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Milano M, Cinaglia P, Guzzi PH, Cannataro M. Aligning Cross-Species Interactomes for Studying Complex and Chronic Diseases. Life (Basel) 2023; 13:1520. [PMID: 37511895 PMCID: PMC10381714 DOI: 10.3390/life13071520] [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: 06/13/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Neurodegenerative diseases (NDs) are a group of complex disorders characterized by the progressive degeneration and dysfunction of neurons in the central nervous system. NDs encompass many conditions, including Alzheimer's disease and Parkinson's disease. Alzheimer's disease (AD) is a complex disease affecting almost forty million people worldwide. AD is characterized by a progressive decline of cognitive functions related to the loss of connections between nerve cells caused by the prevalence of extracellular Aβ plaques and intracellular neurofibrillary tangles plaques. Parkinson's disease (PD) is a neurodegenerative disorder that primarily affects the movement of an individual. The exact cause of Parkinson's disease is not fully understood, but it is believed to involve a combination of genetic and environmental factors. Some cases of PD are linked to mutations in the LRRK2, PARKIN and other genes, which are associated with familial forms of the disease. Different research studies have applied the Protein Protein Interaction (PPI) networks to understand different aspects of disease progression. For instance, Caenorhabditis elegans is widely used as a model organism for the study of AD due to roughly 38% of its genes having a human ortholog. This study's goal consists of comparing PPI network of C. elegans and human by applying computational techniques, widely used for the analysis of PPI networks between species, such as Local Network Alignment (LNA). For this aim, we used L-HetNetAligner algorithm to build a local alignment among two PPI networks, i.e., C. elegans and human PPI networks associated with AD and PD built-in silicon. The results show that L-HetNetAligner can find local alignments representing functionally related subregions. In conclusion, since local alignment enables the extraction of functionally related modules, the method can be used to study complex disease progression.
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Affiliation(s)
- Marianna Milano
- Department of Experimental and Clinical Medicine, University Magna Græcia, 88100 Catanzaro, Italy
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| | - Pietro Cinaglia
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Health Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Pietro Hiram Guzzi
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Medical and Surgical Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Medical and Surgical Sciences, University Magna Græcia, 88100 Catanzaro, Italy
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Paul M, Anand A. Impact of low-confidence interactions on computational identification of protein complexes. J Bioinform Comput Biol 2020; 18:2050025. [PMID: 32757809 DOI: 10.1142/s0219720020500250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Protein complexes are the cornerstones of most of the biological processes. Identifying protein complexes is crucial in understanding the principles of cellular organization with several important applications, including in disease diagnosis. Several computational techniques have been developed to identify protein complexes from protein-protein interaction (PPI) data (equivalently, from PPI networks). These PPI data have a significant amount of false positives, which is a bottleneck in identifying protein complexes correctly. Gene ontology (GO)-based semantic similarity measures can be used to assign a confidence score to PPIs. Consequently, low-confidence PPIs are highly likely to be false positives. In this paper, we systematically study the impact of low-confidence PPIs on the performance of complex detection methods using GO-based semantic similarity measures. We consider five state-of-the-art complex detection algorithms and nine GO-based similarity measures in the evaluation. We find that each complex detection algorithm significantly improves its performance after the filtration of low-similarity scored PPIs. It is also observed that the percentage improvement and the filtration percentage (of low-confidence PPIs) are highly correlated.
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Affiliation(s)
- Madhusudan Paul
- Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India.,Department of Computer and System Sciences, Visva-Bharati, Santiniketan 731235, West Bengal, India
| | - Ashish Anand
- Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
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Jeong JC, Chen X. A New Semantic Functional Similarity over Gene Ontology. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:322-334. [PMID: 26357220 DOI: 10.1109/tcbb.2014.2343963] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Identifying functionally similar or closely related genes and gene products has significant impacts on biological and clinical studies as well as drug discovery. In this paper, we propose an effective and practically useful method measuring both gene and gene product similarity by integrating the topology of gene ontology, known functional domains and their functional annotations. The proposed method is comprehensively evaluated through statistical analysis of the similarities derived from sequence, structure and phylogenetic profiles, and clustering analysis of disease genes clusters. Our results show that the proposed method clearly outperforms other conventional methods. Furthermore, literature analysis also reveals that the proposed method is both statistically and biologically promising for identifying functionally similar genes or gene products. In particular, we demonstrate that the proposed functional similarity metric is capable of discoverying new disease related genes or gene products.
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Deng Y, Gao L, Wang B. ppiPre: predicting protein-protein interactions by combining heterogeneous features. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 2:S8. [PMID: 24565177 PMCID: PMC3851814 DOI: 10.1186/1752-0509-7-s2-s8] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Protein-protein interactions (PPIs) are crucial in cellular processes. Since the current biological experimental techniques are time-consuming and expensive, and the results suffer from the problems of incompleteness and noise, developing computational methods and software tools to predict PPIs is necessary. Although several approaches have been proposed, the species supported are often limited and additional data like homologous interactions in other species, protein sequence and protein expression are often required. And predictive abilities of different features for different kinds of PPI data have not been studied. RESULTS In this paper, we propose ppiPre, an open-source framework for PPI analysis and prediction using a combination of heterogeneous features including three GO-based semantic similarities, one KEGG-based co-pathway similarity and three topology-based similarities. It supports up to twenty species. Only the original PPI data and gold-standard PPI data are required from users. The experiments on binary and co-complex gold-standard yeast PPI data sets show that there exist big differences among the predictive abilities of different features on different kinds of PPI data sets. And the prediction performance on the two data sets shows that ppiPre is capable of handling PPI data in different kinds and sizes. ppiPre is implemented in the R language and is freely available on the CRAN (http://cran.r-project.org/web/packages/ppiPre/). CONCLUSIONS We applied our framework to both binary and co-complex gold-standard PPI data sets. The detailed analysis on three GO aspects suggests that different GO aspects should be used on different kinds of data sets, and that combining all the three aspects of GO often gets the best result. The analysis also shows that using only features based solely on the topology of the PPI network can get a very good result when predicting the co-complex PPI data. ppiPre provides useful functions for analysing PPI data and can be used to predict PPIs for multiple species.
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Vafaee F, Rosu D, Broackes-Carter F, Jurisica I. Novel semantic similarity measure improves an integrative approach to predicting gene functional associations. BMC SYSTEMS BIOLOGY 2013; 7:22. [PMID: 23497449 PMCID: PMC3663825 DOI: 10.1186/1752-0509-7-22] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2012] [Accepted: 03/01/2013] [Indexed: 02/03/2023]
Abstract
BACKGROUND Elucidation of the direct/indirect protein interactions and gene associations is required to fully understand the workings of the cell. This can be achieved through the use of both low- and high-throughput biological experiments and in silico methods. We present GAP (Gene functional Association Predictor), an integrative method for predicting and characterizing gene functional associations. GAP integrates different biological features using a novel taxonomy-based semantic similarity measure in predicting and prioritizing high-quality putative gene associations. The proposed similarity measure increases information gain from the available gene annotations. The annotation information is incorporated from several public pathway databases, Gene Ontology annotations as well as drug and disease associations from the scientific literature. RESULTS We evaluated GAP by comparing its prediction performance with several other well-known functional interaction prediction tools over a comprehensive dataset of known direct and indirect interactions, and observed significantly better prediction performance. We also selected a small set of GAP's highly-scored novel predicted pairs (i.e., currently not found in any known database or dataset), and by manually searching the literature for experimental evidence accessible in the public domain, we confirmed different categories of predicted functional associations with available evidence of interaction. We also provided extra supporting evidence for subset of the predicted functionally-associated pairs using an expert curated database of genes associated to autism spectrum disorders. CONCLUSIONS GAP's predicted "functional interactome" contains ≈1M highly-scored predicted functional associations out of which about 90% are novel (i.e., not experimentally validated). GAP's novel predictions connect disconnected components and singletons to the main connected component of the known interactome. It can, therefore, be a valuable resource for biologists by providing corroborating evidence for and facilitating the prioritization of potential direct or indirect interactions for experimental validation. GAP is freely accessible through a web portal: http://ophid.utoronto.ca/gap.
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Affiliation(s)
- Fatemeh Vafaee
- Ontario Cancer Institute and Campbell Family Cancer Research Institute, Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
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Diversity in genetic in vivo methods for protein-protein interaction studies: from the yeast two-hybrid system to the mammalian split-luciferase system. Microbiol Mol Biol Rev 2012; 76:331-82. [PMID: 22688816 DOI: 10.1128/mmbr.05021-11] [Citation(s) in RCA: 134] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The yeast two-hybrid system pioneered the field of in vivo protein-protein interaction methods and undisputedly gave rise to a palette of ingenious techniques that are constantly pushing further the limits of the original method. Sensitivity and selectivity have improved because of various technical tricks and experimental designs. Here we present an exhaustive overview of the genetic approaches available to study in vivo binary protein interactions, based on two-hybrid and protein fragment complementation assays. These methods have been engineered and employed successfully in microorganisms such as Saccharomyces cerevisiae and Escherichia coli, but also in higher eukaryotes. From single binary pairwise interactions to whole-genome interactome mapping, the self-reassembly concept has been employed widely. Innovative studies report the use of proteins such as ubiquitin, dihydrofolate reductase, and adenylate cyclase as reconstituted reporters. Protein fragment complementation assays have extended the possibilities in protein-protein interaction studies, with technologies that enable spatial and temporal analyses of protein complexes. In addition, one-hybrid and three-hybrid systems have broadened the types of interactions that can be studied and the findings that can be obtained. Applications of these technologies are discussed, together with the advantages and limitations of the available assays.
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Eronen L, Toivonen H. Biomine: predicting links between biological entities using network models of heterogeneous databases. BMC Bioinformatics 2012; 13:119. [PMID: 22672646 PMCID: PMC3505483 DOI: 10.1186/1471-2105-13-119] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2011] [Accepted: 04/17/2012] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Biological databases contain large amounts of data concerning the functions and associations of genes and proteins. Integration of data from several such databases into a single repository can aid the discovery of previously unknown connections spanning multiple types of relationships and databases. RESULTS Biomine is a system that integrates cross-references from several biological databases into a graph model with multiple types of edges, such as protein interactions, gene-disease associations and gene ontology annotations. Edges are weighted based on their type, reliability, and informativeness. We present Biomine and evaluate its performance in link prediction, where the goal is to predict pairs of nodes that will be connected in the future, based on current data. In particular, we formulate protein interaction prediction and disease gene prioritization tasks as instances of link prediction. The predictions are based on a proximity measure computed on the integrated graph. We consider and experiment with several such measures, and perform a parameter optimization procedure where different edge types are weighted to optimize link prediction accuracy. We also propose a novel method for disease-gene prioritization, defined as finding a subset of candidate genes that cluster together in the graph. We experimentally evaluate Biomine by predicting future annotations in the source databases and prioritizing lists of putative disease genes. CONCLUSIONS The experimental results show that Biomine has strong potential for predicting links when a set of selected candidate links is available. The predictions obtained using the entire Biomine dataset are shown to clearly outperform ones obtained using any single source of data alone, when different types of links are suitably weighted. In the gene prioritization task, an established reference set of disease-associated genes is useful, but the results show that under favorable conditions, Biomine can also perform well when no such information is available.The Biomine system is a proof of concept. Its current version contains 1.1 million entities and 8.1 million relations between them, with focus on human genetics. Some of its functionalities are available in a public query interface at http://biomine.cs.helsinki.fi, allowing searching for and visualizing connections between given biological entities.
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Affiliation(s)
- Lauri Eronen
- Biocomputing Platforms Ltd, Innopoli 2, Tekniikantie 14, , FI-02150 Espoo, Finland.
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Raftery AE, Niu X, Hoff PD, Yeung KY. Fast Inference for the Latent Space Network Model Using a Case-Control Approximate Likelihood. J Comput Graph Stat 2012; 21:901-919. [PMID: 27570438 DOI: 10.1080/10618600.2012.679240] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
Network models are widely used in social sciences and genome sciences. The latent space model proposed by (Hoff et al. 2002), and extended by (Handcock et al. 2007) to incorporate clustering, provides a visually interpretable model-based spatial representation of relational data and takes account of several intrinsic network properties. Due to the structure of the likelihood function of the latent space model, the computational cost is of order O(N2), where N is the number of nodes. This makes it infeasible for large networks. In this paper, we propose an approximation of the log likelihood function. We adopt the case-control idea from epidemiology and construct a case-control likelihood which is an unbiased estimator of the full likelihood. Replacing the full likelihood by the case-control likelihood in the MCMC estimation of the latent space model reduces the computational time from O(N2) to O(N), making it feasible for large networks. We evaluate its performance using simulated and real data. We fit the model to a large protein-protein interaction data using the case-control likelihood and use the model fitted link probabilities to identify false positive links.
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Affiliation(s)
- Adrian E Raftery
- Department of Statistics, University of Washington, Seattle, Wash., USA
| | - Xiaoyue Niu
- Department of Statistics, University of Washington, Seattle, Wash., USA
| | - Peter D Hoff
- Department of Statistics, University of Washington, Seattle, Wash., USA
| | - Ka Yee Yeung
- Department of Statistics, University of Washington, Seattle, Wash., USA
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Integrating diverse information to gain more insight into microarray analysis. J Biomed Biotechnol 2009; 2009:648987. [PMID: 19834567 PMCID: PMC2761008 DOI: 10.1155/2009/648987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2008] [Revised: 06/23/2009] [Accepted: 07/17/2009] [Indexed: 11/17/2022] Open
Abstract
Microarray technology provides an opportunity to view transcriptions at genomic level under different conditions controlled by an experiment. From an array experiment using a human cancer cell line that is engineered to differ in expression of tumor antigen, integrin alpha6beta4, few hundreds of differentially expressed genes are selected and are clustered using one of several standard algorithms. The set of genes in a cluster is expected to have similar expression patterns and are most likely to be coregulated and thereby expected to have similar function. The highly expressed set of upregulated genes become candidates for further evaluation as potential biomarkers. Besides these benefits, microarray experiment by itself does not help us to understand or discover potential pathways or to identify important set of genes for potential drug targets. In this paper we discuss about integrating protein-to-protein interaction information, pathway information with array expression data set to identify a set of "important" genes, and potential signal transduction networks that help to target and reverse the oncogenic phenotype induced by tumor antigen such as integrin alpha6beta4. We will illustrate the proposed method with our recent microarray experiment conducted for identifying transcriptional targets of integrin alpha6beta4 for cancer progression.
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Lewis ACF, Saeed R, Deane CM. Predicting protein-protein interactions in the context of protein evolution. MOLECULAR BIOSYSTEMS 2009; 6:55-64. [PMID: 20024067 DOI: 10.1039/b916371a] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Here we review the methods for the prediction of protein interactions and the ideas in protein evolution that relate to them. The evolutionary assumptions implicit in many of the protein interaction prediction methods are elucidated. We draw attention to the caution needed in deploying certain evolutionary assumptions, in particular cross-organism transfer of interactions by sequence homology, and discuss the known issues in deriving interaction predictions from evidence of co-evolution. We also conject that there is evolutionary knowledge yet to be exploited in the prediction of interactions, in particular the heterogeneity of interactions, the increasing availability of interaction data from multiple species, and the models of protein interaction network growth.
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Affiliation(s)
- Anna C F Lewis
- Department of Statistics and Systems Biology DTC, University of Oxford, UK
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Liu M, Chen XW, Jothi R. Knowledge-guided inference of domain-domain interactions from incomplete protein-protein interaction networks. ACTA ACUST UNITED AC 2009; 25:2492-9. [PMID: 19667081 PMCID: PMC2752622 DOI: 10.1093/bioinformatics/btp480] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Motivation: Protein-protein interactions (PPIs), though extremely valuable towards a better understanding of protein functions and cellular processes, do not provide any direct information about the regions/domains within the proteins that mediate the interaction. Most often, it is only a fraction of a protein that directly interacts with its biological partners. Thus, understanding interaction at the domain level is a critical step towards (i) thorough understanding of PPI networks; (ii) precise identification of binding sites; (iii) acquisition of insights into the causes of deleterious mutations at interaction sites; and (iv) most importantly, development of drugs to inhibit pathological protein interactions. In addition, knowledge derived from known domain–domain interactions (DDIs) can be used to understand binding interfaces, which in turn can help discover unknown PPIs. Results: Here, we describe a novel method called K-GIDDI (knowledge-guided inference of DDIs) to narrow down the PPI sites to smaller regions/domains. K-GIDDI constructs an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information, which identifies partial-complete bipartite sub-networks in the DDI network and makes them complete bipartite sub-networks by adding edges. Our results indicate that K-GIDDI can reliably predict DDIs. Most importantly, K-GIDDI's novel network expansion procedure allows prediction of DDIs that are otherwise not identifiable by methods that rely only on PPI data. Contact:xwchen@ku.edu Availability:http://www.ittc.ku.edu/∼xwchen/domainNetwork/ddinet.html Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mei Liu
- Bioinformatics and Computational Life-Sciences Laboratory, ITTC, Department of Electrical Engineering and Computer Science, University of Kansas, 1520 West 15th Street, Lawrence, KS 66045, USA
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