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Uegaki K, Tokunaga Y, Inoue M, Takashima S, Inaba K, Takeuchi K, Ushioda R, Nagata K. The oxidative folding of nascent polypeptides provides electrons for reductive reactions in the ER. Cell Rep 2023; 42:112742. [PMID: 37421625 DOI: 10.1016/j.celrep.2023.112742] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 03/20/2023] [Accepted: 06/19/2023] [Indexed: 07/10/2023] Open
Abstract
The endoplasmic reticulum (ER) maintains an oxidative redox environment that is advantageous for the oxidative folding of nascent polypeptides entering the ER. Reductive reactions within the ER are also crucial for maintaining ER homeostasis. However, the mechanism by which electrons are supplied for the reductase activity within the ER remains unknown. Here, we identify ER oxidoreductin-1α (Ero1α) as an electron donor for ERdj5, an ER-resident disulfide reductase. During oxidative folding, Ero1α catalyzes disulfide formation in nascent polypeptides through protein disulfide isomerase (PDI) and then transfers the electrons to molecular oxygen via flavin adenine dinucleotide (FAD), ultimately yielding hydrogen peroxide (H2O2). Besides this canonical electron pathway, we reveal that ERdj5 accepts electrons from specific cysteine pairs in Ero1α, demonstrating that the oxidative folding of nascent polypeptides provides electrons for reductive reactions in the ER. Moreover, this electron transfer pathway also contributes to maintaining ER homeostasis by reducing H2O2 production in the ER.
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Affiliation(s)
- Kaiku Uegaki
- Department of Molecular Biosciences, Faculty of Life Sciences, Kyoto Sangyo University, Kyoto 603-8555, Japan
| | - Yuji Tokunaga
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan; Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo 113-0033, Japan
| | - Michio Inoue
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Saitama 332-0012, Japan; Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Miyagi 980-8577, Japan
| | - Seiji Takashima
- Department of Medical Biochemistry, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
| | - Kenji Inaba
- Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Saitama 332-0012, Japan; Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, Miyagi 980-8577, Japan
| | - Koh Takeuchi
- Cellular and Molecular Biotechnology Research Institute, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan; Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo 113-0033, Japan
| | - Ryo Ushioda
- Department of Molecular Biosciences, Faculty of Life Sciences, Kyoto Sangyo University, Kyoto 603-8555, Japan; Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Saitama 332-0012, Japan; Institute for Protein Dynamics, Kyoto Sangyo University, Kyoto 603-8555, Japan.
| | - Kazuhiro Nagata
- Department of Molecular Biosciences, Faculty of Life Sciences, Kyoto Sangyo University, Kyoto 603-8555, Japan; Core Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), Saitama 332-0012, Japan; Institute for Protein Dynamics, Kyoto Sangyo University, Kyoto 603-8555, Japan; JT Biohistory Research Hall, Murasaki Town 1-1, Takatsuki City, Osaka 569-1125, Japan.
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2
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Tsuchiya Y, Yamamori Y, Tomii K. Protein-protein interaction prediction methods: from docking-based to AI-based approaches. Biophys Rev 2022; 14:1341-1348. [PMID: 36570321 PMCID: PMC9759050 DOI: 10.1007/s12551-022-01032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
Protein-protein interactions (PPIs), such as protein-protein inhibitor, antibody-antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody-antigen interactions, offering both historical and forward-looking perspectives.
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Affiliation(s)
- Yuko Tsuchiya
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Yu Yamamori
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
| | - Kentaro Tomii
- grid.208504.b0000 0001 2230 7538Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST), 2-4-7 Aomi, Koto-Ku, Tokyo, 135-0064 Japan
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3
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Shimba N, Kamiya N, Nakamura H. Model Building of Antibody–Antigen Complex Structures Using GBSA Scores. J Chem Inf Model 2016; 56:2005-2012. [DOI: 10.1021/acs.jcim.6b00066] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Noriko Shimba
- Device
Research Laboratory, Advanced Research Division, Panasonic Corporation, 3-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan
| | - Narutoshi Kamiya
- Advanced
Institute for Computational Science, RIKEN, QBiC Building B, 6-2-4 Furuedai, Suita, Osaka 565-0874, Japan
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
| | - Haruki Nakamura
- Institute
for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka 565-0871, Japan
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4
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Akbal-Delibas B, Pomplun M, Haspel N. Accurate Prediction of Docked Protein Structure Similarity. J Comput Biol 2016; 22:892-904. [PMID: 26335807 DOI: 10.1089/cmb.2015.0114] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin.
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Affiliation(s)
- Bahar Akbal-Delibas
- Department of Computer Science, University of Massachusetts, Boston , Massachusetts
| | - Marc Pomplun
- Department of Computer Science, University of Massachusetts, Boston , Massachusetts
| | - Nurit Haspel
- Department of Computer Science, University of Massachusetts, Boston , Massachusetts
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5
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Hashmi I, Shehu A. idDock+: Integrating Machine Learning in Probabilistic Search for Protein–Protein Docking. J Comput Biol 2015. [DOI: 10.1089/cmb.2015.0108] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Irina Hashmi
- Department of Computer Science, George Mason University, Fairfax, Virginia
| | - Amarda Shehu
- Department of Computer Science, George Mason University, Fairfax, Virginia
- Department of Bioengineering, George Mason University, Fairfax, Virginia
- School of Systems Biology, George Mason University, Fairfax, Virginia
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6
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Aumentado-Armstrong TT, Istrate B, Murgita RA. Algorithmic approaches to protein-protein interaction site prediction. Algorithms Mol Biol 2015; 10:7. [PMID: 25713596 PMCID: PMC4338852 DOI: 10.1186/s13015-015-0033-9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2014] [Accepted: 01/07/2015] [Indexed: 12/19/2022] Open
Abstract
Interaction sites on protein surfaces mediate virtually all biological activities, and their identification holds promise for disease treatment and drug design. Novel algorithmic approaches for the prediction of these sites have been produced at a rapid rate, and the field has seen significant advancement over the past decade. However, the most current methods have not yet been reviewed in a systematic and comprehensive fashion. Herein, we describe the intricacies of the biological theory, datasets, and features required for modern protein-protein interaction site (PPIS) prediction, and present an integrative analysis of the state-of-the-art algorithms and their performance. First, the major sources of data used by predictors are reviewed, including training sets, evaluation sets, and methods for their procurement. Then, the features employed and their importance in the biological characterization of PPISs are explored. This is followed by a discussion of the methodologies adopted in contemporary prediction programs, as well as their relative performance on the datasets most recently used for evaluation. In addition, the potential utility that PPIS identification holds for rational drug design, hotspot prediction, and computational molecular docking is described. Finally, an analysis of the most promising areas for future development of the field is presented.
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Hashmi I, Shehu A. HopDock: a probabilistic search algorithm for decoy sampling in protein-protein docking. Proteome Sci 2013; 11:S6. [PMID: 24564839 PMCID: PMC3909090 DOI: 10.1186/1477-5956-11-s1-s6] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Elucidating the three-dimensional structure of a higher-order molecular assembly formed by interacting molecular units, a problem commonly known as docking, is central to unraveling the molecular basis of cellular activities. Though protein assemblies are ubiquitous in the cell, it is currently challenging to predict the native structure of a protein assembly in silico. METHODS This work proposes HopDock, a novel search algorithm for protein-protein docking. HopDock efficiently obtains an ensemble of low-energy dimeric configurations, also known as decoys, that can be effectively used by ab-initio docking protocols. HopDock is based on the Basin Hopping (BH) framework which perturbs the structure of a dimeric configuration and then follows it up with an energy minimization to explicitly sample a local minimum of a chosen energy function. This process is repeated in order to sample consecutive energy minima in a trajectory-like fashion. HopDock employs both geometry and evolutionary conservation analysis to narrow down the interaction search space of interest for the purpose of efficiently obtaining a diverse decoy ensemble. RESULTS AND CONCLUSIONS A detailed analysis and a comparative study on seventeen different dimers shows HopDock obtains a broad view of the energy surface near the native dimeric structure and samples many near-native configurations. The results show that HopDock has high sampling capability and can be employed to effectively obtain a large and diverse ensemble of decoy configurations that can then be further refined in greater structural detail in ab-initio docking protocols.
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Affiliation(s)
- Irina Hashmi
- Department of Computer Science, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA
| | - Amarda Shehu
- Department of Computer Science, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA
- Department of Bioengineering, George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA
- School of Systems Biology, George Mason University, 10900 University Blvd., Manassas, VA, 20110, USA
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8
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Andreani J, Faure G, Guerois R. InterEvScore: a novel coarse-grained interface scoring function using a multi-body statistical potential coupled to evolution. ACTA ACUST UNITED AC 2013; 29:1742-9. [PMID: 23652426 DOI: 10.1093/bioinformatics/btt260] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
MOTIVATION Structural prediction of protein interactions currently remains a challenging but fundamental goal. In particular, progress in scoring functions is critical for the efficient discrimination of near-native interfaces among large sets of decoys. Many functions have been developed using knowledge-based potentials, but few make use of multi-body interactions or evolutionary information, although multi-residue interactions are crucial for protein-protein binding and protein interfaces undergo significant selection pressure to maintain their interactions. RESULTS This article presents InterEvScore, a novel scoring function using a coarse-grained statistical potential including two- and three-body interactions, which provides each residue with the opportunity to contribute in its most favorable local structural environment. Combination of this potential with evolutionary information considerably improves scoring results on the 54 test cases from the widely used protein docking benchmark for which evolutionary information can be collected. We analyze how our way to include evolutionary information gradually increases the discriminative power of InterEvScore. Comparison with several previously published scoring functions (ZDOCK, ZRANK and SPIDER) shows the significant progress brought by InterEvScore. AVAILABILITY http://biodev.cea.fr/interevol/interevscore CONTACT guerois@cea.fr SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jessica Andreani
- CEA, iBiTecS, Service de Bioenergetique Biologie Structurale et Mecanismes SB2SM, Laboratoire de Biologie Structurale et Radiobiologie LBSR, F-91191 Gif sur Yvette, France
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9
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Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules. ACTA ACUST UNITED AC 2012. [DOI: 10.1155/2012/674832] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of
the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule,
thus serving as a first step towards the characterization of transition trajectories connecting these states.
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10
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AKBAL-DELIBAS BAHAR, HASHMI IRINA, SHEHU AMARDA, HASPEL NURIT. AN EVOLUTIONARY CONSERVATION-BASED METHOD FOR REFINING AND RERANKING PROTEIN COMPLEX STRUCTURES. J Bioinform Comput Biol 2012; 10:1242002. [DOI: 10.1142/s0219720012420024] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Detection of protein complexes and their structures is crucial for understanding their role in the basic biology of organisms. Computational docking methods can provide researchers with a good starting point for the analysis of protein complexes. However, these methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a refinement method that incorporates evolutionary information into a novel scoring function by employing Evolutionary Trace (ET)-based scores. Our method also takes Van der Waals interactions into account to avoid atomic clashes in refined structures. We tested our method on docked candidates of eight protein complexes and the results suggest that the proposed scoring function helps bias the search toward complexes with native interactions. We show a strong correlation between evolutionary-conserved residues and correct interface packing. Our refinement method is able to produce structures with better lRMSD (least RMSD) with respect to the known complexes and lower energies than initial docked structures. It also helps to filter out false-positive complexes generated by docking methods, by detecting little or no conserved residues on false interfaces. We believe this method is a step toward better ranking and prediction of protein complexes.
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Affiliation(s)
- BAHAR AKBAL-DELIBAS
- Computer Science Department, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA 02125, USA
| | - IRINA HASHMI
- Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
| | - AMARDA SHEHU
- Department of Computer Science, George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
| | - NURIT HASPEL
- Computer Science Department, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA 02125, USA
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11
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HASHMI IRINA, AKBAL-DELIBAS BAHAR, HASPEL NURIT, SHEHU AMARDA. GUIDING PROTEIN DOCKING WITH GEOMETRIC AND EVOLUTIONARY INFORMATION. J Bioinform Comput Biol 2012; 10:1242008. [DOI: 10.1142/s0219720012420085] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Structural modeling of molecular assemblies promises to improve our understanding of molecular interactions and biological function. Even when focusing on modeling structures of protein dimers from knowledge of monomeric native structure, docking two rigid structures onto one another entails exploring a large configurational space. This paper presents a novel approach for docking protein molecules and elucidating native-like configurations of protein dimers. The approach makes use of geometric hashing to focus the docking of monomeric units on geometrically complementary regions through rigid-body transformations. This geometry-based approach improves the feasibility of searching the combined configurational space. The search space is narrowed even further by focusing the sought rigid-body transformations around molecular surface regions composed of amino acids with high evolutionary conservation. This condition is based on recent findings, where analysis of protein assemblies reveals that many functional interfaces are significantly conserved throughout evolution. Different search procedures are employed in this work to search the resulting narrowed configurational space. A proof-of-concept energy-guided probabilistic search procedure is also presented. Results are shown on a broad list of 18 protein dimers and additionally compared with data reported by other labs. Our analysis shows that focusing the search around evolutionary-conserved interfaces results in lower lRMSDs.
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Affiliation(s)
- IRINA HASHMI
- Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA
| | - BAHAR AKBAL-DELIBAS
- Department of Computer Science, University of Massachusetts at Boston, Boston, MA, 02125, USA
| | - NURIT HASPEL
- Department of Computer Science, University of Massachusetts at Boston, Boston, MA, 02125, USA
| | - AMARDA SHEHU
- Department of Computer Science, George Mason University, Fairfax, VA, 22030, USA
- Department of Bioinformatics and Computational Biology, George Mason University, Fairfax, VA, 22030, USA
- Department of Bioengineering, George Mason University, Fairfax, VA, 22030, USA
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12
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Masui S, Vavassori S, Fagioli C, Sitia R, Inaba K. Molecular bases of cyclic and specific disulfide interchange between human ERO1alpha protein and protein-disulfide isomerase (PDI). J Biol Chem 2011; 286:16261-71. [PMID: 21398518 DOI: 10.1074/jbc.m111.231357] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
In the endoplasmic reticulum (ER) of human cells, ERO1α and protein-disulfide isomerase (PDI) constitute one of the major electron flow pathways that catalyze oxidative folding of secretory proteins. Specific and limited PDI oxidation by ERO1α is essential to avoid ER hyperoxidation. To investigate how ERO1α oxidizes PDI selectively among more than 20 ER-resident PDI family member proteins, we performed docking simulations and systematic biochemical analyses. Our findings reveal that a protruding β-hairpin of ERO1α specifically interacts with the hydrophobic pocket present in the redox-inactive PDI b'-domain through the stacks between their aromatic residues, leading to preferred oxidation of the C-terminal PDI a'-domain. ERO1α associated preferentially with reduced PDI, explaining the stepwise disulfide shuttle mechanism, first from ERO1α to PDI and then from oxidized PDI to an unfolded polypeptide bound to its hydrophobic pocket. The interaction of ERO1α with ERp44, another PDI family member protein, was also analyzed. Notably, ERO1α-dependent PDI oxidation was inhibited by a hyperactive ERp44 mutant that lacks the C-terminal tail concealing the substrate-binding hydrophobic regions. The potential ability of ERp44 to inhibit ERO1α activity may suggest its physiological role in ER redox and protein homeostasis.
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Affiliation(s)
- Shoji Masui
- Division of Protein Chemistry, Post-Genome Science Center, Medical Institute of Bioregulation, Kyushu University, Higashi-ku, Fukuoka, Japan
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13
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Kanamori E, Igarashi S, Osawa M, Fukunishi Y, Shimada I, Nakamura H. Structure determination of a protein assembly by amino acid selective cross-saturation. Proteins 2010; 79:179-90. [PMID: 20954264 DOI: 10.1002/prot.22871] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2010] [Revised: 08/25/2010] [Accepted: 08/27/2010] [Indexed: 11/10/2022]
Abstract
Amino acid selective cross-saturation (ASCS) method not only provides information about the interface of a protein assembly by the spin relaxation experiment, but also identifies the amino acid residues in the acceptor protein, which are located close to the selectively labeled amino acid residues in the donor protein. Here, a new method was developed to build a precise structural model of a protein assembly, which satisfies the experimental ASCS values, using simulated annealing computation. This method was applied to the ubiquitin-yeast ubiquitin hydrolase 1 (Ub-YUH1) complex to build a precise complex structure compatible with that determined by X-ray crystallography.
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Affiliation(s)
- Eiji Kanamori
- Japan Biological Informatics Consortium (JBIC), Koto-ku, Tokyo 135-0064, Japan.
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14
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Janin J. Protein–protein docking tested in blind predictions: the CAPRI experiment. MOLECULAR BIOSYSTEMS 2010; 6:2351-62. [DOI: 10.1039/c005060c] [Citation(s) in RCA: 132] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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15
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Xu D, Zhang Y. Generating triangulated macromolecular surfaces by Euclidean Distance Transform. PLoS One 2009; 4:e8140. [PMID: 19956577 PMCID: PMC2779860 DOI: 10.1371/journal.pone.0008140] [Citation(s) in RCA: 77] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2009] [Accepted: 11/09/2009] [Indexed: 11/30/2022] Open
Abstract
Macromolecular surfaces are fundamental representations of their three-dimensional geometric shape. Accurate calculation of protein surfaces is of critical importance in the protein structural and functional studies including ligand-protein docking and virtual screening. In contrast to analytical or parametric representation of macromolecular surfaces, triangulated mesh surfaces have been proved to be easy to describe, visualize and manipulate by computer programs. Here, we develop a new algorithm of EDTSurf for generating three major macromolecular surfaces of van der Waals surface, solvent-accessible surface and molecular surface, using the technique of fast Euclidean Distance Transform (EDT). The triangulated surfaces are constructed directly from volumetric solids by a Vertex-Connected Marching Cube algorithm that forms triangles from grid points. Compared to the analytical result, the relative error of the surface calculations by EDTSurf is <2–4% depending on the grid resolution, which is 1.5–4 times lower than the methods in the literature; and yet, the algorithm is faster and costs less computer memory than the comparative methods. The improvements in both accuracy and speed of the macromolecular surface determination should make EDTSurf a useful tool for the detailed study of protein docking and structure predictions. Both source code and the executable program of EDTSurf are freely available at http://zhang.bioinformatics.ku.edu/EDTSurf.
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Affiliation(s)
- Dong Xu
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, Lawrence, Kansas, United States of America
| | - Yang Zhang
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Center for Bioinformatics and Department of Molecular Bioscience, University of Kansas, Lawrence, Kansas, United States of America
- * E-mail:
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16
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Tsuchiya Y, Kanamori E, Nakamura H, Kinoshita K. Classification of heterodimer interfaces using docking models and construction of scoring functions for the complex structure prediction. Adv Appl Bioinform Chem 2009; 2:79-100. [PMID: 21918618 PMCID: PMC3169947 DOI: 10.2147/aabc.s6347] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Protein–protein docking simulations can provide the predicted complex structural models. In a docking simulation, several putative structural models are selected by scoring functions from an ensemble of many complex models. Scoring functions based on statistical analyses of heterodimers are usually designed to select the complex model with the most abundant interaction mode found among the known complexes, as the correct model. However, because the formation schemes of heterodimers are extremely diverse, a single scoring function does not seem to be sufficient to describe the fitness of the predicted models other than the most abundant interaction mode. Thus, it is necessary to classify the heterodimers in terms of their individual interaction modes, and then to construct multiple scoring functions for each heterodimer type. In this study, we constructed the classification method of heterodimers based on the discriminative characters between near-native and decoy models, which were found in the comparison of the interfaces in terms of the complementarities for the hydrophobicity, the electrostatic potential and the shape. Consequently, we found four heterodimer clusters, and then constructed the multiple scoring functions, each of which was optimized for each cluster. Our multiple scoring functions were applied to the predictions in the unbound docking.
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Affiliation(s)
- Yuko Tsuchiya
- Institute of Medical Science, University of Tokyo, Tokyo, Japan
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17
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Engelen S, Trojan LA, Sacquin-Mora S, Lavery R, Carbone A. Joint evolutionary trees: a large-scale method to predict protein interfaces based on sequence sampling. PLoS Comput Biol 2009; 5:e1000267. [PMID: 19165315 PMCID: PMC2613531 DOI: 10.1371/journal.pcbi.1000267] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2008] [Accepted: 12/04/2008] [Indexed: 11/18/2022] Open
Abstract
The Joint Evolutionary Trees (JET) method detects protein interfaces, the core
residues involved in the folding process, and residues susceptible to
site-directed mutagenesis and relevant to molecular recognition. The approach,
based on the Evolutionary Trace (ET) method, introduces a novel way to treat
evolutionary information. Families of homologous sequences are analyzed through
a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple
alignment and impacts of weakly homologous sequences on distance tree
construction. The sampling method makes sequence analysis more sensitive to
functional and structural importance of individual residues by avoiding effects
of the overrepresentation of highly homologous sequences and improves
computational efficiency. A carefully designed clustering method is parametrized
on the target structure to detect and extend patches on protein surfaces into
predicted interaction sites. Clustering takes into account residues'
physical-chemical properties as well as conservation. Large-scale application of
JET requires the system to be adjustable for different datasets and to guarantee
predictions even if the signal is low. Flexibility was achieved by a careful
treatment of the number of retrieved sequences, the amino acid distance between
sequences, and the selective thresholds for cluster identification. An iterative
version of JET (iJET) that guarantees finding the most likely interface residues
is proposed as the appropriate tool for large-scale predictions. Tests are
carried out on the Huang database of 62 heterodimer, homodimer, and transient
complexes and on 265 interfaces belonging to signal transduction proteins,
enzymes, inhibitors, antibodies, antigens, and others. A specific set of
proteins chosen for their special functional and structural properties
illustrate JET behavior on a large variety of interactions covering proteins,
ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf,
Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant
improvement in performance and computational efficiency is shown. Information obtained on the structure of macromolecular complexes is important
for identifying functionally important partners but also for determining how
such interactions will be perturbed by natural or engineered site mutations.
Hence, to fully understand or control biological processes we need to predict in
the most accurate manner protein interfaces for a protein structure, possibly
without knowing its partners. Joint Evolutionary Trees (JET) is a method
designed to detect very different types of interactions of a protein with
another protein, ligands, DNA, and RNA. It uses a carefully designed sampling
method, making sequence analysis more sensitive to the functional and structural
importance of individual residues, and a clustering method parametrized on the
target structure for the detection of patches on protein surfaces and their
extension into predicted interaction sites. JET is a large-scale method, highly
accurate and potentially applicable to search for protein partners.
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Affiliation(s)
- Stefan Engelen
- Génomique Analytique, Université Pierre et Marie
Curie-Paris 6, UMR S511, Paris, France
- INSERM, U511, Paris, France
| | - Ladislas A. Trojan
- Génomique Analytique, Université Pierre et Marie
Curie-Paris 6, UMR S511, Paris, France
- INSERM, U511, Paris, France
| | | | - Richard Lavery
- Institut de Biologie et Chimie des Protéines, CNRS UMR
5086/IFR 128/Université de Lyon, Lyon, France
| | - Alessandra Carbone
- Génomique Analytique, Université Pierre et Marie
Curie-Paris 6, UMR S511, Paris, France
- INSERM, U511, Paris, France
- * E-mail:
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