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Tahir ul Qamar M, Noor F, Guo YX, Zhu XT, Chen LL. Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions. Comput Struct Biotechnol J 2024; 23:316-329. [PMID: 38192372 PMCID: PMC10772389 DOI: 10.1016/j.csbj.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/11/2023] [Accepted: 12/12/2023] [Indexed: 01/10/2024] Open
Abstract
Host-pathogen interactions (HPIs) are vital in numerous biological activities and are intrinsically linked to the onset and progression of infectious diseases. HPIs are pivotal in the entire lifecycle of diseases: from the onset of pathogen introduction, navigating through the mechanisms that bypass host cellular defenses, to its subsequent proliferation inside the host. At the heart of these stages lies the synergy of proteins from both the host and the pathogen. By understanding these interlinking protein dynamics, we can gain crucial insights into how diseases progress and pave the way for stronger plant defenses and the swift formulation of countermeasures. In the framework of current study, we developed a web-based R/Shiny app, Deep-HPI-pred, that uses network-driven feature learning method to predict the yet unmapped interactions between pathogen and host proteins. Leveraging citrus and CLas bacteria training datasets as case study, we spotlight the effectiveness of Deep-HPI-pred in discerning Protein-protein interaction (PPIs) between them. Deep-HPI-pred use Multilayer Perceptron (MLP) models for HPI prediction, which is based on a comprehensive evaluation of topological features and neural network architectures. When subjected to independent validation datasets, the predicted models consistently surpassed a Matthews correlation coefficient (MCC) of 0.80 in host-pathogen interactions. Remarkably, the use of Eigenvector Centrality as the leading topological feature further enhanced this performance. Further, Deep-HPI-pred also offers relevant gene ontology (GO) term information for each pathogen and host protein within the system. This protein annotation data contributes an additional layer to our understanding of the intricate dynamics within host-pathogen interactions. In the additional benchmarking studies, the Deep-HPI-pred model has proven its robustness by consistently delivering reliable results across different host-pathogen systems, including plant-pathogens (accuracy of 98.4% and 97.9%), human-virus (accuracy of 94.3%), and animal-bacteria (accuracy of 96.6%) interactomes. These results not only demonstrate the model's versatility but also pave the way for gaining comprehensive insights into the molecular underpinnings of complex host-pathogen interactions. Taken together, the Deep-HPI-pred applet offers a unified web service for both identifying and illustrating interaction networks. Deep-HPI-pred applet is freely accessible at its homepage: https://cbi.gxu.edu.cn/shiny-apps/Deep-HPI-pred/ and at github: https://github.com/tahirulqamar/Deep-HPI-pred.
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Affiliation(s)
- Muhammad Tahir ul Qamar
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China
| | - Fatima Noor
- Integrative Omics and Molecular Modeling Laboratory, Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad 38000, Pakistan
| | - Yi-Xiong Guo
- National Key Laboratory of Crop Genetic Improvement, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Xi-Tong Zhu
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China
| | - Ling-Ling Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China
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2
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Zhang Y, Thomas JP, Korcsmaros T, Gul L. Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease. Cell Rep Med 2024; 5:101738. [PMID: 39293401 DOI: 10.1016/j.xcrm.2024.101738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Revised: 08/11/2024] [Accepted: 08/21/2024] [Indexed: 09/20/2024]
Abstract
The gut microbiome is crucial for nutrient metabolism, immune regulation, and intestinal homeostasis with changes in its composition linked to complex diseases like inflammatory bowel disease (IBD). Although the precise host-microbial mechanisms in disease pathogenesis remain unclear, high-throughput sequencing have opened new ways to unravel the role of interspecies interactions in IBD. Systems biology-a holistic computational framework for modeling complex biological systems-is critical for leveraging multi-omics datasets to identify disease mechanisms. This review highlights the significance of multi-omics data in IBD research and provides an overview of state-of-the-art systems biology resources and computational tools for data integration. We explore gaps, challenges, and future directions in the research field aiming to uncover novel biomarkers and therapeutic targets, ultimately advancing personalized treatment strategies. While focusing on IBD, the proposed approaches are applicable for other complex diseases, like cancer, and neurodegenerative diseases, where the microbiome has also been implicated.
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Affiliation(s)
- Yiran Zhang
- Department of Surgery & Cancer, Imperial College London, London W12 0NN, UK; Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK
| | - John P Thomas
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; UKRI MRC Laboratory of Medical Sciences, Hammersmith Hospital Campus, London W12 0HS, UK
| | - Tamas Korcsmaros
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; NIHR Imperial BRC Organoid Facility, Imperial College London, London W12 0NN, UK; Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK.
| | - Lejla Gul
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London W12 0NN, UK; Quadram Institute Bioscience, Norwich Research Park, Norwich NR4 7UQ, UK
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3
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Jia P, Zhang F, Wu C, Li M. A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. Brief Bioinform 2024; 25:bbae162. [PMID: 38739759 PMCID: PMC11089422 DOI: 10.1093/bib/bbae162] [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: 01/01/2024] [Revised: 02/17/2024] [Accepted: 03/31/2024] [Indexed: 05/16/2024] Open
Abstract
Proteins interact with diverse ligands to perform a large number of biological functions, such as gene expression and signal transduction. Accurate identification of these protein-ligand interactions is crucial to the understanding of molecular mechanisms and the development of new drugs. However, traditional biological experiments are time-consuming and expensive. With the development of high-throughput technologies, an increasing amount of protein data is available. In the past decades, many computational methods have been developed to predict protein-ligand interactions. Here, we review a comprehensive set of over 160 protein-ligand interaction predictors, which cover protein-protein, protein-nucleic acid, protein-peptide and protein-other ligands (nucleotide, heme, ion) interactions. We have carried out a comprehensive analysis of the above four types of predictors from several significant perspectives, including their inputs, feature profiles, models, availability, etc. The current methods primarily rely on protein sequences, especially utilizing evolutionary information. The significant improvement in predictions is attributed to deep learning methods. Additionally, sequence-based pretrained models and structure-based approaches are emerging as new trends.
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Affiliation(s)
- Pengzhen Jia
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Fuhao Zhang
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
- College of Information Engineering, Northwest A&F University, No. 3 Taicheng Road, Yangling, Shaanxi 712100, China
| | - Chaojin Wu
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, 932 Lushan Road(S), Changsha 410083, China
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4
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Pan J, Zhang Z, Li Y, Yu J, You Z, Li C, Wang S, Zhu M, Ren F, Zhang X, Sun Y, Wang S. A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts. Brief Bioinform 2024; 25:bbae119. [PMID: 38555472 PMCID: PMC10981679 DOI: 10.1093/bib/bbae119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/23/2024] [Accepted: 03/02/2024] [Indexed: 04/02/2024] Open
Abstract
Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.
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Affiliation(s)
- Jie Pan
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhen Zhang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Ying Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Jiaoyang Yu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Zhuhong You
- School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
| | - Chenyu Li
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shixu Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Minghui Zhu
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Fengzhi Ren
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Xuexia Zhang
- North China Pharmaceutical Group, Shijiazhuang 050015, Hebei, China
- National Microbial Medicine Engineering & Research Center, Shijiazhuang 050015, Hebei, China
| | - Yanmei Sun
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
| | - Shiwei Wang
- Key Laboratory of Resources Biology and Biotechnology in Western China, Ministry of Education, Provincial Key Laboratory of Biotechnology of Shaanxi Province, College of Life Sciences, Northwest University, Xi’an 710069, China
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5
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Yang X, Wuchty S, Liang Z, Ji L, Wang B, Zhu J, Zhang Z, Dong Y. Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM. Brief Bioinform 2024; 25:bbae005. [PMID: 38279649 PMCID: PMC10818167 DOI: 10.1093/bib/bbae005] [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: 11/20/2023] [Revised: 12/25/2023] [Accepted: 01/01/2021] [Indexed: 01/28/2024] Open
Abstract
The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.
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Affiliation(s)
- Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami FL, 33146, USA
- Department of Biology, University of Miami, Miami FL, 33146, USA
- Institute of Data Science and Computation, University of Miami, Miami, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Zeyin Liang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Li Ji
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bingjie Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Jialin Zhu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yujun Dong
- Department of Hematology, Peking University First Hospital, Beijing, China
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6
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Chen HM, Liu JX, Liu D, Hao GF, Yang GF. Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection. Rev Med Virol 2024; 34:e2517. [PMID: 38282401 DOI: 10.1002/rmv.2517] [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: 05/11/2023] [Revised: 09/12/2023] [Accepted: 01/16/2024] [Indexed: 01/30/2024]
Abstract
Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.
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Affiliation(s)
- Hui-Min Chen
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Jia-Xin Liu
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
| | - Di Liu
- CAS Key Laboratory of Special Pathogens and Biosafety, Wuhan Institute of Virology, Center for Biosafety Mega-Science, Chinese Academy of Sciences, Wuhan, China
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang, China
| | - Guang-Fu Yang
- National Key Laboratory of Green Pesticide, Central China Normal University, Wuhan, China
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7
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Ming Z, Chen X, Wang S, Liu H, Yuan Z, Wu M, Xia H. HostNet: improved sequence representation in deep neural networks for virus-host prediction. BMC Bioinformatics 2023; 24:455. [PMID: 38041071 PMCID: PMC10691023 DOI: 10.1186/s12859-023-05582-9] [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: 03/31/2023] [Accepted: 11/24/2023] [Indexed: 12/03/2023] Open
Abstract
BACKGROUND The escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand. RESULTS To overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of "Rabies lyssavirus" and an in-house dataset of "Flavivirus". Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability. CONCLUSION HostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.
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Affiliation(s)
- Zhaoyan Ming
- School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China
| | - Xiangjun Chen
- Polytechnic Institute, Zhejiang University, Hangzhou, 310058, China
| | - Shunlong Wang
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Hong Liu
- Institute of Biomedicine, Shandong University of Technology, Zibo, 255000, China
| | - Zhiming Yuan
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China
- University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Minghui Wu
- School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China.
| | - Han Xia
- Key Laboratory of Virology and Biosafety, Wuhan Institute of Virology, Wuhan, 430071, China.
- University of Chinese Academy of Sciences, Beijing, 100190, China.
- Hubei Jiangxia Laboratory, Wuhan, 430200, China.
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8
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Lee M. Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review. Molecules 2023; 28:5169. [PMID: 37446831 DOI: 10.3390/molecules28135169] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 07/15/2023] Open
Abstract
Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.
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Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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9
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Rogers JR, Nikolényi G, AlQuraishi M. Growing ecosystem of deep learning methods for modeling protein-protein interactions. Protein Eng Des Sel 2023; 36:gzad023. [PMID: 38102755 DOI: 10.1093/protein/gzad023] [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: 10/10/2023] [Revised: 12/06/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
Numerous cellular functions rely on protein-protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.
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Affiliation(s)
- Julia R Rogers
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
| | - Gergő Nikolényi
- Department of Systems Biology, Columbia University, New York, NY 10032, USA
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10
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Iuchi H, Kawasaki J, Kubo K, Fukunaga T, Hokao K, Yokoyama G, Ichinose A, Suga K, Hamada M. Bioinformatics approaches for unveiling virus-host interactions. Comput Struct Biotechnol J 2023; 21:1774-1784. [PMID: 36874163 PMCID: PMC9969756 DOI: 10.1016/j.csbj.2023.02.044] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/22/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023] Open
Abstract
The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network remains veiled. In this review, we comprehensively surveyed algorithms used to predict virus-host interactions. We also discuss the current challenges, such as dataset biases toward highly pathogenic viruses, and the potential solutions. The complete prediction of virus-host interactions remains difficult; however, bioinformatics can contribute to progress in research on infectious diseases and human health.
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Affiliation(s)
- Hitoshi Iuchi
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan
| | - Junna Kawasaki
- Faculty of Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Kento Kubo
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Tsukasa Fukunaga
- Waseda Institute for Advanced Study, Waseda University, Nishi Waseda, Shinjuku-ku, Tokyo 169-0051, Japan
| | - Koki Hokao
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Gentaro Yokoyama
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Akiko Ichinose
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan
| | - Kanta Suga
- School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan
| | - Michiaki Hamada
- Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169-8555, Japan.,School of Advanced Science and Engineering, Waseda University, Okubo Shinjuku-ku, Tokyo 169-8555, Japan.,Graduate School of Medicine, Nippon Medical School, Tokyo 113-8602, Japan
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11
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Murakami Y, Mizuguchi K. Recent developments of sequence-based prediction of protein-protein interactions. Biophys Rev 2022; 14:1393-1411. [PMID: 36589735 PMCID: PMC9789376 DOI: 10.1007/s12551-022-01038-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2022] [Indexed: 12/25/2022] Open
Abstract
The identification of protein-protein interactions (PPIs) can lead to a better understanding of cellular functions and biological processes of proteins and contribute to the design of drugs to target disease-causing PPIs. In addition, targeting host-pathogen PPIs is useful for elucidating infection mechanisms. Although several experimental methods have been used to identify PPIs, these methods can yet to draw complete PPI networks. Hence, computational techniques are increasingly required for the prediction of potential PPIs, which have never been seen experimentally. Recent high-performance sequence-based methods have contributed to the construction of PPI networks and the elucidation of pathogenetic mechanisms in specific diseases. However, the usefulness of these methods depends on the quality and quantity of training data of PPIs. In this brief review, we introduce currently available PPI databases and recent sequence-based methods for predicting PPIs. Also, we discuss key issues in this field and present future perspectives of the sequence-based PPI predictions.
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Affiliation(s)
- Yoichi Murakami
- grid.440890.10000 0004 0640 9413Tokyo University of Information Sciences, 4-1 Onaridai, Wakaba-Ku, Chiba, 265-8501 Japan
| | - Kenji Mizuguchi
- grid.136593.b0000 0004 0373 3971Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita-Shi, Osaka, 565-0871 Japan ,grid.482562.fNational Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito Asagi, Ibaraki, Osaka 567-0085 Japan
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12
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Neumann D, Roy S, Minhas FUAA, Ben-Hur A. On the choice of negative examples for prediction of host-pathogen protein interactions. FRONTIERS IN BIOINFORMATICS 2022; 2:1083292. [PMID: 36591335 PMCID: PMC9798088 DOI: 10.3389/fbinf.2022.1083292] [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: 10/28/2022] [Accepted: 11/14/2022] [Indexed: 12/23/2022] Open
Abstract
As practitioners of machine learning in the area of bioinformatics we know that the quality of the results crucially depends on the quality of our labeled data. While there is a tendency to focus on the quality of positive examples, the negative examples are equally as important. In this opinion paper we revisit the problem of choosing negative examples for the task of predicting protein-protein interactions, either among proteins of a given species or for host-pathogen interactions and describe important issues that are prevalent in the current literature. The challenge in creating datasets for this task is the noisy nature of the experimentally derived interactions and the lack of information on non-interacting proteins. A standard approach is to choose random pairs of non-interacting proteins as negative examples. Since the interactomes of all species are only partially known, this leads to a very small percentage of false negatives. This is especially true for host-pathogen interactions. To address this perceived issue, some researchers have chosen to select negative examples as pairs of proteins whose sequence similarity to the positive examples is sufficiently low. This clearly reduces the chance for false negatives, but also makes the problem much easier than it really is, leading to over-optimistic accuracy estimates. We demonstrate the effect of this form of bias using a selection of recent protein interaction prediction methods of varying complexity, and urge researchers to pay attention to the details of generating their datasets for potential biases like this.
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Affiliation(s)
- Don Neumann
- Department Computer Science, Colorado State University, Fort Collins, CO, United States,*Correspondence: Don Neumann, ; Asa Ben-Hur,
| | - Soumyadip Roy
- Department Computer Science, Colorado State University, Fort Collins, CO, United States
| | | | - Asa Ben-Hur
- Department Computer Science, Colorado State University, Fort Collins, CO, United States,*Correspondence: Don Neumann, ; Asa Ben-Hur,
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13
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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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14
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Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks. Comput Struct Biotechnol J 2022; 20:5564-5573. [PMID: 36249566 PMCID: PMC9546503 DOI: 10.1016/j.csbj.2022.10.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 10/05/2022] [Accepted: 10/05/2022] [Indexed: 11/30/2022] Open
Abstract
Cross-attention PHV implements two key technologies: cross-attention mechanism and 1D-CNN. It accurately predicts PPIs between human and unknown influenza viruses/SARS-CoV-2. It extracts critical taxonomic and evolutionary differences responsible for PPI prediction.
Viral infections represent a major health concern worldwide. The alarming rate at which SARS-CoV-2 spreads, for example, led to a worldwide pandemic. Viruses incorporate genetic material into the host genome to hijack host cell functions such as the cell cycle and apoptosis. In these viral processes, protein–protein interactions (PPIs) play critical roles. Therefore, the identification of PPIs between humans and viruses is crucial for understanding the infection mechanism and host immune responses to viral infections and for discovering effective drugs. Experimental methods including mass spectrometry-based proteomics and yeast two-hybrid assays are widely used to identify human-virus PPIs, but these experimental methods are time-consuming, expensive, and laborious. To overcome this problem, we developed a novel computational predictor, named cross-attention PHV, by implementing two key technologies of the cross-attention mechanism and a one-dimensional convolutional neural network (1D-CNN). The cross-attention mechanisms were very effective in enhancing prediction and generalization abilities. Application of 1D-CNN to the word2vec-generated feature matrices reduced computational costs, thus extending the allowable length of protein sequences to 9000 amino acid residues. Cross-attention PHV outperformed existing state-of-the-art models using a benchmark dataset and accurately predicted PPIs for unknown viruses. Cross-attention PHV also predicted human–SARS-CoV-2 PPIs with area under the curve values >0.95. The Cross-attention PHV web server and source codes are freely available at https://kurata35.bio.kyutech.ac.jp/Cross-attention_PHV/ and https://github.com/kuratahiroyuki/Cross-Attention_PHV, respectively.
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Key Words
- 1D-CNN, One-dimensional-CNN
- AC, Accuracy
- AUC, Area under the curve
- CNN, Convolutional neural network
- Convolutional neural network
- DT, Decision tree
- F1, F1-score
- HV-PPIs, Human-virus PPIs
- HuV-PPI, Human–unknown virus PPI
- Human
- LR, Linear regression
- MCC, Matthews correlation coefficient
- PPIs, Protein-protein interactions
- Protein–protein interaction
- RF, Random forest
- SARS-CoV-2
- SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2
- SN, Sensitivity
- SP, Specificity
- SVM, Support vector machine
- T-SNE, T-distributed stochastic neighbor embedding
- Virus
- W2V, Word2vec
- Word2vec
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15
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Xu D, Liu B, Wang J, Zhang Z. Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers. Front Bioeng Biotechnol 2022; 10:998298. [PMID: 36277390 PMCID: PMC9585160 DOI: 10.3389/fbioe.2022.998298] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background: In the biotechnology and applied microbiology sectors, artificial intelligence (AI) has been extensively used in disease diagnostics, drug research and development, functional genomics, biomarker recognition, and medical imaging diagnostics. In our study, from 2000 to 2021, science publications focusing on AI in biotechnology were reviewed, and quantitative, qualitative, and modeling analyses were performed. Methods: On 6 May 2022, the Web of Science Core Collection (WoSCC) was screened for AI applications in biotechnology and applied microbiology; 3,529 studies were identified between 2000 and 2022, and analyzed. The following information was collected: publication, country or region, references, knowledgebase, institution, keywords, journal name, and research hotspots, and examined using VOSviewer and CiteSpace V bibliometric platforms. Results: We showed that 128 countries published articles related to AI in biotechnology and applied microbiology; the United States had the most publications. In addition, 584 global institutions contributed to publications, with the Chinese Academy of Science publishing the most. Reference clusters from studies were categorized into ten headings: deep learning, prediction, support vector machines (SVM), object detection, feature representation, synthetic biology, amyloid, human microRNA precursors, systems biology, and single cell RNA-Sequencing. Research frontier keywords were represented by microRNA (2012–2020) and protein-protein interactions (PPIs) (2012–2020). Conclusion: We systematically, objectively, and comprehensively analyzed AI-related biotechnology and applied microbiology literature, and additionally, identified current hot spots and future trends in this area. Our review provides researchers with a comprehensive overview of the dynamic evolution of AI in biotechnology and applied microbiology and identifies future key research areas.
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Affiliation(s)
- Dongyu Xu
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
| | - Bing Liu
- Department of Bone Oncology, The People’s Hospital of Liaoning Province, Shenyang, Liaoning, China
| | - Jian Wang
- Department of Pathogenic Biology, School of Basic Medicine, China Medical University, Shenyang, Liaoning, China
| | - Zhichang Zhang
- Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China
- *Correspondence: Zhichang Zhang,
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16
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Alakus TB, Turkoglu I. Prediction of viral-host interactions of COVID-19 by computational methods. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS : AN INTERNATIONAL JOURNAL SPONSORED BY THE CHEMOMETRICS SOCIETY 2022; 228:104622. [PMID: 35879939 PMCID: PMC9301933 DOI: 10.1016/j.chemolab.2022.104622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 06/20/2022] [Accepted: 07/10/2022] [Indexed: 06/15/2023]
Abstract
Experimental approaches are currently used to determine viral-host interactions, but these approaches are both time-consuming and costly. For these reasons, computational-based approaches are recommended. In this study, using computational-based approaches, viral-host interactions of SARS-CoV-2 virus and human proteins were predicted. The study consists of four different stages; in the first stage viral and host protein sequences were obtained. In the second stage, protein sequences were converted into numerical expressions by various protein mapping methods. These methods are entropy-based, AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters, EIIP, AESNN1, Miyazawa energies, Micheletti potentials, Z-scale, and hydrophobicity. In the third stage, a deep learning model was designed and BiLSTM was used for this. In the last stage, the protein sequences were classified, and the viral-host interactions were predicted. The performances of protein mapping methods were determined by accuracy, F1-score, specificity, sensitivity, and AUC scores. According to the classification results, the best classification process was obtained by the entropy-based method. With this method, 94.74% accuracy, and 0.95 AUC score were calculated. Then, the most successful classification process was performed with the Z-scale and 91.23% accuracy, and 0.96 AUC score were obtained. Although other protein mapping methods are not as efficient as Z-scale and entropy-based methods, they have achieved successful classification. AVL-tree, FIBHASH, binary encoding, CPNR, PAM250, BLOSUM62, Atchley factors, Meiler parameters and AESNN1 methods showed over 80% accuracy, F1-score, and AUC score. Accuracy scores of EIIP, Miyazawa energies, Micheletti potentials and hydrophobicity methods remained below 80%. When the results were examined in general, it was observed that the computational approaches were successful in predicting viral-host interactions between SARS-CoV-2 virus and human proteins.
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Affiliation(s)
- Talha Burak Alakus
- Kirklareli University, Department of Software Engineering, Kirklareli, 39000, Turkey
| | - Ibrahim Turkoglu
- Firat University, Department of Software Engineering, Elazig, 23119, Turkey
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17
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Madan S, Demina V, Stapf M, Ernst O, Fröhlich H. Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings. PATTERNS (NEW YORK, N.Y.) 2022; 3:100551. [PMID: 36124304 PMCID: PMC9481957 DOI: 10.1016/j.patter.2022.100551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 03/28/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022]
Abstract
Prediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.
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Affiliation(s)
- Sumit Madan
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Institute of Computer Science, University of Bonn, 53115 Bonn, Germany
| | | | - Marcus Stapf
- NEUWAY Pharma GmbH, In den Dauen 6A, 53117 Bonn, Germany
| | - Oliver Ernst
- NEUWAY Pharma GmbH, In den Dauen 6A, 53117 Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53113 Bonn, Germany
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18
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Koca MB, Nourani E, Abbasoğlu F, Karadeniz İ, Sevilgen FE. Graph convolutional network based virus-human protein-protein interaction prediction for novel viruses. Comput Biol Chem 2022; 101:107755. [PMID: 36037723 DOI: 10.1016/j.compbiolchem.2022.107755] [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: 05/02/2022] [Revised: 07/07/2022] [Accepted: 08/10/2022] [Indexed: 11/03/2022]
Abstract
Computational identification of human-virus protein-protein interactions (PHIs) is a worthwhile step towards understanding infection mechanisms. Analysis of the PHI networks is important for the determination of pathogenic diseases. Prediction of these interactions is a popular problem since experimental detection of PHIs is both time-consuming and expensive. The available methods use biological features like amino acid sequences, molecular structure, or biological activities for prediction. Recent studies show that the topological properties of proteins in protein-protein interaction (PPI) networks increase the performance of the predictions. The basic network projections, random-walk-based models, or graph neural networks are used for generating topologically enriched (hybrid) protein embeddings. In this study, we propose a three-stage machine learning pipeline that generates and uses hybrid embeddings for PHI prediction. In the first stage, numerical features are extracted from the amino acid sequences using the Doc2Vec and Byte Pair Encoding method. The amino acid embeddings are used as node features while training a modified GraphSAGE model, which is an improved version of the graph convolutional network. Lastly, the hybrid protein embeddings are used for training a binary interaction classifier model that predicts whether there is an interaction between the given two proteins or not. The proposed method is evaluated with comprehensive experiments to test its functionality and compare it with the state-of-art methods. The experimental results on the benchmark dataset prove the efficiency of the proposed model by having a 3-23% better area under curve (AUC) score than its competitors.
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Affiliation(s)
- Mehmet Burak Koca
- Department of Computer Engineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkey
| | - Esmaeil Nourani
- Department of Information Technology, Faculty of Computer Engineering and Information Technology, Azarbaijan Shahid Madani University, Tabriz, Iran
| | - Ferda Abbasoğlu
- Department of Computer Engineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkey
| | - İlknur Karadeniz
- Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Işık University, İstanbul, Turkey.
| | - Fatih Erdoğan Sevilgen
- Department of Computer Engineering, Faculty of Engineering, Gebze Technical University, Kocaeli, Turkey; Institute for Data Science and Artificial Intelligence, Boğaziçi University, İstanbul, Turkey
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19
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Wadie B, Kleshchevnikov V, Sandaltzopoulou E, Benz C, Petsalaki E. Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs. Cell Rep 2022; 39:110764. [PMID: 35508127 DOI: 10.1016/j.celrep.2022.110764] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 02/09/2022] [Accepted: 04/08/2022] [Indexed: 12/16/2022] Open
Abstract
Linear motifs have an integral role in dynamic cell functions, including cell signaling. However, due to their small size, low complexity, and frequent mutations, identifying novel functional motifs poses a challenge. Viruses rely extensively on the molecular mimicry of cellular linear motifs. In this study, we apply systematic motif prediction combined with functional filters to identify human linear motifs convergently evolved also in viral proteins. We observe an increase in the sensitivity of motif prediction and improved enrichment in known instances. We identify >7,300 non-redundant motif instances at various confidence levels, 99 of which are supported by all functional and structural filters. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalog of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the associated mechanisms of viral interference.
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Affiliation(s)
- Bishoy Wadie
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Vitalii Kleshchevnikov
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Elissavet Sandaltzopoulou
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Caroline Benz
- Department of Chemistry - BMC, Uppsala University, Uppsala, Sweden
| | - Evangelia Petsalaki
- European Molecular Biology Laboratory - European Bioinformatics Institute, Wellcome Genome Campus, Hinxton CB10 1SD, UK.
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20
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Kaundal R, Loaiza CD, Duhan N, Flann N. deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host-pathogen protein-protein interactions. Brief Bioinform 2022; 23:6576450. [PMID: 35511057 DOI: 10.1093/bib/bbac125] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 02/07/2022] [Accepted: 03/15/2022] [Indexed: 01/06/2023] Open
Abstract
Host-pathogen protein interactions (HPPIs) play vital roles in many biological processes and are directly involved in infectious diseases. With the outbreak of more frequent pandemics in the last couple of decades, such as the recent outburst of Covid-19 causing millions of deaths, it has become more critical to develop advanced methods to accurately predict pathogen interactions with their respective hosts. During the last decade, experimental methods to identify HPIs have been used to decipher host-pathogen systems with the caveat that those techniques are labor-intensive, expensive and time-consuming. Alternatively, accurate prediction of HPIs can be performed by the use of data-driven machine learning. To provide a more robust and accurate solution for the HPI prediction problem, we have developed a deepHPI tool based on deep learning. The web server delivers four host-pathogen model types: plant-pathogen, human-bacteria, human-virus and animal-pathogen, leveraging its operability to a wide range of analyses and cases of use. The deepHPI web tool is the first to use convolutional neural network models for HPI prediction. These models have been selected based on a comprehensive evaluation of protein features and neural network architectures. The best prediction models have been tested on independent validation datasets, which achieved an overall Matthews correlation coefficient value of 0.87 for animal-pathogen using the combined pseudo-amino acid composition and conjoint triad (PAAC_CT) features, 0.75 for human-bacteria using the combined pseudo-amino acid composition, conjoint triad and normalized Moreau-Broto feature (PAAC_CT_NMBroto), 0.96 for human-virus using PAAC_CT_NMBroto and 0.94 values for plant-pathogen interactions using the combined pseudo-amino acid composition, composition and transition feature (PAAC_CTDC_CTDT). Our server running deepHPI is deployed on a high-performance computing cluster that enables large and multiple user requests, and it provides more information about interactions discovered. It presents an enriched visualization of the resulting host-pathogen networks that is augmented with external links to various protein annotation resources. We believe that the deepHPI web server will be very useful to researchers, particularly those working on infectious diseases. Additionally, many novel and known host-pathogen systems can be further investigated to significantly advance our understanding of complex disease-causing agents. The developed models are established on a web server, which is freely accessible at http://bioinfo.usu.edu/deepHPI/.
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Affiliation(s)
- Rakesh Kaundal
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences.,Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences.,Department of Computer Science, College of Science; Utah State University, Logan, 84322 USA
| | - Cristian D Loaiza
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences.,Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences
| | - Naveen Duhan
- Bioinformatics Facility, Center for Integrated BioSystems, College of Agriculture and Applied Sciences.,Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences
| | - Nicholas Flann
- Department of Computer Science, College of Science; Utah State University, Logan, 84322 USA
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21
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Saha D, Iannuccelli M, Brun C, Zanzoni A, Licata L. The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses. Front Microbiol 2022; 13:849781. [PMID: 35531299 PMCID: PMC9069133 DOI: 10.3389/fmicb.2022.849781] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/11/2022] [Indexed: 11/18/2022] Open
Abstract
Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.
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Affiliation(s)
- Deeya Saha
- Aix-Marseille Univ., Inserm, TAGC, UMR_S1090, Marseille, France
| | | | - Christine Brun
- Aix-Marseille Univ., Inserm, TAGC, UMR_S1090, Marseille, France
- CNRS, Marseille, France
| | - Andreas Zanzoni
- Aix-Marseille Univ., Inserm, TAGC, UMR_S1090, Marseille, France
- *Correspondence: Andreas Zanzoni,
| | - Luana Licata
- Department of Biology, University of Rome Tor Vergata, Rome, Italy
- Luana Licata,
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22
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Zhang R, Ghosh S, Pal R. Predicting binding affinities of emerging variants of SARS-CoV-2 using spike protein sequencing data: observations, caveats and recommendations. Brief Bioinform 2022; 23:6569542. [PMID: 35437577 DOI: 10.1093/bib/bbac128] [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: 12/23/2021] [Revised: 02/13/2022] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein-protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance.
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Affiliation(s)
- Ruibo Zhang
- Department of Electrical and Computer Engineering, Texas Tech University, TX, USA
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, NB, USA
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, TX, USA
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23
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Yang X, Yang S, Ren P, Wuchty S, Zhang Z. Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions. Front Microbiol 2022; 13:842976. [PMID: 35495666 PMCID: PMC9051481 DOI: 10.3389/fmicb.2022.842976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Abstract
Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Panyu Ren
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, FL, United States
- Department of Biology, University of Miami, Miami, FL, United States
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, United States
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China
- *Correspondence: Ziding Zhang,
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24
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Lim H, Cankara F, Tsai CJ, Keskin O, Nussinov R, Gursoy A. Artificial intelligence approaches to human-microbiome protein–protein interactions. Curr Opin Struct Biol 2022; 73:102328. [DOI: 10.1016/j.sbi.2022.102328] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/01/2021] [Accepted: 12/31/2021] [Indexed: 02/08/2023]
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25
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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]
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26
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Hu X, Feng C, Ling T, Chen M. Deep learning frameworks for protein–protein interaction prediction. Comput Struct Biotechnol J 2022; 20:3223-3233. [PMID: 35832624 PMCID: PMC9249595 DOI: 10.1016/j.csbj.2022.06.025] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/27/2022] [Accepted: 06/12/2022] [Indexed: 11/26/2022] Open
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27
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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 .
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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
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28
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Carlson CJ, Farrell MJ, Grange Z, Han BA, Mollentze N, Phelan AL, Rasmussen AL, Albery GF, Bett B, Brett-Major DM, Cohen LE, Dallas T, Eskew EA, Fagre AC, Forbes KM, Gibb R, Halabi S, Hammer CC, Katz R, Kindrachuk J, Muylaert RL, Nutter FB, Ogola J, Olival KJ, Rourke M, Ryan SJ, Ross N, Seifert SN, Sironen T, Standley CJ, Taylor K, Venter M, Webala PW. The future of zoonotic risk prediction. Philos Trans R Soc Lond B Biol Sci 2021; 376:20200358. [PMID: 34538140 PMCID: PMC8450624 DOI: 10.1098/rstb.2020.0358] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2021] [Indexed: 01/26/2023] Open
Abstract
In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.
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Affiliation(s)
- Colin J. Carlson
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Maxwell J. Farrell
- Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Ontario, Canada
| | - Zoe Grange
- Public Health Scotland, Glasgow G2 6QE, UK
| | - Barbara A. Han
- Cary Institute of Ecosystem Studies, Millbrook, NY 12545, USA
| | - Nardus Mollentze
- Medical Research Council, University of Glasgow Centre for Virus Research, Glasgow G61 1QH, UK
- Institute of Biodiversity, Animal Health and Comparative Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
| | - Alexandra L. Phelan
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- O'Neill Institute for National and Global Health Law, Georgetown University Law Center, Washington, DC 20001, USA
| | - Angela L. Rasmussen
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Gregory F. Albery
- Department of Biology, Georgetown University, Washington, DC 20007, USA
| | - Bernard Bett
- Animal and Human Health Program, International Livestock Research Institute, PO Box 30709-00100, Nairobi, Kenya
| | - David M. Brett-Major
- Department of Epidemiology, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA
| | - Lily E. Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Tad Dallas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70806, USA
| | - Evan A. Eskew
- Department of Biology, Pacific Lutheran University, Tacoma, WA, USA
| | - Anna C. Fagre
- Department of Microbiology, Immunology, and Pathology, College of Veterinary Medicine and Biomedical Sciences, Colorado State University, Fort Collins, CO, USA
| | - Kristian M. Forbes
- Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Rory Gibb
- Centre on Climate Change and Planetary Health, London School of Hygiene and Tropical Medicine, London, UK
- Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine, London, UK
| | - Sam Halabi
- O'Neill Institute for National and Global Health Law, Georgetown University Law Center, Washington, DC 20001, USA
| | - Charlotte C. Hammer
- Centre for the Study of Existential Risk, University of Cambridge, Cambridge, UK
| | - Rebecca Katz
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Jason Kindrachuk
- Department of Medical Microbiology and Infectious Diseases, University of Manitoba, Winnipeg, Manitoba, Canada R3E 0J9
| | - Renata L. Muylaert
- Molecular Epidemiology and Public Health Laboratory, Hopkirk Research Institute, Massey University, Palmerston North, New Zealand
| | - Felicia B. Nutter
- Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, North Grafton, MA 01536, USA
- Department of Public Health and Community Medicine, School of Medicine, Tufts University, Boston, MA 02111, USA
| | | | | | - Michelle Rourke
- Law Futures Centre, Griffith Law School, Griffith University, Nathan, Queensland 4111, Australia
| | - Sadie J. Ryan
- Department of Geography and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
- School of Life Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Noam Ross
- EcoHealth Alliance, New York, NY 10018, USA
| | - Stephanie N. Seifert
- Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
| | - Tarja Sironen
- Department of Virology, University of Helsinki, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland
| | - Claire J. Standley
- Center for Global Health Science and Security, Georgetown University Medical Center, Washington, DC 20007, USA
- Department of Microbiology and Immunology, Georgetown University Medical Center, Washington, DC 20007, USA
| | - Kishana Taylor
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Marietjie Venter
- Zoonotic Arbo and Respiratory Virus Program, Centre for Viral Zoonoses, Department of Medical Virology, University of Pretoria, Pretoria, South Africa
| | - Paul W. Webala
- Department of Forestry and Wildlife Management, Maasai Mara University, Narok 20500, Kenya
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29
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Hu X, Feng C, Zhou Y, Harrison A, Chen M. DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks. Bioinformatics 2021; 38:694-702. [PMID: 34694333 PMCID: PMC8756175 DOI: 10.1093/bioinformatics/btab737] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 10/05/2021] [Accepted: 10/20/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. RESULTS We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. AVAILABILITY AND IMPLEMENTATION We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaotian Hu
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Cong Feng
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yincong Zhou
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Andrew Harrison
- Department of Mathematical Sciences, University of Essex, Colchester CO4 3SQ, UK
| | - Ming Chen
- To whom correspondence should be addressed.
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30
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Yakimovich A. Machine Learning and Artificial Intelligence for the Prediction of Host-Pathogen Interactions: A Viral Case. Infect Drug Resist 2021; 14:3319-3326. [PMID: 34456575 PMCID: PMC8385421 DOI: 10.2147/idr.s292743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 08/03/2021] [Indexed: 01/27/2023] Open
Abstract
The research of interactions between the pathogens and their hosts is key for understanding the biology of infection. Commencing on the level of individual molecules, these interactions define the behavior of infectious agents and the outcomes they elicit. Discovery of host-pathogen interactions (HPIs) conventionally involves a stepwise laborious research process. Yet, amid the global pandemic the urge for rapid discovery acceleration through the novel computational methodologies has become ever so poignant. This review explores the challenges of HPI discovery and investigates the efforts currently undertaken to apply the latest machine learning (ML) and artificial intelligence (AI) methodologies to this field. This includes applications to molecular and genetic data, as well as image and language data. Furthermore, a number of breakthroughs, obstacles, along with prospects of AI for host-pathogen interactions (HPI), are discussed.
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31
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Wang X, Yang Y, Li K, Li W, Li F, Peng S. BioERP: biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions. Bioinformatics 2021; 37:4793-4800. [PMID: 34329382 DOI: 10.1093/bioinformatics/btab565] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 07/18/2021] [Accepted: 07/29/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Predicting entity relationship can greatly benefit important biomedical problems. Recently, a large amount of biomedical heterogeneous networks (BioHNs) are generated and offer opportunities for developing network-based learning approaches to predict relationships among entities. However, current researches slightly explored BioHNs-based self-supervised representation learning methods, and are hard to simultaneously capturing local- and global-level association information among entities. RESULTS In this study, we propose a biomedical heterogeneous network-based self-supervised representation learning approach for entity relationship predictions, termed BioERP. A self-supervised meta path detection mechanism is proposed to train a deep Transformer encoder model that can capture the global structure and semantic feature in BioHNs. Meanwhile, a biomedical entity mask learning strategy is designed to reflect local associations of vertices. Finally, the representations from different task models are concatenated to generate two-level representation vectors for predicting relationships among entities. The results on eight datasets show BioERP outperforms 30 state-of-the-art methods. In particular, BioERP reveals great performance with results close to 1 in terms of AUC and AUPR on the drug-target interaction predictions. In summary, BioERP is a promising bio-entity relationship prediction approach. AVAILABILITY Source code and data can be downloaded from https://github.com/pengsl-lab/BioERP.git. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Yaning Yang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Kenli Li
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Wentao Li
- School of Computer Science, National University of Defense Technology, Changsha, 410073, China
| | - Fei Li
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100850, China
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.,School of Computer Science, National University of Defense Technology, Changsha, 410073, China.,Peng Cheng Lab, Shenzhen 518000, China
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32
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Yang X, Yang S, Lian X, Wuchty S, Zhang Z. Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction. Bioinformatics 2021; 37:4771-4778. [PMID: 34273146 PMCID: PMC8406877 DOI: 10.1093/bioinformatics/btab533] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/03/2021] [Accepted: 07/16/2021] [Indexed: 11/20/2022] Open
Abstract
Motivation To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human–virus protein–protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. Results To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e. ‘frozen’ type and ‘fine-tuning’ type) that reliably predict interactions in a target human–virus domain based on training in a source human–virus domain, by retraining CNN layers. Finally, we utilize the ‘frozen’ type transfer learning approach to predict human–SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. Availability and implementation: The source codes and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xianyi Lian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Stefan Wuchty
- Dept. of Computer Science, University of Miami, Miami, FL 33146, USA.,Dept. of Biology, University of Miami, Miami, FL 33146, USA.,Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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