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Ahdritz G, Bouatta N, Floristean C, Kadyan S, Xia Q, Gerecke W, O'Donnell TJ, Berenberg D, Fisk I, Zanichelli N, Zhang B, Nowaczynski A, Wang B, Stepniewska-Dziubinska MM, Zhang S, Ojewole A, Guney ME, Biderman S, Watkins AM, Ra S, Lorenzo PR, Nivon L, Weitzner B, Ban YEA, Chen S, Zhang M, Li C, Song SL, He Y, Sorger PK, Mostaque E, Zhang Z, Bonneau R, AlQuraishi M. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat Methods 2024:10.1038/s41592-024-02272-z. [PMID: 38744917 DOI: 10.1038/s41592-024-02272-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.
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Affiliation(s)
- Gustaf Ahdritz
- Department of Systems Biology, Columbia University, New York, NY, USA
- Harvard University, Cambridge, MA, USA
| | - Nazim Bouatta
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
| | | | - Sachin Kadyan
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Qinghui Xia
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - William Gerecke
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Daniel Berenberg
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Ian Fisk
- Flatiron Institute, New York, NY, USA
| | | | - Bo Zhang
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | | | | | | | | | | | | | - Stella Biderman
- EleutherAI, New York, NY, USA
- Booz Allen Hamilton, McLean, VA, USA
| | | | - Stephen Ra
- Prescient Design, Genentech, New York, NY, USA
| | | | | | | | | | | | - Minjia Zhang
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | | | | | | | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Zhao Zhang
- Rutgers University, New Brunswick, NJ, USA
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Chen K, Litfin T, Singh J, Zhan J, Zhou Y. MARS and RNAcmap3: The Master Database of All Possible RNA Sequences Integrated with RNAcmap for RNA Homology Search. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae018. [PMID: 38872612 DOI: 10.1093/gpbjnl/qzae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 09/24/2023] [Accepted: 10/31/2023] [Indexed: 06/15/2024]
Abstract
Recent success of AlphaFold2 in protein structure prediction relied heavily on co-evolutionary information derived from homologous protein sequences found in the huge, integrated database of protein sequences (Big Fantastic Database). In contrast, the existing nucleotide databases were not consolidated to facilitate wider and deeper homology search. Here, we built a comprehensive database by incorporating the non-coding RNA (ncRNA) sequences from RNAcentral, the transcriptome assembly and metagenome assembly from metagenomics RAST (MG-RAST), the genomic sequences from Genome Warehouse (GWH), and the genomic sequences from MGnify, in addition to the nucleotide (nt) database and its subsets in National Center of Biotechnology Information (NCBI). The resulting Master database of All possible RNA sequences (MARS) is 20-fold larger than NCBI's nt database or 60-fold larger than RNAcentral. The new dataset along with a new split-search strategy allows a substantial improvement in homology search over existing state-of-the-art techniques. It also yields more accurate and more sensitive multiple sequence alignments (MSAs) than manually curated MSAs from Rfam for the majority of structured RNAs mapped to Rfam. The results indicate that MARS coupled with the fully automatic homology search tool RNAcmap will be useful for improved structural and functional inference of ncRNAs and RNA language models based on MSAs. MARS is accessible at https://ngdc.cncb.ac.cn/omix/release/OMIX003037, and RNAcmap3 is accessible at http://zhouyq-lab.szbl.ac.cn/download/.
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Affiliation(s)
- Ke Chen
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
- Peking University Shenzhen Graduate School, Shenzhen 518055, China
- University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China
| | - Thomas Litfin
- Institute for Glycomics, Griffith University, Southport, QLD 4222, Australia
| | - Jaswinder Singh
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Jian Zhan
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Yaoqi Zhou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
- Peking University Shenzhen Graduate School, Shenzhen 518055, China
- Institute for Glycomics, Griffith University, Southport, QLD 4222, Australia
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Ramakers J, Blum CF, König S, Harmeling S, Kollmann M. De novo prediction of RNA 3D structures with deep generative models. PLoS One 2024; 19:e0297105. [PMID: 38358972 PMCID: PMC10868834 DOI: 10.1371/journal.pone.0297105] [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: 05/31/2023] [Accepted: 12/24/2023] [Indexed: 02/17/2024] Open
Abstract
We present a Deep Learning approach to predict 3D folding structures of RNAs from their nucleic acid sequence. Our approach combines an autoregressive Deep Generative Model, Monte Carlo Tree Search, and a score model to find and rank the most likely folding structures for a given RNA sequence. We show that RNA de novo structure prediction by deep learning is possible at atom resolution, despite the low number of experimentally measured structures that can be used for training. We confirm the predictive power of our approach by achieving competitive results in a retrospective evaluation of the RNA-Puzzles prediction challenges, without using structural contact information from multiple sequence alignments or additional data from chemical probing experiments. Blind predictions for recent RNA-Puzzle challenges under the name "Dfold" further support the competitive performance of our approach.
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Affiliation(s)
- Julius Ramakers
- Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | | | - Sabrina König
- Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Stefan Harmeling
- Department of Computer Science, Technical University Dortmund, Dortmund, Germany
| | - Markus Kollmann
- Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
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4
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Wang W, Feng C, Han R, Wang Z, Ye L, Du Z, Wei H, Zhang F, Peng Z, Yang J. trRosettaRNA: automated prediction of RNA 3D structure with transformer network. Nat Commun 2023; 14:7266. [PMID: 37945552 PMCID: PMC10636060 DOI: 10.1038/s41467-023-42528-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 10/13/2023] [Indexed: 11/12/2023] Open
Abstract
RNA 3D structure prediction is a long-standing challenge. Inspired by the recent breakthrough in protein structure prediction, we developed trRosettaRNA, an automated deep learning-based approach to RNA 3D structure prediction. The trRosettaRNA pipeline comprises two major steps: 1D and 2D geometries prediction by a transformer network; and 3D structure folding by energy minimization. Benchmark tests suggest that trRosettaRNA outperforms traditional automated methods. In the blind tests of the 15th Critical Assessment of Structure Prediction (CASP15) and the RNA-Puzzles experiments, the automated trRosettaRNA predictions for the natural RNAs are competitive with the top human predictions. trRosettaRNA also outperforms other deep learning-based methods in CASP15 when measured by the Z-score of the Root-Mean-Square Deviation. Nevertheless, it remains challenging to predict accurate structures for synthetic RNAs with an automated approach. We hope this work could be a good start toward solving the hard problem of RNA structure prediction with deep learning.
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Affiliation(s)
- Wenkai Wang
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Chenjie Feng
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
- School of Science, Ningxia Medical University, Yinchuan, 750004, China
| | - Renmin Han
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Ziyi Wang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China
| | - Lisha Ye
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Zongyang Du
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Hong Wei
- School of Mathematical Sciences, Nankai University, Tianjin, 300071, China
| | - Fa Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Zhenling Peng
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
| | - Jianyi Yang
- MOE Frontiers Science Center for Nonlinear Expectations, Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao, 266237, China.
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Li Y, Zhang C, Feng C, Pearce R, Lydia Freddolino P, Zhang Y. Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction. Nat Commun 2023; 14:5745. [PMID: 37717036 PMCID: PMC10505173 DOI: 10.1038/s41467-023-41303-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 08/22/2023] [Indexed: 09/18/2023] Open
Abstract
RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.
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Affiliation(s)
- Yang Li
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore, Singapore
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06511, USA
| | - Chenjie Feng
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- School of Science, Ningxia Medical University, Yinchuan, 750004, China
| | - Robin Pearce
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore, Singapore
| | - P Lydia Freddolino
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
| | - Yang Zhang
- Cancer Science Institute of Singapore, National University of Singapore, 117599, Singapore, Singapore.
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Computer Science, School of Computing, National University of Singapore, 117417, Singapore, Singapore.
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 117596, Singapore, Singapore.
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Li J, Chen SJ. RNAJP: enhanced RNA 3D structure predictions with non-canonical interactions and global topology sampling. Nucleic Acids Res 2023; 51:3341-3356. [PMID: 36864729 PMCID: PMC10123122 DOI: 10.1093/nar/gkad122] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 01/14/2023] [Accepted: 02/25/2023] [Indexed: 03/04/2023] Open
Abstract
RNA 3D structures are critical for understanding their functions. However, only a limited number of RNA structures have been experimentally solved, so computational prediction methods are highly desirable. Nevertheless, accurate prediction of RNA 3D structures, especially those containing multiway junctions, remains a significant challenge, mainly due to the complicated non-canonical base pairing and stacking interactions in the junction loops and the possible long-range interactions between loop structures. Here we present RNAJP ('RNA Junction Prediction'), a nucleotide- and helix-level coarse-grained model for the prediction of RNA 3D structures, particularly junction structures, from a given 2D structure. Through global sampling of the 3D arrangements of the helices in junctions using molecular dynamics simulations and in explicit consideration of non-canonical base pairing and base stacking interactions as well as long-range loop-loop interactions, the model can provide significantly improved predictions for multibranched junction structures than existing methods. Moreover, integrated with additional restraints from experiments, such as junction topology and long-range interactions, the model may serve as a useful structure generator for various applications.
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Affiliation(s)
- Jun Li
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Shi-Jie Chen
- Department of Physics, Department of Biochemistry and Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
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Abstract
RNA molecules carry out various cellular functions, and understanding the mechanisms behind their functions requires the knowledge of their 3D structures. Different types of computational methods have been developed to model RNA 3D structures over the past decade. These methods were widely used by researchers although their performance needs to be further improved. Recently, along with these traditional methods, machine-learning techniques have been increasingly applied to RNA 3D structure prediction and show significant improvement in performance. Here we shall give a brief review of the traditional methods and recent related advances in machine-learning approaches for RNA 3D structure prediction.
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Affiliation(s)
- Xiujuan Ou
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Zhang
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yiduo Xiong
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
| | - Yi Xiao
- Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
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Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions. Comput Struct Biotechnol J 2022; 20:6120-6137. [PMID: 36420155 PMCID: PMC9672130 DOI: 10.1016/j.csbj.2022.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/04/2022] [Accepted: 11/04/2022] [Indexed: 11/13/2022] Open
Abstract
The International Society of RNA Nanotechnology and Nanomedicine (ISRNN) serves to further the development of a wide variety of functional nucleic acids and other related nanotechnology platforms. To aid in the dissemination of the most recent advancements, a biennial discussion focused on biomotors, viral assembly, and RNA nanobiotechnology has been established where international experts in interdisciplinary fields such as structural biology, biophysical chemistry, nanotechnology, cell and cancer biology, and pharmacology share their latest accomplishments and future perspectives. The results summarized here highlight advancements in our understanding of viral biology and the structure-function relationship of frame-shifting elements in genomic viral RNA, improvements in the predictions of SHAPE analysis of 3D RNA structures, and the understanding of dynamic RNA structures through a variety of experimental and computational means. Additionally, recent advances in the drug delivery, vaccine design, nanopore technologies, biomotor and biomachine development, DNA packaging, RNA nanotechnology, and drug delivery are included in this critical review. We emphasize some of the novel accomplishments, major discussion topics, and present current challenges and perspectives of these emerging fields.
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