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Helmold BR, Ahrens A, Fitzgerald Z, Ozdinler PH. Spastin and alsin protein interactome analyses begin to reveal key canonical pathways and suggest novel druggable targets. Neural Regen Res 2025; 20:725-739. [PMID: 38886938 DOI: 10.4103/nrr.nrr-d-23-02068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 04/05/2024] [Indexed: 06/20/2024] Open
Abstract
Developing effective and long-term treatment strategies for rare and complex neurodegenerative diseases is challenging. One of the major roadblocks is the extensive heterogeneity among patients. This hinders understanding the underlying disease-causing mechanisms and building solutions that have implications for a broad spectrum of patients. One potential solution is to develop personalized medicine approaches based on strategies that target the most prevalent cellular events that are perturbed in patients. Especially in patients with a known genetic mutation, it may be possible to understand how these mutations contribute to problems that lead to neurodegeneration. Protein-protein interaction analyses offer great advantages for revealing how proteins interact, which cellular events are primarily involved in these interactions, and how they become affected when key genes are mutated in patients. This line of investigation also suggests novel druggable targets for patients with different mutations. Here, we focus on alsin and spastin, two proteins that are identified as "causative" for amyotrophic lateral sclerosis and hereditary spastic paraplegia, respectively, when mutated. Our review analyzes the protein interactome for alsin and spastin, the canonical pathways that are primarily important for each protein domain, as well as compounds that are either Food and Drug Administration-approved or are in active clinical trials concerning the affected cellular pathways. This line of research begins to pave the way for personalized medicine approaches that are desperately needed for rare neurodegenerative diseases that are complex and heterogeneous.
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Affiliation(s)
- Benjamin R Helmold
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Angela Ahrens
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Zachary Fitzgerald
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - P Hande Ozdinler
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Molecular Innovation and Drug Discovery, Center for Developmental Therapeutics, Chemistry of Life Processes Institute, Northwestern University, Evanston, IL, USA
- Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Feinberg School of Medicine, Les Turner ALS Center at Northwestern University, Chicago, IL, USA
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2
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Liang F, Sun M, Xie L, Zhao X, Liu D, Zhao K, Zhang G. Recent advances and challenges in protein complex model accuracy estimation. Comput Struct Biotechnol J 2024; 23:1824-1832. [PMID: 38707538 PMCID: PMC11066466 DOI: 10.1016/j.csbj.2024.04.049] [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: 01/27/2024] [Revised: 04/18/2024] [Accepted: 04/18/2024] [Indexed: 05/07/2024] Open
Abstract
Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.
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Affiliation(s)
| | | | - Lei Xie
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Xuanfeng Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Dong Liu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Kailong Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Guijun Zhang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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3
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Gillani M, Pollastri G. Protein subcellular localization prediction tools. Comput Struct Biotechnol J 2024; 23:1796-1807. [PMID: 38707539 PMCID: PMC11066471 DOI: 10.1016/j.csbj.2024.04.032] [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: 02/13/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 05/07/2024] Open
Abstract
Protein subcellular localization prediction is of great significance in bioinformatics and biological research. Most of the proteins do not have experimentally determined localization information, computational prediction methods and tools have been acting as an active research area for more than two decades now. Knowledge of the subcellular location of a protein provides valuable information about its functionalities, the functioning of the cell, and other possible interactions with proteins. Fast, reliable, and accurate predictors provides platforms to harness the abundance of sequence data to predict subcellular locations accordingly. During the last decade, there has been a considerable amount of research effort aimed at developing subcellular localization predictors. This paper reviews recent subcellular localization prediction tools in the Eukaryotic, Prokaryotic, and Virus-based categories followed by a detailed analysis. Each predictor is discussed based on its main features, strengths, weaknesses, algorithms used, prediction techniques, and analysis. This review is supported by prediction tools taxonomies that highlight their rele- vant area and examples for uncomplicated categorization and ease of understandability. These taxonomies help users find suitable tools according to their needs. Furthermore, recent research gaps and challenges are discussed to cover areas that need the utmost attention. This survey provides an in-depth analysis of the most recent prediction tools to facilitate readers and can be considered a quick guide for researchers to identify and explore the recent literature advancements.
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Affiliation(s)
- Maryam Gillani
- School of Computer Science, University College Dublin (UCD), Dublin, D04 V1W8, Ireland
| | - Gianluca Pollastri
- School of Computer Science, University College Dublin (UCD), Dublin, D04 V1W8, Ireland
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4
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Zhong B, Sun S, Luo Z, Yang J, Jia L, Zheng K, Tang W, Jiang X, Lyu Z, Chen J, Chen G. Antigen specific VNAR screening in whitespotted bamboo shark (Chiloscyllium plagiosum) with next generation sequencing. FISH & SHELLFISH IMMUNOLOGY 2024; 150:109661. [PMID: 38821227 DOI: 10.1016/j.fsi.2024.109661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 06/02/2024]
Abstract
IgNAR exhibits significant promise in the fields of cancer and anti-virus biotherapies. Notably, the variable regions of IgNAR (VNAR) possess comparable antigen binding affinity with much smaller molecular weight (∼12 kDa) compared to IgNAR. Antigen specific VNAR screening is a changeling work, which limits its application in medicine and therapy fields. Though phage display is a powerful tool for VNAR screening, it has a lot of drawbacks, such as small library coverage, low expression levels, unstable target protein, complicating and time-consuming procedures. Here we report VANR screening with next generation sequencing (NGS) could effectively overcome the limitations of phage display, and we successfully identified approximately 3000 BAFF-specific VNARs in Chiloscyllium plagiosum vaccinated with the BAFF antigen. The results of modelling and molecular dynamics simulation and ELISA assay demonstrated that one out of the top five abundant specific VNARs exhibited higher binding affinity to the BAFF antigen than those obtained through phage display screening. Our data indicates NGS would be an alternative way for VNAR screening with plenty of advantages.
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Affiliation(s)
- Bo Zhong
- School of Life Sciences, Central South University, 410031, Changsha, China; College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Shengjie Sun
- School of Life Sciences, Central South University, 410031, Changsha, China.
| | - Zhan Luo
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Junjie Yang
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Lei Jia
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Kaixi Zheng
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Wenjie Tang
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Xiaofeng Jiang
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China.
| | - Zhengbing Lyu
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China; Zhejiang Sci-Tech University Shaoxing Academy of Biomedicine Co.,Ltd, 312369, Shaoxing, China.
| | - Jianqing Chen
- College of Life Sciences and Medicine, Zhejiang Provincial Key Laboratory of Silkworm Bioreactor and Biomedicine, Zhejiang Sci-Tech University, 310018, Hangzhou, China; Zhejiang Sci-Tech University Shaoxing Academy of Biomedicine Co.,Ltd, 312369, Shaoxing, China; Zhejiang Q-peptide Biotechnology Co., Ltd, 312366, Shaoxing, China.
| | - Guodong Chen
- School of Life Sciences, Central South University, 410031, Changsha, China.
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5
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Agarwal V, McShan AC. The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins. Nat Chem Biol 2024:10.1038/s41589-024-01638-w. [PMID: 38907110 DOI: 10.1038/s41589-024-01638-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [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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6
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Mukhopadhyay S, Garvetto A, Neuhauser S, Pérez-López E. Decoding the Arsenal: Protist Effectors and Their Impact on Photosynthetic Hosts. MOLECULAR PLANT-MICROBE INTERACTIONS : MPMI 2024:MPMI11230196CR. [PMID: 38551366 DOI: 10.1094/mpmi-11-23-0196-cr] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Interactions between various microbial pathogens including viruses, bacteria, fungi, oomycetes, and their plant hosts have traditionally been the focus of phytopathology. In recent years, a significant and growing interest in the study of eukaryotic microorganisms not classified among fungi or oomycetes has emerged. Many of these protists establish complex interactions with photosynthetic hosts, and understanding these interactions is crucial in understanding the dynamics of these parasites within traditional and emerging types of farming, including marine aquaculture. Many phytopathogenic protists are biotrophs with complex polyphasic life cycles, which makes them difficult or impossible to culture, a fact reflected in a wide gap in the availability of comprehensive genomic data when compared to fungal and oomycete plant pathogens. Furthermore, our ability to use available genomic resources for these protists is limited by the broad taxonomic distance that these organisms span, which makes comparisons with other genomic datasets difficult. The current rapid progress in genomics and computational tools for the prediction of protein functions and interactions is revolutionizing the landscape in plant pathology. This is also opening novel possibilities, specifically for a deeper understanding of protist effectors. Tools like AlphaFold2 enable structure-based function prediction of effector candidates with divergent protein sequences. In turn, this allows us to ask better biological questions and, coupled with innovative experimental strategies, will lead into a new era of effector research, especially for protists, to expand our knowledge on these elusive pathogens and their interactions with photosynthetic hosts. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
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Affiliation(s)
- Soham Mukhopadhyay
- Départment de phytologie, Faculté des sciences de l'agriculture et de l'alimentation, Université Laval, Quebec City, Quebec, Canada
- Centre de recherche et d'innovation sur les végétaux (CRIV), Université Laval, Quebec City, Quebec, Canada
- Institute de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
- L'Institute EDS, Université Laval, Quebec City, Quebec, Canada
| | - Andrea Garvetto
- Institute of Microbiology, Universität Innsbruck, Innsbruck, Austria
| | - Sigrid Neuhauser
- Institute of Microbiology, Universität Innsbruck, Innsbruck, Austria
| | - Edel Pérez-López
- Départment de phytologie, Faculté des sciences de l'agriculture et de l'alimentation, Université Laval, Quebec City, Quebec, Canada
- Centre de recherche et d'innovation sur les végétaux (CRIV), Université Laval, Quebec City, Quebec, Canada
- Institute de Biologie Intégrative et des Systèmes (IBIS), Université Laval, Quebec City, Quebec, Canada
- L'Institute EDS, Université Laval, Quebec City, Quebec, Canada
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7
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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 DOI: 10.1126/science.adn6354] [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: 12/19/2023] [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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8
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Waman VP, Ashford P, Lam SD, Sen N, Abbasian M, Woodridge L, Goldtzvik Y, Bordin N, Wu J, Sillitoe I, Orengo CA. Predicting human and viral protein variants affecting COVID-19 susceptibility and repurposing therapeutics. Sci Rep 2024; 14:14208. [PMID: 38902252 PMCID: PMC11190248 DOI: 10.1038/s41598-024-61541-1] [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: 11/07/2023] [Accepted: 05/07/2024] [Indexed: 06/22/2024] Open
Abstract
The COVID-19 disease is an ongoing global health concern. Although vaccination provides some protection, people are still susceptible to re-infection. Ostensibly, certain populations or clinical groups may be more vulnerable. Factors causing these differences are unclear and whilst socioeconomic and cultural differences are likely to be important, human genetic factors could influence susceptibility. Experimental studies indicate SARS-CoV-2 uses innate immune suppression as a strategy to speed-up entry and replication into the host cell. Therefore, it is necessary to understand the impact of variants in immunity-associated human proteins on susceptibility to COVID-19. In this work, we analysed missense coding variants in several SARS-CoV-2 proteins and their human protein interactors that could enhance binding affinity to SARS-CoV-2. We curated a dataset of 19 SARS-CoV-2: human protein 3D-complexes, from the experimentally determined structures in the Protein Data Bank and models built using AlphaFold2-multimer, and analysed the impact of missense variants occurring in the protein-protein interface region. We analysed 468 missense variants from human proteins and 212 variants from SARS-CoV-2 proteins and computationally predicted their impacts on binding affinities for the human viral protein complexes. We predicted a total of 26 affinity-enhancing variants from 13 human proteins implicated in increased binding affinity to SARS-CoV-2. These include key-immunity associated genes (TOMM70, ISG15, IFIH1, IFIT2, RPS3, PALS1, NUP98, AXL, ARF6, TRIMM, TRIM25) as well as important spike receptors (KREMEN1, AXL and ACE2). We report both common (e.g., Y13N in IFIH1) and rare variants in these proteins and discuss their likely structural and functional impact, using information on known and predicted functional sites. Potential mechanisms associated with immune suppression implicated by these variants are discussed. Occurrence of certain predicted affinity-enhancing variants should be monitored as they could lead to increased susceptibility and reduced immune response to SARS-CoV-2 infection in individuals/populations carrying them. Our analyses aid in understanding the potential impact of genetic variation in immunity-associated proteins on COVID-19 susceptibility and help guide drug-repurposing strategies.
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Affiliation(s)
- Vaishali P Waman
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Paul Ashford
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Su Datt Lam
- Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia
| | - Neeladri Sen
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Mahnaz Abbasian
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Laurel Woodridge
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Yonathan Goldtzvik
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Jiaxin Wu
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK
| | - Christine A Orengo
- Institute of Structural and Molecular Biology, University College London, London, WC1E 6BT, UK.
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9
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Nicolas Y, Bret H, Cannavo E, Acharya A, Cejka P, Borde V, Guerois R. Molecular insights into the activation of Mre11-Rad50 endonuclease activity by Sae2/CtIP. Mol Cell 2024; 84:2223-2237.e4. [PMID: 38870937 DOI: 10.1016/j.molcel.2024.05.019] [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: 07/20/2023] [Revised: 02/25/2024] [Accepted: 05/20/2024] [Indexed: 06/15/2024]
Abstract
In Saccharomyces cerevisiae (S. cerevisiae), Mre11-Rad50-Xrs2 (MRX)-Sae2 nuclease activity is required for the resection of DNA breaks with secondary structures or protein blocks, while in humans, the MRE11-RAD50-NBS1 (MRN) homolog with CtIP is needed to initiate DNA end resection of all breaks. Phosphorylated Sae2/CtIP stimulates the endonuclease activity of MRX/N. Structural insights into the activation of the Mre11 nuclease are available only for organisms lacking Sae2/CtIP, so little is known about how Sae2/CtIP activates the nuclease ensemble. Here, we uncover the mechanism of Mre11 activation by Sae2 using a combination of AlphaFold2 structural modeling of biochemical and genetic assays. We show that Sae2 stabilizes the Mre11 nuclease in a conformation poised to cleave substrate DNA. Several designs of compensatory mutations establish how Sae2 activates MRX in vitro and in vivo, supporting the structural model. Finally, our study uncovers how human CtIP, despite considerable sequence divergence, employs a similar mechanism to activate MRN.
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Affiliation(s)
- Yoann Nicolas
- Institut Curie, PSL University, Sorbonne Université, CNRS UMR3244, Dynamics of Genetic Information, 75005 Paris, France
| | - Hélène Bret
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Elda Cannavo
- Institute for Research in Biomedicine, Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Bellinzona 6500, Switzerland
| | - Ananya Acharya
- Institute for Research in Biomedicine, Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Bellinzona 6500, Switzerland
| | - Petr Cejka
- Institute for Research in Biomedicine, Università della Svizzera italiana (USI), Faculty of Biomedical Sciences, Bellinzona 6500, Switzerland.
| | - Valérie Borde
- Institut Curie, PSL University, Sorbonne Université, CNRS UMR3244, Dynamics of Genetic Information, 75005 Paris, France.
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France.
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10
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Biswas G, Mukherjee D, Basu S. Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual. J Comput Biol 2024. [PMID: 38885081 DOI: 10.1089/cmb.2024.0554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024] Open
Abstract
The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.
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Affiliation(s)
- Gargi Biswas
- Department of Chemical and Structural Biology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Sankar Basu
- Department of Microbiology, Asutosh College, University of Calcutta, Kolkata, India
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11
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Kalantar M, Kalanther I, Kumar S, Buxton EK, Raeeszadeh-Sarmazdeh M. Elucidating key determinants of engineered scFv antibody in MMP-9 binding using high throughput screening and machine learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.04.597476. [PMID: 38895413 PMCID: PMC11185642 DOI: 10.1101/2024.06.04.597476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
An imbalance in matrix metalloproteinase-9 (MMP-9) regulation can lead to numerous diseases, including neurological disorders, cancer, and pre-term labor. Engineering single-chain antibody fragments (scFvs) Targeting MMP-9 to develop novel therapeutics for such diseases is desirable. We screened a synthetic scFv antibody library displayed on the yeast surface for binding improvement to MMP-9 using FACS (fluorescent-activated cell sorting). The scFv antibody clones isolated after FACS showed improvement in binding to MMP-9 compared to the endogenous inhibitor. To understand molecular determinants of binding between engineered scFv antibody variants and MMP-9, next-generation DNA sequencing, and computational protein structure analysis were used. Additionally, a deep-learning language model was trained on the synthetic library to predict the binding of scFv variants using their CDR-H3 sequences.
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12
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Ma X, Zou D, Ji A, Jiang C, Zhao Z, Ding X, Han Z, Bao P, Chen K, Ma A, Wei X. Identification of a Novel Chitinase from Bacillus paralicheniformis: Gene Mining, Sequence Analysis, and Enzymatic Characterization. Foods 2024; 13:1777. [PMID: 38891005 PMCID: PMC11171888 DOI: 10.3390/foods13111777] [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: 05/19/2024] [Revised: 05/27/2024] [Accepted: 06/04/2024] [Indexed: 06/20/2024] Open
Abstract
In this study, a novel strain for degrading chitin was identified as Bacillus paralicheniformis HL37, and the key chitinase CH1 was firstly mined through recombinant expression in Bacillus amyloliquefaciens HZ12. Subsequently, the sequence composition and catalytic mechanism of CH1 protein were analyzed. The molecular docking indicated that the triplet of Asp526, Asp528, and Glu530 was a catalytic active center. The enzymatic properties analysis revealed that the optimal reaction temperature and pH was 65 °C and 6.0, respectively. Especially, the chitinase activity showed no significant change below 55 °C and it could maintain over 60% activity after exposure to 85 °C for 30 min. Moreover, the optimal host strain and signal peptide were obtained to enhance the expression of chitinase CH1 significantly. As far as we know, it was the first time finding the highly efficient chitin-degrading enzymes in B. paralicheniformis, and detailed explanations were provided on the catalytic mechanism and enzymatic properties on CH1.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Xuetuan Wei
- State Key Laboratory of Agricultural Microbiology, College of Food Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; (X.M.); (D.Z.); (A.J.); (C.J.); (Z.Z.); (X.D.); (Z.H.); (P.B.); (K.C.); (A.M.)
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13
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Chen J, Li Q, Xia S, Arsala D, Sosa D, Wang D, Long M. The Rapid Evolution of De Novo Proteins in Structure and Complex. Genome Biol Evol 2024; 16:evae107. [PMID: 38753069 PMCID: PMC11149777 DOI: 10.1093/gbe/evae107] [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] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Recent studies in the rice genome-wide have established that de novo genes, evolving from noncoding sequences, enhance protein diversity through a stepwise process. However, the pattern and rate of their evolution in protein structure over time remain unclear. Here, we addressed these issues within a surprisingly short evolutionary timescale (<1 million years for 97% of Oryza de novo genes) with comparative approaches to gene duplicates. We found that de novo genes evolve faster than gene duplicates in the intrinsically disordered regions (such as random coils), secondary structure elements (such as α helix and β strand), hydrophobicity, and molecular recognition features. In de novo proteins, specifically, we observed an 8% to 14% decay in random coils and intrinsically disordered region lengths and a 2.3% to 6.5% increase in structured elements, hydrophobicity, and molecular recognition features, per million years on average. These patterns of structural evolution align with changes in amino acid composition over time as well. We also revealed higher positive charges but smaller molecular weights for de novo proteins than duplicates. Tertiary structure predictions showed that most de novo proteins, though not typically well folded on their own, readily form low-energy and compact complexes with other proteins facilitated by extensive residue contacts and conformational flexibility, suggesting a faster-binding scenario in de novo proteins to promote interaction. These analyses illuminate a rapid evolution of protein structure in de novo genes in rice genomes, originating from noncoding sequences, highlighting their quick transformation into active, protein complex-forming components within a remarkably short evolutionary timeframe.
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Affiliation(s)
- Jianhai Chen
- Department of Ecology and Evolution, The University of Chicago, Chicago, IL 60637, USA
| | - Qingrong Li
- Division of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular & Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Shengqian Xia
- Department of Ecology and Evolution, The University of Chicago, Chicago, IL 60637, USA
| | - Deanna Arsala
- Department of Ecology and Evolution, The University of Chicago, Chicago, IL 60637, USA
| | - Dylan Sosa
- Department of Ecology and Evolution, The University of Chicago, Chicago, IL 60637, USA
| | - Dong Wang
- Division of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA 92093, USA
- Department of Cellular & Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Manyuan Long
- Department of Ecology and Evolution, The University of Chicago, Chicago, IL 60637, USA
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14
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Mubeen H, Masood A, Zafar A, Khan ZQ, Khan MQ, Nisa AU. Insights into AlphaFold's breakthrough in neurodegenerative diseases. Ir J Med Sci 2024:10.1007/s11845-024-03721-6. [PMID: 38833116 DOI: 10.1007/s11845-024-03721-6] [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: 04/16/2024] [Accepted: 05/19/2024] [Indexed: 06/06/2024]
Abstract
Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.
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Affiliation(s)
- Hira Mubeen
- Department of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore, Pakistan.
| | - Ammara Masood
- Department of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore, Pakistan
| | - Asma Zafar
- Department of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore, Pakistan
| | - Zohaira Qayyum Khan
- Department of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore, Pakistan
| | - Muneeza Qayyum Khan
- Department of Biotechnology, Faculty of Science & Technology, University of Central Punjab, Lahore, Pakistan
| | - Alim Un Nisa
- Pakistan Council of Scientific and Industrial Research, Lahore, Pakistan
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15
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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 (OXFORD, ENGLAND) 2024; 40:btae329. [PMID: 38781500 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] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/18/2024] [Accepted: 05/22/2024] [Indexed: 05/25/2024]
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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16
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Dahlström KM, Salminen TA. Apprehensions and emerging solutions in ML-based protein structure prediction. Curr Opin Struct Biol 2024; 86:102819. [PMID: 38631107 DOI: 10.1016/j.sbi.2024.102819] [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: 01/19/2024] [Revised: 03/05/2024] [Accepted: 03/31/2024] [Indexed: 04/19/2024]
Abstract
The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnological, diagnostics, and therapeutic applications. Over the past five years, machine learning (ML)-based modeling has pushed protein structure prediction to the next level with AlphaFold in the front line, predicting the structure for hundreds of millions of proteins. Further advances recently report promising ML-based approaches for solving remaining challenges by incorporating functionally important metals, co-factors, post-translational modifications, structural dynamics, and interdomain and multimer interactions in the structure prediction process.
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Affiliation(s)
- Käthe M Dahlström
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland; InFLAMES Research Flagship Center, Åbo Akademi University, 20520 Turku, Finland
| | - Tiina A Salminen
- Structural Bioinformatics Laboratory, Biochemistry, Faculty of Science and Engineering, Åbo Akademi University, Tykistökatu 6A, 20520 Turku, Finland; InFLAMES Research Flagship Center, Åbo Akademi University, 20520 Turku, Finland.
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17
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Geller AM, Shalom M, Zlotkin D, Blum N, Levy A. Identification of type VI secretion system effector-immunity pairs using structural bioinformatics. Mol Syst Biol 2024; 20:702-718. [PMID: 38658795 PMCID: PMC11148199 DOI: 10.1038/s44320-024-00035-8] [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: 01/02/2024] [Revised: 03/24/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
The type VI secretion system (T6SS) is an important mediator of microbe-microbe and microbe-host interactions. Gram-negative bacteria use the T6SS to inject T6SS effectors (T6Es), which are usually proteins with toxic activity, into neighboring cells. Antibacterial effectors have cognate immunity proteins that neutralize self-intoxication. Here, we applied novel structural bioinformatic tools to perform systematic discovery and functional annotation of T6Es and their cognate immunity proteins from a dataset of 17,920 T6SS-encoding bacterial genomes. Using structural clustering, we identified 517 putative T6E families, outperforming sequence-based clustering. We developed a logistic regression model to reliably quantify protein-protein interaction of new T6E-immunity pairs, yielding candidate immunity proteins for 231 out of the 517 T6E families. We used sensitive structure-based annotation which yielded functional annotations for 51% of the T6E families, again outperforming sequence-based annotation. Next, we validated four novel T6E-immunity pairs using basic experiments in E. coli. In particular, we showed that the Pfam domain DUF3289 is a homolog of Colicin M and that DUF943 acts as its cognate immunity protein. Furthermore, we discovered a novel T6E that is a structural homolog of SleB, a lytic transglycosylase, and identified a specific glutamate that acts as its putative catalytic residue. Overall, this study applies novel structural bioinformatic tools to T6E-immunity pair discovery, and provides an extensive database of annotated T6E-immunity pairs.
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Affiliation(s)
- Alexander M Geller
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Maor Shalom
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - David Zlotkin
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Noam Blum
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Asaf Levy
- Department of Plant Pathology and Microbiology, The Institute of Environmental Science, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel.
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18
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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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19
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Nandigrami P, Fiser A. Assessing the functional impact of protein binding site definition. Protein Sci 2024; 33:e5026. [PMID: 38757384 PMCID: PMC11099757 DOI: 10.1002/pro.5026] [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: 10/06/2023] [Revised: 05/01/2024] [Accepted: 05/03/2024] [Indexed: 05/18/2024]
Abstract
Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.
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Affiliation(s)
- Prithviraj Nandigrami
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Andras Fiser
- Departments of Systems and Computational Biology, and BiochemistryAlbert Einstein College of MedicineBronxNew YorkUSA
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20
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Mizgalska D, Rodríguez-Banqueri A, Veillard F, Książęk M, Goulas T, Guevara T, Eckhard U, Potempa J, Gomis-Rüth FX. Structural and functional insights into the C-terminal signal domain of the Bacteroidetes type-IX secretion system. Open Biol 2024; 14:230448. [PMID: 38862016 DOI: 10.1098/rsob.230448] [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: 12/12/2023] [Accepted: 02/20/2024] [Indexed: 06/13/2024] Open
Abstract
Gram-negative bacteria from the Bacteroidota phylum possess a type-IX secretion system (T9SS) for protein secretion, which requires cargoes to have a C-terminal domain (CTD). Structurally analysed CTDs are from Porphyromonas gingivalis proteins RgpB, HBP35, PorU and PorZ, which share a compact immunoglobulin-like antiparallel 3+4 β-sandwich (β1-β7). This architecture is essential as a P. gingivalis strain with a single-point mutant of RgpB disrupting the interaction of the CTD with its preceding domain prevented secretion of the protein. Next, we identified the C-terminus ('motif C-t.') and the loop connecting strands β3 and β4 ('motif Lβ3β4') as conserved. We generated two strains with insertion and replacement mutants of PorU, as well as three strains with ablation and point mutants of RgpB, which revealed both motifs to be relevant for T9SS function. Furthermore, we determined the crystal structure of the CTD of mirolase, a cargo of the Tannerella forsythia T9SS, which shares the same general topology as in Porphyromonas CTDs. However, motif Lβ3β4 was not conserved. Consistently, P. gingivalis could not properly secrete a chimaeric protein with the CTD of peptidylarginine deiminase replaced with this foreign CTD. Thus, the incompatibility of the CTDs between these species prevents potential interference between their T9SSs.
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Affiliation(s)
- Danuta Mizgalska
- Department of Microbiology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland
| | - Arturo Rodríguez-Banqueri
- Proteolysis Laboratory, Department of Structural Biology, Molecular Biology Institute of Barcelona (CSIC), Barcelona, Catalonia 08028, Spain
| | - Florian Veillard
- Department of Microbiology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland
| | - Mirosław Książęk
- Department of Microbiology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland
| | - Theodoros Goulas
- Department of Food Science and Nutrition, School of Agricultural Sciences, University of Thessaly, Karditsa 43100, Greece
| | - Tibisay Guevara
- Proteolysis Laboratory, Department of Structural Biology, Molecular Biology Institute of Barcelona (CSIC), Barcelona, Catalonia 08028, Spain
| | - Ulrich Eckhard
- Synthetic Structural Biology Group, Department of Structural Biology, Molecular Biology Institute of Barcelona (CSIC), Barcelona, Catalonia 08028, Spain
| | - Jan Potempa
- Department of Microbiology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland
- Department of Oral Immunology and Infectious Diseases, University of Louisville School of Dentistry, Louisville, KY 40202, USA
| | - F Xavier Gomis-Rüth
- Proteolysis Laboratory, Department of Structural Biology, Molecular Biology Institute of Barcelona (CSIC), Barcelona, Catalonia 08028, Spain
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21
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Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, Bodenstein SW, Evans DA, Hung CC, O'Neill M, Reiman D, Tunyasuvunakool K, Wu Z, Žemgulytė A, Arvaniti E, Beattie C, Bertolli O, Bridgland A, Cherepanov A, Congreve M, Cowen-Rivers AI, Cowie A, Figurnov M, Fuchs FB, Gladman H, Jain R, Khan YA, Low CMR, Perlin K, Potapenko A, Savy P, Singh S, Stecula A, Thillaisundaram A, Tong C, Yakneen S, Zhong ED, Zielinski M, Žídek A, Bapst V, Kohli P, Jaderberg M, Hassabis D, Jumper JM. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 2024; 630:493-500. [PMID: 38718835 PMCID: PMC11168924 DOI: 10.1038/s41586-024-07487-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 04/29/2024] [Indexed: 06/13/2024]
Abstract
The introduction of AlphaFold 21 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2-6. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein-ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein-nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody-antigen prediction accuracy compared with AlphaFold-Multimer v.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
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Affiliation(s)
| | - Jonas Adler
- Core Contributor, Google DeepMind, London, UK
| | - Jack Dunger
- Core Contributor, Google DeepMind, London, UK
| | | | - Tim Green
- Core Contributor, Google DeepMind, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | - Zachary Wu
- Core Contributor, Google DeepMind, London, UK
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Yousuf A Khan
- Google DeepMind, London, UK
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
| | | | | | | | | | | | | | | | | | | | - Ellen D Zhong
- Google DeepMind, London, UK
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | | | | | | | | | - Demis Hassabis
- Core Contributor, Google DeepMind, London, UK.
- Core Contributor, Isomorphic Labs, London, UK.
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22
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Schmidt B, Hildebrandt A. From GPUs to AI and quantum: three waves of acceleration in bioinformatics. Drug Discov Today 2024; 29:103990. [PMID: 38663581 DOI: 10.1016/j.drudis.2024.103990] [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: 12/13/2023] [Revised: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/01/2024]
Abstract
The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.
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Affiliation(s)
- Bertil Schmidt
- Institut für Informatik, Johannes Gutenberg University, Mainz, Germany.
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23
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Kohler AR, Scheil A, Hill JL, Allen JR, Al-Haddad JM, Goeckeritz CZ, Strader LC, Telewski FW, Hollender CA. Defying gravity: WEEP promotes negative gravitropism in peach trees by establishing asymmetric auxin gradients. PLANT PHYSIOLOGY 2024; 195:1229-1255. [PMID: 38366651 PMCID: PMC11142379 DOI: 10.1093/plphys/kiae085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 01/09/2024] [Accepted: 01/14/2024] [Indexed: 02/18/2024]
Abstract
Trees with weeping shoot architectures are valued for their beauty and are a resource for understanding how plants regulate posture control. The peach (Prunus persica) weeping phenotype, which has elliptical downward arching branches, is caused by a homozygous mutation in the WEEP gene. Little is known about the function of WEEP despite its high conservation throughout Plantae. Here, we present the results of anatomical, biochemical, biomechanical, physiological, and molecular experiments that provide insight into WEEP function. Our data suggest that weeping peach trees do not have defects in branch structure. Rather, transcriptomes from the adaxial (upper) and abaxial (lower) sides of standard and weeping branch shoot tips revealed flipped expression patterns for genes associated with early auxin response, tissue patterning, cell elongation, and tension wood development. This suggests that WEEP promotes polar auxin transport toward the lower side during shoot gravitropic response, leading to cell elongation and tension wood development. In addition, weeping peach trees exhibited steeper root systems and faster lateral root gravitropic response. This suggests that WEEP moderates root gravitropism and is essential to establishing the set-point angle of lateral roots from the gravity vector. Additionally, size exclusion chromatography indicated that WEEP proteins self-oligomerize, like other proteins with sterile alpha motif domains. Collectively, our results from weeping peach provide insight into polar auxin transport mechanisms associated with gravitropism and lateral shoot and root orientation.
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Affiliation(s)
- Andrea R Kohler
- Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA
| | - Andrew Scheil
- Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA
| | - Joseph L Hill
- Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA
| | - Jeffrey R Allen
- Department of Biology, Duke University, Durham, NC 27708, USA
| | - Jameel M Al-Haddad
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Charity Z Goeckeritz
- Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA
| | - Lucia C Strader
- Department of Biology, Duke University, Durham, NC 27708, USA
| | - Frank W Telewski
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Courtney A Hollender
- Department of Horticulture, Michigan State University, East Lansing, MI 48824, USA
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24
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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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25
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Bayarsaikhan B, Zsidó BZ, Börzsei R, Hetényi C. Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols. Int J Mol Sci 2024; 25:5945. [PMID: 38892133 PMCID: PMC11172440 DOI: 10.3390/ijms25115945] [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: 04/24/2024] [Revised: 05/26/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.
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Affiliation(s)
- Bayartsetseg Bayarsaikhan
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Balázs Zoltán Zsidó
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Rita Börzsei
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
| | - Csaba Hetényi
- Pharmacoinformatics Unit, Department of Pharmacology and Pharmacotherapy, Medical School, University of Pécs, Szigeti út 12, H-7624 Pécs, Hungary; (B.B.); (B.Z.Z.); (R.B.)
- National Laboratory for Drug Research and Development, Magyar tudósok krt. 2, H-1117 Budapest, Hungary
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26
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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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27
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Pratiwi NKC, Tayara H, Chong KT. An Ensemble Classifiers for Improved Prediction of Native-Non-Native Protein-Protein Interaction. Int J Mol Sci 2024; 25:5957. [PMID: 38892144 PMCID: PMC11172808 DOI: 10.3390/ijms25115957] [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: 04/22/2024] [Revised: 05/27/2024] [Accepted: 05/27/2024] [Indexed: 06/21/2024] Open
Abstract
In this study, we present an innovative approach to improve the prediction of protein-protein interactions (PPIs) through the utilization of an ensemble classifier, specifically focusing on distinguishing between native and non-native interactions. Leveraging the strengths of various base models, including random forest, gradient boosting, extreme gradient boosting, and light gradient boosting, our ensemble classifier integrates these diverse predictions using a logistic regression meta-classifier. Our model was evaluated using a comprehensive dataset generated from molecular dynamics simulations. While the gains in AUC and other metrics might seem modest, they contribute to a model that is more robust, consistent, and adaptable. To assess the effectiveness of various approaches, we compared the performance of logistic regression to four baseline models. Our results indicate that logistic regression consistently underperforms across all evaluated metrics. This suggests that it may not be well-suited to capture the complex relationships within this dataset. Tree-based models, on the other hand, appear to be more effective for problems involving molecular dynamics simulations. Extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) are optimized for performance and speed, handling datasets effectively and incorporating regularizations to avoid over-fitting. Our findings indicate that the ensemble method enhances the predictive capability of PPIs, offering a promising tool for computational biology and drug discovery by accurately identifying potential interaction sites and facilitating the understanding of complex protein functions within biological systems.
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Affiliation(s)
- Nor Kumalasari Caecar Pratiwi
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Department of Electrical Engineering, Telkom University, Bandung 40257, West Java, Indonesia
| | - Hilal Tayara
- School of International Engineering and Science, Jeonbuk National University, Jeonju 54896, Republic of Korea
| | - Kil To Chong
- Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea;
- Advances Electronics and Information Research Centre, Jeonbuk National University, Jeonju 54896, Republic of Korea
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28
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Lam HYI, Ong XE, Mutwil M. Large language models in plant biology. TRENDS IN PLANT SCIENCE 2024:S1360-1385(24)00118-3. [PMID: 38797656 DOI: 10.1016/j.tplants.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/29/2024]
Abstract
Large language models (LLMs), such as ChatGPT, have taken the world by storm. However, LLMs are not limited to human language and can be used to analyze sequential data, such as DNA, protein, and gene expression. The resulting foundation models can be repurposed to identify the complex patterns within the data, resulting in powerful, multipurpose prediction tools able to predict the state of cellular systems. This review outlines the different types of LLMs and showcases their recent uses in biology. Since LLMs have not yet been embraced by the plant community, we also cover how these models can be deployed for the plant kingdom.
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Affiliation(s)
- Hilbert Yuen In Lam
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Xing Er Ong
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore
| | - Marek Mutwil
- School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore, 637551, Singapore.
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29
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Shor B, Schneidman-Duhovny D. Integrative modeling meets deep learning: Recent advances in modeling protein assemblies. Curr Opin Struct Biol 2024; 87:102841. [PMID: 38795564 DOI: 10.1016/j.sbi.2024.102841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 04/24/2024] [Accepted: 04/27/2024] [Indexed: 05/28/2024]
Abstract
Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.
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Affiliation(s)
- Ben Shor
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. https://twitter.com/ben_shor
| | - Dina Schneidman-Duhovny
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
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30
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Wilkinson P, Jackson B, Fermor H, Davies R. A new mRNA structure prediction based approach to identifying improved signal peptides for bone morphogenetic protein 2. BMC Biotechnol 2024; 24:34. [PMID: 38783306 PMCID: PMC11112908 DOI: 10.1186/s12896-024-00858-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND Signal peptide (SP) engineering has proven able to improve production of many proteins yet is a laborious process that still relies on trial and error. mRNA structure around the translational start site is important in translation initiation and has rarely been considered in this context, with recent improvements in in silico mRNA structure potentially rendering it a useful predictive tool for SP selection. Here we attempt to create a method to systematically screen candidate signal peptide sequences in silico based on both their nucleotide and amino acid sequences. Several recently released computational tools were used to predict signal peptide activity (SignalP), localization target (DeepLoc) and predicted mRNA structure (MXFold2). The method was tested with Bone Morphogenetic Protein 2 (BMP2), an osteogenic growth factor used clinically for bone regeneration. It was hoped more effective BMP2 SPs could improve BMP2-based gene therapies and reduce the cost of recombinant BMP2 production. RESULTS Amino acid sequence analysis indicated 2,611 SPs from the TGF-β superfamily were predicted to function when attached to BMP2. mRNA structure prediction indicated structures at the translational start site were likely highly variable. The five sequences with the most accessible translational start sites, a codon optimized BMP2 SP variant and the well-established hIL2 SP sequence were taken forward to in vitro testing. The top five candidates showed non-significant improvements in BMP2 secretion in HEK293T cells. All showed reductions in secretion versus the native sequence in C2C12 cells, with several showing large and significant decreases. None of the tested sequences were able to increase alkaline phosphatase activity above background in C2C12s. The codon optimized control sequence and hIL2 SP showed reasonable activity in HEK293T but very poor activity in C2C12. CONCLUSIONS These results support the use of peptide sequence based in silico tools for basic predictions around signal peptide activity in a synthetic biology context. However, mRNA structure prediction requires improvement before it can produce reliable predictions for this application. The poor activity of the codon optimized BMP2 SP variant in C2C12 emphasizes the importance of codon choice, mRNA structure, and cellular context for SP activity.
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Affiliation(s)
- Piers Wilkinson
- Department of Mechanical Engineering, Institute of Medical and Biological Engineering, University of Leeds, Leeds, UK.
| | - Brian Jackson
- Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - Hazel Fermor
- School of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds, UK
| | - Robert Davies
- Oral Biology, Faculty of Medicine and Health, University of Leeds, Leeds, UK
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31
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Xu Z, Sadleir L, Goel H, Jiao X, Niu Y, Zhou Z, de Valles-Ibáñez G, Poke G, Hildebrand M, Lieffering N, Qin J, Yang Z. Genotype and phenotype correlation of PHACTR1-related neurological disorders. J Med Genet 2024; 61:536-542. [PMID: 38272663 DOI: 10.1136/jmg-2023-109638] [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: 09/14/2023] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
BACKGROUND PHACTR1 (phosphatase and actin regulators) plays a key role in cortical migration and synaptic activity by binding and regulating G-actin and PPP1CA. This study aimed to expand the genotype and phenotype of patients with de novo variants in PHACTR1 and analyse the impact of variants on protein-protein interaction. METHODS We identified seven patients with PHACTR1 variants by trio-based whole-exome sequencing. Additional two subjects were ascertained from two centres through GeneMatcher. The genotype-phenotype correlation was determined, and AlphaFold-Multimer was used to predict protein-protein interactions and interfaces. RESULTS Eight individuals carried missense variants and one had CNV in the PHACTR1. Infantile epileptic spasms syndrome (IESS) was the unifying phenotype in eight patients with missense variants of PHACTR1. They could present with other types of seizures and often exhibit drug-resistant epilepsy with a poor prognosis. One patient with CNV displayed a developmental encephalopathy phenotype. Using AlphaFold-Multimer, our findings indicate that PHACTR1 and G-actin-binding sequences overlap with PPP1CA at the RPEL3 domain, which suggests possible competition between PPP1CA and G-actin for binding to PHACTR1 through a similar polymerisation interface. In addition, patients carrying missense variants located at the PHACTR1-PPP1CA or PHACTR1-G-actin interfaces consistently exhibit the IESS phenotype. These missense variants are mostly concentrated in the overlapping sequence (RPEL3 domain). CONCLUSIONS Patients with variants in PHACTR1 can have a phenotype of developmental encephalopathy in addition to IESS. Moreover, our study confirmed that the variants affect the binding of PHACTR1 to G-actin or PPP1CA, resulting in neurological disorders in patients.
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Affiliation(s)
- Zhao Xu
- Department of Pediatrics, Peking University People's Hospital, Beijing, China
- Epilepsy Center, Peking University People's Hospital, Beijing, China
| | - Lynette Sadleir
- Department of Paediatrics and Child Health, University of Otago Wellington, Wellington, New Zealand
| | - Himanshu Goel
- Hunter Genetics, Waratah, New South Wales, Australia
| | - Xianru Jiao
- Department of Pediatrics, Peking University People's Hospital, Beijing, China
- Epilepsy Center, Peking University People's Hospital, Beijing, China
| | - Yue Niu
- Department of Pediatrics, Peking University People's Hospital, Beijing, China
- Epilepsy Center, Peking University People's Hospital, Beijing, China
| | - Zongpu Zhou
- Department of Pediatrics, Peking University People's Hospital, Beijing, China
- Epilepsy Center, Peking University People's Hospital, Beijing, China
| | - Guillem de Valles-Ibáñez
- Department of Paediatrics and Child Health, University of Otago Wellington, Wellington, New Zealand
| | - Gemma Poke
- Department of Paediatrics and Child Health, University of Otago Wellington, Wellington, New Zealand
| | - Michael Hildebrand
- Epilepsy Research Centre, Department of Medicine, The University of Melbourne, Heidelberg, Victoria, Australia
- Neuroscience Research Group, Murdoch Children's Research Institute, Royal Children's Hospital, South Brisbane, Queensland, Australia
| | - Nico Lieffering
- Department of Paediatrics and Child Health, University of Otago Wellington, Wellington, New Zealand
| | - Jiong Qin
- Department of Pediatrics, Peking University People's Hospital, Beijing, China
- Epilepsy Center, Peking University People's Hospital, Beijing, China
| | - Zhixian Yang
- Department of Pediatrics, Peking University People's Hospital, Beijing, China
- Epilepsy Center, Peking University People's Hospital, Beijing, China
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Shen J, Gong L, Sun Y, Lin J, Hu W, Wei J, Miao X, Gao T, Suo J, Xu J, Chai Y, Bao B, Qian Y, Zheng X. Semaphorin3C identified as mediator of neuroinflammation and microglia polarization after spinal cord injury. iScience 2024; 27:109649. [PMID: 38638567 PMCID: PMC11025009 DOI: 10.1016/j.isci.2024.109649] [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: 10/12/2023] [Revised: 02/01/2024] [Accepted: 03/27/2024] [Indexed: 04/20/2024] Open
Abstract
Excessive neuroinflammation after spinal cord injury (SCI) is a major hurdle during nerve repair. Although proinflammatory macrophage/microglia-mediated neuroinflammation plays important roles, the underlying mechanism that triggers neuroinflammation and aggravating factors remain unclear. The present study identified a proinflammatory role of semaphorin3C (SEMA3C) in immunoregulation after SCI. SEMA3C expression level peaked 7 days post-injury (dpi) and decreased by 14 dpi. In vivo and in vitro studies revealed that macrophages/microglia expressed SEMA3C in the local microenvironment, which induced neuroinflammation and conversion of proinflammatory macrophage/microglia. Mechanistic experiments revealed that RAGE/NF-κB was downstream target of SEMA3C. Inhibiting SEMA3C-mediated RAGE signaling considerably suppressed proinflammatory cytokine production, reversed polarization of macrophages/microglia shortly after SCI. In addition, inhibition of SEMA3C-mediated RAGE signaling suggested that the SEMA3C/RAGE axis is a feasible target to preserve axons from neuroinflammation. Taken together, our study provides the first experimental evidence of an immunoregulatory role for SEMA3C in SCI via an autocrine mechanism.
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Affiliation(s)
- Junjie Shen
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Liangzhi Gong
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Yi Sun
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Junqing Lin
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Wencheng Hu
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Jiabao Wei
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Xin Miao
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Tao Gao
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Jinlong Suo
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Jia Xu
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Yimin Chai
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Bingbo Bao
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Yun Qian
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
| | - Xianyou Zheng
- Department of Orthopedic Surgery, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, P.R. China
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Yu LT, Kreutzberger MAB, Hancu MC, Bui TH, Farsheed AC, Egelman EH, Hartgerink JD. Beyond the Triple Helix: Exploration of the Hierarchical Assembly Space of Collagen-like Peptides. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594194. [PMID: 38798367 PMCID: PMC11118445 DOI: 10.1101/2024.05.14.594194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
The de novo design of self-assembling peptides has garnered significant attention in scientific research. While alpha-helical assemblies have been extensively studied, exploration of polyproline type II (PPII) helices, such as those found in collagen, remains relatively limited. In this study, we focused on understanding the sequence-structure relationship in hierarchical assemblies of collagen-like peptides, using defense collagen SP-A as a model. By dissecting the sequence derived from SP-A and synthesizing short collagen-like peptides, we successfully constructed a discrete bundle of hollow triple helices. Mutation studies pinpointed amino acid sequences, including hydrophobic and charged residues that are critical for oligomer formation. These insights guided the de novo design of collagen-like peptides, resulting in the formation of diverse quaternary structures, including discrete and heterogenous bundled oligomers, 2D nanosheets, and pH-responsive nanoribbons. Our study represents a significant advancement in the understanding and harnessing of collagen higher-order assemblies beyond the triple helix.
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Affiliation(s)
- Le Tracy Yu
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Mark A. B. Kreutzberger
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Maria C. Hancu
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Thi H. Bui
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Adam C. Farsheed
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
| | - Edward H. Egelman
- Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA, 22903, USA
| | - Jeffrey D. Hartgerink
- Department of Chemistry, Rice University, 6100 Main Street, Houston, TX, 77005, USA
- Department of Bioengineering, Rice University, 6100 Main Street, Houston, TX, 77005, USA
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Ahdritz G, Bouatta N, Floristean C, Kadyan S, Xia Q, Gerecke W, O'Donnell TJ, Berenberg D, Fisk I, Zanichelli N, Zhang B, Nowaczynski A, Wang B, Stepniewska-Dziubinska MM, Zhang S, Ojewole A, Guney ME, Biderman S, Watkins AM, Ra S, Lorenzo PR, Nivon L, Weitzner B, Ban YEA, Chen S, Zhang M, Li C, Song SL, He Y, Sorger PK, Mostaque E, Zhang Z, Bonneau R, AlQuraishi M. OpenFold: retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization. Nat Methods 2024:10.1038/s41592-024-02272-z. [PMID: 38744917 DOI: 10.1038/s41592-024-02272-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
AlphaFold2 revolutionized structural biology with the ability to predict protein structures with exceptionally high accuracy. Its implementation, however, lacks the code and data required to train new models. These are necessary to (1) tackle new tasks, like protein-ligand complex structure prediction, (2) investigate the process by which the model learns and (3) assess the model's capacity to generalize to unseen regions of fold space. Here we report OpenFold, a fast, memory efficient and trainable implementation of AlphaFold2. We train OpenFold from scratch, matching the accuracy of AlphaFold2. Having established parity, we find that OpenFold is remarkably robust at generalizing even when the size and diversity of its training set is deliberately limited, including near-complete elisions of classes of secondary structure elements. By analyzing intermediate structures produced during training, we also gain insights into the hierarchical manner in which OpenFold learns to fold. In sum, our studies demonstrate the power and utility of OpenFold, which we believe will prove to be a crucial resource for the protein modeling community.
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Affiliation(s)
- Gustaf Ahdritz
- Department of Systems Biology, Columbia University, New York, NY, USA
- Harvard University, Cambridge, MA, USA
| | - Nazim Bouatta
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
| | | | - Sachin Kadyan
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Qinghui Xia
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - William Gerecke
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Daniel Berenberg
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Ian Fisk
- Flatiron Institute, New York, NY, USA
| | | | - Bo Zhang
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | | | | | | | | | | | | | - Stella Biderman
- EleutherAI, New York, NY, USA
- Booz Allen Hamilton, McLean, VA, USA
| | | | - Stephen Ra
- Prescient Design, Genentech, New York, NY, USA
| | | | | | | | | | | | - Minjia Zhang
- University of Illinois at Urbana-Champaign, Champaign, IL, USA
| | | | | | | | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | | | - Zhao Zhang
- Rutgers University, New Brunswick, NJ, USA
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Chen X, Liu J, Park N, Cheng J. A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models. Biomolecules 2024; 14:574. [PMID: 38785981 PMCID: PMC11117562 DOI: 10.3390/biom14050574] [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: 03/01/2024] [Revised: 04/07/2024] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
The quality prediction of quaternary structure models of a protein complex, in the absence of its true structure, is known as the Estimation of Model Accuracy (EMA). EMA is useful for ranking predicted protein complex structures and using them appropriately in biomedical research, such as protein-protein interaction studies, protein design, and drug discovery. With the advent of more accurate protein complex (multimer) prediction tools, such as AlphaFold2-Multimer and ESMFold, the estimation of the accuracy of protein complex structures has attracted increasing attention. Many deep learning methods have been developed to tackle this problem; however, there is a noticeable absence of a comprehensive overview of these methods to facilitate future development. Addressing this gap, we present a review of deep learning EMA methods for protein complex structures developed in the past several years, analyzing their methodologies, data and feature construction. We also provide a prospective summary of some potential new developments for further improving the accuracy of the EMA methods.
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Affiliation(s)
- Xiao Chen
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jian Liu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
| | - Nolan Park
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, USA
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Cao MY, Zainudin S, Daud KM. Protein features fusion using attributed network embedding for predicting protein-protein interaction. BMC Genomics 2024; 25:466. [PMID: 38741045 DOI: 10.1186/s12864-024-10361-8] [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: 01/10/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations. METHODS We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs' prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature. RESULTS When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively. CONCLUSION Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.
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Affiliation(s)
- Mei-Yuan Cao
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia.
| | - Suhaila Zainudin
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia
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Frasnetti E, Magni A, Castelli M, Serapian SA, Moroni E, Colombo G. Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence. Curr Opin Struct Biol 2024; 87:102835. [PMID: 38744148 DOI: 10.1016/j.sbi.2024.102835] [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: 01/23/2024] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.
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Affiliation(s)
- Elena Frasnetti
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Andrea Magni
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Matteo Castelli
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | | | - Giorgio Colombo
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy.
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Gu S, Yang Y, Zhao Y, Qiu J, Wang X, Tong HHY, Liu L, Wan X, Liu H, Hou T, Kang Y. Evaluation of AlphaFold2 Structures for Hit Identification across Multiple Scenarios. J Chem Inf Model 2024; 64:3630-3639. [PMID: 38630855 DOI: 10.1021/acs.jcim.3c01976] [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: 04/19/2024]
Abstract
The introduction of AlphaFold2 (AF2) has sparked significant enthusiasm and generated extensive discussion within the scientific community, particularly among drug discovery researchers. Although previous studies have addressed the performance of AF2 structures in virtual screening (VS), a more comprehensive investigation is still necessary considering the paramount importance of structural accuracy in drug design. In this study, we evaluate the performance of AF2 structures in VS across three common drug discovery scenarios: targets with holo, apo, and AF2 structures; targets with only apo and AF2 structures; and targets exclusively with AF2 structures. We utilized both the traditional physics-based Glide and the deep-learning-based scoring function RTMscore to rank the compounds in the DUD-E, DEKOIS 2.0, and DECOY data sets. The results demonstrate that, overall, the performance of VS on AF2 structures is comparable to that on apo structures but notably inferior to that on holo structures across diverse scenarios. Moreover, when a target has solely AF2 structure, selecting the holo structure of the target from different subtypes within the same protein family produces comparable results with the AF2 structure for VS on the data set of the AF2 structures, and significantly better results than the AF2 structures on its own data set. This indicates that utilizing AF2 structures for docking-based VS may not yield most satisfactory outcomes, even when solely AF2 structures are available. Moreover, we rule out the possibility that the variations in VS performance between the binding pockets of AF2 and holo structures arise from the differences in their biological assembly composition.
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Affiliation(s)
- Shukai Gu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yuwei Yang
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, China
| | - Yihao Zhao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Jiayue Qiu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, China
| | - Xiaorui Wang
- State Key Laboratory of Quality Re-search in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macao 999078, China
| | - Henry Hoi Yee Tong
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, China
| | - Liwei Liu
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Nanjing 210000, Jiangsu, China
| | - Xiaozhe Wan
- Advanced Computing and Storage Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd., Nanjing 210000, Jiangsu, China
| | - Huanxiang Liu
- Faculty of Applied Science, Macao Polytechnic University, Macao 999078, SAR, China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China
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Jiang X, Shu H, Feng S, Wang P, Zhang Z, Wang N. A Hadal Streptomyces-Derived Echinocandin Acylase Discovered through the Prioritization of Protein Families. Mar Drugs 2024; 22:212. [PMID: 38786603 PMCID: PMC11122479 DOI: 10.3390/md22050212] [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: 04/04/2024] [Revised: 04/28/2024] [Accepted: 05/04/2024] [Indexed: 05/25/2024] Open
Abstract
Naturally occurring echinocandin B and FR901379 are potent antifungal lipopeptides featuring a cyclic hexapeptide nucleus and a fatty acid side chain. They are the parent compounds of echinocandin drugs for the treatment of severe fungal infections caused by the Candida and Aspergilla species. To minimize hemolytic toxicity, the native fatty acid side chains in these drug molecules are replaced with designer acyl side chains. The deacylation of the N-acyl side chain is, therefore, a crucial step for the development and manufacturing of echinocandin-type antibiotics. Echinocandin E (ECE) is a novel echinocandin congener with enhanced stability generated via the engineering of the biosynthetic machinery of echinocandin B (ECB). In the present study, we report the discovery of the first echinocandin E acylase (ECEA) using the enzyme similarity tool (EST) for enzymatic function mining across protein families. ECEA is derived from Streptomyces sp. SY1965 isolated from a sediment collected from the Mariana Trench. It was cloned and heterologously expressed in S. lividans TK24. The resultant TKecea66 strain showed efficient cleavage activity of the acyl side chain of ECE, showing promising applications in the development of novel echinocandin-type therapeutics. Our results also provide a showcase for harnessing the essentially untapped biodiversity from the hadal ecosystems for the discovery of functional molecules.
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Affiliation(s)
- Xuejian Jiang
- Ocean College, Zhejiang University, Zhoushan 316021, China; (X.J.); (H.S.); (S.F.); (P.W.); (Z.Z.)
| | - Hongjun Shu
- Ocean College, Zhejiang University, Zhoushan 316021, China; (X.J.); (H.S.); (S.F.); (P.W.); (Z.Z.)
| | - Shuting Feng
- Ocean College, Zhejiang University, Zhoushan 316021, China; (X.J.); (H.S.); (S.F.); (P.W.); (Z.Z.)
| | - Pinmei Wang
- Ocean College, Zhejiang University, Zhoushan 316021, China; (X.J.); (H.S.); (S.F.); (P.W.); (Z.Z.)
- Hainan Institute of Zhejiang University, Sanya 572025, China
| | - Zhizhen Zhang
- Ocean College, Zhejiang University, Zhoushan 316021, China; (X.J.); (H.S.); (S.F.); (P.W.); (Z.Z.)
| | - Nan Wang
- Ocean College, Zhejiang University, Zhoushan 316021, China; (X.J.); (H.S.); (S.F.); (P.W.); (Z.Z.)
- Hainan Institute of Zhejiang University, Sanya 572025, China
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Zhang G, Zhang C, Cai M, Luo C, Zhu F, Liang Z. FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics. Int J Biol Macromol 2024; 266:131180. [PMID: 38552697 DOI: 10.1016/j.ijbiomac.2024.131180] [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: 12/21/2023] [Revised: 03/19/2024] [Accepted: 03/26/2024] [Indexed: 04/01/2024]
Abstract
Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhos-STR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.
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Affiliation(s)
- Guangyu Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Cai Zhang
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China
| | - Mingyue Cai
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China
| | - Cheng Luo
- State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
| | - Zhongjie Liang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China.
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Lahfa M, Barthe P, de Guillen K, Cesari S, Raji M, Kroj T, Le Naour—Vernet M, Hoh F, Gladieux P, Roumestand C, Gracy J, Declerck N, Padilla A. The structural landscape and diversity of Pyricularia oryzae MAX effectors revisited. PLoS Pathog 2024; 20:e1012176. [PMID: 38709846 PMCID: PMC11132498 DOI: 10.1371/journal.ppat.1012176] [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: 11/30/2023] [Revised: 05/28/2024] [Accepted: 04/08/2024] [Indexed: 05/08/2024] Open
Abstract
Magnaporthe AVRs and ToxB-like (MAX) effectors constitute a family of secreted virulence proteins in the fungus Pyricularia oryzae (syn. Magnaporthe oryzae), which causes blast disease on numerous cereals and grasses. In spite of high sequence divergence, MAX effectors share a common fold characterized by a ß-sandwich core stabilized by a conserved disulfide bond. In this study, we investigated the structural landscape and diversity within the MAX effector repertoire of P. oryzae. Combining experimental protein structure determination and in silico structure modeling we validated the presence of the conserved MAX effector core domain in 77 out of 94 groups of orthologs (OG) identified in a previous population genomic study. Four novel MAX effector structures determined by NMR were in remarkably good agreement with AlphaFold2 (AF2) predictions. Based on the comparison of the AF2-generated 3D models we propose a classification of the MAX effectors superfamily in 20 structural groups that vary in the canonical MAX fold, disulfide bond patterns, and additional secondary structures in N- and C-terminal extensions. About one-third of the MAX family members remain singletons, without strong structural relationship to other MAX effectors. Analysis of the surface properties of the AF2 MAX models also highlights the high variability within the MAX family at the structural level, potentially reflecting the wide diversity of their virulence functions and host targets.
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Affiliation(s)
- Mounia Lahfa
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Philippe Barthe
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Karine de Guillen
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Stella Cesari
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | - Mouna Raji
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Thomas Kroj
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | - Marie Le Naour—Vernet
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | - François Hoh
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Pierre Gladieux
- PHIM Plant Health Institute, Univ Montpellier, INRAE, CIRAD, Institut Agro, IRD, Montpellier, France
| | - Christian Roumestand
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Jérôme Gracy
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - Nathalie Declerck
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
| | - André Padilla
- Centre de Biologie Structurale, Univ Montpellier, CNRS UMR 5048, INSERM U 1054, Montpellier, France
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Takihira S, Yamada D, Osone T, Takao T, Sakaguchi M, Hakozaki M, Itano T, Nakata E, Fujiwara T, Kunisada T, Ozaki T, Takarada T. PRRX1-TOP2A interaction is a malignancy-promoting factor in human malignant peripheral nerve sheath tumours. Br J Cancer 2024; 130:1493-1504. [PMID: 38448751 PMCID: PMC11058259 DOI: 10.1038/s41416-024-02632-8] [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: 08/19/2023] [Revised: 02/15/2024] [Accepted: 02/19/2024] [Indexed: 03/08/2024] Open
Abstract
BACKGROUND Paired related-homeobox 1 (PRRX1) is a transcription factor in the regulation of developmental morphogenetic processes. There is growing evidence that PRRX1 is highly expressed in certain cancers and is critically involved in human survival prognosis. However, the molecular mechanism of PRRX1 in cancer malignancy remains to be elucidated. METHODS PRRX1 expression in human Malignant peripheral nerve sheath tumours (MPNSTs) samples was detected immunohistochemically to evaluate survival prognosis. MPNST models with PRRX1 gene knockdown or overexpression were constructed in vitro and the phenotype of MPNST cells was evaluated. Bioinformatics analysis combined with co-immunoprecipitation, mass spectrometry, RNA-seq and structural prediction were used to identify proteins interacting with PRRX1. RESULTS High expression of PRRX1 was associated with a poor prognosis for MPNST. PRRX1 knockdown suppressed the tumorigenic potential. PRRX1 overexpressed in MPNSTs directly interacts with topoisomerase 2 A (TOP2A) to cooperatively promote epithelial-mesenchymal transition and increase expression of tumour malignancy-related gene sets including mTORC1, KRAS and SRC signalling pathways. Etoposide, a TOP2A inhibitor used in the treatment of MPNST, may exhibit one of its anticancer effects by inhibiting the PRRX1-TOP2A interaction. CONCLUSION Targeting the PRRX1-TOP2A interaction in malignant tumours with high PRRX1 expression might provide a novel tumour-selective therapeutic strategy.
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Affiliation(s)
- Shota Takihira
- Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
- Department of Orthopedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Daisuke Yamada
- Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Tatsunori Osone
- Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Tomoka Takao
- Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Masakiyo Sakaguchi
- Department of Cell Biology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Michiyuki Hakozaki
- Department of Orthopedic Surgery, Fukushima Medical University School of Medicine, Fukushima, 960-1295, Japan
| | - Takuto Itano
- Department of Orthopedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Eiji Nakata
- Department of Orthopedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Tomohiro Fujiwara
- Department of Orthopedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Toshiyuki Kunisada
- Department of Orthopedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Toshifumi Ozaki
- Department of Orthopedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan
| | - Takeshi Takarada
- Department of Regenerative Science, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, 700-8558, Japan.
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Mischley V, Maier J, Chen J, Karanicolas J. PPIscreenML: Structure-based screening for protein-protein interactions using AlphaFold. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.16.585347. [PMID: 38559274 PMCID: PMC10979958 DOI: 10.1101/2024.03.16.585347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Protein-protein interactions underlie nearly all cellular processes. With the advent of protein structure prediction methods such as AlphaFold2 (AF2), models of specific protein pairs can be built extremely accurately in most cases. However, determining the relevance of a given protein pair remains an open question. It is presently unclear how to use best structure-based tools to infer whether a pair of candidate proteins indeed interact with one another: ideally, one might even use such information to screen amongst candidate pairings to build up protein interaction networks. Whereas methods for evaluating quality of modeled protein complexes have been co-opted for determining which pairings interact (e.g., pDockQ and iPTM), there have been no rigorously benchmarked methods for this task. Here we introduce PPIscreenML, a classification model trained to distinguish AF2 models of interacting protein pairs from AF2 models of compelling decoy pairings. We find that PPIscreenML out-performs methods such as pDockQ and iPTM for this task, and further that PPIscreenML exhibits impressive performance when identifying which ligand/receptor pairings engage one another across the structurally conserved tumor necrosis factor superfamily (TNFSF). Analysis of benchmark results using complexes not seen in PPIscreenML development strongly suggest that the model generalizes beyond training data, making it broadly applicable for identifying new protein complexes based on structural models built with AF2.
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44
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Elofsson A, Han L, Bianchi E, Wright GJ, Jovine L. Deep learning insights into the architecture of the mammalian egg-sperm fusion synapse. eLife 2024; 13:RP93131. [PMID: 38666763 PMCID: PMC11052572 DOI: 10.7554/elife.93131] [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: 04/28/2024] Open
Abstract
A crucial event in sexual reproduction is when haploid sperm and egg fuse to form a new diploid organism at fertilization. In mammals, direct interaction between egg JUNO and sperm IZUMO1 mediates gamete membrane adhesion, yet their role in fusion remains enigmatic. We used AlphaFold to predict the structure of other extracellular proteins essential for fertilization to determine if they could form a complex that may mediate fusion. We first identified TMEM81, whose gene is expressed by mouse and human spermatids, as a protein having structural homologies with both IZUMO1 and another sperm molecule essential for gamete fusion, SPACA6. Using a set of proteins known to be important for fertilization and TMEM81, we then systematically searched for predicted binary interactions using an unguided approach and identified a pentameric complex involving sperm IZUMO1, SPACA6, TMEM81 and egg JUNO, CD9. This complex is structurally consistent with both the expected topology on opposing gamete membranes and the location of predicted N-glycans not modeled by AlphaFold-Multimer, suggesting that its components could organize into a synapse-like assembly at the point of fusion. Finally, the structural modeling approach described here could be more generally useful to gain insights into transient protein complexes difficult to detect experimentally.
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Affiliation(s)
- Arne Elofsson
- Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm UniversitySolnaSweden
| | - Ling Han
- Department of Biosciences and Nutrition, Karolinska InstitutetHuddingeSweden
| | - Enrica Bianchi
- Department of Biology, Hull York Medical School, York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Gavin J Wright
- Department of Biology, Hull York Medical School, York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Luca Jovine
- Department of Biosciences and Nutrition, Karolinska InstitutetHuddingeSweden
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45
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Cho T, Hoeg L, Setiaputra D, Durocher D. NFATC2IP is a mediator of SUMO-dependent genome integrity. Genes Dev 2024; 38:233-252. [PMID: 38503515 PMCID: PMC11065178 DOI: 10.1101/gad.350914.123] [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: 07/05/2023] [Accepted: 03/04/2024] [Indexed: 03/21/2024]
Abstract
The post-translational modification of proteins by SUMO is crucial for cellular viability and mammalian development in part due to the contribution of SUMOylation to genome duplication and repair. To investigate the mechanisms underpinning the essential function of SUMO, we undertook a genome-scale CRISPR/Cas9 screen probing the response to SUMOylation inhibition. This effort identified 130 genes whose disruption reduces or enhances the toxicity of TAK-981, a clinical-stage inhibitor of the SUMO E1-activating enzyme. Among the strongest hits, we validated and characterized NFATC2IP, an evolutionarily conserved protein related to the fungal Esc2 and Rad60 proteins that harbors tandem SUMO-like domains. Cells lacking NFATC2IP are viable but are hypersensitive to SUMO E1 inhibition, likely due to the accumulation of mitotic chromosome bridges and micronuclei. NFATC2IP primarily acts in interphase and associates with nascent DNA, suggesting a role in the postreplicative resolution of replication or recombination intermediates. Mechanistically, NFATC2IP interacts with the SMC5/6 complex and UBC9, the SUMO E2, via its first and second SUMO-like domains, respectively. AlphaFold-Multimer modeling suggests that NFATC2IP positions and activates the UBC9-NSMCE2 complex, the SUMO E3 ligase associated with SMC5/SMC6. We conclude that NFATC2IP is a key mediator of SUMO-dependent genomic integrity that collaborates with the SMC5/6 complex.
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Affiliation(s)
- Tiffany Cho
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Lisa Hoeg
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Dheva Setiaputra
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, British Columbia V5A 1S6, Canada
| | - Daniel Durocher
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada;
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada
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46
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Long XB, Yao CR, Li SY, Zhang JG, Lu ZJ, Ma DD, Chen CE, Ying GG, Shi WJ. Screening androgen receptor agonists of fish species using machine learning and molecular model in NORMAN water-relevant list. JOURNAL OF HAZARDOUS MATERIALS 2024; 468:133844. [PMID: 38394900 DOI: 10.1016/j.jhazmat.2024.133844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 02/14/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
Androgen receptor (AR) agonists have strong endocrine disrupting effects in fish. Most studies mainly investigate AR binding capacity using human AR in vitro. However, there is still few methods to rapidly predict AR agonists in aquatic organisms. This study aimed to screen AR agonists of fish species using machine learning and molecular models in water-relevant list from NORMAN, a network of reference laboratories for monitoring contaminants of emerging concern in the environment. In this study, machine learning approaches (e.g., Deep Forest (DF)), Random Forests and artificial neural networks) were applied to predict AR agonists. Zebrafish, fathead minnow, mosquitofish, medaka fish and grass carp are all important aquatic model organisms widely used to evaluate the toxicity of new pollutants, and the molecular models of ARs from these five fish species were constructed to further screen AR agonists using AlphaFold2. The DF method showed the best performances with 0.99 accuracy, 0.97 sensitivity and 1 precision. The Asn705, Gln711, Arg752, and Thr877 residues in human AR and the corresponding sites in ARs from the five fish species were responsible for agonist binding. Overall, 245 substances were predicted as suspect AR agonists in the five fish species, including, certain glucocorticoids, cholesterol metabolites, and cardiovascular drugs in the NORMAN list. Using machine learning and molecular modeling hybrid methods rapidly and accurately screened AR agonists in fish species, and helping evaluate their ecological risk in fish populations.
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Affiliation(s)
- Xiao-Bing Long
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Chong-Rui Yao
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Si-Ying Li
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Jin-Ge Zhang
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Zhi-Jie Lu
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Dong-Dong Ma
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Chang-Er Chen
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Guang-Guo Ying
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China
| | - Wen-Jun Shi
- SCNU Environmental Research Institute, Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key Laboratory of Theoretical Chemistry of Environment, South China Normal University, Guangzhou 510006, China; School of Environment, South China Normal University, University Town, Guangzhou 510006, China.
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47
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Schmid EW, Walter JC. Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.09.588596. [PMID: 38645019 PMCID: PMC11030396 DOI: 10.1101/2024.04.09.588596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Protein-protein interactions (PPIs) are ubiquitous in biology, yet a comprehensive structural characterization of the PPIs underlying biochemical processes is lacking. Although AlphaFold-Multimer (AF-M) has the potential to fill this knowledge gap, standard AF-M confidence metrics do not reliably separate relevant PPIs from an abundance of false positive predictions. To address this limitation, we used machine learning on well curated datasets to train a Structure Prediction and Omics informed Classifier called SPOC that shows excellent performance in separating true and false PPIs, including in proteome-wide screens. We applied SPOC to an all-by-all matrix of nearly 300 human genome maintenance proteins, generating ~40,000 predictions that can be viewed at predictomes.org, where users can also score their own predictions with SPOC. High confidence PPIs discovered using our approach suggest novel hypotheses in genome maintenance. Our results provide a framework for interpreting large scale AF-M screens and help lay the foundation for a proteome-wide structural interactome.
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Affiliation(s)
- Ernst W. Schmid
- Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Johannes C. Walter
- Department of Biological Chemistry & Molecular Pharmacology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Howard Hughes Medical Institute, Boston, MA 02115, USA
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48
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Grassmann G, Miotto M, Desantis F, Di Rienzo L, Tartaglia GG, Pastore A, Ruocco G, Monti M, Milanetti E. Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments. Chem Rev 2024; 124:3932-3977. [PMID: 38535831 PMCID: PMC11009965 DOI: 10.1021/acs.chemrev.3c00550] [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: 07/31/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 04/11/2024]
Abstract
Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.
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Affiliation(s)
- Greta Grassmann
- Department
of Biochemical Sciences “Alessandro Rossi Fanelli”, Sapienza University of Rome, Rome 00185, Italy
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Mattia Miotto
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Fausta Desantis
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- The
Open University Affiliated Research Centre at Istituto Italiano di
Tecnologia, Genoa 16163, Italy
| | - Lorenzo Di Rienzo
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
| | - Gian Gaetano Tartaglia
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
- Center
for Human Technologies, Genoa 16152, Italy
| | - Annalisa Pastore
- Experiment
Division, European Synchrotron Radiation
Facility, Grenoble 38043, France
| | - Giancarlo Ruocco
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
| | - Michele Monti
- RNA
System Biology Lab, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Genoa 16163, Italy
| | - Edoardo Milanetti
- Center
for Life Nano & Neuro Science, Istituto
Italiano di Tecnologia, Rome 00161, Italy
- Department
of Physics, Sapienza University, Rome 00185, Italy
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49
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Li RQ, Yan L, Zhang L, Zhao Y, Lian J. CD74 as a prognostic and M1 macrophage infiltration marker in a comprehensive pan-cancer analysis. Sci Rep 2024; 14:8125. [PMID: 38582956 PMCID: PMC10998849 DOI: 10.1038/s41598-024-58899-7] [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: 12/30/2023] [Accepted: 04/04/2024] [Indexed: 04/08/2024] Open
Abstract
CD74 is a type-II transmembrane glycoprotein that has been linked to tumorigenesis. However, this association was based only on phenotypic studies, and, to date, no in-depth mechanistic studies have been conducted. In this study, combined with a multi-omics study, CD74 levels were significantly upregulated in most cancers relative to normal tissues and were found to be predictive of prognosis. Elevated CD74 expression was associated with reduced levels of mismatch-repair genes and homologous repair gene signatures in over 10 tumor types. Multiple fluorescence staining and bulk, spatial, single-cell transcriptional analyses indicated its potential as a marker for M1 macrophage infiltration in pan-cancer. In addition, CD74 expression was higher in BRCA patients responsive to conventional chemotherapy and was able to predict the prognosis of these patients. Potential CD74-activating drugs (HNHA and BRD-K55186349) were identified through molecular docking to CD74. The findings indicate activation of CD74 may have potential in tumor immunotherapy.
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Affiliation(s)
- Ruo Qi Li
- Department of Pathology, Cancer Hospital Affiliated to Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
- General Surgery Department, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, 030032, China
| | - Lei Yan
- Department of Orthopedics, The Second Hospital of Shanxi Medical University, Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair, 382 Wuyi Road, Taiyuan, Shanxi, China
| | - Ling Zhang
- Department of Pathology, Shanxi Medical University, Taiyuan, 030001, Shanxi, China
| | - Yanli Zhao
- Department of Pathology, Cancer Hospital Affiliated to Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
| | - Jing Lian
- Department of Pathology, Cancer Hospital Affiliated to Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China.
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50
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Peng J, Svetec N, Molina H, Zhao L. The Origin and Evolution of Sex Peptide and Sex Peptide Receptor Interactions. Mol Biol Evol 2024; 41:msae065. [PMID: 38518286 PMCID: PMC11017328 DOI: 10.1093/molbev/msae065] [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: 11/09/2023] [Revised: 02/22/2024] [Accepted: 03/18/2024] [Indexed: 03/24/2024] Open
Abstract
Post-mating responses play a vital role in successful reproduction across diverse species. In fruit flies, sex peptide binds to the sex peptide receptor, triggering a series of post-mating responses. However, the origin of sex peptide receptor predates the emergence of sex peptide. The evolutionary origins of the interactions between sex peptide and sex peptide receptor and the mechanisms by which they interact remain enigmatic. In this study, we used ancestral sequence reconstruction, AlphaFold2 predictions, and molecular dynamics simulations to study sex peptide-sex peptide receptor interactions and their origination. Using AlphaFold2 and long-time molecular dynamics simulations, we predicted the structure and dynamics of sex peptide-sex peptide receptor interactions. We show that sex peptide potentially binds to the ancestral states of Diptera sex peptide receptor. Notably, we found that only a few amino acid changes in sex peptide receptor are sufficient for the formation of sex peptide-sex peptide receptor interactions. Ancestral sequence reconstruction and molecular dynamics simulations further reveal that sex peptide receptor interacts with sex peptide through residues that are mostly involved in the interaction interface of an ancestral ligand, myoinhibitory peptides. We propose a potential mechanism whereby sex peptide-sex peptide receptor interactions arise from the preexisting myoinhibitory peptides-sex peptide receptor interface as well as early chance events both inside and outside the preexisting interface that created novel sex peptide-specific sex peptide-sex peptide receptor interactions. Our findings provide new insights into the origin and evolution of sex peptide-sex peptide receptor interactions and their relationship with myoinhibitory peptides-sex peptide receptor interactions.
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Affiliation(s)
- Junhui Peng
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Nicolas Svetec
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Henrik Molina
- Proteomics Resource Center, The Rockefeller University, New York, NY, USA
| | - Li Zhao
- Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY, USA
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