Single‐Gene and Whole‐Genome Duplications and the Evolution of Protein–Protein Interaction Networks

Amoutzias, Grigoris D.; Peer, Yves Van de

doi:10.1002/9780470570418.ch19

Cited by 8 publications

(6 citation statements)

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“…The pervasive duplication and subsequent divergence in retention and copy number, as well as arm switching, that we have identified among chelicerate microRNAs may have led to their subfunctionalization and neofunctionalization ( Ohno 1970 ; Taylor and Raes 2004 ; Li et al 2008 ; Amoutzias and Van de Peer 2010 ; Innan and Kondrashov 2010 ; Abrouk et al 2012 ; Huminiecki and Conant 2012 ; Wang and Adams 2015 ). These evolutionary differences, therefore, may have contributed to the divergence in the developmental programs of chelicerates.…”

Section: Discussionmentioning

confidence: 99%

Pervasive microRNA Duplication in Chelicerates: Insights from the Embryonic microRNA Repertoire of the Spider Parasteatoda tepidariorum

Leite

Ninova

Hilbrant

et al. 2016

Genome Biology and Evolution

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MicroRNAs are small (∼22 nt) noncoding RNAs that repress translation and therefore regulate the production of proteins from specific target mRNAs. microRNAs have been found to function in diverse aspects of gene regulation within animal development and many other processes. Among invertebrates, both conserved and novel, lineage specific, microRNAs have been extensively studied predominantly in holometabolous insects such as Drosophila melanogaster. However little is known about microRNA repertoires in other arthropod lineages such as the chelicerates. To understand the evolution of microRNAs in this poorly sampled subphylum, we characterized the microRNA repertoire expressed during embryogenesis of the common house spider Parasteatoda tepidariorum. We identified a total of 148 microRNAs in P. tepidariorum representing 66 families. Approximately half of these microRNA families are conserved in other metazoans, while the remainder are specific to this spider. Of the 35 conserved microRNAs families 15 had at least two copies in the P. tepidariorum genome. A BLAST-based approach revealed a similar pattern of duplication in other spiders and a scorpion, but not among other chelicerates and arthropods, with the exception of a horseshoe crab. Among the duplicated microRNAs we found examples of lineage-specific tandem duplications, and the duplication of entire microRNA clusters in three spiders, a scorpion, and in a horseshoe crab. Furthermore, we found that paralogs of many P. tepidariorum microRNA families exhibit arm switching, which suggests that duplication was often followed by sub- or neofunctionalization. Our work shows that understanding the evolution of microRNAs in the chelicerates has great potential to provide insights into the process of microRNA duplication and divergence and the evolution of animal development.

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Section: Discussionmentioning

confidence: 99%

Pervasive microRNA Duplication in Chelicerates: Insights from the Embryonic microRNA Repertoire of the Spider Parasteatoda tepidariorum

Leite

Ninova

Hilbrant

et al. 2016

Genome Biology and Evolution

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“…In an attempt to exclude proteins participating in obligate interactions, we generated our training set by selecting only proteins which can form stable structures in vivo without needing to be bound to other proteins [ 30 ]; crystal structures of obligate interactions should not satisfy this criteria. We only took proteins with structural deposits in the PDB as it is a necessary requirement for the analysis of structural feature that contribute to PPIs.…”

Section: Methodsmentioning

confidence: 99%

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

et al. 2014

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BackgroundTransient protein-protein interactions (PPIs), which underly most biological processes, are a prime target for therapeutic development. Immense progress has been made towards computational prediction of PPIs using methods such as protein docking and sequence analysis. However, docking generally requires high resolution structures of both of the binding partners and sequence analysis requires that a significant number of recurrent patterns exist for the identification of a potential binding site. Researchers have turned to machine learning to overcome some of the other methods’ restrictions by generalising interface sites with sets of descriptive features. Best practices for dataset generation, features, and learning algorithms have not yet been identified or agreed upon, and an analysis of the overall efficacy of machine learning based PPI predictors is due, in order to highlight potential areas for improvement.ResultsThe presence of unknown interaction sites as a result of limited knowledge about protein interactions in the testing set dramatically reduces prediction accuracy. Greater accuracy in labelling the data by enforcing higher interface site rates per domain resulted in an average 44% improvement across multiple machine learning algorithms. A set of 10 biologically unrelated proteins that were consistently predicted on with high accuracy emerged through our analysis. We identify seven features with the most predictive power over multiple datasets and machine learning algorithms. Through our analysis, we created a new predictor, RAD-T, that outperforms existing non-structurally specializing machine learning protein interface predictors, with an average 59% increase in MCC score on a dataset with a high number of interactions.ConclusionCurrent methods of evaluating machine-learning based PPI predictors tend to undervalue their performance, which may be artificially decreased by the presence of un-identified interaction sites. Changes to predictors’ training sets will be integral to the future progress of interface prediction by machine learning methods. We reveal the need for a larger test set of well studied proteins or domain-specific scoring algorithms to compensate for poor interaction site identification on proteins in general.

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“…In a non-obligate complex, a protein forms a stable well-folded structure without any assistance from other proteins. However, some proteins cannot make a stable well-folded structure themselves and form protein complexes to stabilize the constituent proteins, leading to obligate protein complexes [2] .…”

Section: Introductionmentioning

confidence: 99%

Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

Omranian

Nikoloski

Grimm

2022

Computational and Structural Biotechnology Journal

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Single‐Gene and Whole‐Genome Duplications and the Evolution of Protein–Protein Interaction Networks

Cited by 8 publications

References 100 publications

Pervasive microRNA Duplication in Chelicerates: Insights from the Embryonic microRNA Repertoire of the Spider Parasteatoda tepidariorum

Pervasive microRNA Duplication in Chelicerates: Insights from the Embryonic microRNA Repertoire of the Spider Parasteatoda tepidariorum

Transient protein-protein interface prediction: datasets, features, algorithms, and the RAD-T predictor

Computational identification of protein complexes from network interactions: Present state, challenges, and the way forward

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