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Capdevila DA, Rondón JJ, Edmonds KA, Rocchio JS, Dujovne MV, Giedroc DP. Bacterial Metallostasis: Metal Sensing, Metalloproteome Remodeling, and Metal Trafficking. Chem Rev 2024; 124:13574-13659. [PMID: 39658019 DOI: 10.1021/acs.chemrev.4c00264] [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: 12/12/2024]
Abstract
Transition metals function as structural and catalytic cofactors for a large diversity of proteins and enzymes that collectively comprise the metalloproteome. Metallostasis considers all cellular processes, notably metal sensing, metalloproteome remodeling, and trafficking (or allocation) of metals that collectively ensure the functional integrity and adaptability of the metalloproteome. Bacteria employ both protein and RNA-based mechanisms that sense intracellular transition metal bioavailability and orchestrate systems-level outputs that maintain metallostasis. In this review, we contextualize metallostasis by briefly discussing the metalloproteome and specialized roles that metals play in biology. We then offer a comprehensive perspective on the diversity of metalloregulatory proteins and metal-sensing riboswitches, defining general principles within each sensor superfamily that capture how specificity is encoded in the sequence, and how selectivity can be leveraged in downstream synthetic biology and biotechnology applications. This is followed by a discussion of recent work that highlights selected metalloregulatory outputs, including metalloproteome remodeling and metal allocation by metallochaperones to both client proteins and compartments. We close by briefly discussing places where more work is needed to fill in gaps in our understanding of metallostasis.
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Affiliation(s)
- Daiana A Capdevila
- Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), C1405 BWE Buenos Aires, Argentina
| | - Johnma J Rondón
- Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), C1405 BWE Buenos Aires, Argentina
| | - Katherine A Edmonds
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405-7102, United States
| | - Joseph S Rocchio
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405-7102, United States
| | - Matias Villarruel Dujovne
- Fundación Instituto Leloir, Instituto de Investigaciones Bioquímicas de Buenos Aires (IIBBA-CONICET), C1405 BWE Buenos Aires, Argentina
| | - David P Giedroc
- Department of Chemistry, Indiana University, Bloomington, Indiana 47405-7102, United States
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2
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Abe M, Sakai M, Kanaly RA, Mori JF. Identification of a putative novel polycyclic aromatic hydrocarbon-biodegrading gene cluster in a marine Roseobacteraceae bacterium Sagittula sp. MA-2. Microbiol Spectr 2024:e0107424. [PMID: 39601554 DOI: 10.1128/spectrum.01074-24] [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: 05/09/2024] [Accepted: 10/28/2024] [Indexed: 11/29/2024] Open
Abstract
The ability to biodegrade polycyclic aromatic hydrocarbons (PAHs) and the catabolic enzymes responsible for PAH biotransformation in marine bacteria belonging to the family Roseobacteraceae remain largely unexplored despite their wide distribution and highly diverse physiological traits. A bacterial isolate within Roseobacteraceae originating from coastal seawater, Sagittula sp. strain MA-2, that biotransformed phenanthrene and utilized it as a growth substrate was found to possess a putative PAH-degrading gene cluster on one of the eight circular plasmids in its genome. Subsequent comprehensive investigations utilizing bacterial genomes in public databases revealed that gene clusters potentially homologous to this newly found cluster are widely but heterogeneously distributed within Roseobacteraceae and a few non-Roseobacteraceae (Paracoccaceae and Rhizobiaceae) strains from saline environments. Catabolic functions of the enzymes encoded in strain MA-2 were predicted through the profiling of phenanthrene biotransformation products by liquid chromatography-electrospray ionization high-resolution mass spectrometry and substrate docking simulations using predicted three-dimensional structures of selected proteins, and phenanthrene biodegradation pathways were proposed. Strain MA-2 appeared to biodegrade phenanthrene via two separated, concurrent pathways, namely the salicylate and phthalate pathways. This study serves as the first investigation into the functional genes potentially responsible for PAH biodegradation conserved in Roseobacteraceae bacteria, expanding scientific understanding of the physiological repertoire evolved in this ubiquitous marine bacterial group. IMPORTANCE The ocean is often characterized as the terminal destination for persistent polycyclic aromatic hydrocarbon (PAH) environmental pollutants; however, the ability to biodegrade PAHs and the corresponding enzymes conserved among marine bacteria are less understood compared to their terrestrial counterparts. A marine bacterial isolate, Sagittula sp. strain MA-2, belonging to the family Roseobacteraceae-a widely distributed and physiologically diverse marine bacterial group-was found to possess a functional gene cluster encoding enzymes potentially responsible for PAH biodegradation in its genome and exhibit the ability to biodegrade the three-ring PAH, phenanthrene. Intriguingly, gene clusters potentially homologous to this cluster were also distributed broadly across genomes from different Roseobacteraceae genera in public databases, which has not been previously investigated. The knowledge provided here expands our understanding of the physiology of Roseobacteraceae and may be applied to explore biotechnologically useful bacteria that contribute to the remediation of polluted marine environments or high-salinity wastewater.
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Affiliation(s)
- Mayuko Abe
- Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan
| | - Miharu Sakai
- Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan
| | - Robert A Kanaly
- Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan
| | - Jiro F Mori
- Graduate School of Nanobioscience, Yokohama City University, Yokohama, Japan
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3
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Pei W, Zhang X, Zeng Y, Li J, Li Z, Yang J, Du X, Zhu Y, Sun Y. Rational design of a zinc-dependence secondary alcohol dehydrogenase to boost its oxidation activity for α-hydroxyketones biosynthesis. Int J Biol Macromol 2024; 282:137183. [PMID: 39488308 DOI: 10.1016/j.ijbiomac.2024.137183] [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: 09/25/2024] [Revised: 10/28/2024] [Accepted: 10/31/2024] [Indexed: 11/04/2024]
Abstract
α-Hydroxyketones, which have significant industrial applications, can be sustainably synthesized through the oxidation of secondary alcohols using secondary alcohol dehydrogenase (SADH). However, the activity of the SADH oxidation reaction is generally low, making it unsuitable for large-scale production. In this study, a rational design approach was employed to computationally engineer SADH derived from Ogataea parapolymorpha (OpSADH), significantly enhancing its oxidation activity towards (R)-1,2-propanediol (PDO). The mutant M2 (S222T/S316A) exhibited a 14.2-fold increase in specific enzyme activity compared to the wild type (WT) and was employed as a catalyst for high-concentration hydroxyacetone production, facilitated by an NAD+ regeneration system. By applying an appropriate Zn2+ ion force field in molecular dynamics (MD) simulations, it was found that two mutation sites could stabilize the conformations of NAD+ and PDO, thereby revealing the molecular mechanism behind the enhanced activity of this metalloenzyme mutant. Notably, we first uncovered the dynamic mechanism by which four key residues (Cys39, His75, Glu76, and Glu175) and PDO within the active pocket contribute to the formation of coordination bonds with Zn2+. The findings of this study provide robust support for researching the catalytic mechanisms and dynamics processes of metalloenzymes.
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Affiliation(s)
- Wenwen Pei
- College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China; National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Xuewen Zhang
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Yan Zeng
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Jiao Li
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Ziyi Li
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Jiangang Yang
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
| | - Xinjun Du
- College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China.
| | - Yueming Zhu
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China.
| | - Yuanxia Sun
- National Engineering Laboratory for Industrial Enzymes, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China; National Technology Innovation Center of Synthetic Biology, Tianjin 300308, China
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Li Q, Hong K, Jia Y, Hu J. Biomineralized Nuclear Receptor Protein-Selection Mass Spectrometry for the Discovery of Drugs and Toxics. Anal Chem 2024; 96:16926-16936. [PMID: 39383328 DOI: 10.1021/acs.analchem.4c03943] [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: 10/11/2024]
Abstract
Protein-selection mass spectrometry is cost-effective for the discovery of drugs and toxics. Nuclear receptors (NRs) are major targets for pharmaceuticals and endocrine-disrupting chemicals and are, thus, widely used as "bait" proteins. However, their application is limited due to the tendency to lose protein activity during cold storage. To address this problem, we introduced a novel biomineralization-based approach to preserve activity in NRs, exemplified by human retinoic acid receptor alpha (hRARα), a target for cancer and leucocythemia therapy. Since information on the coordination chemistry of metal ion and NR protein complexes is almost unavailable, we applied peptide mapping analysis for the first time for the rational design of his-hRARα-Co phosphate nanobiomaterial with high bioactivity. This nanobiomaterial successfully captured hRARα bioactive chemicals from a Chinese herb and environmental water and discovered an unsaturated fatty acid, (±)-(9Z,11E)-13-hydroxy-9,11-octadecadienoic acid ((±)13-HODE), which exhibited strong hRARα antagonistic activity.
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Affiliation(s)
- Qiang Li
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Kaisheng Hong
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Yingting Jia
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
| | - Jianying Hu
- Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
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Zhang M, Wang X, Xu S, Ge F, Paixao IC, Song J, Yu DJ. MetalTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in Protein Metal-Binding Sites. J Chem Inf Model 2024; 64:6216-6229. [PMID: 39092854 DOI: 10.1021/acs.jcim.4c00739] [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: 08/04/2024]
Abstract
The critical importance of accurately predicting mutations in protein metal-binding sites for advancing drug discovery and enhancing disease diagnostic processes cannot be overstated. In response to this imperative, MetalTrans emerges as an accurate predictor for disease-associated mutations in protein metal-binding sites. The core innovation of MetalTrans lies in its seamless integration of multifeature splicing with the Transformer framework, a strategy that ensures exhaustive feature extraction. Central to MetalTrans's effectiveness is its deep feature combination strategy, which merges evolutionary-scale modeling amino acid embeddings with ProtTrans embeddings, thus shedding light on the biochemical properties of proteins. Employing the Transformer component, MetalTrans leverages the self-attention mechanism to delve into higher-level representations. Utilizing mutation site information for feature fusion not only enriches the feature set but also sidesteps the common pitfall of overestimation linked to protein sequence-based predictions. This nuanced approach to feature fusion is a key differentiator, enabling MetalTrans to outperform existing methods significantly, as evidenced by comparative analyses. Our evaluations across varied metal binding site data sets (specifically Zn, Ca, Mg, and Mix) underscore MetalTrans's superior performance, which achieved the average AUC values of 0.971, 0.965, 0.980, and 0.945 on multiple 5-fold cross-validation, respectively. Remarkably, against the multichannel convolutional neural network method on a benchmark independent test set, MetalTrans demonstrated unparalleled robustness and superiority, boasting the AUC score of 0.998 on multiple 5-fold cross-validation. Our comprehensive examination of the predicted outcomes further confirms the effectiveness of the model. The source codes, data sets, and prediction results for MetalTrans can be accessed for academic usage at https://github.com/EduardWang/MetalTrans.
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Affiliation(s)
- Ming Zhang
- School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Xiaohua Wang
- School of Computer, Jiangsu University of Science and Technology, 666 Changhui Road, Zhenjiang 212100, China
| | - Shanruo Xu
- Duke Kunshan University, Duke Avenue, Kunshan, Jiangsu 215316, China
| | - Fang Ge
- State Key Laboratory of Organic Electronics and Information Displays & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
| | - Ian Costa Paixao
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Jiangning Song
- Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria 3800, Australia
- Monash Data Futures Institute, Monash University, Melbourne, Victoria 3800, Australia
| | - Dong-Jun Yu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
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6
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Jamasb AR, Morehead A, Joshi CK, Zhang Z, Didi K, Mathis S, Harris C, Tang J, Cheng J, Liò P, Blundell TL. Evaluating Representation Learning on the Protein Structure Universe. ARXIV 2024:arXiv:2406.13864v1. [PMID: 38947934 PMCID: PMC11213157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
We introduce ProteinWorkshop, a comprehensive benchmark suite for representation learning on protein structures with Geometric Graph Neural Networks. We consider large-scale pre-training and downstream tasks on both experimental and predicted structures to enable the systematic evaluation of the quality of the learned structural representation and their usefulness in capturing functional relationships for downstream tasks. We find that: (1) large-scale pretraining on AlphaFold structures and auxiliary tasks consistently improve the performance of both rotation-invariant and equivariant GNNs, and (2) more expressive equivariant GNNs benefit from pretraining to a greater extent compared to invariant models. We aim to establish a common ground for the machine learning and computational biology communities to rigorously compare and advance protein structure representation learning. Our open-source codebase reduces the barrier to entry for working with large protein structure datasets by providing: (1) storage-efficient dataloaders for large-scale structural databases including AlphaFoldDB and ESM Atlas, as well as (2) utilities for constructing new tasks from the entire PDB. ProteinWorkshop is available at: github.com/a-r-j/ProteinWorkshop.
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7
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Blanc FEC, Hummer G. Mechanism of proton-powered c-ring rotation in a mitochondrial ATP synthase. Proc Natl Acad Sci U S A 2024; 121:e2314199121. [PMID: 38451940 PMCID: PMC10945847 DOI: 10.1073/pnas.2314199121] [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: 08/17/2023] [Accepted: 01/10/2024] [Indexed: 03/09/2024] Open
Abstract
Proton-powered c-ring rotation in mitochondrial ATP synthase is crucial to convert the transmembrane protonmotive force into torque to drive the synthesis of adenosine triphosphate (ATP). Capitalizing on recent cryo-EM structures, we aim at a structural and energetic understanding of how functional directional rotation is achieved. We performed multi-microsecond atomistic simulations to determine the free energy profiles along the c-ring rotation angle before and after the arrival of a new proton. Our results reveal that rotation proceeds by dynamic sliding of the ring over the a-subunit surface, during which interactions with conserved polar residues stabilize distinct intermediates. Ordered water chains line up for a Grotthuss-type proton transfer in one of these intermediates. After proton transfer, a high barrier prevents backward rotation and an overall drop in free energy favors forward rotation, ensuring the directionality of c-ring rotation required for the thermodynamically disfavored ATP synthesis. The essential arginine of the a-subunit stabilizes the rotated configuration through a salt bridge with the c-ring. Overall, we describe a complete mechanism for the rotation step of the ATP synthase rotor, thereby illuminating a process critical to all life at atomic resolution.
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Affiliation(s)
- Florian E. C. Blanc
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main60438, Germany
| | - Gerhard Hummer
- Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Frankfurt am Main60438, Germany
- Institute for Biophysics, Goethe University Frankfurt, Frankfurt am Main60438, Germany
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8
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Edholm F, Nandy A, Reinhardt CR, Kastner DW, Kulik HJ. Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify. J Comput Chem 2024; 45:352-361. [PMID: 37873926 DOI: 10.1002/jcc.27242] [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: 08/09/2023] [Revised: 09/27/2023] [Accepted: 10/03/2023] [Indexed: 10/25/2023]
Abstract
Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.
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Affiliation(s)
- Freya Edholm
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Aditya Nandy
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Clorice R Reinhardt
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - David W Kastner
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Heather J Kulik
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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9
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Zhan X, Zhang K, Wang C, Fan Q, Tang X, Zhang X, Wang K, Fu Y, Liang H. A c-di-GMP signaling module controls responses to iron in Pseudomonas aeruginosa. Nat Commun 2024; 15:1860. [PMID: 38424057 PMCID: PMC10904736 DOI: 10.1038/s41467-024-46149-3] [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: 10/28/2023] [Accepted: 02/15/2024] [Indexed: 03/02/2024] Open
Abstract
Cyclic dimeric guanosine monophosphate (c-di-GMP) serves as a bacterial second messenger that modulates various processes including biofilm formation, motility, and host-microbe symbiosis. Numerous studies have conducted comprehensive analysis of c-di-GMP. However, the mechanisms by which certain environmental signals such as iron control intracellular c-di-GMP levels are unclear. Here, we show that iron regulates c-di-GMP levels in Pseudomonas aeruginosa by modulating the interaction between an iron-sensing protein, IsmP, and a diguanylate cyclase, ImcA. Binding of iron to the CHASE4 domain of IsmP inhibits the IsmP-ImcA interaction, which leads to increased c-di-GMP synthesis by ImcA, thus promoting biofilm formation and reducing bacterial motility. Structural characterization of the apo-CHASE4 domain and its binding to iron allows us to pinpoint residues defining its specificity. In addition, the cryo-electron microscopy structure of ImcA in complex with a c-di-GMP analog (GMPCPP) suggests a unique conformation in which the compound binds to the catalytic pockets and to the membrane-proximal side located at the cytoplasm. Thus, our results indicate that a CHASE4 domain directly senses iron and modulates the crosstalk between c-di-GMP metabolic enzymes.
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Affiliation(s)
- Xueliang Zhan
- College of Life Sciences, Northwest University, Xi'an, ShaanXi, China
| | - Kuo Zhang
- College of Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Chenchen Wang
- College of Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Qiao Fan
- College of Life Sciences, Northwest University, Xi'an, ShaanXi, China
| | - Xiujia Tang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xi Zhang
- College of Life Sciences, Northwest University, Xi'an, ShaanXi, China
| | - Ke Wang
- Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yang Fu
- College of Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Haihua Liang
- College of Medicine, Southern University of Science and Technology, Shenzhen, China.
- University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, China.
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Zheng H, Zhang H, Zhong J, Gucwa M, Zhang Y, Ma H, Deng L, Mao L, Minor W, Wang N. PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules. RESEARCH SQUARE 2024:rs.3.rs-3908734. [PMID: 38463967 PMCID: PMC10925427 DOI: 10.21203/rs.3.rs-3908734/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.
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Affiliation(s)
| | | | | | | | | | | | - Lei Deng
- Hunan University College of Biology
| | | | | | - Nasui Wang
- Division of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College
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11
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Laveglia V, Bazayeva M, Andreini C, Rosato A. Hunting down zinc(II)-binding sites in proteins with distance matrices. Bioinformatics 2023; 39:btad653. [PMID: 37878807 PMCID: PMC10630175 DOI: 10.1093/bioinformatics/btad653] [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: 08/14/2023] [Revised: 10/17/2023] [Accepted: 10/23/2023] [Indexed: 10/27/2023] Open
Abstract
MOTIVATION In recent years, high-throughput sequencing technologies have made available the genome sequences of a huge variety of organisms. However, the functional annotation of the encoded proteins often still relies on low-throughput and costly experimental studies. Bioinformatics approaches offer a promising alternative to accelerate this process. In this work, we focus on the binding of zinc(II) ions, which is needed for 5%-10% of any organism's proteins to achieve their physiologically relevant form. RESULTS To implement a predictor of zinc(II)-binding sites in the 3D structures of proteins, we used a neural network, followed by a filter of the network output against the local structure of all known sites. The latter was implemented as a function comparing the distance matrices of the Cα and Cβ atoms of the sites. We called the resulting tool Master of Metals (MOM). The structural models for the entire proteome of an organism generated by AlphaFold can be used as input to our tool in order to achieve annotation at the whole organism level within a few hours. To demonstrate this, we applied MOM to the yeast proteome, obtaining a precision of about 76%, based on data for homologous proteins. AVAILABILITY AND IMPLEMENTATION Master of Metals has been implemented in Python and is available at https://github.com/cerm-cirmmp/Master-of-metals.
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Affiliation(s)
- Vincenzo Laveglia
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
| | - Milana Bazayeva
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy
| | - Claudia Andreini
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Sesto Fiorentino 50019, Italy
| | - Antonio Rosato
- Department of Chemistry, University of Florence, Sesto Fiorentino 50019, Italy
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino 50019, Italy
- Consorzio Interuniversitario di Risonanze Magnetiche di Metallo Proteine, Sesto Fiorentino 50019, Italy
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