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Scietti L, Forneris F. Modeling of Protein Complexes. Methods Mol Biol 2023; 2627:349-371. [PMID: 36959458 DOI: 10.1007/978-1-0716-2974-1_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/25/2023]
Abstract
The recent advances in structural biology, combined with continuously increasing computational capabilities and development of advanced softwares, have drastically simplified the workflow for protein homology modeling. Modeling of individual proteins is nowadays quick and straightforward for a large variety of protein targets, thanks to guided pipelines relying on advanced computational tools and user-friendly interfaces, which have extended and promoted the use of modeling also to scientists not focusing on molecular structures of proteins. Nevertheless, construction of models of multi-protein complexes remains quite challenging for the non-experts, often due to the usage of specific procedures depending on the system under investigation and the need for experimental validation approaches to strengthen the generated output.In this chapter, we provide a brief overview of the approaches enabling generation of multi-protein complex models starting from homology models of individual protein components. Using real-life examples, we include two examples to guide the reader in the generation of homomeric and heteromeric protein models.
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Affiliation(s)
- Luigi Scietti
- Department of Biology and Biotechnology, The Armenise-Harvard Laboratory of Structural Biology, University of Pavia, Pavia, Italy.
| | - Federico Forneris
- Department of Biology and Biotechnology, The Armenise-Harvard Laboratory of Structural Biology, University of Pavia, Pavia, Italy.
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2
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Harmalkar A, Gray JJ. Advances to tackle backbone flexibility in protein docking. Curr Opin Struct Biol 2020; 67:178-186. [PMID: 33360497 DOI: 10.1016/j.sbi.2020.11.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/18/2020] [Accepted: 11/25/2020] [Indexed: 12/11/2022]
Abstract
Computational docking methods can provide structural models of protein-protein complexes, but protein backbone flexibility upon association often thwarts accurate predictions. In recent blind challenges, medium or high accuracy models were submitted in less than 20% of the 'difficult' targets (with significant backbone change or uncertainty). Here, we describe recent developments in protein-protein docking and highlight advances that tackle backbone flexibility. In molecular dynamics and Monte Carlo approaches, enhanced sampling techniques have reduced time-scale limitations. Internal coordinate formulations can now capture realistic motions of monomers and complexes using harmonic dynamics. And machine learning approaches adaptively guide docking trajectories or generate novel binding site predictions from deep neural networks trained on protein interfaces. These tools poise the field to break through the longstanding challenge of correctly predicting complex structures with significant conformational change.
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Affiliation(s)
- Ameya Harmalkar
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jeffrey J Gray
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA; Program in Molecular Biophysics, Institute for Nanobiotechnology, and Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
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3
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Cao Y, Shen Y. Bayesian Active Learning for Optimization and Uncertainty Quantification in Protein Docking. J Chem Theory Comput 2020; 16:5334-5347. [PMID: 32558561 DOI: 10.1021/acs.jctc.0c00476] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Ab initio protein docking represents a major challenge for optimizing a noisy and costly "black box"-like function in a high-dimensional space. Despite progress in this field, there is a lack of rigorous uncertainty quantification (UQ). To fill the gap, we introduce a novel algorithm, Bayesian active learning (BAL), for optimization and UQ of such black-box functions with applications to flexible protein docking. BAL directly models the posterior distribution of the global optimum (i.e., native structures) with active sampling and posterior estimation iteratively feeding each other. Furthermore, it uses complex normal modes to span a homogeneous, Euclidean conformation space suitable for high-dimensional optimization and constructs funnel-like energy models for quality estimation of encounter complexes. Over a protein-docking benchmark set and a CAPRI set including homology docking, we establish that BAL significantly improves against starting points from rigid docking and refinements by particle swarm optimization, providing a top-3 near-native prediction for one third targets. Quality assessment empowered with UQ leads to tight quality intervals with half range around 25% of the actual interface root-mean-square deviation and confidence level at 85%. BAL's estimated probability of a prediction being near-native achieves binary classification AUROC at 0.93 and area under the precision recall curve over 0.60 (compared to 0.50 and 0.14, respectively, by chance), which also improves ranking predictions. This study represents the first UQ solution for protein docking, with rigorous theoretical frameworks and comprehensive empirical assessments.
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Affiliation(s)
- Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas 77843, United States.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77840, United States
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4
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Cao Y, Shen Y. Energy-based graph convolutional networks for scoring protein docking models. Proteins 2020; 88:1091-1099. [PMID: 32144844 DOI: 10.1002/prot.25888] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 01/15/2020] [Accepted: 02/26/2020] [Indexed: 12/18/2022]
Abstract
Structural information about protein-protein interactions, often missing at the interactome scale, is important for mechanistic understanding of cells and rational discovery of therapeutics. Protein docking provides a computational alternative for such information. However, ranking near-native docked models high among a large number of candidates, often known as the scoring problem, remains a critical challenge. Moreover, estimating model quality, also known as the quality assessment problem, is rarely addressed in protein docking. In this study, the two challenging problems in protein docking are regarded as relative and absolute scoring, respectively, and addressed in one physics-inspired deep learning framework. We represent protein and complex structures as intra- and inter-molecular residue contact graphs with atom-resolution node and edge features. And we propose a novel graph convolutional kernel that aggregates interacting nodes' features through edges so that generalized interaction energies can be learned directly from 3D data. The resulting energy-based graph convolutional networks (EGCN) with multihead attention are trained to predict intra- and inter-molecular energies, binding affinities, and quality measures (interface RMSD) for encounter complexes. Compared to a state-of-the-art scoring function for model ranking, EGCN significantly improves ranking for a critical assessment of predicted interactions (CAPRI) test set involving homology docking; and is comparable or slightly better for Score_set, a CAPRI benchmark set generated by diverse community-wide docking protocols not known to training data. For Score_set quality assessment, EGCN shows about 27% improvement to our previous efforts. Directly learning from 3D structure data in graph representation, EGCN represents the first successful development of graph convolutional networks for protein docking.
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Affiliation(s)
- Yue Cao
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas
| | - Yang Shen
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas.,TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas
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5
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Kawaguchi M, Dashzeveg N, Cao Y, Jia Y, Liu X, Shen Y, Liu H. Extracellular Domains I and II of cell-surface glycoprotein CD44 mediate its trans-homophilic dimerization and tumor cluster aggregation. J Biol Chem 2020; 295:2640-2649. [PMID: 31969394 DOI: 10.1074/jbc.ra119.010252] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Revised: 01/16/2020] [Indexed: 01/09/2023] Open
Abstract
CD44 molecule (CD44) is a well-known surface glycoprotein on tumor-initiating cells or cancer stem cells. However, its utility as a therapeutic target for managing metastases remains to be fully evaluated. We previously demonstrated that CD44 mediates homophilic interactions for circulating tumor cell (CTC) cluster formation, which enhances cancer stemness and metastatic potential in association with an unfavorable prognosis. Furthermore, CD44 self-interactions activate the P21-activated kinase 2 (PAK2) signaling pathway. Here, we further examined the biochemical properties of CD44 in homotypic tumor cell aggregation. The standard CD44 form (CD44s) mainly assembled as intercellular homodimers (trans-dimers) in tumor clusters rather than intracellular dimers (cis-dimers) present in single cells. Machine learning-based computational modeling combined with experimental mutagenesis tests revealed that the extracellular Domains I and II of CD44 are essential for its trans-dimerization and predicted high-score residues to be required for dimerization. Substitutions of 10 these residues in Domain I (Ser-45, Glu-48, Phe-74, Cys-77, Arg-78, Tyr-79, Ile-88, Arg-90, Asn-94, and Cys-97) or 5 residues in Domain II (Ile-106, Tyr-155, Val-156, Gln-157, and Lys-158) abolished CD44 dimerization and reduced tumor cell aggregation in vitro Importantly, the substitutions in Domain II dramatically inhibited lung colonization in mice. The CD44 dimer-disrupting substitutions decreased downstream PAK2 activation without affecting the interaction between CD44 and PAK2, suggesting that PAK2 activation in tumor cell clusters is CD44 trans-dimer-dependent. These results shed critical light on the biochemical mechanisms of CD44-mediated tumor cell cluster formation and may help inform the development of therapeutic strategies to prevent tumor cluster formation and block cluster-mediated metastases.
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Affiliation(s)
- Madoka Kawaguchi
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611; Laboratory of Functional Biology, Graduate School of Biostudies, Kyoto University, Kyoto, 6068501, Japan
| | - Nurmaa Dashzeveg
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611
| | - Yue Cao
- Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77843
| | - Yuzhi Jia
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611
| | - Xia Liu
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611; Department of Toxicology and Cancer Biology, College of Medicine, University of Kentucky, Lexington, Kentucky 40536.
| | - Yang Shen
- Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas 77843.
| | - Huiping Liu
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611; Department of Medicine, Hematology/Oncology Division, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611.
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Abstract
Many of the biological functions of the cell are driven by protein-protein interactions. However, determining which proteins interact and exactly how they do so to enable their functions, remain major research questions. Functional interactions are dependent on a number of complicated factors; therefore, modeling the three-dimensional structure of protein-protein complexes is still considered a complex endeavor. Nevertheless, the rewards for modeling protein interactions to atomic level detail are substantial, and there are numerous examples of how models can provide useful information for drug design, protein engineering, systems biology, and understanding of the immune system. Here, we provide practical guidelines for docking proteins using the web-server, SwarmDock, a flexible protein-protein docking method. Moreover, we provide an overview of the factors that need to be considered when deciding whether docking is likely to be successful.
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Affiliation(s)
- Iain H Moal
- European Bioinformatics Institute, Hinxton, UK
| | | | | | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK.
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7
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Unsupervised classification of PSII with and without water-oxidizing complex samples by PARAFAC resolution of excitation-emission fluorescence images. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY 2019; 195:58-66. [PMID: 31100638 DOI: 10.1016/j.jphotobiol.2019.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 03/12/2019] [Accepted: 03/13/2019] [Indexed: 11/19/2022]
Abstract
The potential of excitation-emission fluorescence spectroscopy combined with three-way analysis was investigated for discriminating the photosystem II (PSII) (with the water-oxidizing complex) and without the water-oxidizing complex (wPSII) using unsupervised classification methods. The water-oxidizing complex within PSII carry out the reaction of water splitting which is as a vital process on the earth. Therefore, discriminating the presence of the water-oxidizing complex in protein samples is crucial. Low cost and accurate spectroscopic determination of the amount of clusters inside PSII or any other protein containing species are important when investigating the inclusion and exclusion of such clusters into and from species. Fluorescence data of samples were similar, and we showed the potential usefulness of multivariate methods, such as parallel factor analysis (PARAFAC) and principal component analysis (PCA) for recognition of the two types of samples. Both techniques were applied to the excitation-emission fluorescence matrices (EEM) of solutions at two of different pH values (2.0 and 12.0). Three fluorescent components were found for all samples that are related to tyrosine (Tyr), tryptophan (Trp) and phenylalanine (Phe) amino acids. These three amino acids are representative of all datasets and indicate their similarities and differences. We then found the effectual wavelengths for separation of samples in a specific acidity, including the excitation wavelengths of 220 and 230 nm and the emission wavelengths of 300 and 305 nm. The acidity of the solutions has various influences on the conformation of proteins. In PSII and PSII the without water-oxidizing complex samples conformational changes can change their spectra which was applied for discrimination purpose. This separation was better in pH = 12.0. We also showed the effect of time on small conformational changes within datasets were higher in pH = 2.0. In the end, for indicating the high distribution of spectral data from proteins which is the result of conformational changes, we compared the distribution of measured spectral data with that from a simple organic molecule, fluorescein. Altogether, we could distinguish between the two groups of protein samples properly at pH = 12.0 using low-cost EEM spectral images and PARAFAC.
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Liu X, Taftaf R, Kawaguchi M, Chang YF, Chen W, Entenberg D, Zhang Y, Gerratana L, Huang S, Patel DB, Tsui E, Adorno-Cruz V, Chirieleison SM, Cao Y, Harney AS, Patel S, Patsialou A, Shen Y, Avril S, Gilmore HL, Lathia JD, Abbott DW, Cristofanilli M, Condeelis JS, Liu H. Homophilic CD44 Interactions Mediate Tumor Cell Aggregation and Polyclonal Metastasis in Patient-Derived Breast Cancer Models. Cancer Discov 2018; 9:96-113. [PMID: 30361447 DOI: 10.1158/2159-8290.cd-18-0065] [Citation(s) in RCA: 207] [Impact Index Per Article: 34.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 08/11/2018] [Accepted: 10/17/2018] [Indexed: 02/07/2023]
Abstract
Circulating tumor cells (CTC) seed cancer metastases; however, the underlying cellular and molecular mechanisms remain unclear. CTC clusters were less frequently detected but more metastatic than single CTCs of patients with triple-negative breast cancer and representative patient-derived xenograft models. Using intravital multiphoton microscopic imaging, we found that clustered tumor cells in migration and circulation resulted from aggregation of individual tumor cells rather than collective migration and cohesive shedding. Aggregated tumor cells exhibited enriched expression of the breast cancer stem cell marker CD44 and promoted tumorigenesis and polyclonal metastasis. Depletion of CD44 effectively prevented tumor cell aggregation and decreased PAK2 levels. The intercellular CD44-CD44 homophilic interactions directed multicellular aggregation, requiring its N-terminal domain, and initiated CD44-PAK2 interactions for further activation of FAK signaling. Our studies highlight that CD44+ CTC clusters, whose presence is correlated with a poor prognosis of patients with breast cancer, can serve as novel therapeutic targets of polyclonal metastasis. SIGNIFICANCE: CTCs not only serve as important biomarkers for liquid biopsies, but also mediate devastating metastases. CD44 homophilic interactions and subsequent CD44-PAK2 interactions mediate tumor cluster aggregation. This will lead to innovative biomarker applications to predict prognosis, facilitate development of new targeting strategies to block polyclonal metastasis, and improve clinical outcomes.See related commentary by Rodrigues and Vanharanta, p. 22.This article is highlighted in the In This Issue feature, p. 1.
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Affiliation(s)
- Xia Liu
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.
| | - Rokana Taftaf
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Madoka Kawaguchi
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Ya-Fang Chang
- The Ben May Department for Cancer Research, The University of Chicago, Chicago, Illinois
| | - Wenjing Chen
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - David Entenberg
- Deparment of Anatomy and Structural Biology, Gruss Lipper Biophotonics Center, Integrated Imaging Program, Albert Einstein College of Medicine, Bronx, New York
| | - Youbin Zhang
- Department of Medicine, Hematology/Oncology Division, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Lorenzo Gerratana
- Department of Medicine, Hematology/Oncology Division, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.,Department of Medicine (DAME), University of Udine, Udine, Italy
| | - Simo Huang
- Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Dhwani B Patel
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Elizabeth Tsui
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - Valery Adorno-Cruz
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.,Department of Pharmacology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Steven M Chirieleison
- Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Yue Cao
- Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas
| | - Allison S Harney
- Deparment of Anatomy and Structural Biology, Gruss Lipper Biophotonics Center, Integrated Imaging Program, Albert Einstein College of Medicine, Bronx, New York
| | - Shivani Patel
- Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Antonia Patsialou
- Deparment of Anatomy and Structural Biology, Gruss Lipper Biophotonics Center, Integrated Imaging Program, Albert Einstein College of Medicine, Bronx, New York
| | - Yang Shen
- Department of Electrical and Computer Engineering, TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering, Texas A&M University, College Station, Texas
| | - Stefanie Avril
- Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Hannah L Gilmore
- Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Justin D Lathia
- Department of Cellular and Molecular Medicine, Cleveland Clinic Lerner Research Institute, Cleveland, Ohio.,The Case Comprehensive Cancer Center, Cleveland, Ohio
| | - Derek W Abbott
- Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Massimo Cristofanilli
- Department of Medicine, Hematology/Oncology Division, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.,Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
| | - John S Condeelis
- Deparment of Anatomy and Structural Biology, Gruss Lipper Biophotonics Center, Integrated Imaging Program, Albert Einstein College of Medicine, Bronx, New York.
| | - Huiping Liu
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. .,Department of Medicine, Hematology/Oncology Division, Feinberg School of Medicine, Northwestern University, Chicago, Illinois.,Deparment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, Ohio.,Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, Illinois
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9
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Abstract
The atomic structures of protein complexes can provide useful information for drug design, protein engineering, systems biology, and understanding pathology. Obtaining this information experimentally can be challenging. However, if the structures of the subunits are known, then it is often possible to model the complex computationally. This chapter provide practical guidelines for docking proteins using the SwarmDock flexible protein-protein docking method, providing an overview of the factors that need to be considered when deciding whether docking is likely to be successful, the preparation of structural input, generation of docked poses, analysis and ranking of docked poses, and the validation of models using external data.
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Affiliation(s)
- Iain H Moal
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
| | | | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, London, UK
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Najafpour MM, Madadkhani S, Akbarian S, Hołyńska M, Kompany-Zareh M, Tomo T, Singh JP, Chae KH, Allakhverdiev SI. A new strategy to make an artificial enzyme: photosystem II around nanosized manganese oxide. Catal Sci Technol 2017. [DOI: 10.1039/c7cy01654a] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
A new strategy to make an artificial enzyme was reported.
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Affiliation(s)
- Mohammad Mahdi Najafpour
- Department of Chemistry
- Institute for Advanced Studies in Basic Sciences (IASBS)
- Zanjan
- Iran
- Center of Climate Change and Global Warming
| | - Sepideh Madadkhani
- Department of Chemistry
- Institute for Advanced Studies in Basic Sciences (IASBS)
- Zanjan
- Iran
| | - Somayyeh Akbarian
- Department of Chemistry
- Institute for Advanced Studies in Basic Sciences (IASBS)
- Zanjan
- Iran
| | - Małgorzata Hołyńska
- Fachbereich Chemie and Wissenschaftliches Zentrum für Materialwissenschaften (WZMW)
- Philipps-Universität Marburg
- D-35032 Marburg
- Germany
| | - Mohsen Kompany-Zareh
- Department of Chemistry
- Institute for Advanced Studies in Basic Sciences (IASBS)
- Zanjan
- Iran
- Center of Climate Change and Global Warming
| | - Tatsuya Tomo
- Department of Biology
- Faculty of Science
- Tokyo University of Science
- Tokyo 162-8601
- Japan
| | - Jitendra Pal Singh
- Advanced Analysis Center
- Korea Institute of Science and Technology (KIST)
- Seoul 02792
- Republic of Korea
| | - Keun Hwa Chae
- Advanced Analysis Center
- Korea Institute of Science and Technology (KIST)
- Seoul 02792
- Republic of Korea
| | - Suleyman I. Allakhverdiev
- Controlled Photobiosynthesis Laboratory
- Institute of Plant Physiology
- Russian Academy of Sciences
- Moscow 127276
- Russia
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