided experimental structures to challenge the predictors. In this article, these scientists offer a unique and valuable analysis of both the successes and areas for improvement in the predicted models. All 10 RNA-only targets yielded predictions topologically similar to experimentally determined structures. For one target, experimentalists were able to phase their x-ray diffraction data by molecular replacement, showing a potential application of structure predictions for RNA structural biologists. Recommended areas for improvement include: enhancing the accuracy in local interaction predictions and increased consideration of the experimental conditions such as multimerization, structure determination method, and time along folding pathways. The prediction of RNA-protein complexes remains the most significant challenge. Finally, given the intrinsic flexibility of many RNAs, we propose the consideration of ensemble models.
Collapse
Affiliation(s)
- Rachael C. Kretsch
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
| | - Ebbe S. Andersen
- Interdisciplinary Nanoscience Center and Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
| | - Janusz M. Bujnicki
- International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
| | - Wah Chiu
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Bioengineering and James H. Clark Center, Stanford University, Stanford, CA, USA
- Division of CryoEM and Bioimaging, SSRL, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Rhiju Das
- Biophysics Program, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Bingnan Luo
- The State Key Laboratory of Biotherapy, Frontiers Medical Center of Tianfu Jincheng Laboratory, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610044, Sichuan, China
| | - Benoît Masquida
- UMR 7156, CNRS – Universite de Strasbourg, Strasbourg, France
| | - Ewan K.S. McRae
- Center for RNA Therapeutics, Houston Methodist Research Institute, Houston, TX 77030, USA
| | - Griffin M. Schroeder
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Zhaoming Su
- The State Key Laboratory of Biotherapy, Frontiers Medical Center of Tianfu Jincheng Laboratory, Department of Geriatrics and National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610044, Sichuan, China
| | - Joseph E. Wedekind
- Department of Biochemistry and Biophysics, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
- Center for RNA Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, 14642, USA
| | - Lily Xu
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kaiming Zhang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Ivan N. Zheludev
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - John Moult
- Department of Cell Biology and Molecular Genetics, Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, Maryland, USA
| | | |
Collapse
|
6
|
Kryshtafovych A, Antczak M, Szachniuk M, Zok T, Kretsch RC, Rangan R, Pham P, Das R, Robin X, Studer G, Durairaj J, Eberhardt J, Sweeney A, Topf M, Schwede T, Fidelis K, Moult J. New prediction categories in CASP15. Proteins 2023; 91:1550-1557. [PMID: 37306011 PMCID: PMC10713864 DOI: 10.1002/prot.26515] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023]
Abstract
Prediction categories in the Critical Assessment of Structure Prediction (CASP) experiments change with the need to address specific problems in structure modeling. In CASP15, four new prediction categories were introduced: RNA structure, ligand-protein complexes, accuracy of oligomeric structures and their interfaces, and ensembles of alternative conformations. This paper lists technical specifications for these categories and describes their integration in the CASP data management system.
Collapse
Affiliation(s)
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of TechnologyPoznanPoland
- Institute of Bioorganic Chemistry, Polish Academy of SciencesPoznanPoland
| | - Marta Szachniuk
- Institute of Computing Science, Poznan University of TechnologyPoznanPoland
- Institute of Bioorganic Chemistry, Polish Academy of SciencesPoznanPoland
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of TechnologyPoznanPoland
- Institute of Bioorganic Chemistry, Polish Academy of SciencesPoznanPoland
| | - Rachael C. Kretsch
- Biophysics Program, Stanford University School of MedicineStanfordCaliforniaUSA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of MedicineStanfordCaliforniaUSA
| | - Phillip Pham
- Biochemistry DepartmentStanford University School of MedicineStanfordCaliforniaUSA
| | - Rhiju Das
- Biochemistry DepartmentStanford University School of MedicineStanfordCaliforniaUSA
- Howard Hughes Medical Institute, Stanford UniversityStanfordCaliforniaUSA
| | - Xavier Robin
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Gabriel Studer
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Janani Durairaj
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Jerome Eberhardt
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | - Aaron Sweeney
- Centre for Structural Systems Biology (CSSB), Leibniz‐Institut für Virologie (LIV)HamburgGermany
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz‐Institut für Virologie (LIV)HamburgGermany
- Universitätsklinikum Hamburg Eppendorf (UKE)HamburgGermany
| | - Torsten Schwede
- Biozentrum, University of BaselBaselSwitzerland
- SIB Swiss Institute of BioinformaticsBaselSwitzerland
| | | | - John Moult
- Institute for Bioscience and Biotechnology Research, Department of Cell Biology and Molecular genetics, University of MarylandRockvilleMarylandUSA
| |
Collapse
|
7
|
Kretsch RC, Xu L, Zheludev IN, Zhou X, Huang R, Nye G, Li S, Zhang K, Chiu W, Das R. Tertiary folds of the SL5 RNA from the 5' proximal region of SARS-CoV-2 and related coronaviruses. bioRxiv 2023:2023.11.22.567964. [PMID: 38076883 PMCID: PMC10705266 DOI: 10.1101/2023.11.22.567964] [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] [Indexed: 12/23/2023]
Abstract
Coronavirus genomes sequester their start codons within stem-loop 5 (SL5), a structured, 5' genomic RNA element. In most alpha- and betacoronaviruses, the secondary structure of SL5 is predicted to contain a four-way junction of helical stems, some of which are capped with UUYYGU hexaloops. Here, using cryogenic electron microscopy (cryo-EM) and computational modeling with biochemically-determined secondary structures, we present three-dimensional structures of SL5 from six coronaviruses. The SL5 domain of betacoronavirus SARS-CoV-2, resolved at 4.7 Å resolution, exhibits a T-shaped structure, with its UUYYGU hexaloops at opposing ends of a coaxial stack, the T's "arms." Further analysis of SL5 domains from SARS-CoV-1 and MERS (7.1 and 6.4-6.9 Å resolution, respectively) indicate that the junction geometry and inter-hexaloop distances are conserved features across the studied human-infecting betacoronaviruses. The MERS SL5 domain displays an additional tertiary interaction, which is also observed in the non-human-infecting betacoronavirus BtCoV-HKU5 (5.9-8.0 Å resolution). SL5s from human-infecting alphacoronaviruses, HCoV-229E and HCoV-NL63 (6.5 and 8.4-9.0 Å resolution, respectively), exhibit the same coaxial stacks, including the UUYYGU-capped arms, but with a phylogenetically distinct crossing angle, an X-shape. As such, all SL5 domains studied herein fold into stable tertiary structures with cross-genus similarities, with implications for potential protein-binding modes and therapeutic targets.
Collapse
Affiliation(s)
| | - Lily Xu
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Ivan N. Zheludev
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Xueting Zhou
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA
| | - Rui Huang
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Grace Nye
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Shanshan Li
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Kaiming Zhang
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Wah Chiu
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Bioengineering, James H. Clark Center, Stanford University, Stanford, CA, USA
- CryoEM and Bioimaging Division, Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA
| | - Rhiju Das
- Biophysics Program, Stanford University, Stanford, CA, USA
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| |
Collapse
|
8
|
Das R, Kretsch RC, Simpkin AJ, Mulvaney T, Pham P, Rangan R, Bu F, Keegan RM, Topf M, Rigden DJ, Miao Z, Westhof E. Assessment of three-dimensional RNA structure prediction in CASP15. bioRxiv 2023:2023.04.25.538330. [PMID: 37162955 PMCID: PMC10168427 DOI: 10.1101/2023.04.25.538330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The prediction of RNA three-dimensional structures remains an unsolved problem. Here, we report assessments of RNA structure predictions in CASP15, the first CASP exercise that involved RNA structure modeling. Forty two predictor groups submitted models for at least one of twelve RNA-containing targets. These models were evaluated by the RNA-Puzzles organizers and, separately, by a CASP-recruited team using metrics (GDT, lDDT) and approaches (Z-score rankings) initially developed for assessment of proteins and generalized here for RNA assessment. The two assessments independently ranked the same predictor groups as first (AIchemy_RNA2), second (Chen), and third (RNAPolis and GeneSilico, tied); predictions from deep learning approaches were significantly worse than these top ranked groups, which did not use deep learning. Further analyses based on direct comparison of predicted models to cryogenic electron microscopy (cryo-EM) maps and X-ray diffraction data support these rankings. With the exception of two RNA-protein complexes, models submitted by CASP15 groups correctly predicted the global fold of the RNA targets. Comparisons of CASP15 submissions to designed RNA nanostructures as well as molecular replacement trials highlight the potential utility of current RNA modeling approaches for RNA nanotechnology and structural biology, respectively. Nevertheless, challenges remain in modeling fine details such as non-canonical pairs, in ranking among submitted models, and in prediction of multiple structures resolved by cryo-EM or crystallography.
Collapse
Affiliation(s)
- Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, CA USA
- Biophysics Program, Stanford University School of Medicine, CA USA
- Howard Hughes Medical Institute, Stanford University, CA USA
| | | | - Adam J. Simpkin
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Thomas Mulvaney
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Phillip Pham
- Department of Biochemistry, Stanford University School of Medicine, CA USA
| | - Ramya Rangan
- Biophysics Program, Stanford University School of Medicine, CA USA
| | - Fan Bu
- Guangzhou Laboratory, Guangzhou International Bio Island, Guangzhou 510005, China
- Division of Life Sciences and Medicine,University of Science and Technology of China, Hefei 230036, Anhui, China
| | - Ronan M. Keegan
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
- Life Science, Diamond Light Source, Harwell Science, UK
| | - Maya Topf
- Centre for Structural Systems Biology (CSSB), Leibniz-Institut für Virologie (LIV)
- University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Daniel J. Rigden
- Institute of Systems, Molecular & Integrative Biology, The University of Liverpool, UK
| | - Zhichao Miao
- GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou National Laboratory, Guangzhou Medical University
- Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People’s Hospital, School of Medicine, Tongji University, Shanghai 200434, China
| | - Eric Westhof
- Architecture et Réactivité de l’ARN, Institut de Biologie Moléculaire et Cellulaire du CNRS, Université de Strasbourg, F-67084, Strasbourg, France
| |
Collapse
|
9
|
Mulvaney T, Kretsch RC, Elliott L, Beton J, Kryshtafovych A, Rigden DJ, Das R, Topf M. CASP15 cryoEM protein and RNA targets: refinement and analysis using experimental maps. bioRxiv 2023:2023.08.07.552287. [PMID: 37609268 PMCID: PMC10441278 DOI: 10.1101/2023.08.07.552287] [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: 08/24/2023]
Abstract
CASP assessments primarily rely on comparing predicted coordinates with experimental reference structures. However, errors in the reference structures can potentially reduce the accuracy of the assessment. This issue is particularly prominent in cryoEM-determined structures, and therefore, in the assessment of CASP15 cryoEM targets, we directly utilized density maps to evaluate the predictions. A method for ranking the quality of protein chain predictions based on rigid fitting to experimental density was found to correlate well with the CASP assessment scores. Overall, the evaluation against the density map indicated that the models are of high accuracy although local assessment of predicted side chains in a 1.52 Å resolution map showed that side-chains are sometimes poorly positioned. The top 136 predictions associated with 9 protein target reference structures were selected for refinement, in addition to the top 40 predictions for 11 RNA targets. To this end, we have developed an automated hierarchical refinement pipeline in cryoEM maps. For both proteins and RNA, the refinement of CASP15 predictions resulted in structures that are close to the reference target structure, including some regions with better fit to the density. This refinement was successful despite large conformational changes and secondary structure element movements often being required, suggesting that predictions from CASP-assessed methods could serve as a good starting point for building atomic models in cryoEM maps for both proteins and RNA. Loop modeling continued to pose a challenge for predictors with even short loops failing to be accurately modeled or refined at times. The lack of consensus amongst models suggests that modeling holds the potential for identifying more flexible regions within the structure.
Collapse
|
10
|
Billesbølle CB, Azumaya CM, Kretsch RC, Powers AS, Gonen S, Schneider S, Arvedson T, Dror RO, Cheng Y, Manglik A. Structure of hepcidin-bound ferroportin reveals iron homeostatic mechanisms. Nature 2020; 586:807-811. [PMID: 32814342 PMCID: PMC7906036 DOI: 10.1038/s41586-020-2668-z] [Citation(s) in RCA: 138] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/15/2020] [Indexed: 01/01/2023]
Abstract
The serum iron level in humans is tightly controlled by the action of the hormone hepcidin on the iron efflux transporter ferroportin. Hepcidin regulates iron absorption and recycling by inducing ferroportin internalization and degradation1. Aberrant ferroportin activity can lead to diseases of iron overload, like hemochromatosis, or iron limitation anemias2. Here, we determined cryogenic electron microscopy (cryo-EM) structures of ferroportin in lipid nanodiscs, both in the apo state and in complex with cobalt, an iron mimetic, and hepcidin. These structures and accompanying molecular dynamics simulations identify two metal binding sites within the N- and C-domains of ferroportin. Hepcidin binds ferroportin in an outward-open conformation and completely occludes the iron efflux pathway to inhibit transport. The carboxy-terminus of hepcidin directly contacts the divalent metal in the ferroportin C-domain. We further show that hepcidin binding to ferroportin is coupled to iron binding, with an 80-fold increase in hepcidin affinity in the presence of iron. These results suggest a model for hepcidin regulation of ferroportin, where only iron loaded ferroportin molecules are targeted for degradation. More broadly, our structural and functional insights are likely to enable more targeted manipulation of the hepcidin-ferroportin axis in disorders of iron homeostasis.
Collapse
Affiliation(s)
- Christian B Billesbølle
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Caleigh M Azumaya
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Rachael C Kretsch
- Department of Computer Science, Stanford University, Stanford, CA, USA.,Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.,Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.,Biophysics Program, Stanford University, Stanford, CA, USA
| | - Alexander S Powers
- Department of Computer Science, Stanford University, Stanford, CA, USA.,Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.,Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.,Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Shane Gonen
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.,Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA.,Department of Molecular Biology and Biochemistry, University of California, Irvine, Biological Sciences III, Irvine, CA, USA
| | - Simon Schneider
- Institute of Biochemistry, Goethe University Frankfurt, Max-von-Laue-Straße 9, Frankfurt am Main, Germany
| | - Tara Arvedson
- Department of Oncology Research, Amgen Inc., South San Francisco, CA, USA
| | - Ron O Dror
- Department of Computer Science, Stanford University, Stanford, CA, USA.,Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA, USA.,Department of Structural Biology, Stanford University School of Medicine, Stanford, CA, USA.,Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA.,Biophysics Program, Stanford University, Stanford, CA, USA
| | - Yifan Cheng
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA. .,Howard Hughes Medical Institute, University of California San Francisco, San Francisco, CA, USA.
| | - Aashish Manglik
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA. .,Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, USA.
| |
Collapse
|