​Cloning and Sequence Analysis of Transferrin Binding Protein Gene A of Field and Vaccine Strains of Pasteurella multocida B:2

Background: Pasteurella multocida is an important bacterial pathogen that causes many major diseases of which haemorrhagic septiciemia (HS) in cattle and buffalo is responsible for catastrophic epizootics in India and South Asia. In India, the disease haemorrhagic septiciemia is considered as the most dreaded bacterial disease. Various host- and pathogen- specific determinants are responsible for disease outcome. Various bacterial virulence genes (tbpA, pfhA, toxA, hgbB, hgbA, nanH, nanB, sodA, sodC, oma87 and ptfA) have been proposed to play a key role in this interaction. Methods: The present study was done to compare the gene and deduced amino acid sequence of transferrin binding protein gene (tbpA) gene of field isolates and vaccine strain of P. multocida B: 2. Result: It was observed that tbpA gene of field and vaccine strains have similar nucleotide sequence except at positions 574 and 620. The sequence of tbpA gene was used for prediction of matured TbpA protein characteristics. The deduced amino acid sequences of 242 amino acids revealed 99% homology with TbpA of P. multocida and with a variety of other TonB-dependent receptor proteins, indicating that it belongs to the family of outer membrane receptors. Deduced amino acid sequence was found to be similar in field and vaccine strains except at 207th amino acid. In field isolates Leucine was there while in vaccine strain Phenyl alanine was found. These both amino acids are hydrophobic in nature so no change in physico-chemical property of TbpA is expected. From this study it is concluded that single amino acid difference between field isolate and vaccine strain might not cause change in its binding and physico-chemical property.

Graph Neural Networks with Multiple Feature Extraction Paths for Chemical Property Estimation

Feature extraction is essential for chemical property estimation of molecules using machine learning. Recently, graph neural networks have attracted attention for feature extraction from molecules. However, existing methods focus only on specific structural information, such as node relationship. In this paper, we propose a novel graph convolutional neural network that performs feature extraction with simultaneously considering multiple structures. Specifically, we propose feature extraction paths specialized in node, edge, and three-dimensional structures. Moreover, we propose an attention mechanism to aggregate the features extracted by the paths. The attention aggregation enables us to select useful features dynamically. The experimental results showed that the proposed method outperformed previous methods.

Automating the IRC Analysis within Eyringpy

To analyze the evolution of a chemical property along the reaction path, we have to extract all the necessary information from a set of electronic structure computations. However, this process is time-consuming and prone to human error. Here we introduce IRC-Analysis, a new extension in Eyringpy, to monitor the evolution of chemical properties along the intrinsic reaction coordinate, including complete reaction force analysis. IRC-Analysis collects the entire data set for each point on the reaction coordinate, eliminating human error in data capture and allowing the study of several chemical reactions in seconds, regardless of the complexity of the systems. Eyringpy has a simple input format, and no programming skills are required. A tracer has been included to visualize the evolution of a given chemical property along the reaction coordinate. Several properties can be analyzed at the same time. This version can analysis the evolution of bond distances and angles, Wiberg bond indices, natural charges, dipole moments, and orbital energies (and related properties).