Mathematical Modelling Indicates Th-cell Targeted Antibody-Dependent Cellular Cytotoxic Is a Crucial Obstacle Hurdling HIV Vaccine Development (preprint)

HIV poses a significant threat to human health. Although some progress has been made in the development of an HIV vaccine, there is currently no reported success in achieving an effective and fully functional vaccine for HIV. This highlights the challenges involved in HIV vaccine development. Through mathematical modeling, we have conducted a systematic study on the impact of antibody-dependent cellular cytotoxicity (ADCC) on HIV-specific immune responses. Unlike other viral infections, the ADCC effect following HIV infection may cause significant damage to the follicular center Th cells, leading to apoptosis of follicular center cells and rapid death of effector Th cells. This impedes the generation of neutralizing antibodies and creates barriers to viral clearance, thereby contributing to long-term infection. Another challenge posed by this effect is the substantial reduction in vaccine effectiveness, as effective antigenic substances such as gp120 bind to Th cell surfaces, resulting in the apoptosis of follicular center Th cells due to ADCC, hindering antibody regeneration. To address this issue, we propose the concept of using bispecific antibodies. By genetically editing B cells to insert the bispecific antibody gene, which consists of two parts targeting the CD4 binding site of HIV, such as the broadly neutralizing antibody 3BNC117, and the other targeting antibodies against other viruses, such as the spike protein of SARS-CoV-2. We can simultaneously enhance the levels of two pathogen-specific antibodies through stimulation with non-HIV-antigens corresponding to the other part of the chimeric antibody, such as the spike protein. This study contributes to the elucidation of the pathophysiology of HIV, while also providing a theoretical framework for the successful development of an HIV vaccine.

Bioinformatic Analysis of Defective Viral Genomes in SARS-CoV-2 and Its Impact on Population Infection Characteristics (preprint)

DVGs (Defective Viral Genomes) and SIP (Semi-Infectious Particle) are commonly present in RNA virus infections. In this study, we analyzed high-throughput sequencing data and found that DVGs or SIPs are also widely present in SARS-CoV-2. Comparison of SARS-CoV-2 with various DNA viruses revealed that the SARS-CoV-2 genome is more susceptible to damage and has greater sequencing sample heterogeneity. Variability analysis at the whole-genome sequencing depth showed a higher coefficient of variation for SARS-CoV-2, and DVG analysis indicated a high proportion of splicing sites, suggesting significant genome heterogeneity and implying that most virus particles assembled are enveloped with incomplete RNA sequences. We further analyzed the characteristics of different strains in terms of sequencing depth and DVG content differences and found that as the virus evolves, the proportion of intact genomes in virus particles increases, which can be significantly reflected in third-generation sequencing data, while the proportion of DVG gradually decreases. Specifically, the proportion of intact genome of Omicron was greater than that of Delta and Alpha strains. This can well explain why Omicron strain is more infectious than Delta and Alpha strains. We also speculate that this improvement in completeness is due to the enhancement of virus assembly ability, as the Omicron strain can quickly realize the binding of RNA and capsid protein, thereby shortening the exposure time of exposed virus RNA in the host environment and greatly reducing its degradation level. Finally, by using mathematical modeling, we simulated how DVG effects under different environmental factors affect the infection characteristics and evolution of the population. We can explain well why the severity of symptoms is closely related to the amount of virus invasion and why the same strain causes huge differences in population infection characteristics under different environmental conditions. Our study provides a new approach for future virus research and vaccine development.

Running ahead of evolution - AI based simulation for predicting future high-risk SARS-CoV-2 variants (preprint)

The never-ending emergence of SARS-CoV-2 variations of concern (VOCs) has challenged the whole world for pandemic control. In order to develop effective drugs and vaccines, one needs to efficiently simulate SARS- CoV-2 spike receptor binding domain (RBD) mutations and identify high-risk variants. We pretrain a large pro- tein language model on approximately 408 million pro- tein sequences and construct a high-throughput screen- ing for the prediction of binding affinity and antibody escape. As the first work on SARS-CoV-2 RBD mu- tation simulation, we successfully identify mutations in the RBD regions of 5 VOCs and can screen millions of potential variants in seconds. Our workflow scales to 4096 NPUs with 96.5% scalability and 493.9X speedup in mixed precision computing, while achieving a peak performance of 366.8 PFLOPS (reaching 34.9% theo- retical peak) on Pengcheng Cloudbrain-II. Our method paves the way for simulating coronavirus evolution in or- der to prepare for a future pandemic that will inevitably take place.

Emerging mutations in spike and other structural proteins of SARS-CoV-2 (preprint)

The structural proteins, spike (S), nucleocapsid (N), membrane (M), and envelope (E), of severe acute respiratory syndrome (SARS-CoV-2) play a critical role from attachment to replication and virulency. Recently a bulk of genomes have been sequenced from different geographical regions with significant number of variations. Therefore, the current study was aimed to find variations in the structural proteins. This is the first comprehensive study in which we screened 2,95,000 complete genomes in global initiative on sharing all influenza data (GISAID), submitted from December 2019 to December 2020. We detected 4725 non-synonymous mutations in S, 627 in M, 259 in E, and 1631 mutations in N protein, among which the most frequently occurring mutations in S protein are D614G (n=2,66,513), A222V (n=59,697), L18F (n=28,015) and that of M protein are; T175M (n=1286), D3G (n=968), L17I (n=621), A2V (n=463), and A2S (n=460). The most commonly circulating variants in E includes, S68F (n=419), P71S (n=264), and L73F (n=218). Similarly, the N protein also harbored the most common variants which include; R203K (n=82,570), G204R (n=81,858), and A220V (n=39,729). The frequency of N501Y (n=4362) in S is determining a tight interaction of CoV-2 RBD with ACE2. These wide range of mutations in structural proteins may not only affect the therapeutic efforts but also the vaccines efficacy and diagnostics specificity. We suggest that geographically strain specific variations should be investigated for effective drugs, vaccine, and the antibodies combinations. Alternatively, immune boosting compounds might be very useful for successful eradication of CoV-2 infections.

Lectin-like Intestinal Defensin Inhibits 2019-nCoV Spike binding to ACE2 (preprint)

The burgeoning epidemic caused by novel coronavirus 2019 (2019-nCoV) is currently a global concern. Angiotensin-converting enzyme-2 (ACE2) is a receptor of 2019-nCoV spike 1 protein (S1) and mediates viral entry into host cells. Despite the abundance of ACE2 in small intestine, few digestive symptoms are observed in patients infected by 2019-nCoV. Herein, we investigated the interactions between ACE2 and human defensins (HDs) specifically secreted by intestinal Paneth cells. The lectin-like HD5, rather than HD6, bound ACE2 with a high affinity of 39.3 nM and weakened the subsequent recruitment of 2019-nCoV S1. The cloak of HD5 on the ligand-binding domain of ACE2 was confirmed by molecular dynamic simulation. A remarkable dose-dependent preventive effect of HD5 on 2019-nCoV S1 binding to intestinal epithelial cells was further evidenced by in vitro experiments. Our findings unmasked the innate defense function of lectin-like intestinal defensin against 2019-nCoV, which may provide new insights into the prevention and treatment of 2019-nCoV infection.