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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.

The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into the host cells. Recent studies mainly focus on annotating new T4SE from the huge amount of sequencing data, and various computational tools are therefore developed to accelerate T4SE annotation. However, these tools are reported as heavily dependent on the selected methods and their annotation performance need to be further enhanced. Herein, a convolution neural network (CNN) technique was used to annotate T4SEs by integrating multiple protein encoding strategies. First, the annotation accuracies of nine encoding strategies integrated with CNN were assessed and compared with that of the popular T4SE annotation tools based on independent benchmark. Second, false discovery rates of various models were systematically evaluated by (1) scanning the genome of Legionella pneumophila subsp. ATCC 33152 and (2) predicting the real-world non-T4SEs validated using published experiments. Based on the above analyses, the encoding strategies, (a) position-specific scoring matrix (PSSM), (b) protein secondary structure & solvent accessibility (PSSSA) and (c) one-hot encoding scheme (Onehot), were identified as well-performing when integrated with CNN. Finally, a novel strategy that collectively considers the three well-performing models (CNN-PSSM, CNN-PSSSA and CNN-Onehot) was proposed, and a new tool (CNN-T4SE, https://idrblab.org/cnnt4se/) was constructed to facilitate T4SE annotation. All in all, this study conducted a comprehensive analysis on the performance of a collection of encoding strategies when integrated with CNN, which could facilitate the suppression of T4SS in infection and limit the spread of antimicrobial resistance.

The southeastern US has experienced recurrent drought during recent decades. Increasing demand for water, as precipitation decreases, exacerbates stress on the aquatic biota of the Southeast: a global hotspot for freshwater mussel, crayfish, and fish diversity. Freshwater unionid mussels are ideal candidates to study linkages between ecophysiological and behavioral responses to drought. Previous work on co-occurring mussel species suggests a coupling of physiology and behavior along a gradient ranging from intolerant species such as Pyganodon grandis (giant floater) that track receding waters and rarely burrow in the substrates to tolerant species such as Uniomerus tetralasmus (pondhorn) that rarely track receding waters, but readily burrow into the drying sediments. We utilized a next-generation sequencing-based RNA-Seq approach to examine heat/desiccation-induced transcriptomic profiles of these two species in order to identify linkages between patterns of gene expression, physiology and behavior. Sequencing produced over 425 million 100 bp reads. Using the de novo assembly package Trinity, we assembled the short reads into 321,250 contigs from giant floater (average length 835 bp) and 385,735 contigs from pondhorn (average length 929 bp). BLAST-based annotation and gene expression analysis revealed 2,832 differentially expressed genes in giant floater and 2,758 differentially expressed genes in pondhorn. Trancriptomic responses included changes in molecular chaperones, oxidative stress profiles, cell cycling, energy metabolism, immunity, and cytoskeletal rearrangements. Comparative analyses between species indicated significantly higher induction of molecular chaperones and cytoskeletal elements in the intolerant P. grandis as well as important differences in genes regulating apoptosis and immunity.

Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome–DME interaction (MICBIO), xenobiotics–DME interaction (XEOTIC) and host protein–DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/