1
|
Rao PS, Downie DL, David-Ferdon C, Beekmann SE, Santibanez S, Polgreen PM, Kuehnert M, Courtney S, Lee JS, Chaitram J, Salerno RM, Gundlapalli AV. Pathogen-Agnostic Advanced Molecular Diagnostic Testing for Difficult-to-Diagnose Clinical Syndromes-Results of an Emerging Infections Network Survey of Frontline US Infectious Disease Clinicians, May 2023. Open Forum Infect Dis 2024; 11:ofae395. [PMID: 39113826 PMCID: PMC11304606 DOI: 10.1093/ofid/ofae395] [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: 04/04/2024] [Accepted: 07/10/2024] [Indexed: 08/10/2024] Open
Abstract
During routine clinical practice, infectious disease physicians encounter patients with difficult-to-diagnose clinical syndromes and may order advanced molecular testing to detect pathogens. These tests may identify potential infectious causes for illness and allow clinicians to adapt treatments or stop unnecessary antimicrobials. Cases of pathogen-agnostic disease testing also provide an important window into known, emerging, and reemerging pathogens and may be leveraged as part of national sentinel surveillance. A survey of Emerging Infections Network members, a group of infectious disease providers in North America, was conducted in May 2023. The objective of the survey was to gain insight into how and when infectious disease physicians use advanced molecular testing for patients with difficult-to-diagnose infectious diseases, as well as to explore the usefulness of advanced molecular testing and barriers to use. Overall, 643 providers answered at least some of the survey questions; 478 (74%) of those who completed the survey had ordered advanced molecular testing in the last two years, and formed the basis for this study. Respondents indicated that they most often ordered broad-range 16S rRNA gene sequencing, followed by metagenomic next-generation sequencing and whole genome sequencing; and commented that in clinical practice, some, but not all tests were useful. Many physicians also noted several barriers to use, including a lack of national guidelines and cost, while others commented that whole genome sequencing had potential for use in outbreak surveillance. Improving frontline physician access, availability, affordability, and developing clear national guidelines for interpretation and use of advanced molecular testing could potentially support clinical practice and public health surveillance.
Collapse
Affiliation(s)
- Preetika S Rao
- Office of Public Health Data, Surveillance and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Diane L Downie
- Office of Readiness and Response, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Corinne David-Ferdon
- National Center for Injury Prevention and Control, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Susan E Beekmann
- Emerging Infections Network, University of Iowa, Iowa City, Iowa, USA
| | - Scott Santibanez
- National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Philip M Polgreen
- Emerging Infections Network, University of Iowa, Iowa City, Iowa, USA
| | - Matthew Kuehnert
- National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Sean Courtney
- Office of Laboratory Systems and Response, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Justin S Lee
- Global Health Center, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Jasmine Chaitram
- Office of Laboratory Systems and Response, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Reynolds M Salerno
- Office of Laboratory Systems and Response, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| | - Adi V Gundlapalli
- Office of Public Health Data, Surveillance and Technology, Centers for Disease Control and Prevention, Atlanta, Georgia, USA
| |
Collapse
|
2
|
Boys IN, Johnson AG, Quinlan MR, Kranzusch PJ, Elde NC. Structural homology screens reveal host-derived poxvirus protein families impacting inflammasome activity. Cell Rep 2023; 42:112878. [PMID: 37494187 DOI: 10.1016/j.celrep.2023.112878] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 06/20/2023] [Accepted: 07/11/2023] [Indexed: 07/28/2023] Open
Abstract
Viruses acquire host genes via horizontal transfer and can express them to manipulate host biology during infections. Some homologs retain sequence identity, but evolutionary divergence can obscure host origins. We use structural modeling to compare vaccinia virus proteins with metazoan proteomes. We identify vaccinia A47L as a homolog of gasdermins, the executioners of pyroptosis. An X-ray crystal structure of A47 confirms this homology, and cell-based assays reveal that A47 interferes with caspase function. We also identify vaccinia C1L as the product of a cryptic gene fusion event coupling a Bcl-2-related fold with a pyrin domain. C1 associates with components of the inflammasome, a cytosolic innate immune sensor involved in pyroptosis, yet paradoxically enhances inflammasome activity, suggesting differential modulation during infections. Our findings demonstrate the increasing power of structural homology screens to reveal proteins with unique combinations of domains that viruses capture from host genes and combine in unique ways.
Collapse
Affiliation(s)
- Ian N Boys
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Alex G Johnson
- Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Meghan R Quinlan
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Philip J Kranzusch
- Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA; Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02115, USA
| | - Nels C Elde
- Department of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA.
| |
Collapse
|
3
|
Boys IN, Johnson AG, Quinlan M, Kranzusch PJ, Elde NC. Structural homology screens reveal poxvirus-encoded proteins impacting inflammasome-mediated defenses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.26.529821. [PMID: 36909515 PMCID: PMC10002665 DOI: 10.1101/2023.02.26.529821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
Viruses acquire host genes via horizontal gene transfer and can express them to manipulate host biology during infections. Some viral and host homologs retain sequence identity, but evolutionary divergence can obscure host origins. We used structural modeling to compare vaccinia virus proteins with metazoan proteomes. We identified vaccinia A47L as a homolog of gasdermins, the executioners of pyroptosis. An X-ray crystal structure of A47 confirmed this homology and cell-based assays revealed that A47 inhibits pyroptosis. We also identified vaccinia C1L as the product of a cryptic gene fusion event coupling a Bcl-2 related fold with a pyrin domain. C1 associates with components of the inflammasome, a cytosolic innate immune sensor involved in pyroptosis, yet paradoxically enhances inflammasome activity, suggesting a benefit to poxvirus replication in some circumstances. Our findings demonstrate the potential of structural homology screens to reveal genes that viruses capture from hosts and repurpose to benefit viral fitness.
Collapse
Affiliation(s)
- Ian N. Boys
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, 84112 USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, 20815, USA
| | - Alex G. Johnson
- Department of Microbiology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Meghan Quinlan
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, 84112 USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, 20815, USA
| | - Philip J. Kranzusch
- Department of Microbiology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Nels C. Elde
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, 84112 USA
- Howard Hughes Medical Institute, Chevy Chase, Maryland, 20815, USA
| |
Collapse
|
4
|
Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
Collapse
Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
5
|
Nussinov R, Zhang M, Liu Y, Jang H. AlphaFold, Artificial Intelligence (AI), and Allostery. J Phys Chem B 2022; 126:6372-6383. [PMID: 35976160 PMCID: PMC9442638 DOI: 10.1021/acs.jpcb.2c04346] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 08/03/2022] [Indexed: 02/08/2023]
Abstract
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
- Department
of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Mingzhen Zhang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| | - Yonglan Liu
- Cancer
Innovation Laboratory, National Cancer Institute, Frederick, Maryland 21702, United States
| | - Hyunbum Jang
- Computational
Structural Biology Section, Frederick National
Laboratory for Cancer Research, Frederick, Maryland 21702, United States
| |
Collapse
|
6
|
Guven-Maiorov E, Sakakibara N, Ponnamperuma RM, Dong K, Matar H, King KE, Weinberg WC. Delineating functional mechanisms of the p53/p63/p73 family of transcription factors through identification of protein-protein interactions using interface mimicry. Mol Carcinog 2022; 61:629-642. [PMID: 35560453 PMCID: PMC9949960 DOI: 10.1002/mc.23405] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 11/08/2022]
Abstract
Members of the p53 family of transcription factors-p53, p63, and p73-share a high degree of homology; however, members can be activated in response to different stimuli, perform distinct (sometimes opposing) roles and are expressed in different tissues. The level of complexity is increased further by the transcription of multiple isoforms of each homolog, which may interact or interfere with each other and can impact cellular outcome. Proteins perform their functions through interacting with other proteins (and/or with nucleic acids). Therefore, identification of the interactors of a protein and how they interact in 3D is essential to fully comprehend their roles. By utilizing an in silico protein-protein interaction prediction method-HMI-PRED-we predicted interaction partners of p53 family members and modeled 3D structures of these protein interaction complexes. This method recovered experimentally known interactions while identifying many novel candidate partners. We analyzed the similarities and differences observed among the interaction partners to elucidate distinct functions of p53 family members and provide examples of how this information may yield mechanistic insight to explain their overlapping versus distinct/opposing outcomes in certain contexts. While some interaction partners are common to p53, p63, and p73, the majority are unique to each member. Nevertheless, most of the enriched pathways associated with these partners are common to all members, indicating that the members target the same biological pathways but through unique mediators. p63 and p73 have more common enriched pathways compared to p53, supporting their similar developmental roles in different tissues.
Collapse
Affiliation(s)
- Emine Guven-Maiorov
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.,National Cancer Institute, Bethesda, MD, United States.,Postal and email addresses of corresponding authors FDA/CDER/OPQ/OBP, Building 52-72/2306, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States, ,
| | - Nozomi Sakakibara
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Roshini M. Ponnamperuma
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Kun Dong
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.,National Cancer Institute, Bethesda, MD, United States
| | - Hector Matar
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Kathryn E. King
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States
| | - Wendy C. Weinberg
- Laboratory of Molecular Oncology, Office of Biotechnology Products, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, United States.,Postal and email addresses of corresponding authors FDA/CDER/OPQ/OBP, Building 52-72/2306, 10903 New Hampshire Avenue, Silver Spring, MD 20993, United States, ,
| |
Collapse
|
7
|
Marques-Pereira C, Pires M, Moreira IS. Discovery of Virus-Host interactions using bioinformatic tools. Methods Cell Biol 2022; 169:169-198. [DOI: 10.1016/bs.mcb.2022.02.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
8
|
Ovek D, Taweel A, Abali Z, Tezsezen E, Koroglu YE, Tsai CJ, Nussinov R, Keskin O, Gursoy A. SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions. Bioinformatics 2021; 38:1455-1457. [PMID: 34864889 PMCID: PMC8690264 DOI: 10.1093/bioinformatics/btab799] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/09/2021] [Accepted: 11/25/2021] [Indexed: 01/05/2023] Open
Abstract
SUMMARY We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. AVAILABILITY AND IMPLEMENTATION The server is available at https://interactome.ku.edu.tr/sars/.
Collapse
Affiliation(s)
- Damla Ovek
- Graduate School of Science and Engineering, Koc University, Istanbul, 24450, Turkey,Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey
| | - Ameer Taweel
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey
| | - Zeynep Abali
- Graduate School of Science and Engineering, Koc University, Istanbul, 24450, Turkey
| | - Ece Tezsezen
- Graduate School of Science and Engineering, Koc University, Istanbul, 24450, Turkey
| | - Yunus Emre Koroglu
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey
| | - Chung-Jung Tsai
- Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Computational Structural Biology Section, Frederick, MD, 21702, U.S.A
| | - Ruth Nussinov
- Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Computational Structural Biology Section, Frederick, MD, 21702, U.S.A,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey,To whom correspondence should be addressed. E-mail:
| |
Collapse
|
9
|
Dong TN, Brogden G, Gerold G, Khosla M. A multitask transfer learning framework for the prediction of virus-human protein-protein interactions. BMC Bioinformatics 2021; 22:572. [PMID: 34837942 PMCID: PMC8626732 DOI: 10.1186/s12859-021-04484-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 11/15/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses. RESULTS We developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model. CONCLUSIONS Our approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-COV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer .
Collapse
Affiliation(s)
- Thi Ngan Dong
- L3S Research Center, Leibniz University Hannover, Hannover, Germany.
| | - Graham Brogden
- Institute for Biochemistry, University of Veterinary Medicine, Hannover, Germany.,Institute of Experimental Virology, TWINCORE, Center for Experimental and Clinical Infection Research Hannover, Hannover, Germany
| | - Gisa Gerold
- Institute for Biochemistry, University of Veterinary Medicine, Hannover, Germany.,Institute of Experimental Virology, TWINCORE, Center for Experimental and Clinical Infection Research Hannover, Hannover, Germany.,Department of Clinical Microbiology, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden
| | - Megha Khosla
- L3S Research Center, Leibniz University Hannover, Hannover, Germany
| |
Collapse
|
10
|
Murray D, Petrey D, Honig B. Integrating 3D structural information into systems biology. J Biol Chem 2021; 296:100562. [PMID: 33744294 PMCID: PMC8095114 DOI: 10.1016/j.jbc.2021.100562] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/18/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected via structural similarity offer a rich source of information not available from sequence similarity, and homology modeling can be used to leverage Protein Data Bank structures to produce 3D models for a significant fraction of many proteomes. A number of structure-informed genomic and cross-species (i.e., virus–host) interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.
Collapse
Affiliation(s)
- Diana Murray
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Donald Petrey
- Department of Systems Biology, Columbia University, New York, New York, USA
| | - Barry Honig
- Department of Systems Biology, Department of Biochemistry and Molecular Biophysics, Department of Medicine, Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, New York, USA.
| |
Collapse
|
11
|
Lasso G, Honig B, Shapira SD. A Sweep of Earth's Virome Reveals Host-Guided Viral Protein Structural Mimicry and Points to Determinants of Human Disease. Cell Syst 2020; 12:82-91.e3. [PMID: 33053371 PMCID: PMC7552982 DOI: 10.1016/j.cels.2020.09.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 09/03/2020] [Accepted: 09/18/2020] [Indexed: 12/17/2022]
Abstract
Viruses deploy genetically encoded strategies to coopt host machinery and support viral replicative cycles. Here, we use protein structure similarity to scan for molecular mimicry, manifested by structural similarity between viral and endogenous host proteins, across thousands of cataloged viruses and hosts spanning broad ecological niches and taxonomic range, including bacteria, plants and fungi, invertebrates, and vertebrates. This survey identified over 6,000,000 instances of structural mimicry; more than 70% of viral mimics cannot be discerned through protein sequence alone. We demonstrate that the manner and degree to which viruses exploit molecular mimicry varies by genome size and nucleic acid type and identify 158 human proteins that are mimicked by coronaviruses, providing clues about cellular processes driving pathogenesis. Our observations point to molecular mimicry as a pervasive strategy employed by viruses and indicate that the protein structure space used by a given virus is dictated by the host proteome. A record of this paper's transparent peer review process is included in the Supplemental Information.
Collapse
Affiliation(s)
- Gorka Lasso
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Albert Einstein College of Medicine, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY, USA; Zuckerman Mind Brain Behavior Institute, Columbia University Medical Center, New York, NY, USA; Department of Medicine, Columbia University, New York, NY, USA
| | - Sagi D Shapira
- Department of Systems Biology, Columbia University Medical Center, New York, NY, USA; Department of Microbiology and Immunology, Columbia University Medical Center, New York, NY, USA.
| |
Collapse
|
12
|
Khorsand B, Savadi A, Naghibzadeh M. SARS-CoV-2-human protein-protein interaction network. INFORMATICS IN MEDICINE UNLOCKED 2020; 20:100413. [PMID: 32838020 PMCID: PMC7425553 DOI: 10.1016/j.imu.2020.100413] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2020] [Revised: 07/11/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022] Open
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.
Collapse
Affiliation(s)
- Babak Khorsand
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Abdorreza Savadi
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahmoud Naghibzadeh
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| |
Collapse
|
13
|
Guven-Maiorov E, Hakouz A, Valjevac S, Keskin O, Tsai CJ, Gursoy A, Nussinov R. HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry. J Mol Biol 2020; 432:3395-3403. [PMID: 32061934 PMCID: PMC7261632 DOI: 10.1016/j.jmb.2020.01.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/28/2019] [Accepted: 01/14/2020] [Indexed: 02/07/2023]
Abstract
Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.
Collapse
Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Asma Hakouz
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Sukejna Valjevac
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ozlem Keskin
- Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA.
| | - Attila Gursoy
- Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey.
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, 21702, USA; Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel.
| |
Collapse
|
14
|
Bose T, Venkatesh KV, Mande SS. Investigating host-bacterial interactions among enteric pathogens. BMC Genomics 2019; 20:1022. [PMID: 31881845 PMCID: PMC6935094 DOI: 10.1186/s12864-019-6398-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 12/15/2019] [Indexed: 01/07/2023] Open
Abstract
Background In 2017, World Health Organization (WHO) published a catalogue of 12 families of antibiotic-resistant “priority pathogens” that are posing the greatest threats to human health. Six of these dreaded pathogens are known to infect the human gastrointestinal system. In addition to causing gastrointestinal and systemic infections, these pathogens can also affect the composition of other microbes constituting the healthy gut microbiome. Such aberrations in gut microbiome can significantly affect human physiology and immunity. Identifying the virulence mechanisms of these enteric pathogens are likely to help in developing newer therapeutic strategies to counter them. Results Using our previously published in silico approach, we have evaluated (and compared) Host-Pathogen Protein-Protein Interaction (HPI) profiles of four groups of enteric pathogens, namely, different species of Escherichia, Shigella, Salmonella and Vibrio. Results indicate that in spite of genus/ species specific variations, most enteric pathogens possess a common repertoire of HPIs. This core set of HPIs are probably responsible for the survival of these pathogen in the harsh nutrient-limiting environment within the gut. Certain genus/ species specific HPIs were also observed. Conslusions The identified bacterial proteins involved in the core set of HPIs are expected to be helpful in understanding the pathogenesis of these dreaded gut pathogens in greater detail. Possible role of genus/ species specific variations in the HPI profiles in the virulence of these pathogens are also discussed. The obtained results are likely to provide an opportunity for development of novel therapeutic strategies against the most dreaded gut pathogens.
Collapse
Affiliation(s)
- Tungadri Bose
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, Pune, India.,Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - K V Venkatesh
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Innovation Labs, Tata Consultancy Services Limited, Pune, India.
| |
Collapse
|
15
|
Guven-Maiorov E, Tsai CJ, Nussinov R. Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry. Front Oncol 2019; 9:1236. [PMID: 31803618 PMCID: PMC6872517 DOI: 10.3389/fonc.2019.01236] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/28/2019] [Indexed: 01/17/2023] Open
Abstract
Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.
Collapse
Affiliation(s)
- Emine Guven-Maiorov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
| | - Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD, United States
- Department of Human Genetics and Molecular Medicine, Sackler Institute of Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| |
Collapse
|
16
|
Nussinov R, Tsai CJ, Shehu A, Jang H. Computational Structural Biology: Successes, Future Directions, and Challenges. Molecules 2019; 24:molecules24030637. [PMID: 30759724 PMCID: PMC6384756 DOI: 10.3390/molecules24030637] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 02/05/2019] [Accepted: 02/10/2019] [Indexed: 02/06/2023] Open
Abstract
Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.
Collapse
Affiliation(s)
- Ruth Nussinov
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
- Sackler Institute of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel.
| | - Chung-Jung Tsai
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| | - Amarda Shehu
- Departments of Computer Science, Department of Bioengineering, and School of Systems Biology, George Mason University, Fairfax, VA 22030, USA.
| | - Hyunbum Jang
- Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA.
| |
Collapse
|