Horizontal Gene Transfer (HGT) is defined as the movement of genetic material from one strain of species to another. Bacteria, being an asexual organism were always believed to transfer genes vertically. But recent studies provide evidence that shows bacteria can also transfer genes horizontally. HGT plays a major role in evolution and medicine. It is the major contributor in bacterial evolution, enabling species to acquire genes to adapt to the new environments. Bacteria are also believed to develop drug resistance to antibiotics through the phenomenon of HGT. Therefore further study of HGT and its implications is necessary to understand the effects of HGT in biology and to study techniques to enable or disable the process based on its effects. Methods to detect HGT events have been studied extensively but no method can accurately detect all the transfers between the organisms. This paper presents an HGT identification method based on approximate searches on bacterial protein structures. This method makes use of Z- score similarities between the protein structures and also uses functions of BLAST and DaliLite to work with protein sequence and structural similarities. In addition, Jmol, a java viewer tool is used for visual structural comparisons and sequence alignment. We also present experimental results regarding HGTs between the Firmicutes bacterium Bacillus subtilis and various Proteobacteria bacteria.
Protein is of utmost importance in the human body. It is considered as the building blocks of life. Scientists, for a long, have been studying its properties and functionalities in order to improve proteins and design completely new proteins that perform new functions and processes. Recently, an innovation came into being when researchers in the United States and Taiwan explored how to create new proteins by using machine learning to translate protein structures into musical scores, presenting an unusual way to translate physics concepts across disparate domains, noted APL Bioengineering. A deep learning model has been employed to design de novo proteins, based on the interplay of elementary building blocks via hierarchical patterns.
The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate tool to predict subcellular localization becomes increasingly important. Many efforts have been made to predict protein subcellular localization. This paper aims to merge the artificial neural networks and bioinformatics to predict the location of protein in yeast genome. We introduce a new subcellular prediction method based on a backpropagation neural network. The results show that the prediction within an error limit of 5 to 10 percentage can be achieved with the system.
DeepMind, a Google-owned artificial intelligence (AI) company based in the United Kingdom, made scientific history when it announced last November that it had a solution to a 50-year-old grand challenge in biology--protein folding. This AI machine learning breakthrough may help accelerate the discovery of new medications and novel treatments for diseases. On July 15, 2021 DeepMind revealed details on how its AI works in a new peer-reviewed paper published in Nature, and made its revolutionary AlphaFold version 2.0 model available as open-source on GitHub. The three-dimensional (3D) shape and function of proteins are determined by the sequence of its amino acids. AlphaFold predicts three-dimensional (3D) models of protein structures.