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Zeng X, Meng FF, Wen ML, Li SJ, Li Y. GNNGL-PPI: multi-category prediction of protein-protein interactions using graph neural networks based on global graphs and local subgraphs. BMC Genomics 2024; 25:406. [PMID: 38724906 PMCID: PMC11080243 DOI: 10.1186/s12864-024-10299-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 04/10/2024] [Indexed: 05/13/2024] Open
Abstract
Most proteins exert their functions by interacting with other proteins, making the identification of protein-protein interactions (PPI) crucial for understanding biological activities, pathological mechanisms, and clinical therapies. Developing effective and reliable computational methods for predicting PPI can significantly reduce the time-consuming and labor-intensive associated traditional biological experiments. However, accurately identifying the specific categories of protein-protein interactions and improving the prediction accuracy of the computational methods remain dual challenges. To tackle these challenges, we proposed a novel graph neural network method called GNNGL-PPI for multi-category prediction of PPI based on global graphs and local subgraphs. GNNGL-PPI consisted of two main components: using Graph Isomorphism Network (GIN) to extract global graph features from PPI network graph, and employing GIN As Kernel (GIN-AK) to extract local subgraph features from the subgraphs of protein vertices. Additionally, considering the imbalanced distribution of samples in each category within the benchmark datasets, we introduced an Asymmetric Loss (ASL) function to further enhance the predictive performance of the method. Through evaluations on six benchmark test sets formed by three different dataset partitioning algorithms (Random, BFS, DFS), GNNGL-PPI outperformed the state-of-the-art multi-category prediction methods of PPI, as measured by the comprehensive performance evaluation metric F1-measure. Furthermore, interpretability analysis confirmed the effectiveness of GNNGL-PPI as a reliable multi-category prediction method for predicting protein-protein interactions.
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Affiliation(s)
- Xin Zeng
- College of Mathematics and Computer Science, Dali University, 671003, Dali, China
| | - Fan-Fang Meng
- College of Mathematics and Computer Science, Dali University, 671003, Dali, China
| | - Meng-Liang Wen
- State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Yunnan University, 650000, Kunming, China
| | - Shu-Juan Li
- Yunnan Institute of Endemic Diseases Control & Prevention, 671000, Dali, China
| | - Yi Li
- College of Mathematics and Computer Science, Dali University, 671003, Dali, China.
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Kewalramani N, Emili A, Crovella M. State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage. Proteomics 2023; 23:e2200292. [PMID: 37401192 DOI: 10.1002/pmic.202200292] [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: 04/02/2023] [Revised: 05/24/2023] [Accepted: 06/09/2023] [Indexed: 07/05/2023]
Abstract
Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.
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Affiliation(s)
- Neal Kewalramani
- Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
| | - Andrew Emili
- OHSU Knight Cancer Institute, Portland, Oregon, USA
| | - Mark Crovella
- Department of Computer Science and Program in Bioinformatics, Boston University, Boston, Massachusetts, USA
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [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: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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Abstract
Since the large-scale experimental characterization of protein–protein interactions (PPIs) is not possible for all species, several computational PPI prediction methods have been developed that harness existing data from other species. While PPI network prediction has been extensively used in eukaryotes, microbial network inference has lagged behind. However, bacterial interactomes can be built using the same principles and techniques; in fact, several methods are better suited to bacterial genomes. These predicted networks allow systems-level analyses in species that lack experimental interaction data. This review describes the current network inference and analysis techniques and summarizes the use of computationally-predicted microbial interactomes to date.
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Wan XH. Artificial intelligence reveals roles of gut microbiota in driving human colorectal cancer evolution. Artif Intell Cancer 2021; 2:69-78. [DOI: 10.35713/aic.v2.i5.69] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 10/24/2021] [Accepted: 10/27/2021] [Indexed: 02/06/2023] Open
Abstract
With the rapid development of high-throughput sequencing and artificial intelligence (AI) techniques, gut mucosal microbiota begins to be recognized as critical drivers of human colorectal cancer (CRC). Various AI approaches have been designed to obtain effective information from enormous numbers of microbial cells residing in gut mucosal as well as cancer cells. These mainly include detection of microbial markers for early clinical diagnosis of stage-specific CRC, characterization of pathogenic bacterial activities via genomic and transcriptomic analyses, and prediction of interplay between bacterial drivers and host immune systems. Here I review the current progresses of AI applications in profiling gut microbiomes linked to CRC initiation and development. I further look forward to future AI research for improving our understanding of the roles of gut microbiota in CRC evolution.
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Affiliation(s)
- Xue-Hua Wan
- TEDA Institute of Biological Sciences and Biotechnology, Nankai University, Tianjin 300457, China
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Pitta JLDLP, Vasconcelos CRDS, Wallau GDL, Campos TDL, Rezende AM. In silico predictions of protein interactions between Zika virus and human host. PeerJ 2021; 9:e11770. [PMID: 34513323 PMCID: PMC8395582 DOI: 10.7717/peerj.11770] [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: 11/25/2020] [Accepted: 06/23/2021] [Indexed: 11/20/2022] Open
Abstract
Background The ZIKA virus (ZIKV) belongs to the Flaviviridae family, was first isolated in the 1940s, and remained underreported until its global threat in 2016, where drastic consequences were reported as Guillan-Barre syndrome and microcephaly in newborns. Understanding molecular interactions of ZIKV proteins during the host infection is important to develop treatments and prophylactic measures; however, large-scale experimental approaches normally used to detect protein-protein interaction (PPI) are onerous and labor-intensive. On the other hand, computational methods may overcome these challenges and guide traditional approaches on one or few protein molecules. The prediction of PPIs can be used to study host-parasite interactions at the protein level and reveal key pathways that allow viral infection. Results Applying Random Forest and Support Vector Machine (SVM) algorithms, we performed predictions of PPI between two ZIKV strains and human proteomes. The consensus number of predictions of both algorithms was 17,223 pairs of proteins. Functional enrichment analyses were executed with the predicted networks to access the biological meanings of the protein interactions. Some pathways related to viral infection and neurological development were found for both ZIKV strains in the enrichment analysis, but the JAK-STAT pathway was observed only for strain PE243 when compared with the FSS13025 strain. Conclusions The consensus network of PPI predictions made by Random Forest and SVM algorithms allowed an enrichment analysis that corroborates many aspects of ZIKV infection. The enrichment results are mainly related to viral infection, neuronal development, and immune response, and presented differences among the two compared ZIKV strains. Strain PE243 presented more predicted interactions between proteins from the JAK-STAT signaling pathway, which could lead to a more inflammatory immune response when compared with the FSS13025 strain. These results show that the methodology employed in this study can potentially reveal new interactions between the ZIKV and human cells.
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Affiliation(s)
| | | | | | - Túlio de Lima Campos
- Bioinformatics Platform, Aggeu Magalhães Institute-FIOCRUZ/PE, Recife, PE, Brasil
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Li J, Wang S, Chen Z, Wang Y. A Bipartite Network Module-Based Project to Predict Pathogen-Host Association. Front Genet 2020; 10:1357. [PMID: 32038713 PMCID: PMC6992693 DOI: 10.3389/fgene.2019.01357] [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: 09/27/2019] [Accepted: 12/11/2019] [Indexed: 12/23/2022] Open
Abstract
Pathogen-host interactions play an important role in understanding the mechanism by which a pathogen can infect its host. Some approaches for predicting pathogen-host association have been developed, but prediction accuracy is still low. In this paper, we propose a bipartite network module-based approach to improve prediction accuracy. First, a bipartite network with pathogens and hosts is constructed. Next, pathogens and hosts are divided into different modules respectively. Then, modular information on the pathogens and hosts is added into a bipartite network projection model and the association scores between pathogens and hosts are calculated. Finally, leave-one-out cross-validation is used to estimate the performance of the proposed method. Experimental results show that the proposed method performs better in predicting pathogen-host association than other methods, and some potential pathogen-host associations with higher prediction scores are also confirmed by the results of biological experiments in the publically available literature.
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Affiliation(s)
- Jie Li
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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Yang X, Yang S, Li Q, Wuchty S, Zhang Z. Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method. Comput Struct Biotechnol J 2019; 18:153-161. [PMID: 31969974 PMCID: PMC6961065 DOI: 10.1016/j.csbj.2019.12.005] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 11/29/2019] [Accepted: 12/10/2019] [Indexed: 12/11/2022] Open
Abstract
The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.
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Key Words
- AC, Auto Covariance
- ACC, Accuracy
- AUC, area under the ROC curve
- AUPRC, area under the PR curve
- Adaboost, Adaptive Boosting
- CT, Conjoint Triad
- Doc2vec
- Embedding
- Human-virus interaction
- LD, Local Descriptor
- MCC, Matthews correlation coefficient
- ML, machine learning
- MLP, Multiple Layer Perceptron
- MS, mass spectroscopy
- Machine learning
- PPIs, protein-protein interactions
- PR, Precision-Recall
- Prediction
- Protein-protein interaction
- RBF, radial basis function
- RF, Random Forest
- ROC, Receiver Operating Characteristic
- SGD, stochastic gradient descent
- SVM, Support Vector Machine
- Y2H, yeast two-hybrid
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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
| | - Qinmengge Li
- National Demonstration Center for Experimental Biological Sciences Education, 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
- Center of Computational Science, 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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Lian X, Yang S, Li H, Fu C, Zhang Z. Machine-Learning-Based Predictor of Human–Bacteria Protein–Protein Interactions by Incorporating Comprehensive Host-Network Properties. J Proteome Res 2019; 18:2195-2205. [DOI: 10.1021/acs.jproteome.9b00074] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Xianyi Lian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Hong Li
- Key Laboratory of Tropical Biological Resources of Ministry of Education, Hainan University, Haikou, 570228, China
| | - Chen Fu
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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