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Asim MN, Fazeel A, Ibrahim MA, Dengel A, Ahmed S. MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses. Front Med (Lausanne) 2022; 9:1025887. [PMID: 36465911 PMCID: PMC9709337 DOI: 10.3389/fmed.2022.1025887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/17/2022] [Indexed: 09/19/2023] Open
Abstract
Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.
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Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Ahtisham Fazeel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, Kaiserslautern, Germany
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, Kaiserslautern, Germany
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Cakir M, Obernier K, Forget A, Krogan NJ. Target Discovery for Host-Directed Antiviral Therapies: Application of Proteomics Approaches. mSystems 2021; 6:e0038821. [PMID: 34519533 PMCID: PMC8547474 DOI: 10.1128/msystems.00388-21] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Current epidemics, such as AIDS or flu, and the emergence of new threatening pathogens, such as the one causing the current coronavirus disease 2019 (COVID-19) pandemic, represent major global health challenges. While vaccination is an important part of the arsenal to counter the spread of viral diseases, it presents limitations and needs to be complemented by efficient therapeutic solutions. Intricate knowledge of host-pathogen interactions is a powerful tool to identify host-dependent vulnerabilities that can be exploited to dampen viral replication. Such host-directed antiviral therapies are promising and are less prone to the development of drug-resistant viral strains. Here, we first describe proteomics-based strategies that allow the rapid characterization of host-pathogen interactions. We then discuss how such data can be exploited to help prioritize compounds with potential host-directed antiviral activity that can be tested in preclinical models.
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Affiliation(s)
- Merve Cakir
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, California, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
| | - Kirsten Obernier
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, California, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
| | - Antoine Forget
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, California, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
| | - Nevan J. Krogan
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, California, USA
- Quantitative Biosciences Institute (QBI) COVID-19 Research Group (QCRG), San Francisco, California, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California, USA
- Gladstone Institute of Data Science and Biotechnology, J. David Gladstone Institutes, San Francisco, California, USA
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Richards AL, Eckhardt M, Krogan NJ. Mass spectrometry-based protein-protein interaction networks for the study of human diseases. Mol Syst Biol 2021; 17:e8792. [PMID: 33434350 PMCID: PMC7803364 DOI: 10.15252/msb.20188792] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 09/23/2020] [Accepted: 11/03/2020] [Indexed: 12/13/2022] Open
Abstract
A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein-protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)-based approaches have allowed unbiased mapping of these disease-mediated changes in protein-protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein-protein interactions at a system-level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS-based protein-protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.
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Affiliation(s)
- Alicia L Richards
- Quantitative Biosciences Institute (QBI)University of California San FranciscoSan FranciscoCAUSA
- J. David Gladstone InstitutesSan FranciscoCAUSA
- Department of Cellular and Molecular PharmacologyUniversity of California San FranciscoSan FranciscoCAUSA
| | - Manon Eckhardt
- Quantitative Biosciences Institute (QBI)University of California San FranciscoSan FranciscoCAUSA
- J. David Gladstone InstitutesSan FranciscoCAUSA
- Department of Cellular and Molecular PharmacologyUniversity of California San FranciscoSan FranciscoCAUSA
| | - Nevan J Krogan
- Quantitative Biosciences Institute (QBI)University of California San FranciscoSan FranciscoCAUSA
- J. David Gladstone InstitutesSan FranciscoCAUSA
- Department of Cellular and Molecular PharmacologyUniversity of California San FranciscoSan FranciscoCAUSA
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