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1. Integration of anatomy ontology data with protein–protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

   https://doi.org/10.1186/s12859-020-03773-2  

   Fernando, Pasan C.; Mabee, Paula M.; Zeng, Erliang (December 2020, BMC Bioinformatics)

   Abstract Background Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. Results According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. Conclusion Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities. 

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

   https://doi.org/10.1038/s42003-021-02867-8  

   Klein, Brennan; Holmér, Ludvig; Smith, Keith M.; Johnson, Mackenzie M.; Swain, Anshuman; Stolp, Laura; Teufel, Ashley I.; Kleppe, April S. (December 2021, Communications Biology)

   Abstract Protein–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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3. Selection Maintains Protein Interactome Resilience in the Long-Term Evolution Experiment with Escherichia coli

   https://doi.org/10.1093/gbe/evab074  

   Maddamsetti, Rohan (June 2021, Genome Biology and Evolution)

   Golding, Brian (Ed.)

   Abstract Most cellular functions are carried out by a dynamic network of interacting proteins. An open question is whether the network properties of protein interactomes represent phenotypes under natural selection. One proposal is that protein interactomes have evolved to be resilient, such that they tend to maintain connectivity when proteins are removed from the network. This hypothesis predicts that interactome resilience should be maintained by natural selection during long-term experimental evolution. I tested this prediction by modeling the evolution of protein–protein interaction (PPI) networks in Lenski’s long-term evolution experiment with Escherichia coli (LTEE). In this test, I removed proteins affected by nonsense, insertion, deletion, and transposon mutations in evolved LTEE strains, and measured the resilience of the resulting networks. I compared the rate of change of network resilience in each LTEE population to the rate of change of network resilience for corresponding randomized networks. The evolved PPI networks are significantly more resilient than networks in which random proteins have been deleted. Moreover, the evolved networks are generally more resilient than networks in which the random deletion of proteins was restricted to those disrupted in LTEE. These results suggest that evolution in the LTEE has favored PPI networks that are, on average, more resilient than expected from the genetic variation across the evolved strains. My findings therefore support the hypothesis that selection maintains protein interactome resilience over evolutionary time. 

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4. NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity

   https://doi.org/10.1093/bioinformatics/btab098  

   Barot, Meet; Gligorijević, Vladimir; Cho, Kyunghyun; Bonneau, Richard (February 2021, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract Motivation Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, most of these methods are tied to a network for a single species, and many species lack biological networks. Results In this work, we integrate sequence and network information across multiple species by computing IsoRank similarity scores to create a meta-network profile of the proteins of multiple species. We use this integrated multispecies meta-network as input to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and consequently leads to significant improvements in function prediction performance compared to two network-based methods, a deep learning sequence-based method and the BLAST annotation method used in the Critial Assessment of Functional Annotation. We are able to demonstrate that our approach performs well even in cases where a species has no network information available: when an organism’s PPI network is left out we can use our multi-species method to make predictions for the left-out organism with good performance. Availability and implementation The code is freely available at https://github.com/nowittynamesleft/NetQuilt. The data, including sequences, PPI networks and GO annotations are available at https://string-db.org/. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. ConnectedAlign: a PPI network alignment method for identifying conserved protein complexes across multiple species

   Jianliang Gao, Bo Song (May 2019, BMC bioinformatics)

   Abstract Background: In bioinformatics, network alignment algorithms have been applied to protein-protein interaction (PPI) networks to discover evolutionary conserved substructures at the system level. However, most previous methods aim to maximize the similarity of aligned proteins in pairwise networks, while concerning little about the feature of connectivity in these substructures, such as the protein complexes. Results: In this paper, we identify the problem of finding conserved protein complexes, which requires the aligned proteins in a PPI network to form a connected subnetwork. By taking the feature of connectivity into consideration, we propose ConnectedAlign, an efficient method to find conserved protein complexes from multiple PPI networks. The proposed method improves the coverage significantly without compromising of the consistency in the aligned results. In this way, the knowledge of protein complexes in well-studied species can be extended to that of poor-studied species. Conclusions: We conducted extensive experiments on real PPI networks of four species, including human, yeast, fruit fly and worm. The experimental results demonstrate dominant benefits of the proposed method in finding protein complexes across multiple species. 

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