mCSM-PPI2: predicting the effects of mutations on protein–protein interactions

AbstractProtein–protein Interactions are involved in most fundamental biological processes, with disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a novel machine learning computational tool designed to more accurately predict the effects of missense mutations on protein–protein interaction binding affinity. mCSM-PPI2 uses graph-based structural signatures to model effects of variations on the inter-residue interaction network, evolutionary information, complex network metrics and energetic terms to generate an optimised predictor. We demonstrate that our method outperforms previous methods, ranking first among 26 others on CAPRI blind tests. mCSM-PPI2 is freely available as a user friendly webserver at http://biosig.unimelb.edu.au/mcsm_ppi2/.

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Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab010 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Sun Sook Chung ◽

Joseph C F Ng ◽

Anna Laddach ◽

N Shaun B Thomas ◽

Franca Fraternali

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Interaction Network ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Ppi Networks ◽

Short Loop ◽

New Strategy ◽

Loop Network ◽

Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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PIPINO: A Software Package to Facilitate the Identification of Protein-Protein Interactions from Affinity Purification Mass Spectrometry Data

BioMed Research International ◽

10.1155/2016/2891918 ◽

2016 ◽

Vol 2016 ◽

pp. 1-13

Author(s):

Stefan Kalkhof ◽

Stefan Schildbach ◽

Conny Blumert ◽

Friedemann Horn ◽

Martin von Bergen ◽

...

Keyword(s):

Mass Spectrometry ◽

Protein Interactions ◽

Isotope Labeling ◽

Affinity Purification ◽

Interaction Network ◽

Mass Spectrometry Data ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Binding Behavior ◽

Bona Fide

The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6.

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Dynamic rewiring of the human interactome by interferon signalling

10.1101/766808 ◽

2019 ◽

Cited By ~ 2

Author(s):

Craig H. Kerr ◽

Michael A. Skinnider ◽

Angel M. Madero ◽

Daniel D.T. Andrews ◽

R. Greg Stacey ◽

...

Keyword(s):

Protein Interactions ◽

Interaction Network ◽

Cell Types ◽

Antiviral Response ◽

Type I ◽

Protein Protein Interactions ◽

Human Interactome ◽

Viral Pathogens ◽

Protein Protein Interaction ◽

Dependent Protein

ABSTRACTBackgroundThe type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes (ISGs). Transcriptomic and biochemical approaches have established comprehensive catalogues of ISGs across species and cell types, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to delineate the effects of IFN signalling on the human proteome, culminating in the use of protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network.ResultsWe identified >27,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer ISG protein synthesis.ConclusionsOur map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing ISGs in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.

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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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Interactome Data and Databases: Different Types of Protein Interaction

Comparative and Functional Genomics ◽

10.1002/cfg.377 ◽

2004 ◽

Vol 5 (2) ◽

pp. 173-178 ◽

Cited By ~ 25

Author(s):

Javier De Las Rivas ◽

Alberto de Luis

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Interaction Network ◽

Short Review ◽

Experimental Methods ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Genome Wide ◽

Bioinformatic Tool ◽

Different Types

In recent years, the biomolecular sciences have been driven forward by overwhelming advances in new biotechnological high-throughput experimental methods and bioinformatic genome-wide computational methods. Such breakthroughs are producing huge amounts of new data that need to be carefully analysed to obtain correct and useful scientific knowledge. One of the fields where this advance has become more intense is the study of the network of ‘protein–protein interactions’, i.e. the ‘interactome’. In this short review we comment on the main data and databases produced in this field in last 5 years. We also present a rationalized scheme of biological definitions that will be useful for a better understanding and interpretation of ‘what a protein–protein interaction is’ and ‘which types of protein–protein interactions are found in a living cell’. Finally, we comment on some assignments of interactome data to defined types of protein interaction and we present a new bioinformatic tool called APIN (Agile Protein Interaction Network browser), which is in development and will be applied to browsing protein interaction databases.

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De NovoAssembly and Characterization ofSophora japonicaTranscriptome Using RNA-seq

BioMed Research International ◽

10.1155/2014/750961 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 5

Author(s):

Liucun Zhu ◽

Ying Zhang ◽

Wenna Guo ◽

Xin-Jian Xu ◽

Qiang Wang

Keyword(s):

Protein Interactions ◽

De Novo ◽

Average Length ◽

Genomic Analysis ◽

Interaction Network ◽

Protein Protein Interactions ◽

Gene Expressions ◽

Sophora Japonica ◽

Protein Protein Interaction ◽

Functional Genomic Analysis

Sophora japonicaLinn (Chinese Scholar Tree) is a shrub species belonging to the subfamily Faboideae of the pea family Fabaceae. In this study, RNA sequencing ofS. japonicatranscriptome was performed to produce large expression datasets for functional genomic analysis. Approximate 86.1 million high-quality clean reads were generated and assembledde novointo 143010 unique transcripts and 57614 unigenes. The average length of unigenes was 901 bps with an N50 of 545 bps. Four public databases, including the NCBI nonredundant protein (NR), Swiss-Prot, Kyoto Encyclopedia of Genes and Genomes (KEGG), and the Cluster of Orthologous Groups (COG), were used to annotate unigenes through NCBI BLAST procedure. A total of 27541 of 57614 unigenes (47.8%) were annotated for gene descriptions, conserved protein domains, or gene ontology. Moreover, an interaction network of unigenes inS. japonicawas predicted based on known protein-protein interactions of putative orthologs of well-studied plant genomes. The transcriptome data ofS. japonicareported here represents first genome-scale investigation of gene expressions in Faboideae plants. We expect that our study will provide a useful resource for further studies on gene expression, genomics, functional genomics, and protein-protein interaction inS. japonica.

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PINAWeb: A web-based tool for the comparison of Protein-Protein Interaction Networks Aligners

10.1101/2021.04.23.441154 ◽

2021 ◽

Author(s):

A. Alcalá ◽

G. Riera ◽

I. García ◽

R. Alberich ◽

M. Llabrés

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Interaction Networks ◽

Interaction Networks ◽

Protein Protein Interactions ◽

Considerable Effort ◽

Web Based ◽

Protein Protein Interaction ◽

Protein Protein Interaction Networks ◽

User Friendly

AbstractMotivationSeveral protein-protein interaction networks (PPIN) aligners have been developed during the last 15 years. One of their goals is to help the functional annotation of proteins and the prediction of protein-protein interactions. A correct aligner must preserve the network’s topology as well as the biological coherence. However, this is a trade-off that is hard to achieve. In addition, most aligners require a considerable effort to use in practice and many researchers must choose an aligner without the opportunity to previously compare the performance of different aligners.ResultsWe developed PINAWeb, a user-friendly web-based tool to obtain and compare the results produced by the aligners: AligNet, HubAlign, L-GRAAL, PINALOG and SPINAL. PPINs can be uploaded either from the STRING database or from a user database. The source code of PINAWeb is freely available on GitHub to enable researchers to add other aligners, network databases or alignment score metrics. In addition, PINAWeb provides a report with the analysis for every alignment in terms of topological and functional information scores, as well as the visualization of the alignments’ comparison (agreement/differences) when more than one aligner are considered.Availabilityhttps://bioinfo.uib.es/~recerca/PINAWeb

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Prediction of Protein-Protein Interactions Related to Protein Complexes Based on Protein Interaction Networks

BioMed Research International ◽

10.1155/2015/259157 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Peng Liu ◽

Lei Yang ◽

Daming Shi ◽

Xianglong Tang

Keyword(s):

Protein Interaction ◽

Protein Interactions ◽

Protein Complexes ◽

Statistical Tests ◽

Interaction Network ◽

Biological Data ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Small Parts

A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptivek-cores method to predict protein-protein interactions associated with the complexes. The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.

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Using Micro- and Macro-Level Network Metrics Unveils Top Communicative Gene Modules in Psoriasis

Genes ◽

10.3390/genes11080914 ◽

2020 ◽

Vol 11 (8) ◽

pp. 914

Author(s):

Reyhaneh Naderi ◽

Homa Saadati Mollaei ◽

Arne Elofsson ◽

Saman Hosseini Ashtiani

Keyword(s):

Network Analysis ◽

Protein Interactions ◽

Interaction Network ◽

Gene Interaction ◽

Macro Level ◽

Inflammatory Disorder ◽

Microarray Gene Expression ◽

Chronic Inflammatory Disorder ◽

Protein Protein Interaction ◽

Network Metrics

(1) Background: Psoriasis is a multifactorial chronic inflammatory disorder of the skin, with significant morbidity, characterized by hyperproliferation of the epidermis. Even though psoriasis’ etiology is not fully understood, it is believed to be multifactorial, with numerous key components. (2) Methods: In order to cast light on the complex molecular interactions in psoriasis vulgaris at both protein–protein interactions and transcriptomics levels, we studied a set of microarray gene expression analyses consisting of 170 paired lesional and non-lesional samples. Afterwards, a network analysis was conducted on the protein–protein interaction network of differentially expressed genes based on micro- and macro-level network metrics at a systemic level standpoint. (3) Results: We found 17 top communicative genes, all of which were experimentally proven to be pivotal in psoriasis, which were identified in two modules, namely the cell cycle and immune system. Intra- and inter-gene interaction subnetworks from the top communicative genes might provide further insight into the corresponding characteristic interactions. (4) Conclusions: Potential gene combinations for therapeutic/diagnostics purposes were identified. Moreover, our proposed workflow could be of interest to a broader range of future biological network analysis studies.

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Identification of the Key Genes of Autism Spectrum Disorder Through Protein-Protein Interaction Network

10.31661/gmj.v8i0.1367 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 1

Author(s):

Mona Zamanian Azodi ◽

Mostafa Rezaei-Tavirani ◽

Majid Rezaei-Tavirani

Keyword(s):

Gene Expression ◽

Autism Spectrum Disorder ◽

Protein Interactions ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

The Given

Background: Currently, the prevalence of autism spectrum disorder (ASD) is increasing, which widely spurs the interest in the molecular investigation. Thereby, a better understanding of the given disorder mechanisms is likely to be achieved. Bioinformatics suiting protein-protein interactions analysis via the application of high-throughput studies, such as protein array, is one of these achievements.Materials and Methods: The gene expression data from Gene Expression Omnibus (GEO) database were downloaded, and the expression profile of patients with developmental delay and autistic features were analyzed via Cytoscape and its relevant plug-ins.Results: Our findings indicated that EGFR, ACTB, RHOA, CALM1, MAPK1, and JUN genes as the hub-bottlenecks and their related terms could be important in ASD risk. In other words, any expression modification in these genes could trigger dysfunctions in the corresponding biological processes.Conclusion: We suggest that differentially expressed genes could be used as suitable targets for ASD after being validated.[GMJ.2019;8:e1367]

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