The smallest near-infrared fluorescence complementation system for imaging protein-protein and RNA-protein interactions

Bimolecular fluorescence complementation (BiFC) and its derivative molecular biosensor systems provide effective tools for visualizing biomolecular interactions. The introduction of red and near-infrared fluorescence emission proteins has expanded the spectrum...

Social Media Validation for Pre-Screened Ontologies As a Driver for Biomolecular Interaction Significance in COVID-19 and Hematological Malignancies

With the rise of social media use during the COVID-19 pandemic, impressions from online content can affect behavioral changes resulting in exacerbating disparities in care. Thus, there exists a need to utilize social media platforms, like Twitter, to help augment preparedness, especially at the intersection between oncology and COVID-19, where tweets could help hint at potential biomolecular interactions. To address this, a study was developed to assess relationship and ontologies on the interaction between hematological malignancies and COVID-19 on Twitter. Ontologies are groupings of terms and related identifiers, such as genes, for general search terms, such as "Blood Cancer", were found utilizing the Human Phenotype Ontology. These were combined with the term "COVID-19" and used as search terms for Twitter's Standard Search API. The resulting tweets were cross-checked to assess if they included any of the other terms or genes related to the starting ontologies to then determine how many terms or genes each tweet was associated with. Once the most associated tweets to the ontologies were found, the genes related to those ontologies were utilized to find biological structures within the AlphaFold EMBL database, before being used in binding using HEX Docking software's shape based binding tool in 3D. Finally, Root Mean Square (RMS) Deviations were performed between the top 2000 conformations for each bound structure to determine if the binding was statistically significant. Results showed strong clustering of top tweets around keyword combinations. In the case of the starting entry, "Blood COVID-19", the ontologies that were found were linked to 45 terms that each had 100 or more tweets linked to them (Figure 1a). One such term of significance was Acute Myeloid Leukemia, which was linked to the gene BRCA1. The biological significance of the molecular interaction between BRCA1 and SARS CoV-2 was determined using the predicted protein structure from the AlphaFold-EMBL database for BRCA1 and the RCSB Protein Bank structure for the SARS CoV-2 spike (PDB# 6VSB), which can be found in Figure 1b. This interaction was found to be significant based on the average RMS Deviation of 82.97 Angstroms that ranged across the top 2000 conformation. Each model had an average RMS of 85.05 Angstroms between BRCA1 and the COVID-19 spike, with binding occurring on the spike's carbohydrate recognition domain within its S1 segment that is typically used for cell entry. Thus, human phenotype ontology was effective in classifying tweets to specific biomolecular interactions. Therefore, this approach could be utilized to proactively influence treatment designs for blood cancer patients infected with COVID-19, as well as in other areas where medical illnesses are already well defined by ontologies or other literature data. Forward looking, future studies will help to ensure that terms that are not well characterized by ontologies can still be utilized in this type of analysis by employing de novo ontology production methods. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.