Abstract Predicting protein properties from amino acid sequences is an important problem in biology and pharmacology. Protein–protein interactions among SARS-CoV-2 spike protein, human receptors and antibodies are key determinants of the potency of this virus and its ability to evade the human immune response. As a rapidly evolving virus, SARS-CoV-2 has already developed into many variants with considerable variation in virulence among these variants. Utilizing the proteomic data of SARS-CoV-2 to predict its viral characteristics will, therefore, greatly aid in disease control and prevention. In this paper, we review and compare recent successful prediction methods based on long short-term memory (LSTM), transformer, convolutional neural network (CNN) and a similarity-based topological regression (TR) model and offer recommendations about appropriate predictive methodology depending on the similarity between training and test datasets. We compare the effectiveness of these models in predicting the binding affinity and expression of SARS-CoV-2 spike protein sequences. We also explore how effective these predictive methods are when trained on laboratory-created data and are tasked with predicting the binding affinity of the in-the-wild SARS-CoV-2 spike protein sequences obtained from the GISAID datasets. We observe that TR is a better method when the sample size is small and test protein sequences are sufficiently similar to the training sequence. However, when the training sample size is sufficiently large and prediction requires extrapolation, LSTM embedding and CNN-based predictive model show superior performance.
more »
« less
Single-Particle Characterization of SARS-CoV-2 Isoelectric Point and Comparison to Variants of Interest
SARS-CoV-2, the cause of COVID-19, is a new, highly pathogenic coronavirus, which is the third coronavirus to emerge in the past 2 decades and the first to become a global pandemic. The virus has demonstrated itself to be extremely transmissible and deadly. Recent data suggest that a targeted approach is key to mitigating infectivity. Due to the proliferation of cataloged protein and nucleic acid sequences in databases, the function of the nucleic acid, and genetic encoded proteins, we make predictions by simply aligning sequences and exploring their homology. Thus, similar amino acid sequences in a protein usually confer similar biochemical function, even from distal or unrelated organisms. To understand viral transmission and adhesion, it is key to elucidate the structural, surface, and functional properties of each viral protein. This is typically first modeled in highly pathogenic species by exploring folding, hydrophobicity, and isoelectric point (IEP). Recent evidence from viral RNA sequence modeling and protein crystals have been inadequate, which prevent full understanding of the IEP and other viral properties of SARS-CoV-2. We have thus experimentally determined the IEP of SARS-CoV-2. Our findings suggest that for enveloped viruses, such as SARS-CoV-2, estimates of IEP by the amino acid sequence alone may be unreliable. We compared the experimental IEP of SARS-CoV-2 to variants of interest (VOIs) using their amino acid sequence, thus providing a qualitative comparison of the IEP of VOIs.
more »
« less
- Award ID(s):
- 1451959
- NSF-PAR ID:
- 10329679
- Date Published:
- Journal Name:
- Microorganisms
- Volume:
- 9
- Issue:
- 8
- ISSN:
- 2076-2607
- Page Range / eLocation ID:
- 1606
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 —human interactomenull (Ed.)Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dramatically change clinical phenotypes of the virus, including transmission rates and severity of the disease. On the other hand, highly dissimilar virus families such as Coronaviridae, Ebola, and HIV have overlap in functions. In this work we aim to analyze the role of protein sequence in the binding of SARS-CoV-2 virus proteins towards human proteins and compare it to that of the above other viruses. We build supervised machine learning models, using Generalized Additive Models to predict interactions based on sequence features and find that our models perform well with an AUC-PR of 0.65 in a class-skew of 1:10. Analysis of the novel predictions using an independent dataset showed statistically significant enrichment. We further map the importance of specific amino-acid sequence features in predicting binding and summarize what combinations of sequences from the virus and the host is correlated with an interaction. By analyzing the sequence-based embeddings of the interactomes from different viruses and clustering them together we find some functionally similar proteins from different viruses. For example, vif protein from HIV-1, vp24 from Ebola and orf3b from SARS-CoV all function as interferon antagonists. Furthermore, we can differentiate the functions of similar viruses, for example orf3a’s interactions are more diverged than orf7b interactions when comparing SARS-CoV and SARS-CoV-2.more » « less
-
Abstract The coronavirus disease 2019 (COVID-19) is a highly contagious and fatal disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In general, the diagnostic tests for COVID-19 are based on the detection of nucleic acid, antibodies, and protein. Among different analytes, the gold standard of the COVID-19 test is the viral nucleic acid detection performed by the quantitative reverse transcription polymerase chain reaction (qRT-PCR) method. However, the gold standard test is time-consuming and requires expensive instrumentation, as well as trained personnel. Herein, we report an ultrasensitive electrochemical biosensor based on zinc sulfide/graphene (ZnS/graphene) nanocomposite for rapid and direct nucleic acid detection of SARS-CoV-2. We demonstrated a simple one-step route for manufacturing ZnS/graphene by employing an ultrafast (90 s) microwave-based non-equilibrium heating approach. The biosensor assay involves the hybridization of target DNA or RNA samples with probes that are immersed into a redox active electrolyte, which are detectable by electrochemical measurements. In this study, we have performed the tests for synthetic DNA samples and, SARS-CoV-2 standard samples. Experimental results revealed that the proposed biosensor could detect low concentrations of all different SARS-CoV-2 samples, using such as S, ORF 1a, and ORF 1b gene sequences as targets. This microwave-synthesized ZnS/graphene-based biosensor could be reliably used as an on-site, real-time, and rapid diagnostic test for COVID-19.more » « less
-
Site specific N- and O-glycosylation mapping of the spike proteins of SARS-CoV-2 variants of concernmore » « less
Abstract The glycosylation on the spike (S) protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, modulates the viral infection by altering conformational dynamics, receptor interaction and host immune responses. Several variants of concern (VOCs) of SARS-CoV-2 have evolved during the pandemic, and crucial mutations on the S protein of the virus have led to increased transmissibility and immune escape. In this study, we compare the site-specific glycosylation and overall glycomic profiles of the wild type Wuhan-Hu-1 strain (WT) S protein and five VOCs of SARS-CoV-2: Alpha, Beta, Gamma, Delta and Omicron. Interestingly, both N- and O-glycosylation sites on the S protein are highly conserved among the spike mutant variants, particularly at the sites on the receptor-binding domain (RBD). The conservation of glycosylation sites is noteworthy, as over 2 million SARS-CoV-2 S protein sequences have been reported with various amino acid mutations. Our detailed profiling of the glycosylation at each of the individual sites of the S protein across the variants revealed intriguing possible association of glycosylation pattern on the variants and their previously reported infectivity. While the sites are conserved, we observed changes in the N- and O-glycosylation profile across the variants. The newly emerged variants, which showed higher resistance to neutralizing antibodies and vaccines, displayed a decrease in the overall abundance of complex-type glycans with both fucosylation and sialylation and an increase in the oligomannose-type glycans across the sites. Among the variants, the glycosylation sites with significant changes in glycan profile were observed at both the
N -terminal domain and RBD of S protein, with Omicron showing the highest deviation. The increase in oligomannose-type happens sequentially from Alpha through Delta. Interestingly, Omicron does not contain more oligomannose-type glycans compared to Delta but does contain more compared to the WT and other VOCs. O-glycosylation at the RBD showed lower occupancy in the VOCs in comparison to the WT. Our study on the sites and pattern of glycosylation on the SARS-CoV-2 S proteins across the VOCs may help to understand how the virus evolved to trick the host immune system. Our study also highlights how the SARS-CoV-2 virus has conserved bothN - andO - glycosylation sites on the S protein of the most successful variants even after undergoing extensive mutations, suggesting a correlation between infectivity/ transmissibility and glycosylation. -
more » « less
Abstract Understanding the molecular evolution of the SARS‐CoV‐2 virus as it continues to spread in communities around the globe is important for mitigation and future pandemic preparedness. Three‐dimensional structures of SARS‐CoV‐2 proteins and those of other coronavirusess archived in the Protein Data Bank were used to analyze viral proteome evolution during the first 6 months of the COVID‐19 pandemic. Analyses of spatial locations, chemical properties, and structural and energetic impacts of the observed amino acid changes in >48 000 viral isolates revealed how each one of 29 viral proteins have undergone amino acid changes. Catalytic residues in active sites and binding residues in protein–protein interfaces showed modest, but significant, numbers of substitutions, highlighting the mutational robustness of the viral proteome. Energetics calculations showed that the impact of substitutions on the thermodynamic stability of the proteome follows a universal bi‐Gaussian distribution. Detailed results are presented for potential drug discovery targets and the four structural proteins that comprise the virion, highlighting substitutions with the potential to impact protein structure, enzyme activity, and protein–protein and protein–nucleic acid interfaces. Characterizing the evolution of the virus in three dimensions provides testable insights into viral protein function and should aid in structure‐based drug discovery efforts as well as the prospective identification of amino acid substitutions with potential for drug resistance.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/microorganisms9081606
