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Fongang B, Wadop YN, Zhu Y, Wagner EJ, Kudlicki A, Rowicka M. Coevolution combined with molecular dynamics simulations provides structural and mechanistic insights into the interactions between the integrator complex subunits. Comput Struct Biotechnol J 2023; 21:5686-5697. [PMID: 38074468 PMCID: PMC10700540 DOI: 10.1016/j.csbj.2023.11.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/10/2023] [Accepted: 11/10/2023] [Indexed: 01/18/2024] Open
Abstract
Finding the 3D structure of large, multi-subunit complexes is difficult, despite recent advances in cryo-EM technology, due to remaining challenges to expressing and purifying subunits. Computational approaches that predict protein-protein interactions, including Direct Coupling Analysis (DCA), represent an attractive alternative for dissecting interactions within protein complexes. However, they are readily applicable only to small proteins due to high computational complexity and a high number of false positives. To solve this problem, we proposed a modified DCA approach, a powerful tool to predict the most likely interfaces of protein complexes. Since our modified approach cannot provide structural and mechanistic details of interacting peptides, we combine it with Molecular Dynamics (MD) simulations. To illustrate this novel approach, we predict interacting domains and structural details of interactions of two Integrator complex subunits, INTS9 and INTS11. Our predictions of interacting residues of INTS9/INTS11 are highly consistent with crystallographic structure. We then expand our procedure to two complexes whose structures are not well-studied: 1) The heterodimer formed by the Cleavage and Polyadenylation Specificity Factor 100-kD (CPSF100) and 73-kD (CPSF73); 2) The heterotrimer formed by INTS4/INTS9/INTS11. Experimental data supports our predictions of interactions within these two complexes, demonstrating that combining DCA and MD simulations is a powerful approach to revealing structural insights of large protein complexes.
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Affiliation(s)
- Bernard Fongang
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
- Department of Biochemistry and Structural Biology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
- Department of Population Health Sciences, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
- Institute for Translational Sciences, The University of Texas Medical Branch, Galveston, TX, United States
| | - Yannick N. Wadop
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
- Institute for Translational Sciences, The University of Texas Medical Branch, Galveston, TX, United States
| | - Yingjie Zhu
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States
- Institute for Translational Sciences, The University of Texas Medical Branch, Galveston, TX, United States
| | - Eric J. Wagner
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States
- Department of Biochemistry and Biophysics, The University of Rochester Medical Center, Rochester, NY, United States
- Institute for Translational Sciences, The University of Texas Medical Branch, Galveston, TX, United States
| | - Andrzej Kudlicki
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States
- Institute for Translational Sciences, The University of Texas Medical Branch, Galveston, TX, United States
- Informatics Service Center, The University of Texas Medical Branch, Galveston, TX, United States
| | - Maga Rowicka
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, United States
- Institute for Translational Sciences, The University of Texas Medical Branch, Galveston, TX, United States
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Zaidi N, Soban M, Chen F, Kinkead H, Mathew J, Yarchoan M, Armstrong TD, Haider S, Jaffee EM. Role of in silico structural modeling in predicting immunogenic neoepitopes for cancer vaccine development. JCI Insight 2020; 5:136991. [PMID: 32879142 PMCID: PMC7526456 DOI: 10.1172/jci.insight.136991] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/24/2020] [Indexed: 12/30/2022] Open
Abstract
In prior studies, we delineated the landscape of neoantigens arising from nonsynonymous point mutations in a murine pancreatic cancer model, Panc02. We developed a peptide vaccine by targeting neoantigens predicted using a pipeline that incorporates the MHC binding algorithm NetMHC. The vaccine, when combined with immune checkpoint modulators, elicited a robust neoepitope-specific antitumor immune response and led to tumor clearance. However, only a small fraction of the predicted neoepitopes induced T cell immunity, similarly to that reported for neoantigen vaccines tested in clinical studies. While these studies have used binding affinities to MHC I as surrogates for T cell immunity, this approach does not include spatial information on the mutated residue that is crucial for TCR activation. Here, we investigate conformational alterations in and around the MHC binding groove induced by selected minimal neoepitopes, and we examine the influence of a given mutated residue as a function of its spatial position. We found that structural parameters, including the solvent-accessible surface area (SASA) of the neoepitope and the position and spatial configuration of the mutated residue within the sequence, can be used to improve the prediction of immunogenic neoepitopes for inclusion in cancer vaccines. Structural parameters, including the solvent exposed surface area of the neoepitope and the position and spatial configuration of the mutated residue can be used to improve the prediction of immunogenic neoepitopes for inclusion in cancer vaccines.
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Affiliation(s)
- Neeha Zaidi
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mariya Soban
- Department of Pharmaceutical and Biological Chemistry, University College London School of Pharmacy, London, United Kingdom.,Department of Biochemistry, Faculty of Life Sciences, Aligarh Muslim University, Aligarh, India
| | - Fangluo Chen
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Heather Kinkead
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jocelyn Mathew
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark Yarchoan
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Todd D Armstrong
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Shozeb Haider
- Department of Pharmaceutical and Biological Chemistry, University College London School of Pharmacy, London, United Kingdom
| | - Elizabeth M Jaffee
- The Sidney Kimmel Comprehensive Cancer Center, The Skip Viragh Center for Pancreatic Cancer, The Bloomberg-Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Khatun MS, Shoombuatong W, Hasan MM, Kurata H. Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction. Curr Genomics 2020; 21:454-463. [PMID: 33093807 PMCID: PMC7536797 DOI: 10.2174/1389202921999200625103936] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Revised: 03/19/2020] [Accepted: 05/27/2020] [Indexed: 12/22/2022] Open
Abstract
Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.
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Affiliation(s)
| | | | - Md. Mehedi Hasan
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
| | - Hiroyuki Kurata
- Address correspondence to these authors at the Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Japan Society for the Promotion of Science, 5-3-1 Kojimachi, Chiyoda-ku, Tokyo 102-0083, Japan; Tel: +81-948-297-828; E-mail: and Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan; Tel: +81-948-297-828; E-mail:
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Abstract
The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.
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Affiliation(s)
- Donald Petrey
- Center for Computational Biology and Bioinformatics, Department of Systems Biology
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Ochoa D, Pazos F. Practical aspects of protein co-evolution. Front Cell Dev Biol 2014; 2:14. [PMID: 25364721 PMCID: PMC4207036 DOI: 10.3389/fcell.2014.00014] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2014] [Accepted: 04/02/2014] [Indexed: 11/15/2022] Open
Abstract
Co-evolution is a fundamental aspect of Evolutionary Theory. At the molecular level, co-evolutionary linkages between protein families have been used as indicators of protein interactions and functional relationships from long ago. Due to the complexity of the problem and the amount of genomic data required for these approaches to achieve good performances, it took a relatively long time from the appearance of the first ideas and concepts to the quotidian application of these approaches and their incorporation to the standard toolboxes of bioinformaticians and molecular biologists. Today, these methodologies are mature (both in terms of performance and usability/implementation), and the genomic information that feeds them large enough to allow their general application. This review tries to summarize the current landscape of co-evolution-based methodologies, with a strong emphasis on describing interesting cases where their application to important biological systems, alone or in combination with other computational and experimental approaches, allowed getting new insight into these.
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Affiliation(s)
- David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) Hinxton, UK
| | - Florencio Pazos
- Computational Systems Biology Group, National Centre for Biotechnology (CNB-CSIC) Madrid, Spain
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