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Crowdsourcing to predict RNA degradation and secondary structure. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00615-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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Martin NS, Ahnert SE. Fast free-energy-based neutral set size estimates for the RNA genotype-phenotype map. J R Soc Interface 2022; 19:20220072. [PMID: 35702868 PMCID: PMC9198509 DOI: 10.1098/rsif.2022.0072] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/23/2022] [Indexed: 12/30/2022] Open
Abstract
The genotype-phenotype (GP) map of RNA secondary structure links each RNA sequence to its corresponding secondary structure. Previous research has shown that the large-scale structural properties of GP maps, such as the size of neutral sets in genotype space, can influence evolutionary outcomes. In order to use neutral set sizes, efficient and accurate computational methods are needed to compute them. Here, we propose a new method, which is based on free energy estimates and is much faster than existing sample-based methods. Moreover, this approach can give insight into the reasons behind neutral set size variations, for example, why structures with fewer stacks tend to have larger neutral set sizes. In addition, we generalize neutral set size calculations from the previously studied many-to-one framework, where each sequence folds into a single energetically preferred structure, to a fuller many-to-many framework, where several low-energy structures are included. We find that structures with high neutral sets in one framework also tend to have large neutral sets in the other framework for a range of parameters and thus the choice of GP map does not fundamentally affect which structures have the largest neutral set sizes.
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Affiliation(s)
- Nora S. Martin
- Theory of Condensed Matter Group, Cavendish Laboratory, University of Cambridge, JJ Thomson Avenue, Cambridge CB3 0HE, UK
- Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
- Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Parks Road, Oxford OX1 3PU, UK
| | - Sebastian E. Ahnert
- Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK
- The Alan Turing Institute, British Library, Euston Road, London NW1 2DB, UK
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Saman Booy M, Ilin A, Orponen P. RNA secondary structure prediction with convolutional neural networks. BMC Bioinformatics 2022; 23:58. [PMID: 35109787 PMCID: PMC8812003 DOI: 10.1186/s12859-021-04540-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 12/08/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Predicting the secondary, i.e. base-pairing structure of a folded RNA strand is an important problem in synthetic and computational biology. First-principle algorithmic approaches to this task are challenging because existing models of the folding process are inaccurate, and even if a perfect model existed, finding an optimal solution would be in general NP-complete. RESULTS In this paper, we propose a simple, yet effective data-driven approach. We represent RNA sequences in the form of three-dimensional tensors in which we encode possible relations between all pairs of bases in a given sequence. We then use a convolutional neural network to predict a two-dimensional map which represents the correct pairings between the bases. Our model achieves significant accuracy improvements over existing methods on two standard datasets, RNAStrAlign and ArchiveII, for 10 RNA families, where our experiments show excellent performance of the model across a wide range of sequence lengths. Since our matrix representation and post-processing approaches do not require the structures to be pseudoknot-free, we get similar good performance also for pseudoknotted structures. CONCLUSION We show how to use an artificial neural network design to predict the structure for a given RNA sequence with high accuracy only by learning from samples whose native structures have been experimentally characterized, independent of any energy model.
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Affiliation(s)
- Mehdi Saman Booy
- Department of Computer Science, Aalto University, Espoo, Finland.
| | - Alexander Ilin
- Department of Computer Science, Aalto University, Espoo, Finland
| | - Pekka Orponen
- Department of Computer Science, Aalto University, Espoo, Finland
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Cincilla G, Masoni S, Blobel J. Individual and collective human intelligence in drug design: evaluating the search strategy. J Cheminform 2021; 13:80. [PMID: 34635158 PMCID: PMC8507178 DOI: 10.1186/s13321-021-00556-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 09/18/2021] [Indexed: 11/10/2022] Open
Abstract
In recent years, individual and collective human intelligence, defined as the knowledge, skills, reasoning and intuition of individuals and groups, have been used in combination with computer algorithms to solve complex scientific problems. Such approach was successfully used in different research fields such as: structural biology, comparative genomics, macromolecular crystallography and RNA design. Herein we describe an attempt to use a similar approach in small-molecule drug discovery, specifically to drive search strategies of de novo drug design. This is assessed with a case study that consists of a series of public experiments in which participants had to explore the huge chemical space in silico to find predefined compounds by designing molecules and analyzing the score associate with them. Such a process may be seen as an instantaneous surrogate of the classical design-make-test cycles carried out by medicinal chemists during the drug discovery hit to lead phase but not hindered by long synthesis and testing times. We present first findings on (1) assessing human intelligence in chemical space exploration, (2) comparing individual and collective human intelligence performance in this task and (3) contrasting some human and artificial intelligence achievements in de novo drug design.
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Affiliation(s)
- Giovanni Cincilla
- Molomics, Barcelona Science Park, c/Baldiri i Reixac 4-12, 08028, Barcelona, Spain.
| | - Simone Masoni
- Molomics, Barcelona Science Park, c/Baldiri i Reixac 4-12, 08028, Barcelona, Spain.
| | - Jascha Blobel
- Molomics, Barcelona Science Park, c/Baldiri i Reixac 4-12, 08028, Barcelona, Spain.
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Kofman C, Lee J, Jewett MC. Engineering molecular translation systems. Cell Syst 2021; 12:593-607. [PMID: 34139167 DOI: 10.1016/j.cels.2021.04.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 02/19/2021] [Accepted: 03/31/2021] [Indexed: 12/16/2022]
Abstract
Molecular translation systems provide a genetically encoded framework for protein synthesis, which is essential for all life. Engineering these systems to incorporate non-canonical amino acids (ncAAs) into peptides and proteins has opened many exciting opportunities in chemical and synthetic biology. Here, we review recent advances that are transforming our ability to engineer molecular translation systems. In cell-based systems, new processes to synthesize recoded genomes, tether ribosomal subunits, and engineer orthogonality with high-throughput workflows have emerged. In cell-free systems, adoption of flexizyme technology and cell-free ribosome synthesis and evolution platforms are expanding the limits of chemistry at the ribosome's RNA-based active site. Looking forward, innovations will deepen understanding of molecular translation and provide a path to polymers with previously unimaginable structures and functions.
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Affiliation(s)
- Camila Kofman
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
| | - Joongoo Lee
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA; Department of Chemical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Michael C Jewett
- Department of Chemical and Biological Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA; Interdisplinary Biological Sciences Graduate Program, Northwestern University, Evanston, IL 60208, USA; Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA; Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Evanston, IL 60208, USA; Simpson Querrey Institute, Northwestern University, Evanston, IL 60208, USA; Center for Synthetic Biology, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA.
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6
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Thavarajah W, Hertz LM, Bushhouse DZ, Archuleta CM, Lucks JB. RNA Engineering for Public Health: Innovations in RNA-Based Diagnostics and Therapeutics. Annu Rev Chem Biomol Eng 2021; 12:263-286. [PMID: 33900805 PMCID: PMC9714562 DOI: 10.1146/annurev-chembioeng-101420-014055] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
RNA is essential for cellular function: From sensing intra- and extracellular signals to controlling gene expression, RNA mediates a diverse and expansive list of molecular processes. A long-standing goal of synthetic biology has been to develop RNA engineering principles that can be used to harness and reprogram these RNA-mediated processes to engineer biological systems to solve pressing global challenges. Recent advances in the field of RNA engineering are bringing this to fruition, enabling the creation of RNA-based tools to combat some of the most urgent public health crises. Specifically, new diagnostics using engineered RNAs are able to detect both pathogens and chemicals while generating an easily detectable fluorescent signal as an indicator. New classes of vaccines and therapeutics are also using engineered RNAs to target a wide range of genetic and pathogenic diseases. Here, we discuss the recent breakthroughs in RNA engineering enabling these innovations and examine how advances in RNA design promise to accelerate the impact of engineered RNA systems.
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Affiliation(s)
- Walter Thavarajah
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA; .,Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Center for Water Research, Northwestern University, Evanston, Illinois 60208, USA
| | - Laura M Hertz
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Interdisciplinary Biological Sciences Graduate Program, Northwestern University, Evanston, Illinois 60208, USA
| | - David Z Bushhouse
- Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Interdisciplinary Biological Sciences Graduate Program, Northwestern University, Evanston, Illinois 60208, USA
| | - Chloé M Archuleta
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA; .,Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Center for Water Research, Northwestern University, Evanston, Illinois 60208, USA
| | - Julius B Lucks
- Department of Chemical and Biological Engineering, Northwestern University, Evanston, Illinois 60208, USA; .,Center for Synthetic Biology, Northwestern University, Evanston, Illinois 60208, USA.,Center for Water Research, Northwestern University, Evanston, Illinois 60208, USA.,Center for Engineering Sustainability and Resilience, Northwestern University, Evanston, Illinois 60208, USA
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Rice GM, Shivashankar V, Ma EJ, Baryza JL, Nutiu R. Functional Atlas of Primary miRNA Maturation by the Microprocessor. Mol Cell 2020; 80:892-902.e4. [DOI: 10.1016/j.molcel.2020.10.028] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 08/27/2020] [Accepted: 10/16/2020] [Indexed: 12/26/2022]
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Badu S, Melnik R, Singh S. Mathematical and computational models of RNA nanoclusters and their applications in data-driven environments. MOLECULAR SIMULATION 2020. [DOI: 10.1080/08927022.2020.1804564] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Shyam Badu
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
| | - Roderick Melnik
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
- BCAM-Basque Center for Applied Mathematics, Bilbao, Spain
| | - Sundeep Singh
- MS2Discovery Interdisciplinary Research Institute, Wilfrid Laurier University, Waterloo, Ontario, Canada
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