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Epidemic spread of new pathogens is quite a frequent event that affects not only humans but also animals and plants, and specifically livestock and crops. In the last few years, many novel pathogenic viruses have threatened human life. Some were mutations of the traditional influenza viruses, and some were viruses that crossed the animal-human divide.In both cases, when a novel virus or bacterial strain for which there is no pre-existing immunity or a vaccine released, there is the possibility of an epidemic or even a pandemic event, as the one we are experiencing today with COVID-19.In this context, we defined an ELIXIR Service Bundle for Epidemic Response: a set of tools and workflows to facilitate and speed up the study of new pathogens, viruses or bacteria. The final goal of the bundle is to provide tools and resources to collect and analyse data on new pathogens (bacteria and viruses) and their relation to hosts (humans, animals, plants).

TransMembrane β-Barrel (TMBB) proteins located in the outer membranes of Gram-negative bacteria are crucial for many important biological processes and primary candidates as drug targets. Structure determination of TMBB proteins is challenging and hence computational methods devised for the analysis of TMBB proteins are important for complementing experimental approaches. Here, we present a novel web server called BetAware-Deep that is able to accurately identify the topology of TMBB proteins (i.e. the number and orientation of membrane-spanning segments along the protein sequence) and to discriminate them from other protein types. The method in BetAware-Deep defines new features by exploiting a non-canonical computation of the hydrophobic moment and by adopting sequence-profile weighting of the White&Wimley hydrophobicity scale. These features are processed using a two-step approach based on deep learning and probabilistic graphical models. BetAware-Deep has been trained on a dataset comprising 58 TMBBs and benchmarked on a novel set of 15 TMBB proteins. Results showed that BetAware-Deep outperforms two recently released state-of-the-art methods for topology prediction, predicting correct topologies of 10 out of 15 proteins. TMBB detection was also assessed on a larger dataset comprising 1009 TMBB proteins and 7571 non-TMBB proteins. Even in this benchmark, BetAware-Deep scored at the level of top-performing methods. A web server has been developed allowing users to analyze input protein sequences and providing topology prediction together with a rich set of information including a graphical representation of the residue-level annotations and prediction probabilities. BetAware-Deep is available at https://busca.biocomp.unibo.it/betaware2 .