SARS-CoV-2 protein structure and sequence mutations: evolutionary analysis and effects on virus variants SARS-CoV-2 protein structure and sequence mutations: (preprint)

Proteins sequence, structure, and function are related, so that any changes in the protein sequence may cause modifications in its structure and function. Thanks to the exponential growth of data availability, many studies have addressed different questions such as: (i) how structure evolves based on the sequence changes, (ii) how structure and function change over time. Computational experiments have contributed to the study of viral protein structures. For instance the Spike (S) protein has been investigated for its role in binding receptors and infection activity in COVID-19, hence the interest of scientific researchers in studying the effects of virus mutations due to sequence, structure and vaccination effects. Protein Contact Networks (PCNs) can be used for investigating protein structures to detect biological properties thorough network topology. We apply topological studies based on graph theory of the PCNs to compare the structural changes with sequence changes, and find that both node centrality and community extraction analysis play a relevant role in changes in protein stability and functionality caused by mutations. We compare the structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. We finally highlight a timeline correlation between Omicron variant identification and the vaccination campaign.

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing.

MOTIVATION: The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. RESULTS: Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. CONTACT: hguzzi@unicz.it, sroy01@cus.ac.in.

Regional Resource Assessment During the COVID-19 Pandemic in Italy: Modeling Study.

BACKGROUND: COVID-19 has been declared a worldwide emergency and a pandemic by the World Health Organization. It started in China in December 2019, and it rapidly spread throughout Italy, which was the most affected country after China. The pandemic affected all countries with similarly negative effects on the population and health care structures. OBJECTIVE: The evolution of the COVID-19 infections and the way such a phenomenon can be characterized in terms of resources and planning has to be considered. One of the most critical resources has been intensive care units (ICUs) with respect to the infection trend and critical hospitalization. METHODS: We propose a model to estimate the needed number of places in ICUs during the most acute phase of the infection. We also define a scalable geographic model to plan emergency and future management of patients with COVID-19 by planning their reallocation in health structures of other regions. RESULTS: We applied and assessed the prediction method both at the national and regional levels. ICU bed prediction was tested with respect to real data provided by the Italian government. We showed that our model is able to predict, with a reliable error in terms of resource complexity, estimation parameters used in health care structures. In addition, the proposed method is scalable at different geographic levels. This is relevant for pandemics such as COVID-19, which has shown different case incidences even among northern and southern Italian regions. CONCLUSIONS: Our contribution can be useful for decision makers to plan resources to guarantee patient management, but it can also be considered as a reference model for potential upcoming waves of COVID-19 and similar emergency situations.