RRM Prediction of Erythrocyte Band3 Protein as Alternative Receptor for SARS-CoV-2 Virus

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new coronavirus causing a worldwide pandemic. It is infecting respiratory organs and, in more severe cases, the lungs, where it is infecting the human cells through the angiotensin-converting enzyme 2 (ACE2) receptor. In severe cases, it is characterized not only by difficulties in breathing through infected lungs, but also with disproportionally and, thus far, unexplained low levels of oxygen in the blood. Here, we propose that, besides the infection of respiratory organs through ACE2 receptors, there is an additional infection in the red blood cells (erythrocytes). There could be a possible for SARS-CoV-2 to pass through the alveoli membrane in the lungs and infect the red blood cells through another receptor. Using our own biophysical model, the Resonant Recognition Model, we propose that the red blood cell (RBC) Band3 protein on the surface of red blood cells is a possible entry point for the SARS-CoV-2 virus into red blood cells.

Analysis of Protein–Receptor Interactions on an Example of Leptin–Leptin Receptor Interaction Using the Resonant Recognition Model

Obesity is a medical condition in which excess body fat may have a negative effect on health and lifestyle, and it is becoming an increasing problem within modern society. Leptin is the key protein that regulates body energy balance by inhibiting hunger, and it could potentially be used in treatment of obesity and overweight. Here, we applied our own Resonant Recognition Model, which is capable of analyzing the selectivity of any protein–receptor interaction on an example of leptin–leptin receptor. We have identified a specific characteristic parameter for leptin activity through the leptin receptor, and this parameter could be used in development of new treatments for obesity.

Explanation of Osteoblastic Differentiation of Stem Cells by Photo Biomodulation Using the Resonant Recognition Model

Differentiation of stem cells into different tissues is a promising approach to treat a large number of diseases, as well as for tissue transplantation and repair. It has been shown that parathyroid hormone, similarly to stromal self-derived factor, and the radiation of specific electromagnetic frequencies of blue and green light, can encourage stem cell differentiation into osteoblasts. Here, we analysed parathyroid hormone, its receptor and stromal self-derived factor using the Resonant Recognition Model, which proposes that protein function is based on specific frequencies of electromagnetic radiation within ultra-violet, visible, infra-red and far infra-red light. The purpose of this research is to predict the characteristic frequencies related to parathyroid hormone activities, particularly differentiation of stem cells into osteoblasts. We have found that the most effective wavelength for stem cell differentiation would be 502 nm, which is between 420 nm and 540 nm, already experimentally proven to be effective in stimulating osteoblast differentiation. Thus, we propose that wavelength radiation of 502 nm will be even more efficient for differentiation of stem cells into osteoblasts.