Prediction of Protein-Protein Interactions by Evidence Combining Methods.

Most cellular functions involve proteins' features based on their physical interactions with other partner proteins. Sketching a map of protein-protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy.

Prediction and functional analysis of the sweet orange protein-protein interaction network.

BACKGROUND: Sweet orange (Citrus sinensis) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species. RESULTS: In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks. CONCLUSIONS: Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.

Clinical study of periodontal endoscope-assisted subgingival scaling in the treatment of residual pocket / 华西口腔医学杂志

OBJECTIVES@#To compare the treatment effects of periodontal endoscope-assisted and traditional subgingival scaling on residual pockets.@*METHODS@#A total of 13 patients with periodontitis from Dept. of Periodontics, West China Hospital of Stomatology, Sichuan University were recruited. After 4-6 weeks of initial treatment, the residual pockets with a probing depth (PD) of ≥4 mm and attachment loss (AL) of ≥4 mm and bleeding on probing were examined with traditional (control group) and periodontal endoscope-assisted subgingival scaling (endoscopy group) in a randomly controlled split-mouth design. At baseline and 6 weeks and 3 months after treatment, plaque index (PLI), PD, AL, and bleeding index (BI) were measured. Differences in these clinical parameters within and between groups and patient-reported outcomes were compared.@*RESULTS@#A total of the 694 sites of 251 teeth were included in this trial. Both groups showed significant improvement in each periodontal parameters 6 weeks and 3 months after treatment (@*CONCLUSIONS@#Periodontal endoscope-assisted subgingival scaling resulted in better effects than traditional subgingival scaling when the residual pockets were in a single-rooted tooth, with a PD of ≥5 mm but without vertical alveolar bone resorption and furcation involvement.

Advances in salivary protein glycosylation and its relationship with systemic and oral diseases / 华西口腔医学杂志

Protein glycosylation is one of the most important protein post-translational modifications that can affect life activities by endowing the protein with various structural and functional features. Saliva is an easy-to-obtain, noninvasive body fluid that contains components originating from serum, gingival crevicular fluid, and oropharyngeal mucosae. In recent years, understanding of saliva has been constantly updated with the developments in related research. Studies have shown that salivary proteins can be used as diagnostic markers for certain diseases, and changes of protein glycosylation in saliva are generally considered to be related to many diseases. In this review, salivary protein glycosylation and its relationship with systemic and oral diseases were discussed.