Characterization of BcaA, a Putative Classical Autotransporter Protein in Burkholderia pseudomallei

ABSTRACT Burkholderia pseudomallei is a tier 1 select agent, and the causative agent of melioidosis, a disease with effects ranging from chronic abscesses to fulminant pneumonia and septic shock, which can be rapidly fatal. Autotransporters (ATs) are outer membrane proteins belonging to the type V secretion system family, and many have been shown to play crucial roles in pathogenesis. The open reading frame Bp1026b_II1054 ( bcaA ) in B. pseudomallei strain 1026b is predicted to encode a classical autotransporter protein with an approximately 80-kDa passenger domain that contains a subtilisin-related domain. Immediately 3′ to bcaA is Bp11026_II1055 ( bcaB ), which encodes a putative prolyl 4-hydroxylase. To investigate the role of these genes in pathogenesis, large in-frame deletion mutations of bcaA and bcaB were constructed in strain Bp340, an efflux pump mutant derivative of the melioidosis clinical isolate 1026b. Comparison of Bp340Δ bcaA and Bp340Δ bcaB mutants to wild-type B. pseudomallei in vitro demonstrated similar levels of adherence to A549 lung epithelial cells, but the mutant strains were defective in their ability to invade these cells and to form plaques. In a BALB/c mouse model of intranasal infection, similar bacterial burdens were observed after 48 h in the lungs and liver of mice infected with Bp340Δ bcaA , Bp340Δ bcaB , and wild-type bacteria. However, significantly fewer bacteria were recovered from the spleen of Bp340Δ bcaA -infected mice, supporting the idea of a role for this AT in dissemination or in survival in the passage from the site of infection to the spleen

Machine learning tools for protein annotation: the cases of transmembrane β-barrel and myristoylated proteins

Biology is now a “Big Data Science” thanks to technological advancements allowing the characterization of the whole macromolecular content of a cell or a collection of cells. This opens interesting perspectives, but only a small portion of this data may be experimentally characterized. From this derives the demand of accurate and efficient computational tools for automatic annotation of biological molecules. This is even more true when dealing with membrane proteins, on which my research project is focused leading to the development of two machine learning-based methods: BetAware-Deep and SVMyr. BetAware-Deep is a tool for the detection and topology prediction of transmembrane beta-barrel proteins found in Gram-negative bacteria. These proteins are involved in many biological processes and primary candidates as drug targets. BetAware-Deep exploits the combination of a deep learning framework (bidirectional long short-term memory) and a probabilistic graphical model (grammatical-restrained hidden conditional random field). Moreover, it introduced a modified formulation of the hydrophobic moment, designed to include the evolutionary information. BetAware-Deep outperformed all the available methods in topology prediction and reported high scores in the detection task. Glycine myristoylation in Eukaryotes is the binding of a myristic acid on an N-terminal glycine. SVMyr is a fast method based on support vector machines designed to predict this modification in dataset of proteomic scale. It uses as input octapeptides and exploits computational scores derived from experimental examples and mean physicochemical features. SVMyr outperformed all the available methods for co-translational myristoylation prediction. In addition, it allows (as a unique feature) the prediction of post-translational myristoylation. Both the tools here described are designed having in mind best practices for the development of machine learning-based tools outlined by the bioinformatics community. Moreover, they are made available via user-friendly web servers. All this make them valuable tools for filling the gap between sequential and annotated data

Active Materials

What is an active material? This book aims to redefine perceptions of the materials that respond to their environment. Through the theory of the structure and functionality of materials found in nature a scientific approach to active materials is first identified. Further interviews with experts from the natural sciences and humanities then seeks to question and redefine this view of materials to create a new definition of active materials