Standardizing macromolecular structure files: further efforts are needed.

Investigating large datasets of biological information by automatic procedures may offer chances of progress in knowledge. Recently, tremendous improvements in structural biology have allowed the number of structures in the Protein Data Bank (PDB) archive to increase rapidly, in particular those for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-associated proteins. However, their automatic analysis can be hampered by the nonuniform descriptors used by authors in some records of the PDB and PDBx/mmCIF files. In this opinion article we highlight the difficulties encountered in automating the analysis of hundreds of structures, suggesting that further standardization of the description of these molecular entities and of their attributes, generalized to the macromolecular structures contained in the PDB, might generate files more suitable for automatized analyses of a large number of structures.

In Silico Analysis of the Effects of Omicron Spike Amino Acid Changes on the Interactions with Human Proteins.

The SARS-CoV-2 variant Omicron is characterized, among others, by more than 30 amino acid changes occurring on the spike glycoprotein with respect to the original SARS-CoV-2 spike protein. We report a comprehensive analysis of the effects of the Omicron spike amino acid changes in the interaction with human antibodies, obtained by modeling them into selected publicly available resolved 3D structures of spike-antibody complexes and investigating the effects of these mutations at structural level. We predict that the interactions of Omicron spike with human antibodies can be either negatively or positively affected by amino acid changes, with a predicted total loss of interactions only in a few complexes. Moreover, our analysis applied also to the spike-ACE2 interaction predicts that these amino acid changes may increase Omicron transmissibility. Our approach can be used to better understand SARS-CoV-2 transmissibility, detectability, and epidemiology and represents a model to be adopted also in the case of other variants.

A review on drug repurposing applicable to COVID-19.

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.