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Hemmer S, Siedhoff NE, Werner S, Ölçücü G, Schwaneberg U, Jaeger KE, Davari MD, Krauss U. Machine Learning-Assisted Engineering of Light, Oxygen, Voltage Photoreceptor Adduct Lifetime. JACS AU 2023; 3:3311-3323. [PMID: 38155650 PMCID: PMC10751770 DOI: 10.1021/jacsau.3c00440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/07/2023] [Accepted: 11/07/2023] [Indexed: 12/30/2023]
Abstract
Naturally occurring and engineered flavin-binding, blue-light-sensing, light, oxygen, voltage (LOV) photoreceptor domains have been used widely to design fluorescent reporters, optogenetic tools, and photosensitizers for the visualization and control of biological processes. In addition, natural LOV photoreceptors with engineered properties were recently employed for optimizing plant biomass production in the framework of a plant-based bioeconomy. Here, the understanding and fine-tuning of LOV photoreceptor (kinetic) properties is instrumental for application. In response to blue-light illumination, LOV domains undergo a cascade of photophysical and photochemical events that yield a transient covalent FMN-cysteine adduct, allowing for signaling. The rate-limiting step of the LOV photocycle is the dark-recovery process, which involves adduct scission and can take between seconds and days. Rational engineering of LOV domains with fine-tuned dark recovery has been challenging due to the lack of a mechanistic model, the long time scale of the process, which hampers atomistic simulations, and a gigantic protein sequence space covering known mutations (combinatorial challenge). To address these issues, we used machine learning (ML) trained on scarce literature data and iteratively generated and implemented experimental data to design LOV variants with faster and slower dark recovery. Over the three prediction-validation cycles, LOV domain variants were successfully predicted, whose adduct-state lifetimes spanned 7 orders of magnitude, yielding optimized tools for synthetic (opto)biology. In summary, our results demonstrate ML as a viable method to guide the design of proteins even with limited experimental data and when no mechanistic model of the underlying physical principles is available.
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Affiliation(s)
- Stefanie Hemmer
- Institute
of Molecular Enzyme Technology, Heinrich
Heine University Düsseldorf, Wilhelm Johnen Strasse, Jülich 52426, Germany
| | - Niklas Erik Siedhoff
- Institute
of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074 Aachen, Germany
- DWI-Leibniz
Institute for Interactive Materials, Forckenbeckstraße 50, 52074 Aachen, Germany
| | - Sophia Werner
- Institute
of Molecular Enzyme Technology, Heinrich
Heine University Düsseldorf, Wilhelm Johnen Strasse, Jülich 52426, Germany
| | - Gizem Ölçücü
- Institute
of Molecular Enzyme Technology, Heinrich
Heine University Düsseldorf, Wilhelm Johnen Strasse, Jülich 52426, Germany
| | - Ulrich Schwaneberg
- Institute
of Biotechnology, RWTH Aachen University, Worringer Weg 3, 52074 Aachen, Germany
- DWI-Leibniz
Institute for Interactive Materials, Forckenbeckstraße 50, 52074 Aachen, Germany
| | - Karl-Erich Jaeger
- Institute
of Molecular Enzyme Technology, Heinrich
Heine University Düsseldorf, Wilhelm Johnen Strasse, Jülich 52426, Germany
- Institute
of Bio-and Geosciences IBG 1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm Johnen Strasse, Jülich 52426, Germany
| | - Mehdi D. Davari
- Department
of Bioorganic Chemistry, Leibniz Institute
of Plant Biochemistry, Weinberg 3, 06120 Halle, Germany
| | - Ulrich Krauss
- Institute
of Molecular Enzyme Technology, Heinrich
Heine University Düsseldorf, Wilhelm Johnen Strasse, Jülich 52426, Germany
- Institute
of Bio-and Geosciences IBG 1: Biotechnology, Forschungszentrum Jülich GmbH, Wilhelm Johnen Strasse, Jülich 52426, Germany
- Department
of Biochemistry, University of Bayreuth, 95447 Bayreuth, Germany
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Charest N, Shen Y, Lai YC, Chen IA, Shea JE. Discovering pathways through ribozyme fitness landscapes using information theoretic quantification of epistasis. RNA (NEW YORK, N.Y.) 2023; 29:1644-1657. [PMID: 37580126 PMCID: PMC10578471 DOI: 10.1261/rna.079541.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 07/29/2023] [Indexed: 08/16/2023]
Abstract
The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary "bridge" between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.
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Affiliation(s)
- Nathaniel Charest
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
| | - Yuning Shen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
| | - Yei-Chen Lai
- Department of Chemistry, National Chung Hsing University, Taichung City 40227, Taiwan
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, USA
| | - Irene A Chen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, USA
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
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Roberts JM, Beck JD, Pollock TB, Bendixsen DP, Hayden EJ. RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes. eLife 2023; 12:80360. [PMID: 36655987 PMCID: PMC9901934 DOI: 10.7554/elife.80360] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 12/28/2022] [Indexed: 01/20/2023] Open
Abstract
Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence-to-function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here, we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heatmap-based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures, and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.
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Affiliation(s)
- Jessica M Roberts
- Biomolecular Sciences Graduate Programs, Boise State UniversityBoiseUnited States
| | - James D Beck
- Computing PhD Program, Boise State UniversityBoiseUnited States
| | - Tanner B Pollock
- Department of Biological Science, Boise State UniversityBoiseUnited States
| | - Devin P Bendixsen
- Biomolecular Sciences Graduate Programs, Boise State UniversityBoiseUnited States
| | - Eric J Hayden
- Biomolecular Sciences Graduate Programs, Boise State UniversityBoiseUnited States
- Computing PhD Program, Boise State UniversityBoiseUnited States
- Department of Biological Science, Boise State UniversityBoiseUnited States
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