1
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Colom MS, Vučinić J, Adolf‐Bryfogle J, Bowman JW, Verel S, Moczygemba I, Schiex T, Simoncini D, Bahl CD. Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity. Protein Sci 2024; 33:e5109. [PMID: 38989563 PMCID: PMC11237556 DOI: 10.1002/pro.5109] [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: 02/17/2024] [Revised: 05/20/2024] [Accepted: 06/25/2024] [Indexed: 07/12/2024]
Abstract
Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.
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Affiliation(s)
- Mireia Solà Colom
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
| | - Jelena Vučinić
- Université Fédérale de Toulouse, IRIT UMR 5505, ANITI, Université Toulouse CapitoleToulouseFrance
| | - Jared Adolf‐Bryfogle
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - James W. Bowman
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
| | | | - Isabelle Moczygemba
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
| | - Thomas Schiex
- MIAT, Université Fédérale de Toulouse, ANITI, INRAE UR 875ToulouseFrance
| | - David Simoncini
- Université Fédérale de Toulouse, IRIT UMR 5505, ANITI, Université Toulouse CapitoleToulouseFrance
| | - Christopher D. Bahl
- Institute for Protein InnovationBostonMassachusettsUSA
- Division of Hematology/OncologyBoston Children's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Present address:
AI ProteinsBostonMassachusettsUSA
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2
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Wu Y, Zhang S, York DM, Wang L. Adsorption of Flavonoids in a Transcriptional Regulator TtgR: Relative Binding Free Energies and Intermolecular Interactions. J Phys Chem B 2024; 128:6529-6541. [PMID: 38935925 DOI: 10.1021/acs.jpcb.4c02303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024]
Abstract
Antimicrobial resistance in bacteria often arises from their ability to actively identify and expel toxic compounds. The bacterium strain Pseudomonas putida DOT-T1E utilizes its TtgABC efflux pump to confer robust resistance against antibiotics, flavonoids, and organic solvents. This resistance mechanism is intricately regulated at the transcriptional level by the TtgR protein. Through molecular dynamics and alchemical free energy simulations, we systematically examine the binding of seven flavonoids and their derivatives with the TtgR transcriptional regulator. Our simulations reveal distinct binding geometries and free energies for the flavonoids in the active site of the protein, which are driven by a range of noncovalent forces encompassing van der Waals, electrostatic, and hydrogen bonding interactions. The interplay of molecular structures, substituent patterns, and intermolecular interactions effectively stabilizes the bound flavonoids, confining their movements within the TtgR binding pocket. These findings yield valuable insights into the molecular determinants that govern ligand recognition in TtgR and shed light on the mechanism of antimicrobial resistance in P. putida DOT-T1E.
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Affiliation(s)
- Yuxuan Wu
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Laboratory for Biomolecular Simulation Research, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Shi Zhang
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Laboratory for Biomolecular Simulation Research, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Darrin M York
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Laboratory for Biomolecular Simulation Research, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Lu Wang
- Department of Chemistry and Chemical Biology, Institute for Quantitative Biomedicine, Laboratory for Biomolecular Simulation Research, Rutgers University, Piscataway, New Jersey 08854, United States
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3
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Liu Z, Gillis TG, Raman S, Cui Q. A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery. eLife 2024; 12:RP92262. [PMID: 38836839 PMCID: PMC11152574 DOI: 10.7554/elife.92262] [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] [Indexed: 06/06/2024] Open
Abstract
New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.
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Affiliation(s)
- Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Thomas G Gillis
- Department of Biochemistry, University of WisconsinMadisonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of WisconsinMadisonUnited States
- Department of Chemistry, University of WisconsinMadisonUnited States
- Department of Bacteriology, University of WisconsinMadisonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
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4
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Rao J, Xin R, Macdonald C, Howard MK, Estevam GO, Yee SW, Wang M, Fraser JS, Coyote-Maestas W, Pimentel H. Rosace: a robust deep mutational scanning analysis framework employing position and mean-variance shrinkage. Genome Biol 2024; 25:138. [PMID: 38789982 PMCID: PMC11127319 DOI: 10.1186/s13059-024-03279-7] [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: 10/31/2023] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.
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Affiliation(s)
- Jingyou Rao
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Ruiqi Xin
- Computational and Systems Biology Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Christian Macdonald
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
| | - Matthew K Howard
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Tetrad Graduate Program, UCSF, San Francisco, CA, USA
- Department of Pharmaceutical Chemistry, UCSF, San Francisco, CA, USA
| | - Gabriella O Estevam
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Tetrad Graduate Program, UCSF, San Francisco, CA, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
| | - Mingsen Wang
- Department of Mathematics, Baruch College, CUNY, New York, NY, USA
| | - James S Fraser
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA
- Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA
| | - Willow Coyote-Maestas
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA.
- Quantitative Biosciences Institute, UCSF, San Francisco, CA, USA.
| | - Harold Pimentel
- Department of Computer Science, UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine, UCLA, Los Angeles, CA, USA.
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5
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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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6
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Billerbeck S, Walker RSK, Pretorius IS. Killer yeasts: expanding frontiers in the age of synthetic biology. Trends Biotechnol 2024:S0167-7799(24)00067-2. [PMID: 38575438 DOI: 10.1016/j.tibtech.2024.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/07/2024] [Accepted: 03/07/2024] [Indexed: 04/06/2024]
Abstract
Killer yeasts secrete protein toxins that are selectively lethal to other yeast and filamentous fungi. These exhibit exceptional genetic and functional diversity, and have several biotechnological applications. However, despite decades of research, several limitations hinder their widespread adoption. In this perspective we contend that technical advances in synthetic biology present an unprecedented opportunity to unlock the full potential of yeast killer systems across a spectrum of applications. By leveraging these new technologies, engineered killer toxins may emerge as a pivotal new tool to address antifungal resistance and food security. Finally, we speculate on the biotechnological potential of re-engineering host double-stranded (ds) RNA mycoviruses, from which many toxins derive, as a safe and noninfectious system to produce designer RNA.
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Affiliation(s)
- Sonja Billerbeck
- Molecular Microbiology, Groningen Biomolecular Sciences and Biotechnology institute, University of Groningen, Groningen 9747, AG, The Netherlands
| | - Roy S K Walker
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales 2109, Australia; ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia
| | - Isak S Pretorius
- ARC Centre of Excellence in Synthetic Biology, Macquarie University, Sydney, New South Wales 2109, Australia.
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7
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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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8
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Liu Z, Gillis T, Raman S, Cui Q. A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.06.552196. [PMID: 37662419 PMCID: PMC10473640 DOI: 10.1101/2023.08.06.552196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.
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Affiliation(s)
- Zhuang Liu
- Department of Physics, Boston University, Boston, United States
| | - Thomas Gillis
- Department of Biochemistry, University of Wisconsin, Madison, United States
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, United States
- Department of Chemistry, University of Wisconsin, Madison, United States
- Department of Bacteriology, University of Wisconsin, Madison, United States
| | - Qiang Cui
- Department of Physics, Boston University, Boston, United States
- Department of Chemistry, Boston University, Boston, United States
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9
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Deng J, Yuan Y, Cui Q. Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR. J Am Chem Soc 2024; 146:2757-2768. [PMID: 38231868 PMCID: PMC10843641 DOI: 10.1021/jacs.3c12494] [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] [Indexed: 01/19/2024]
Abstract
Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of cooperativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semiquantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different rescuabilities observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same noninducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscores the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions and therefore provides quantitative guidance to allostery modulation for therapeutic and engineering applications.
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Affiliation(s)
- Jiahua Deng
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Yuchen Yuan
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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10
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Jardón-Valadez E, Ulloa-Aguirre A. Tracking conformational transitions of the gonadotropin hormone receptors in a bilayer of (SDPC) poly-unsaturated lipids from all-atom molecular dynamics simulations. PLoS Comput Biol 2024; 20:e1011415. [PMID: 38206994 PMCID: PMC10807830 DOI: 10.1371/journal.pcbi.1011415] [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: 08/08/2023] [Revised: 01/24/2024] [Accepted: 12/15/2023] [Indexed: 01/13/2024] Open
Abstract
Glycoprotein hormone receptors [thyrotropin (TSHR), luteinizing hormone/chorionic gonadotropin (LHCGR), and follicle stimulating hormone (FSHR) receptors] are rhodopsin-like G protein-coupled receptors. These receptors display common structural features including a prominent extracellular domain with leucine-rich repeats (LRR) stabilized by β-sheets and a long and flexible loop known as the hinge region (HR), and a transmembrane (TM) domain with seven α-helices interconnected by intra- and extracellular loops. Binding of the ligand to the LRR resembles a hand coupling transversally to the α- and β-subunits of the hormone, with the thumb being the HR. The structure of the FSH-FSHR complex suggests an activation mechanism in which Y335 at the HR binds into a pocket between the α- and β-chains of the hormone, leading to an adjustment of the extracellular loops. In this study, we performed molecular dynamics (MD) simulations to identify the conformational changes of the FSHR and LHCGR. We set up a FSHR structure as predicted by AlphaFold (AF-P23945); for the LHCGR structure we took the cryo-electron microscopy structure for the active state (PDB:7FII) as initial coordinates. Specifically, the flexibility of the HR domain and the correlated motions of the LRR and TM domain were analyzed. From the conformational changes of the LRR, TM domain, and HR we explored the conformational landscape by means of MD trajectories in all-atom approximation, including a membrane of polyunsaturated phospholipids. The distances and procedures here defined may be useful to propose reaction coordinates to describe diverse processes, such as the active-to-inactive transition, and to identify intermediaries suited for allosteric regulation and biased binding to cellular transducers in a selective activation strategy.
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Affiliation(s)
- Eduardo Jardón-Valadez
- Departamento de Recursos de la Tierra, Unidad Lerma, Universidad Autónoma Metropolitana, Lerma de Villada, Estado de México, Mexico
| | - Alfredo Ulloa-Aguirre
- Instituto Nacional de Ciencias Medicas y Nutrición “Salvador Zubiran”. Mexico City, Mexico
- Red de Apoyo a la Investigación, Universidad Nacional Autónoma de México. Mexico City, Mexico
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11
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Deng J, Yuan Y, Cui Q. Modulation of Allostery with Multiple Mechanisms by Hotspot Mutations in TetR. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.29.555381. [PMID: 37905112 PMCID: PMC10614727 DOI: 10.1101/2023.08.29.555381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Modulating allosteric coupling offers unique opportunities for biomedical applications. Such efforts can benefit from efficient prediction and evaluation of allostery hotspot residues that dictate the degree of co-operativity between distant sites. We demonstrate that effects of allostery hotspot mutations can be evaluated qualitatively and semi-quantitatively by molecular dynamics simulations in a bacterial tetracycline repressor (TetR). The simulations recapitulate the effects of these mutations on abolishing the induction function of TetR and provide a rationale for the different degrees of rescuability observed to restore allosteric coupling of the hotspot mutations. We demonstrate that the same non-inducible phenotype could be the result of perturbations in distinct structural and energetic properties of TetR. Our work underscore the value of explicitly computing the functional free energy landscapes to effectively evaluate and rank hotspot mutations despite the prevalence of compensatory interactions, and therefore provide quantitative guidance to allostery modulation for therapeutic and engineering applications.
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Affiliation(s)
- Jiahua Deng
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Yuchen Yuan
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
| | - Qiang Cui
- Department of Chemistry, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Physics, Boston University, 590 Commonwealth Avenue, Boston, Massachusetts 02215, United States
- Department of Biomedical Engineering, Boston University, 44 Cummington Mall, Boston, Massachusetts 02215, United States
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12
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Nussinov R, Liu Y, Zhang W, Jang H. Protein conformational ensembles in function: roles and mechanisms. RSC Chem Biol 2023; 4:850-864. [PMID: 37920394 PMCID: PMC10619138 DOI: 10.1039/d3cb00114h] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 09/02/2023] [Indexed: 11/04/2023] Open
Abstract
The sequence-structure-function paradigm has dominated twentieth century molecular biology. The paradigm tacitly stipulated that for each sequence there exists a single, well-organized protein structure. Yet, to sustain cell life, function requires (i) that there be more than a single structure, (ii) that there be switching between the structures, and (iii) that the structures be incompletely organized. These fundamental tenets called for an updated sequence-conformational ensemble-function paradigm. The powerful energy landscape idea, which is the foundation of modernized molecular biology, imported the conformational ensemble framework from physics and chemistry. This framework embraces the recognition that proteins are dynamic and are always interconverting between conformational states with varying energies. The more stable the conformation the more populated it is. The changes in the populations of the states are required for cell life. As an example, in vivo, under physiological conditions, wild type kinases commonly populate their more stable "closed", inactive, conformations. However, there are minor populations of the "open", ligand-free states. Upon their stabilization, e.g., by high affinity interactions or mutations, their ensembles shift to occupy the active states. Here we discuss the role of conformational propensities in function. We provide multiple examples of diverse systems, including protein kinases, lipid kinases, and Ras GTPases, discuss diverse conformational mechanisms, and provide a broad outlook on protein ensembles in the cell. We propose that the number of molecules in the active state (inactive for repressors), determine protein function, and that the dynamic, relative conformational propensities, rather than the rigid structures, are the hallmark of cell life.
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Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research Frederick MD 21702 USA
- Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University Tel Aviv 69978 Israel
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
| | - Yonglan Liu
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
| | - Wengang Zhang
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
| | - Hyunbum Jang
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research Frederick MD 21702 USA
- Cancer Innovation Laboratory, National Cancer Institute Frederick MD 21702 USA
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13
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Swint-Kruse L, Dougherty LL, Page B, Wu T, O’Neil PT, Prasannan CB, Timmons C, Tang Q, Parente DJ, Sreenivasan S, Holyoak T, Fenton AW. PYK-SubstitutionOME: an integrated database containing allosteric coupling, ligand affinity and mutational, structural, pathological, bioinformatic and computational information about pyruvate kinase isozymes. Database (Oxford) 2023; 2023:baad030. [PMID: 37171062 PMCID: PMC10176505 DOI: 10.1093/database/baad030] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/29/2023] [Accepted: 04/11/2023] [Indexed: 05/13/2023]
Abstract
Interpreting changes in patient genomes, understanding how viruses evolve and engineering novel protein function all depend on accurately predicting the functional outcomes that arise from amino acid substitutions. To that end, the development of first-generation prediction algorithms was guided by historic experimental datasets. However, these datasets were heavily biased toward substitutions at positions that have not changed much throughout evolution (i.e. conserved). Although newer datasets include substitutions at positions that span a range of evolutionary conservation scores, these data are largely derived from assays that agglomerate multiple aspects of function. To facilitate predictions from the foundational chemical properties of proteins, large substitution databases with biochemical characterizations of function are needed. We report here a database derived from mutational, biochemical, bioinformatic, structural, pathological and computational studies of a highly studied protein family-pyruvate kinase (PYK). A centerpiece of this database is the biochemical characterization-including quantitative evaluation of allosteric regulation-of the changes that accompany substitutions at positions that sample the full conservation range observed in the PYK family. We have used these data to facilitate critical advances in the foundational studies of allosteric regulation and protein evolution and as rigorous benchmarks for testing protein predictions. We trust that the collected dataset will be useful for the broader scientific community in the further development of prediction algorithms. Database URL https://github.com/djparente/PYK-DB.
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Affiliation(s)
- Liskin Swint-Kruse
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Larissa L Dougherty
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Braelyn Page
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Tiffany Wu
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Pierce T O’Neil
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Charulata B Prasannan
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Cody Timmons
- Chemistry Department, Southwestern Oklahoma State University, 100 Campus Dr., Weatherford, OK 73096, USA
| | - Qingling Tang
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Daniel J Parente
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
- Department of Family Medicine and Community Health, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Shwetha Sreenivasan
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Todd Holyoak
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
- Department of Biology, University of Waterloo, 200 University Ave. W, Waterloo, ON N2L 3G1, Canada
| | - Aron W Fenton
- Department of Biochemistry and Molecular Biology, The University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
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14
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Verkhivker G, Alshahrani M, Gupta G, Xiao S, Tao P. From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches. Int J Mol Sci 2023; 24:7747. [PMID: 37175454 PMCID: PMC10178073 DOI: 10.3390/ijms24097747] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/22/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023] Open
Abstract
The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena. In recent years, machine learning approaches have been developed and actively deployed to facilitate computational and experimental studies of protein dynamics and allosteric mechanisms. In this review, we discuss in detail new developments along two major directions of allosteric research through the lens of data-intensive biochemical approaches and AI-based computational methods. Despite considerable progress in applications of AI methods for protein structure and dynamics studies, the intersection between allosteric regulation, the emerging structural biology technologies and AI approaches remains largely unexplored, calling for the development of AI-augmented integrative structural biology. In this review, we focus on the latest remarkable progress in deep high-throughput mining and comprehensive mapping of allosteric protein landscapes and allosteric regulatory mechanisms as well as on the new developments in AI methods for prediction and characterization of allosteric binding sites on the proteome level. We also discuss new AI-augmented structural biology approaches that expand our knowledge of the universe of protein dynamics and allostery. We conclude with an outlook and highlight the importance of developing an open science infrastructure for machine learning studies of allosteric regulation and validation of computational approaches using integrative studies of allosteric mechanisms. The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of the allosteric functions can provide a much-needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field.
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Affiliation(s)
- Gennady Verkhivker
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
- Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Mohammed Alshahrani
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Grace Gupta
- Keck Center for Science and Engineering, Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; (M.A.); (G.G.)
| | - Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75275, USA; (S.X.); (P.T.)
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15
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Xiao S, Verkhivker GM, Tao P. Machine learning and protein allostery. Trends Biochem Sci 2023; 48:375-390. [PMID: 36564251 PMCID: PMC10023316 DOI: 10.1016/j.tibs.2022.12.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022]
Abstract
The fundamental biological importance and complexity of allosterically regulated proteins stem from their central role in signal transduction and cellular processes. Recently, machine-learning approaches have been developed and actively deployed to facilitate theoretical and experimental studies of protein dynamics and allosteric mechanisms. In this review, we survey recent developments in applications of machine-learning methods for studies of allosteric mechanisms, prediction of allosteric effects and allostery-related physicochemical properties, and allosteric protein engineering. We also review the applications of machine-learning strategies for characterization of allosteric mechanisms and drug design targeting SARS-CoV-2. Continuous development and task-specific adaptation of machine-learning methods for protein allosteric mechanisms will have an increasingly important role in bridging a wide spectrum of data-intensive experimental and theoretical technologies.
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Affiliation(s)
- Sian Xiao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
| | - Gennady M Verkhivker
- Graduate Program in Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange, CA 92866, USA; Department of Biomedical and Pharmaceutical Sciences, Chapman University School of Pharmacy, Irvine, CA 92618, USA
| | - Peng Tao
- Department of Chemistry, Center for Research Computing, Center for Drug Discovery, Design, and Delivery (CD4), Southern Methodist University, Dallas, TX 75205, USA.
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16
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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: 13] [Impact Index Per Article: 13.0] [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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17
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Wei H, Li X. Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes. Front Genet 2023; 14:1087267. [PMID: 36713072 PMCID: PMC9878224 DOI: 10.3389/fgene.2023.1087267] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023] Open
Abstract
Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.
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Affiliation(s)
- Huijin Wei
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
| | - Xianghua Li
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China,Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom,The Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China,Biomedical and Health Translational Centre of Zhejiang Province, Haining, Zhejiang, China,*Correspondence: Xianghua Li,
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18
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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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