Andrew James Perry

Chemokines are key regulators of leukocyte trafficking and attractive targets for anti-inflammatory therapy. Evasins are chemokine-binding proteins from tick saliva, whose application as anti-inflammatory therapeutics will require manipulation of their chemokine target selectivity. Here we describe subclass A3 evasins, which are unique to the tick genus Amblyomma and distinguished from “classical” class A1 evasins by an additional disulfide bond near the chemokine recognition interface. The A3 evasin EVA-AAM1001 (EVA-A) bound to CC chemokines and inhibited their receptor activation. Unlike A1 evasins, EVA-A was not highly dependent on N- and C-terminal regions to differentiate chemokine targets. Structures of chemokine-bound EVA-A revealed a deep hydrophobic pocket, unique to A3 evasins, that interacts with the residue immediately following the CC motif of the chemokine. Mutations to this pocket altered the chemokine selectivity of EVA-A. Thus, class A3 evasins provide a suitable platform for engineering proteins with applications in research, diagnosis or anti-inflammatory therapy.

View Video Presentation: https://doi.org/10.2514/6.2022-3729.vid The Kentucky Re-entry Universal Payload System (KRUPS) is a small re-entry capsule designed as a technology testbed. For its first incarnation, KRUPS has been designed to test Thermal Protection System (TPS) material and instrumentation. TPS are used to protect spacecraft, and its payload, from the extreme conditions of planetary entry. KRUPS has been developed at the University of Kentucky over the past 6 years. This specific launch aimed to produce system verification, software implementation, and launch qualifications, using an atmospheric balloon platform. The capsule released from the balloon in this experiment, was designed to transmit data using the Iridium satellite network. Recovery of the capsule was planned, using a parachute that aimed at preventing a crash landing and thus protecting the internal components. The capsule was activated and ejected as planned. Thermal measurements of the TPS were received by the capsule as it returned to Earth. This satisfied the main objective of the flight. However, the capsule did not have a successful parachute deployment, due to a timing issue. Therefore, the capsule was not able to be recovered in one piece. The thermal data measured during the descent of this capsule demonstrated the insulative properties of the TPS.

Understanding spatial gene expression and regulation is key to uncovering developmental and physiological processes, during homeostasis and disease. Numerous techniques exist to gain gene expression and regulation information, but very few utilise intuitive true-to-life three-dimensional representations to analyze and visualize results. Here we combined spatial transcriptomics with 3D modelling to represent and interrogate, transcriptome-wide, three-dimensional gene expression and location in the mouse adult heart. Our study has unveiled specific subsets of genes that display complex spatial expression in organ sub-compartments. Also, we created a web-based user interface for spatial transcriptome analysis and visualization. The application may be accessed from http://3d-cardiomics.erc.monash.edu/ .

The annotation of surface exposed bacterial membrane proteins is an important step in interpretation and validation of proteomic experiments. In particular, proteins detected by cell surface protease shaving experiments can indicate exposed regions of membrane proteins that may contain antigenic determinants or constitute vaccine targets in pathogenic bacteria.

The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.