SARS-CoV-2 neutralizing antibodies: Longevity, breadth, and evasion by emerging viral variants

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antibody neutralization response and its evasion by emerging viral variants and variant of concern (VOC) are unknown, but critical to understand reinfection risk and breakthrough infection following vaccination. Antibody immunoreactivity against SARS-CoV-2 antigens and Spike variants, inhibition of Spike-driven virus–cell fusion, and infectious SARS-CoV-2 neutralization were characterized in 807 serial samples from 233 reverse transcription polymerase chain reaction (RT-PCR)–confirmed Coronavirus Disease 2019 (COVID-19) individuals with detailed demographics and followed up to 7 months. A broad and sustained polyantigenic immunoreactivity against SARS-CoV-2 Spike, Membrane, and Nucleocapsid proteins, along with high viral neutralization, was associated with COVID-19 severity. A subgroup of “high responders” maintained high neutralizing responses over time, representing ideal convalescent plasma donors. Antibodies generated against SARS-CoV-2 during the first COVID-19 wave had reduced immunoreactivity and neutralization potency to emerging Spike variants and VOC. Accurate monitoring of SARS-CoV-2 antibody responses would be essential for selection of optimal responders and vaccine monitoring and design.

Neutralizing response against E484K variant after original SARS-CoV-2 infection

Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) variants, which are spreading in the United Kingdom (UK) and elsewhere, have been found in infected individuals in Japan. The virus mutates, to facilitate its life in the host, during the process of repeated proliferation in the body of the host, including humans. In other words, it is natural that a human-compatible mutant strain always predominates in infection and proliferation. As a result, the viral mutants acquire strong proliferative potential in the host and are highly pathogenic. The number of people infected with the mutated SARS-CoV-2 variant E484K, which is different from the SARS-CoV-2 variants that are spreading in the UK, South Africa, and Brazil, is increasing in Tokyo. It has been pointed out that the effects of immunity and vaccines may be reduced against the Tokyo-type SARS-CoV-2 variant E484K. We have investigated the neutralization response to various mutations in the spike glycoprotein using the serum of people already infected with the original SARS-CoV-2. The results showed that SARS-CoV-2 variants with Y543F or N501Y mutations in the spike glycoprotein affect the neutralization reaction. However, single E484K mutations within the spiked glycoprotein of the Tokyo-type SARS-CoV-2 variant are unlikely to have a significant effect on the affinity of the host antibody for the virus.

Potential neutralizing antibodies discovered for novel corona virus using machine learning

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.

SARS-CoV-2 neutralizing antibodies; longevity, breadth, and evasion by emerging viral variants

The SARS-CoV-2 antibody neutralization response and its evasion by emerging viral variants are unknown. Antibody immunoreactivity against SARS-CoV-2 antigens and Spike variants, inhibition of Spike-driven virus-cell fusion, and infectious SARS-CoV-2 neutralization were characterized in 807 serial samples from 233 RT-PCR-confirmed COVID-19 individuals with detailed demographics and followed up to seven months. A broad and sustained polyantigenic immunoreactivity against SARS-CoV-2 Spike, Membrane, and Nucleocapsid proteins, along with high viral neutralization were associated with COVID-19 severity. A subgroup of ‘high responders’ maintained high neutralizing responses over time, representing ideal convalescent plasma therapy donors. Antibodies generated against SARS-CoV-2 during the first COVID-19 wave had reduced immunoreactivity and neutralization potency to emerging Spike variants. Accurate monitoring of SARS-CoV-2 antibody responses would be essential for selection of optimal plasma donors and vaccine monitoring and design.One Sentence SummaryNeutralizing antibody responses to SARS-CoV-2 are sustained, associated with COVID19 severity, and evaded by emerging viral variants

Potential Neutralizing Antibodies Discovered for Novel Corona Virus Using Machine Learning

The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. Recent outbreak of novel coronavirus infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of COVID-19 will save the life of thousands. In this paper, we devised a machine learning (ML) model to predict the possible inhibitory synthetic antibodies for Corona virus. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, we screened thousands of hypothetical antibody sequences and found 8 stable antibodies that potentially inhibit COVID-19. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit the Corona virus.