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  1. Article ; Online: Antimicrobial Lasso Peptide Cloacaenodin Utilizes a Unique TonB-Dependent Transporter to Access Susceptible Bacteria.

    Carson, Drew V / Juarez, Reecan J / Do, Truc / Yang, Zhongyue J / Link, A James

    ACS chemical biology

    2024  Volume 19, Issue 4, Page(s) 981–991

    Abstract: The development of new antimicrobial agents effective against Gram-negative bacteria remains a major challenge in drug discovery. The lasso peptide cloacaenodin has potent antimicrobial activity against multiple strains in ... ...

    Abstract The development of new antimicrobial agents effective against Gram-negative bacteria remains a major challenge in drug discovery. The lasso peptide cloacaenodin has potent antimicrobial activity against multiple strains in the
    MeSH term(s) Anti-Bacterial Agents/pharmacology ; Antimicrobial Peptides/pharmacology ; Bacteria/drug effects ; Membrane Transport Proteins/metabolism ; Peptides ; Enterobacter/drug effects ; Enterobacter/metabolism ; Molecular Dynamics Simulation ; Bacterial Proteins
    Chemical Substances Anti-Bacterial Agents ; Antimicrobial Peptides ; Membrane Transport Proteins ; Peptides ; tonB protein, Bacteria ; Bacterial Proteins
    Language English
    Publishing date 2024-03-25
    Publishing country United States
    Document type Journal Article
    ISSN 1554-8937
    ISSN (online) 1554-8937
    DOI 10.1021/acschembio.4c00009
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: LassoHTP: A High-Throughput Computational Tool for Lasso Peptide Structure Construction and Modeling.

    Juarez, Reecan J / Jiang, Yaoyukun / Tremblay, Matthew / Shao, Qianzhen / Link, A James / Yang, Zhongyue J

    Journal of chemical information and modeling

    2023  Volume 63, Issue 2, Page(s) 522–530

    Abstract: Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates ... ...

    Abstract Lasso peptides are a subclass of ribosomally synthesized and post-translationally modified peptides with a slipknot conformation. With superior thermal stability, protease resistance, and antimicrobial activity, lasso peptides are promising candidates for bioengineering and pharmaceutical applications. To enable high-throughput computational prediction and design of lasso peptides, we developed a software, LassoHTP, for automatic lasso peptide structure construction and modeling. LassoHTP consists of three modules, including the scaffold constructor, mutant generator, and molecular dynamics (MD) simulator. With a user-provided sequence and conformational annotation, LassoHTP can either generate the structure and conformational ensemble as is or conduct random mutagenesis. We used LassoHTP to construct eight known lasso peptide structures
    MeSH term(s) Peptides/chemistry ; Molecular Dynamics Simulation ; Software ; Molecular Conformation ; Magnetic Resonance Spectroscopy
    Chemical Substances Peptides
    Language English
    Publishing date 2023-01-03
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.2c00945
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates

    Jiang, Yaoyukun / Yan, Bailu / Chen, Yu / Juarez, Reecan J. / Yang, Zhongyue J.

    Journal of physical chemistry. 2022 Mar. 24, v. 126, no. 13

    2022  

    Abstract: Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers ... ...

    Abstract Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase SsoPox with both native and non-native substrates. The study consists of 10 enzyme–substrate complexes resulting from a combination of five enzyme variants with two substrates. For each complex, we derived 15 descriptors from molecular dynamics simulations and applied principal component analysis to rank the predictive capability of the descriptors. A top-ranked descriptor was identified, which is the solvent-accessible surface area (SASA) ratio of the substrate to the active site pocket. A uniform volcano-shaped plot was observed in the distribution of experimental activation free energy against the SASA ratio. To achieve efficient lactonase hydrolysis, a non-native substrate-bound enzyme variant needs to involve a similar range of the SASA ratio to the native substrate-bound wild-type enzyme. The descriptor reflects how well the enzyme active site pocket accommodates a substrate for reaction, which has the potential of guiding optimization of enzyme reaction turnover for non-native chemical transformations.
    Keywords Gibbs free energy ; active sites ; catalytic activity ; enzymatic reactions ; enzyme substrates ; enzymes ; hydrolysis ; molecular dynamics ; mutation ; principal component analysis ; surface area
    Language English
    Dates of publication 2022-0324
    Size p. 2486-2495.
    Publishing place American Chemical Society
    Document type Article
    ISSN 1520-5207
    DOI 10.1021/acs.jpcb.2c00142
    Database NAL-Catalogue (AGRICOLA)

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  4. Article ; Online: Molecular Dynamics-Derived Descriptor Informs the Impact of Mutation on the Catalytic Turnover Number in Lactonase Across Substrates.

    Jiang, Yaoyukun / Yan, Bailu / Chen, Yu / Juarez, Reecan J / Yang, Zhongyue J

    The journal of physical chemistry. B

    2022  Volume 126, Issue 13, Page(s) 2486–2495

    Abstract: Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers ... ...

    Abstract Molecular dynamics simulations have been extensively employed to reveal the roles of protein dynamics in mediating enzyme catalysis. However, simulation-derived predictive descriptors that inform the impacts of mutations on catalytic turnover numbers remain largely unexplored. In this work, we report the identification of molecular modeling-derived descriptors to predict mutation effect on the turnover number of lactonase
    MeSH term(s) Catalysis ; Catalytic Domain/genetics ; Molecular Dynamics Simulation ; Mutation/genetics ; Mutation/physiology ; Substrate Specificity
    Language English
    Publishing date 2022-03-24
    Publishing country United States
    Document type Journal Article
    ISSN 1520-5207
    ISSN (online) 1520-5207
    DOI 10.1021/acs.jpcb.2c00142
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Mutexa: A Computational Ecosystem for Intelligent Protein Engineering.

    Yang, Zhongyue J / Shao, Qianzhen / Jiang, Yaoyukun / Jurich, Christopher / Ran, Xinchun / Juarez, Reecan J / Yan, Bailu / Stull, Sebastian L / Gollu, Anvita / Ding, Ning

    Journal of chemical theory and computation

    2023  Volume 19, Issue 21, Page(s) 7459–7477

    Abstract: Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, ... ...

    Abstract Protein engineering holds immense promise in shaping the future of biomedicine and biotechnology. This Review focuses on our ongoing development of Mutexa, a computational ecosystem designed to enable "intelligent protein engineering". In this vision, researchers will seamlessly acquire sequences of protein variants with desired functions as biocatalysts, therapeutic peptides, and diagnostic proteins through a finely-tuned computational machine, akin to Amazon Alexa's role as a versatile virtual assistant. The technical foundation of Mutexa has been established through the development of a database that combines and relates enzyme structures and their respective functions (e.g., IntEnzyDB), workflow software packages that enable high-throughput protein modeling (e.g., EnzyHTP and LassoHTP), and scoring functions that map the sequence-structure-function relationship of proteins (e.g., EnzyKR and DeepLasso). We will showcase the applications of these tools in benchmarking the convergence conditions of enzyme functional descriptors across mutants, investigating protein electrostatics and cavity distributions in SAM-dependent methyltransferases, and understanding the role of nonelectrostatic dynamic effects in enzyme catalysis. Finally, we will conclude by addressing the future steps and fundamental challenges in our endeavor to develop new Mutexa applications that assist the identification of beneficial mutants in protein engineering.
    MeSH term(s) Protein Engineering ; Proteins
    Chemical Substances Proteins
    Language English
    Publishing date 2023-10-12
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 1549-9626
    ISSN (online) 1549-9626
    DOI 10.1021/acs.jctc.3c00602
    Database MEDical Literature Analysis and Retrieval System OnLINE

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