Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19.

Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 ('Delta plus') is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group framework.

anti-HCoV: A web resource to collect natural compounds against human coronaviruses.

BACKGROUND: A novel coronavirus, the SARS-CoV2, was revealed to be the cause of COVID19, the pandemic disease that already provoked more than 555.324 deaths in the world (July 10, 2020). No vaccine treatment has been defined against SARS-CoV2 or other human coronaviruses (HCoVs), including those causing epidemic infections, neither appropriate strategies for prevention and care are yet officially suggested. SCOPE AND APPROACH: We reviewed scientific literature on natural compounds that were defined as potentially effective against human coronaviruses. Our desk research identified non-chemically modified natural compounds that were shown (in vitro) and/or predicted (in silico) to act against one or more phases of human coronaviruses cell cycle.We selected all available information, merged and annotated the data to define a comprehensive list of natural compounds, describing their chemical classification, the source, the action, the specific target in the viral infection. Our aim was to collect possible compounds for prevention and care against human coronaviruses. KEY FINDINGS AND CONCLUSIONS: The definition of appropriate interventions against viral diseases need a comprehensive view on the infection dynamics and on necessary treatments. Viral targeting compounds to be exploited in food sciences could be of relevant interest to this aim.We collected 174 natural compounds showing effects against human infecting coronaviruses, providing a curated annotation on actions and targets.The data are available in anti-HCoV, a web accessible resource to be exploited for testing and in vivo trials. The website is here launched to favour a community based cooperative effort to call for contribution and expand the collection. To be ready to fight.