1
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Roberts SE, Martin HL, Al-Qallaf D, Tang AA, Tiede C, Gaule TG, Dobon-Alonso A, Overman R, Shah S, Peyret H, Saunders K, Bon R, Manfield IW, Bell SM, Lomonossoff GP, Speirs V, Tomlinson DC. Affimer reagents enable targeted delivery of therapeutic agents and RNA via virus-like particles. iScience 2024; 27:110461. [PMID: 39104409 PMCID: PMC11298639 DOI: 10.1016/j.isci.2024.110461] [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: 11/17/2023] [Revised: 05/09/2024] [Accepted: 07/02/2024] [Indexed: 08/07/2024] Open
Abstract
Monoclonal antibodies have revolutionized therapies, but non-immunoglobulin scaffolds are becoming compelling alternatives owing to their adaptability. Their ability to be labeled with imaging or cytotoxic compounds and to create multimeric proteins is an attractive strategy for therapeutics. Focusing on HER2, a frequently overexpressed receptor in breast cancer, this study addresses some limitations of conventional targeting moieties by harnessing the potential of these scaffolds. HER2-binding Affimers were isolated and characterized, demonstrating potency as binding reagents and efficient internalization by HER2-overexpressing cells. Affimers conjugated with cytotoxic agent achieved dose-dependent reductions in cell viability within HER2-overexpressing cell lines. Bispecific Affimers, targeting HER2 and virus-like particles, facilitated efficient internalization of virus-like particles carrying enhanced green fluorescent protein (eGFP)-encoding RNA, leading to protein expression. Anti-HER2 affibody or designed ankyrin repeat protein (DARPin) fusion constructs with the anti-VLP Affimer further underscore the adaptability of this approach. This study demonstrates the versatility of scaffolds for precise delivery of cargos into cells, advancing biotechnology and therapeutic research.
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Affiliation(s)
- Sophie E. Roberts
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Heather L. Martin
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Danah Al-Qallaf
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
| | - Anna A. Tang
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
| | - Christian Tiede
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
| | - Thembaninkosi G. Gaule
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
- Institutue of Cardiovascular & Metabolic Medicine, University of Leeds, Leeds, UK
| | | | - Ross Overman
- Leaf Expression Systems, Norwich Research Park, Norwich, UK
| | - Sachin Shah
- Department of Biochemistry and Metabolism, John Innes Centre, Norwich Research Park, Norwich, UK
| | - Hadrien Peyret
- Department of Biochemistry and Metabolism, John Innes Centre, Norwich Research Park, Norwich, UK
| | - Keith Saunders
- Department of Biochemistry and Metabolism, John Innes Centre, Norwich Research Park, Norwich, UK
| | - Robin Bon
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK
| | - Iain W. Manfield
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
| | - Sandra M. Bell
- Leeds Institute of Medical Research at St James’s, St James’s University Hospital, University of Leeds, Leeds, UK
| | - George P. Lomonossoff
- Department of Biochemistry and Metabolism, John Innes Centre, Norwich Research Park, Norwich, UK
| | - Valerie Speirs
- Institute of Medical Sciences, University of Aberdeen, Aberdeen, UK
| | - Darren C. Tomlinson
- School of Molecular and Cellular Biology, University of Leeds, Leeds, UK
- Astbury Centre for Structural and Molecular Biology, University of Leeds, Leeds, UK
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2
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Wang L, Yang Z, Satoshi F, Prasanna X, Yan Z, Vihinen H, Chen Y, Zhao Y, He X, Bu Q, Li H, Zhao Y, Jiang L, Qin F, Dai Y, Zhang N, Qin M, Kuang W, Zhao Y, Jokitalo E, Vattulainen I, Kajander T, Zhao H, Cen X. Membrane remodeling by FAM92A1 during brain development regulates neuronal morphology, synaptic function, and cognition. Nat Commun 2024; 15:6209. [PMID: 39043703 PMCID: PMC11266426 DOI: 10.1038/s41467-024-50565-w] [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: 09/18/2023] [Accepted: 07/12/2024] [Indexed: 07/25/2024] Open
Abstract
The Bin/Amphiphysin/Rvs (BAR) domain protein FAM92A1 is a multifunctional protein engaged in regulating mitochondrial ultrastructure and ciliogenesis, but its physiological role in the brain remains unclear. Here, we show that FAM92A1 is expressed in neurons starting from embryonic development. FAM92A1 knockout in mice results in altered brain morphology and age-associated cognitive deficits, potentially due to neuronal degeneration and disrupted synaptic plasticity. Specifically, FAM92A1 deficiency impairs diverse neuronal membrane morphology, including the mitochondrial inner membrane, myelin sheath, and synapses, indicating its roles in membrane remodeling and maintenance. By determining the crystal structure of the FAM92A1 BAR domain, combined with atomistic molecular dynamics simulations, we uncover that FAM92A1 interacts with phosphoinositide- and cardiolipin-containing membranes to induce lipid-clustering and membrane curvature. Altogether, these findings reveal the physiological role of FAM92A1 in the brain, highlighting its impact on synaptic plasticity and neural function through the regulation of membrane remodeling and endocytic processes.
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Affiliation(s)
- Liang Wang
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
- Faculty of Biological and Environmental Sciences, University of Helsinki, 00014, Helsinki, Finland
| | - Ziyun Yang
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Fudo Satoshi
- Helsinki Institute of Life Science - Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Xavier Prasanna
- Department of Physics, University of Helsinki, Helsinki, Finland
| | - Ziyi Yan
- Faculty of Biological and Environmental Sciences, University of Helsinki, 00014, Helsinki, Finland
| | - Helena Vihinen
- Helsinki Institute of Life Science (HiLIFE) - Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Yaxing Chen
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Yue Zhao
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Xiumei He
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
- School of Life Sciences, Guangxi Normal University, Guilin, China
- Guangxi Universities Key Laboratory of Stem Cell and Biopharmaceutical Technology, Guangxi Normal University, Guilin, 541004, China
| | - Qian Bu
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Hongchun Li
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Ying Zhao
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Linhong Jiang
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Feng Qin
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Yanping Dai
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Ni Zhang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Meng Qin
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Weihong Kuang
- Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yinglan Zhao
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Eija Jokitalo
- Helsinki Institute of Life Science (HiLIFE) - Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Ilpo Vattulainen
- Department of Physics, University of Helsinki, Helsinki, Finland
| | - Tommi Kajander
- Helsinki Institute of Life Science - Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Hongxia Zhao
- Faculty of Biological and Environmental Sciences, University of Helsinki, 00014, Helsinki, Finland.
- School of Life Sciences, Guangxi Normal University, Guilin, China.
- Guangxi Universities Key Laboratory of Stem Cell and Biopharmaceutical Technology, Guangxi Normal University, Guilin, 541004, China.
| | - Xiaobo Cen
- Mental Health Center and National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, China.
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3
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Pala D, Clark DE. Caught between a ROCK and a hard place: current challenges in structure-based drug design. Drug Discov Today 2024; 29:104106. [PMID: 39029868 DOI: 10.1016/j.drudis.2024.104106] [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: 04/11/2024] [Revised: 06/27/2024] [Accepted: 07/13/2024] [Indexed: 07/21/2024]
Abstract
The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.
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Affiliation(s)
- Daniele Pala
- Medicinal Chemistry and Drug Design Technologies Department, Chiesi Farmaceutici S.p.A, Research Center, Largo Belloli 11/a, 43122 Parma, Italy
| | - David E Clark
- Charles River, 6-9 Spire Green Centre, Flex Meadow, Harlow CM19 5TR, UK.
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4
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Sun Y, Ko DH, Gao J, Fu K, Mao Y, He Y, Tian H. Engineering psychrophilic polymerase for nanopore long-read sequencing. Front Bioeng Biotechnol 2024; 12:1406722. [PMID: 39011153 PMCID: PMC11246872 DOI: 10.3389/fbioe.2024.1406722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 05/30/2024] [Indexed: 07/17/2024] Open
Abstract
Unveiling the potential application of psychrophilic polymerases as candidates for polymerase-nanopore long-read sequencing presents a departure from conventional choices such as thermophilic Bacillus stearothermophilus (Bst) renowned for its limitation in temperature and mesophilic Bacillus subtilis phage (phi29) polymerases for limitations in strong exonuclease activity and weak salt tolerance. Exploiting the PB-Bst fusion DNA polymerases from Psychrobacillus (PB) and Bacillus stearothermophilus (Bst), our structural and biochemical analysis reveal a remarkable enhancement in salt tolerance and a concurrent reduction in exonuclease activity, achieved through targeted substitution of a pivotal functional domain. The sulfolobus 7-kDa protein (Sso7d) emerges as a standout fusion domain, imparting significant improvements in PB-Bst processivity. Notably, this study elucidates additional functional sites regulating exonuclease activity (Asp43 and Glu45) and processivity using artificial nucleotides (Glu266, Gln283, Leu334, Glu335, Ser426, and Asp430). By disclosing the intricate dynamics in exonuclease activity, strand displacement, and artificial nucleotide-based processivity at specific functional sites, our findings not only advance the fundamental understanding of psychrophilic polymerases but also provide novel insights into polymerase engineering.
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Affiliation(s)
| | | | | | | | | | - Yun He
- Research Center of Molecular Diagnostics and Sequencing, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
| | - Hui Tian
- Research Center of Molecular Diagnostics and Sequencing, Research Institute of Tsinghua University in Shenzhen, Shenzhen, China
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5
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Ciaco S, Aronne R, Fiabane M, Mori M. The Rise of Bacterial G-Quadruplexes in Current Antimicrobial Discovery. ACS OMEGA 2024; 9:24163-24180. [PMID: 38882119 PMCID: PMC11170735 DOI: 10.1021/acsomega.4c01731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/10/2024] [Accepted: 05/17/2024] [Indexed: 06/18/2024]
Abstract
Antimicrobial resistance (AMR) is a silent critical issue that poses several challenges to health systems. While the discovery of novel antibiotics is currently stalled and prevalently focused on chemical variations of the scaffolds of available drugs, novel targets and innovative strategies are urgently needed to face this global threat. In this context, bacterial G-quadruplexes (G4s) are emerging as timely and profitable targets for the design and development of antimicrobial agents. Indeed, they are expressed in regulatory regions of bacterial genomes, and their modulation has been observed to provide antimicrobial effects with translational perspectives in the context of AMR. In this work, we review the current knowledge of bacterial G4s as well as their modulation by small molecules, including tools and techniques suitable for these investigations. Finally, we critically analyze the needs and future directions in the field, with a focus on the development of small molecules as bacterial G4s modulators endowed with remarkable drug-likeness.
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Affiliation(s)
- Stefano Ciaco
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy
| | - Rossella Aronne
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy
| | - Martina Fiabane
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy
| | - Mattia Mori
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy
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6
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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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7
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Yang C, Sun X, Wu G. New insights into GATOR2-dependent interactions and its conformational changes in amino acid sensing. Biosci Rep 2024; 44:BSR20240038. [PMID: 38372438 PMCID: PMC10938194 DOI: 10.1042/bsr20240038] [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: 02/01/2024] [Revised: 02/08/2024] [Accepted: 02/13/2024] [Indexed: 02/20/2024] Open
Abstract
Eukaryotic cells coordinate growth under different environmental conditions via mechanistic target of rapamycin complex 1 (mTORC1). In the amino-acid-sensing signalling pathway, the GATOR2 complex, containing five evolutionarily conserved subunits (WDR59, Mios, WDR24, Seh1L and Sec13), is required to regulate mTORC1 activity by interacting with upstream CASTOR1 (arginine sensor) and Sestrin2 (leucine sensor and downstream GATOR1 complex). GATOR2 complex utilizes β-propellers to engage with CASTOR1, Sestrin2 and GATOR1, removal of these β-propellers results in substantial loss of mTORC1 capacity. However, structural information regarding the interface between amino acid sensors and GATOR2 remains elusive. With the recent progress of the AI-based tool AlphaFold2 (AF2) for protein structure prediction, structural models were predicted for Sentrin2-WDR24-Seh1L and CASTOR1-Mios β-propeller. Furthermore, the effectiveness of relevant residues within the interface was examined using biochemical experiments combined with molecular dynamics (MD) simulations. Notably, fluorescence resonance energy transfer (FRET) analysis detected the structural transition of GATOR2 in response to amino acid signals, and the deletion of Mios β-propeller severely impeded that change at distinct arginine levels. These findings provide structural perspectives on the association between GATOR2 and amino acid sensors and can facilitate future research on structure determination and function.
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Affiliation(s)
- Can Yang
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, the Joint International Research Laboratory of Metabolic and Developmental Sciences MOE, Shanghai Jiao Tong University, Shanghai, China
| | - Xuan Sun
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, the Joint International Research Laboratory of Metabolic and Developmental Sciences MOE, Shanghai Jiao Tong University, Shanghai, China
| | - Geng Wu
- State Key Laboratory of Microbial Metabolism, School of Life Sciences and Biotechnology, the Joint International Research Laboratory of Metabolic and Developmental Sciences MOE, Shanghai Jiao Tong University, Shanghai, China
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8
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Hoque M, Li FQ, Weber WD, Chen JJ, Kim EN, Kuo PL, Visconti PE, Takemaru KI. The Cby3/ciBAR1 complex positions the annulus along the sperm flagellum during spermiogenesis. J Cell Biol 2024; 223:e202307147. [PMID: 38197861 PMCID: PMC10783431 DOI: 10.1083/jcb.202307147] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/24/2023] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
Proper compartmentalization of the sperm flagellum is essential for fertility. The annulus is a septin-based ring that demarcates the midpiece (MP) and the principal piece (PP). It is assembled at the flagellar base, migrates caudally, and halts upon arriving at the PP. However, the mechanisms governing annulus positioning remain unknown. We report that a Chibby3 (Cby3)/Cby1-interacting BAR domain-containing 1 (ciBAR1) complex is required for this process. Ablation of either gene in mice results in male fertility defects, caused by kinked sperm flagella with the annulus mispositioned in the PP. Cby3 and ciBAR1 interact and colocalize to the annulus near the curved membrane invagination at the flagellar pocket. In the absence of Cby3, periannular membranes appear to be deformed, allowing the annulus to migrate over the fibrous sheath into the PP. Collectively, our results suggest that the Cby3/ciBAR1 complex regulates local membrane properties to position the annulus at the MP/PP junction.
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Affiliation(s)
- Mohammed Hoque
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, NY, USA
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Feng-Qian Li
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - William David Weber
- Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA, USA
| | - Jun Jie Chen
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, NY, USA
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Eunice N. Kim
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, NY, USA
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
| | - Pao-Lin Kuo
- Department of Obstetrics and Gynecology, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Pablo E. Visconti
- Department of Veterinary and Animal Sciences, University of Massachusetts, Amherst, MA, USA
| | - Ken-Ichi Takemaru
- Molecular and Cellular Biology Graduate Program, Stony Brook University, Stony Brook, NY, USA
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, NY, USA
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9
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Guo J, He WB, Dai L, Tian F, Luo Z, Shen F, Tu M, Zheng Y, Zhao L, Tan C, Guo Y, Meng LL, Liu W, Deng M, Wu X, Peng Y, Zhang S, Lu GX, Lin G, Wang H, Tan YQ, Yang Y. Mosaic variegated aneuploidy syndrome with tetraploid, and predisposition to male infertility triggered by mutant CEP192. HGG ADVANCES 2024; 5:100256. [PMID: 37981762 PMCID: PMC10716027 DOI: 10.1016/j.xhgg.2023.100256] [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: 08/03/2023] [Revised: 11/15/2023] [Accepted: 11/15/2023] [Indexed: 11/21/2023] Open
Abstract
In this study, we report on mosaic variegated aneuploidy (MVA) syndrome with tetraploidy and predisposition to infertility in a family. Sequencing analysis identified that the CEP192 biallelic variants (c.1912C>T, p.His638Tyr and c.5750A>G, p.Asn1917Ser) segregated with microcephaly, short stature, limb-extremity dysplasia, and reduced testicular size, while CEP192 monoallelic variants segregated with infertility and/or reduced testicular size in the family. In 1,264 unrelated patients, variant screening for CEP192 identified a same variant (c.5750A>G, p.Asn1917Ser) and other variants significantly associated with infertility. Two lines of Cep192 mice model that are equivalent to human variants were generated. Embryos with Cep192 biallelic variants arrested at E7 because of cell apoptosis mediated by MVA/tetraploidy cell acumination. Mice with heterozygous variants replicated the predisposition to male infertility. Mouse primary embryonic fibroblasts with Cep192 biallelic variants cultured in vitro showed abnormal morphology, mitotic arresting, and disruption of spindle formation. In patient epithelial cells with biallelic variants cultured in vitro, the number of cells arrested during the prophase increased because of the failure of spindle formation. Accordingly, we present mutant CEP192, which is a link for the MVA syndrome with tetraploidy and the predisposition to male infertility.
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Affiliation(s)
- Jihong Guo
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Wen-Bin He
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Lei Dai
- Department of Obstetrics, Xiangya Hospital of Central South University, Changsha, China
| | - Fen Tian
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Zhenqing Luo
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Fang Shen
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Ming Tu
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Zheng
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Liu Zhao
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Chen Tan
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Yongteng Guo
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Lan-Lan Meng
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Wei Liu
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Mei Deng
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Xinghan Wu
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Yu Peng
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Shuju Zhang
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Guang-Xiu Lu
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Ge Lin
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China
| | - Hua Wang
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China
| | - Yue-Qiu Tan
- Hunan Guangxiu Hospital, Hunan Normal University School of Medicine, Changsha, China; Institute of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, School of Basic Medical Science, Central South University, Changsha, China.
| | - Yongjia Yang
- Department of Medical Genetics, Hunan Children's Hospital, Xiangya Medical School & Reproductive Medicine Center, Xiangya Hospital, Central South University, Changsha, China.
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10
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Roy BG, Choi J, Fuchs MF. Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality. Biomolecules 2024; 14:62. [PMID: 38254661 PMCID: PMC10813169 DOI: 10.3390/biom14010062] [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: 11/29/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/24/2024] Open
Abstract
Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.
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Affiliation(s)
- Brandon G. Roy
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, 15 Castle Creek Drive, Geneva, NY 14456, USA; (J.C.); (M.F.F.)
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11
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Terwilliger TC, Liebschner D, Croll TI, Williams CJ, McCoy AJ, Poon BK, Afonine PV, Oeffner RD, Richardson JS, Read RJ, Adams PD. AlphaFold predictions are valuable hypotheses and accelerate but do not replace experimental structure determination. Nat Methods 2024; 21:110-116. [PMID: 38036854 PMCID: PMC10776388 DOI: 10.1038/s41592-023-02087-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 10/11/2023] [Indexed: 12/02/2023]
Abstract
Artificial intelligence-based protein structure prediction methods such as AlphaFold have revolutionized structural biology. The accuracies of these predictions vary, however, and they do not take into account ligands, covalent modifications or other environmental factors. Here, we evaluate how well AlphaFold predictions can be expected to describe the structure of a protein by comparing predictions directly with experimental crystallographic maps. In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses. We further suggest that it is important to consider the confidence in prediction when interpreting AlphaFold predictions and to carry out experimental structure determination to verify structural details, particularly those that involve interactions not included in the prediction.
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Affiliation(s)
- Thomas C Terwilliger
- New Mexico Consortium, Los Alamos, NM, USA.
- Los Alamos National Laboratory, Los Alamos, NM, USA.
| | - Dorothee Liebschner
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Tristan I Croll
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Airlie J McCoy
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Billy K Poon
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Pavel V Afonine
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Robert D Oeffner
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | | | - Randy J Read
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, UK
| | - Paul D Adams
- Molecular Biophysics & Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Bioengineering, University of California, Berkeley, CA, USA
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12
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Bea-Mascato B, Valverde D. Genotype-phenotype associations in Alström syndrome: a systematic review and meta-analysis. J Med Genet 2023; 61:18-26. [PMID: 37321834 PMCID: PMC10803979 DOI: 10.1136/jmg-2023-109175] [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: 01/20/2023] [Accepted: 05/29/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND Alström syndrome (ALMS; #203800) is an ultrarare monogenic recessive disease. This syndrome is associated with variants in the ALMS1 gene, which encodes a centrosome-associated protein involved in the regulation of several ciliary and extraciliary processes, such as centrosome cohesion, apoptosis, cell cycle control and receptor trafficking. The type of variant associated with ALMS is mostly complete loss-of-function variants (97%) and they are mainly located in exons 8, 10 and 16 of the gene. Other studies in the literature have tried to establish a genotype-phenotype correlation in this syndrome with limited success. The difficulty in recruiting a large cohort in rare diseases is the main barrier to conducting this type of study. METHODS In this study we collected all cases of ALMS published to date. We created a database of patients who had a genetic diagnosis and an individualised clinical history. Lastly, we attempted to establish a genotype-phenotype correlation using the truncation site of the patient's longest allele as a grouping criteria. RESULTS We collected a total of 357 patients, of whom 227 had complete clinical information, complete genetic diagnosis and meta-information on sex and age. We have seen that there are five variants with high frequency, with p.(Arg2722Ter) being the most common variant, with 28 alleles. No gender differences in disease progression were detected. Finally, truncating variants in exon 10 seem to be correlated with a higher prevalence of liver disorders in patients with ALMS. CONCLUSION Pathogenic variants in exon 10 of the ALMS1 gene were associated with a higher prevalence of liver disease. However, the location of the variant in the ALMS1 gene does not have a major impact on the phenotype developed by the patient.
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Affiliation(s)
- Brais Bea-Mascato
- CINBIO, Universidad de Vigo, 36310 Vigo, Spain
- Grupo de Investigación en Enfermedades Raras y Medicina Pediátrica, Instituto de Investigación Sanitaria Galicia Sur (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
| | - Diana Valverde
- CINBIO, Universidad de Vigo, 36310 Vigo, Spain
- Grupo de Investigación en Enfermedades Raras y Medicina Pediátrica, Instituto de Investigación Sanitaria Galicia Sur (IIS Galicia Sur), SERGAS-UVIGO, Vigo, Spain
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13
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Hou Y, Xie T, He L, Tao L, Huang J. Topological links in predicted protein complex structures reveal limitations of AlphaFold. Commun Biol 2023; 6:1098. [PMID: 37898666 PMCID: PMC10613300 DOI: 10.1038/s42003-023-05489-4] [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: 02/21/2023] [Accepted: 10/19/2023] [Indexed: 10/30/2023] Open
Abstract
AlphaFold is making great progress in protein structure prediction, not only for single-chain proteins but also for multi-chain protein complexes. When using AlphaFold-Multimer to predict protein‒protein complexes, we observed some unusual structures in which chains are looped around each other to form topologically intertwining links at the interface. Based on physical principles, such topological links should generally not exist in native protein complex structures unless covalent modifications of residues are involved. Although it is well known and has been well studied that protein structures may have topologically complex shapes such as knots and links, existing methods are hampered by the chain closure problem and show poor performance in identifying topologically linked structures in protein‒protein complexes. Therefore, we address the chain closure problem by using sliding windows from a local perspective and propose an algorithm to measure the topological-geometric features that can be used to identify topologically linked structures. An application of the method to AlphaFold-Multimer-predicted protein complex structures finds that approximately 1.72% of the predicted structures contain topological links. The method presented in this work will facilitate the computational study of protein‒protein interactions and help further improve the structural prediction of multi-chain protein complexes.
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Affiliation(s)
- Yingnan Hou
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Tengyu Xie
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Liuqing He
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Liang Tao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
- Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China
| | - Jing Huang
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China.
- Westlake AI Therapeutics Lab, Westlake Laboratory of Life Sciences and Biomedicine, 18 Shilongshan Road, Hangzhou, 310024, Zhejiang, China.
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14
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Park JG, Jeon H, Shin S, Song C, Lee H, Kim NK, Kim EE, Hwang KY, Lee BJ, Lee IG. Structural basis for CEP192-mediated regulation of centrosomal AURKA. SCIENCE ADVANCES 2023; 9:eadf8582. [PMID: 37083534 PMCID: PMC10121170 DOI: 10.1126/sciadv.adf8582] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Aurora kinase A (AURKA) performs critical functions in mitosis. Thus, the activity and subcellular localization of AURKA are tightly regulated and depend on diverse factors including interactions with the multiple binding cofactors. How these different cofactors regulate AURKA to elicit different levels of activity at distinct subcellular locations and times is poorly understood. Here, we identified a conserved region of CEP192, the major cofactor of AURKA, that mediates the interaction with AURKA. Quantitative binding studies were performed to map the interactions of a conserved helix (Helix-1) within CEP192. The crystal structure of Helix-1 bound to AURKA revealed a distinct binding site that is different from other cofactor proteins such as TPX2. Inhibiting the interaction between Helix-1 and AURKA in cells led to the mitotic defects, demonstrating the importance of the interaction. Collectively, we revealed a structural basis for the CEP192-mediated AURKA regulation at the centrosome, which is distinct from TPX2-mediated regulation on the spindle microtubule.
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Affiliation(s)
- Jin-Gyeong Park
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, South Korea
| | - Hanul Jeon
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Sangchul Shin
- Technology Support Center, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Chiman Song
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Department of Biological Chemistry, University of Science and Technology, Daejeon 34113, South Korea
| | - Hyomin Lee
- Department of Biological Chemistry, University of Science and Technology, Daejeon 34113, South Korea
- Chemical Kinomics Research Center, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Nak-Kyoon Kim
- Advanced Analysis Center, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Eunice EunKyeong Kim
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, South Korea
| | - Kwang Yeon Hwang
- Department of Biotechnology, College of Life Sciences and Biotechnology, Korea University, Seoul 02841, South Korea
| | - Bong-Jin Lee
- The Research Institute of Pharmaceutical Sciences, College of Pharmacy, Seoul National University, Seoul 08826, South Korea
| | - In-Gyun Lee
- Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, South Korea
- Department of Biological Chemistry, University of Science and Technology, Daejeon 34113, South Korea
- Corresponding author.
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15
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Gutnik D, Evseev P, Miroshnikov K, Shneider M. Using AlphaFold Predictions in Viral Research. Curr Issues Mol Biol 2023; 45:3705-3732. [PMID: 37185764 PMCID: PMC10136805 DOI: 10.3390/cimb45040240] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023] Open
Abstract
Elucidation of the tertiary structure of proteins is an important task for biological and medical studies. AlphaFold, a modern deep-learning algorithm, enables the prediction of protein structure to a high level of accuracy. It has been applied in numerous studies in various areas of biology and medicine. Viruses are biological entities infecting eukaryotic and procaryotic organisms. They can pose a danger for humans and economically significant animals and plants, but they can also be useful for biological control, suppressing populations of pests and pathogens. AlphaFold can be used for studies of molecular mechanisms of viral infection to facilitate several activities, including drug design. Computational prediction and analysis of the structure of bacteriophage receptor-binding proteins can contribute to more efficient phage therapy. In addition, AlphaFold predictions can be used for the discovery of enzymes of bacteriophage origin that are able to degrade the cell wall of bacterial pathogens. The use of AlphaFold can assist fundamental viral research, including evolutionary studies. The ongoing development and improvement of AlphaFold can ensure that its contribution to the study of viral proteins will be significant in the future.
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Affiliation(s)
- Daria Gutnik
- Limnological Institute of the Siberian Branch of the Russian Academy of Sciences, 3 Ulan-Batorskaya Str., 664033 Irkutsk, Russia
| | - Peter Evseev
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| | - Konstantin Miroshnikov
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
| | - Mikhail Shneider
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 16/10 Miklukho-Maklaya Str., GSP-7, 117997 Moscow, Russia
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16
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Varadi M, Bordin N, Orengo C, Velankar S. The opportunities and challenges posed by the new generation of deep learning-based protein structure predictors. Curr Opin Struct Biol 2023; 79:102543. [PMID: 36807079 DOI: 10.1016/j.sbi.2023.102543] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 01/04/2023] [Accepted: 01/13/2023] [Indexed: 02/21/2023]
Abstract
The function of proteins can often be inferred from their three-dimensional structures. Experimental structural biologists spent decades studying these structures, but the accelerated pace of protein sequencing continuously increases the gaps between sequences and structures. The early 2020s saw the advent of a new generation of deep learning-based protein structure prediction tools that offer the potential to predict structures based on any number of protein sequences. In this review, we give an overview of the impact of this new generation of structure prediction tools, with examples of the impacted field in the life sciences. We discuss the novel opportunities and new scientific and technical challenges these tools present to the broader scientific community. Finally, we highlight some potential directions for the future of computational protein structure prediction.
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Affiliation(s)
- Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK. https://twitter.com/nicolabordin
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College, London, London, WC1E 6BT, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Welcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
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17
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Bordin N, Dallago C, Heinzinger M, Kim S, Littmann M, Rauer C, Steinegger M, Rost B, Orengo C. Novel machine learning approaches revolutionize protein knowledge. Trends Biochem Sci 2023; 48:345-359. [PMID: 36504138 PMCID: PMC10570143 DOI: 10.1016/j.tibs.2022.11.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 10/24/2022] [Accepted: 11/17/2022] [Indexed: 12/10/2022]
Abstract
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.
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Affiliation(s)
- Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK
| | - Christian Dallago
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; VantAI, 151 W 42nd Street, New York, NY 10036, USA
| | - Michael Heinzinger
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; TUM Graduate School, Center of Doctoral Studies in Informatics and its Applications (CeDoSIA), Boltzmannstr. 11, 85748 Garching, Germany
| | - Stephanie Kim
- School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Maria Littmann
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany
| | - Clemens Rauer
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK
| | - Martin Steinegger
- School of Biological Sciences, Seoul National University, Seoul, South Korea; Artificial Intelligence Institute, Seoul National University, Seoul, South Korea
| | - Burkhard Rost
- Technical University of Munich (TUM) Department of Informatics, Bioinformatics and Computational Biology - i12, Boltzmannstr. 3, 85748 Garching/Munich, Germany; Institute for Advanced Study (TUM-IAS), Lichtenbergstr. 2a, 85748 Garching/Munich, Germany; TUM School of Life Sciences Weihenstephan (TUM-WZW), Alte Akademie 8, Freising, Germany
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, Gower St, WC1E 6BT London, UK.
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18
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Yang Z, Zeng X, Zhao Y, Chen R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct Target Ther 2023; 8:115. [PMID: 36918529 PMCID: PMC10011802 DOI: 10.1038/s41392-023-01381-z] [Citation(s) in RCA: 78] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/27/2022] [Accepted: 02/16/2023] [Indexed: 03/16/2023] Open
Abstract
AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.
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Affiliation(s)
- Zhenyu Yang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Xiaoxi Zeng
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
| | - Yi Zhao
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, 100190, China.
| | - Runsheng Chen
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, Shenzhen, 518118, China.
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19
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Zhao H, Zhang H, She Z, Gao Z, Wang Q, Geng Z, Dong Y. Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein. Int J Mol Sci 2023; 24:ijms24032740. [PMID: 36769074 PMCID: PMC9916901 DOI: 10.3390/ijms24032740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 02/04/2023] Open
Abstract
Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.
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Affiliation(s)
- Haifan Zhao
- School of Life Sciences, University of Science and Technology of China, Hefei 230027, China
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Heng Zhang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Zhun She
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Zengqiang Gao
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Qi Wang
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhi Geng
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (Z.G.); (Y.D.)
| | - Yuhui Dong
- Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (Z.G.); (Y.D.)
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20
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Si D, Chen J, Nakamura A, Chang L, Guan H. Smart de novo Macromolecular Structure Modeling from Cryo-EM Maps. J Mol Biol 2023; 435:167967. [PMID: 36681181 DOI: 10.1016/j.jmb.2023.167967] [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: 10/31/2022] [Revised: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 01/20/2023]
Abstract
The study of macromolecular structures has expanded our understanding of the amazing cell machinery and such knowledge has changed how the pharmaceutical industry develops new vaccines in recent years. Traditionally, X-ray crystallography has been the main method for structure determination, however, cryogenic electron microscopy (cryo-EM) has increasingly become more popular due to recent advancements in hardware and software. The number of cryo-EM maps deposited in the EMDataResource (formerly EMDatabase) since 2002 has been dramatically increasing and it continues to do so. De novo macromolecular complex modeling is a labor-intensive process, therefore, it is highly desirable to develop software that can automate this process. Here we discuss our automated, data-driven, and artificial intelligence approaches including map processing, feature extraction, modeling building, and target identification. Recently, we have enabled DNA/RNA modeling in our deep learning-based prediction tool, DeepTracer. We have also developed DeepTracer-ID, a tool that can identify proteins solely based on the cryo-EM map. In this paper, we will present our accumulated experiences in developing deep learning-based methods surrounding macromolecule modeling applications.
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Affiliation(s)
- Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, United States.
| | - Jason Chen
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, United States
| | - Andrew Nakamura
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, United States
| | - Luca Chang
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, United States
| | - Haowen Guan
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, United States
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Structural Insights into the Dimeric Form of Bacillus subtilis RNase Y Using NMR and AlphaFold. Biomolecules 2022; 12:biom12121798. [PMID: 36551226 PMCID: PMC9775385 DOI: 10.3390/biom12121798] [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: 09/23/2022] [Revised: 11/23/2022] [Accepted: 11/28/2022] [Indexed: 12/04/2022] Open
Abstract
RNase Y is a crucial component of genetic translation, acting as the key enzyme initiating mRNA decay in many Gram-positive bacteria. The N-terminal domain of Bacillus subtilis RNase Y (Nter-BsRNaseY) is thought to interact with various protein partners within a degradosome complex. Bioinformatics and biophysical analysis have previously shown that Nter-BsRNaseY, which is in equilibrium between a monomeric and a dimeric form, displays an elongated fold with a high content of α-helices. Using multidimensional heteronuclear NMR and AlphaFold models, here, we show that the Nter-BsRNaseY dimer is constituted of a long N-terminal parallel coiled-coil structure, linked by a turn to a C-terminal region composed of helices that display either a straight or bent conformation. The structural organization of the N-terminal domain is maintained within the AlphaFold model of the full-length RNase Y, with the turn allowing flexibility between the N- and C-terminal domains. The catalytic domain is globular, with two helices linking the KH and HD modules, followed by the C-terminal region. This latter region, with no function assigned up to now, is most likely involved in the dimerization of B. subtilis RNase Y together with the N-terminal coiled-coil structure.
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Sun Y, Jiao Y, Shi C, Zhang Y. Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2. Comput Struct Biotechnol J 2022; 20:5014-5027. [PMID: 36091720 PMCID: PMC9448712 DOI: 10.1016/j.csbj.2022.09.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 08/03/2022] [Accepted: 09/03/2022] [Indexed: 11/26/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), has led to a global pandemic. Deep learning (DL) technology and molecular dynamics (MD) simulation are two mainstream computational approaches to investigate the geometric, chemical and structural features of protein and guide the relevant drug design. Despite a large amount of research papers focusing on drug design for SARS-COV-2 using DL architectures, it remains unclear how the binding energy of the protein-protein/ligand complex dynamically evolves which is also vital for drug development. In addition, traditional deep neural networks usually have obvious deficiencies in predicting the interaction sites as protein conformation changes. In this review, we introduce the latest progresses of the DL and DL-based MD simulation approaches in structure-based drug design (SBDD) for SARS-CoV-2 which could address the problems of protein structure and binding prediction, drug virtual screening, molecular docking and complex evolution. Furthermore, the current challenges and future directions of DL-based MD simulation for SBDD are also discussed.
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Affiliation(s)
- Yao Sun
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yanqi Jiao
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Chengcheng Shi
- State Key Lab of Urban Water Resource and Environment, School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- School of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
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