Dataset Information

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.

ABSTRACT: Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and "interologs" in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria.

SUBMITTER: Lv Q

PROVIDER: S-EPMC4614683 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.

Lv Qi Q Ma Weimin W Liu Hui H Li Jiang J Wang Huan H Lu Fang F Zhao Chen C Shi Tieliu T

Scientific reports 20151022

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and "interologs" in conservation. Combined t ...[more]

PMID: 26490033

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Project description:BACKGROUND: Serine/threonine kinases (STKs) have been found in an increasing number of prokaryotes, showing important roles in signal transduction that supplement the well known role of two-component system. Cyanobacteria are photoautotrophic prokaryotes able to grow in a wide range of ecological environments, and their signal transduction systems are important in adaptation to the environment. Sequence information from several cyanobacterial genomes offers a unique opportunity to conduct a comprehensive comparative analysis of this kinase family. In this study, we extracted information regarding Ser/Thr kinases from 21 species of sequenced cyanobacteria and investigated their diversity, conservation, domain structure, and evolution. RESULTS: 286 putative STK homologues were identified. STKs are absent in four Prochlorococcus strains and one marine Synechococcus strain and abundant in filamentous nitrogen-fixing cyanobacteria. Motifs and invariant amino acids typical in eukaryotic STKs were conserved well in these proteins, and six more cyanobacteria- or bacteria-specific conserved residues were found. These STK proteins were classified into three major families according to their domain structures. Fourteen types and a total of 131 additional domains were identified, some of which are reported to participate in the recognition of signals or substrates. Cyanobacterial STKs show rather complicated phylogenetic relationships that correspond poorly with phylogenies based on 16S rRNA and those based on additional domains. CONCLUSION: The number of STK genes in different cyanobacteria is the result of the genome size, ecophysiology, and physiological properties of the organism. Similar conserved motifs and amino acids indicate that cyanobacterial STKs make use of a similar catalytic mechanism as eukaryotic STKs. Gene gain-and-loss is significant during STK evolution, along with domain shuffling and insertion. This study has established an overall framework of sequence-structure-function interactions for the STK gene family, which may facilitate further studies of the role of STKs in various organisms.

Project description:Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain "dark"-i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.

Dataset Information

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.

Publications

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria.

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