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Pang F, Long Q, Liang S. Designing a multi-epitope subunit vaccine against Orf virus using molecular docking and molecular dynamics. Virulence 2024; 15:2398171. [PMID: 39258802 PMCID: PMC11404621 DOI: 10.1080/21505594.2024.2398171] [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: 01/08/2024] [Revised: 03/04/2024] [Accepted: 05/19/2024] [Indexed: 09/12/2024] Open
Abstract
Orf virus (ORFV) is an acute contact, epitheliotropic, zoonotic, and double-stranded DNA virus that causes significant economic losses in the livestock industry. The objective of this study is to design an immunoinformatics-based multi-epitope subunit vaccine against ORFV. Various immunodominant cytotoxic T lymphocytes (CTL), helper T lymphocytes (HTL), and B-cell epitopes from the B2L, F1L, and 080 protein of ORFV were selected and linked by short connectors to construct a multi-epitope subunit vaccine. Immunogenicity was enhanced by adding an adjuvant β-defensin to the N-terminal of the vaccine using the EAAAK linker. The vaccine exhibited a significant degree of antigenicity and solubility, without allergenicity or toxicity. The 3D formation of the vaccine was subsequently anticipated, improved, and verified. The optimized model exhibited a lower Z-score of -4.33, indicating higher quality. Molecular docking results demonstrated that the vaccine strongly binds to TLR2 and TLR4. Molecular dynamics results indicated that the docked vaccine-TLR complexes were stable. Immune simulation analyses further confirmed that the vaccine can induce a marked increase in IgG and IgM antibody titers, and elevated levels of IFN-γ and IL-2. Finally, the optimized DNA sequence of the vaccine was cloned into the vector pET28a (+) for high expression in the E.coli expression system. Overall, the designed multi-epitope subunit vaccine is highly stable and can induce robust humoral and cellular immunity, making it a promising vaccine candidate against ORFV.
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MESH Headings
- Vaccines, Subunit/immunology
- Vaccines, Subunit/genetics
- Vaccines, Subunit/chemistry
- Molecular Docking Simulation
- Animals
- Orf virus/immunology
- Orf virus/genetics
- Viral Vaccines/immunology
- Viral Vaccines/chemistry
- Viral Vaccines/genetics
- Molecular Dynamics Simulation
- Mice
- Epitopes, B-Lymphocyte/immunology
- Epitopes, B-Lymphocyte/genetics
- Epitopes, B-Lymphocyte/chemistry
- Epitopes, T-Lymphocyte/immunology
- Epitopes, T-Lymphocyte/genetics
- Epitopes, T-Lymphocyte/chemistry
- Antibodies, Viral/immunology
- Antibodies, Viral/blood
- Toll-Like Receptor 4/immunology
- Toll-Like Receptor 4/chemistry
- Ecthyma, Contagious/prevention & control
- Ecthyma, Contagious/immunology
- Ecthyma, Contagious/virology
- Mice, Inbred BALB C
- Female
- T-Lymphocytes, Cytotoxic/immunology
- Immunoglobulin G/blood
- Immunoglobulin G/immunology
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Affiliation(s)
- Feng Pang
- Department of Veterinary Medicine, College of Animal Science, Guizhou University, Guiyang, China
| | - Qinqin Long
- Department of Veterinary Medicine, College of Animal Science, Guizhou University, Guiyang, China
| | - Shaobo Liang
- Department of Veterinary Medicine, College of Animal Science, Guizhou University, Guiyang, China
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2
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Li J, Lyu C, An R, Wang D. Interaction Between SARS-CoV-2 Spike Protein S1 Subunit and Oyster Heat Shock Protein 70. FOOD AND ENVIRONMENTAL VIROLOGY 2024; 16:380-390. [PMID: 38635140 DOI: 10.1007/s12560-024-09599-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/19/2024] [Indexed: 04/19/2024]
Abstract
There is growing evidence that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) contaminates the marine environment and is bioaccumulated in filter-feeding shellfish. Previous study shows the Pacific oyster tissues can bioaccumulate the SARS-CoV-2, and the oyster heat shock protein 70 (oHSP70) may play as the primary attachment receptor to bind SARS-CoV-2's recombinant spike protein S1 subunit (rS1). However, detailed information about the interaction between rS1 and oHSP70 is still unknown. In this study, we confirmed that the affinity of recombinant oHSP70 (roHSP70) for rS1 (KD = 20.4 nM) is comparable to the receptor-binding affinity of rACE2 for rS1 (KD = 16.7 nM) by surface plasmon resonance (SPR)-based Biacore and further validated by enzyme-linked immunosorbent assay (ELISA). Three truncated proteins (roHSP70-N/C/M) and five mutated proteins (p.I229del, p.D457del, p.V491_K495del, p.K556I, and p.ΣroHSP70) were constructed according to the molecular docking results. All three truncated proteins have significantly lower affinity for rS1 than the full-length roHSP70, indicating that all three segments of roHSP70 are involved in binding to rS1. Further, the results of SPR and ELISA showed that all five mutant proteins had significantly lower affinity for rS1 than roHSP70, suggesting that amino acids at these sites are involved in binding to rS1. This study provides a preliminary theoretical basis for the bioaccumulation of SARS-CoV-2 in oyster tissues or using roHSP70 as the capture unit to selectively enrich virus particles for detection.
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Affiliation(s)
- Jingwen Li
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Chenang Lyu
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Ran An
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Dapeng Wang
- Department of Food Science and Technology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China.
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Oriol F, Alberto M, Joachim AP, Patrick G, M BP, Ruben MF, Jaume B, Altair CH, Ferran P, Oriol G, Narcis FF, Baldo O. Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity in cis-regulatory elements. NAR Genom Bioinform 2024; 6:lqae068. [PMID: 38867914 PMCID: PMC11167492 DOI: 10.1093/nargab/lqae068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/18/2024] [Accepted: 05/23/2024] [Indexed: 06/14/2024] Open
Abstract
Transcription factor (TF) binding is a key component of genomic regulation. There are numerous high-throughput experimental methods to characterize TF-DNA binding specificities. Their application, however, is both laborious and expensive, which makes profiling all TFs challenging. For instance, the binding preferences of ∼25% human TFs remain unknown; they neither have been determined experimentally nor inferred computationally. We introduce a structure-based learning approach to predict the binding preferences of TFs and the automated modelling of TF regulatory complexes. We show the advantage of using our approach over the classical nearest-neighbor prediction in the limits of remote homology. Starting from a TF sequence or structure, we predict binding preferences in the form of motifs that are then used to scan a DNA sequence for occurrences. The best matches are either profiled with a binding score or collected for their subsequent modeling into a higher-order regulatory complex with DNA. Co-operativity is modelled by: (i) the co-localization of TFs and (ii) the structural modeling of protein-protein interactions between TFs and with co-factors. We have applied our approach to automatically model the interferon-β enhanceosome and the pioneering complexes of OCT4, SOX2 (or SOX11) and KLF4 with a nucleosome, which are compared with the experimentally known structures.
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Affiliation(s)
- Fornes Oriol
- Centre for Molecular Medicine and Therapeutics. BC Children's Hospital Research Institute. Department of Medical Genetics. University of British Columbia, Vancouver, BC V5Z 4H4, Canada
| | - Meseguer Alberto
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | | | - Gohl Patrick
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Bota Patricia M
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Molina-Fernández Ruben
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Bonet Jaume
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
- Laboratory of Protein Design & Immunoengineering. School of Engineering. Ecole Polytechnique Federale de Lausanne. Lausanne 1015, Vaud, Switzerland
| | - Chinchilla-Hernandez Altair
- Live-Cell Structural Biology. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Pegenaute Ferran
- Live-Cell Structural Biology. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Gallego Oriol
- Live-Cell Structural Biology. Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
| | - Fernandez-Fuentes Narcis
- Institute of Biological, Environmental and Rural Science. Aberystwyth University, SY23 3DA Aberystwyth, UK
| | - Oliva Baldo
- Structural Bioinformatics Lab (GRIB-IMIM). Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona 08005 Catalonia, Spain
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Gohl P, Bonet J, Fornes O, Planas-Iglesias J, Fernandez-Fuentes N, Oliva B. SBILib: a handle for protein modeling and engineering. Bioinformatics 2023; 39:btad613. [PMID: 37796837 PMCID: PMC10589914 DOI: 10.1093/bioinformatics/btad613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/22/2023] [Accepted: 10/04/2023] [Indexed: 10/07/2023] Open
Abstract
SUMMARY The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library. AVAILABILITY AND IMPLEMENTATION SBILib is made available on Github at https://github.com/structuralbioinformatics/SBILib.
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Affiliation(s)
- Patrick Gohl
- Department of Medicine and Life Sciences, SBI-GRIB, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Jaume Bonet
- Department of Medicine and Life Sciences, SBI-GRIB, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
| | - Oriol Fornes
- Department of Medical Genetics, Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, University of British Columbia, Vancouver, BC V5Z 4H4, Canada
| | - Joan Planas-Iglesias
- Loschmidt Laboratories, Department of Experimental Biology and RECETOX, Faculty of Science, Masaryk University, 625 00 Brno, Czech Republic
- International Clinical Research Center, St Anne’s University Hospital Brno, 656 916 Brno, Czech Republic
| | - Narcís Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Science, Aberystwyth University, Aberystwyth SY23 3DA, United Kingdom
| | - Baldo Oliva
- Department of Medicine and Life Sciences, SBI-GRIB, Universitat Pompeu Fabra, 08003 Barcelona, Catalonia, Spain
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Mirela Bota P, Hernandez AC, Segura J, Gallego O, Oliva B, Fernandez-Fuentes N. CM2D3: Furnishing the human interactome with structural models of protein complexes derived by comparative modeling and docking. J Mol Biol 2023:168055. [PMID: 36958605 DOI: 10.1016/j.jmb.2023.168055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 03/25/2023]
Abstract
The human interactome is composed of around half a million interactions according to recent estimations and it is only for a small fraction of those that three-dimensional structural information is available. Indeed, the structural coverage of the human interactome is very low and given the complexity and time-consuming requirements of solving protein structures this problem will remain for the foreseeable future. Structural models, or predictions, of protein complexes can provide valuable information when the experimentally determined 3D structures are not available. Here we present CM2D3, a relational database containing structural models of the whole human interactome derived both from comparative modeling and data-driven docking. Starting from a consensus interactome derived from integrating several interactomics databases, a strategy was devised to derive structural models by computational means. Currently, CM2D3 includes 33338 structural models of which 5121 derived from comparative modeling and the remaining from docking. Of the latter, the structures of 14554 complexes were derived from monomers modeled by M4T while the rest were modeled with structures as predicted by AlphaFold2. Lastly, CM2D3 complements existing resources by focusing on models derived from both free-docking, as opposed to template-based docking, and hence expanding the available structural information on protein complexes to the scientific community. Database URL:http://www.bioinsilico.org/CM2D3.
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Affiliation(s)
- Patricia Mirela Bota
- Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, 08950 Barcelona, Catalonia, Spain
| | - Altair C Hernandez
- Live-cell Structural Biology, Department of Medicine and Life Sciences, University Pompeu Fabra, Barcelona 08005, Catalonia, Spain
| | - Joan Segura
- Research Collaboratory for Structural Bioinformatics Protein Data Bank, San Diego Supercomputer Center, University of California, La Jolla, CA 92093, USA
| | - Oriol Gallego
- Live-cell Structural Biology, Department of Medicine and Life Sciences, University Pompeu Fabra, Barcelona 08005, Catalonia, Spain
| | - Baldo Oliva
- Structural Bioinformatics Lab (GRIB-IMIM), Universitat Pompeu Fabra, 08950 Barcelona, Catalonia, Spain.
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences. Aberystwyth University, SY233EE Aberystwyth, United Kingdom.
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Sharma S, Solanki V, Tiwari V. Reverse vaccinology approach to design a vaccine targeting membrane lipoproteins of Salmonella typhi. J Biomol Struct Dyn 2023; 41:954-969. [PMID: 34939517 DOI: 10.1080/07391102.2021.2015443] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Typhoid fever caused by Salmonella is one of the major health issues worldwide, resulting in millions of cases and has very high rates of morbidities. The therapeutic approaches need to be updated for the effective elimination of the bacterial pathogen. The designing of the multiepitope vaccine against Salmonella using comparative proteomics and reverse vaccinology has covered up all the epitopes that induce sufficient immune responses in the host body. Out of the 4293 proteins, 15 outer membrane proteins have been selected based on their antigenicity, low transmembrane helix (<1), and virulence-associated factors. With the help of the reverse vaccinology approach, the epitopes of MHC Class I, Class II, and B-cell with antigenic, low toxicity, and that have the potential to generate immunogenic response have been identified. Based on the comparative analysis of all the epitopes, a multiepitope-based construct has been designed. Based on physicochemical properties and docking scores for HLA and TLR4, the VC5 construct has been selected, and the molecular dynamic simulation studies have confirmed their interaction. The dissociation constant of the VC5 and TLR4 was found to be 3.1 x 10-9. Different immune cell activation has been analyzed, representing the potentiality of the VC5 construct as an effective vaccine target. In silico cloning of VC5 in pET28a has also been performed, which requires experimental validation. Therefore, the present study designs a multi-epitope vaccine VC5 targeted to the membrane lipoproteins of Salmonella typhi.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Saroj Sharma
- Department of Biochemistry, Central University of Rajasthan, Ajmer, India
| | - Vandana Solanki
- Department of Biochemistry, Central University of Rajasthan, Ajmer, India
| | - Vishvanath Tiwari
- Department of Biochemistry, Central University of Rajasthan, Ajmer, India
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7
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Bota PM, Oliva B, Fernandez-Fuentes N. Theoretical 3D Modeling of NLRP3 Inflammasome Complex. Methods Mol Biol 2023; 2696:269-280. [PMID: 37578729 DOI: 10.1007/978-1-0716-3350-2_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The NOD-like receptor pyrin domain containing 3 (NLRP3) is a multidomain protein that plays a key role in innate immune response. Structures of NLRP3 in different conformational states and bound to cognate partners are available. In this chapter we present an approach to model the oligomeric structure of NLRP3 by homology modeling using multiple templates, symmetry, and refinement. The overall process presented here represents advanced exercise in structural modeling that provides unique insights into the biological role and activation of NLRP3 oligomer. Finally, the same approach can be easily adapted to the rest of the members of the NLRP family.
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Affiliation(s)
- Patricia Mirela Bota
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain
| | - Baldo Oliva
- Structural Bioinformatics Lab (GRIB-IMIM), Department of Experimental and Health Science, Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
| | - Narcis Fernandez-Fuentes
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK
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Badkas A, De Landtsheer S, Sauter T. Construction and contextualization approaches for protein-protein interaction networks. Comput Struct Biotechnol J 2022; 20:3280-3290. [PMID: 35832626 PMCID: PMC9251778 DOI: 10.1016/j.csbj.2022.06.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 06/15/2022] [Accepted: 06/15/2022] [Indexed: 11/17/2022] Open
Abstract
Protein-protein interaction network (PPIN) analysis is a widely used method to study the contextual role of proteins of interest, to predict novel disease genes, disease or functional modules, and to identify novel drug targets. PPIN-based analysis uses both generic and context-specific networks. Multiple contextualization methodologies have been described, such as shortest-path algorithms, neighborhood-based methods, and diffusion/propagation algorithms. This review discusses these methods, provides intuitive representations of PPIN contextualization, and also examines how the quality of such context-specific networks could be improved by considering additional sources of evidence. As a heuristic, we observe that tasks such as identifying disease genes, drug targets, and protein complexes should consider local neighborhoods, while uncovering disease mechanisms and discovering disease-pathways would gain from diffusion-based construction.
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Casadio R, Lenhard B, Sternberg MJE. Computational Resources for Molecular Biology 2021. J Mol Biol 2021; 433:166962. [PMID: 33774035 DOI: 10.1016/j.jmb.2021.166962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Rita Casadio
- Biocomputing Group, FABIT-University of Bologna, Italy
| | - Boris Lenhard
- Institute of Clinical Sciences, Faculty of Medicine. Imperial College London, Hammersmith Campus, Du Cane Road, London W12 0NN, UK; Computational Regulatory Genomics, MRC London Institute of Medical Sciences, Du Cane Road, London W12 0NN, UK
| | - Michael J E Sternberg
- Structural Bioinformatics Group, Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
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