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  1. AU="Reynolds, Kimberly A"
  2. AU=Hua Cheng-Ge AU=Hua Cheng-Ge
  3. AU="Sorli, Luisa"
  4. AU=Ahmad Shah Adil Ishtiyaq
  5. AU="Bock, Thomas"
  6. AU=Chang Hyejung
  7. AU="Messer, Alison"
  8. AU="Samarzija, Miroslav"
  9. AU="Oh, Yun-Hee"
  10. AU="Ramos, Jairo"
  11. AU="Chauhan Kushwah, Vinita"
  12. AU="Winter, Katrin"
  13. AU="Berro, Julien"
  14. AU=Cummins Claire B.
  15. AU="Damholt, A"
  16. AU="Muthu, Santhosh Kumar"
  17. AU="Tysinger, Emma"
  18. AU=Covarrubias David
  19. AU="Dino Papeš"
  20. AU="Assis, Daniel Barbosa"
  21. AU="Lauquin, Guy J-M"

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  1. Artikel ; Online: The genetic landscape of a metabolic interaction.

    Nguyen, Thuy N / Ingle, Christine / Thompson, Samuel / Reynolds, Kimberly A

    Nature communications

    2024  Band 15, Heft 1, Seite(n) 3351

    Abstract: While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To ... ...

    Abstract While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focus on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We use deep mutational scanning to quantify the growth rate effect of 2696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.
    Mesh-Begriff(e) Mutation ; Thymidylate Synthase/metabolism
    Chemische Substanzen Thymidylate Synthase (EC 2.1.1.45)
    Sprache Englisch
    Erscheinungsdatum 2024-04-18
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-024-47671-0
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Engineering Proteins Using Statistical Models of Coevolutionary Sequence Information.

    Dinan, Jerry C / McCormick, James W / Reynolds, Kimberly A

    Cold Spring Harbor perspectives in biology

    2024  Band 16, Heft 4

    Abstract: Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large ... ...

    Abstract Homologous protein sequences are wonderfully diverse, indicating many possible evolutionary "solutions" to the encoding of function. Consequently, one can construct statistical models of protein sequence by analyzing amino acid frequency across a large multiple sequence alignment. A central premise is that covariance between amino acid positions reflects coevolution due to a shared functional or biophysical constraint. In this review, we describe the implementation and discuss the advantages, limitations, and recent progress on two coevolution-based modeling approaches: (1) Potts models of protein sequence (direct coupling analysis [DCA]-like), and (2) the statistical coupling analysis (SCA). Each approach detects interesting features of protein sequence and structure-the former emphasizes local physical contacts throughout the structure, while the latter identifies larger evolutionarily coupled networks of residues. Recent advances in large-scale gene synthesis and high-throughput functional selection now motivate additional work to benchmark model performance across quantitative function prediction and de novo design tasks.
    Mesh-Begriff(e) Proteins/metabolism ; Amino Acids/genetics ; Models, Statistical ; Evolution, Molecular ; Biological Evolution
    Chemische Substanzen Proteins ; Amino Acids
    Sprache Englisch
    Erscheinungsdatum 2024-04-01
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1943-0264
    ISSN (online) 1943-0264
    DOI 10.1101/cshperspect.a041463
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: A continuous epistasis model for predicting growth rate given combinatorial variation in gene expression and environment.

    Otto, Ryan M / Turska-Nowak, Agata / Brown, Philip M / Reynolds, Kimberly A

    Cell systems

    2024  Band 15, Heft 2, Seite(n) 134–148.e7

    Abstract: Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression- ... ...

    Abstract Quantifying and predicting growth rate phenotype given variation in gene expression and environment is complicated by epistatic interactions and the vast combinatorial space of possible perturbations. We developed an approach for mapping expression-growth rate landscapes that integrates sparsely sampled experimental measurements with an interpretable machine learning model. We used mismatch CRISPRi across pairs and triples of genes to create over 8,000 titrated changes in E. coli gene expression under varied environmental contexts, exploring epistasis in up to 22 distinct environments. Our results show that a pairwise model previously used to describe drug interactions well-described these data. The model yielded interpretable parameters related to pathway architecture and generalized to predict the combined effect of up to four perturbations when trained solely on pairwise perturbation data. We anticipate this approach will be broadly applicable in optimizing bacterial growth conditions, generating pharmacogenomic models, and understanding the fundamental constraints on bacterial gene expression. A record of this paper's transparent peer review process is included in the supplemental information.
    Mesh-Begriff(e) Epistasis, Genetic/genetics ; Escherichia coli/genetics ; Bacteria/genetics ; Gene Expression
    Sprache Englisch
    Erscheinungsdatum 2024-02-09
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2854138-8
    ISSN 2405-4720 ; 2405-4712
    ISSN (online) 2405-4720
    ISSN 2405-4712
    DOI 10.1016/j.cels.2024.01.003
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: The Genetic Landscape of a Metabolic Interaction.

    Nguyen, Thuy N / Ingle, Christine / Thompson, Samuel / Reynolds, Kimberly A

    bioRxiv : the preprint server for biology

    2023  

    Abstract: Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein ...

    Abstract Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.
    Sprache Englisch
    Erscheinungsdatum 2023-08-17
    Erscheinungsland United States
    Dokumenttyp Preprint
    DOI 10.1101/2023.05.28.542639
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: A new test of computational protein design: predicting posttranslational modification specificity for the enzyme SMYD2.

    Reynolds, Kimberly A

    Structure (London, England : 1993)

    2015  Band 23, Heft 1, Seite(n) 11–12

    Abstract: In this issue of Structure, Lanouette and colleagues use a combination of computation and experiment to define a specificity motif for the lysine methyltransferase SMYD2. Using this motif, they predict and experimentally verify four new SMYD2 substrates. ...

    Abstract In this issue of Structure, Lanouette and colleagues use a combination of computation and experiment to define a specificity motif for the lysine methyltransferase SMYD2. Using this motif, they predict and experimentally verify four new SMYD2 substrates.
    Mesh-Begriff(e) Computational Biology/methods ; Histone-Lysine N-Methyltransferase/chemistry ; Histone-Lysine N-Methyltransferase/metabolism ; Humans ; Protein Interaction Maps
    Chemische Substanzen Histone-Lysine N-Methyltransferase (EC 2.1.1.43)
    Sprache Englisch
    Erscheinungsdatum 2015-01-06
    Erscheinungsland United States
    Dokumenttyp Comment ; Journal Article
    ZDB-ID 1213087-4
    ISSN 1878-4186 ; 0969-2126
    ISSN (online) 1878-4186
    ISSN 0969-2126
    DOI 10.1016/j.str.2014.12.004
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  6. Artikel ; Online: Finding a common path: predicting gene function using inferred evolutionary trees.

    Reynolds, Kimberly A

    Developmental cell

    2014  Band 30, Heft 1, Seite(n) 4–5

    Abstract: Reporting in Cell, Li and colleagues (2014) describe an innovative method to functionally classify genes using evolutionary information. This approach demonstrates broad utility for eukaryotic gene annotation and suggests an intriguing new decomposition ... ...

    Abstract Reporting in Cell, Li and colleagues (2014) describe an innovative method to functionally classify genes using evolutionary information. This approach demonstrates broad utility for eukaryotic gene annotation and suggests an intriguing new decomposition of pathways and complexes into evolutionarily conserved modules.
    Mesh-Begriff(e) Algorithms ; Cluster Analysis ; Humans ; Phylogeny
    Sprache Englisch
    Erscheinungsdatum 2014-07-14
    Erscheinungsland United States
    Dokumenttyp Comment ; Journal Article
    ZDB-ID 2054967-2
    ISSN 1878-1551 ; 1534-5807
    ISSN (online) 1878-1551
    ISSN 1534-5807
    DOI 10.1016/j.devcel.2014.06.029
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: A simplified strategy for titrating gene expression reveals new relationships between genotype, environment, and bacterial growth.

    Mathis, Andrew D / Otto, Ryan M / Reynolds, Kimberly A

    Nucleic acids research

    2020  Band 49, Heft 1, Seite(n) e6

    Abstract: A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate ... ...

    Abstract A lack of high-throughput techniques for making titrated, gene-specific changes in expression limits our understanding of the relationship between gene expression and cell phenotype. Here, we present a generalizable approach for quantifying growth rate as a function of titrated changes in gene expression level. The approach works by performing CRISPRi with a series of mutated single guide RNAs (sgRNAs) that modulate gene expression. To evaluate sgRNA mutation strategies, we constructed a library of 5927 sgRNAs targeting 88 genes in Escherichia coli MG1655 and measured the effects on growth rate. We found that a compounding mutational strategy, through which mutations are incrementally added to the sgRNA, presented a straightforward way to generate a monotonic and gradated relationship between mutation number and growth rate effect. We also implemented molecular barcoding to detect and correct for mutations that 'escape' the CRISPRi targeting machinery; this strategy unmasked deleterious growth rate effects obscured by the standard approach of ignoring escapers. Finally, we performed controlled environmental variations and observed that many gene-by-environment interactions go completely undetected at the limit of maximum knockdown, but instead manifest at intermediate expression perturbation strengths. Overall, our work provides an experimental platform for quantifying the phenotypic response to gene expression variation.
    Mesh-Begriff(e) CRISPR-Cas Systems/genetics ; Cell Division/genetics ; Computational Biology/methods ; Escherichia coli/genetics ; Escherichia coli/growth & development ; Gene Expression Regulation, Bacterial ; Gene-Environment Interaction ; Genetic Techniques ; Genotype ; Mutation ; RNA, Guide, CRISPR-Cas Systems
    Sprache Englisch
    Erscheinungsdatum 2020-11-13
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 186809-3
    ISSN 1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
    ISSN (online) 1362-4962 ; 1362-4954
    ISSN 0301-5610 ; 0305-1048
    DOI 10.1093/nar/gkaa1073
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: Structurally distributed surface sites tune allosteric regulation.

    McCormick, James W / Russo, Marielle Ax / Thompson, Samuel / Blevins, Aubrie / Reynolds, Kimberly A

    eLife

    2021  Band 10

    Abstract: Our ability to rationally optimize allosteric regulation is limited by incomplete knowledge of the mutations that tune allostery. Are these mutations few or abundant, structurally localized or distributed? To examine this, we conducted saturation ... ...

    Abstract Our ability to rationally optimize allosteric regulation is limited by incomplete knowledge of the mutations that tune allostery. Are these mutations few or abundant, structurally localized or distributed? To examine this, we conducted saturation mutagenesis of a synthetic allosteric switch in which Dihydrofolate reductase (DHFR) is regulated by a blue-light sensitive LOV2 domain. Using a high-throughput assay wherein DHFR catalytic activity is coupled to
    Mesh-Begriff(e) Allosteric Regulation/genetics ; Allosteric Site/genetics ; Computational Biology ; Escherichia coli/genetics ; Models, Molecular ; Mutation/genetics ; Protein Binding/genetics ; Protein Domains/genetics ; Recombinant Fusion Proteins/chemistry ; Recombinant Fusion Proteins/genetics ; Recombinant Fusion Proteins/metabolism ; Tetrahydrofolate Dehydrogenase/chemistry ; Tetrahydrofolate Dehydrogenase/genetics ; Tetrahydrofolate Dehydrogenase/metabolism
    Chemische Substanzen Recombinant Fusion Proteins ; Tetrahydrofolate Dehydrogenase (EC 1.5.1.3)
    Sprache Englisch
    Erscheinungsdatum 2021-06-16
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.68346
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  9. Artikel: Strategies for Engineering and Rewiring Kinase Regulation.

    McCormick, James W / Pincus, David / Resnekov, Orna / Reynolds, Kimberly A

    Trends in biochemical sciences

    2019  Band 45, Heft 3, Seite(n) 259–271

    Abstract: Eukaryotic protein kinases (EPKs) catalyze the transfer of a phosphate group onto another protein in response to appropriate regulatory cues. In doing so, they provide a primary means for cellular information transfer. Consequently, EPKs play crucial ... ...

    Abstract Eukaryotic protein kinases (EPKs) catalyze the transfer of a phosphate group onto another protein in response to appropriate regulatory cues. In doing so, they provide a primary means for cellular information transfer. Consequently, EPKs play crucial roles in cell differentiation and cell-cycle progression, and kinase dysregulation is associated with numerous disease phenotypes including cancer. Nonnative cues for synthetically regulating kinases are thus much sought after, both for dissecting cell signaling pathways and for pharmaceutical development. In recent years advances in protein engineering and sequence analysis have led to new approaches for manipulating kinase activity, localization, and in some instances specificity. These tools have revealed fundamental principles of intracellular signaling and suggest paths forward for the design of therapeutic allosteric kinase regulators.
    Mesh-Begriff(e) Allosteric Regulation ; Eukaryota/enzymology ; Humans ; Neoplasms/metabolism ; Neoplasms/pathology ; Protein Engineering ; Protein Kinases/chemistry ; Protein Kinases/metabolism ; Sequence Analysis, Protein ; Signal Transduction
    Chemische Substanzen Protein Kinases (EC 2.7.-)
    Sprache Englisch
    Erscheinungsdatum 2019-12-20
    Erscheinungsland England
    Dokumenttyp Journal Article ; Review
    ZDB-ID 194216-5
    ISSN 1362-4326 ; 0968-0004 ; 0376-5067
    ISSN (online) 1362-4326
    ISSN 0968-0004 ; 0376-5067
    DOI 10.1016/j.tibs.2019.11.005
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  10. Artikel ; Online: Altered expression of a quality control protease in

    Thompson, Samuel / Zhang, Yang / Ingle, Christine / Reynolds, Kimberly A / Kortemme, Tanja

    eLife

    2020  Band 9

    Abstract: Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in ... ...

    Abstract Protein mutational landscapes are shaped by the cellular environment, but key factors and their quantitative effects are often unknown. Here we show that Lon, a quality control protease naturally absent in common
    Mesh-Begriff(e) Escherichia coli/enzymology ; Escherichia coli/genetics ; Escherichia coli Proteins/genetics ; Escherichia coli Proteins/metabolism ; Gene Expression ; Mutation ; Protease La/genetics ; Protease La/metabolism
    Chemische Substanzen Escherichia coli Proteins ; Lon protein, E coli (EC 3.4.21.53) ; Protease La (EC 3.4.21.53)
    Sprache Englisch
    Erscheinungsdatum 2020-07-23
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2687154-3
    ISSN 2050-084X ; 2050-084X
    ISSN (online) 2050-084X
    ISSN 2050-084X
    DOI 10.7554/eLife.53476
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