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.     

RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials

A new software package, RASPA, for simulating adsorption and diffusion of molecules in flexible nanoporous materials is presented. The code implements the latest state-of-the-art algorithms for molecular dynamics and Monte Carlo (MC) in various ensembles including symplectic/measure-preserving integrators, Ewald summation, configurational-bias MC, continuous fractional component MC, reactive MC and Baker's minimisation. We show example applications of RASPA in computing coexistence properties, adsorption isotherms for single and multiple components, self- and collective diffusivities, reaction systems and visualisation. The software is released under the GNU General Public License.     

Dynamic changes of microtubule and actin structures in CV-1 cells during electrofusion

To study the involvement of the cytoskeletal system in the fusion of animal cells, we examined the dynamic changes of cytoskeletal proteins during the various stages of cell fusion. CV-1 cells were fused by applying a radio-frequency electrical pulse. Structural changes of microtubules (MTs) and F-actin were monitored simultaneously by double-label fluorescence microscopy. It was observed that in a few minutes after the initiation of cell fusion, MT bundles began to extend into the cytoplasmic bridges which were formed by fusing the membranes of neighboring cells. Later, a network of parallel MT bundles appeared between the adjacent nuclei of the fusing cells; such MT bundles may provide the mechanical links that are responsible for nuclear aggregation. The structural changes of F-actin during cell fusion were more complicated. We observed many different patterns of actin distribution in the fusing cells, including some giant, ring-shaped structures. Reorganization of actin is unlikely to be involved in the nuclear aggregation process. Instead, actin bundles condensed at the cell edges may help to widen the cytoplasmic bridges to allow merging of cellular contents between the fusing cells.     

Low-level inversion of the L component of pseudorabies virus is not dependent on sequence homology.

Pseudorabies virus has a class 2 genome in which the S component is found in two orientations relative to the L component. The L component is bracketed by sequences that are partially homologous; it is found mainly in one orientation, but a small proportion is inverted (J. M. DeMarchi, Z. Lu, G. Rall, S. Kuperschmidt, and T. Ben-Porat, J. Virol. 64:4968-4977, 1990). We have ascertained the role of the patchy homologous sequences bracketing the L component in its inversion. A viral mutant, vYa, from which the sequences at the right end of the L component were deleted was constructed. Despite the absence of homologous sequences bracketing the L component in vYa, its L component inverted to an extent similar to that of the L component in the wild-type virus. These results show the following. (i) The low-frequency inversion of the L component of PrV is not mediated by homologous sequences bracketing this component. (ii) Cleavage of concatemeric DNA at the internal junction between the S and L components is responsible for the appearance of the minority of genomes with an inverted L component in populations of pseudorabies virus. (iii) The signals present near or at the end of the S component are sufficient to allow low-frequency cleavage of concatemeric DNA; the sequences at the end of the L component are not essential for cleavage, although they enhance it considerably.     

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.