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Shankar SS, Banarjee R, Jathar SM, Rajesh S, Ramasamy S, Kulkarni MJ. De novo structure prediction of meteorin and meteorin-like protein for identification of domains, functional receptor binding regions, and their high-risk missense variants. J Biomol Struct Dyn 2024; 42:4522-4536. [PMID: 37288801 DOI: 10.1080/07391102.2023.2220804] [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: 03/07/2023] [Accepted: 05/29/2023] [Indexed: 06/09/2023]
Abstract
Meteorin (Metrn) and Meteorin-like (Metrnl) are homologous secreted proteins involved in neural development and metabolic regulation. In this study, we have performed de novo structure prediction and analysis of both Metrn and Metrnl using Alphafold2 (AF2) and RoseTTAfold (RF). Based on the domain and structural homology analysis of the predicted structures, we have identified that these proteins are composed of two functional domains, a CUB domain and an NTR domain, connected by a hinge/loop region. We have identified the receptor binding regions of Metrn and Metrnl using the machine-learning tools ScanNet and Masif. These were further validated by docking Metrnl with its reported KIT receptor, thus establishing the role of each domain in the receptor interaction. Also, we have studied the effect of non-synonymous SNPs on the structure and function of these proteins using an array of bioinformatics tools and selected 16 missense variants in Metrn and 10 in Metrnl that can affect the protein stability. This is the first study to comprehensively characterize the functional domains of Metrn and Metrnl at their structural level and identify the functional domains, and protein binding regions. This study also highlights the interaction mechanism of the KIT receptor and Metrnl. The predicted deleterious SNPs will allow further understanding of the role of these variants in modulating the plasma levels of these proteins in disease conditions such as diabetes.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- S Shiva Shankar
- Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Reema Banarjee
- Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India
| | - Swaraj M Jathar
- Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - S Rajesh
- Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India
| | - Sureshkumar Ramasamy
- Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India
| | - Mahesh J Kulkarni
- Proteomics Facility, Division of Biochemical Sciences, CSIR-National Chemical Laboratory, Pune, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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2
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Wells J, Hawkins-Hooker A, Bordin N, Sillitoe I, Paige B, Orengo C. Chainsaw: protein domain segmentation with fully convolutional neural networks. Bioinformatics 2024; 40:btae296. [PMID: 38718225 PMCID: PMC11256964 DOI: 10.1093/bioinformatics/btae296] [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: 01/02/2024] [Revised: 03/23/2024] [Accepted: 05/07/2024] [Indexed: 05/23/2024] Open
Abstract
MOTIVATION Protein domains are fundamental units of protein structure and play a pivotal role in understanding folding, function, evolution, and design. The advent of accurate structure prediction techniques has resulted in an influx of new structural data, making the partitioning of these structures into domains essential for inferring evolutionary relationships and functional classification. RESULTS This article presents Chainsaw, a supervised learning approach to domain parsing that achieves accuracy that surpasses current state-of-the-art methods. Chainsaw uses a fully convolutional neural network which is trained to predict the probability that each pair of residues is in the same domain. Domain predictions are then derived from these pairwise predictions using an algorithm that searches for the most likely assignment of residues to domains given the set of pairwise co-membership probabilities. Chainsaw matches CATH domain annotations in 78% of protein domains versus 72% for the next closest method. When predicting on AlphaFold models, expert human evaluators were twice as likely to prefer Chainsaw's predictions versus the next best method. AVAILABILITY AND IMPLEMENTATION github.com/JudeWells/Chainsaw.
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Affiliation(s)
- Jude Wells
- Centre for Artificial Intelligence, University College London, WC1E 6BT, United Kingdom
| | - Alex Hawkins-Hooker
- Centre for Artificial Intelligence, University College London, WC1E 6BT, United Kingdom
| | - Nicola Bordin
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, United Kingdom
| | - Ian Sillitoe
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, United Kingdom
| | - Brooks Paige
- Centre for Artificial Intelligence, University College London, WC1E 6BT, United Kingdom
| | - Christine Orengo
- Institute of Structural and Molecular Biology, University College London, WC1E 6BT, United Kingdom
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3
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Chen J, Zia A, Luo A, Meng H, Wang F, Hou J, Cao R, Si D. Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration. Brief Bioinform 2024; 25:bbae118. [PMID: 38609330 PMCID: PMC11014792 DOI: 10.1093/bib/bbae118] [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: 06/27/2023] [Revised: 01/23/2024] [Accepted: 03/02/2024] [Indexed: 04/14/2024] Open
Abstract
Understanding the protein structures is invaluable in various biomedical applications, such as vaccine development. Protein structure model building from experimental electron density maps is a time-consuming and labor-intensive task. To address the challenge, machine learning approaches have been proposed to automate this process. Currently, the majority of the experimental maps in the database lack atomic resolution features, making it challenging for machine learning-based methods to precisely determine protein structures from cryogenic electron microscopy density maps. On the other hand, protein structure prediction methods, such as AlphaFold2, leverage evolutionary information from protein sequences and have recently achieved groundbreaking accuracy. However, these methods often require manual refinement, which is labor intensive and time consuming. In this study, we present DeepTracer-Refine, an automated method that refines AlphaFold predicted structures by aligning them to DeepTracers modeled structure. Our method was evaluated on 39 multi-domain proteins and we improved the average residue coverage from 78.2 to 90.0% and average local Distance Difference Test score from 0.67 to 0.71. We also compared DeepTracer-Refine with Phenixs AlphaFold refinement and demonstrated that our method not only performs better when the initial AlphaFold model is less precise but also surpasses Phenix in run-time performance.
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Affiliation(s)
- Jason Chen
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Ayisha Zia
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Albert Luo
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
| | - Hanze Meng
- Department of Computer Science, Duke University, Durham, NC 27708, USA
| | - Fengbin Wang
- Department of Biochemistry and Molecular Genetics, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Jie Hou
- Department of Computer Science, Saint Louis University, Saint Louis, MO 63103, USA
| | - Renzhi Cao
- Department of Computer Science, Pacific Lutheran University, Tacoma, WA 98447, USA
| | - Dong Si
- Division of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA
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4
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Bongiovanni TR, Latario CJ, Le Cras Y, Trus E, Robitaille S, Swartz K, Schmidtke D, Vincent M, Kosta A, Orth J, Stengel F, Pellarin R, Rocha EPC, Ross BD, Durand E. Assembly of a unique membrane complex in type VI secretion systems of Bacteroidota. Nat Commun 2024; 15:429. [PMID: 38200008 PMCID: PMC10781749 DOI: 10.1038/s41467-023-44426-1] [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: 06/08/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024] Open
Abstract
The type VI secretion system (T6SS) of Gram-negative bacteria inhibits competitor cells through contact-dependent translocation of toxic effector proteins. In Proteobacteria, the T6SS is anchored to the cell envelope through a megadalton-sized membrane complex (MC). However, the genomes of Bacteroidota with T6SSs appear to lack genes encoding homologs of canonical MC components. Here, we identify five genes in Bacteroides fragilis (tssNQOPR) that are essential for T6SS function and encode a Bacteroidota-specific MC. We purify this complex, reveal its dimensions using electron microscopy, and identify a protein-protein interaction network underlying the assembly of the MC including the stoichiometry of the five TssNQOPR components. Protein TssN mediates the connection between the Bacteroidota MC and the conserved baseplate. Although MC gene content and organization varies across the phylum Bacteroidota, no MC homologs are detected outside of T6SS loci, suggesting ancient co-option and functional convergence with the non-homologous MC of Pseudomonadota.
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Affiliation(s)
- Thibault R Bongiovanni
- Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM), Institut de Microbiologie, Bioénergies et Biotechnologie (IM2B), Aix-Marseille Université - Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 7255, Institut national de la santé et de la recherche médicale (INSERM), Marseille, France
- Laboratoire de Chimie Bactérienne (LCB), Institut de Microbiologie, Bioénergies et Biotechnologie (IM2B), Aix-Marseille Université - Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 7255, Institut national de la santé et de la recherche médicale (INSERM), Marseille, France
| | - Casey J Latario
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03755, USA
| | - Youn Le Cras
- Institut Pasteur, Université Paris Cité, CNRS UMR3525, Microbial Evolutionary Genomics, Paris, France
| | - Evan Trus
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03755, USA
| | - Sophie Robitaille
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03755, USA
| | - Kerry Swartz
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03755, USA
| | - Danica Schmidtke
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03755, USA
- Department of Microbiology, University of Washington, Seattle, WA, 98109, USA
| | - Maxence Vincent
- Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM), Institut de Microbiologie, Bioénergies et Biotechnologie (IM2B), Aix-Marseille Université - Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 7255, Institut national de la santé et de la recherche médicale (INSERM), Marseille, France
- Laboratoire de Chimie Bactérienne (LCB), Institut de Microbiologie, Bioénergies et Biotechnologie (IM2B), Aix-Marseille Université - Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 7255, Institut national de la santé et de la recherche médicale (INSERM), Marseille, France
| | - Artemis Kosta
- Microscopy Core Facility, Institut de Microbiologie de la Méditerranée (IMM), FR3479, CNRS, Aix-Marseille University, Marseille, France
| | - Jan Orth
- Department of Biology, University of Konstanz, Universitätsstraße 10, 78457, Konstanz, Germany
- Konstanz Research School Chemical Biology, University of Konstanz, Universitätsstraße 10, 78457, Konstanz, Germany
| | - Florian Stengel
- Department of Biology, University of Konstanz, Universitätsstraße 10, 78457, Konstanz, Germany
- Konstanz Research School Chemical Biology, University of Konstanz, Universitätsstraße 10, 78457, Konstanz, Germany
| | - Riccardo Pellarin
- Molecular Microbiology and Structural Biochemistry (MMSB, UMR 5086), CNRS & University of Lyon, 7 Passage du Vercors, 69007, Lyon, France
| | - Eduardo P C Rocha
- Institut Pasteur, Université Paris Cité, CNRS UMR3525, Microbial Evolutionary Genomics, Paris, France
| | - Benjamin D Ross
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth College, Hanover, NH, 03755, USA.
- Department of Microbiology, University of Washington, Seattle, WA, 98109, USA.
| | - Eric Durand
- Laboratoire d'Ingénierie des Systèmes Macromoléculaires (LISM), Institut de Microbiologie, Bioénergies et Biotechnologie (IM2B), Aix-Marseille Université - Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 7255, Institut national de la santé et de la recherche médicale (INSERM), Marseille, France.
- Laboratoire de Chimie Bactérienne (LCB), Institut de Microbiologie, Bioénergies et Biotechnologie (IM2B), Aix-Marseille Université - Centre National de la Recherche Scientifique (CNRS), Unité Mixte de Recherche (UMR) 7255, Institut national de la santé et de la recherche médicale (INSERM), Marseille, France.
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Vander Meersche Y, Cretin G, Gheeraert A, Gelly JC, Galochkina T. ATLAS: protein flexibility description from atomistic molecular dynamics simulations. Nucleic Acids Res 2024; 52:D384-D392. [PMID: 37986215 PMCID: PMC10767941 DOI: 10.1093/nar/gkad1084] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 10/15/2023] [Accepted: 10/30/2023] [Indexed: 11/22/2023] Open
Abstract
Dynamical behaviour is one of the most crucial protein characteristics. Despite the advances in the field of protein structure resolution and prediction, analysis and prediction of protein dynamic properties remains a major challenge, mostly due to the low accessibility of data and its diversity and heterogeneity. To address this issue, we present ATLAS, a database of standardised all-atom molecular dynamics simulations, accompanied by their analysis in the form of interactive diagrams and trajectory visualisation. ATLAS offers a large-scale view and valuable insights on protein dynamics for a large and representative set of proteins, by combining data obtained through molecular dynamics simulations with information extracted from experimental structures. Users can easily analyse dynamic properties of functional protein regions, such as domain limits (hinge positions) and residues involved in interaction with other biological molecules. Additionally, the database enables exploration of proteins with uncommon dynamic properties conditioned by their environment such as chameleon subsequences and Dual Personality Fragments. The ATLAS database is freely available at https://www.dsimb.inserm.fr/ATLAS.
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Affiliation(s)
- Yann Vander Meersche
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Gabriel Cretin
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Aria Gheeraert
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Jean-Christophe Gelly
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75014 Paris, France
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6
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Kryshtafovych A, Rigden DJ. To split or not to split: CASP15 targets and their processing into tertiary structure evaluation units. Proteins 2023; 91:1558-1570. [PMID: 37254889 PMCID: PMC10687315 DOI: 10.1002/prot.26533] [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: 03/10/2023] [Revised: 05/02/2023] [Accepted: 05/18/2023] [Indexed: 06/01/2023]
Abstract
Processing of CASP15 targets into evaluation units (EUs) and assigning them to evolutionary-based prediction classes is presented in this study. The targets were first split into structural domains based on compactness and similarity to other proteins. Models were then evaluated against these domains and their combinations. The domains were joined into larger EUs if predictors' performance on the combined units was similar to that on individual domains. Alternatively, if most predictors performed better on the individual domains, then they were retained as EUs. As a result, 112 evaluation units were created from 77 tertiary structure prediction targets. The EUs were assigned to four prediction classes roughly corresponding to target difficulty categories in previous CASPs: TBM (template-based modeling, easy or hard), FM (free modeling), and the TBM/FM overlap category. More than a third of CASP15 EUs were attributed to the historically most challenging FM class, where homology or structural analogy to proteins of known fold cannot be detected.
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Affiliation(s)
| | - Daniel J. Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, England
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Cretin G, Périn C, Zimmermann N, Galochkina T, Gelly JC. ICARUS: flexible protein structural alignment based on Protein Units. Bioinformatics 2023; 39:btad459. [PMID: 37498544 PMCID: PMC10400377 DOI: 10.1093/bioinformatics/btad459] [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: 10/13/2022] [Revised: 07/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023] Open
Abstract
MOTIVATION Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships. RESULTS We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments. AVAILABILITY AND IMPLEMENTATION Code is freely available online at https://github.com/DSIMB/ICARUS.
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Affiliation(s)
- Gabriel Cretin
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75015 Paris, France
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
| | - Charlotte Périn
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75015 Paris, France
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
- TBI, Université de Toulouse, CNRS, INRAE, INSA, 31077 Toulouse, France
| | - Nicolas Zimmermann
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75015 Paris, France
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
| | - Tatiana Galochkina
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75015 Paris, France
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
| | - Jean-Christophe Gelly
- Université Paris Cité and Université des Antilles and Université de la Réunion, INSERM, BIGR, F-75015 Paris, France
- Laboratoire d’Excellence GR-Ex, 75015 Paris, France
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8
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Wein T, Johnson AG, Millman A, Lange K, Yirmiya E, Hadary R, Garb J, Steinruecke F, Hill AB, Kranzusch PJ, Sorek R. CARD-like domains mediate anti-phage defense in bacterial gasdermin systems. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.28.542683. [PMID: 37398489 PMCID: PMC10312443 DOI: 10.1101/2023.05.28.542683] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
Caspase recruitment domains (CARDs) and pyrin domains are important facilitators of inflammasome activity and pyroptosis. Upon pathogen recognition by NLR proteins, CARDs recruit and activate caspases, which, in turn, activate gasdermin pore forming proteins to and induce pyroptotic cell death. Here we show that CARD-like domains are present in defense systems that protect bacteria against phage. The bacterial CARD is essential for protease-mediated activation of certain bacterial gasdermins, which promote cell death once phage infection is recognized. We further show that multiple anti-phage defense systems utilize CARD-like domains to activate a variety of cell death effectors. We find that these systems are triggered by a conserved immune evasion protein that phages use to overcome the bacterial defense system RexAB, demonstrating that phage proteins inhibiting one defense system can activate another. We also detect a phage protein with a predicted CARD-like structure that can inhibit the CARD-containing bacterial gasdermin system. Our results suggest that CARD domains represent an ancient component of innate immune systems conserved from bacteria to humans, and that CARD-dependent activation of gasdermins is conserved in organisms across the tree of life.
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Affiliation(s)
- Tanita Wein
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Alex G. Johnson
- Department of Microbiology, Harvard Medical School, Boston, Ma, USA
- Deparment of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Adi Millman
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Katharina Lange
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Erez Yirmiya
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Romi Hadary
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Jeremy Garb
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Felix Steinruecke
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Aidan B. Hill
- Deparment of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Philip J. Kranzusch
- Department of Microbiology, Harvard Medical School, Boston, Ma, USA
- Deparment of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA
- Parker Institute for Cancer Immunotherapy at Dana-Farber Cancer Institute, Boston, MA, USA
| | - Rotem Sorek
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
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