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Functional annotation of protein sequence with high accuracy has become one of the most important issues in modern biomedical studies, and computational approaches of significantly accelerated analysis process and enhanced accuracy are greatly desired. Although a variety of methods have been developed to elevate protein annotation accuracy, their ability in controlling false annotation rates remains either limited or not systematically evaluated. In this study, a protein encoding strategy, together with a deep learning algorithm, was proposed to control the false discovery rate in protein function annotation, and its performances were systematically compared with that of the traditional similarity-based and de novo approaches. Based on a comprehensive assessment from multiple perspectives, the proposed strategy and algorithm were found to perform better in both prediction stability and annotation accuracy compared with other de novo methods. Moreover, an in-depth assessment revealed that it possessed an improved capacity of controlling the false discovery rate compared with traditional methods. All in all, this study not only provided a comprehensive analysis on the performances of the newly proposed strategy but also provided a tool for the researcher in the fields of protein function annotation.

Biodegradable porous biomaterial scaffolds play a critical role in bone regeneration. In this study, the porous nano-hydroxyapatite/collagen/poly(lactic-co-glycolic acid)/graphene oxide (nHAC/PLGA/GO) composite scaffolds containing different amount of GO were fabricated by freeze-drying method. The results show that the synthesized scaffolds possess a three-dimensional porous structure. GO slightly improves the hydrophilicity of the scaffolds and reinforces their mechanical strength. Young’s modulus of the 1.5 wt% GO incorporated scaffold is greatly increased compared to the control sample. The in vitro experiments show that the nHAC/PLGA/GO (1.5 wt%) scaffolds significantly cell adhesion and proliferation of osteoblast cells (MC3T3-E1). This present study indicates that the nHAC/PLGA/GO scaffolds have excellent cytocompatibility and bone regeneration ability, thus it has high potential to be used as scaffolds in the field of bone tissue engineering.Electronic supplementary materialThe online version of this article (10.1186/s11671-018-2432-6) contains supplementary material, which is available to authorized users.

High-quality label-free proteome quantification (LFQ) is valuable for clinical and pharmaceutical studies yet remains extremely challenging despite technical advances. Particularly, fluctuating precision, limited robustness, and compromised accuracy are known issues. Here, we described and validated a new strategy enabling the discovery of the LFQs of simultaneously enhanced precision, robustness, and accuracy from thousands of LFQ manipulation chains. In the proof-of-concept study, this strategy showed superior ability in identifying well-performing LFQs. An online tool incorporating this novel strategy was also developed.