1
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Chen H, Lu D, Xiao Z, Li S, Zhang W, Luan X, Zhang W, Zheng G. Comprehensive applications of the artificial intelligence technology in new drug research and development. Health Inf Sci Syst 2024; 12:41. [PMID: 39130617 PMCID: PMC11310389 DOI: 10.1007/s13755-024-00300-y] [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: 08/31/2023] [Accepted: 07/27/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field. Methods Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")]. Results In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery. Conclusion Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.
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Affiliation(s)
- Hongyu Chen
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Lu
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ziyi Xiao
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Shensuo Li
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wen Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Luan
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Weidong Zhang
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Guangyong Zheng
- Shanghai Frontiers Science Center for Chinese Medicine Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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2
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Li M, Qing R, Tao F, Xu P, Zhang S. Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy. Comput Struct Biotechnol J 2024; 23:278-286. [PMID: 38173876 PMCID: PMC10762321 DOI: 10.1016/j.csbj.2023.12.008] [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: 10/20/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.
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Affiliation(s)
- Mengke Li
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Rui Qing
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Fei Tao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Ping Xu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Shuguang Zhang
- Laboratory of Molecular Architecture, Media Lab, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
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3
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Shi W, Yang H, Xie L, Yin XX, Zhang Y. A review of machine learning-based methods for predicting drug-target interactions. Health Inf Sci Syst 2024; 12:30. [PMID: 38617016 PMCID: PMC11014838 DOI: 10.1007/s13755-024-00287-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 03/04/2024] [Indexed: 04/16/2024] Open
Abstract
The prediction of drug-target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.
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Affiliation(s)
- Wen Shi
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Linhai Xie
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206 China
| | - Xiao-Xia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006 China
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004 China
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000 China
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4
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Kwon H, Du Z, Li Y. AlphaFold 2-based stacking model for protein solubility prediction and its transferability on seed storage proteins. Int J Biol Macromol 2024; 278:134601. [PMID: 39137857 DOI: 10.1016/j.ijbiomac.2024.134601] [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: 05/07/2024] [Revised: 07/29/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024]
Abstract
Accurate protein solubility prediction is crucial in screening suitable candidates for food application. Existing models often rely only on sequences, overlooking important structural details. In this study, a regression model for protein solubility was developed using both the sequences and predicted structures of 2983 E. coli proteins. The sequence and structural level properties of the proteins were bioinformatically extracted and subjected to multilayer perceptron (MLP). Moreover, residue level features and contact maps were utilized to construct a graph convolutional network (GCN). The out-of-fold predictions of the two models were combined and fed into multiple meta-regressors to create a stacking model. The stacking model with support vector regressor (SVR) achieved R2 of 0.502 and 0.468 on test and external validation datasets, respectively, displaying higher performance compared to existing regression models. Based on the improved performance compared to its based models, the stacking model effectively captured the strength of its base models as well as the significance of the different features used. Furthermore, the model's transferability was indirectly validated on a dataset of seed storage proteins using Osborne definition as well as on a case study using molecular dynamic simulation, showing potential for application beyond microbial proteins to food and agriculture-related ones.
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Affiliation(s)
- Hyukjin Kwon
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506, USA
| | - Zhenjiao Du
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506, USA
| | - Yonghui Li
- Department of Grain Science and Industry, Kansas State University, Manhattan, KS 66506, USA.
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5
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Ohno S, Manabe N, Yamaguchi Y. Prediction of protein structure and AI. J Hum Genet 2024; 69:477-480. [PMID: 38177398 DOI: 10.1038/s10038-023-01215-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 12/10/2023] [Indexed: 01/06/2024]
Abstract
AlphaFold, an artificial intelligence (AI)-based tool for predicting the 3D structure of proteins, is now widely recognized for its high accuracy and versatility in the folding of human proteins. AlphaFold is useful for understanding structure-function relationships from protein 3D structure models and can serve as a template or a reference for experimental structural analysis including X-ray crystallography, NMR and cryo-EM analysis. Its use is expanding among researchers, not only in structural biology but also in other research fields. Researchers are currently exploring the full potential of AlphaFold-generated protein models. Predicting disease severity caused by missense mutations is one such application. This article provides an overview of the 3D structural modeling of AlphaFold based on deep learning techniques and highlights the challenges in predicting the pathogenicity of missense mutations.
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Affiliation(s)
- Shiho Ohno
- Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan
| | - Noriyoshi Manabe
- Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan
| | - Yoshiki Yamaguchi
- Division of Structural Glycobiology, Institute of Molecular Biomembrane and Glycobiology, Tohoku Medical and Pharmaceutical University, 4-4-1 Komatsushima, Aoba-ku, Sendai, Miyagi, 981-8558, Japan.
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6
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Neubergerová M, Pleskot R. Plant protein-lipid interfaces studied by molecular dynamics simulations. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:5237-5250. [PMID: 38761107 DOI: 10.1093/jxb/erae228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/16/2024] [Indexed: 05/20/2024]
Abstract
The delineation of protein-lipid interfaces is essential for understanding the mechanisms of various membrane-associated processes crucial to plant development and growth, including signalling, trafficking, and membrane transport. Due to their highly dynamic nature, the precise characterization of lipid-protein interactions by experimental techniques is challenging. Molecular dynamics simulations provide a powerful computational alternative with a spatial-temporal resolution allowing the atomistic-level description. In this review, we aim to introduce plant scientists to molecular dynamics simulations. We describe different steps of performing molecular dynamics simulations and provide a broad survey of molecular dynamics studies investigating plant protein-lipid interfaces. Our aim is also to illustrate that combining molecular dynamics simulations with artificial intelligence-based protein structure determination opens up unprecedented possibilities for future investigations of dynamic plant protein-lipid interfaces.
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Affiliation(s)
- Michaela Neubergerová
- Institute of Experimental Botany, Czech Academy of Sciences, Prague, Czech Republic
- Department of Experimental Plant Biology, Faculty of Science, Charles University in Prague, Prague, Czech Republic
| | - Roman Pleskot
- Institute of Experimental Botany, Czech Academy of Sciences, Prague, Czech Republic
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7
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Bradley D, Hogrebe A, Dandage R, Dubé AK, Leutert M, Dionne U, Chang A, Villén J, Landry CR. The fitness cost of spurious phosphorylation. EMBO J 2024:10.1038/s44318-024-00200-7. [PMID: 39256561 DOI: 10.1038/s44318-024-00200-7] [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: 10/25/2023] [Revised: 07/23/2024] [Accepted: 07/24/2024] [Indexed: 09/12/2024] Open
Abstract
The fidelity of signal transduction requires the binding of regulatory molecules to their cognate targets. However, the crowded cell interior risks off-target interactions between proteins that are functionally unrelated. How such off-target interactions impact fitness is not generally known. Here, we use Saccharomyces cerevisiae to inducibly express tyrosine kinases. Because yeast lacks bona fide tyrosine kinases, the resulting tyrosine phosphorylation is biologically spurious. We engineered 44 yeast strains each expressing a tyrosine kinase, and quantitatively analysed their phosphoproteomes. This analysis resulted in ~30,000 phosphosites mapping to ~3500 proteins. The number of spurious pY sites generated correlates strongly with decreased growth, and we predict over 1000 pY events to be deleterious. However, we also find that many of the spurious pY sites have a negligible effect on fitness, possibly because of their low stoichiometry. This result is consistent with our evolutionary analyses demonstrating a lack of phosphotyrosine counter-selection in species with tyrosine kinases. Our results suggest that, alongside the risk for toxicity, the cell can tolerate a large degree of non-functional crosstalk as interaction networks evolve.
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Affiliation(s)
- David Bradley
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexander Hogrebe
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Rohan Dandage
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexandre K Dubé
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Mario Leutert
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Ugo Dionne
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada
- Department of Biology, Université Laval, Québec, QC, Canada
| | - Alexis Chang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Judit Villén
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Christian R Landry
- Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec, QC, Canada.
- Department of Biochemistry, Microbiology and Bioinformatics, Université Laval, Québec, QC, Canada.
- Quebec Network for Research on Protein Function, Engineering, and Applications (PROTEO), Université du Québec à Montréal, Montréal, QC, Canada.
- Université Laval Big Data Research Center (BDRC_UL), Québec, QC, Canada.
- Department of Biology, Université Laval, Québec, QC, Canada.
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8
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Heinzinger M, Rost B. Artificial Intelligence Learns Protein Prediction. Cold Spring Harb Perspect Biol 2024; 16:a041458. [PMID: 38858069 PMCID: PMC11368192 DOI: 10.1101/cshperspect.a041458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024]
Abstract
From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.
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Affiliation(s)
- Michael Heinzinger
- Technical University of Munich (TUM) School of School of Computation, Information and Technology (CIT), Bioinformatics and Computational Biology - i12, 85748 Garching/Munich, Germany
| | - Burkhard Rost
- Technical University of Munich (TUM) School of School of Computation, Information and Technology (CIT), Bioinformatics and Computational Biology - i12, 85748 Garching/Munich, Germany
- Institute for Advanced Study (TUM-IAS), 85748 Garching/Munich, Germany
- TUM School of Life Sciences Weihenstephan (WZW), 85354 Freising, Germany
- Department of Biochemistry and Molecular Biophysics, Columbia University, New York, New York 10032, USA
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9
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Huang Y, Wang T, Zhong L, Zhang W, Zhang Y, Yu X, Yuan S, Ni T. Molecular architecture of coronavirus double-membrane vesicle pore complex. Nature 2024; 633:224-231. [PMID: 39143215 PMCID: PMC11374677 DOI: 10.1038/s41586-024-07817-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 07/11/2024] [Indexed: 08/16/2024]
Abstract
Coronaviruses remodel the intracellular host membranes during replication, forming double-membrane vesicles (DMVs) to accommodate viral RNA synthesis and modifications1,2. SARS-CoV-2 non-structural protein 3 (nsp3) and nsp4 are the minimal viral components required to induce DMV formation and to form a double-membrane-spanning pore, essential for the transport of newly synthesized viral RNAs3-5. The mechanism of DMV pore complex formation remains unknown. Here we describe the molecular architecture of the SARS-CoV-2 nsp3-nsp4 pore complex, as resolved by cryogenic electron tomography and subtomogram averaging in isolated DMVs. The structures uncover an unexpected stoichiometry and topology of the nsp3-nsp4 pore complex comprising 12 copies each of nsp3 and nsp4, organized in 4 concentric stacking hexamer rings, mimicking a miniature nuclear pore complex. The transmembrane domains are interdigitated to create a high local curvature at the double-membrane junction, coupling double-membrane reorganization with pore formation. The ectodomains form extensive contacts in a pseudo-12-fold symmetry, belting the pore complex from the intermembrane space. A central positively charged ring of arginine residues coordinates the putative RNA translocation, essential for virus replication. Our work establishes a framework for understanding DMV pore formation and RNA translocation, providing a structural basis for the development of new antiviral strategies to combat coronavirus infection.
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Affiliation(s)
- Yixin Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Tongyun Wang
- State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Lijie Zhong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Wenxin Zhang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yu Zhang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Xiulian Yu
- State Key Laboratory of Chemical Biology and Drug Discovery, Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China
| | - Shuofeng Yuan
- State Key Laboratory of Emerging Infectious Diseases, Carol Yu Centre for Infection, Department of Microbiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
| | - Tao Ni
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
- Materials Innovation Institute for Life Sciences and Energy (MILES), HKU-SIRI, Shenzhen, China.
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10
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Wang K, Hu G, Basu S, Kurgan L. flDPnn2: Accurate and Fast Predictor of Intrinsic Disorder in Proteins. J Mol Biol 2024; 436:168605. [PMID: 39237195 DOI: 10.1016/j.jmb.2024.168605] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/16/2024] [Accepted: 05/04/2024] [Indexed: 09/07/2024]
Abstract
Prediction of the intrinsic disorder in protein sequences is an active research area, with well over 100 predictors that were released to date. These efforts are motivated by the functional importance and high levels of abundance of intrinsic disorder, combined with relatively low amounts of experimental annotations. The disorder predictors are periodically evaluated by independent assessors in the Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiments. The recently completed CAID2 experiment assessed close to 40 state-of-the-art methods demonstrating that some of them produce accurate results. In particular, flDPnn2 method, which is the successor of flDPnn that performed well in the CAID1 experiment, secured the overall most accurate results on the Disorder-NOX dataset in CAID2. flDPnn2 implements a number of improvements when compared to its predecessor including changes to the inputs, increased size of the deep network model that we retrained on a larger training set, and addition of an alignment module. Using results from CAID2, we show that flDPnn2 produces accurate predictions very quickly, modestly improving over the accuracy of flDPnn and reducing the runtime by half, to about 27 s per protein. flDPnn2 is freely available as a convenient web server at http://biomine.cs.vcu.edu/servers/flDPnn2/.
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Affiliation(s)
- Kui Wang
- NITFID, School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Gang Hu
- NITFID, School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Sushmita Basu
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| | - Lukasz Kurgan
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
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11
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Beltrao P. The power of scientific collaborations and the future of structural biology. Nat Struct Mol Biol 2024; 31:1309-1310. [PMID: 39009854 DOI: 10.1038/s41594-024-01358-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Affiliation(s)
- Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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12
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Smith K. The biology of smell is a mystery - AI is helping to solve it. Nature 2024; 633:26-29. [PMID: 39227712 DOI: 10.1038/d41586-024-02833-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
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13
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McBride JM, Tlusty T. AI-Predicted Protein Deformation Encodes Energy Landscape Perturbation. PHYSICAL REVIEW LETTERS 2024; 133:098401. [PMID: 39270162 DOI: 10.1103/physrevlett.133.098401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 02/27/2024] [Accepted: 07/24/2024] [Indexed: 09/15/2024]
Abstract
AI algorithms have proven to be excellent predictors of protein structure, but whether and how much these algorithms can capture the underlying physics remains an open question. Here, we aim to test this question using the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy ΔΔG. Unexpectedly, we find that physical strain alone-without any additional data or computation-correlates almost as well with ΔΔG as state-of-the-art energy-based and machine-learning predictors. This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution.
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Affiliation(s)
- John M McBride
- Center for Algorithmic and Robotized Synthesis, Institute for Basic Science, Ulsan 44919, South Korea
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14
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Chen Y, Xu Y, Liu D, Xing Y, Gong H. An end-to-end framework for the prediction of protein structure and fitness from single sequence. Nat Commun 2024; 15:7400. [PMID: 39191788 DOI: 10.1038/s41467-024-51776-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 08/19/2024] [Indexed: 08/29/2024] Open
Abstract
Significant research progress has been made in the field of protein structure and fitness prediction. Particularly, single-sequence-based structure prediction methods like ESMFold and OmegaFold achieve a balance between inference speed and prediction accuracy, showing promise for many downstream prediction tasks. Here, we propose SPIRED, a single-sequence-based structure prediction model that exhibits comparable performance to the state-of-the-art methods but with approximately 5-fold acceleration in inference and at least one order of magnitude reduction in training consumption. By integrating SPIRED with downstream neural networks, we compose an end-to-end framework named SPIRED-Fitness for the rapid prediction of both protein structure and fitness from single sequence with satisfactory accuracy. Moreover, SPIRED-Stab, the derivative of SPIRED-Fitness, achieves state-of-the-art performance in predicting the mutational effects on protein stability.
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Affiliation(s)
- Yinghui Chen
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China
| | - Yunxin Xu
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China
| | - Di Liu
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China
| | - Yaoguang Xing
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
- Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China
| | - Haipeng Gong
- MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China.
- Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing, China.
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15
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Ali MA, Caetano-Anollés G. AlphaFold2 Reveals Structural Patterns of Seasonal Haplotype Diversification in SARS-CoV-2 Nucleocapsid Protein Variants. Viruses 2024; 16:1358. [PMID: 39339835 PMCID: PMC11435742 DOI: 10.3390/v16091358] [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: 07/05/2024] [Revised: 08/10/2024] [Accepted: 08/21/2024] [Indexed: 09/30/2024] Open
Abstract
The COVID-19 pandemic saw the emergence of various Variants of Concern (VOCs) that took the world by storm, often replacing the ones that preceded them. The characteristic mutant constellations of these VOCs increased viral transmissibility and infectivity. Their origin and evolution remain puzzling. With the help of data mining efforts and the GISAID database, a chronology of 22 haplotypes described viral evolution up until 23 July 2023. Since the three-dimensional atomic structures of proteins corresponding to the identified haplotypes are not available, ab initio methods were here utilized. Regions of intrinsic disorder proved to be important for viral evolution, as evidenced by the targeted change to the nucleocapsid (N) protein at the sequence, structure, and biochemical levels. The linker region of the N-protein, which binds to the RNA genome and self-oligomerizes for efficient genome packaging, was greatly impacted by mutations throughout the pandemic, followed by changes in structure and intrinsic disorder. Remarkably, VOC constellations acted co-operatively to balance the more extreme effects of individual haplotypes. Our strategy of mapping the dynamic evolutionary landscape of genetically linked mutations to the N-protein structure demonstrates the utility of ab initio modeling and deep learning tools for therapeutic intervention.
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Affiliation(s)
| | - Gustavo Caetano-Anollés
- Evolutionary Bioinformatics Laboratory, Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA;
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16
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Kovalevskiy O, Mateos-Garcia J, Tunyasuvunakool K. AlphaFold two years on: Validation and impact. Proc Natl Acad Sci U S A 2024; 121:e2315002121. [PMID: 39133843 PMCID: PMC11348012 DOI: 10.1073/pnas.2315002121] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/29/2024] Open
Abstract
Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.
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17
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Nakajima Y, Oda A, Baatartsogt N, Kashiwakura Y, Ohmori T, Nogami K. The combination of Asp519Val/Glu665Val and Lys1813Ala mutations in FVIII markedly increases coagulation potential. Blood Adv 2024; 8:3929-3940. [PMID: 38820442 PMCID: PMC11321387 DOI: 10.1182/bloodadvances.2023012391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 05/06/2024] [Accepted: 05/18/2024] [Indexed: 06/02/2024] Open
Abstract
ABSTRACT A2 domain dissociation in activated factor VIII (FVIIIa) results in reduced activity. Previous studies demonstrated that some FVIII mutants (D519V/E665V and K1813A) with delayed A2 dissociation enhanced coagulation potential. We speculated, therefore, that FVIII encompassing a combination of these mutations might further enhance coagulant activity. The aim was to assess the D519V/E665V/K1813A-FVIII mutation as a gain of function. The FVIII mutants, D519V/E665V/K1813A, D519V/E665V, and K1813A were expressed in a baby hamster kidney cell system, and global coagulation potential of these mutants was compared with wild-type (WT) FVIII in vitro and in hemophilia A mice in vivo. Kinetic analyses indicated that the apparent Kd for FIXa on the tenase assembly with D519V/E665V and D519V/E665V/K1813A mutants were lower, and that the generated FXa for D519V/E665V/K1813A was significantly greater than WT-FVIII. WT-FVIII activity after thrombin activation increased by ∼12-fold within 5 minutes, and returned to initial levels within 30 minutes. In contrast, The FVIII-related activity of D519V/E665V/K1813A increased further with time after thrombin activation, and showed an ∼25-fold increase at 2 hours. The A2 dissociation rate of D519V/E665V/K1813A was ∼50-fold slower than the WT in a 1-stage clotting assay. Thrombin generation assays demonstrated that D519V/E665V/K1813A (0.125 nM) exhibited coagulation potential comparable with that of the WT (1 nM). In animal studies, rotational thromboelastometry and tail-clip assays showed that the coagulation potential of D519V/E665V/K1813A (0.25 μg/kg) was equal to that of the WT (2 μg/kg). FVIII-D519V/E665V/K1813A mutant could provide an approximately eightfold increase in hemostatic function of WT-FVIII because of increased FVIIIa stability and the association between FVIIIa and FIXa.
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Affiliation(s)
- Yuto Nakajima
- Department of Pediatrics, Nara Medical University, Kashihara, Japan
- Advanced Medical Science of Thrombosis and Hemostasis, Nara Medical University, Kashihara City, Japan
| | - Akihisa Oda
- Department of Pediatrics, Nara Medical University, Kashihara, Japan
| | | | - Yuji Kashiwakura
- Department of Biochemistry, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Tsukasa Ohmori
- Department of Biochemistry, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Keiji Nogami
- Department of Pediatrics, Nara Medical University, Kashihara, Japan
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18
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Santos LABDO, Feitosa TDAL, Batista MVDA. Comparative structural studies on Bovine papillomavirus E6 oncoproteins: Novel insights into viral infection and cell transformation from homology modeling and molecular dynamics simulations. Genet Mol Biol 2024; 47:e20230346. [PMID: 39136577 PMCID: PMC11320664 DOI: 10.1590/1678-4685-gmb-2023-0346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 06/24/2024] [Indexed: 08/16/2024] Open
Abstract
Bovine papillomavirus (BPV) infects cattle cells worldwide, leading to hyperproliferative lesions and the potential development of cancer, driven by E5, E6, and E7 oncoproteins along with other cofactors. E6 oncoprotein binds experimentally to various proteins, primarily paxillin and MAML1, as well as hMCM7 and CBP/p300. However, the molecular and structural mechanisms underlying BPV-induced malignant transformation remain unclear. Therefore, we have modeled the E6 oncoprotein structure from non-oncogenic BPV-5 and compared them with oncogenic BPV-1 to assess the relationship between structural features and oncogenic potential. Our analysis elucidated crucial structural aspects of E6, highlighting both conserved elements across genotypes and genotype-specific variations potentially implicated in the oncogenic process, particularly concerning primary target interactions. Additionally, we predicted the location of the hMCM7 binding site on the N-terminal of BPV-5 E6. This study enhances our understanding of the structural characteristics of BPV E6 oncoproteins and their interactions with host proteins, clarifying structural differences and similarities between high and low-risk BPVs. This is important to understand better the mechanisms involved in cell transformation in BPV infection, which could be used as a possible target for therapy.
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Affiliation(s)
- Lucas Alexandre Barbosa de Oliveira Santos
- Universidade Federal de Sergipe, Centro de Ciências Biológicas e da Saúde, Departamento de Biologia, Laboratório de Genética Molecular e Biotecnologia (GMBio), São Cristóvão, SE, Brazil
| | - Tales de Albuquerque Leite Feitosa
- Universidade Federal de Sergipe, Centro de Ciências Biológicas e da Saúde, Departamento de Biologia, Laboratório de Genética Molecular e Biotecnologia (GMBio), São Cristóvão, SE, Brazil
| | - Marcus Vinicius de Aragão Batista
- Universidade Federal de Sergipe, Centro de Ciências Biológicas e da Saúde, Departamento de Biologia, Laboratório de Genética Molecular e Biotecnologia (GMBio), São Cristóvão, SE, Brazil
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19
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Díaz-Holguín A, Saarinen M, Vo DD, Sturchio A, Branzell N, Cabeza de Vaca I, Hu H, Mitjavila-Domènech N, Lindqvist A, Baranczewski P, Millan MJ, Yang Y, Carlsson J, Svenningsson P. AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine-associated receptor 1. SCIENCE ADVANCES 2024; 10:eadn1524. [PMID: 39110804 PMCID: PMC11305387 DOI: 10.1126/sciadv.adn1524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/25/2023] [Accepted: 06/28/2024] [Indexed: 08/10/2024]
Abstract
Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using protein structures generated by the AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of the trace amine-associated receptor 1 (TAAR1), a G protein-coupled receptor of unknown structure and target for treating neuropsychiatric disorders. Sets of 30 and 32 highly ranked compounds from the AlphaFold and homology model screens, respectively, were experimentally evaluated. Of these, 25 were TAAR1 agonists with potencies ranging from 12 to 0.03 μM. The AlphaFold screen yielded a more than twofold higher hit rate (60%) than the homology model and discovered the most potent agonists. A TAAR1 agonist with a promising selectivity profile and drug-like properties showed physiological and antipsychotic-like effects in wild-type but not in TAAR1 knockout mice. These results demonstrate that AlphaFold structures can accelerate drug discovery.
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Affiliation(s)
- Alejandro Díaz-Holguín
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Marcus Saarinen
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Duc Duy Vo
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Andrea Sturchio
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
| | - Niclas Branzell
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Israel Cabeza de Vaca
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Huabin Hu
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Núria Mitjavila-Domènech
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Annika Lindqvist
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Pawel Baranczewski
- Department of Pharmacy, SciLifeLab Drug Discovery and Development Platform, Uppsala University, Box 580, SE-751 23 Uppsala, Sweden
| | - Mark J. Millan
- Neuroinflammation Therapeutic Area, Institut de Recherches Servier, Centre de Recherches de Croissy, Paris, France and Institute of Neuroscience and Psychology, College of Medicine, Vet and Life Sciences, Glasgow University, Scotland, Glasgow, UK
| | - Yunting Yang
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
| | - Jens Carlsson
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Box 596, SE-751 24 Uppsala, Sweden
| | - Per Svenningsson
- Neuro Svenningsson, Department of Clinical Neuroscience, Karolinska Institute, SE-171 76 Stockholm, Sweden
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20
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Yang Q, Bai Y, Liu S, Han X, Liu T, Ma D, Mao J. Multicopper Oxidase from Lactobacillus hilgardii: Mechanism of Degradation of Tyramine and Phenylethylamine in Fermented Food. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:17465-17480. [PMID: 39046216 DOI: 10.1021/acs.jafc.4c02319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
Elevated levels of biogenic amines (BAs) in fermented food can have negative effects on both the flavor and health. Mining enzymes that degrade BAs is an effective strategy for controlling their content. The study screened a strain of Lactobacillus hilgardii 1614 from fermented food system that can degrade BAs. The multiple copper oxidase genes LHMCO1614 were successfully mined after the whole genome protein sequences of homologous strains were clustered and followed by homology modeling. The enzyme molecules can interact with BAs to stabilize composite structures for catalytic degradation, as shown by molecular docking results. Ingeniously, the kinetic data showed that purified LHMCO1614 was less sensitive to the substrate inhibition of tyramine and phenylethylamine. The degradation rates of tyramine and phenylethylamine in huangjiu (18% vol) after adding LHMCO1614 were 41.35 and 40.21%, respectively. Furthermore, LHMCO1614 demonstrated universality in degrading tyramine and phenylethylamine present in other fermented foods as well. HS-SPME-GC-MS analysis revealed that, except for aldehydes, the addition of enzyme treatment did not significantly alter the levels of major flavor compounds in enzymatically treated fermented foods (p > 0.05). This study presents an enzymatic approach for regulating tyramine and phenylethylamine levels in fermented foods with potential applications both targeted and universal.
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Affiliation(s)
- Qilin Yang
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Yitao Bai
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Shuangping Liu
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, Zhejiang, China
- National Engineering Research Center of Huangjiu, Zhejiang Guyuelongshan Shaoxing Wine Co., Ltd., Shaoxing 312000, Zhejiang, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Xiao Han
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, Zhejiang, China
- National Engineering Research Center of Huangjiu, Zhejiang Guyuelongshan Shaoxing Wine Co., Ltd., Shaoxing 312000, Zhejiang, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Tiantian Liu
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, Zhejiang, China
- National Engineering Research Center of Huangjiu, Zhejiang Guyuelongshan Shaoxing Wine Co., Ltd., Shaoxing 312000, Zhejiang, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Dongna Ma
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, Zhejiang, China
- National Engineering Research Center of Huangjiu, Zhejiang Guyuelongshan Shaoxing Wine Co., Ltd., Shaoxing 312000, Zhejiang, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
| | - Jian Mao
- National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, State Key Laboratory of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
- Shaoxing Key Laboratory of Traditional Fermentation Food and Human Health, Jiangnan University (Shaoxing) Industrial Technology Research Institute, Shaoxing 312000, Zhejiang, China
- National Engineering Research Center of Huangjiu, Zhejiang Guyuelongshan Shaoxing Wine Co., Ltd., Shaoxing 312000, Zhejiang, China
- Jiangsu Provincial Engineering Research Center for Bioactive Product Processing Technology, Jiangnan University, Wuxi 214122, Jiangsu, China
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21
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Takahashi K, Lee Y, Fago A, Bautista NM, Storz JF, Kawamoto A, Kurisu G, Nishizawa T, Tame JRH. The unique allosteric property of crocodilian haemoglobin elucidated by cryo-EM. Nat Commun 2024; 15:6505. [PMID: 39090102 PMCID: PMC11294572 DOI: 10.1038/s41467-024-49947-x] [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: 11/26/2023] [Accepted: 06/25/2024] [Indexed: 08/04/2024] Open
Abstract
The principal effect controlling the oxygen affinity of vertebrate haemoglobins (Hbs) is the allosteric switch between R and T forms with relatively high and low oxygen affinity respectively. Uniquely among jawed vertebrates, crocodilians possess Hb that shows a profound drop in oxygen affinity in the presence of bicarbonate ions. This allows them to stay underwater for extended periods by consuming almost all the oxygen present in the blood-stream, as metabolism releases carbon dioxide, whose conversion to bicarbonate and hydrogen ions is catalysed by carbonic anhydrase. Despite the apparent universal utility of bicarbonate as an allosteric regulator of Hb, this property evolved only in crocodilians. We report here the molecular structures of both human and a crocodilian Hb in the deoxy and liganded states, solved by cryo-electron microscopy. We reveal the precise interactions between two bicarbonate ions and the crocodilian protein at symmetry-related sites found only in the T state. No other known effector of vertebrate Hbs binds anywhere near these sites.
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Affiliation(s)
- Katsuya Takahashi
- Graduate School of Medical Life Science, Yokohama City University, Suehiro 1-7-29, Yokohama, 230-0045, Japan
| | - Yongchan Lee
- Graduate School of Medical Life Science, Yokohama City University, Suehiro 1-7-29, Yokohama, 230-0045, Japan
| | - Angela Fago
- Department of Biology, Aarhus University, C. F. Møllers Alle 3, Aarhus, DK-8000, Aarhus C, Denmark
| | - Naim M Bautista
- School of Biological Sciences, University of Nebraska, 1104 T St., Lincoln, NE 68588-0118, NE, USA
| | - Jay F Storz
- School of Biological Sciences, University of Nebraska, 1104 T St., Lincoln, NE 68588-0118, NE, USA
| | - Akihiro Kawamoto
- Institute for Protein Research, Osaka University, 3-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Genji Kurisu
- Institute for Protein Research, Osaka University, 3-2, Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Tomohiro Nishizawa
- Graduate School of Medical Life Science, Yokohama City University, Suehiro 1-7-29, Yokohama, 230-0045, Japan.
| | - Jeremy R H Tame
- Graduate School of Medical Life Science, Yokohama City University, Suehiro 1-7-29, Yokohama, 230-0045, Japan.
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22
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Skawinski CLS, Shah PS. I'm Walking into Spiderwebs: Making Sense of Protein-Protein Interaction Data. J Proteome Res 2024; 23:2723-2732. [PMID: 38556766 PMCID: PMC11296932 DOI: 10.1021/acs.jproteome.3c00892] [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: 04/02/2024]
Abstract
Protein-protein interactions (PPIs) are at the heart of the molecular landscape permeating life. Proteomics studies can explore this protein interaction landscape using mass spectrometry (MS). Thanks to their high sensitivity, mass spectrometers can easily identify thousands of proteins within a single sample, but that same sensitivity generates tangled spiderwebs of data that hide biologically relevant findings. So, what does a researcher do when she finds herself walking into spiderwebs? In a field focused on discovery, MS data require rigor in their analysis, experimental validation, or a combination of both. In this Review, we provide a brief primer on MS-based experimental methods to identify PPIs. We discuss approaches to analyze the resulting data and remove the proteomic background. We consider the advantages between comprehensive and targeted studies. We also discuss how scoring might be improved through AI-based protein structure information. Women have been essential to the development of proteomics, so we will specifically highlight work by women that has made this field thrive in recent years.
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Affiliation(s)
| | - Priya S. Shah
- Department of Chemical Engineering, University of California – Davis, California
- Department of Microbiology and Molecular Genetics, University of California – Davis, California
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23
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Geist JL, Lee CY, Strom JM, de Jesús Naveja J, Luck K. Generation of a high confidence set of domain-domain interface types to guide protein complex structure predictions by AlphaFold. Bioinformatics 2024; 40:btae482. [PMID: 39171834 PMCID: PMC11361816 DOI: 10.1093/bioinformatics/btae482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 07/10/2024] [Accepted: 08/20/2024] [Indexed: 08/23/2024] Open
Abstract
MOTIVATION While the release of AlphaFold (AF) represented a breakthrough for the prediction of protein complex structures, its sensitivity, especially when using full length protein sequences, still remains limited. Modeling success rates might increase if AF predictions were guided by likely interacting protein fragments. This approach requires available sets of highly confident protein-protein interface types. Computational resources, such as 3did, infer interacting globular domain types from observed contacts in protein structures. Assessing the accuracy of these predicted interface types is difficult because we lack hand-curated reference sets of verified domain-domain interface (DDI) types. RESULTS To improve protein complex modeling of DDIs by AF, we manually inspected 80 randomly selected DDI types from the 3did resource to generate a first reference set of DDI types. Identified cases of DDI type nonapproval (40%) primarily resulted from inaccurate Pfam domain matches, crystal contacts, and synthetic protein constructs. Using logistic regression, we predicted a subset of 2411 out of 5724 considered DDI types in 3did to be of high confidence, which we subsequently applied to 53 000 human-protein interactions to predict DDIs followed by AF modeling. We obtained highly confident AF models for 604 out of 1129 predicted DDIs. Of note, for 47% of them no confident AF structural model could be obtained using full length protein sequences. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/KatjaLuckLab/DDI_manuscript.
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Affiliation(s)
| | - Chop Yan Lee
- Institute of Molecular Biology (IMB) gGmbH, Mainz 55128, Germany
| | | | - José de Jesús Naveja
- Institute of Molecular Biology (IMB) gGmbH, Mainz 55128, Germany
- 3rd Medical Department, University Medical Center, Johannes Gutenberg University Mainz, Mainz 55131, Germany
- University Cancer Center, University Medical Center, Johannes Gutenberg University Mainz, Mainz 55131, Germany
| | - Katja Luck
- Institute of Molecular Biology (IMB) gGmbH, Mainz 55128, Germany
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24
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Correa Marrero M, Jänes J, Baptista D, Beltrao P. Integrating Large-Scale Protein Structure Prediction into Human Genetics Research. Annu Rev Genomics Hum Genet 2024; 25:123-140. [PMID: 38621234 DOI: 10.1146/annurev-genom-120622-020615] [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] [Indexed: 04/17/2024]
Abstract
The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.
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Affiliation(s)
- Miguel Correa Marrero
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland;
| | - Jürgen Jänes
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland;
| | | | - Pedro Beltrao
- Instituto Gulbenkian de Ciência, Oeiras, Portugal
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland;
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25
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Agarwal V, McShan AC. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol 2024; 20:950-959. [PMID: 38907110 DOI: 10.1038/s41589-024-01638-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 04/29/2024] [Indexed: 06/23/2024]
Abstract
Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.
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Affiliation(s)
- Vinayak Agarwal
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Andrew C McShan
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, GA, USA.
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26
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Medvedev KE, Schaeffer RD, Grishin NV. DrugDomain: The evolutionary context of drugs and small molecules bound to domains. Protein Sci 2024; 33:e5116. [PMID: 38979784 PMCID: PMC11231930 DOI: 10.1002/pro.5116] [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: 03/22/2024] [Revised: 06/27/2024] [Accepted: 06/29/2024] [Indexed: 07/10/2024]
Abstract
Interactions between proteins and small organic compounds play a crucial role in regulating protein functions. These interactions can modulate various aspects of protein behavior, including enzymatic activity, signaling cascades, and structural stability. By binding to specific sites on proteins, small organic compounds can induce conformational changes, alter protein-protein interactions, or directly affect catalytic activity. Therefore, many drugs available on the market today are small molecules (72% of all approved drugs in the last 5 years). Proteins are composed of one or more domains: evolutionary units that convey function or fitness either singly or in concert with others. Understanding which domain(s) of the target protein binds to a drug can lead to additional opportunities for discovering novel targets. The evolutionary classification of protein domains (ECOD) classifies domains into an evolutionary hierarchy that focuses on distant homology. Previously, no structure-based protein domain classification existed that included information about both the interaction between small molecules or drugs and the structural domains of a target protein. This data is especially important for multidomain proteins and large complexes. Here, we present the DrugDomain database that reports the interaction between ECOD of human target proteins and DrugBank molecules and drugs. The pilot version of DrugDomain describes the interaction of 5160 DrugBank molecules associated with 2573 human proteins. It describes domains for all experimentally determined structures of these proteins and incorporates AlphaFold models when such structures are unavailable. The DrugDomain database is available online: http://prodata.swmed.edu/DrugDomain/.
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Affiliation(s)
- Kirill E. Medvedev
- Department of BiophysicsUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - R. Dustin Schaeffer
- Department of BiophysicsUniversity of Texas Southwestern Medical CenterDallasTexasUSA
| | - Nick V. Grishin
- Department of BiophysicsUniversity of Texas Southwestern Medical CenterDallasTexasUSA
- Department of BiochemistryUniversity of Texas Southwestern Medical CenterDallasTexasUSA
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27
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Wang W, Li H, Liu Z, Xu D, Pu H, Hu L, Mo H. Identification of flavor peptides based on virtual screening and molecular docking from Hypsizygus marmoreuss. Food Chem 2024; 448:139071. [PMID: 38552458 DOI: 10.1016/j.foodchem.2024.139071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/11/2024] [Accepted: 03/16/2024] [Indexed: 04/24/2024]
Abstract
Hypsizygus marmoreuss is an under-explored source of flavor peptides that can enhance the flavor of NaCl or MSG, allowing products to be reformulated in line with reduction policies. This study utilized advanced techniques, including UPLC-Q-TOF MS/MS and molecular docking, to identify H. marmoreuss peptides. Sensory evaluations revealed 10 peptides with pronounced umami flavors and seven with dominantly salty tastes. VLPVPQK scored highest for umami intensity (5.2), and EGNPAHQK for salty intensity (6.2). Further investigation influenced by 0.35 % MSG or 0.35 % NaCl exposed peptides with elevated umami and salty thresholds. LDSPATPEK, VVEGEPSLK, and QKLPEKPER had umami-enhancing thresholds of 0.18, 0.18, and 0.35 mM, while LDSPATPEK and VVEGEPSLK had similar thresholds for salt (0.09 mM). Molecular docking revealed that taste receptor proteins interacted with umami peptides through hydrogen, carbon-hydrogen, alkyl, and van der Waals forces. Specific amino acids in the umami receptor T1R1 had roles in bonding with umami peptides through hydrogen and carbon-hydrogen interactions. In conclusion, molecular docking proved to be an effective and efficient method for flavor peptide screening. Further, this study demonstrated that flavor peptides from H. marmoreuss had the capacity to enhance NaCl and MSG flavours and might be useful tools for reformulation, reducing salt and MSG contents.
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Affiliation(s)
- Wenting Wang
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China; Shaanxi Agricultural Products Processing Technology Research Institute, Xi'an 710021, Shaanxi, China
| | - Hongbo Li
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China.
| | - Zhenbin Liu
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China; Shaanxi Agricultural Products Processing Technology Research Institute, Xi'an 710021, Shaanxi, China
| | - Dan Xu
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China; Shaanxi Agricultural Products Processing Technology Research Institute, Xi'an 710021, Shaanxi, China
| | - Huayin Pu
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China
| | - Liangbin Hu
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China
| | - Haizhen Mo
- School of Food Science and Engineering,Shaanxi University of Science and Technology, Xi'an 710021, Shaanxi, China.
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28
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Zhang H, Lan J, Wang H, Lu R, Zhang N, He X, Yang J, Chen L. AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease. Front Mol Biosci 2024; 11:1414916. [PMID: 39139810 PMCID: PMC11319189 DOI: 10.3389/fmolb.2024.1414916] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Accepted: 07/15/2024] [Indexed: 08/15/2024] Open
Abstract
Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.
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Affiliation(s)
- Hong Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Jiajing Lan
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Huijie Wang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Ruijie Lu
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Nanqi Zhang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Xiaobai He
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, Hangzhou, China
| | - Jun Yang
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
| | - Linjie Chen
- School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China
- Zhejiang Engineering Research Centre for Key Technology of Diagnostic Testing, Hangzhou, China
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29
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Lane WJ, Vege S, Mah HH, Ochoa-Garay G, Lomas-Francis C, Westhoff CM. Three novel Er blood group system alleles and insights from protein modeling. Transfusion 2024. [PMID: 39051122 DOI: 10.1111/trf.17965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/23/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND The Er blood group system was recently shown to be defined by PIEZO1. The system consists of high prevalence antigens Era, Er3, ERSA, and ERAMA; and low prevalence antigen Erb. Era/Erb are antithetical with Er(a-b+) defined by the ER*B allele [c.7180G>A p.(Gly2394Ser)]. A nonsense variant c.5289C>G p.(Tyr1763*) is associated with a predicted Ernull phenotype, and a missense variant c.7174G>A p.(Glu2392Lys) in close proximity to p.2394 causes loss of both Era and Erb expression. STUDY DESIGN AND METHODS We investigated PIEZO1 in four Er(a-) individuals who presented with anti-Era. Whole genome sequencing (WGS) and Sanger sequencing were performed. The location and structural differences of predicted protein changes were visualized using the predicted 3-D structure of Piezo1 created using AlphaFold2. RESULTS One individual was homozygous for the reported ER*B. A second had a novel heterozygous nonsense variant c.3331C>T p.(Gln1111*), but a second allelic variant was not found. In the remaining two individuals, two different heterozygous novel missense variants, c.7184C>T p.(Ala2395Val) or c.7195G>A p.(Gly2399Ser), were in trans to the reported c.7180G>A variant, ER*B. AlphaFold2 protein modeling showed that each of the missense variants is predicted to encode an altered structural conformation near Era and Erb. CONCLUSIONS Investigation of archived samples resulted in the identification of three novel PIEZO1 alleles including a predicted Ernull and two missense variants. Structural modeling suggests that the missense changes potentially alter Era/Erb epitope expression with p.2399Ser resulting in a small increase in the negative electrostatic potential.
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Affiliation(s)
- William J Lane
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Sunitha Vege
- Immunohematology and Genomics Laboratory, New York Blood Center Enterprises, New York, New York, USA
| | - Helen H Mah
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Gorka Ochoa-Garay
- Immunohematology and Genomics Laboratory, New York Blood Center Enterprises, New York, New York, USA
| | - Christine Lomas-Francis
- Immunohematology and Genomics Laboratory, New York Blood Center Enterprises, New York, New York, USA
| | - Connie M Westhoff
- Immunohematology and Genomics Laboratory, New York Blood Center Enterprises, New York, New York, USA
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30
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Wilson MP, Kentache T, Althoff CR, Schulz C, de Bettignies G, Mateu Cabrera G, Cimbalistiene L, Burnyte B, Yoon G, Costain G, Vuillaumier-Barrot S, Cheillan D, Rymen D, Rychtarova L, Hansikova H, Bury M, Dewulf JP, Caligiore F, Jaeken J, Cantagrel V, Van Schaftingen E, Matthijs G, Foulquier F, Bommer GT. A pseudoautosomal glycosylation disorder prompts the revision of dolichol biosynthesis. Cell 2024; 187:3585-3601.e22. [PMID: 38821050 PMCID: PMC11250103 DOI: 10.1016/j.cell.2024.04.041] [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: 07/12/2023] [Revised: 02/21/2024] [Accepted: 04/29/2024] [Indexed: 06/02/2024]
Abstract
Dolichol is a lipid critical for N-glycosylation as a carrier for activated sugars and nascent oligosaccharides. It is commonly thought to be directly produced from polyprenol by the enzyme SRD5A3. Instead, we found that dolichol synthesis requires a three-step detour involving additional metabolites, where SRD5A3 catalyzes only the second reaction. The first and third steps are performed by DHRSX, whose gene resides on the pseudoautosomal regions of the X and Y chromosomes. Accordingly, we report a pseudoautosomal-recessive disease presenting as a congenital disorder of glycosylation in patients with missense variants in DHRSX (DHRSX-CDG). Of note, DHRSX has a unique dual substrate and cofactor specificity, allowing it to act as a NAD+-dependent dehydrogenase and as a NADPH-dependent reductase in two non-consecutive steps. Thus, our work reveals unexpected complexity in the terminal steps of dolichol biosynthesis. Furthermore, we provide insights into the mechanism by which dolichol metabolism defects contribute to disease.
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Affiliation(s)
- Matthew P Wilson
- Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium
| | - Takfarinas Kentache
- Metabolic Research Group, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium; WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Charlotte R Althoff
- Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium; Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, 59000 Lille, France
| | - Céline Schulz
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, 59000 Lille, France
| | - Geoffroy de Bettignies
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, 59000 Lille, France
| | - Gisèle Mateu Cabrera
- Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium
| | - Loreta Cimbalistiene
- Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Birute Burnyte
- Institute of Biomedical Sciences, Faculty of Medicine, Vilnius University, Vilnius, Lithuania
| | - Grace Yoon
- Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, ON, Canada; Division of Neurology, Hospital for Sick Children, Toronto, ON, Canada; Department of Paediatrics, University of Toronto, Toronto, ON, Canada
| | - Gregory Costain
- Division of Clinical and Metabolic Genetics, Hospital for Sick Children, Toronto, ON, Canada; Department of Paediatrics, University of Toronto, Toronto, ON, Canada; Program in Genetics and Genome Biology, SickKids Research Institute, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Sandrine Vuillaumier-Barrot
- AP-HP, Biochimie Métabolique et Cellulaire and Département de Génétique, Hôpital Bichat-Claude Bernard, and Université de Paris, Faculté de Médecine Xavier Bichat, INSERM U1149, CRI, Paris, France
| | - David Cheillan
- Service Biochimie et Biologie Moléculaire - Hospices Civils de Lyon; Laboratoire Carmen - Inserm U1060, INRAE UMR1397, Université Claude Bernard Lyon 1, Lyon, France
| | - Daisy Rymen
- Department of Pediatrics, Center for Metabolic Diseases, University Hospitals Leuven, Leuven, Belgium
| | - Lucie Rychtarova
- Laboratory for Study of Mitochondrial Disorders, Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine and General University Hospital in Prague, Charles University, Prague, Czechia
| | - Hana Hansikova
- Laboratory for Study of Mitochondrial Disorders, Department of Paediatrics and Inherited Metabolic Disorders, First Faculty of Medicine and General University Hospital in Prague, Charles University, Prague, Czechia
| | - Marina Bury
- Metabolic Research Group, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium; WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Joseph P Dewulf
- Metabolic Research Group, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium; WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Francesco Caligiore
- Metabolic Research Group, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium; WELBIO Department, WEL Research Institute, Wavre, Belgium
| | - Jaak Jaeken
- Department of Pediatrics, Center for Metabolic Diseases, University Hospitals Leuven, Leuven, Belgium
| | - Vincent Cantagrel
- Developmental Brain Disorders Laboratory, Université Paris Cité, INSERM UMR1163, Imagine Institute, Paris, France
| | - Emile Van Schaftingen
- Metabolic Research Group, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium; WELBIO Department, WEL Research Institute, Wavre, Belgium.
| | - Gert Matthijs
- Laboratory for Molecular Diagnosis, Center for Human Genetics, KU Leuven, Leuven, Belgium.
| | - François Foulquier
- Univ. Lille, CNRS, UMR 8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, 59000 Lille, France.
| | - Guido T Bommer
- Metabolic Research Group, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium; WELBIO Department, WEL Research Institute, Wavre, Belgium.
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31
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Stephenson JD, Totoo P, Burke D, Jänes J, Beltrao P, Martin M. ProtVar: mapping and contextualizing human missense variation. Nucleic Acids Res 2024; 52:W140-W147. [PMID: 38769064 PMCID: PMC11223857 DOI: 10.1093/nar/gkae413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 04/26/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024] Open
Abstract
Genomic variation can impact normal biological function in complex ways and so understanding variant effects requires a broad range of data to be coherently assimilated. Whilst the volume of human variant data and relevant annotations has increased, the corresponding increase in the breadth of participating fields, standards and versioning mean that moving between genomic, coding, protein and structure positions is increasingly complex. In turn this makes investigating variants in diverse formats and assimilating annotations from different resources challenging. ProtVar addresses these issues to facilitate the contextualization and interpretation of human missense variation with unparalleled flexibility and ease of accessibility for use by the broadest range of researchers. By precalculating all possible variants in the human proteome it offers near instantaneous mapping between all relevant data types. It also combines data and analyses from a plethora of resources to bring together genomic, protein sequence and function annotations as well as structural insights and predictions to better understand the likely effect of missense variation in humans. It is offered as an intuitive web server https://www.ebi.ac.uk/protvar where data can be explored and downloaded, and can be accessed programmatically via an API.
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Affiliation(s)
| | - Prabhat Totoo
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, Cambridgeshire, UK
| | | | - Jürgen Jänes
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
| | - Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Maria J Martin
- EMBL-EBI, Wellcome Genome Campus, Hinxton CB10 1SD, Cambridgeshire, UK
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32
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Bryant P, Noé F. Improved protein complex prediction with AlphaFold-multimer by denoising the MSA profile. PLoS Comput Biol 2024; 20:e1012253. [PMID: 39052676 DOI: 10.1371/journal.pcbi.1012253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 08/06/2024] [Accepted: 06/14/2024] [Indexed: 07/27/2024] Open
Abstract
Structure prediction of protein complexes has improved significantly with AlphaFold2 and AlphaFold-multimer (AFM), but only 60% of dimers are accurately predicted. Here, we learn a bias to the MSA representation that improves the predictions by performing gradient descent through the AFM network. We demonstrate the performance on seven difficult targets from CASP15 and increase the average MMscore to 0.76 compared to 0.63 with AFM. We evaluate the procedure on 487 protein complexes where AFM fails and obtain an increased success rate (MMscore>0.75) of 33% on these difficult targets. Our protocol, AFProfile, provides a way to direct predictions towards a defined target function guided by the MSA. We expect gradient descent over the MSA to be useful for different tasks.
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Affiliation(s)
- Patrick Bryant
- Department of Mathematics and Informatics, Freie Universität Berlin, Germany
- The Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
- Science for Life Laboratory, Solna, Sweden
| | - Frank Noé
- Department of Mathematics and Informatics, Freie Universität Berlin, Germany
- Microsoft Research AI4Science, Berlin, Germany
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33
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Ebrahimikondori H, Sutherland D, Yanai A, Richter A, Salehi A, Li C, Coombe L, Kotkoff M, Warren RL, Birol I. Structure-aware deep learning model for peptide toxicity prediction. Protein Sci 2024; 33:e5076. [PMID: 39196703 PMCID: PMC11193153 DOI: 10.1002/pro.5076] [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: 01/09/2024] [Revised: 04/26/2024] [Accepted: 05/28/2024] [Indexed: 08/30/2024]
Abstract
Antimicrobial resistance is a critical public health concern, necessitating the exploration of alternative treatments. While antimicrobial peptides (AMPs) show promise, assessing their toxicity using traditional wet lab methods is both time-consuming and costly. We introduce tAMPer, a novel multi-modal deep learning model designed to predict peptide toxicity by integrating the underlying amino acid sequence composition and the three-dimensional structure of peptides. tAMPer adopts a graph-based representation for peptides, encoding ColabFold-predicted structures, where nodes represent amino acids and edges represent spatial interactions. Structural features are extracted using graph neural networks, and recurrent neural networks capture sequential dependencies. tAMPer's performance was assessed on a publicly available protein toxicity benchmark and an AMP hemolysis data we generated. On the latter, tAMPer achieves an F1-score of 68.7%, outperforming the second-best method by 23.4%. On the protein benchmark, tAMPer exhibited an improvement of over 3.0% in the F1-score compared to current state-of-the-art methods. We anticipate tAMPer to accelerate AMP discovery and development by reducing the reliance on laborious toxicity screening experiments.
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Affiliation(s)
- Hossein Ebrahimikondori
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Bioinformatics Graduate ProgramUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Darcy Sutherland
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
- Department of Pathology and Laboratory MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Anat Yanai
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
| | - Amelia Richter
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
| | - Ali Salehi
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
| | - Chenkai Li
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Bioinformatics Graduate ProgramUniversity of British ColumbiaVancouverBritish ColumbiaCanada
| | - Lauren Coombe
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
| | - Monica Kotkoff
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
| | - René L. Warren
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences CentreBC Cancer AgencyVancouverBritish ColumbiaCanada
- Public Health LaboratoryBritish Columbia Centre for Disease ControlVancouverBritish ColumbiaCanada
- Department of Pathology and Laboratory MedicineUniversity of British ColumbiaVancouverBritish ColumbiaCanada
- Department of Medical GeneticsUniversity of British ColumbiaVancouverBritish ColumbiaCanada
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34
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Krishnan SR, Sharma D, Nazeer Y, Bose M, Rajkumar T, Jayaraman G, Madaboosi N, Gromiha MM. rAbDesFlow: a novel workflow for computational recombinant antibody design for healthcare engineering. Antib Ther 2024; 7:256-265. [PMID: 39262441 PMCID: PMC11384895 DOI: 10.1093/abt/tbae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/11/2024] [Indexed: 09/13/2024] Open
Abstract
Recombinant antibodies (rAbs) have emerged as a promising solution to tackle antigen specificity, enhancement of immunogenic potential and versatile functionalization to treat human diseases. The development of single chain variable fragments has helped accelerate treatment in cancers and viral infections, due to their favorable pharmacokinetics and human compatibility. However, designing rAbs is traditionally viewed as a genetic engineering problem, with phage display and cell free systems playing a major role in sequence selection for gene synthesis. The process of antibody engineering involves complex and time-consuming laboratory techniques, which demand substantial resources and expertise. The success rate of obtaining desired antibody candidates through experimental approaches can be modest, necessitating iterative cycles of selection and optimization. With ongoing advancements in technology, in silico design of diverse antibody libraries, screening and identification of potential candidates for in vitro validation can be accelerated. To meet this need, we have developed rAbDesFlow, a unified computational workflow for recombinant antibody engineering with open-source programs and tools for ease of implementation. The workflow encompasses five computational modules to perform antigen selection, antibody library generation, antigen and antibody structure modeling, antigen-antibody interaction modeling, structure analysis, and consensus ranking of potential antibody sequences for synthesis and experimental validation. The proposed workflow has been demonstrated through design of rAbs for the ovarian cancer antigen Mucin-16 (CA-125). This approach can serve as a blueprint for designing similar engineered molecules targeting other biomarkers, allowing for a simplified adaptation to different cancer types or disease-specific antigens.
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Affiliation(s)
- Sowmya Ramaswamy Krishnan
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Divya Sharma
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Yasin Nazeer
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Mayilvahanan Bose
- Department of Molecular Oncology, Cancer Institute (WIA), Adyar, Chennai 600020, India
| | - Thangarajan Rajkumar
- Department of Applied Mechanics and Biomedical Engineering, Indian Institute of Technology Madras, Chennai 600036, India
- MedGenome, Bengaluru 560099, Karnataka, India
- Department of Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences, Kochi 682041, Kerala, India
| | - Guhan Jayaraman
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - Narayanan Madaboosi
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
| | - M Michael Gromiha
- Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India
- International Research Frontiers Initiative, School of Computing, Tokyo Institute of Technology, Yokohama 226-8501, Japan
- School of Computing, National University of Singapore (NUS), Singapore 119077, Singapore
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35
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Joshi SHN, Jenkins C, Ulaeto D, Gorochowski TE. Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology. BIODESIGN RESEARCH 2024; 6:0037. [PMID: 38919711 PMCID: PMC11197468 DOI: 10.34133/bdr.0037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/23/2024] [Indexed: 06/27/2024] Open
Abstract
Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.
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Affiliation(s)
| | - Christopher Jenkins
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - David Ulaeto
- CBR Division, Defence Science and Technology Laboratory, Porton Down, Wiltshire SP4 0JQ, UK
| | - Thomas E. Gorochowski
- School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, UK
- BrisEngBio,
School of Chemistry, University of Bristol, Bristol BS8 1TS, UK
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36
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Sawada R, Sakajiri Y, Shibata T, Yamanishi Y. Predicting therapeutic and side effects from drug binding affinities to human proteome structures. iScience 2024; 27:110032. [PMID: 38868195 PMCID: PMC11167438 DOI: 10.1016/j.isci.2024.110032] [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: 12/09/2023] [Revised: 04/08/2024] [Accepted: 05/16/2024] [Indexed: 06/14/2024] Open
Abstract
Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.
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Affiliation(s)
- Ryusuke Sawada
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Department of Pharmacology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan
| | - Yuko Sakajiri
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
| | - Tomokazu Shibata
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
| | - Yoshihiro Yamanishi
- Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Japan
- Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, Japan
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37
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Lyu J, Kapolka N, Gumpper R, Alon A, Wang L, Jain MK, Barros-Álvarez X, Sakamoto K, Kim Y, DiBerto J, Kim K, Glenn IS, Tummino TA, Huang S, Irwin JJ, Tarkhanova OO, Moroz Y, Skiniotis G, Kruse AC, Shoichet BK, Roth BL. AlphaFold2 structures guide prospective ligand discovery. Science 2024; 384:eadn6354. [PMID: 38753765 PMCID: PMC11253030 DOI: 10.1126/science.adn6354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/24/2024] [Indexed: 05/18/2024]
Abstract
AlphaFold2 (AF2) models have had wide impact but mixed success in retrospective ligand recognition. We prospectively docked large libraries against unrefined AF2 models of the σ2 and serotonin 2A (5-HT2A) receptors, testing hundreds of new molecules and comparing results with those obtained from docking against the experimental structures. Hit rates were high and similar for the experimental and AF2 structures, as were affinities. Success in docking against the AF2 models was achieved despite differences between orthosteric residue conformations in the AF2 models and the experimental structures. Determination of the cryo-electron microscopy structure for one of the more potent 5-HT2A ligands from the AF2 docking revealed residue accommodations that resembled the AF2 prediction. AF2 models may sample conformations that differ from experimental structures but remain low energy and relevant for ligand discovery, extending the domain of structure-based drug design.
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Affiliation(s)
- Jiankun Lyu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
- The Evnin Family Laboratory of Computational Molecular Discovery, The Rockefeller University, New York, NY 10065, USA
| | - Nicholas Kapolka
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ryan Gumpper
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Assaf Alon
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liang Wang
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Manish K. Jain
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Ximena Barros-Álvarez
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Kensuke Sakamoto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Yoojoong Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Jeffrey DiBerto
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Kuglae Kim
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
| | - Isabella S. Glenn
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Tia A. Tummino
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Sijie Huang
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - John J. Irwin
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | | | - Yurii Moroz
- Chemspace LLC, Kyiv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Kyiv 01601, Ukraine
- Enamine Ltd., Kyiv 02094, Ukraine
| | - Georgios Skiniotis
- Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94035, USA
- Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Andrew C. Kruse
- Department of Biological Chemistry and Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Brian K. Shoichet
- Department of Pharmaceutical Chemistry, University of California, San Francisco, CA 94158, USA
| | - Bryan L. Roth
- Department of Pharmacology, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP), School of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC 27599, USA
- Division of Chemical Biology and Medicinal Chemistry, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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38
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Bradley D, Garand C, Belda H, Gagnon-Arsenault I, Treeck M, Elowe S, Landry CR. The substrate quality of CK2 target sites has a determinant role on their function and evolution. Cell Syst 2024; 15:544-562.e8. [PMID: 38861992 DOI: 10.1016/j.cels.2024.05.005] [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: 07/03/2023] [Revised: 02/29/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
Most biological processes are regulated by signaling modules that bind to short linear motifs. For protein kinases, substrates may have full or only partial matches to the kinase recognition motif, a property known as "substrate quality." However, it is not clear whether differences in substrate quality represent neutral variation or if they have functional consequences. We examine this question for the kinase CK2, which has many fundamental functions. We show that optimal CK2 sites are phosphorylated at maximal stoichiometries and found in many conditions, whereas minimal substrates are more weakly phosphorylated and have regulatory functions. Optimal CK2 sites tend to be more conserved, and substrate quality is often tuned by selection. For intermediate sites, increases or decreases in substrate quality may be deleterious, as we demonstrate for a CK2 substrate at the kinetochore. The results together suggest a strong role for substrate quality in phosphosite function and evolution. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- David Bradley
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec City, QC G1V 0A6, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC G1V 0A6, Canada; PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec City, QC G1V 0A6, Canada; Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec City, QC G1V 0A6, Canada; Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec City, QC G1V 0A6, Canada.
| | - Chantal Garand
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec City, QC G1V 0A6, Canada; Axe de Reproduction, Santé de la mère et de l'enfant, CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Hugo Belda
- Signalling in Host-Pathogen Interaction Laboratory, The Francis Crick Institute, London NW11AT, UK
| | - Isabelle Gagnon-Arsenault
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec City, QC G1V 0A6, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC G1V 0A6, Canada; PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec City, QC G1V 0A6, Canada; Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec City, QC G1V 0A6, Canada; Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec City, QC G1V 0A6, Canada
| | - Moritz Treeck
- Signalling in Host-Pathogen Interaction Laboratory, The Francis Crick Institute, London NW11AT, UK; Cell Biology of Host-Pathogen Interaction Laboratory, The Gulbenkian Institute of Science, Oeiras 2780-156, Portugal
| | - Sabine Elowe
- PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec City, QC G1V 0A6, Canada; Axe de Reproduction, Santé de la mère et de l'enfant, CHU de Québec, Université Laval, Québec City, QC, Canada; Department of Pediatrics, Faculty of Medicine, Université Laval, Québec City, QC, Canada; Centre de Recherche sur le Cancer, CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Christian R Landry
- Département de Biochimie, de Microbiologie et de Bio-informatique, Faculté des Sciences et de Génie, Université Laval, Québec City, QC G1V 0A6, Canada; Institut de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Québec City, QC G1V 0A6, Canada; PROTEO, Le regroupement québécois de recherche sur la fonction, l'ingénierie et les applications des protéines, Université Laval, Québec City, QC G1V 0A6, Canada; Centre de Recherche sur les Données Massives (CRDM), Université Laval, Québec City, QC G1V 0A6, Canada; Département de Biologie, Faculté des Sciences et de Génie, Université Laval, Québec City, QC G1V 0A6, Canada.
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Chim HY, Elofsson A. MoLPC2: improved prediction of large protein complex structures and stoichiometry using Monte Carlo Tree Search and AlphaFold2. Bioinformatics 2024; 40:btae329. [PMID: 38781500 PMCID: PMC11194477 DOI: 10.1093/bioinformatics/btae329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/18/2024] [Accepted: 05/22/2024] [Indexed: 05/25/2024] Open
Abstract
MOTIVATION Today, the prediction of structures of large protein complexes solely from their sequence information requires prior knowledge of the stoichiometry of the complex. To address this challenge, we have enhanced the Monte Carlo Tree Search algorithms in MoLPC to enable the assembly of protein complexes while simultaneously predicting their stoichiometry. RESULTS In MoLPC2, we have improved the predictions by allowing sampling alternative AlphaFold predictions. Using MoLPC2, we accurately predicted the structures of 50 out of 175 nonredundant protein complexes (TM-score ≥ 0.8) without knowing the stoichiometry. MoLPC2 provides new opportunities for predicting protein complex structures without stoichiometry information. AVAILABILITY AND IMPLEMENTATION MoLPC2 is freely available at https://github.com/hychim/molpc2. A notebook is also available from the repository for easy use.
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Affiliation(s)
- Ho Yeung Chim
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 106 91, Sweden
| | - Arne Elofsson
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Stockholm 106 91, Sweden
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40
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Wang L, Wen Z, Liu SW, Zhang L, Finley C, Lee HJ, Fan HJS. Overview of AlphaFold2 and breakthroughs in overcoming its limitations. Comput Biol Med 2024; 176:108620. [PMID: 38761500 DOI: 10.1016/j.compbiomed.2024.108620] [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: 10/29/2023] [Revised: 05/01/2024] [Accepted: 05/14/2024] [Indexed: 05/20/2024]
Abstract
Predicting three-dimensional (3D) protein structures has been challenging for decades. The emergence of AlphaFold2 (AF2), a deep learning-based machine learning method developed by DeepMind, became a game changer in the protein folding community. AF2 can predict a protein's three-dimensional structure with high confidence based on its amino acid sequence. Accurate prediction of protein structures can dramatically accelerate our understanding of biological mechanisms and provide a solid foundation for reliable drug design. Although AF2 breaks through the barriers in predicting protein structures, many rooms remain to be further studied. This review provides a brief historical overview of the development of protein structure prediction, covering template-based, template-free, and machine learning-based methods. In addition to reviewing the potential benefits (Pros) and considerations (Cons) of using AF2, this review summarizes the diverse applications, including protein structure predictions, dynamic changes, point mutation, integration of language model and experimental data, protein complex, and protein-peptide interaction. It underscores recent advancements in efficiency, reliability, and broad application of AF2. This comprehensive review offers valuable insights into the applications of AF2 and AF2-inspired AI methods in structural biology and its potential for clinically significant drug target discovery.
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Affiliation(s)
- Lei Wang
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Zehua Wen
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Shi-Wei Liu
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China
| | - Lihong Zhang
- Digestive Department, Binhai New Area Hospital of TCM Tianjin, Tianjin, 300451, China
| | - Cierra Finley
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA
| | - Ho-Jin Lee
- Department of Natural Sciences, Southwest Tennessee Community College, Memphis, TN, 38015, USA; Division of Natural & Mathematical Sciences, LeMoyne-Own College, Memphis, TN, 38126, USA.
| | - Hua-Jun Shawn Fan
- College of Chemical Engineering, Sichuan University of Science and Engineering, Zigong City, Sichuan Province, 64300, China.
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41
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Middendorf L, Eicholt LA. Random, de novo, and conserved proteins: How structure and disorder predictors perform differently. Proteins 2024; 92:757-767. [PMID: 38226524 DOI: 10.1002/prot.26652] [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: 07/18/2023] [Revised: 10/18/2023] [Accepted: 12/01/2023] [Indexed: 01/17/2024]
Abstract
Understanding the emergence and structural characteristics of de novo and random proteins is crucial for unraveling protein evolution and designing novel enzymes. However, experimental determination of their structures remains challenging. Recent advancements in protein structure prediction, particularly with AlphaFold2 (AF2), have expanded our knowledge of protein structures, but their applicability to de novo and random proteins is unclear. In this study, we investigate the structural predictions and confidence scores of AF2 and protein language model-based predictor ESMFold for de novo and conserved proteins from Drosophila and a dataset of comparable random proteins. We find that the structural predictions for de novo and random proteins differ significantly from conserved proteins. Interestingly, a positive correlation between disorder and confidence scores (pLDDT) is observed for de novo and random proteins, in contrast to the negative correlation observed for conserved proteins. Furthermore, the performance of structure predictors for de novo and random proteins is hampered by the lack of sequence identity. We also observe fluctuating median predicted disorder among different sequence length quartiles for random proteins, suggesting an influence of sequence length on disorder predictions. In conclusion, while structure predictors provide initial insights into the structural composition of de novo and random proteins, their accuracy and applicability to such proteins remain limited. Experimental determination of their structures is necessary for a comprehensive understanding. The positive correlation between disorder and pLDDT could imply a potential for conditional folding and transient binding interactions of de novo and random proteins.
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Affiliation(s)
- Lasse Middendorf
- Institute for Evolution and Biodiversity, University of Muenster, Muenster, Germany
| | - Lars A Eicholt
- Institute for Evolution and Biodiversity, University of Muenster, Muenster, Germany
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42
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Lyratzakis A, Daskalakis V, Xie H, Tsiotis G. The synergy between the PscC subunits for electron transfer to the P 840 special pair in Chlorobaculum tepidum. PHOTOSYNTHESIS RESEARCH 2024; 160:87-96. [PMID: 38625595 PMCID: PMC11108878 DOI: 10.1007/s11120-024-01093-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 03/08/2024] [Indexed: 04/17/2024]
Abstract
The primary photochemical reaction of photosynthesis in green sulfur bacteria occurs in the homodimer PscA core proteins by a special chlorophyll pair. The light induced excited state of the special pair producing P840+ is rapidly reduced by electron transfer from one of the two PscC subunits. Molecular dynamics (MD) simulations are combined with bioinformatic tools herein to provide structural and dynamic insight into the complex between the two PscA core proteins and the two PscC subunits. The microscopic dynamic model involves extensive sampling at atomic resolution and at a cumulative time-scale of 22µs and reveals well defined protein-protein interactions. The membrane complex is composed of the two PscA and the two PscC subunits and macroscopic connections are revealed within a putative electron transfer pathway from the PscC subunit to the special pair P840 located within the PscA subunits. Our results provide a structural basis for understanding the electron transport to the homodimer RC of the green sulfur bacteria. The MD based approach can provide the basis to further probe the PscA-PscC complex dynamics and observe electron transfer therein at the quantum level. Furthermore, the transmembrane helices of the different PscC subunits exert distinct dynamics in the complex.
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Affiliation(s)
- Alexandros Lyratzakis
- Department of Chemistry, School of Science and Engineering, University of Crete, Heraklion, 70013, Greece
| | - Vangelis Daskalakis
- Department of Chemical Engineering, School of Engineering, University of Patras, Rion, Patras, 26504, Greece
| | - Hao Xie
- Max Planck Institute of Biophysics, 60438, Frankfurt am Main, Germany
| | - Georgios Tsiotis
- Department of Chemistry, School of Science and Engineering, University of Crete, Heraklion, 70013, Greece.
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43
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Stincone P, Naimi A, Saviola AJ, Reher R, Petras D. Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions. Proteomics 2024; 24:e2200533. [PMID: 37929699 DOI: 10.1002/pmic.202200533] [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: 07/07/2023] [Revised: 10/15/2023] [Accepted: 10/23/2023] [Indexed: 11/07/2023]
Abstract
With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.
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Affiliation(s)
- Paolo Stincone
- University of Tuebingen, CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Infection Medicine, Tuebingen, Germany
- University of Tuebingen, Center for Plant Molecular Biology, Tuebingen, Germany
| | - Amira Naimi
- University of Marburg, Institute of Pharmaceutical Biology and Biotechnology, Marburg, Germany
| | | | - Raphael Reher
- University of Marburg, Institute of Pharmaceutical Biology and Biotechnology, Marburg, Germany
| | - Daniel Petras
- University of Tuebingen, CMFI Cluster of Excellence, Interfaculty Institute of Microbiology and Infection Medicine, Tuebingen, Germany
- University of California Riverside, Department of Biochemistry, Riverside, USA
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44
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Chen HW, Ma CP, Chin E, Chen YT, Wang TC, Kuo YP, Su CH, Huang PJ, Tan BCM. Imbalance in Unc80 RNA Editing Disrupts Dynamic Neuronal Activity and Olfactory Perception. Int J Mol Sci 2024; 25:5985. [PMID: 38892173 PMCID: PMC11172567 DOI: 10.3390/ijms25115985] [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: 03/29/2024] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
A-to-I RNA editing, catalyzed by the ADAR protein family, significantly contributes to the diversity and adaptability of mammalian RNA signatures, aligning with developmental and physiological needs. Yet, the functions of many editing sites are still to be defined. The Unc80 gene stands out in this context due to its brain-specific expression and the evolutionary conservation of its codon-altering editing event. The precise biological functions of Unc80 and its editing, however, are still largely undefined. In this study, we first demonstrated that Unc80 editing occurs in an ADAR2-dependent manner and is exclusive to the brain. By employing the CRISPR/Cas9 system to generate Unc80 knock-in mouse models that replicate the natural editing variations, our findings revealed that mice with the "gain-of-editing" variant (Unc80G/G) exhibit heightened basal neuronal activity in critical olfactory regions, compared to the "loss-of-editing" (Unc80S/S) counterparts. Moreover, an increase in glutamate levels was observed in the olfactory bulbs of Unc80G/G mice, indicating altered neurotransmitter dynamics. Behavioral analysis of odor detection revealed distinctive responses to novel odors-both Unc80 deficient (Unc80+/-) and Unc80S/S mice demonstrated prolonged exploration times and heightened dishabituation responses. Further elucidating the olfactory connection of Unc80 editing, transcriptomic analysis of the olfactory bulb identified significant alterations in gene expression that corroborate the behavioral and physiological findings. Collectively, our research advances the understanding of Unc80's neurophysiological functions and the impact of its editing on the olfactory sensory system, shedding light on the intricate molecular underpinnings of olfactory perception and neuronal activity.
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Affiliation(s)
- Hui-Wen Chen
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (H.-W.C.); (C.-P.M.); (E.C.); (Y.-T.C.); (P.-J.H.)
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
| | - Chung-Pei Ma
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (H.-W.C.); (C.-P.M.); (E.C.); (Y.-T.C.); (P.-J.H.)
| | - En Chin
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (H.-W.C.); (C.-P.M.); (E.C.); (Y.-T.C.); (P.-J.H.)
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
| | - Yi-Tung Chen
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (H.-W.C.); (C.-P.M.); (E.C.); (Y.-T.C.); (P.-J.H.)
- Molecular Medicine Research Center, Chang Gung University, Taoyuan 333, Taiwan;
| | - Teh-Cheng Wang
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
| | - Yu-Ping Kuo
- Molecular Medicine Research Center, Chang Gung University, Taoyuan 333, Taiwan;
| | - Chia-Hao Su
- Center for General Education, Chang Gung University, Taoyuan 333, Taiwan;
- Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan
| | - Po-Jung Huang
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (H.-W.C.); (C.-P.M.); (E.C.); (Y.-T.C.); (P.-J.H.)
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
- Genomic Medicine Core Laboratory, Lin-Kou Medical Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Bertrand Chin-Ming Tan
- Department of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan; (H.-W.C.); (C.-P.M.); (E.C.); (Y.-T.C.); (P.-J.H.)
- Graduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan;
- Division of Colon and Rectal Surgery, Lin-Kou Medical Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
- Department of Neurosurgery, Lin-Kou Medical Center, Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
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45
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Hoffman J, Tan H, Sandoval-Cooper C, de Villiers K, Reed SM. GTExome: Modeling commonly expressed missense mutations in the human genome. PLoS One 2024; 19:e0303604. [PMID: 38814966 PMCID: PMC11139294 DOI: 10.1371/journal.pone.0303604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 04/26/2024] [Indexed: 06/01/2024] Open
Abstract
A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/.
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Affiliation(s)
- Jill Hoffman
- Computational Bioscience, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
| | - Henry Tan
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America
| | - Clara Sandoval-Cooper
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America
| | - Kaelyn de Villiers
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America
| | - Scott M. Reed
- Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America
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Yazaki J, Yamanashi T, Nemoto S, Kobayashi A, Han YW, Hasegawa T, Iwase A, Ishikawa M, Konno R, Imami K, Kawashima Y, Seita J. Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry. Biol Methods Protoc 2024; 9:bpae039. [PMID: 38884001 PMCID: PMC11180226 DOI: 10.1093/biomethods/bpae039] [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: 05/01/2024] [Revised: 05/19/2024] [Accepted: 05/28/2024] [Indexed: 06/18/2024] Open
Abstract
Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.
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Affiliation(s)
- Junshi Yazaki
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Faculty of Agriculture, Laboratory for Genome Biology, Setsunan University, Osaka, 573-0101, Japan
| | - Takashi Yamanashi
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Medical Data Deep Learning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, 103-0027, Japan
- School of Integrative and Global Majors, University of Tsukuba, Tsukuba, 305-8577, Japan
| | - Shino Nemoto
- Laboratory for Intestinal Ecosystem, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Atsuo Kobayashi
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Yong-Woon Han
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Tomoko Hasegawa
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Akira Iwase
- Cell Function Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, 230-0045, Japan
| | - Masaki Ishikawa
- Department of Applied Genomics, Technology Development Team, Kazusa DNA Research Institute, Kisarazu, 292-0818, Japan
| | - Ryo Konno
- Department of Applied Genomics, Technology Development Team, Kazusa DNA Research Institute, Kisarazu, 292-0818, Japan
| | - Koshi Imami
- Proteome Homeostasis Research Unit, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
| | - Yusuke Kawashima
- Department of Applied Genomics, Technology Development Team, Kazusa DNA Research Institute, Kisarazu, 292-0818, Japan
| | - Jun Seita
- Laboratory for Integrative Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, 230-0045, Japan
- Medical Data Deep Learning Team, Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Tokyo, 103-0027, Japan
- School of Integrative and Global Majors, University of Tsukuba, Tsukuba, 305-8577, Japan
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47
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Jänes J, Müller M, Selvaraj S, Manoel D, Stephenson J, Gonçalves C, Lafita A, Polacco B, Obernier K, Alasoo K, Lemos MC, Krogan N, Martin M, Saraiva LR, Burke D, Beltrao P. Predicted mechanistic impacts of human protein missense variants. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.29.596373. [PMID: 38854010 PMCID: PMC11160786 DOI: 10.1101/2024.05.29.596373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Genome sequencing efforts have led to the discovery of tens of millions of protein missense variants found in the human population with the majority of these having no annotated role and some likely contributing to trait variation and disease. Sequence-based artificial intelligence approaches have become highly accurate at predicting variants that are detrimental to the function of proteins but they do not inform on mechanisms of disruption. Here we combined sequence and structure-based methods to perform proteome-wide prediction of deleterious variants with information on their impact on protein stability, protein-protein interactions and small-molecule binding pockets. AlphaFold2 structures were used to predict approximately 100,000 small-molecule binding pockets and stability changes for over 200 million variants. To inform on protein-protein interfaces we used AlphaFold2 to predict structures for nearly 500,000 protein complexes. We illustrate the value of mechanism-aware variant effect predictions to study the relation between protein stability and abundance and the structural properties of interfaces underlying trans protein quantitative trait loci (pQTLs). We characterised the distribution of mechanistic impacts of protein variants found in patients and experimentally studied example disease linked variants in FGFR1.
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Affiliation(s)
- Jürgen Jänes
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marc Müller
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Senthil Selvaraj
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Diogo Manoel
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - James Stephenson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Catarina Gonçalves
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Benjamin Polacco
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kirsten Obernier
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Manuel C. Lemos
- CICS-UBI, Health Sciences Research Centre, University of Beira Interior, 6200-506, Covilhã, Portugal
| | - Nevan Krogan
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Maria Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
| | - Luis R. Saraiva
- Sidra Medicine, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - David Burke
- Faculty of Life Sciences and Medicine, King’s College, London, UK
| | - Pedro Beltrao
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
- Open Targets, Wellcome Genome Campus, Cambridge, CB10 1SA, UK
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48
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Gong R, Wang J, Xing Y, Wang J, Chen X, Lei K, Yu Q, Zhao C, Li S, Zhang Y, Wang H, Ren H. Expression landscape of cancer-FOXP3 and its prognostic value in pancreatic adenocarcinoma. Cancer Lett 2024; 590:216838. [PMID: 38561039 DOI: 10.1016/j.canlet.2024.216838] [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: 02/05/2024] [Revised: 03/19/2024] [Accepted: 03/27/2024] [Indexed: 04/04/2024]
Abstract
FOXP3, a key identifier of Treg, has also been identified in tumor cells, which is referred to as cancer-FOXP3 (c-FOXP3). Human c-FOXP3 undergoes multiple alternative splicing events, generating several isoforms, like c-FOXP3FL and c-FOXP3Δ3. Previous research on c-FOXP3 often ignore its cellular source (immune or tumor cells) and isoform expression patterns, which may obscure our understanding of its clinical significance. Our immunohistochemistry investigations which conducted across 18 tumors using validated c-FOXP3 antibodies revealed distinct expression landscapes for c-FOXP3 and its variants, with the majority of tumors exhibited a predominantly expression of c-FOXP3Δ3. In pancreatic ductal adenocarcinoma (PDAC), we further discovered a potential link between nuclear c-FOXP3Δ3 in tumor cells and poor prognosis. Overexpression of c-FOXP3Δ3 in tumor cells was associated with metastasis. This work elucidates the expression pattern of c-FOXP3 in pan-cancer and indicates its potential as a prognostic biomarker in clinical settings, offering new perspectives for its clinical application.
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Affiliation(s)
- Ruining Gong
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China; Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Jia Wang
- Qingdao Medical College, Qingdao University, Qingdao, 266000, China
| | - Yihai Xing
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China; Department of Gastroenterology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Jigang Wang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, 266555, China
| | - Xianghan Chen
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China; State Key Laboratory of Cancer Biology, Department of Pathology, Xijing Hospital and School of Basic Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Ke Lei
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Qian Yu
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Chenyang Zhao
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Sainan Li
- Key Laboratory of Biofuels and Shandong Provincial Key Laboratory of Synthetic Biology, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, China
| | - Yuxing Zhang
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China
| | - Hongxia Wang
- Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China.
| | - He Ren
- Shandong Provincial Key Laboratory of Clinical Research for Pancreatic Diseases, Center for GI Cancer Diagnosis and Treatment, Tumor Immunology and Cytotherapy, Medical Research Center, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
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49
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Li J, Li Y, Koide A, Kuang H, Torres VJ, Koide S, Wang DN, Traaseth NJ. Proton-coupled transport mechanism of the efflux pump NorA. Nat Commun 2024; 15:4494. [PMID: 38802368 PMCID: PMC11130294 DOI: 10.1038/s41467-024-48759-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 05/13/2024] [Indexed: 05/29/2024] Open
Abstract
Efflux pump antiporters confer drug resistance to bacteria by coupling proton import with the expulsion of antibiotics from the cytoplasm. Despite efforts there remains a lack of understanding as to how acid/base chemistry drives drug efflux. Here, we uncover the proton-coupling mechanism of the Staphylococcus aureus efflux pump NorA by elucidating structures in various protonation states of two essential acidic residues using cryo-EM. Protonation of Glu222 and Asp307 within the C-terminal domain stabilized the inward-occluded conformation by forming hydrogen bonds between the acidic residues and a single helix within the N-terminal domain responsible for occluding the substrate binding pocket. Remarkably, deprotonation of both Glu222 and Asp307 is needed to release interdomain tethering interactions, leading to opening of the pocket for antibiotic entry. Hence, the two acidic residues serve as a "belt and suspenders" protection mechanism to prevent simultaneous binding of protons and drug that enforce NorA coupling stoichiometry and confer antibiotic resistance.
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Affiliation(s)
- Jianping Li
- Department of Chemistry, New York University, New York, NY, USA
| | - Yan Li
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA
| | - Akiko Koide
- Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
- Department of Medicine, New York University School of Medicine, New York, NY, USA
| | - Huihui Kuang
- Simons Electron Microscopy Center, New York Structural Biology Center, New York, NY, USA
| | - Victor J Torres
- Department of Microbiology, New York University School of Medicine, New York, NY, USA
- Antimicrobial-Resistant Pathogens Program, New York University School of Medicine, New York, NY, USA
| | - Shohei Koide
- Perlmutter Cancer Center, New York University School of Medicine, New York, NY, USA
- Department of Biochemistry and Molecular Pharmacology, New York University School of Medicine, New York, NY, USA
| | - Da-Neng Wang
- Department of Cell Biology, New York University School of Medicine, New York, NY, USA.
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50
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Passi G, Lieberman S, Zahdeh F, Murik O, Renbaum P, Beeri R, Linial M, May D, Levy-Lahad E, Schneidman-Duhovny D. Discovering predisposing genes for hereditary breast cancer using deep learning. Brief Bioinform 2024; 25:bbae346. [PMID: 39038933 PMCID: PMC11262808 DOI: 10.1093/bib/bbae346] [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: 01/16/2024] [Revised: 04/18/2024] [Accepted: 07/04/2024] [Indexed: 07/24/2024] Open
Abstract
Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.
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Affiliation(s)
- Gal Passi
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Sari Lieberman
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Ein Kerem PO Box 12271 Jerusalem 9112102, Israel
| | - Fouad Zahdeh
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Omer Murik
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Paul Renbaum
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Rachel Beeri
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel
| | - Dalit May
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
- Clalit Health Services, Jerusalem, Israel
| | - Ephrat Levy-Lahad
- The Fuld Family Medical Genetics Institute, Shaare Zedek Medical Center 12 Bayit St., Jerusalem 9103101, Israel
- The Eisenberg R&D Authority, Shaare Zedek Medical Center, 12 Bayit St., Jerusalem 9103101, Israel
- Faculty of Medicine, The Hebrew University of Jerusalem, Ein Kerem PO Box 12271 Jerusalem 9112102, Israel
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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