No abstract

Tissue factor pathway inhibitor (TFPI) is a physiological inhibitor of extrinsic pathway of coagulation and has biological functions of anticoagulation and anti-inflammation. Although TFPI has been proved to be a good therapeutic agent of sepsis, inflammatory shock, and DIC, the clinical application and therapeutic effects of TFPI are impeded because of its short half-life in vivo. In order to prolong the half-life of TFPI, homology modeling and molecule docking were performed on a computer workstation principally in protein structural biology and binding characteristics between TFPI and its receptor LRP (low-density lipoprotein receptor related protein). Two recombinant long half-life human TFPI mutants coined TFPI-Mut1 and TFPI-Mut4 were designed and expressed in E.coli. In comparison with the wild-type TFPI, TFPI-Mut1 and TFPI-Mut4 presented a few of changes in spatial configuration and a decrease in relative Gibbs free energy of docking complex by 17.3% and 21.5%, respectively, as indicated by a computer simulation. After refolding and purification, anticoagulant activities, anti-TF/FVIIa and anti-FXa activities of the mutants were found to be the same as those of wide-type TFPI. The pharmacokinetics research indicated that alpha phase half-life (t1/2 alpha) of TFPI-Mut1 and TFPI-Mut4 were prolonged 1.33-fold and 1.96-fold respectively, beta phase half-life (t1/2 beta) of TFPI-Mut1 and TFPI-Mut4 were prolonged 1.62-fold and 4.22-fold respectively. These results suggested that TFPI-Mut1 and TFPI-Mut4 maintained the bioactivities of wild-type TFPI, prolonged half-life in vivo simultaneously and were expected for better clinical value and therapeutic effect.

Network motifs are subnetworks that appear in the network far more frequently than in randomized networks. They have gathered much attention for uncovering structural design principles of complex networks. One of the previous approaches for motif detection is sampling method, in- troduced to perform the computational challenging task. However, it suffers from sampling bias and probability assignment. In addition, subgraph search, being very time-consuming, is a critical process in motif detection as we need to enumerate subgraphs of given sizes in the original input graph and an ensemble of random generated graphs. Therefore, we present a Degree-based Sampling Method with Partition-based Subgraph Finder for larger motif detection. Inspired by the intrinsic feature of real biological networks, Degree-based Sampling is a new solution for probability assignment based on degree. And, Partition-based Subgraph Finder takes its inspiration from the idea of partition, which improves computational efficiency and lowers space consumption. Experimental study on UETZ and E.COLI data set shows that the proposed method achieves more accuracy and efficiency than previous methods and scales better with increasing subgraph size.