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Filipek J, Chalaskiewicz K, Kosmider A, Nielipinski M, Michalak A, Bednarkiewicz M, Goslawski-Zeligowski M, Prucnal F, Sekula B, Pietrzyk-Brzezinska AJ. Comprehensive structural overview of the C-terminal ligand-binding domains of the TetR family regulators. J Struct Biol 2024; 216:108071. [PMID: 38401830 DOI: 10.1016/j.jsb.2024.108071] [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: 01/05/2024] [Revised: 02/20/2024] [Accepted: 02/20/2024] [Indexed: 02/26/2024]
Abstract
TetR family regulators (TFRs) represent a large group of one-component bacterial signal transduction systems which recognize environmental signals, like the presence of antibiotics or other bactericidal compounds, and trigger the cell response by regulating the expression of genes that secure bacterial survival in harsh environmental conditions. TFRs act as homodimers, each protomer is composed of a conserved DNA-binding N-terminal domain (NTD) and a variable ligand-binding C-terminal domain (CTD). Currently, there are about 500 structures of TFRs available in the Protein Data Bank and one-fourth of them represent the structures of TFR-ligand complexes. In this review, we summarized information on the ligands interacting with TFRs and based on structural data, we compared the CTDs of the TFR family members, as well as their ligand-binding cavities. Additionally, we divided the whole TFR family, including more than half of a million sequences, into subfamilies according to calculated multiple sequence alignment and phylogenetic tree. We also highlighted structural elements characteristic of some of the subfamilies. The presented comprehensive overview of the TFR CTDs provides good bases and future directions for further studies on TFRs that are not only important targets for battling multidrug resistance but also good candidates for many biotechnological approaches, like TFR-based biosensors.
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Affiliation(s)
- Jakub Filipek
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland
| | - Katarzyna Chalaskiewicz
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland; Institute of Molecular and Industrial Biotechnology, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Stefanowskiego 2/22, Lodz 90-537, Poland
| | - Aleksandra Kosmider
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland
| | - Maciej Nielipinski
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland; Institute of Molecular and Industrial Biotechnology, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Stefanowskiego 2/22, Lodz 90-537, Poland
| | - Agnieszka Michalak
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland
| | - Maria Bednarkiewicz
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland
| | - Mieszko Goslawski-Zeligowski
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland
| | - Filip Prucnal
- Biotechnology Students Association Ferment, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Wolczanska 171/173, 90-530 Lodz, Poland
| | - Bartosz Sekula
- Institute of Molecular and Industrial Biotechnology, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Stefanowskiego 2/22, Lodz 90-537, Poland
| | - Agnieszka J Pietrzyk-Brzezinska
- Institute of Molecular and Industrial Biotechnology, Faculty of Biotechnology and Food Sciences, Lodz University of Technology, Stefanowskiego 2/22, Lodz 90-537, Poland.
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2
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Gonzales J, Kim I, Hwang W, Cho JH. Evolutionary rewiring of the dynamic network underpinning allosteric epistasis in NS1 of influenza A virus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.24.595776. [PMID: 38826371 PMCID: PMC11142230 DOI: 10.1101/2024.05.24.595776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Viral proteins frequently mutate to evade or antagonize host innate immune responses, yet the impact of these mutations on the molecular energy landscape remains unclear. Epistasis, the intramolecular communications between mutations, often renders the combined mutational effects unpredictable. Nonstructural protein 1 (NS1) is a major virulence factor of the influenza A virus (IAV) that activates host PI3K by binding to its p85β subunit. Here, we present the deep analysis for the impact of evolutionary mutations in NS1 that emerged between the 1918 pandemic IAV strain and its descendant PR8 strain. Our analysis reveal how the mutations rewired inter-residue communications which underlies long-range allosteric and epistatic networks in NS1. Our findings show that PR8 NS1 binds to p85β with approximately 10-fold greater affinity than 1918 NS1 due to allosteric mutational effects. Notably, these mutations also exhibited long-range epistatic effects. NMR chemical shift perturbation and methyl-axis order parameter analyses revealed that the mutations induced long-range structural and dynamic changes in PR8 NS1, enhancing its affinity to p85β. Complementary MD simulations and graph-based network analysis uncover how these mutations rewire dynamic residue interaction networks, which underlies the long-range epistasis and allosteric effects on p85β-binding affinity. Significantly, we find that conformational dynamics of residues with high betweenness centrality play a crucial role in communications between network communities and are highly conserved across influenza A virus evolution. These findings advance our mechanistic understanding of the allosteric and epistatic communications between distant residues and provides insight into their role in the molecular evolution of NS1.
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3
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Nishikawa KK, Chen J, Acheson JF, Harbaugh SV, Huss P, Frenkel M, Novy N, Sieren HR, Lodewyk EC, Lee DH, Chávez JL, Fox BG, Raman S. Highly multiplexed design of an allosteric transcription factor to sense novel ligands. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583947. [PMID: 38496486 PMCID: PMC10942455 DOI: 10.1101/2024.03.07.583947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.
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Affiliation(s)
- Kyle K Nishikawa
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jackie Chen
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Justin F Acheson
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Svetlana V Harbaugh
- 711th Human Performance Wing, Air Force Research Laboratory Wright Patterson Air Force Base, OH, USA
| | - Phil Huss
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Max Frenkel
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Nathan Novy
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Hailey R Sieren
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Ella C Lodewyk
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Daniel H Lee
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Jorge L Chávez
- 711th Human Performance Wing, Air Force Research Laboratory Wright Patterson Air Force Base, OH, USA
| | - Brian G Fox
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI, USA
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, Madison, WI, USA
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4
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Swint-Kruse L, Fenton AW. Rheostats, toggles, and neutrals, Oh my! A new framework for understanding how amino acid changes modulate protein function. J Biol Chem 2024; 300:105736. [PMID: 38336297 PMCID: PMC10914490 DOI: 10.1016/j.jbc.2024.105736] [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: 11/15/2023] [Revised: 01/09/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Advances in personalized medicine and protein engineering require accurately predicting outcomes of amino acid substitutions. Many algorithms correctly predict that evolutionarily-conserved positions show "toggle" substitution phenotypes, which is defined when a few substitutions at that position retain function. In contrast, predictions often fail for substitutions at the less-studied "rheostat" positions, which are defined when different amino acid substitutions at a position sample at least half of the possible functional range. This review describes efforts to understand the impact and significance of rheostat positions: (1) They have been observed in globular soluble, integral membrane, and intrinsically disordered proteins; within single proteins, their prevalence can be up to 40%. (2) Substitutions at rheostat positions can have biological consequences and ∼10% of substitutions gain function. (3) Although both rheostat and "neutral" (defined when all substitutions exhibit wild-type function) positions are nonconserved, the two classes have different evolutionary signatures. (4) Some rheostat positions have pleiotropic effects on function, simultaneously modulating multiple parameters (e.g., altering both affinity and allosteric coupling). (5) In structural studies, substitutions at rheostat positions appear to cause only local perturbations; the overall conformations appear unchanged. (6) Measured functional changes show promising correlations with predicted changes in protein dynamics; the emergent properties of predicted, dynamically coupled amino acid networks might explain some of the complex functional outcomes observed when substituting rheostat positions. Overall, rheostat positions provide unique opportunities for using single substitutions to tune protein function. Future studies of these positions will yield important insights into the protein sequence/function relationship.
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Affiliation(s)
- Liskin Swint-Kruse
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas, USA.
| | - Aron W Fenton
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, Kansas City, Kansas, USA
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5
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Zeng M, Sarker B, Rondthaler SN, Vu V, Andrews LB. Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape. ACS Synth Biol 2024; 13:568-589. [PMID: 38206199 DOI: 10.1021/acssynbio.3c00543] [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] [Indexed: 01/12/2024]
Abstract
Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.
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Affiliation(s)
- Min Zeng
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Biprodev Sarker
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Stephen N Rondthaler
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Vanessa Vu
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
| | - Lauren B Andrews
- Department of Chemical Engineering, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Molecular and Cellular Biology Graduate Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
- Biotechnology Training Program, University of Massachusetts Amherst, Amherst, Massachusetts 01003, United States
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6
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Xi C, Diao J, Moon TS. Advances in ligand-specific biosensing for structurally similar molecules. Cell Syst 2023; 14:1024-1043. [PMID: 38128482 PMCID: PMC10751988 DOI: 10.1016/j.cels.2023.10.009] [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: 05/21/2023] [Revised: 08/23/2023] [Accepted: 10/19/2023] [Indexed: 12/23/2023]
Abstract
The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.
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Affiliation(s)
- Chenggang Xi
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Jinjin Diao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tae Seok Moon
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA; Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, MO, USA.
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7
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Guo Z, Smutok O, Ayva CE, Walden P, Parker J, Whitfield J, Vickers CE, Ungerer JPJ, Katz E, Alexandrov K. Development of epistatic YES and AND protein logic gates and their assembly into signalling cascades. NATURE NANOTECHNOLOGY 2023; 18:1327-1334. [PMID: 37500780 DOI: 10.1038/s41565-023-01450-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 06/09/2023] [Indexed: 07/29/2023]
Abstract
The construction and assembly of artificial allosteric protein switches into information and energy processing networks connected to both biological and non-biological systems is a central goal of synthetic biology and bionanotechnology. However, designing protein switches with the desired input, output and performance parameters is challenging. Here we use a range of reporter proteins to demonstrate that their chimeras with duplicated receptor domains produce YES gate protein switches with large (up to 9,000-fold) dynamic ranges and fast (minutes) response rates. In such switches, the epistatic interactions between largely independent synthetic allosteric sites result in an OFF state with minimal background noise. We used YES gate protein switches based on β-lactamase to develop quantitative biosensors of therapeutic drugs and protein biomarkers. Furthermore, we demonstrated the reconfiguration of YES gate switches into AND gate switches controlled by two different inputs, and their assembly into signalling networks regulated at multiple nodes.
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Affiliation(s)
- Zhong Guo
- ARC Centre of Excellence in Synthetic Biology, Brisbane, Queensland, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Oleh Smutok
- Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY, USA
| | - Cagla Ergun Ayva
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Patricia Walden
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jake Parker
- Yakka Bio, Canberra, New South Wales, Australia
| | - Jason Whitfield
- UNSW Founders, University of New South Wales, Sydney, New South Wales, Australia
| | - Claudia E Vickers
- ARC Centre of Excellence in Synthetic Biology, Brisbane, Queensland, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jacobus P J Ungerer
- Department of Chemical Pathology, Pathology Queensland, Brisbane, Queensland, Australia
- Faculty of Health and Behavioural Sciences, University of Queensland, Brisbane, Queensland, Australia
| | - Evgeny Katz
- Department of Chemistry and Biomolecular Science, Clarkson University, Potsdam, NY, USA
| | - Kirill Alexandrov
- ARC Centre of Excellence in Synthetic Biology, Brisbane, Queensland, Australia.
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology, Brisbane, Queensland, Australia.
- School of Biology and Environmental Science, Queensland University of Technology, Brisbane, Queensland, Australia.
- CSIRO-QUT Synthetic Biology Alliance, Brisbane, Queensland, Australia.
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, Australia.
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8
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Agajanian S, Alshahrani M, Bai F, Tao P, Verkhivker GM. Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches. J Chem Inf Model 2023; 63:1413-1428. [PMID: 36827465 PMCID: PMC11162550 DOI: 10.1021/acs.jcim.2c01634] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
Allosteric mechanisms are commonly employed regulatory tools used by proteins to orchestrate complex biochemical processes and control communications in cells. The quantitative understanding and characterization of allosteric molecular events are among major challenges in modern biology and require integration of innovative computational experimental approaches to obtain atomistic-level knowledge of the allosteric states, interactions, and dynamic conformational landscapes. The growing body of computational and experimental studies empowered by emerging artificial intelligence (AI) technologies has opened up new paradigms for exploring and learning the universe of protein allostery from first principles. In this review we analyze recent developments in high-throughput deep mutational scanning of allosteric protein functions; applications and latest adaptations of Alpha-fold structural prediction methods for studies of protein dynamics and allostery; new frontiers in integrating machine learning and enhanced sampling techniques for characterization of allostery; and recent advances in structural biology approaches for studies of allosteric systems. We also highlight recent computational and experimental studies of the SARS-CoV-2 spike (S) proteins revealing an important and often hidden role of allosteric regulation driving functional conformational changes, binding interactions with the host receptor, and mutational escape mechanisms of S proteins which are critical for viral infection. We conclude with a summary and outlook of future directions suggesting that AI-augmented biophysical and computer simulation approaches are beginning to transform studies of protein allostery toward systematic characterization of allosteric landscapes, hidden allosteric states, and mechanisms which may bring about a new revolution in molecular biology and drug discovery.
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Affiliation(s)
- Steve Agajanian
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
| | - Fang Bai
- Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology and Information Science and Technology, Shanghai Tech University, 393 Middle Huaxia Road, Shanghai 201210, China
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, United States
| | - Gennady M Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, California 92866, United States
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, California 92618, United States
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9
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Huss P, Kieft K, Meger A, Nishikawa K, Anantharaman K, Raman S. Deep metagenomic mining reveals bacteriophage sequence motifs driving host specificity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.07.527309. [PMID: 36798209 PMCID: PMC9934549 DOI: 10.1101/2023.02.07.527309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Bacteriophage Library Informed Sequence Scoring (BLISS), to discover sequence motifs in metagenomic datasets governing phage activity. BLISS uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested 10,073 BLISS-derived sequence motifs for the receptor-binding protein of the T7 phage. The screen revealed hundreds of T7 variants with novel host specificity with functional motifs sourced from distant families besides other major phyla. Position, substitution and location preferences on T7 dictated different specificities. To demonstrate therapeutic utility, we engineered highly active T7 variants against urinary tract pathogens. BLISS is a powerful tool to unlock the functional potential encoded in phage metagenomes.
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10
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Hu R, Fu L, Chen Y, Chen J, Qiao Y, Si T. Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments. Brief Bioinform 2023; 24:6958505. [PMID: 36562723 DOI: 10.1093/bib/bbac570] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.
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Affiliation(s)
- Ruyun Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Lihao Fu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen 518055, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yongcan Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen 518055, China
| | - Junyu Chen
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yu Qiao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Tong Si
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.,CAS Key Laboratory for Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen 518055, China.,University of Chinese Academy of Sciences, Beijing 100049, China
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11
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Krishnan K, Tian H, Tao P, Verkhivker GM. Probing conformational landscapes and mechanisms of allosteric communication in the functional states of the ABL kinase domain using multiscale simulations and network-based mutational profiling of allosteric residue potentials. J Chem Phys 2022; 157:245101. [PMID: 36586979 PMCID: PMC11184971 DOI: 10.1063/5.0133826] [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: 11/06/2022] [Accepted: 12/05/2022] [Indexed: 12/12/2022] Open
Abstract
In the current study, multiscale simulation approaches and dynamic network methods are employed to examine the dynamic and energetic details of conformational landscapes and allosteric interactions in the ABL kinase domain that determine the kinase functions. Using a plethora of synergistic computational approaches, we elucidate how conformational transitions between the active and inactive ABL states can employ allosteric regulatory switches to modulate intramolecular communication networks between the ATP site, the substrate binding region, and the allosteric binding pocket. A perturbation-based network approach that implements mutational profiling of allosteric residue propensities and communications in the ABL states is proposed. Consistent with biophysical experiments, the results reveal functionally significant shifts of the allosteric interaction networks in which preferential communication paths between the ATP binding site and substrate regions in the active ABL state become suppressed in the closed inactive ABL form, which in turn features favorable allosteric coupling between the ATP site and the allosteric binding pocket. By integrating the results of atomistic simulations with dimensionality reduction methods and Markov state models, we analyze the mechanistic role of macrostates and characterize kinetic transitions between the ABL conformational states. Using network-based mutational scanning of allosteric residue propensities, this study provides a comprehensive computational analysis of long-range communications in the ABL kinase domain and identifies conserved regulatory hotspots that modulate kinase activity and allosteric crosstalk between the allosteric pocket, ATP binding site, and substrate binding regions.
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Affiliation(s)
| | - Hao Tian
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, Texas 75205, USA
| | - Gennady M. Verkhivker
- Author to whom correspondence should be addressed: . Telephone: 714-516-4586. Fax: 714-532-6048
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12
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Leander M, Liu Z, Cui Q, Raman S. Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins. eLife 2022; 11:e79932. [PMID: 36226916 PMCID: PMC9662819 DOI: 10.7554/elife.79932] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/13/2022] [Indexed: 01/29/2023] Open
Abstract
A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.
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Affiliation(s)
- Megan Leander
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
| | - Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin-MadisonMadisonUnited States
- Department of Bacteriology, University of Wisconsin-MadisonMadisonUnited States
- Department of Chemical and Biological Engineering, University of Wisconsin-MadisonMadisonUnited States
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Environmental selection and epistasis in an empirical phenotype-environment-fitness landscape. Nat Ecol Evol 2022; 6:427-438. [PMID: 35210579 DOI: 10.1038/s41559-022-01675-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 12/14/2021] [Indexed: 11/08/2022]
Abstract
Fitness landscapes, mappings of genotype/phenotype to their effects on fitness, are invaluable concepts in evolutionary biochemistry. Although widely discussed, measurements of phenotype-fitness landscapes in proteins remain scarce. Here, we quantify all single mutational effects on fitness and phenotype (EC50) of VIM-2 β-lactamase across a 64-fold range of ampicillin concentrations. We then construct a phenotype-fitness landscape that takes variations in environmental selection pressure into account. We found that a simple, empirical landscape accurately models the ~39,000 mutational data points, suggesting that the evolution of VIM-2 can be predicted on the basis of the selection environment. Our landscape provides new quantitative knowledge on the evolution of the β-lactamases and proteins in general, particularly their evolutionary dynamics under subinhibitory antibiotic concentrations, as well as the mechanisms and environmental dependence of non-specific epistasis.
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