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Kobayashi N, Ishii Y. Analysis of solid-state NMR data facilitated by MagRO_NMRViewJ with Graph_Robot: Application for membrane protein and amyloid. Biophys Chem 2025; 318:107356. [PMID: 39637606 DOI: 10.1016/j.bpc.2024.107356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 11/05/2024] [Accepted: 11/14/2024] [Indexed: 12/07/2024]
Abstract
Solid-state NMR (ssNMR) methods have continued to be developed in recent years for the efficient assignment of signals and 3D structure modeling of biomacromolecules. Consequently, we are approaching an era in which vigorous applications of these methods are more widespread in research, including functional elucidation of biomacromolecules and drug discovery. However, multidimensional ssNMR methods are not as advanced as solution NMR methods, especially for automated data analysis. This article describes how a newly developed Graph_Robot module, implemented in MagRO-NMRViewJ, evolved from integrated tools for NMR data analysis named Kujira (developed by Kobayashi et al. [1]). These packaged tools systematically utilize flexible, sophisticated, yet simple libraries that facilitate only for solution-NMR data analysis, offering an intuitive interface accessible even to novice users. In this study, semi-automated assignments of backbone and side chain signals of ssNMR datasets for uniformly 13C/15N labeled aquaporin Z and 42-residue amyloid-β fibril were examined as examples to demonstrate how Graph_Robot can expedite the visual inspection and handling of multidimensional ssNMR spectral data. In addition, the functionality of the Graph_Robot system enables a computer to interpret the behavior of magnetization transfer based on a finite automaton model.
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Affiliation(s)
- Naohiro Kobayashi
- RIKEN, RIKEN Center for Biosystems Dynamics Research (BDR), Yokohama 230-0045, Japan.
| | - Yoshitaka Ishii
- RIKEN, RIKEN Center for Biosystems Dynamics Research (BDR), Yokohama 230-0045, Japan; School of Life Science and Technology, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama 226-8503, Japan
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2
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Kalaj BN, La Clair JJ, Shen Y, Schwieters CD, Deshmukh L, Burkart MD. Quantitative Characterization of Chain-Flipping of Acyl Carrier Protein of Escherichia coli Using Chemical Exchange NMR. J Am Chem Soc 2024; 146:18650-18660. [PMID: 38875499 PMCID: PMC11299499 DOI: 10.1021/jacs.4c05509] [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: 06/16/2024]
Abstract
The acyl carrier protein of Escherichia coli, termed AcpP, is a prototypical example of type II fatty acid synthase systems found in many bacteria. It serves as a central hub by accepting diverse acyl moieties (4-18 carbons) and shuttling them between its multiple enzymatic partners to generate fatty acids. Prior structures of acyl-AcpPs established that thioester-linked acyl cargos are sequestered within AcpP's hydrophobic lumen. In contrast, structures of enzyme-bound acyl-AcpPs showed translocation of AcpP-tethered acyl chains into the active sites of enzymes. The mechanistic underpinnings of this conformational interplay, termed chain-flipping, are unclear. Here, using heteronuclear NMR spectroscopy, we reveal that AcpP-tethered acyl chains (6-10 carbons) spontaneously adopt lowly populated solvent-exposed conformations. To this end, we devised a new strategy to replace AcpP's thioester linkages with 15N-labeled amide bonds, which facilitated direct "visualization" of these excited states using NMR chemical exchange saturation transfer and relaxation dispersion measurements. Global fitting of the corresponding data yielded kinetic rate constants of the underlying equilibrium and populations and lifetimes of solvent-exposed states. The latter were influenced by acyl chain composition and ranged from milliseconds to submilliseconds for chains containing six, eight, and ten carbons, owing to their variable interactions with AcpP's hydrophobic core. Although transient, the exposure of AcpP-tethered acyl chains to the solvent may allow relevant enzymes to gain access to its active thioester, and the enzyme-induced selection of this conformation will culminate in the production of fatty acids.
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Affiliation(s)
- Brianna N. Kalaj
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
| | - James J. La Clair
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
| | - Yang Shen
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Charles D. Schwieters
- Computational Biomolecular Magnetic Resonance Core, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lalit Deshmukh
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
| | - Michael D. Burkart
- Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, CA 92093, USA
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3
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Aute R, Waghela N, Deshmukh MV. Key arginine residues in R2D2 dsRBD1 and dsRBD2 lead the siRNA recognition in Drosophila melanogaster RNAi pathway. Biophys Chem 2024; 310:107247. [PMID: 38663122 DOI: 10.1016/j.bpc.2024.107247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 04/04/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
In Drosophila melanogaster, Dcr-2:R2D2 heterodimer binds to the 21 nucleotide siRNA duplex to form the R2D2/Dcr-2 Initiator (RDI) complex, which is critical for the initiation of siRNA-induced silencing complex (RISC) assembly. During RDI complex formation, R2D2, a protein that contains three dsRNA binding domains (dsRBD), senses two aspects of the siRNA: thermodynamically more stable end (asymmetry sensing) and the 5'-phosphate (5'-P) recognition. Despite several detailed studies to date, the molecular determinants arising from R2D2 for performing these two tasks remain elusive. In this study, we have performed structural, biophysical, and biochemical characterization of R2D2 dsRBDs. We found that the solution NMR-derived structure of R2D2 dsRBD1 yielded a canonical α1-β1-β2-β3-α2 fold, wherein two arginine salt bridges provide additional stability to the R2D2 dsRBD1. Furthermore, we show that R2D2 dsRBD1 interacts with thermodynamically asymmetric siRNA duplex independent of its 5'-phosphorylation state, whereas R2D2 dsRBD2 prefers to interact with 5'-P siRNA duplex. The mutation of key arginine residues, R53 and R101, in concatenated dsRBDs of R2D2 results in a significant loss of siRNA duplex recognition. Our study deciphers the active roles of R2D2 dsRBDs by showing that dsRBD1 initiates siRNA recognition, whereas dsRBD2 senses 5'-phosphate as an authentic mark on functional siRNA.
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Affiliation(s)
- Ramdas Aute
- CSIR - Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research, Uppal Road, Hyderabad 500007, Telangana, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Nilam Waghela
- CSIR - Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research, Uppal Road, Hyderabad 500007, Telangana, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Mandar V Deshmukh
- CSIR - Centre for Cellular and Molecular Biology, Council of Scientific and Industrial Research, Uppal Road, Hyderabad 500007, Telangana, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India.
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4
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Kliza KW, Song W, Pinzuti I, Schaubeck S, Kunzelmann S, Kuntin D, Fornili A, Pandini A, Hofmann K, Garnett JA, Stieglitz B, Husnjak K. N4BP1 functions as a dimerization-dependent linear ubiquitin reader which regulates TNF signalling. Cell Death Discov 2024; 10:183. [PMID: 38643192 PMCID: PMC11032371 DOI: 10.1038/s41420-024-01913-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2024] [Revised: 03/05/2024] [Accepted: 03/11/2024] [Indexed: 04/22/2024] Open
Abstract
Signalling through TNFR1 modulates proinflammatory gene transcription and programmed cell death, and its impairment causes autoimmune diseases and cancer. NEDD4-binding protein 1 (N4BP1) is a critical suppressor of proinflammatory cytokine production that acts as a regulator of innate immune signalling and inflammation. However, our current understanding about the molecular properties that enable N4BP1 to exert its suppressive potential remain limited. Here, we show that N4BP1 is a novel linear ubiquitin reader that negatively regulates NFκB signalling by its unique dimerization-dependent ubiquitin-binding module that we named LUBIN. Dimeric N4BP1 strategically positions two non-selective ubiquitin-binding domains to ensure preferential recognition of linear ubiquitin. Under proinflammatory conditions, N4BP1 is recruited to the nascent TNFR1 signalling complex, where it regulates duration of proinflammatory signalling in LUBIN-dependent manner. N4BP1 deficiency accelerates TNFα-induced cell death by increasing complex II assembly. Under proapoptotic conditions, caspase-8 mediates proteolytic processing of N4BP1, resulting in rapid degradation of N4BP1 by the 26 S proteasome, and acceleration of apoptosis. In summary, our findings demonstrate that N4BP1 dimerization creates a novel type of ubiquitin reader that selectively recognises linear ubiquitin which enables the timely and coordinated regulation of TNFR1-mediated inflammation and cell death.
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Affiliation(s)
- Katarzyna W Kliza
- Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt (Main), Germany.
- Max Planck Institute of Molecular Physiology, Otto-Hahn-Straße 11, 44227, Dortmund, Germany.
| | - Wei Song
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Irene Pinzuti
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Simone Schaubeck
- Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt (Main), Germany
| | - Simone Kunzelmann
- Structural Biology Science Technology Platform, Francis Crick Institute, London, UK
| | - David Kuntin
- Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt (Main), Germany
- Department of Biology, University of York, Wentworth Way, York, UK
| | - Arianna Fornili
- School of Physical and Chemical Sciences, Queen Mary University of London, London, UK
| | | | - Kay Hofmann
- Institute for Genetics, University of Cologne, Cologne, Germany
| | - James A Garnett
- Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, UK
| | - Benjamin Stieglitz
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK.
| | - Koraljka Husnjak
- Institute of Biochemistry II, Goethe University School of Medicine, Frankfurt (Main), Germany.
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Abdollahi H, Prestegard JH, Valafar H. Computational modeling multiple conformational states of proteins with residual dipolar coupling data. Curr Opin Struct Biol 2023; 82:102655. [PMID: 37454402 DOI: 10.1016/j.sbi.2023.102655] [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: 03/21/2023] [Revised: 06/06/2023] [Accepted: 06/20/2023] [Indexed: 07/18/2023]
Abstract
Solution nuclear magnetic resonance spectroscopy provides unique opportunities to study the structure and dynamics of biomolecules in aqueous environments. While spin relaxation methods are well recognized for their ability to probe timescales of motion, residual dipolar couplings (RDCs) provide access to amplitudes and directions of motion, characteristics that are important to the function of these molecules. Although observed in the 1960s, the acquisition and computational analysis of RDCs has gained significant momentum in recent years, and particularly applications to motion in proteins have become more numerous. This trend may well continue as RDCs can easily leverage structures produced by new computational methods (e.g., AlphaFold) to produce functional descriptions. In this report, we provide examples and a summary of the ways that RDCs have been used to confirm the existence of internal dynamics, characterize the type of dynamics, and recover atomic-scale structural ensembles that define the full range of conformational sampling.
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Affiliation(s)
- Hamed Abdollahi
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, USA.
| | - James H Prestegard
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA.
| | - Homayoun Valafar
- Department of Computer Science and Engineering, University of South Carolina, 29201, Columbia, SC, USA.
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6
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Koehler Leman J, Künze G. Recent Advances in NMR Protein Structure Prediction with ROSETTA. Int J Mol Sci 2023; 24:ijms24097835. [PMID: 37175539 PMCID: PMC10178863 DOI: 10.3390/ijms24097835] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/15/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023] Open
Abstract
Nuclear magnetic resonance (NMR) spectroscopy is a powerful method for studying the structure and dynamics of proteins in their native state. For high-resolution NMR structure determination, the collection of a rich restraint dataset is necessary. This can be difficult to achieve for proteins with high molecular weight or a complex architecture. Computational modeling techniques can complement sparse NMR datasets (<1 restraint per residue) with additional structural information to elucidate protein structures in these difficult cases. The Rosetta software for protein structure modeling and design is used by structural biologists for structure determination tasks in which limited experimental data is available. This review gives an overview of the computational protocols available in the Rosetta framework for modeling protein structures from NMR data. We explain the computational algorithms used for the integration of different NMR data types in Rosetta. We also highlight new developments, including modeling tools for data from paramagnetic NMR and hydrogen-deuterium exchange, as well as chemical shifts in CS-Rosetta. Furthermore, strategies are discussed to complement and improve structure predictions made by the current state-of-the-art AlphaFold2 program using NMR-guided Rosetta modeling.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA
| | - Georg Künze
- Institute for Drug Discovery, Medical Faculty, University of Leipzig, Brüderstr. 34, D-04103 Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, University of Leipzig, Härtelstr. 16-18, D-04107 Leipzig, Germany
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7
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Jørgensen FK, Reinholdt P, Hedegård ED, Kongsted J. Nuclear Magnetic Shielding Constants with the Polarizable Density Embedding Model. J Chem Theory Comput 2022; 18:7384-7393. [PMID: 36332108 DOI: 10.1021/acs.jctc.2c00829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We extend the polarizable density embedding (PDE) model to support the calculation of nuclear magnetic resonance (NMR) shielding constants using gauge-including atomic orbitals (GIAOs) within a density functional theory (DFT) framework. The PDE model divides the total system into fragments, describing some by quantum mechanics (QM) and the others through an embedding model. The PDE model uses anisotropic polarizabilities, inter-fragment two-electron Coulomb integrals, and a non-local repulsion operator to emulate the QM effects. The terms involving Coulomb integrals are straightforwardly extended with GIAOs. In contrast, we consider two approaches to handle the gauge dependency of the non-local operator, employing either simple symmetrization or a gauge transformation. We find the latter approach to be most stable with respect to increasing the basis set size of the QM region. We examine the accuracy of the PDE model for calculating NMR shielding constants on several solutes in a water solution. The performance is compared with the classical polarizable embedding (PE) model in addition to supermolecular reference calculations. Based on these systems, we address the basis set convergence characteristics and the QM region size requirements. Furthermore, we investigate the performance of the PDE model for a system with significant electron spill-out. In many cases, we find that the PDE model outperforms the PE model, especially regarding the accuracy of nuclear shielding constants when using small QM region sizes and in systems with significant electron spill-out.
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Affiliation(s)
- Frederik Kamper Jørgensen
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230Odense M, Denmark
| | - Peter Reinholdt
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230Odense M, Denmark
| | - Erik Donovan Hedegård
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230Odense M, Denmark
| | - Jacob Kongsted
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, DK-5230Odense M, Denmark
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8
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Meyer BH, Adam PS, Wagstaff BA, Kolyfetis GE, Probst AJ, Albers SV, Dorfmueller HC. Agl24 is an ancient archaeal homolog of the eukaryotic N-glycan chitobiose synthesis enzymes. eLife 2022; 11:e67448. [PMID: 35394422 PMCID: PMC8993221 DOI: 10.7554/elife.67448] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 03/13/2022] [Indexed: 11/13/2022] Open
Abstract
Protein N-glycosylation is a post-translational modification found in organisms of all domains of life. The crenarchaeal N-glycosylation begins with the synthesis of a lipid-linked chitobiose core structure, identical to that in Eukaryotes, although the enzyme catalyzing this reaction remains unknown. Here, we report the identification of a thermostable archaeal β-1,4-N-acetylglucosaminyltransferase, named archaeal glycosylation enzyme 24 (Agl24), responsible for the synthesis of the N-glycan chitobiose core. Biochemical characterization confirmed its function as an inverting β-D-GlcNAc-(1→4)-α-D-GlcNAc-diphosphodolichol glycosyltransferase. Substitution of a conserved histidine residue, found also in the eukaryotic and bacterial homologs, demonstrated its functional importance for Agl24. Furthermore, bioinformatics and structural modeling revealed similarities of Agl24 to the eukaryotic Alg14/13 and a distant relation to the bacterial MurG, which are catalyzing the same or a similar reaction, respectively. Phylogenetic analysis of Alg14/13 homologs indicates that they are ancient in Eukaryotes, either as a lateral transfer or inherited through eukaryogenesis.
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Affiliation(s)
- Benjamin H Meyer
- Environmental Microbiology and Biotechnology (EMB), Aquatic Microbial Ecology, University of Duisburg-EssenEssenGermany
- Division of Molecular Microbiology, School of Life Sciences, University of DundeeDundeeUnited Kingdom
- Molecular Biology of Archaea, Faculty of Biology, University of FreiburgFreiburgGermany
| | - Panagiotis S Adam
- Group for Aquatic Microbial Ecology, Environmental Microbiology and Biotechnology, Faculty of Chemistry University Duisburg-EssenEssenGermany
| | - Ben A Wagstaff
- Division of Molecular Microbiology, School of Life Sciences, University of DundeeDundeeUnited Kingdom
| | - George E Kolyfetis
- Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of AthensAthensGreece
| | - Alexander J Probst
- Centre of Water and Environmental Research (ZWU), University of Duisburg-EssenEssenGermany
| | - Sonja V Albers
- Molecular Biology of Archaea, Faculty of Biology, University of FreiburgFreiburgGermany
| | - Helge C Dorfmueller
- Division of Molecular Microbiology, School of Life Sciences, University of DundeeDundeeUnited Kingdom
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9
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van Breugel M, Rosa E Silva I, Andreeva A. Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes. Commun Biol 2022; 5:312. [PMID: 35383272 PMCID: PMC8983713 DOI: 10.1038/s42003-022-03269-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 02/28/2022] [Indexed: 11/21/2022] Open
Abstract
Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.
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Affiliation(s)
- Mark van Breugel
- Queen Mary University of London, School of Biological and Behavioural Sciences, 4 Newark Street, London, E1 2AT, UK.
- Medical Research Council-Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK.
| | - Ivan Rosa E Silva
- Queen Mary University of London, School of Biological and Behavioural Sciences, 4 Newark Street, London, E1 2AT, UK
- Medical Research Council-Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
- University of Campinas, Faculty of Pharmaceutical Sciences, Cândido Portinari Street, Campinas, 13083-871, Brazil
| | - Antonina Andreeva
- Medical Research Council-Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, CB2 0QH, UK
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10
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Structural Analysis of the Effect of Asn107Ser Mutation on Alg13 Activity and Alg13-Alg14 Complex Formation and Expanding the Phenotypic Variability of ALG13-CDG. Biomolecules 2022; 12:biom12030398. [PMID: 35327592 PMCID: PMC8945535 DOI: 10.3390/biom12030398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/02/2022] [Indexed: 11/16/2022] Open
Abstract
Congenital Disorders of Glycosylation (CDG) are multisystemic metabolic disorders showing highly heterogeneous clinical presentation, molecular etiology, and laboratory results. Here, we present different transferrin isoform patterns (obtained by isoelectric focusing) from three female patients harboring the ALG13 c.320A>G mutation. Contrary to other known variants of type I CDGs, where transferrin isoelectric focusing revealed notably increased asialo- and disialotransferrin fractions, a normal glycosylation pattern was observed in the probands. To verify this data and give novel insight into this variant, we modeled the human Alg13 protein and analyzed the dynamics of the apo structure and the complex with the UDP-GlcNAc substrate. We also modeled the Alg13-Alg14 heterodimer and ran multiple simulations of the complex in the presence of the substrate. Finally, we proposed a plausible complex formation mechanism.
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11
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Guo X, Lin H, Xu S, He L. Recent Advances in Spectroscopic Techniques for the Analysis of Microplastics in Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:1410-1422. [PMID: 35099960 DOI: 10.1021/acs.jafc.1c06085] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Microplastic pollution has become a worldwide concern in aquatic and terrestrial environments. Microplastics could also enter the food chain, causing potential harm to human health. To facilitate the risk assessment of microplastics to humans, it is critically important to have a reliable analytical technique to detect, quantify, and identify microplastics of various materials, sizes, and shapes from environmental, agricultural, and food matrices. Spectroscopic techniques, mainly vibrational spectroscopy (Raman and infrared), are commonly used techniques for microplastic analysis. This review focuses on recent advances of these spectroscopic techniques for the analysis of microplastics in food. The fundamental, recent technical advances of the spectroscopic techniques and their advantages and limitations were summarized. The food sample pretreatment methods and recent applications for detecting and quantifying microplastics in different types of food were reviewed. In addition, the current technical challenges and future research directions were discussed. It is anticipated that the advances in instrument development and methodology innovation will enable spectroscopic techniques to solve critical analytical challenges in microplastic analysis in food, which will facilitate the reliable risk assessment.
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Affiliation(s)
- Xin Guo
- Department of Food Science, University of Massachusetts Amherst, Chenoweth Laboratory, 102 Holdsworth Way, Amherst, Massachusetts 01003, United States
| | - Helen Lin
- Department of Food Science, University of Massachusetts Amherst, Chenoweth Laboratory, 102 Holdsworth Way, Amherst, Massachusetts 01003, United States
| | - Shuping Xu
- State Key Laboratory of Supramolecular Structure and Materials, Institute of Theorical Chemistry, College of Chemistry, Jilin University, Changchun, Jilin 130012, People's Republic of China
| | - Lili He
- Department of Food Science, University of Massachusetts Amherst, Chenoweth Laboratory, 102 Holdsworth Way, Amherst, Massachusetts 01003, United States
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12
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Omar H, Hein A, Cole CA, Valafar H. Concurrent Identification and Characterization of Protein Structure and Continuous Internal Dynamics with REDCRAFT. Front Mol Biosci 2022; 9:806584. [PMID: 35187082 PMCID: PMC8856112 DOI: 10.3389/fmolb.2022.806584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/10/2022] [Indexed: 11/13/2022] Open
Abstract
Internal dynamics of proteins can play a critical role in the biological function of some proteins. Several well documented instances have been reported such as MBP, DHFR, hTS, DGCR8, and NSP1 of the SARS-CoV family of viruses. Despite the importance of internal dynamics of proteins, there currently are very few approaches that allow for meaningful separation of internal dynamics from structural aspects using experimental data. Here we present a computational approach named REDCRAFT that allows for concurrent characterization of protein structure and dynamics. Here, we have subjected DHFR (PDB-ID 1RX2), a 159-residue protein, to a fictitious, mixed mode model of internal dynamics. In this simulation, DHFR was segmented into 7 regions where 4 of the fragments were fixed with respect to each other, two regions underwent rigid-body dynamics, and one region experienced uncorrelated and melting event. The two dynamical and rigid-body segments experienced an average orientational modification of 7° and 12° respectively. Observable RDC data for backbone C′-N, N-HN, and C′-HN were generated from 102 uniformly sampled frames that described the molecular trajectory. The structure calculation of DHFR with REDCRAFT by using traditional Ramachandran restraint produced a structure with 29 Å of structural difference measured over the backbone atoms (bb-rmsd) over the entire length of the protein and an average bb-rmsd of more than 4.7 Å over each of the dynamical fragments. The same exercise repeated with context-specific dihedral restraints generated by PDBMine produced a structure with bb-rmsd of 21 Å over the entire length of the protein but with bb-rmsd of less than 3 Å over each of the fragments. Finally, utilization of the Dynamic Profile generated by REDCRAFT allowed for the identification of different dynamical regions of the protein and the recovery of individual fragments with bb-rmsd of less than 1 Å. Following the recovery of the fragments, our assembly procedure of domains (larger segments consisting of multiple fragments with a common dynamical profile) correctly assembled the four fragments that are rigid with respect to each other, categorized the two domains that underwent rigid-body dynamics, and identified one dynamical region for which no conserved structure could be defined. In conclusion, our approach was successful in identifying the dynamical domains, recovery of structure where it is meaningful, and relative assembly of the domains when possible.
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13
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Heimhalt M, Berndt A, Wagstaff J, Anandapadamanaban M, Perisic O, Maslen S, McLaughlin S, Yu CWH, Masson GR, Boland A, Ni X, Yamashita K, Murshudov GN, Skehel M, Freund SM, Williams RL. Bipartite binding and partial inhibition links DEPTOR and mTOR in a mutually antagonistic embrace. eLife 2021; 10:e68799. [PMID: 34519269 PMCID: PMC8439657 DOI: 10.7554/elife.68799] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/31/2021] [Indexed: 12/16/2022] Open
Abstract
The mTORC1 kinase complex regulates cell growth, proliferation, and survival. Because mis-regulation of DEPTOR, an endogenous mTORC1 inhibitor, is associated with some cancers, we reconstituted mTORC1 with DEPTOR to understand its function. We find that DEPTOR is a unique partial mTORC1 inhibitor that may have evolved to preserve feedback inhibition of PI3K. Counterintuitively, mTORC1 activated by RHEB or oncogenic mutation is much more potently inhibited by DEPTOR. Although DEPTOR partially inhibits mTORC1, mTORC1 prevents this inhibition by phosphorylating DEPTOR, a mutual antagonism that requires no exogenous factors. Structural analyses of the mTORC1/DEPTOR complex showed DEPTOR's PDZ domain interacting with the mTOR FAT region, and the unstructured linker preceding the PDZ binding to the mTOR FRB domain. The linker and PDZ form the minimal inhibitory unit, but the N-terminal tandem DEP domains also significantly contribute to inhibition.
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Affiliation(s)
- Maren Heimhalt
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Alex Berndt
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Jane Wagstaff
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | | | - Olga Perisic
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Sarah Maslen
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | | | | | - Glenn R Masson
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | - Andreas Boland
- Department of Molecular Biology, University of GenevaGenevaSwitzerland
| | - Xiaodan Ni
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
| | | | | | - Mark Skehel
- MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
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14
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Gaalswyk K, Liu Z, Vogel HJ, MacCallum JL. An Integrative Approach to Determine 3D Protein Structures Using Sparse Paramagnetic NMR Data and Physical Modeling. Front Mol Biosci 2021; 8:676268. [PMID: 34476238 PMCID: PMC8407082 DOI: 10.3389/fmolb.2021.676268] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/29/2021] [Indexed: 11/13/2022] Open
Abstract
Paramagnetic nuclear magnetic resonance (NMR) methods have emerged as powerful tools for structure determination of large, sparsely protonated proteins. However traditional applications face several challenges, including a need for large datasets to offset the sparsity of restraints, the difficulty in accounting for the conformational heterogeneity of the spin-label, and noisy experimental data. Here we propose an integrative approach to structure determination combining sparse paramagnetic NMR with physical modelling to infer approximate protein structural ensembles. We use calmodulin in complex with the smooth muscle myosin light chain kinase peptide as a model system. Despite acquiring data from samples labeled only at the backbone amide positions, we are able to produce an ensemble with an average RMSD of ∼2.8 Å from a reference X-ray crystal structure. Our approach requires only backbone chemical shifts and measurements of the paramagnetic relaxation enhancement and residual dipolar couplings that can be obtained from sparsely labeled samples.
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Affiliation(s)
- Kari Gaalswyk
- Department of Chemistry, University of Calgary, Calgary, AB, Canada
| | - Zhihong Liu
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
| | - Hans J. Vogel
- Department of Biological Sciences, University of Calgary, Calgary, AB, Canada
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15
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Xu L, Xu Z, Liao X. A review of fruit juice authenticity assessments: Targeted and untargeted analyses. Crit Rev Food Sci Nutr 2021; 62:6081-6102. [PMID: 33683157 DOI: 10.1080/10408398.2021.1895713] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Fruit juices are becoming more and more popular in the whole world. However, the increasing fruit juice fraud cases are undermining the healthy development of fruit juice industry. Fruit juice authenticity represents an important food quality and safety parameter. Many techniques have been applied in fruit juices authenticity assessment. The purpose of this review is to provide a research overview of the targeted and untargeted analyses of fruit authentication, and a method selection guide for fruit juice authenticity assessment. Targeted markers, such as stable isotopes, phenolics, carbohydrates, organic acids, volatile components, DNAs, amino acids and proteins, as well as carotenoids, will be discussed. And untargeted techniques, including liquid/gas chromatography-mass spectrometer, nuclear magnetic resonance, infrared spectroscopy, inductively-coupled plasma-mass spectrometry/optical emission spectrometer, fluorescence spectra, electronic sensors and others, will be reviewed. The emerging untargeted for novel targeted marker analysis will be also summarized.
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Affiliation(s)
- Lei Xu
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-food Safety and Quality, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China.,College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.,Beijing Key Laboratory for Food Nonthermal Processing, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing, China
| | - Zhenzhen Xu
- Institute of Quality Standard & Testing Technology for Agro-Products, Key Laboratory of Agro-food Safety and Quality, Ministry of Agriculture and Rural Affairs, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaojun Liao
- College of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.,Beijing Key Laboratory for Food Nonthermal Processing, Key Lab of Fruit and Vegetable Processing, Ministry of Agriculture and Rural Affairs, Beijing, China
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16
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Structure of membrane diacylglycerol kinase in lipid bilayers. Commun Biol 2021; 4:282. [PMID: 33674677 PMCID: PMC7935881 DOI: 10.1038/s42003-021-01802-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 02/05/2021] [Indexed: 12/17/2022] Open
Abstract
Diacylglycerol kinase (DgkA) is a small integral membrane protein, responsible for the ATP-dependent phosphorylation of diacylglycerol to phosphatidic acid. Its structures reported in previous studies, determined in detergent micelles by solution NMR and in monoolein cubic phase by X-ray crystallography, differ significantly. These differences point to the need to validate these detergent-based structures in phospholipid bilayers. Here, we present a well-defined homo-trimeric structure of DgkA in phospholipid bilayers determined by magic angle spinning solid-state NMR (ssNMR) spectroscopy, using an approach combining intra-, inter-molecular paramagnetic relaxation enhancement (PRE)-derived distance restraints and CS-Rosetta calculations. The DgkA structure determined in lipid bilayers is different from the solution NMR structure. In addition, although ssNMR structure of DgkA shows a global folding similar to that determined by X-ray, these two structures differ in monomeric symmetry and dynamics. A comparative analysis of DgkA structures determined in three different detergent/lipid environments provides a meaningful demonstration of the influence of membrane mimetic environments on the structure and dynamics of membrane proteins. Jianping Li et al. present the homo-trimeric structure of the small integral membrane protein diacylglycerol kinase (DgkA) in phospholipid bilayers determined by magic angle spinning solid-state NMR spectroscopy. They compare the structure with structures solved by solution NMR and X-ray crystallography and provide insights into the influence of membrane mimetic environments on membrane proteins.
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17
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Lawrence C, Grishaev A. Chemical shifts-based similarity restraints improve accuracy of RNA structures determined via NMR. RNA (NEW YORK, N.Y.) 2020; 26:2051-2061. [PMID: 32917774 PMCID: PMC7668244 DOI: 10.1261/rna.074617.119] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 08/19/2020] [Indexed: 06/01/2023]
Abstract
Determination of structure of RNA via NMR is complicated in large part by the lack of a precise parameterization linking the observed chemical shifts to the underlying geometric parameters. In contrast to proteins, where numerous high-resolution crystal structures serve as coordinate templates for this mapping, such models are rarely available for smaller oligonucleotides accessible via NMR, or they exhibit crystal packing and counter-ion binding artifacts that prevent their use for the chemical shifts analysis. On the other hand, NMR-determined structures of RNA often are not solved at the density of restraints required to precisely define the variable degrees of freedom. In this study we sidestep the problems of direct parameterization of the RNA chemical shifts/structure relationship and examine the effects of imposing local fragmental coordinate similarity restraints based on similarities of the experimental secondary ribose 13C/1H chemical shifts instead. The effect of such chemical shift similarity (CSS) restraints on the structural accuracy is assessed via residual dipolar coupling (RDC)-based cross-validation. Improvements in the coordinate accuracy are observed for all of the six RNA constructs considered here as test cases, which argues for routine inclusion of these terms during NMR-based oligonucleotide structure determination. Such accuracy improvements are expected to facilitate derivation of the chemical shift/structure relationships for RNA.
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Affiliation(s)
- Chad Lawrence
- Institute for Bioscience and Biotechnology Research, Rockville, Maryland 20850, USA
| | - Alexander Grishaev
- Institute for Bioscience and Biotechnology Research, Rockville, Maryland 20850, USA
- Biomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA
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18
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Gloaguen E, Mons M, Schwing K, Gerhards M. Neutral Peptides in the Gas Phase: Conformation and Aggregation Issues. Chem Rev 2020; 120:12490-12562. [PMID: 33152238 DOI: 10.1021/acs.chemrev.0c00168] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Combined IR and UV laser spectroscopic techniques in molecular beams merged with theoretical approaches have proven to be an ideal tool to elucidate intrinsic structural properties on a molecular level. It offers the possibility to analyze structural changes, in a controlled molecular environment, when successively adding aggregation partners. By this, it further makes these techniques a valuable starting point for a bottom-up approach in understanding the forces shaping larger molecular systems. This bottom-up approach was successfully applied to neutral amino acids starting around the 1990s. Ever since, experimental and theoretical methods developed further, and investigations could be extended to larger peptide systems. Against this background, the review gives an introduction to secondary structures and experimental methods as well as a summary on theoretical approaches. Vibrational frequencies being characteristic probes of molecular structure and interactions are especially addressed. Archetypal biologically relevant secondary structures investigated by molecular beam spectroscopy are described, and the influences of specific peptide residues on conformational preferences as well as the competition between secondary structures are discussed. Important influences like microsolvation or aggregation behavior are presented. Beyond the linear α-peptides, the main results of structural analysis on cyclic systems as well as on β- and γ-peptides are summarized. Overall, this contribution addresses current aspects of molecular beam spectroscopy on peptides and related species and provides molecular level insights into manifold issues of chemical and biochemical relevance.
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Affiliation(s)
- Eric Gloaguen
- CEA, CNRS, Université Paris-Saclay, CEA Paris-Saclay, Bât 522, 91191 Gif-sur-Yvette, France
| | - Michel Mons
- CEA, CNRS, Université Paris-Saclay, CEA Paris-Saclay, Bât 522, 91191 Gif-sur-Yvette, France
| | - Kirsten Schwing
- TU Kaiserslautern & Research Center Optimas, Erwin-Schrödinger-Straße 52, D-67663 Kaiserslautern, Germany
| | - Markus Gerhards
- TU Kaiserslautern & Research Center Optimas, Erwin-Schrödinger-Straße 52, D-67663 Kaiserslautern, Germany
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19
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Trindade IB, Invernici M, Cantini F, Louro RO, Piccioli M. PRE-driven protein NMR structures: an alternative approach in highly paramagnetic systems. FEBS J 2020; 288:3010-3023. [PMID: 33124176 DOI: 10.1111/febs.15615] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 09/10/2020] [Accepted: 10/28/2020] [Indexed: 01/29/2023]
Abstract
Metalloproteins play key roles across biology, and knowledge of their structure is essential to understand their physiological role. For those metalloproteins containing paramagnetic states, the enhanced relaxation caused by the unpaired electrons often makes signal detection unfeasible near the metal center, precluding adequate structural characterization right where it is more biochemically relevant. Here, we report a protein structure determination by NMR where two different sets of restraints, one containing Nuclear Overhauser Enhancements (NOEs) and another containing Paramagnetic Relaxation Enhancements (PREs), are used separately and eventually together. The protein PioC from Rhodopseudomonas palustris TIE-1 is a High Potential Iron-Sulfur Protein (HiPIP) where the [4Fe-4S] cluster is paramagnetic in both oxidation states at room temperature providing the source of PREs used as alternative distance restraints. Comparison of the family of structures obtained using NOEs only, PREs only, and the combination of both reveals that the pairwise root-mean-square deviation (RMSD) between them is similar and comparable with the precision within each family. This demonstrates that, under favorable conditions in terms of protein size and paramagnetic effects, PREs can efficiently complement and eventually replace NOEs for the structural characterization of small paramagnetic metalloproteins and de novo-designed metalloproteins by NMR. DATABASES: The 20 conformers with the lowest target function constituting the final family obtained using the full set of NMR restraints were deposited to the Protein Data Bank (PDB ID: 6XYV). The 20 conformers with the lowest target function obtained using NOEs only (PDB ID: 7A58) and PREs only (PDB ID: 7A4L) were also deposited to the Protein Data Bank. The chemical shift assignments were deposited to the BMRB (code 34487).
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Affiliation(s)
- Inês B Trindade
- Instituto de Tecnologia Química e Biológica António Xavier (ITQB-NOVA), Universidade Nova de Lisboa, Oeiras, Portugal
| | - Michele Invernici
- Magnetic Resonance Center and Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Francesca Cantini
- Magnetic Resonance Center and Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Ricardo O Louro
- Instituto de Tecnologia Química e Biológica António Xavier (ITQB-NOVA), Universidade Nova de Lisboa, Oeiras, Portugal
| | - Mario Piccioli
- Magnetic Resonance Center and Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
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20
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Flores-Solis D, Mendoza A, Rentería-González I, Casados-Vazquez LE, Trasviña-Arenas CH, Jiménez-Sandoval P, Benítez-Cardoza CG, Del Río-Portilla F, Brieba LG. Solution structure of the inhibitor of cysteine proteases 1 from Entamoeba histolytica reveals a possible auto regulatory mechanism. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1868:140512. [PMID: 32731033 DOI: 10.1016/j.bbapap.2020.140512] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 07/07/2020] [Accepted: 07/24/2020] [Indexed: 10/23/2022]
Abstract
The genome of Entamoeba histolytica encodes approximately 50 Cysteine Proteases (CPs) whose activity is regulated by two Inhibitors of Cysteine Proteases (ICPs), EhICP1 and EhICP2. The main difference between both EhICPs is the acquisition of a 17 N-terminal targeting signal in EhICP2 and three exposed cysteine residues in EhICP1. The three exposed cysteines in EhICP1 potentiate the formation of cross-linking species that drive heterogeneity. Here we solved the NMR structure of EhICP1 using a mutant protein without accessible cysteines. Our structural data shows that EhICP1 adopts an immunoglobulin fold composed of seven β-strands, and three solvent exposed loops that resemble the structures of EhICP2 and chagasin. EhICP1 and EhICP2 are able to inhibit the archetypical cysteine protease papain by intercalating their BC loops into the protease active site independently of the character of the residue (serine or threonine) responsible to interact with the active site of papain. EhICP1 and EhICP2 present signals of functional divergence as they clustered in different clades. Two of the three exposed cysteines in EhICP1 are located at the DE loop that intercalates into the CP substrate-binding cleft. We propose that the solvent exposed cysteines of EhICP1 play a role in regulating its inhibitory activity and that in oxidative conditions, the cysteines of EhICP1 react to form intra and intermolecular disulfide bonds that render an inactive inhibitor. EhICP2 is not subject to redox regulation, as this inhibitor does not contain a single cysteine residue. This proposed redox regulation may be related to the differential cellular localization between EhICP1 and EhICP2.
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Affiliation(s)
- David Flores-Solis
- Departamento de Química de Biomacromoléculas, Instituto de Química, Universidad Nacional Autónoma de México, Circuito exterior s/n, Coyoacán, Ciudad de Mexico 04510, Mexico
| | - Angeles Mendoza
- Departamento de Química de Biomacromoléculas, Instituto de Química, Universidad Nacional Autónoma de México, Circuito exterior s/n, Coyoacán, Ciudad de Mexico 04510, Mexico
| | - Itzel Rentería-González
- Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, km. 9.6 Libramiento Norte Carretera Irapuato-León, CP 36821 Irapuato, Guanajuato, Mexico
| | - Luz E Casados-Vazquez
- Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, km. 9.6 Libramiento Norte Carretera Irapuato-León, CP 36821 Irapuato, Guanajuato, Mexico
| | - Carlos H Trasviña-Arenas
- Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, km. 9.6 Libramiento Norte Carretera Irapuato-León, CP 36821 Irapuato, Guanajuato, Mexico
| | - Pedro Jiménez-Sandoval
- Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, km. 9.6 Libramiento Norte Carretera Irapuato-León, CP 36821 Irapuato, Guanajuato, Mexico
| | - Claudia G Benítez-Cardoza
- Laboratorio de Investigación Bioquímica, Programa Institucional en Biomedicina Molecular ENMyH-Instituto Politécnico Nacional, Guillermo Massieu Helguera No. 239, La Escalera Ticoman, 07320, D.F, Mexico
| | - Federico Del Río-Portilla
- Departamento de Química de Biomacromoléculas, Instituto de Química, Universidad Nacional Autónoma de México, Circuito exterior s/n, Coyoacán, Ciudad de Mexico 04510, Mexico.
| | - Luis G Brieba
- Laboratorio Nacional de Genómica para la Biodiversidad, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, km. 9.6 Libramiento Norte Carretera Irapuato-León, CP 36821 Irapuato, Guanajuato, Mexico.
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21
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Leman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, et alLeman JK, Weitzner BD, Lewis SM, Adolf-Bryfogle J, Alam N, Alford RF, Aprahamian M, Baker D, Barlow KA, Barth P, Basanta B, Bender BJ, Blacklock K, Bonet J, Boyken SE, Bradley P, Bystroff C, Conway P, Cooper S, Correia BE, Coventry B, Das R, De Jong RM, DiMaio F, Dsilva L, Dunbrack R, Ford AS, Frenz B, Fu DY, Geniesse C, Goldschmidt L, Gowthaman R, Gray JJ, Gront D, Guffy S, Horowitz S, Huang PS, Huber T, Jacobs TM, Jeliazkov JR, Johnson DK, Kappel K, Karanicolas J, Khakzad H, Khar KR, Khare SD, Khatib F, Khramushin A, King IC, Kleffner R, Koepnick B, Kortemme T, Kuenze G, Kuhlman B, Kuroda D, Labonte JW, Lai JK, Lapidoth G, Leaver-Fay A, Lindert S, Linsky T, London N, Lubin JH, Lyskov S, Maguire J, Malmström L, Marcos E, Marcu O, Marze NA, Meiler J, Moretti R, Mulligan VK, Nerli S, Norn C, Ó'Conchúir S, Ollikainen N, Ovchinnikov S, Pacella MS, Pan X, Park H, Pavlovicz RE, Pethe M, Pierce BG, Pilla KB, Raveh B, Renfrew PD, Burman SSR, Rubenstein A, Sauer MF, Scheck A, Schief W, Schueler-Furman O, Sedan Y, Sevy AM, Sgourakis NG, Shi L, Siegel JB, Silva DA, Smith S, Song Y, Stein A, Szegedy M, Teets FD, Thyme SB, Wang RYR, Watkins A, Zimmerman L, Bonneau R. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nat Methods 2020; 17:665-680. [PMID: 32483333 PMCID: PMC7603796 DOI: 10.1038/s41592-020-0848-2] [Show More Authors] [Citation(s) in RCA: 497] [Impact Index Per Article: 99.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 04/22/2020] [Indexed: 12/12/2022]
Abstract
The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.
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Affiliation(s)
- Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
| | - Brian D Weitzner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Steven M Lewis
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biochemistry, Duke University, Durham, NC, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Jared Adolf-Bryfogle
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Nawsad Alam
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Rebecca F Alford
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Melanie Aprahamian
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - David Baker
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Kyle A Barlow
- Graduate Program in Bioinformatics, University of California San Francisco, San Francisco, CA, USA
| | - Patrick Barth
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Benjamin Basanta
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Biological Physics Structure and Design PhD Program, University of Washington, Seattle, WA, USA
| | - Brian J Bender
- Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Kristin Blacklock
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Jaume Bonet
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Scott E Boyken
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Lyell Immunopharma Inc., Seattle, WA, USA
| | - Phil Bradley
- Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Bystroff
- Department of Biological Sciences, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Patrick Conway
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Seth Cooper
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bruno E Correia
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Brian Coventry
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Frank DiMaio
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Lorna Dsilva
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Roland Dunbrack
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Alexander S Ford
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Brandon Frenz
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Darwin Y Fu
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Caleb Geniesse
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ragul Gowthaman
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Program in Molecular Biophysics, Johns Hopkins University, Baltimore, MD, USA
| | - Dominik Gront
- Faculty of Chemistry, Biological and Chemical Research Centre, University of Warsaw, Warsaw, Poland
| | - Sharon Guffy
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott Horowitz
- Department of Chemistry & Biochemistry, University of Denver, Denver, CO, USA
- The Knoebel Institute for Healthy Aging, University of Denver, Denver, CO, USA
| | - Po-Ssu Huang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Tim M Jacobs
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - David K Johnson
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Kalli Kappel
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - John Karanicolas
- Institute for Cancer Research, Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Hamed Khakzad
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
| | - Karen R Khar
- Cyrus Biotechnology, Seattle, WA, USA
- Center for Computational Biology, University of Kansas, Lawrence, KS, USA
| | - Sagar D Khare
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Firas Khatib
- Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA
| | - Alisa Khramushin
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Indigo C King
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Robert Kleffner
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Brian Koepnick
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
| | - Brian Kuhlman
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daisuke Kuroda
- Medical Device Development and Regulation Research Center, School of Engineering, University of Tokyo, Tokyo, Japan
- Department of Bioengineering, School of Engineering, University of Tokyo, Tokyo, Japan
| | - Jason W Labonte
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Chemistry, Franklin & Marshall College, Lancaster, PA, USA
| | - Jason K Lai
- Baylor College of Medicine, Department of Pharmacology, Houston, TX, USA
| | - Gideon Lapidoth
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Andrew Leaver-Fay
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH, USA
| | - Thomas Linsky
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Nir London
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Joseph H Lubin
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Sergey Lyskov
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jack Maguire
- Program in Bioinformatics and Computational Biology, Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lars Malmström
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute for Computational Science, University of Zurich, Zurich, Switzerland
- S3IT, University of Zurich, Zurich, Switzerland
- Division of Infection Medicine, Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden
| | - Enrique Marcos
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Research in Biomedicine Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Orly Marcu
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Nicholas A Marze
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jens Meiler
- Center for Structural Biology, Vanderbilt University, Nashville, TN, USA
- Departments of Chemistry, Pharmacology and Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
- Institute for Chemical Biology, Vanderbilt University, Nashville, TN, USA
| | - Rocco Moretti
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Vikram Khipple Mulligan
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Santrupti Nerli
- Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Christoffer Norn
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
| | - Shane Ó'Conchúir
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Noah Ollikainen
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Sergey Ovchinnikov
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Molecular and Cellular Biology Program, University of Washington, Seattle, WA, USA
| | - Michael S Pacella
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xingjie Pan
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Hahnbeom Park
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ryan E Pavlovicz
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Manasi Pethe
- Department of Chemistry and Chemical Biology, The State University of New Jersey, Piscataway, NJ, USA
- Center for Integrative Proteomics Research, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Brian G Pierce
- University of Maryland Institute for Bioscience and Biotechnology Research, Rockville, MD, USA
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Kala Bharath Pilla
- Research School of Chemistry, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Barak Raveh
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - P Douglas Renfrew
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Shourya S Roy Burman
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Aliza Rubenstein
- Institute of Quantitative Biomedicine, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
- Computational Biology and Molecular Biophysics Program, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Marion F Sauer
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Andreas Scheck
- Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - William Schief
- Department of Immunology and Microbiology, The Scripps Research Institute, La Jolla, CA, USA
| | - Ora Schueler-Furman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yuval Sedan
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alexander M Sevy
- Chemical and Physical Biology Program, Vanderbilt Vaccine Center, Vanderbilt University, Nashville, TN, USA
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Lei Shi
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
| | - Justin B Siegel
- Department of Chemistry, University of California, Davis, Davis, CA, USA
- Department of Biochemistry and Molecular Medicine, University of California, Davis, Davis, California, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | | | - Shannon Smith
- Department of Chemistry, Vanderbilt University, Nashville, TN, USA
| | - Yifan Song
- Department of Biochemistry, University of Washington, Seattle, WA, USA
- Institute for Protein Design, University of Washington, Seattle, WA, USA
- Cyrus Biotechnology, Seattle, WA, USA
| | - Amelie Stein
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA
| | - Maria Szegedy
- Department of Chemistry and Chemical Biology, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Frank D Teets
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Summer B Thyme
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Ray Yu-Ruei Wang
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Andrew Watkins
- Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA
| | - Lior Zimmerman
- Department of Microbiology and Molecular Genetics, IMRIC, Ein Kerem Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA.
- Department of Biology, New York University, New York, New York, USA.
- Department of Computer Science, New York University, New York, NY, USA.
- Center for Data Science, New York University, New York, NY, USA.
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22
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Karamanos TK, Tugarinov V, Clore GM. Determining methyl sidechain conformations in a CS-ROSETTA model using methyl 1H- 13C residual dipolar couplings. JOURNAL OF BIOMOLECULAR NMR 2020; 74:111-118. [PMID: 31950428 PMCID: PMC7083688 DOI: 10.1007/s10858-019-00294-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 12/17/2019] [Indexed: 05/12/2023]
Abstract
Modelling of protein structures based on backbone chemical shifts, using programs such as CS-ROSETTA, is becoming increasingly popular, especially for systems where few restraints are available or where homologous structures are already known. While the reliability of CS-ROSETTA calculations can be improved by incorporation of some additional backbone NMR data such as those afforded by residual dipolar couplings or minimal NOE data sets involving backbone amide protons, the sidechain conformations are largely modelled by statistical energy terms. Here, we present a simple method based on methyl residual dipolar couplings that can be used to determine the rotameric state of the threefold symmetry axis of methyl groups that occupy a single rotamer, determine rotameric distributions, and identify regions of high flexibility. The method is demonstrated for methyl side chains of a deletion variant of the human chaperone DNAJB6b.
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Affiliation(s)
- Theodoros K Karamanos
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892-0520, USA
| | - Vitali Tugarinov
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892-0520, USA.
| | - G Marius Clore
- Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, 20892-0520, USA.
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23
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Orton H, Huber T, Otting G. Paramagpy: software for fitting magnetic susceptibility tensors using paramagnetic effects measured in NMR spectra. MAGNETIC RESONANCE (GOTTINGEN, GERMANY) 2020; 1:1-12. [PMID: 37904891 PMCID: PMC10500712 DOI: 10.5194/mr-1-1-2020] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 01/20/2020] [Indexed: 11/01/2023]
Abstract
Paramagnetic metal ions with fast-relaxing electrons generate pseudocontact shifts (PCSs), residual dipolar couplings (RDCs), paramagnetic relaxation enhancements (PREs) and cross-correlated relaxation (CCR) in the nuclear magnetic resonance (NMR) spectra of the molecules they bind to. These effects offer long-range structural information in molecules equipped with binding sites for such metal ions. Here we present the new open-source software Paramagpy, which has been written in Python 3 with a graphic user interface. Paramagpy combines the functionalities of different currently available programs to support the fitting of magnetic susceptibility tensors using PCS, RDC, PRE and CCR data and molecular coordinates in Protein Data Bank (PDB) format, including a convenient graphical user interface. Paramagpy uses efficient fitting algorithms to avoid local minima and supports corrections to back-calculated PCS and PRE data arising from cross-correlation effects with chemical shift tensors. The source code is available from 10.5281/zenodo.3594568 .
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Affiliation(s)
- Henry William Orton
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Thomas Huber
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
| | - Gottfried Otting
- Research School of Chemistry, Australian National University, Canberra, ACT 2601, Australia
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24
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Yahalom A, Davidov G, Kolusheva S, Shaked H, Barber-Zucker S, Zarivach R, Chill JH. Structure and membrane-targeting of a Bordetella pertussis effector N-terminal domain. BIOCHIMICA ET BIOPHYSICA ACTA-BIOMEMBRANES 2019; 1861:183054. [DOI: 10.1016/j.bbamem.2019.183054] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/05/2019] [Accepted: 08/22/2019] [Indexed: 01/07/2023]
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25
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Psenakova K, Hexnerova R, Srb P, Obsilova V, Veverka V, Obsil T. The redox‐active site of thioredoxin is directly involved in apoptosis signal‐regulating kinase 1 binding that is modulated by oxidative stress. FEBS J 2019; 287:1626-1644. [DOI: 10.1111/febs.15101] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 09/10/2019] [Accepted: 10/16/2019] [Indexed: 12/11/2022]
Affiliation(s)
- Katarina Psenakova
- Department of Physical and Macromolecular Chemistry Faculty of Science Charles University Prague Czech Republic
- Department of Structural Biology of Signaling Proteins, Division BIOCEV Institute of Physiology of the Czech Academy of Sciences Vestec Czech Republic
| | - Rozalie Hexnerova
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic
| | - Pavel Srb
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic
| | - Veronika Obsilova
- Department of Structural Biology of Signaling Proteins, Division BIOCEV Institute of Physiology of the Czech Academy of Sciences Vestec Czech Republic
| | - Vaclav Veverka
- Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Prague Czech Republic
- Department of Cell Biology Faculty of Science Charles University Prague Czech Republic
| | - Tomas Obsil
- Department of Physical and Macromolecular Chemistry Faculty of Science Charles University Prague Czech Republic
- Department of Structural Biology of Signaling Proteins, Division BIOCEV Institute of Physiology of the Czech Academy of Sciences Vestec Czech Republic
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26
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Sala D, Huang YJ, Cole CA, Snyder DA, Liu G, Ishida Y, Swapna GVT, Brock KP, Sander C, Fidelis K, Kryshtafovych A, Inouye M, Tejero R, Valafar H, Rosato A, Montelione GT. Protein structure prediction assisted with sparse NMR data in CASP13. Proteins 2019; 87:1315-1332. [PMID: 31603581 DOI: 10.1002/prot.25837] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 09/26/2019] [Accepted: 09/27/2019] [Indexed: 01/05/2023]
Abstract
CASP13 has investigated the impact of sparse NMR data on the accuracy of protein structure prediction. NOESY and 15 N-1 H residual dipolar coupling data, typical of that obtained for 15 N,13 C-enriched, perdeuterated proteins up to about 40 kDa, were simulated for 11 CASP13 targets ranging in size from 80 to 326 residues. For several targets, two prediction groups generated models that are more accurate than those produced using baseline methods. Real NMR data collected for a de novo designed protein were also provided to predictors, including one data set in which only backbone resonance assignments were available. Some NMR-assisted prediction groups also did very well with these data. CASP13 also assessed whether incorporation of sparse NMR data improves the accuracy of protein structure prediction relative to nonassisted regular methods. In most cases, incorporation of sparse, noisy NMR data results in models with higher accuracy. The best NMR-assisted models were also compared with the best regular predictions of any CASP13 group for the same target. For six of 13 targets, the most accurate model provided by any NMR-assisted prediction group was more accurate than the most accurate model provided by any regular prediction group; however, for the remaining seven targets, one or more regular prediction method provided a more accurate model than even the best NMR-assisted model. These results suggest a novel approach for protein structure determination, in which advanced prediction methods are first used to generate structural models, and sparse NMR data is then used to validate and/or refine these models.
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Affiliation(s)
- Davide Sala
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York
| | - Casey A Cole
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - David A Snyder
- Department of Chemistry, College of Science and Health, William Paterson University, Wayne, New Jersey
| | - Gaohua Liu
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Nexomics Biosciences, Bordentown, New Jersey
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - G V T Swapna
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, Massachusetts.,cBio Center, Dana-Farber Cancer Institute, Boston, Massachusetts
| | | | | | - Masayori Inouye
- Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
| | - Roberto Tejero
- Departamento de Quimica Fisica, Universidad de Valencia, Valencia, Spain
| | - Homayoun Valafar
- Department of Computer Science & Engineering, University of South Carolina, Columbia, South Carolina
| | - Antonio Rosato
- Magnetic Resonance Center, University of Florence, Sesto Fiorentino, Italy.,Department of Chemistry, University of Florence, Sesto Fiorentino, Italy
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, and Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, New Jersey.,Department of Chemistry and Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, New York.,Department of Biochemistry and Molecular Biology, The Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, New Jersey
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27
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Unraveling the structure and dynamics of the human DNAJB6b chaperone by NMR reveals insights into Hsp40-mediated proteostasis. Proc Natl Acad Sci U S A 2019; 116:21529-21538. [PMID: 31591220 DOI: 10.1073/pnas.1914999116] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
J-domain chaperones are involved in the efficient handover of misfolded/partially folded proteins to Hsp70 but also function independently to protect against cell death. Due to their high flexibility, the mechanism by which they regulate the Hsp70 cycle and how specific substrate recognition is performed remains unknown. Here we focus on DNAJB6b, which has been implicated in various human diseases and represents a key player in protection against neurodegeneration and protein aggregation. Using a variant that exists mainly in a monomeric form, we report the solution structure of an Hsp40 containing not only the J and C-terminal substrate binding (CTD) domains but also the functionally important linkers. The structure reveals a highly dynamic protein in which part of the linker region masks the Hsp70 binding site. Transient interdomain interactions via regions crucial for Hsp70 binding create a closed, autoinhibited state and help retain the monomeric form of the protein. Detailed NMR analysis shows that the CTD (but not the J domain) self-associates to form an oligomer comprising ∼35 monomeric units, revealing an intricate balance between intramolecular and intermolecular interactions. The results shed light on the mechanism of autoregulation of the Hsp70 cycle via conserved parts of the linker region and reveal the mechanism of DNAJB6b oligomerization and potentially antiaggregation.
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28
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Arthanari H, Takeuchi K, Dubey A, Wagner G. Emerging solution NMR methods to illuminate the structural and dynamic properties of proteins. Curr Opin Struct Biol 2019; 58:294-304. [PMID: 31327528 PMCID: PMC6778509 DOI: 10.1016/j.sbi.2019.06.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 06/03/2019] [Accepted: 06/10/2019] [Indexed: 12/20/2022]
Abstract
The first recognition of protein breathing was more than 50 years ago. Today, we are able to detect the multitude of interaction modes, structural polymorphisms, and binding-induced changes in protein structure that direct function. Solution-state NMR spectroscopy has proved to be a powerful technique, not only to obtain high-resolution structures of proteins, but also to provide unique insights into the functional dynamics of proteins. Here, we summarize recent technical landmarks in solution NMR that have enabled characterization of key biological macromolecular systems. These methods have been fundamental to atomic resolution structure determination and quantitative analysis of dynamics over a wide range of time scales by NMR. The ability of NMR to detect lowly populated protein conformations and transiently formed complexes plays a critical role in its ability to elucidate functionally important structural features of proteins and their dynamics.
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Affiliation(s)
- Haribabu Arthanari
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, United States; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, United States.
| | - Koh Takeuchi
- Molecular Profiling Research Center for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 135-0064 Tokyo, Japan.
| | - Abhinav Dubey
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, United States; Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, United States
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, United States.
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29
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Kuenze G, Bonneau R, Leman JK, Meiler J. Integrative Protein Modeling in RosettaNMR from Sparse Paramagnetic Restraints. Structure 2019; 27:1721-1734.e5. [PMID: 31522945 DOI: 10.1016/j.str.2019.08.012] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 08/05/2019] [Accepted: 08/23/2019] [Indexed: 12/17/2022]
Abstract
Computational methods to predict protein structure from nuclear magnetic resonance (NMR) restraints that only require assignment of backbone signals, hold great potential to study larger proteins. Ideally, computational methods designed to work with sparse data need to add atomic detail that is missing in the experimental restraints. We introduce a comprehensive framework into the Rosetta suite that uses NMR restraints derived from paramagnetic labeling. Specifically, RosettaNMR incorporates pseudocontact shifts, residual dipolar couplings, and paramagnetic relaxation enhancements. It continues to use backbone chemical shifts and nuclear Overhauser effect distance restraints. We assess RosettaNMR for protein structure prediction by folding 28 monomeric proteins and 8 homo-oligomeric proteins. Furthermore, the general applicability of RosettaNMR is demonstrated on two protein-protein and three protein-ligand docking examples. Paramagnetic restraints generated more accurate models for 85% of the benchmark proteins and, when combined with chemical shifts, sampled high-accuracy models (≤2Å) in 50% of the cases.
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Affiliation(s)
- Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA.
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA; Department of Computer Science, New York University, New York, NY 10012, USA
| | - Julia Koehler Leman
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY 10010, USA; Department of Biology and Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA.
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN 37232, USA; Center for Structural Biology, Vanderbilt University, Nashville, TN 37240, USA.
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30
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Bahramzadeh A, Huber T, Otting G. Three-Dimensional Protein Structure Determination Using Pseudocontact Shifts of Backbone Amide Protons Generated by Double-Histidine Co 2+-Binding Motifs at Multiple Sites. Biochemistry 2019; 58:3243-3250. [PMID: 31282649 DOI: 10.1021/acs.biochem.9b00404] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Pseudocontact shifts (PCSs) generated by paramagnetic metal ions contribute highly informative long-range structure restraints that can be measured in solution and are ideally suited to guide structure prediction algorithms in determining global protein folds. We recently demonstrated that PCSs, which are relatively small but of high quality, can be generated by a double-histidine (dHis) motif in an α-helix, which provides a well-defined binding site for a single Co2+ ion. Here we show that PCSs of backbone amide protons generated by dHis-Co2+ motifs positioned in four different α-helices of a protein deliver excellent restraints to determine the three-dimensional (3D) structure of a protein in a way akin to the global positioning system (GPS). We demonstrate the approach with GPS-Rosetta calculations of the 3D structure of the C-terminal domain of the chaperone ERp29 (ERp29-C). Despite the relatively small size of the PCSs generated by the dHis-Co2+ motifs, the structure calculations converged readily. Generating PCSs by the dHis-Co2+ motif thus presents an excellent alternative to the use of lanthanide tags.
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Affiliation(s)
- Alireza Bahramzadeh
- Research School of Chemistry , Australian National University , Canberra , ACT 2601 , Australia
| | - Thomas Huber
- Research School of Chemistry , Australian National University , Canberra , ACT 2601 , Australia
| | - Gottfried Otting
- Research School of Chemistry , Australian National University , Canberra , ACT 2601 , Australia
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31
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Kuenze G, Meiler J. Protein structure prediction using sparse NOE and RDC restraints with Rosetta in CASP13. Proteins 2019; 87:1341-1350. [PMID: 31292988 DOI: 10.1002/prot.25769] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Revised: 05/25/2019] [Accepted: 07/06/2019] [Indexed: 12/30/2022]
Abstract
Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.
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Affiliation(s)
- Georg Kuenze
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
| | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee.,Center for Structural Biology, Vanderbilt University, Nashville, Tennessee
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Chen D, Drombosky KW, Hou Z, Sari L, Kashmer OM, Ryder BD, Perez VA, Woodard DR, Lin MM, Diamond MI, Joachimiak LA. Tau local structure shields an amyloid-forming motif and controls aggregation propensity. Nat Commun 2019; 10:2493. [PMID: 31175300 PMCID: PMC6555816 DOI: 10.1038/s41467-019-10355-1] [Citation(s) in RCA: 112] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 05/08/2019] [Indexed: 11/09/2022] Open
Abstract
Tauopathies are neurodegenerative diseases characterized by intracellular amyloid deposits of tau protein. Missense mutations in the tau gene (MAPT) correlate with aggregation propensity and cause dominantly inherited tauopathies, but their biophysical mechanism driving amyloid formation is poorly understood. Many disease-associated mutations localize within tau's repeat domain at inter-repeat interfaces proximal to amyloidogenic sequences, such as 306VQIVYK311. We use cross-linking mass spectrometry, recombinant protein and synthetic peptide systems, in silico modeling, and cell models to conclude that the aggregation-prone 306VQIVYK311 motif forms metastable compact structures with its upstream sequence that modulates aggregation propensity. We report that disease-associated mutations, isomerization of a critical proline, or alternative splicing are all sufficient to destabilize this local structure and trigger spontaneous aggregation. These findings provide a biophysical framework to explain the basis of early conformational changes that may underlie genetic and sporadic tau pathogenesis.
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Affiliation(s)
- Dailu Chen
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Molecular Biophysics Graduate Program, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Kenneth W Drombosky
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zhiqiang Hou
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Levent Sari
- Green Center for Molecular, Computational and Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Omar M Kashmer
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Bryan D Ryder
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Molecular Biophysics Graduate Program, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Valerie A Perez
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Molecular Biophysics Graduate Program, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - DaNae R Woodard
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Milo M Lin
- Green Center for Molecular, Computational and Systems Biology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
- Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Marc I Diamond
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Lukasz A Joachimiak
- Center for Alzheimer's and Neurodegenerative Diseases, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
- Department of Biochemistry, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
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NMR structure determination of Ixolaris and factor X(a) interaction reveals a noncanonical mechanism of Kunitz inhibition. Blood 2019; 134:699-708. [PMID: 31133602 DOI: 10.1182/blood.2018889493] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2018] [Accepted: 05/05/2019] [Indexed: 11/20/2022] Open
Abstract
Ixolaris is a potent tick salivary anticoagulant that binds coagulation factor Xa (FXa) and zymogen FX, with formation of a quaternary tissue factor (TF)/FVIIa/ FX(a)/Ixolaris inhibitory complex. Ixolaris blocks TF-induced coagulation and PAR2 signaling and prevents thrombosis, tumor growth, and immune activation. We present a high-resolution structure and dynamics of Ixolaris and describe the structural basis for recognition of FX. Ixolaris consists of 2 Kunitz domains (K1 and K2) in which K2 is strikingly dynamic and encompasses several residues involved in FX binding. This indicates that the backbone plasticity of K2 is critical for Ixolaris biological activity. Notably, a nuclear magnetic resonance-derived model reveals a mechanism for an electrostatically guided, high-affinity interaction between Ixolaris and FX heparin-binding (pro)exosite, resulting in an allosteric switch in the catalytic site. This is the first report revealing the structure-function relationship of an anticoagulant targeting a zymogen serving as a scaffold for TF inhibition.
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Wang L, Lim L, Dang M, Song J. A novel mechanism for ATP to enhance the functional oligomerization of TDP-43 by specific binding. Biochem Biophys Res Commun 2019; 514:809-814. [PMID: 31079926 DOI: 10.1016/j.bbrc.2019.05.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 05/01/2019] [Indexed: 11/16/2022]
Abstract
Pathological TDP-43 aggregation has been found in ∼98% ALS and other neurodegenerative diseases including Alzheimer's. TDP-43 N-terminal domain (NTD) was recently shown to form a tubular super-helical filament by oligomerization in vivo, which functions to prevent its pathological aggregation. ATP, the universal energy currency with very high concentrations in all living cells, was recently decoded to act as a biological hydrotrope to maintain protein homeostasis. Here by NMR spectroscopy, we reveal for the first time that at physiological concentrations ATP binds the TDP-43 NTD to enhance its oligomerization. Most strikingly, this binding is specifically coupled with oligomerization because three mutants with the capacity of oligomerization eliminated lose the ability to bind ATP. Our study thus provides a novel mechanism for ATP to prevent pathological aggregation by specific binding; and further implies that ATP might have many previously-unknown functions in cells by binding to proteins other than the classic ATP-dependent proteins/enzymes.
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Affiliation(s)
- Lu Wang
- Department of Biological Sciences, Faculty of Science, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore
| | - Liangzhong Lim
- Department of Biological Sciences, Faculty of Science, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore
| | - Mei Dang
- Department of Biological Sciences, Faculty of Science, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore
| | - Jianxing Song
- Department of Biological Sciences, Faculty of Science, National University of Singapore, 10 Kent Ridge Crescent, 119260, Singapore.
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Wang CK, Craik DJ. Toward Structure Determination of Disulfide-Rich Peptides Using Chemical Shift-Based Methods. J Phys Chem B 2019; 123:1903-1912. [PMID: 30730741 DOI: 10.1021/acs.jpcb.8b10649] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Disulfide-rich peptides are a class of molecules for which NMR spectroscopy has been the primary tool for structural characterization. Here, we explore whether the process can be achieved by using structural information encoded in chemical shifts. We examine (i) a representative set of five cyclic disulfide-rich peptides that have high-resolution NMR and X-ray structures and (ii) a larger set of 100 disulfide-rich peptides from the PDB. Accuracy of the calculated structures was dependent on the methods used for searching through conformational space and for identifying native conformations. Although Hα chemical shifts could be predicted reasonably well using SHIFTX, agreement between predicted and experimental chemical shifts was sufficient for identifying native conformations for only some peptides in the representative set. Combining chemical shift data with the secondary structure information and potential energy calculations improved the ability to identify native conformations. Additional use of sparse distance restraints or homology information to restrict the search space also improved the resolution of the calculated structures. This study demonstrates that abbreviated methods have potential for elucidation of peptide structures to high resolution and further optimization of these methods, e.g., improvement in chemical shift prediction accuracy, will likely help transition these methods into the mainstream of disulfide-rich peptide structural biology.
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Affiliation(s)
- Conan K Wang
- Institute for Molecular Bioscience , The University of Queensland , Brisbane , Queensland 4072 , Australia
| | - David J Craik
- Institute for Molecular Bioscience , The University of Queensland , Brisbane , Queensland 4072 , Australia
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36
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Fast NMR method to probe solvent accessibility and disordered regions in proteins. Sci Rep 2019; 9:1647. [PMID: 30733478 PMCID: PMC6367444 DOI: 10.1038/s41598-018-37599-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 12/10/2018] [Indexed: 01/12/2023] Open
Abstract
Understanding protein structure and dynamics, which govern key cellular processes, is crucial for basic and applied research. Intrinsically disordered protein (IDP) regions display multifunctionality via alternative transient conformations, being key players in disease mechanisms. IDP regions are abundant, namely in small viruses, allowing a large number of functions out of a small proteome. The relation between protein function and structure is thus now seen from a different perspective: as IDP regions enable transient structural arrangements, each conformer can play different roles within the cell. However, as IDP regions are hard and time-consuming to study via classical techniques (optimized for globular proteins with unique conformations), new methods are required. Here, employing the dengue virus (DENV) capsid (C) protein and the immunoglobulin-binding domain of streptococcal protein G, we describe a straightforward NMR method to differentiate the solvent accessibility of single amino acid N-H groups in structured and IDP regions. We also gain insights into DENV C flexible fold region biological activity. The method, based on minimal pH changes, uses the well-established 1H-15N HSQC pulse sequence and is easily implementable in current protein NMR routines. The data generated are simple to interpret, with this rapid approach being an useful first-choice IDPs characterization method.
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Huang YJ, Brock KP, Ishida Y, Swapna GVT, Inouye M, Marks DS, Sander C, Montelione GT. Combining Evolutionary Covariance and NMR Data for Protein Structure Determination. Methods Enzymol 2018; 614:363-392. [PMID: 30611430 PMCID: PMC6640129 DOI: 10.1016/bs.mie.2018.11.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Accurate protein structure determination by solution-state NMR is challenging for proteins greater than about 20kDa, for which extensive perdeuteration is generally required, providing experimental data that are incomplete (sparse) and ambiguous. However, the massive increase in evolutionary sequence information coupled with advances in methods for sequence covariance analysis can provide reliable residue-residue contact information for a protein from sequence data alone. These "evolutionary couplings (ECs)" can be combined with sparse NMR data to determine accurate 3D protein structures. This hybrid "EC-NMR" method has been developed using NMR data for several soluble proteins and validated by comparison with corresponding reference structures determined by X-ray crystallography and/or conventional NMR methods. For small proteins, only backbone resonance assignments are utilized, while for larger proteins both backbone and some sidechain methyl resonance assignments are generally required. ECs can be combined with sparse NMR data obtained on deuterated, selectively protonated protein samples to provide structures that are more accurate and complete than those obtained using such sparse NMR data alone. EC-NMR also has significant potential for analysis of protein structures from solid-state NMR data and for studies of integral membrane proteins. The requirement that ECs are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
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Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Kelly P Brock
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Yojiro Ishida
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Gurla V T Swapna
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Masayori Inouye
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, United States
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School and cBio Center, Dana-Farber Cancer Institute, Boston, MA, United States
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, United States; Department of Biochemistry and Molecular Biology, Robert Wood Johnson Medical School, Rutgers, The State University of New Jersey, Piscataway, NJ, United States.
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Kossowska D, Kwak K, Cho M. Do Osmolytes Impact the Structure and Dynamics of Myoglobin? Molecules 2018; 23:E3189. [PMID: 30513982 PMCID: PMC6321238 DOI: 10.3390/molecules23123189] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 11/30/2018] [Accepted: 12/02/2018] [Indexed: 11/16/2022] Open
Abstract
Osmolytes are small organic compounds that can affect the stability of proteins in living cells. The mechanism of osmolytes' protective effects on protein structure and dynamics has not been fully explained, but in general, two possibilities have been suggested and examined: a direct interaction of osmolytes with proteins (water replacement hypothesis), and an indirect interaction (vitrification hypothesis). Here, to investigate these two possible mechanisms, we studied myoglobin-osmolyte systems using FTIR, UV-vis, CD, and femtosecond IR pump-probe spectroscopy. Interestingly, noticeable changes are observed in both the lifetime of the CO stretch of CO-bound myoglobin and the spectra of UV-vis, CD, and FTIR upon addition of the osmolytes. In addition, the temperature-dependent CD studies reveal that the protein's thermal stability depends on molecular structure, hydrogen-bonding ability, and size of osmolytes. We anticipate that the present experimental results provide important clues about the complicated and intricate mechanism of osmolyte effects on protein structure and dynamics in a crowded cellular environment.
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Affiliation(s)
- Dorota Kossowska
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, Korea.
| | - Kyungwon Kwak
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, Korea.
- Department of Chemistry, Korea University, Seoul 136-713, Korea.
| | - Minhaeng Cho
- Center for Molecular Spectroscopy and Dynamics, Institute for Basic Science (IBS), Seoul 02841, Korea.
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Abstract
Chemical Shift-Rosetta (CS-Rosetta) is an automated method that employs NMR chemical shifts to model protein structures de novo. In this chapter, we introduce the terminology and central concepts of CS-Rosetta. We describe the architecture and functionality of automatic NOESY assignment (AutoNOE) and structure determination protocols (Abrelax and RASREC) within the CS-Rosetta framework. We further demonstrate how CS-Rosetta can discriminate near-native structures against a large conformational search space using restraints obtained from NMR data, and/or sequence and structure homology. We highlight how CS-Rosetta can be combined with alternative automated approaches to (i) model oligomeric systems and (ii) create NMR-based structure determination pipeline. To show its practical applicability, we emphasize on the computational requirements and performance of CS-Rosetta for protein targets of varying molecular weight and complexity. Finally, we discuss the current Python interface, which enables easy execution of protocols for rapid and accurate high-resolution structure determination.
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Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA, United States.
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40
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Bédard F, Hammami R, Zirah S, Rebuffat S, Fliss I, Biron E. Synthesis, antimicrobial activity and conformational analysis of the class IIa bacteriocin pediocin PA-1 and analogs thereof. Sci Rep 2018; 8:9029. [PMID: 29899567 PMCID: PMC5998028 DOI: 10.1038/s41598-018-27225-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Accepted: 05/29/2018] [Indexed: 12/25/2022] Open
Abstract
The antimicrobial peptide pediocin PA-1 is a class IIa bacteriocin that inhibits several clinically relevant pathogens including Listeria spp. Here we report the synthesis and characterization of whole pediocin PA-1 and novel analogs thereof using a combination of solid- and solution-phase strategies to overcome difficulties due to instability and undesired reactions. Pediocin PA-1 thus synthesized was a potent inhibitor of Listeria monocytogenes (MIC = 6.8 nM), similar to the bacteriocin produced naturally by Pediococcus acidilactici. Of particular interest is that linear analogs lacking both of the disulfide bridges characterizing pediocin PA-1 were as potent. One linear analog was also a strong inhibitor of Clostridium perfringens, another important food-borne pathogen. These results are discussed in light of conformational information derived from circular dichroism, solution NMR spectroscopy and structure-activity relationship studies.
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Affiliation(s)
- François Bédard
- Faculté de pharmacie, Université Laval and Laboratoire de chimie médicinale, Centre de recherche du CHU de Québec, 2705 Boulevard Laurier, Québec, Québec, G1V 0A6, Canada
- STELA Dairy Research Centre, Institute of Nutrition and Functional Foods, Université Laval, Québec, Québec, G1V 0A6, Canada
| | - Riadh Hammami
- STELA Dairy Research Centre, Institute of Nutrition and Functional Foods, Université Laval, Québec, Québec, G1V 0A6, Canada
- School of Nutrition Sciences, University of Ottawa, Ottawa, ON, Canada, K1N 6N5
| | - Séverine Zirah
- Molécules de Communication et Adaptation des Microorganismes (MCAM, UMR 7245), Muséum national d'Histoire Naturelle, Sorbonne Universités, CNRS, CP 54, 57 rue Cuvier, 75005, Paris, France
| | - Sylvie Rebuffat
- Molécules de Communication et Adaptation des Microorganismes (MCAM, UMR 7245), Muséum national d'Histoire Naturelle, Sorbonne Universités, CNRS, CP 54, 57 rue Cuvier, 75005, Paris, France
| | - Ismail Fliss
- STELA Dairy Research Centre, Institute of Nutrition and Functional Foods, Université Laval, Québec, Québec, G1V 0A6, Canada
| | - Eric Biron
- Faculté de pharmacie, Université Laval and Laboratoire de chimie médicinale, Centre de recherche du CHU de Québec, 2705 Boulevard Laurier, Québec, Québec, G1V 0A6, Canada.
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41
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Nerli S, McShan AC, Sgourakis NG. Chemical shift-based methods in NMR structure determination. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2018; 106-107:1-25. [PMID: 31047599 PMCID: PMC6788782 DOI: 10.1016/j.pnmrs.2018.03.002] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/09/2018] [Accepted: 03/09/2018] [Indexed: 05/08/2023]
Abstract
Chemical shifts are highly sensitive probes harnessed by NMR spectroscopists and structural biologists as conformational parameters to characterize a range of biological molecules. Traditionally, assignment of chemical shifts has been a labor-intensive process requiring numerous samples and a suite of multidimensional experiments. Over the past two decades, the development of complementary computational approaches has bolstered the analysis, interpretation and utilization of chemical shifts for elucidation of high resolution protein and nucleic acid structures. Here, we review the development and application of chemical shift-based methods for structure determination with a focus on ab initio fragment assembly, comparative modeling, oligomeric systems, and automated assignment methods. Throughout our discussion, we point out practical uses, as well as advantages and caveats, of using chemical shifts in structure modeling. We additionally highlight (i) hybrid methods that employ chemical shifts with other types of NMR restraints (residual dipolar couplings, paramagnetic relaxation enhancements and pseudocontact shifts) that allow for improved accuracy and resolution of generated 3D structures, (ii) the utilization of chemical shifts to model the structures of sparsely populated excited states, and (iii) modeling of sidechain conformations. Finally, we briefly discuss the advantages of contemporary methods that employ sparse NMR data recorded using site-specific isotope labeling schemes for chemical shift-driven structure determination of larger molecules. With this review, we aim to emphasize the accessibility and versatility of chemical shifts for structure determination of challenging biological systems, and to point out emerging areas of development that lead us towards the next generation of tools.
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Affiliation(s)
- Santrupti Nerli
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States; Department of Computer Science, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Andrew C McShan
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States
| | - Nikolaos G Sgourakis
- Department of Chemistry and Biochemistry, University of California Santa Cruz, Santa Cruz, CA 95064, United States.
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42
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Automated NMR resonance assignments and structure determination using a minimal set of 4D spectra. Nat Commun 2018; 9:384. [PMID: 29374165 PMCID: PMC5786013 DOI: 10.1038/s41467-017-02592-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Accepted: 12/12/2017] [Indexed: 12/22/2022] Open
Abstract
Automated methods for NMR structure determination of proteins are continuously becoming more robust. However, current methods addressing larger, more complex targets rely on analyzing 6-10 complementary spectra, suggesting the need for alternative approaches. Here, we describe 4D-CHAINS/autoNOE-Rosetta, a complete pipeline for NOE-driven structure determination of medium- to larger-sized proteins. The 4D-CHAINS algorithm analyzes two 4D spectra recorded using a single, fully protonated protein sample in an iterative ansatz where common NOEs between different spin systems supplement conventional through-bond connectivities to establish assignments of sidechain and backbone resonances at high levels of completeness and with a minimum error rate. The 4D-CHAINS assignments are then used to guide automated assignment of long-range NOEs and structure refinement in autoNOE-Rosetta. Our results on four targets ranging in size from 15.5 to 27.3 kDa illustrate that the structures of proteins can be determined accurately and in an unsupervised manner in a matter of days.
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43
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Halle-Bikovski A, Fried S, Rozentur-Shkop E, Biber G, Shaked H, Joseph N, Barda-Saad M, Chill JH. New Structural Insights into Formation of the Key Actin Regulating WIP-WASp Complex Determined by NMR and Molecular Imaging. ACS Chem Biol 2018; 13:100-109. [PMID: 29215267 DOI: 10.1021/acschembio.7b00486] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Wiskott-Aldrich syndrome protein (WASp) is exclusively expressed in hematopoietic cells and responsible for actin-dependent processes, including cellular activation, migration, and invasiveness. The C-terminal domain of WASp-Interacting Protein (WIP) binds to WASp and regulates its activity by shielding it from degradation in a phosphorylation dependent manner as we previously demonstrated. Mutations in the WAS-encoding gene lead to the primary immunodeficiencies Wiskott-Aldrich syndrome (WAS) and X-linked thrombocytopenia (XLT). Here, we shed a first structural light upon this function of WIP using nuclear magnetic resonance (NMR) and in vivo molecular imaging. Coexpression of fragments WASp(20-158) and WIP(442-492) allowed the purification and structural characterization of a natively folded complex, determined to form a characteristic pleckstrin homology domain with a mixed α/β-fold and central two-winged β-sheet. The WIP-derived peptide, unstructured in its free form, wraps around and interacts with WASp through short structural elements. Förster resonance energy transfer (FRET) and biochemical experiments demonstrated that, of these elements, WIP residues 454-456 are the major contributor to WASp affinity, and the previously overlooked residues 449-451 were found to have the largest effect upon WASp ubiquitylation and, presumably, degradation. Results obtained from this complementary combination of technologies link WIP-WASp affinity to protection from degradation. Our findings about the nature of WIP·WASp complex formation are relevant for ongoing efforts to understand hematopoietic cell behavior, paving the way for new therapeutic approaches to WAS and XLT.
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Affiliation(s)
- Adi Halle-Bikovski
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Sophia Fried
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Eva Rozentur-Shkop
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Guy Biber
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Hadassa Shaked
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Noah Joseph
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Mira Barda-Saad
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
| | - Jordan H. Chill
- Department
of Chemistry, and ‡Mina and Everard Goodman Faculty of Life Sciences, Bar Ilan University, Ramat Gan, 52900, Israel
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Singh A, Kaushik R, Kuntal H, Jayaram B. PvaxDB: a comprehensive structural repository of Plasmodium vivax proteome. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4938395. [PMID: 29688373 PMCID: PMC5852996 DOI: 10.1093/database/bay021] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Accepted: 02/06/2018] [Indexed: 12/20/2022]
Abstract
The severity of malaria caused by Plasmodium vivax worldwide and its resistance against the available general antimalarial drugs has created an urgent need for a comprehensive insight into its biology and biochemistry for developing some novel potential vaccines and therapeutics. P.vivax comprises 5392 proteins mostly predicted, out of which 4211 are soluble proteins and 2205 of these belong to blood and liver stages of malarial cycle. Presently available public resources report functional annotation (gene ontology) of only 28% (627 proteins) of the enzymatic soluble proteins and experimental structures are determined for only 42 proteins P. vivax proteome. In this milieu of severe paucity of structural and functional data, we have generated structures of 2205 soluble proteins, validated them thoroughly, identified their binding pockets (including active sites) and annotated their function increasing the coverage from the existing 28% to 100%. We have pooled all this information together and created a database christened as PvaxDB, which furnishes extensive sequence, structure, ligand binding site and functional information. We believe PvaxDB could be helpful in identifying novel protein drug targets, expediting development of new drugs to combat malaria. This is also the first attempt to create a reliable comprehensive computational structural repository of all the soluble proteins of P. vivax. Database URL: http://www.scfbio-iitd.res.in/PvaxDB
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Affiliation(s)
- Ankita Singh
- Department of Bioinformatics, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India.,Supercomputing Facility for Bioinformatics and Computational Biology, IIT Delhi, Delhi, India
| | - Rahul Kaushik
- Supercomputing Facility for Bioinformatics and Computational Biology, IIT Delhi, Delhi, India.,Kusuma School of Biological Sciences, IIT Delhi, Delhi, India
| | - Himani Kuntal
- Department of Bioinformatics, Banasthali Vidyapith, Banasthali 304022, Rajasthan, India
| | - B Jayaram
- Supercomputing Facility for Bioinformatics and Computational Biology, IIT Delhi, Delhi, India.,Kusuma School of Biological Sciences, IIT Delhi, Delhi, India.,Department of Chemistry, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, Delhi, India
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45
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Huang YJ, Brock KP, Sander C, Marks DS, Montelione GT. A Hybrid Approach for Protein Structure Determination Combining Sparse NMR with Evolutionary Coupling Sequence Data. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2018; 1105:153-169. [PMID: 30617828 DOI: 10.1007/978-981-13-2200-6_10] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
While 3D structure determination of small (<15 kDa) proteins by solution NMR is largely automated and routine, structural analysis of larger proteins is more challenging. An emerging hybrid strategy for modeling protein structures combines sparse NMR data that can be obtained for larger proteins with sequence co-variation data, called evolutionary couplings (ECs), obtained from multiple sequence alignments of protein families. This hybrid "EC-NMR" method can be used to accurately model larger (15-60 kDa) proteins, and more rapidly determine structures of smaller (5-15 kDa) proteins using only backbone NMR data. The resulting structures have accuracies relative to reference structures comparable to those obtained with full backbone and sidechain NMR resonance assignments. The requirement that evolutionary couplings (ECs) are consistent with NMR data recorded on a specific member of a protein family, under specific conditions, potentially also allows identification of ECs that reflect alternative allosteric or excited states of the protein structure.
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Affiliation(s)
- Yuanpeng Janet Huang
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Kelly P Brock
- cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- cBio Center, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Debora S Marks
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Gaetano T Montelione
- Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers, The State University of New Jersey, Piscataway, NJ, USA.
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46
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Karaca E, Rodrigues JPGLM, Graziadei A, Bonvin AMJJ, Carlomagno T. M3: an integrative framework for structure determination of molecular machines. Nat Methods 2017; 14:897-902. [DOI: 10.1038/nmeth.4392] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 07/05/2017] [Indexed: 01/22/2023]
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Lampe JN. Advances in the Understanding of Protein-Protein Interactions in Drug Metabolizing Enzymes through the Use of Biophysical Techniques. Front Pharmacol 2017; 8:521. [PMID: 28848438 PMCID: PMC5550701 DOI: 10.3389/fphar.2017.00521] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Accepted: 07/24/2017] [Indexed: 02/01/2023] Open
Abstract
In recent years, a growing appreciation has developed for the importance of protein-protein interactions to modulate the function of drug metabolizing enzymes. Accompanied with this appreciation, new methods and technologies have been designed for analyzing protein-protein interactions both in vitro and in vivo. These technologies have been applied to several classes of drug metabolizing enzymes, including: cytochrome P450's (CYPs), monoamine oxidases (MAOs), UDP-glucuronosyltransferases (UGTs), glutathione S-transferases (GSTs), and sulfotransferases (SULTs). In this review, we offer a brief description and assessment of the impact of many of these technologies to the study of protein-protein interactions in drug disposition. The still expanding list of these techniques and assays has the potential to revolutionize our understanding of how these enzymes carry out their important functions in vivo.
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Affiliation(s)
- Jed N Lampe
- Department of Pharmacology, Toxicology, and Therapeutics, University of Kansas Medical CenterKansas City, MO, United States
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48
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Kassem MM, Wang Y, Boomsma W, Lindorff-Larsen K. Structure of the Bacterial Cytoskeleton Protein Bactofilin by NMR Chemical Shifts and Sequence Variation. Biophys J 2017; 110:2342-2348. [PMID: 27276252 DOI: 10.1016/j.bpj.2016.04.039] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Revised: 04/19/2016] [Accepted: 04/21/2016] [Indexed: 12/28/2022] Open
Abstract
Bactofilins constitute a recently discovered class of bacterial proteins that form cytoskeletal filaments. They share a highly conserved domain (DUF583) of which the structure remains unknown, in part due to the large size and noncrystalline nature of the filaments. Here, we describe the atomic structure of a bactofilin domain from Caulobacter crescentus. To determine the structure, we developed an approach that combines a biophysical model for proteins with recently obtained solid-state NMR spectroscopy data and amino acid contacts predicted from a detailed analysis of the evolutionary history of bactofilins. Our structure reveals a triangular β-helical (solenoid) conformation with conserved residues forming the tightly packed core and polar residues lining the surface. The repetitive structure explains the presence of internal repeats as well as strongly conserved positions, and is reminiscent of other fibrillar proteins. Our work provides a structural basis for future studies of bactofilin biology and for designing molecules that target them, as well as a starting point for determining the organization of the entire bactofilin filament. Finally, our approach presents new avenues for determining structures that are difficult to obtain by traditional means.
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Affiliation(s)
- Maher M Kassem
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Yong Wang
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Wouter Boomsma
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Kresten Lindorff-Larsen
- Structural Biology and NMR Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
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Ziarek JJ, Baptista D, Wagner G. Recent developments in solution nuclear magnetic resonance (NMR)-based molecular biology. J Mol Med (Berl) 2017. [PMID: 28643003 DOI: 10.1007/s00109-017-1560-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Visualizing post-translational modifications, conformations, and interaction surfaces of protein structures at atomic resolution underpins the development of novel therapeutics to combat disease. As computational resources expand, in silico calculations coupled with experimentally derived structures and functional assays have led to an explosion in structure-based drug design (SBDD) with several compounds in clinical trials. It is increasingly clear that "hidden" transition-state structures along activation trajectories can be harnessed to develop novel classes of allosteric inhibitors. The goal of this mini-review is to empower the clinical researcher with a general knowledge of the strengths and weaknesses of nuclear magnetic resonance (NMR) spectroscopy in molecular medicine. Although NMR can determine protein structures at atomic resolution, its unrivaled strength lies in sensing subtle changes in a nuclei's chemical environment as a result of intrinsic conformational dynamics, solution conditions, and binding interactions. These can be recorded at atomic resolution, without explicit structure determination, and then incorporated with static structures or molecular dynamics simulations to produce a complete biological picture.
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Affiliation(s)
- Joshua J Ziarek
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Ave, Boston, MA, 02115, USA
| | - Diego Baptista
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Ave, Boston, MA, 02115, USA
| | - Gerhard Wagner
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, 240 Longwood Ave, Boston, MA, 02115, USA.
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Pilla KB, Gaalswyk K, MacCallum JL. Molecular modeling of biomolecules by paramagnetic NMR and computational hybrid methods. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2017. [PMID: 28648524 DOI: 10.1016/j.bbapap.2017.06.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The 3D atomic structures of biomolecules and their complexes are key to our understanding of biomolecular function, recognition, and mechanism. However, it is often difficult to obtain structures, particularly for systems that are complex, dynamic, disordered, or exist in environments like cell membranes. In such cases sparse data from a variety of paramagnetic NMR experiments offers one possible source of structural information. These restraints can be incorporated in computer modeling algorithms that can accurately translate the sparse experimental data into full 3D atomic structures. In this review, we discuss various types of paramagnetic NMR/computational hybrid modeling techniques that can be applied to successful modeling of not only the atomic structure of proteins but also their interacting partners. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.
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Affiliation(s)
| | - Kari Gaalswyk
- Department of Chemistry, University of Calgary, Calgary, AB, Canada
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