A Computational Workflow to Predict Biological Target Mutations: The Spike Glycoprotein Case Study.

The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins' mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. "Mutations rules" that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.

How a Blockchain Approach Can Improve Data Reliability in the COVID-19 Pandemic.

The rapid spread of COVID-19 made it necessary to quickly collect and share viral genomic sequences, sometimes making quantity prevail over the quality of information. Can research pay this price? Blockchain technology, based on the concept of a ledger that guarantees the authenticity and traceability of information, could be the best applicable solution.