An integrated understanding of the evolutionary and structural features of the SARS-CoV-2 spike receptor binding domain (RBD).

Conventional drug development strategies typically use pocket in protein structures as drug-target sites. They overlook the plausible effects of protein evolvability and resistant mutations on protein structure which in turn may impair protein-drug interaction. In this study, we used an integrated evolution and structure guided strategy to develop potential evolutionary-escape resistant therapeutics using receptor binding domain (RBD) of SARS-CoV-2 spike-protein/S-protein as a model. Deploying an ensemble of sequence space exploratory tools including co-evolutionary analysis and deep mutational scans we provide a quantitative insight into the evolutionarily constrained subspace of the RBD sequence-space. Guided by molecular simulation and structure network analysis we highlight regions inside the RBD, which are critical for providing structural integrity and conformational flexibility. Using fuzzy C-means clustering we combined evolutionary and structural features of RBD and identified a critical region. Subsequently, we used computational drug screening using a library of 1615 small molecules and identified one lead molecule, which is expected to target the identified region, critical for evolvability and structural stability of RBD. This integrated evolution-structure guided strategy to develop evolutionary-escape resistant lead molecules have potential general applications beyond SARS-CoV-2.

Cellular automata in the light of COVID-19.

Currently, the world has been facing the brunt of a pandemic due to a disease called COVID-19 for the last 2 years. To study the spread of such infectious diseases it is important to not only understand their temporal evolution but also the spatial evolution. In this work, the spread of this disease has been studied with a cellular automata (CA) model to find the temporal and the spatial behavior of it. Here, we have proposed a neighborhood criteria which will help us to measure the social confinement at the time of the disease spread. The two main parameters of our model are (i) disease transmission probability (q) which helps us to measure the infectivity of a disease and (ii) exponent (n) which helps us to measure the degree of the social confinement. Here, we have studied various spatial growths of the disease by simulating this CA model. Finally we have tried to fit our model with the COVID-19 data of India for various waves and have attempted to match our model predictions with regards to each wave to see how the different parameters vary with respect to infectivity and restrictions in social interaction.

Universality and herd immunity threshold: Revisiting the SIR model for COVID-19

COVID-19 pandemic has been raging all around the world for almost a year now, as of November 1, 2020. In this paper, we try to analyze the variation of the COVID-19 pandemic in different countries in the light of some modifications to the susceptible-infected-recovered (SIR) model. The SIR model was modified by taking time-dependent rate parameters. From this modified SIR model, the basic reproduction number, effective reproduction number, herd immunity, and herd immunity threshold are redefined. The re-outbreak of the COVID-19 is a real threat to various countries. We have used the above-mentioned quantities to find the reasons behind the re-outbreak of this disease. Also, the effectiveness of herd immunity to prevent an epidemic has been analyzed with respect to this model. We have also tried to show that there are certain universal aspects in the spread and containment of the disease in various countries for a short period of time. Finally, we have also analyzed the current pandemic situation in India and have attempted to discuss the possibilities in order to predict its future behavior using our model.