Urgent Need for Field Surveys of Coronaviruses in Southeast Asia to Understand the SARS-CoV-2 Phylogeny and Risk Assessment for Future Outbreaks.

Phylogenetic analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on a single isolate of bat coronaviruses (bat CoVs) which does not adequately represent genetically related coronaviruses (CoVs) [...].

Pathogenic perspective of missense mutations of ORF3a protein of SARS-CoV-2.

One of the most important proteins for COVID-19 pathogenesis in SARS-CoV-2 is the ORF3a which is the largest accessory protein among others coded by the SARS-CoV-2 genome. The major roles of the protein include virulence, infectivity, ion channel activity, morphogenesis, and virus release. The coronavirus, SARS-CoV-2 is mutating rapidly, therefore, critical study of mutations in ORF3a is certainly important from the pathogenic perspective. Here, a sum of 175 non-synonymous mutations in the ORF3a of SARS-CoV-2 were identified from 7194 complete genomes of SARS-CoV-2 available from NCBI database. Effects of these mutations on structural stability, and functions of ORF3a were also studied. Broadly, three different classes of mutations, such as neutral, disease, and mixed (neutral and disease) types of mutations were observed. Consecutive phenomena of mutations in ORF3a protein were studied based on the timeline of detection of the mutations. Considering the amino acid compositions of the ORF3a protein, twenty clusters were detected using the K-means clustering method. The present findings on 175 novel mutations of ORF3a proteins will extend our knowledge on ORF3a, a vital accessory protein in SARS-CoV-2, to enlighten the pathogenicity of this life-threatening virus.

Notable sequence homology of the ORF10 protein introspects the architecture of SARS-CoV-2.

The current Coronavirus Disease 19 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) shows similar pathology to MERS and SARS-CoV, with a current estimated fatality rate of 1.4%. Open reading frame 10 (ORF10) is a unique SARS-CoV-2 accessory protein, which contains eleven cytotoxic T lymphocyte (CTL) epitopes each of nine amino acids in length. Twenty-two unique SARS-CoV-2 ORF10 variants have been identified based on missense mutations found in sequence databases. Some of these mutations are predicted to decrease the stability of ORF10 in silico physicochemical and structural comparative analyses were carried out on SARS-CoV-2 and Pangolin-CoV ORF10 proteins, which share 97.37% amino acid (aa) homology. Though there is a high degree of ORF10 protein similarity of SARS-CoV-2 and Pangolin-CoV, there are differences of these two ORF10 proteins related to their sub-structure (loop/coil region), solubility, antigenicity and shift from strand to coil at aa position 26 (tyrosine). SARS-CoV-2 ORF10, which is apparently expressed in vivo since reactive T cell clones are found in convalescent patients should be monitored for changes which could correlate with the pathogenesis of COVID-19.

A unique view of SARS-CoV-2 through the lens of ORF8 protein.

Immune evasion is one of the unique characteristics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) attributed to its ORF8 protein. This protein modulates the adaptive host immunity through down-regulation of MHC-1 (Major Histocompatibility Complex) molecules and innate immune responses by surpassing the host's interferon-mediated antiviral response. To understand the host's immune perspective in reference to the ORF8 protein, a comprehensive study of the ORF8 protein and mutations possessed by it have been performed. Chemical and structural properties of ORF8 proteins from different hosts, such as human, bat, and pangolin, suggest that the ORF8 of SARS-CoV-2 is much closer to ORF8 of Bat RaTG13-CoV than to that of Pangolin-CoV. Eighty-seven mutations across unique variants of ORF8 in SARS-CoV-2 can be grouped into four classes based on their predicted effects (Hussain et al., 2021) [1]. Based on the geo-locations and timescale of sample collection, a possible flow of mutations was built. Furthermore, conclusive flows of amalgamation of mutations were found upon sequence similarity analyses and consideration of the amino acid conservation phylogenies. Therefore, this study seeks to highlight the uniqueness of the rapidly evolving SARS-CoV-2 through the ORF8.

Possible Transmission Flow of SARS-CoV-2 Based on ACE2 Features.

Angiotensin-converting enzyme 2 (ACE2) is the cellular receptor for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) that is engendering the severe coronavirus disease 2019 (COVID-19) pandemic. The spike (S) protein receptor-binding domain (RBD) of SARS-CoV-2 binds to the three sub-domains viz. amino acids (aa) 22-42, aa 79-84, and aa 330-393 of ACE2 on human cells to initiate entry. It was reported earlier that the receptor utilization capacity of ACE2 proteins from different species, such as cats, chimpanzees, dogs, and cattle, are different. A comprehensive analysis of ACE2 receptors of nineteen species was carried out in this study, and the findings propose a possible SARS-CoV-2 transmission flow across these nineteen species.

The structural basis of accelerated host cell entry by SARS-CoV-2†.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the pandemic coronavirus disease 2019 (COVID-19) that exhibits an overwhelming contagious capacity over other human coronaviruses (HCoVs). This structural snapshot describes the structural bases underlying the pandemic capacity of SARS-CoV-2 and explains its fast motion over respiratory epithelia that allow its rapid cellular entry. Based on notable viral spike (S) protein features, we propose that the flat sialic acid-binding domain at the N-terminal domain (NTD) of the S1 subunit leads to more effective first contact and interaction with the sialic acid layer over the epithelium, and this, in turn, allows faster viral 'surfing' of the epithelium and receptor scanning by SARS-CoV-2. Angiotensin-converting enzyme 2 (ACE-2) protein on the epithelial surface is the primary entry receptor for SARS-CoV-2, and protein-protein interaction assays demonstrate high-affinity binding of the spike protein (S protein) to ACE-2. To date, no high-frequency mutations were detected at the C-terminal domain of the S1 subunit in the S protein, where the receptor-binding domain (RBD) is located. Tight binding to ACE-2 by a conserved viral RBD suggests the ACE2-RBD interaction is likely optimal. Moreover, the viral S subunit contains a cleavage site for furin and other proteases, which accelerates cell entry by SARS-CoV-2. The model proposed here describes a structural basis for the accelerated host cell entry by SARS-CoV-2 relative to other HCoVs and also discusses emerging hypotheses that are likely to contribute to the development of antiviral strategies to combat the pandemic capacity of SARS-CoV-2.

Predictive model with analysis of the initial spread of COVID-19 in India.

OBJECTIVE: The Coronavirus Disease 2019 (COVID-19) has currently ravaged through the world, resulting in over thirteen million confirmed cases and over five hundred thousand deaths, a complete change in daily life as we know it, worldwide lockdowns, travel restrictions, as well as heightened hygiene measures and physical distancing. Being able to analyse and predict the spread of this epidemic-causing disease is hence of utmost importance now, especially as it would help in the reasoning behind important decisions drastically affecting countries and their people, as well as in ensuring efficient resource and utility management. However, the needs of the people and specific conditions of the spread are varying widely from country to country. Hence, this article has two fold objectives: (i) conduct an in-depth statistical analysis of COVID-19 affected patients in India, (ii) propose a mathematical model for the prediction of spread of COVID-19 cases in India. MATERIALS AND METHOD: There has been limited research in modeling and predicting the spread of COVID-19 in India, owing both to the ongoing nature of the pandemic and limited availability of data. Currently famous SIR and non-SIR based Gauss-error-function and Monte Carlo simulation models do not perform well in the context of COVID-19 spread in India. We propose a 'change-factor' or 'rate-of-change' based mathematical model to predict the spread of the pandemic in India, with data drawn from hundreds of sources. RESULTS: Average age of affected patients was found to be 38.54 years, with 66.76% males, and 33.24% females. Most patients were in the age range of 18-40 years. Optimal parameter values of the prediction model are identified (α = 1.35, N = 3 and T = 10) by extensive experiments. Over the entire course of time since the outbreak started in India, the model has been 90.36% accurate in predicting the total number of cases the next day, correctly predicting the range in 150 out of the 166 days looked at. CONCLUSION: The proposed system showed an accuracy of 90.36% for prediction since the first COVID-19 case in India, and 96.67% accuracy over the month of April. Predicted number of cases for the next day is found to be a function of the numbers over the last 3 days, but with an 'increase' factor influenced by the last 10 days. It is noticed that males are affected more than females. It is also noticed that in India, the number of people in each age bucket is steadily decreasing, with the largest number of adults infected being the youngest ones-a departure from the world trend. The model is self-correcting as it improves its predictions every day, by incorporating the previous day's data into the trend-line for the following days. This model can thus be used dynamically not only to predict the spread of COVID-19 in India, but also to check the effect of various government measures in a short span of time after they are implemented.