Stability and expression of SARS-CoV-2 spike-protein mutations

Decoding SARS-CoV-2 variants: Mutations, viral stability, and breakthroughs in vaccines and therapies

This study investigates the infectivity of SARS-CoV-2 and its immune evasion mechanisms, particularly through mutations in the spike protein that enable the virus to escape host immune responses. As global vaccination efforts continue, understanding viral evolution and immune evasion strategies remains critical. This analysis focuses on fourteen key mutations (Arg346Lys, Lys417Asp, Leu452Glu, Leu452Arg, Phe456Leu, Ser477Asp, Thr478Lys, Glu484Ala, Glu484Lys, Glu484Gln, Gln493Arg, Gly496Ser, Glu498Arg, and His655Y) within the receptor-binding domain (RBD) of the spike protein. The results reveal consistent patterns of immune escape across various SARS-CoV-2 variants, with specific mutations influencing protein stability, binding affinity to the hACE2 receptor, and antibody recognition. These findings demonstrate how single-point mutations can destabilize the spike protein and reduce the efficacy of the immune response. By correlating expression levels and thermodynamic stability with immune evasion, this study provides valuable insights into the functional characteristics of the spike protein. The findings contribute to the understanding of immune escape variants and identify potential targets for enhancing vaccine efficacy and developing therapeutic approaches in response to the evolving SARS-CoV-2 landscape. SHORT SUMMARY: The study investigates the infectivity of SARS-CoV-2 and its implications for immune evasion. It focuses on fourteen key mutations within the spike protein's Receptor-Binding Domain (S-RBD) and reveals consistent patterns associated with immune escape in various SARS-CoV-2 variants. The research highlights the influence of factors such as protein fold stability, hACE2 binding, and antibody evasion on spike protein evolution. Single-point immune escape variants alter virus stability, impacting antibody response success. The study provides valuable insights into immune escape variants and suggests avenues for enhancing vaccine efficacy. It also opens the way for novel therapeutic approaches in the context of SARS-CoV-2 variants.

Prompt engineering-enabled LLM or MLLM and instigative bioinformatics pave the way to identify and characterize the significant SARS-CoV-2 antibody escape mutations

The research aims to identify and characterize the antibody escape mutations of NTD and RBD regions of SARS-CoV-2 using prompt engineering-enabled combined LLMs (large language models) and instigative bioinformatics techniques. We used two LLMs (ChatGPT and Mistral 7B) and one MLLM (Gemini model) to retrieve the significant NTD and RBD mutations. The retrieved significant mutations were characterized through the in silico servers. The retrieved 15 NTD significant mutations (six deletions and nine-point mutations) and 17 RBD point mutations were noted. We further characterized them in terms of distribution, count, ΔΔG of mutation (ΔΔG stability mCSM, ΔΔGstability DUET, ΔΔGstabilitySDM) to understand the stabilized or destabilized mutation, interaction interface, distance to PPI interface, Δvibrational entropy energy (ΔΔSVib ENCoM), and change in the flexibility. Here, we analyzed every mutation's ΔΔG, interaction, and related parameters using the trimeric Spike protein complex. In NTD mutations, our five analyzed mutations show two destabilising (G142D, R190S) and three showing stabilising properties (D215G, A222V, and R246I). Some RBD mutations are noted as entirely destabilising (K417N, K417T, L452R, F490S). N440K, N460K, and Q493R show stabilising and neutral properties. Combined LLMs and instigative bioinformatics techniques were used to identify and characterize the antibody escape mutations. With our strategy, the LLM and MLLM can help to fight future pandemic viruses by quickly identifying mutations and their significance.

Dynamics of SARS-CoV-2 Spike RBD Protein Mutation and Pathogenicity Consequences in Indonesian Circulating Variants in 2020-2022

BACKGROUND

Since the beginning of the coronavirus disease 2019 (COVID-19) outbreak, dynamic mutations in the receptor-binding domain (RBD) in the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein have altered the pathogenicity of the variants of the virus circulating in Indonesia. This research analyzes the mutation trend in various RBD samples from Indonesia published in the Global Initiative on Sharing All Influenza Data (GISAID) database using genomic profiling.

METHOD

Patients in Indonesia infected with SARS-CoV-2, whose samples have been published in genomic databases, were selected for this research. The collected data were processed for analysis following several bioinformatics protocols: visualization into phylogenetic trees, 3D rendering, and the assessment of mutational impact.

RESULTS

In Indonesia, there are 25 unique SARS-CoV-2 clades and 318 unique SARS-CoV-2 RBD mutations from the earliest COVID-19 sample to samples collected in 2022, with T478K being the most prevalent RBD mutation and 22B being the most abundant clade. The Omicron variant has a lower docking score, higher protein destabilization, and higher KD than the Delta variant and the original virus.

CONCLUSIONS

The study findings reveal a decreasing trend in virus pathogenicity as a potential trade-off to increase transmissibility via mutations in RBD over the years.

Understanding large scale sequencing datasets through changes to protein folding

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

Fast Viral Diagnostics: FTIR-Based Identification, Strain-Typing, and Structural Characterization of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered an ongoing global pandemic, necessitating rapid and accurate diagnostic tools to monitor emerging variants and preparedness for the next outbreak. This study introduces a multidisciplinary approach combining Fourier Transform Infrared (FTIR) microspectroscopy and Machine learning to comprehensively characterize and strain-type SARS-CoV-2 variants. FTIR analysis of pharyngeal swabs from different pandemic waves revealed distinct vibrational profiles, particularly in nucleic acid and protein vibrations. The spectral wavenumber range between 1150 and 1240 cm-1 was identified as the classification marker, distinguishing Healthy (noninfected) and infected samples. Machine learning algorithms, with neural networks exhibiting superior performance, successfully classified SARS-CoV-2 variants with a remarkable accuracy of 98.6%. Neural networks were also able to identify and differentiate a small cohort infected with influenza A variants, H1N1 and H3N2, from SARS-CoV-2-infected and Healthy samples. FTIR measurements further show distinct red shifts in vibrational energy and secondary structural alterations in the spike proteins of more transmissible forms of SARS-CoV-2 variants, providing experimental validation of the computational data. This integrated approach presents a promising avenue for rapid and reliable SARS-CoV-2 variant identification, enhancing our understanding of viral evolution and aiding in diagnostic advancements, particularly for an infectious disease with unknown etiology.

Learning from Protein Engineering by Deconvolution of Multi-Mutational Variants

This review analyzes a development in biochemistry, enzymology and biotechnology that originally came as a surprise. Following the establishment of directed evolution of stereoselective enzymes in organic chemistry, the concept of partial or complete deconvolution of selective multi-mutational variants was introduced. Early deconvolution experiments of stereoselective variants led to the finding that mutations can interact cooperatively or antagonistically with one another, not just additively. During the past decade, this phenomenon was shown to be general. In some studies, molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) computations were performed in order to shed light on the origin of non-additivity at all stages of an evolutionary upward climb. Data of complete deconvolution can be used to construct unique multi-dimensional rugged fitness pathway landscapes, which provide mechanistic insights different from traditional fitness landscapes. Along a related line, biochemists have long tested the result of introducing two point mutations in an enzyme for mechanistic reasons, followed by a comparison of the respective double mutant in so-called double mutant cycles, which originally showed only additive effects, but more recently also uncovered cooperative and antagonistic non-additive effects. We conclude with suggestions for future work, and call for a unified overall picture of non-additivity and epistasis.