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.     

A study of the genetic polymorphism of human inter-alpha-trypsin-inhibitor (ITI) in the Han population,Chengdu, China

Phenotypes of inter-alpha-trypsin-inhibitor (ITI) have been determined by isoelectric focusing on polyacrylamide gels followed by immunofixation. The phenotype frequencies of ITI in the Han population in Chengdu, P. R. China have been investigated using this method. In addition, family studies have been conducted in 21 families. The results show that ITI is polymorphic in the Han population in Chengdu, China. The allele frequencies are as follows: ITI*1 = 0.5763. ITI*2 = 0.4107, ITI*3 = 0.0130. ITI is thus a new and promising genetic marker that can be used in the field of forensic haematogenetics.     

U-Shaped Photonic Crystal Fiber Based Surface Plasmon Resonance Sensors

A surface plasmon resonance sensor based on a U-shaped photonic crystal fiber with a rectangular lattice has been designed through finite element method. The U-shaped fiber exhibits not only stronger mechanical strength but also better sensor performance than our previous scheme. The upper detection limit extends to higher analyze refractive index, 1.384, for phase interrogation. We introduce a ratio to evaluate the impact of higher order plasmonic mode. For wavelength modulation scheme, the parameter to describe the performance of a sensor is chosen to be the figure of merit, which can be up to 533.8[RIU^?1] around complete coupling condition.     

Diversity of endophytic bacteria and fungi in seeds of Elymus nutans growing in four locations of Qinghai Tibet Plateau,China

Plant and Soil - Seeds are involved in the transmission of microorganisms from one plant generation to the next, acting as initial inoculum for the plant microbiome, therefore provide a key source...     

Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma

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A study on giant panda recognition based on images of a large proportion of captive pandas

1. As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image‐based panda face recognition method.
2. In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.
3. The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.
4. This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost‐effective than the approaches used in the previous panda surveys.