Analysis of conformational motions and related key residue interactions responsible for a specific function of proteins with elastic network model

Protein collective motions play a critical role in many biochemical processes. How to predict the functional motions and the related key residue interactions in proteins is important for our understanding in the mechanism of the biochemical processes. Normal mode analysis (NMA) of the elastic network model (ENM) is one of the effective approaches to investigate the structure-encoded motions in proteins. However, the motion modes revealed by the conventional NMA approach do not necessarily correspond to a specific function of protein. In the present work, a new analysis method was proposed to identify the motion modes responsible for a specific function of proteins and then predict the key residue interactions involved in the functional motions by using a perturbation approach. In our method, an internal coordinate that accounts for the specific function was introduced, and the Cartesian coordinate space was transformed into the internal/Cartesian space by using linear approximation, where the introduced internal coordinate serves as one of the axes of the coordinate space. NMA of ENM in this internal/Cartesian space was performed and the function-relevant motion modes were identified according to their contributions to the specific function of proteins. Then the key residue interactions important for the functional motions of the protein were predicted as the interactions whose perturbation largely influences the fluctuation along the internal coordinate. Using our proposed methods, the maltose transporter (MalFGK₂) from E. Coli was studied. The functional motions and the key residue interactions that are related to the channel-gating function of this protein were successfully identified.     

Protein factors that bind to the murine 2',5'-oligoadenylate synthetase ME-12 gene 5' upstream regulatory region

The murine 2',5'-oligoadenylate synthetase ME-12 gene regulatory region AB forms six complexes with protein factors in murine BALB/c 3T3 cells as demonstrated by the mobility shift electrophoresis assay under the reaction conditions used. The complexes, designated C1-C6 in order of their decreasing electrophoretic mobility, showed three distinctive specificities with regulatory region AB, element A, and element B as probes or competing DNA: 1) C1 is region AB-specific (this complex did not form with either element A or B used alone or as a mixture); 2) C5 formed both with element A and element B; 3) C2, C3, C4, and C6 formed with element B, but not A. The protein factors that give rise to these complexes show differential DNA binding activities in various buffer solutions at different pH values. The C4-forming protein factor is the interferon (IFN)-alpha/beta-stimulated response factor (ISRF) which shows element B specificity. It preexists in the cytoplasm. ISRF appears to be complexed to an inhibitor (ISRFI) in the cytoplasm and to dissociate from the inhibitor and to translocate into the nucleus upon treatment of cells with IFN-alpha/beta. We propose that IFN-alpha/beta treatment of BALB/c 3T3 can trigger at least two events: 1) loosening of a tight inhibitor-ISRF complex with the release of free ISRF; this may be mediated via phosphorylation of ISRF or ISRFI; 2) translocation of ISRF into the nucleus and binding to the enhancer element B, which results in the activation of 2',5'-oligoadenylate synthetase gene expression.     

Capturing pair-wise epistatic effects associated with three agronomic traits in barley

Genetic association mapping has been widely applied to determine genetic markers favorably associated with a trait of interest and provide information for marker-assisted selection. Many association mapping studies commonly focus on main effects due to intolerable computing intensity. This study aims to select several sets of DNA markers with potential epistasis to maximize genetic variations of some key agronomic traits in barley. By doing so, we integrated a MDR (multifactor dimensionality reduction) method with a forward variable selection approach. This integrated approach was used to determine single nucleotide polymorphism pairs with epistasis effects associated with three agronomic traits: heading date, plant height, and grain yield in barley from the barley Coordinated Agricultural Project. Our results showed that four, seven, and five SNP pairs accounted for 51.06, 45.66 and 40.42% for heading date, plant height, and grain yield, respectively with epistasis being considered, while corresponding contributions to these three traits were 45.32, 31.39, 31.31%, respectively without epistasis being included. The results suggested that epistasis model was more effective than non-epistasis model in this study and can be more preferred for other applications.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.