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.     

Influence of Phytase Transgenic Corn on the Intestinal Microflora and the Fate of Transgenic DNA and Protein in Digesta and Tissues of Broilers

An experiment was conducted to investigate the effect of phytase transgenic corn (PTC) on intestinal microflora, and the fate of transgenic DNA and protein in the digesta and tissues of broilers. A total of 160 1-day-old Arbor Acres commercial male broilers were randomly assigned to 20 cages (8 chicks per cage) with 10 cages (replicates) for each treatment. Birds were fed with a diet containing either PTC (54.0% during 1–21 days and 61.0% during 22–42 days) or non-transgenic isogenic control corn (CC) for a duration of 42 days. There were no significant differences (P>0.05) between birds fed with the PTC diets and those fed with the CC diets in the quantities of aerobic bacteria, anaerobic bacteria, colibacillus and lactobacilli, or microbial diversities in the contents of ileum and cecum. Transgenic phyA2 DNA was not detected, but phyA2 protein was detected in the digesta of duodenum and jejunum of broilers fed with the PTC diets. Both transgenic phyA2 DNA and protein fragments were not found in the digesta of the ileum and rectum, heart, liver, kidney, and breast or thigh muscles of broilers fed with the PTC diets. It was concluded that PTC had no adverse effect on the quantity and diversity of gut microorganisms; Transgenic phyA2 DNA or protein was rapidly degraded in the intestinal tract and was not transferred to the tissues of broilers.     

Multi-model recursive identification for nonlinear systems with non-uniformly sampling

A recursive least squares based on Multi-model is proposed for non-uniformly sampled-data nonlinear (NUSDN) systems. The corresponding state space model of an NUSDN system is derived using lifting technique. Taking advantage of the Fuzzy c-Mean Clustering algorithm, NUSDN is divided into several local models. The basic idea is that the NUSDN system is viewed as a model switching system under a given rule. Once the local models are identified, the global model is determined. A pH neutralization process validate the performance of the proposed algorithm.