Engagement of key bioinformatics tools that retrieves data from protein sequences can be compared with structure of ACE2 proteins from same individuals and patients of different clinical status for COVID-19 (for individuals that are non-infected, infected asymptomatic and symptomatic). This can either be from data obtained from disease states or from already deposited annotated data generated from past studies stored in protein databases. This can be geared towards investigating effectiveness of a pool of biological attributes such a structural neighbor profiles which help describe micro-environment of single amino polymorphisms (SAPs). Analysis can be done for SAPs (also known as non-synchronous single nucleotide polymorphism (nsSNPs). Then engage predictive and modeling tools to predict key mutational loci- particularly those involving SAPs and their functional significance in relation to association with disease states in known and predicted protein sequences. Generated biological data can contribute to ontology and associated molecular pathways. Similar data can be obtained from in-vivo studies in SARS-CoV-2 infected animal models.
This is an opinion study approach can open up paths to new designs and development protocols in therapeutics and diagnostics biomedicine.
|Publication status||Published - 6 Aug 2023|
|Name||Cambridge Open Engage|