1
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Bonilla SL, Jang K. Challenges, advances, and opportunities in RNA structural biology by Cryo-EM. Curr Opin Struct Biol 2024; 88:102894. [PMID: 39121532 DOI: 10.1016/j.sbi.2024.102894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 07/03/2024] [Accepted: 07/15/2024] [Indexed: 08/12/2024]
Abstract
RNAs are remarkably versatile molecules that can fold into intricate three-dimensional (3D) structures to perform diverse cellular and viral functions. Despite their biological importance, relatively few RNA 3D structures have been solved, and our understanding of RNA structure-function relationships remains in its infancy. This limitation partly arises from challenges posed by RNA's complex conformational landscape, characterized by structural flexibility, formation of multiple states, and a propensity to misfold. Recently, cryo-electron microscopy (cryo-EM) has emerged as a powerful tool for the visualization of conformationally dynamic RNA-only 3D structures. However, RNA's characteristics continue to pose challenges. We discuss experimental methods developed to overcome these hurdles, including the engineering of modular modifications that facilitate the visualization of small RNAs, improve particle alignment, and validate structural models.
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Affiliation(s)
- Steve L Bonilla
- Laboratory of RNA Structural Biology and Biophysics, The Rockefeller University, New York, NY, 10065, USA.
| | - Karen Jang
- Laboratory of RNA Structural Biology and Biophysics, The Rockefeller University, New York, NY, 10065, USA
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2
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Li J, Zhou Y, Chen SJ. Embracing exascale computing in nucleic acid simulations. Curr Opin Struct Biol 2024; 87:102847. [PMID: 38815519 PMCID: PMC11283969 DOI: 10.1016/j.sbi.2024.102847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 04/18/2024] [Accepted: 05/09/2024] [Indexed: 06/01/2024]
Abstract
This mini-review reports the recent advances in biomolecular simulations, particularly for nucleic acids, and provides the potential effects of the emerging exascale computing on nucleic acid simulations, emphasizing the need for advanced computational strategies to fully exploit this technological frontier. Specifically, we introduce recent breakthroughs in computer architectures for large-scale biomolecular simulations and review the simulation protocols for nucleic acids regarding force fields, enhanced sampling methods, coarse-grained models, and interactions with ligands. We also explore the integration of machine learning methods into simulations, which promises to significantly enhance the predictive modeling of biomolecules and the analysis of complex data generated by the exascale simulations. Finally, we discuss the challenges and perspectives for biomolecular simulations as we enter the dawning exascale computing era.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA
| | - Yuanzhe Zhou
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, 223 Physics Bldg., Columbia, 65211, MO, USA.
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3
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Nithin C, Kmiecik S, Błaszczyk R, Nowicka J, Tuszyńska I. Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions. Nucleic Acids Res 2024; 52:7465-7486. [PMID: 38917327 PMCID: PMC11260495 DOI: 10.1093/nar/gkae541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/23/2024] [Accepted: 06/16/2024] [Indexed: 06/27/2024] Open
Abstract
Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web-based tools due to intellectual property concerns. We focus on reproducing the RNA structure existing in RNA-small molecule complexes, particularly on the ability to model ligand binding sites. Using a comprehensive set of RNA structures from the PDB, which includes diverse structural elements, we found that machine learning (ML)-based methods effectively predict global RNA folds but are less accurate with local interactions. Conversely, non-ML-based methods demonstrate higher precision in modeling intramolecular interactions, particularly with secondary structure restraints. Importantly, ligand-binding site accuracy can remain sufficiently high for practical use, even if the overall model quality is not optimal. With the recent release of AlphaFold 3, we included this advanced method in our tests. Benchmark subsets containing new structures, not used in the training of the tested ML methods, show that AlphaFold 3's performance was comparable to other ML-based methods, albeit with some challenges in accurately modeling ligand binding sites. This study underscores the importance of enhancing binding site prediction accuracy and the challenges in modeling RNA-ligand interactions accurately.
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Affiliation(s)
- Chandran Nithin
- Molecure SA, 02-089 Warsaw, Poland
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, 02-089 Warsaw, Poland
| | - Sebastian Kmiecik
- Laboratory of Computational Biology, Biological and Chemical Research Center, Faculty of Chemistry, University of Warsaw, 02-089 Warsaw, Poland
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4
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Steffen FD, Cunha RA, Sigel RKO, Börner R. FRET-guided modeling of nucleic acids. Nucleic Acids Res 2024; 52:e59. [PMID: 38869063 PMCID: PMC11260485 DOI: 10.1093/nar/gkae496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/29/2024] [Indexed: 06/14/2024] Open
Abstract
The functional diversity of RNAs is encoded in their innate conformational heterogeneity. The combination of single-molecule spectroscopy and computational modeling offers new attractive opportunities to map structural transitions within nucleic acid ensembles. Here, we describe a framework to harmonize single-molecule Förster resonance energy transfer (FRET) measurements with molecular dynamics simulations and de novo structure prediction. Using either all-atom or implicit fluorophore modeling, we recreate FRET experiments in silico, visualize the underlying structural dynamics and quantify the reaction coordinates. Using multiple accessible-contact volumes as a post hoc scoring method for fragment assembly in Rosetta, we demonstrate that FRET can be used to filter a de novo RNA structure prediction ensemble by refuting models that are not compatible with in vitro FRET measurement. We benchmark our FRET-assisted modeling approach on double-labeled DNA strands and validate it against an intrinsically dynamic manganese(II)-binding riboswitch. We show that a FRET coordinate describing the assembly of a four-way junction allows our pipeline to recapitulate the global fold of the riboswitch displayed by the crystal structure. We conclude that computational fluorescence spectroscopy facilitates the interpretability of dynamic structural ensembles and improves the mechanistic understanding of nucleic acid interactions.
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Affiliation(s)
- Fabio D Steffen
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Richard A Cunha
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Roland K O Sigel
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
| | - Richard Börner
- Department of Chemistry, University of Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland
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5
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Moafinejad SN, de Aquino BRH, Boniecki M, Pandaranadar Jeyeram IN, Nikolaev G, Magnus M, Farsani M, Badepally N, Wirecki T, Stefaniak F, Bujnicki J. SimRNAweb v2.0: a web server for RNA folding simulations and 3D structure modeling, with optional restraints and enhanced analysis of folding trajectories. Nucleic Acids Res 2024; 52:W368-W373. [PMID: 38738621 PMCID: PMC11223799 DOI: 10.1093/nar/gkae356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/07/2024] [Accepted: 04/29/2024] [Indexed: 05/14/2024] Open
Abstract
Research on ribonucleic acid (RNA) structures and functions benefits from easy-to-use tools for computational prediction and analyses of RNA three-dimensional (3D) structure. The SimRNAweb server version 2.0 offers an enhanced, user-friendly platform for RNA 3D structure prediction and analysis of RNA folding trajectories based on the SimRNA method. SimRNA employs a coarse-grained model, Monte Carlo sampling and statistical potentials to explore RNA conformational space, optionally guided by spatial restraints. Recognized for its accuracy in RNA 3D structure prediction in RNA-Puzzles and CASP competitions, SimRNA is particularly useful for incorporating restraints based on experimental data. The new server version introduces performance optimizations and extends user control over simulations and the processing of results. It allows the application of various hard and soft restraints, accommodating alternative structures involving canonical and noncanonical base pairs and unpaired residues, while also integrating data from chemical probing methods. Enhanced features include an improved analysis of folding trajectories, offering advanced clustering options and multiple analyses of the generated trajectories. These updates provide comprehensive tools for detailed RNA structure analysis. SimRNAweb v2.0 significantly broadens the scope of RNA modeling, emphasizing flexibility and user-defined parameter control. The web server is available at https://genesilico.pl/SimRNAweb.
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Affiliation(s)
- S Naeim Moafinejad
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Belisa R H de Aquino
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Michał J Boniecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Iswarya P N Pandaranadar Jeyeram
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Grigory Nikolaev
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Marcin Magnus
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford St, Cambridge, MA 02138, USA
| | - Masoud Amiri Farsani
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Nagendar Goud Badepally
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Tomasz K Wirecki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Filip Stefaniak
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
| | - Janusz M Bujnicki
- Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, ul. Ks. Trojdena 4, PL-02-109 Warsaw, Poland
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6
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Waterhouse AM, Studer G, Robin X, Bienert S, Tauriello G, Schwede T. The structure assessment web server: for proteins, complexes and more. Nucleic Acids Res 2024; 52:W318-W323. [PMID: 38634802 PMCID: PMC11223858 DOI: 10.1093/nar/gkae270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/21/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
The 'structure assessment' web server is a one-stop shop for interactive evaluation and benchmarking of structural models of macromolecular complexes including proteins and nucleic acids. A user-friendly web dashboard links sequence with structure information and results from a variety of state-of-the-art tools, which facilitates the visual exploration and evaluation of structure models. The dashboard integrates stereochemistry information, secondary structure information, global and local model quality assessment of the tertiary structure of comparative protein models, as well as prediction of membrane location. In addition, a benchmarking mode is available where a model can be compared to a reference structure, providing easy access to scores that have been used in recent CASP experiments and CAMEO. The structure assessment web server is available at https://swissmodel.expasy.org/assess.
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Affiliation(s)
- Andrew M Waterhouse
- Biozentrum, University of Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Gabriel Studer
- Biozentrum, University of Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Xavier Robin
- Biozentrum, University of Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Stefan Bienert
- Biozentrum, University of Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Gerardo Tauriello
- Biozentrum, University of Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
| | - Torsten Schwede
- Biozentrum, University of Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Computational Structural Biology, Basel, Switzerland
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7
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Tarafder S, Roche R, Bhattacharya D. The landscape of RNA 3D structure modeling with transformer networks. Biol Methods Protoc 2024; 9:bpae047. [PMID: 39006460 PMCID: PMC11244692 DOI: 10.1093/biomethods/bpae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/22/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Transformers are a powerful subclass of neural networks catalyzing the development of a growing number of computational methods for RNA structure modeling. Here, we conduct an objective and empirical study of the predictive modeling accuracy of the emerging transformer-based methods for RNA structure prediction. Our study reveals multi-faceted complementarity between the methods and underscores some key aspects that affect the prediction accuracy.
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Affiliation(s)
- Sumit Tarafder
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
| | - Rahmatullah Roche
- Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States
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8
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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, Das R. Ribonanza: deep learning of RNA structure through dual crowdsourcing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 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] [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.
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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
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9
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Gren BA, Antczak M, Zok T, Sulkowska JI, Szachniuk M. Knotted artifacts in predicted 3D RNA structures. PLoS Comput Biol 2024; 20:e1011959. [PMID: 38900780 PMCID: PMC11218946 DOI: 10.1371/journal.pcbi.1011959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 07/02/2024] [Accepted: 06/01/2024] [Indexed: 06/22/2024] Open
Abstract
Unlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms.
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Affiliation(s)
- Bartosz A. Gren
- Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Maciej Antczak
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
| | - Tomasz Zok
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | | | - Marta Szachniuk
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland
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10
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Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, Bodenstein SW, Evans DA, Hung CC, O'Neill M, Reiman D, Tunyasuvunakool K, Wu Z, Žemgulytė A, Arvaniti E, Beattie C, Bertolli O, Bridgland A, Cherepanov A, Congreve M, Cowen-Rivers AI, Cowie A, Figurnov M, Fuchs FB, Gladman H, Jain R, Khan YA, Low CMR, Perlin K, Potapenko A, Savy P, Singh S, Stecula A, Thillaisundaram A, Tong C, Yakneen S, Zhong ED, Zielinski M, Žídek A, Bapst V, Kohli P, Jaderberg M, Hassabis D, Jumper JM. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024; 630:493-500. [PMID: 38718835 PMCID: PMC11168924 DOI: 10.1038/s41586-024-07487-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/29/2024] [Indexed: 06/13/2024]
Abstract
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
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Affiliation(s)
| | - Jonas Adler
- Core Contributor, Google DeepMind, London, UK
| | - Jack Dunger
- Core Contributor, Google DeepMind, London, UK
| | | | - Tim Green
- Core Contributor, Google DeepMind, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Zachary Wu
- Core Contributor, Google DeepMind, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yousuf A Khan
- Google DeepMind, London, UK
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
| | | | | | | | | | | | | | | | | | | | - Ellen D Zhong
- Google DeepMind, London, UK
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | | | | | | | | | - Demis Hassabis
- Core Contributor, Google DeepMind, London, UK.
- Core Contributor, Isomorphic Labs, London, UK.
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11
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Bernard C, Postic G, Ghannay S, Tahi F. State-of-the-RNArt: benchmarking current methods for RNA 3D structure prediction. NAR Genom Bioinform 2024; 6:lqae048. [PMID: 38745991 PMCID: PMC11091930 DOI: 10.1093/nargab/lqae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/05/2024] [Accepted: 05/08/2024] [Indexed: 05/16/2024] Open
Abstract
RNAs are essential molecules involved in numerous biological functions. Understanding RNA functions requires the knowledge of their 3D structures. Computational methods have been developed for over two decades to predict the 3D conformations from RNA sequences. These computational methods have been widely used and are usually categorised as either ab initio or template-based. The performances remain to be improved. Recently, the rise of deep learning has changed the sight of novel approaches. Deep learning methods are promising, but their adaptation to RNA 3D structure prediction remains difficult. In this paper, we give a brief review of the ab initio, template-based and novel deep learning approaches. We highlight the different available tools and provide a benchmark on nine methods using the RNA-Puzzles dataset. We provide an online dashboard that shows the predictions made by benchmarked methods, freely available on the EvryRNA platform: https://evryrna.ibisc.univ-evry.fr/evryrna/state_of_the_rnart/.
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Affiliation(s)
- Clément Bernard
- Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France
- LISN - CNRS/Université Paris-Saclay, 91400 Orsay, France
| | - Guillaume Postic
- Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France
| | - Sahar Ghannay
- LISN - CNRS/Université Paris-Saclay, 91400 Orsay, France
| | - Fariza Tahi
- Université Paris-Saclay, Univ. Evry, IBISC, 91020 Evry-Courcouronnes, France
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12
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Bryant P, Kelkar A, Guljas A, Clementi C, Noé F. Structure prediction of protein-ligand complexes from sequence information with Umol. Nat Commun 2024; 15:4536. [PMID: 38806453 PMCID: PMC11133481 DOI: 10.1038/s41467-024-48837-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 05/15/2024] [Indexed: 05/30/2024] Open
Abstract
Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly from sequence information. We find that classical docking methods are still superior, but depend upon having crystal structures of the target protein. In addition to predicting flexible all-atom structures, predicted confidence metrics (plDDT) can be used to select accurate predictions as well as to distinguish between strong and weak binders. The advances presented here suggest that the goal of AI-based drug discovery is one step closer, but there is still a way to go to grasp the complexity of protein-ligand interactions fully. Umol is available at: https://github.com/patrickbryant1/Umol .
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Affiliation(s)
- Patrick Bryant
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany.
- The Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Svante Arrhenius väg 20C, 114 18, Stockholm, Sweden.
- Science for Life Laboratory, 172 21, Solna, Sweden.
| | - Atharva Kelkar
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
| | - Andrea Guljas
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
| | - Cecilia Clementi
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
| | - Frank Noé
- Department of Mathematics and Computer Science, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
- Department of Physics, Freie Universität Berlin, Arnimallee 12, 14195, Berlin, Germany
- Microsoft Research AI4Science, Karl-Liebknecht Str. 32, 10178, Berlin, Germany
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13
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Shulgina Y, Trinidad MI, Langeberg CJ, Nisonoff H, Chithrananda S, Skopintsev P, Nissley AJ, Patel J, Boger RS, Shi H, Yoon PH, Doherty EE, Pande T, Iyer AM, Doudna JA, Cate JHD. RNA language models predict mutations that improve RNA function. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.05.588317. [PMID: 38617247 PMCID: PMC11014562 DOI: 10.1101/2024.04.05.588317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. While advances in deep learning enable the prediction of accurate protein structural models, RNA structure prediction is not possible at present due to a lack of abundant high-quality reference data. Furthermore, available sequence data are generally not associated with organismal phenotypes that could inform RNA function. We created GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences derived from GTDB genomes to experimental and predicted optimal growth temperatures of GTDB reference organisms. This enables construction of deep and diverse RNA sequence alignments to be used for machine learning. Using GARNET, we define the minimal requirements for a sequence- and structure-aware RNA generative model. We also develop a GPT-like language model for RNA in which triplet tokenization provides optimal encoding. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identified mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.
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Affiliation(s)
- Yekaterina Shulgina
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
| | - Marena I Trinidad
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
| | - Conner J Langeberg
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
| | - Hunter Nisonoff
- Center for Computational Biology, University of California, Berkeley, CA, United States
| | - Seyone Chithrananda
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Petr Skopintsev
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
| | - Amos J Nissley
- Department of Chemistry, University of California, Berkeley, CA, USA
| | - Jaymin Patel
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
| | - Ron S Boger
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Biophysics Graduate Program, University of California, Berkeley, CA, USA
| | - Honglue Shi
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
| | - Peter H Yoon
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
| | - Erin E Doherty
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
| | - Tara Pande
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA
| | - Aditya M Iyer
- Department of Physics, University of California, Berkeley, CA, USA
| | - Jennifer A Doudna
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
- Howard Hughes Medical Institute, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
- MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Jamie H D Cate
- Innovative Genomics Institute, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
- California Institute for Quantitative Biosciences, University of California, Berkeley, CA, USA
- Department of Chemistry, University of California, Berkeley, CA, USA
- MBIB Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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14
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Vallat B, Berman HM. Structural highlights of macromolecular complexes and assemblies. Curr Opin Struct Biol 2024; 85:102773. [PMID: 38271778 DOI: 10.1016/j.sbi.2023.102773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 12/22/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024]
Abstract
The structures of macromolecular assemblies have given us deep insights into cellular processes and have profoundly impacted biological research and drug discovery. We highlight the structures of macromolecular assemblies that have been modeled using integrative and computational methods and describe how open access to these structures from structural archives has empowered the research community. The arsenal of experimental and computational methods for structure determination ensures a future where whole organelles and cells can be modeled.
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Affiliation(s)
- Brinda Vallat
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA.
| | - Helen M Berman
- Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles CA 90089, USA
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15
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Szikszai M, Magnus M, Sanghi S, Kadyan S, Bouatta N, Rivas E. RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.30.578025. [PMID: 38352531 PMCID: PMC10862857 DOI: 10.1101/2024.01.30.578025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser availability and lower structural diversity of the experimentally resolved RNA structures in comparison to protein structures. These challenges are often poorly addressed by the existing literature, many of which report inflated performance due to using training and testing sets with significant structural overlap. Further, the most recent Critical Assessment of Structure Prediction (CASP15) has shown that deep learning models for RNA structure are currently outperformed by traditional methods. In this paper we present RNA3DB, a dataset of structured RNAs, derived from the Protein Data Bank (PDB), that is designed for training and benchmarking deep learning models. The RNA3DB method arranges the RNA 3D chains into distinct groups (Components) that are non-redundant both with regard to sequence as well as structure, providing a robust way of dividing training, validation, and testing sets. Any split of these structurally-dissimilar Components are guaranteed to produce test and validations sets that are distinct by sequence and structure from those in the training set. We provide the RNA3DB dataset, a particular train/test split of the RNA3DB Components (in an approximate 70/30 ratio) that will be updated periodically. We also provide the RNA3DB methodology along with the source-code, with the goal of creating a reproducible and customizable tool for producing structurally-dissimilar dataset splits for structural RNAs.
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Affiliation(s)
- Marcell Szikszai
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, MA, USA
| | - Marcin Magnus
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, MA, USA
| | - Siddhant Sanghi
- Department of Systems Biology, Columbia University, New York, 10027, NY, USA
- College of Biological Sciences, UC Davis, Davis, 95616, CA, USA
| | - Sachin Kadyan
- Department of Systems Biology, Columbia University, New York, 10027, NY, USA
| | - Nazim Bouatta
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, 02115, MA, USA
| | - Elena Rivas
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, 02138, MA, USA
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16
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Zhang Y, Lang M, Jiang J, Gao Z, Xu F, Litfin T, Chen K, Singh J, Huang X, Song G, Tian Y, Zhan J, Chen J, Zhou Y. Multiple sequence alignment-based RNA language model and its application to structural inference. Nucleic Acids Res 2024; 52:e3. [PMID: 37941140 PMCID: PMC10783488 DOI: 10.1093/nar/gkad1031] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 10/21/2023] [Indexed: 11/10/2023] Open
Abstract
Compared with proteins, DNA and RNA are more difficult languages to interpret because four-letter coded DNA/RNA sequences have less information content than 20-letter coded protein sequences. While BERT (Bidirectional Encoder Representations from Transformers)-like language models have been developed for RNA, they are ineffective at capturing the evolutionary information from homologous sequences because unlike proteins, RNA sequences are less conserved. Here, we have developed an unsupervised multiple sequence alignment-based RNA language model (RNA-MSM) by utilizing homologous sequences from an automatic pipeline, RNAcmap, as it can provide significantly more homologous sequences than manually annotated Rfam. We demonstrate that the resulting unsupervised, two-dimensional attention maps and one-dimensional embeddings from RNA-MSM contain structural information. In fact, they can be directly mapped with high accuracy to 2D base pairing probabilities and 1D solvent accessibilities, respectively. Further fine-tuning led to significantly improved performance on these two downstream tasks compared with existing state-of-the-art techniques including SPOT-RNA2 and RNAsnap2. By comparison, RNA-FM, a BERT-based RNA language model, performs worse than one-hot encoding with its embedding in base pair and solvent-accessible surface area prediction. We anticipate that the pre-trained RNA-MSM model can be fine-tuned on many other tasks related to RNA structure and function.
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Affiliation(s)
- Yikun Zhang
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
- AI for Science (AI4S)-Preferred Program, Peking University Shenzhen Graduate School, Shenzen 518055, China
| | - Mei Lang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Jiuhong Jiang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Zhiqiang Gao
- Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
- Peng Cheng Laboratory, Shenzhen 518066, China
| | - Fan Xu
- Peng Cheng Laboratory, Shenzhen 518066, China
| | - Thomas Litfin
- Institute for Glycomics, Griffith University, Parklands Dr, Southport, QLD 4215, Australia
| | - Ke Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Jaswinder Singh
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | | | - Guoli Song
- Peng Cheng Laboratory, Shenzhen 518066, China
| | | | - Jian Zhan
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
| | - Jie Chen
- School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China
- Peng Cheng Laboratory, Shenzhen 518066, China
| | - Yaoqi Zhou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China
- Institute for Glycomics, Griffith University, Parklands Dr, Southport, QLD 4215, Australia
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17
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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] [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.
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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
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