BackgroundAccurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict the function of proteins. However, the precision of these predictions still needs to be improved, due to the incompletion and noise in PPI networks. Integrating network topology and biological information could improve the accuracy of protein function prediction and may also lead to the discovery of multiple interaction types between proteins. Current algorithms generate a single network, which is archived using a weighted sum of all types of protein interactions.MethodThe influences of different types of interactions on the prediction of protein functions are not the same. To address this, we construct multilayer protein networks (MPN) by integrating PPI networks, the domain of proteins, and information on protein complexes. In the MPN, there is more than one type of connections between pairwise proteins. Different types of connections reflect different roles and importance in protein function prediction. Based on the MPN, we propose a new protein function prediction method, named function prediction based on multilayer protein networks (FP-MPN). Given an un-annotated protein, the FP-MPN method visits each layer of the MPN in turn and generates a set of candidate neighbors with known functions. A set of predicted functions for the testing protein is then formed and all of these functions are scored and sorted. Each layer plays different importance on the prediction of protein functions. A number of top-ranking functions are selected to annotate the unknown protein.ConclusionsThe method proposed in this paper was a better predictor when used on Saccharomyces cerevisiae protein data than other function prediction methods previously used. The proposed FP-MPN method takes different roles of connections in protein function prediction into account to reduce the artificial noise by introducing biological information.Electronic supplementary materialThe online version of this article (doi:10.1186/s40246-016-0087-x) contains supplementary material, which is available to authorized users.
Quantum correlation plays an important role in quantum information processing, for which various quantifiers have been proposed so far. In this paper, we address the dynamics of local quantum uncertainty (LQU) as a reliable quantifier of quantum correlation in a two-qubit Heisenberg spin chain in the presence of nonuniform external magnetic field and Dzyaloshinski-Moriya interaction with intrinsic decoherence. The influences of the initial states, external magnetic field strength, Dzyaloshinski-Moriya interaction strength and intrinsic decoherence rate on the dynamics of LQU have been in detail investigated. Our analytical results show that the dynamics of LQU is strongly depended on the form of initial states. For an initial correlated state, the dynamical behaviors of LQU exhibit either monotonic decay or damped oscillations with respect to time. While for an initial separable state, quantum correlation quantified by LQU can be created due to the Dzyaloshinski-Moriya interaction and Heisenberg anisotropic interaction. Besides, the relationship between LQU and l-norm coherence or concurrence is also demonstrated in the present model.
Alcohol dependence (AD) is a complex disorder characterized by psychiatric and physiological dependence on alcohol. AD is reflected by regular alcohol drinking, which is highly inheritable. In this study, to identify susceptibility genes associated with alcohol drinking, we performed a genome-wide association study of copy number variants (CNVs) in 2,286 Caucasian subjects with Affymetrix SNP6.0 genotyping array. We replicated our findings in 1,627 Chinese subjects with the same genotyping array. We identified two CNVs, CNV207 (combined p-value 1.91E-03) and CNV1836 (combined p-value 3.05E-03) that were associated with alcohol drinking. CNV207 and CNV1836 are located at the downstream of genes LTBP1 (870 kb) and FGD4 (400 kb), respectively. LTBP1, by interacting TGFB1, may down-regulate enzymes directly participating in alcohol metabolism. FGD4 plays a role in clustering and trafficking GABAA receptor and subsequently influence alcohol drinking through activating CDC42. Our results provide suggestive evidence that the newly identified CNV regions and relevant genes may contribute to the genetic mechanism of alcohol dependence.
In the present study, ultra-performance liquid chromatography (UPLC) coupled to electrospray ionization (ESI(+)) tandem mass spectrometry (MS) was developed to identify and characterize the diarylheptanoids in the supercritical fluid extract (SFE) of Alpinia officinarum. The method established provides good reproducibility of UPLC and shows high precision with all the mass accuracy of less than 5 ppm. The ESI-MS-MS fragmentation behavior of every group and their appropriate characteristic pathways were proposed. On the basis of analyzing the fragmentation pathways, elemental composition provided by software Masslynx, mass data of the standard compounds and the information regarding polarity obtained from retention time data, in all, 23 diarylheptanods were characterized. All of them have been reported in Alpinia officinarum. They were classified into six distinct groups (homologous series). Compared to the references, the fragmentation pathways of the first and second group were detailed much more and complementary. Further more, the fragmentation pathways of the last four groups were firstly discussed. The fragmentation rules deduced and the data provided could aid in the characterization of other diarylheptanoids of these types and would be useful for the further research of diarylheptanoids in Alpinia officinarum or the other plants.
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