Resilience and evolvability of protein-protein interaction networks

AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

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A computational exploration of resilience and evolvability of protein–protein interaction networks

Communications Biology ◽

10.1038/s42003-021-02867-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Brennan Klein ◽

Ludvig Holmér ◽

Keith M. Smith ◽

Mackenzie M. Johnson ◽

Anshuman Swain ◽

...

Keyword(s):

Gene Expression ◽

Protein Interaction ◽

Protein Interactions ◽

Preferential Attachment ◽

Cellular Protein ◽

Phenotypic Traits ◽

General Notion ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Computational Exploration

AbstractProtein–protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. Here, we explore the influence of gene expression and network properties on PPI networks’ resilience. We use publicly available data of PPIs for E. coli, S. cerevisiae, and H. sapiens, where we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network in all three species, regardless of gene expression distribution or network structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.

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A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization

Frontiers in Genetics ◽

10.3389/fgene.2021.709660 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhihong Zhang ◽

Meiping Jiang ◽

Dongjie Wu ◽

Wang Zhang ◽

Wei Yan ◽

...

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Negative Impact ◽

False Negative ◽

Interaction Network ◽

Biological Information ◽

Ppi Network ◽

Essential Proteins ◽

Protein Protein Interaction ◽

Ppi Networks

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

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A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

10.21203/rs.3.rs-537545/v1 ◽

2021 ◽

Author(s):

Zhihong Zhang ◽

Sai Hu ◽

Wei Yan ◽

Bihai Zhao ◽

Lei Wang

Keyword(s):

Protein Interaction ◽

Matrix Factorization ◽

Biological Data ◽

Protein Domain ◽

Biological Information ◽

Ppi Network ◽

Essential Proteins ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Non Negative Matrix Factorization

Abstract BackgroundIdentification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks.ResultsIn this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins.ConclusionEmploying the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks.

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PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽

10.12688/f1000research.12947.1 ◽

2017 ◽

Vol 6 ◽

pp. 1969

Author(s):

Dongmin Jung ◽

Xijin Ge

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Biological Processes ◽

Bioconductor Package ◽

Biological Functions ◽

Ppi Network ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available STRING database, we use network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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PPInfer: a Bioconductor package for inferring functionally related proteins using protein interaction networks

F1000Research ◽

10.12688/f1000research.12947.3 ◽

2018 ◽

Vol 6 ◽

pp. 1969 ◽

Cited By ~ 3

Author(s):

Dongmin Jung ◽

Xijin Ge

Keyword(s):

Protein Interaction ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Biological Processes ◽

Bioconductor Package ◽

Biological Functions ◽

Ppi Network ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Related Proteins

Interactions between proteins occur in many, if not most, biological processes. This fact has motivated the development of a variety of experimental methods for the identification of protein-protein interaction (PPI) networks. Leveraging PPI data available in the STRING database, we use a network-based statistical learning methods to infer the putative functions of proteins from the known functions of neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. The package is freely available at the Bioconductor web site (http://bioconductor.org/packages/PPInfer/).

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Extracting Biological Significant Subnetworks from Protein-Protein Interactions Induced by Differentially Expressed Genes of HIV-1 Vpr Variants

International Journal of System Dynamics Applications ◽

10.4018/ijsda.2015100103 ◽

2015 ◽

Vol 4 (4) ◽

pp. 35-51 ◽

Cited By ~ 1

Author(s):

Bandana Barman ◽

Anirban Mukhopadhyay

Keyword(s):

Differentially Expressed Genes ◽

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Network ◽

Interaction Network ◽

Differentially Expressed ◽

Wild Type ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Hiv 1

Identification of protein interaction network is very important to find the cell signaling pathway for a particular disease. The authors have found the differentially expressed genes between two sample groups of HIV-1. Samples are wild type HIV-1 Vpr and HIV-1 mutant Vpr. They did statistical t-test and found false discovery rate (FDR) to identify the genes increased in expression (up-regulated) or decreased in expression (down-regulated). In the test, the authors have computed q-values of test to identify minimum FDR which occurs. As a result they found 172 differentially expressed genes between their sample wild type HIV-1 Vpr and HIV-1 mutant Vpr, R80A. They found 68 up-regulated genes and 104 down-regulated genes. From the 172 differentially expressed genes the authors found protein-protein interaction network with string-db and then clustered (subnetworks) the PPI networks with cytoscape3.0. Lastly, the authors studied significance of subnetworks with performing gene ontology and also studied the KEGG pathway of those subnetworks.

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Nonessential-Nonhub Proteins in the Protein-Protein Interaction Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.934.159 ◽

2014 ◽

Vol 934 ◽

pp. 159-164

Author(s):

Yun Yuan Dong ◽

Xian Chun Zhang

Keyword(s):

Protein Interaction ◽

Interaction Network ◽

Clustering Coefficient ◽

Centrality Measures ◽

Ppi Network ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Comparison Results ◽

A Cell ◽

High Degree

Protein-protein interaction (PPI) networks provide a simplified overview of the web of interactions that take place inside a cell. According to the centrality-lethality rule, hub proteins (proteins with high degree) tend to be essential in the PPI network. Moreover, there are also many low degree proteins in the PPI network, but they have different lethality. Some of them are essential proteins (essential-nonhub proteins), and the others are not (nonessential-nonhub proteins). In order to explain why nonessential-nonhub proteins don’t have essentiality, we propose a new measure n-iep (the number of essential neighbors) and compare nonessential-nonhub proteins with essential-nonhub proteins from topological, evolutionary and functional view. The comparison results show that there are statistical differences between nonessential-nonhub proteins and essential-nonhub proteins in centrality measures, clustering coefficient, evolutionary rate and the number of essential neighbors. These are reasons why nonessential-nonhub proteins don’t have lethality.

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Identification of Lung-Cancer-Related Genes with the Shortest Path Approach in a Protein-Protein Interaction Network

BioMed Research International ◽

10.1155/2013/267375 ◽

2013 ◽

Vol 2013 ◽

pp. 1-8 ◽

Cited By ~ 8

Author(s):

Bi-Qing Li ◽

Jin You ◽

Lei Chen ◽

Jian Zhang ◽

Ning Zhang ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Shortest Path ◽

Protein Interaction ◽

Expression Profiles ◽

Shortest Paths ◽

Gene Expression Profiles ◽

Cancer Genes ◽

Ppi Network ◽

Protein Protein Interaction

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra’s algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutationPvalue less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.

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A thorough analysis of the contribution of experimental, derived and sequence-based predicted protein-protein interactions for functional annotation of proteins

10.1101/832253 ◽

2019 ◽

Cited By ~ 1

Author(s):

Stavros Makrodimitris ◽

Marcel Reinders ◽

Roeland van Ham

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Biological Process ◽

Poor Performance ◽

Protein Interaction Data ◽

Physical Interaction ◽

Cellular Functions ◽

Data Resource ◽

Protein Protein Interaction ◽

Ppi Networks

AbstractPhysical interaction between two proteins is strong evidence that the proteins are involved in the same biological process, making Protein-Protein Interaction (PPI) networks a valuable data resource for predicting the cellular functions of proteins. However, PPI networks are largely incomplete for non-model species. Here, we tested to what extened these incomplete networks are still useful for genome-wide function prediction. We used two network-based classifiers to predict Biological Process Gene Ontology terms from protein interaction data in four species: Saccharomyces cerevisiae, Escherichia coli, Arabidopsis thaliana and Solanum lycopersicum (tomato). The classifiers had reasonable performance in the well-studied yeast, but performed poorly in the other species. We showed that this poor performance can be considerably improved by adding edges predicted from various data sources, such as text mining, and that associations from the STRING database are more useful than interactions predicted by a neural network from sequence-based features.

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MIPPIE: the mouse integrated protein–protein interaction reference

Database ◽

10.1093/database/baaa035 ◽

2020 ◽

Vol 2020 ◽

Cited By ~ 3

Author(s):

Gregorio Alanis-Lobato ◽

Jannik S Möllmann ◽

Martin H Schaefer ◽

Miguel A Andrade-Navarro

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Model Organism ◽

Web Interface ◽

Protein Protein Interactions ◽

Environmental Signals ◽

Protein Protein Interaction ◽

Ppi Networks ◽

User Friendly

Abstract Cells operate and react to environmental signals thanks to a complex network of protein–protein interactions (PPIs), the malfunction of which can severely disrupt cellular homeostasis. As a result, mapping and analyzing protein networks are key to advancing our understanding of biological processes and diseases. An invaluable part of these endeavors has been the house mouse (Mus musculus), the mammalian model organism par excellence, which has provided insights into human biology and disorders. The importance of investigating PPI networks in the context of mouse prompted us to develop the Mouse Integrated Protein–Protein Interaction rEference (MIPPIE). MIPPIE inherits a robust infrastructure from HIPPIE, its sister database of human PPIs, allowing for the assembly of reliable networks supported by different evidence sources and high-quality experimental techniques. MIPPIE networks can be further refined with tissue, directionality and effect information through a user-friendly web interface. Moreover, all MIPPIE data and meta-data can be accessed via a REST web service or downloaded as text files, thus facilitating the integration of mouse PPIs into follow-up bioinformatics pipelines.

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