- Award ID(s):
- 1632976
- Publication Date:
- NSF-PAR ID:
- 10321469
- Journal Name:
- Communications Biology
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2399-3642
- Sponsoring Org:
- National Science Foundation
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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, onmore »
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Abstract Protein-protein interactions (PPIs) play essential roles in many biological processes. A PPI network provides crucial information on how biological pathways are structured and coordinated from individual protein functions. In the past two decades, large-scale PPI networks of a handful of organisms were determined by experimental techniques. However, these experimental methods are time-consuming, expensive, and are not easy to perform on new target organisms. Large-scale PPI data is particularly sparse in plant organisms. Here, we developed a computational approach for detecting PPIs trained and tested on known PPIs of
Arabidopsis thaliana and applied to three plants,Arabidopsis thaliana ,Glycine max (soybean), andZea mays (maize) to discover new PPIs on a genome-scale. Our method considers a variety of features including protein sequences, gene co-expression, functional association, and phylogenetic profiles. This is the first work where a PPI prediction method was developed for is the first PPI prediction method applied on benchmark datasets ofArabidopsis . The method showed a high prediction accuracy of over 90% and very high precision of close to 1.0. We predicted 50,220 PPIs inArabidopsis thaliana , 13,175,414 PPIs in corn, and 13,527,834 PPIs in soybean. Newly predicted PPIs were classified into three confidence levels according to the availability of existing supporting evidence and discussed. Predicted PPIs inmore » -
Leveraging protein-protein interaction networks to identify groups of proteins and their common functionality is an important problem in bioinformatics. Systems-level analysis of protein-protein interactions is made possible through network science and modeling of high-throughput data. From these analyses, small protein complexes are traditionally represented graphically as complete graphs or dense clusters of nodes. However, there are certain graph theoretic properties that have not been extensively studied in PPI networks, especially as they pertain to cluster discovery, such as planarity. Planarity of graphs have been used to reflect the physical constraints of real-world systems outside of bioinformatics, in areas such as mapping and imaging. Here, we investigate the planarity property in network models of protein complexes. We hypothesize that complexes represented as PPI subgraphs will tend to be planar, reflecting the actual physical interface and limits of components in the complex. When testing the planarity of known complex subgraphs in S. cerevisiae and selected mammalian PPIs, we find that a majority of validated complexes possess this planar property. We discuss the biological motivation of planar versus nonplanar subgraphs, observing that planar subgraphs tend to have longer protein components. Functional classification of planar versus nonplanar complex subgraphs reveals differences in annotation ofmore »
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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 obtainedmore »
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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 themore »