Antiviral Biodegradable Food Packaging and Edible Coating Materials in the COVID-19 Era: A Mini-Review

With the onset of the COVID-19 pandemic in late 2019, and the catastrophe faced by the world in 2020, the food industry was one of the most affected industries. On the one hand, the pandemic-induced fear and lockdown in several countries increased the online delivery of food products, resulting in a drastic increase in single-use plastic packaging waste. On the other hand, several reports revealed the spread of the viral infection through food products and packaging. This significantly affected consumer behavior, which directly influenced the market dynamics of the food industry. Still, a complete recovery from this situation seems a while away, and there is a need to focus on a potential solution that can address both of these issues. Several biomaterials that possess antiviral activities, in addition to being natural and biodegradable, are being studied for food packaging applications. However, the research community has been ignorant of this aspect, as the focus has mainly been on antibacterial and antifungal activities for the enhancement of food shelf life. This review aims to cover the different perspectives of antiviral food packaging materials using established technology. It focuses on the basic principles of antiviral activity and its mechanisms. Furthermore, the antiviral activities of several nanomaterials, biopolymers, natural oils and extracts, polyphenolic compounds, etc., are discussed.

Mutation-Induced Long-Range Allosteric Interactions in the Spike Protein Determine the Infectivity of SARS-CoV-2 Emerging Variants.

The emergence of a variety of highly transmissible SARS-CoV-2 variants, the causative agent of COVID-19, with multiple spike mutations poses serious challenges in overcoming the ongoing deadly pandemic. It is, therefore, essential to understand how these variants gain enhanced ability to evade immune responses with a higher rate of spreading infection. To address this question, here we have individually assessed the effects of SARS-CoV-2 variant-specific spike (S) protein receptor-binding domain (RBD) mutations E484K, K417N, L452Q, L452R, N501Y, and T478K that characterize and differentiate several emerging variants. Despite the hundreds of apparently neutral mutations observed in the domains other than the RBD, we have shown that each RBD mutation site is differentially engaged in an interdomain allosteric network involving mutation sites from a distant domain, affecting interactions with the human receptor angiotensin-converting enzyme-2 (ACE2). This allosteric network couples the residues of the N-terminal domain (NTD) and the RBD, which are modulated by the RBD-specific mutations and are capable of propagating mutation-induced perturbations between these domains through a combination of structural changes and effector-dependent modulations of dynamics. One key feature of this network is the inclusion of compensatory mutations segregated into three characteristically different clusters, where each cluster residue site is allosterically coupled with specific RBD mutation sites. Notably, each RBD mutation acted like a positive allosteric modulator; nevertheless, K417N was shown to have the largest effects among all of the mutations on the allostery and thereby holds the highest binding affinity with ACE2. This result will be useful for designing the targeted control measure and therapeutic efforts aiming at allosteric modulators.

Characterizing genomic variants and mutations in SARS-CoV-2 proteins from Indian isolates.

SARS-CoV-2 is mutating and creating divergent variants by altering the composition of essential constituent proteins. Pharmacologically, it is crucial to understand the diverse mechanism of mutations for stable vaccine or anti-viral drug design. Our current study concentrates on all the constituent proteins of 469 SARS-CoV-2 genome samples, derived from Indian patients. However, the study may easily be extended to the samples across the globe. We perform clustering analysis towards identifying unique variants in each of the SARS-CoV-2 proteins. A total of 536 mutated positions within the coding regions of SARS-CoV-2 proteins are detected among the identified variants from Indian isolates. We quantify mutations by focusing on the unique variants of each SARS-CoV-2 protein. We report the average number of mutation per variant, percentage of mutated positions, synonymous and non-synonymous mutations, mutations occurring in three codon positions and so on. Our study reveals the most susceptible six (06) proteins, which are ORF1ab, Spike (S), Nucleocapsid (N), ORF3a, ORF7a, and ORF8. Several non-synonymous substitutions are observed to be unique in different SARS-CoV-2 proteins. A total of 57 possible deleterious amino acid substitutions are predicted, which may impact on the protein functions. Several mutations show a large decrease in protein stability and are observed in putative functional domains of the proteins that might have some role in disease pathogenesis. We observe a good number of physicochemical property change during above deleterious substitutions.

Data science in unveiling COVID-19 pathogenesis and diagnosis: evolutionary origin to drug repurposing.

MOTIVATION: The outbreak of novel severe acute respiratory syndrome coronavirus (SARS-CoV-2, also known as COVID-19) in Wuhan has attracted worldwide attention. SARS-CoV-2 causes severe inflammation, which can be fatal. Consequently, there has been a massive and rapid growth in research aimed at throwing light on the mechanisms of infection and the progression of the disease. With regard to this data science is playing a pivotal role in in silico analysis to gain insights into SARS-CoV-2 and the outbreak of COVID-19 in order to forecast, diagnose and come up with a drug to tackle the virus. The availability of large multiomics, radiological, bio-molecular and medical datasets requires the development of novel exploratory and predictive models, or the customisation of existing ones in order to fit the current problem. The high number of approaches generates the need for surveys to guide data scientists and medical practitioners in selecting the right tools to manage their clinical data. RESULTS: Focusing on data science methodologies, we conduct a detailed study on the state-of-the-art of works tackling the current pandemic scenario. We consider various current COVID-19 data analytic domains such as phylogenetic analysis, SARS-CoV-2 genome identification, protein structure prediction, host-viral protein interactomics, clinical imaging, epidemiological research and drug discovery. We highlight data types and instances, their generation pipelines and the data science models currently in use. The current study should give a detailed sketch of the road map towards handling COVID-19 like situations by leveraging data science experts in choosing the right tools. We also summarise our review focusing on prime challenges and possible future research directions. CONTACT: hguzzi@unicz.it, sroy01@cus.ac.in.

Comparative analysis of human coronaviruses focusing on nucleotide variability and synonymous codon usage patterns.

The prevailing COVID-19 pandemic has drawn the attention of the scientific community to study the evolutionary origin of Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2). This study is a comprehensive quantitative analysis of the protein-coding sequences of seven human coronaviruses (HCoVs) to decipher the nucleotide sequence variability and codon usage patterns. It is essential to understand the survival ability of the viruses, their adaptation to hosts, and their evolution. The current analysis revealed a high abundance of the relative dinucleotide (odds ratio), GC and CT pairs in the first and last two codon positions, respectively, as well as a low abundance of the CG pair in the last two positions of the codon, which might be related to the evolution of the viruses. A remarkable level of variability of GC content in the third position of the codon among the seven coronaviruses was observed. Codons with high RSCU values are primarily from the aliphatic and hydroxyl amino acid groups, and codons with low RSCU values belong to the aliphatic, cyclic, positively charged, and sulfur-containing amino acid groups. In order to elucidate the evolutionary processes of the seven coronaviruses, a phylogenetic tree (dendrogram) was constructed based on the RSCU scores of the codons. The severe and mild categories CoVs were positioned in different clades. A comparative phylogenetic study with other coronaviruses depicted that SARS-CoV-2 is close to the CoV isolated from pangolins (Manis javanica, Pangolin-CoV) and cats (Felis catus, SARS(r)-CoV). Further analysis of the effective number of codon (ENC) usage bias showed a relatively higher bias for SARS-CoV and MERS-CoV compared to SARS-CoV-2. The ENC plot against GC3 suggested that the mutational bias might have a role in determining the codon usage variation among candidate viruses. A codon adaptability study on a few human host parasites (from different kingdoms), including CoVs, showed a diverse adaptability pattern. SARS-CoV-2 and SARS-CoV exhibit relatively lower but similar codon adaptability compared to MERS-CoV.

Analyzing host-viral interactome of SARS-CoV-2 for identifying vulnerable host proteins during COVID-19 pathogenesis.

The development of therapeutic targets for COVID-19 relies on understanding the molecular mechanism of pathogenesis. Identifying genes or proteins involved in the infection mechanism is the key to shedding light on the complex molecular mechanisms. The combined effort of many laboratories distributed throughout the world has produced protein and genetic interactions. We integrated available results and obtained a host protein-protein interaction network composed of 1432 human proteins. Next, we performed network centrality analysis to identify critical proteins in the derived network. Finally, we performed a functional enrichment analysis of central proteins. We observed that the identified proteins are primarily associated with several crucial pathways, including cellular process, signaling transduction, neurodegenerative diseases. We focused on the proteins that are involved in human respiratory tract diseases. We highlighted many potential therapeutic targets, including RBX1, HSPA5, ITCH, RAB7A, RAB5A, RAB8A, PSMC5, CAPZB, CANX, IGF2R, and HSPA1A, which are central and also associated with multiple diseases.

A scheme for inferring viral-host associations based on codon usage patterns identifies the most affected signaling pathways during COVID-19.

Understanding the molecular mechanism of COVID-19 pathogenesis helps in the rapid therapeutic target identification. Usually, viral protein targets host proteins in an organized fashion. The expression of any viral gene depends mostly on the host translational machinery. Recent studies report the great significance of codon usage biases in establishing host-viral protein-protein interactions (PPI). Exploring the codon usage patterns between a pair of co-evolved host and viral proteins may present novel insight into the host-viral protein interactomes during disease pathogenesis. Leveraging the similarity in codon usage patterns, we propose a computational scheme to recreate the host-viral protein-protein interaction network. We use host proteins from seventeen (17) essential signaling pathways for our current work towards understanding the possible targeting mechanism of SARS-CoV-2 proteins. We infer both negatively and positively interacting edges in the network. Further, extensive analysis is performed to understand the host PPI network topologically and the attacking behavior of the viral proteins. Our study reveals that viral proteins mostly utilize codons, rare in the targeted host proteins (negatively correlated interaction). Among them, non-structural proteins, NSP3 and structural protein, Spike (S), are the most influential proteins in interacting with multiple host proteins. While ranking the most affected pathways, MAPK pathways observe to be the worst affected during the SARS-CoV-2 infection. Several proteins participating in multiple pathways are highly central in host PPI and mostly targeted by multiple viral proteins. We observe many potential targets (host proteins) from the affected pathways associated with the various drug molecules, including Arsenic trioxide, Dexamethasone, Hydroxychloroquine, Ritonavir, and Interferon beta, which are either under clinical trial or in use during COVID-19.

A study on non-synonymous mutational patterns in structural proteins of SARS-CoV-2.

SARS-CoV-2 is mutating and creating divergent variants across the world. An in-depth investigation of the amino acid substitutions in the genomic signature of SARS-CoV-2 proteins is highly essential for understanding its host adaptation and infection biology. A total of 9587 SARS-CoV-2 structural protein sequences collected from 49 different countries are used to characterize protein-wise variants, substitution patterns (type and location), and major substitution changes. The majority of the substitutions are distinct, mostly in a particular location, and lead to a change in an amino acid's biochemical properties. In terms of mutational changes, envelope (E) and membrane (M) proteins are relatively more stable than nucleocapsid (N) and spike (S) proteins. Several co-occurrence substitutions are observed, particularly in S and N proteins. Substitution specific to active sub-domains reveals that heptapeptide repeat, fusion peptides, transmembrane in S protein, and N-terminal and C-terminal domains in the N protein are remarkably mutated. We also observe a few deleterious mutations in the above domains. The overall study on non-synonymous mutation in structural proteins of SARS-CoV-2 at the start of the pandemic indicates a diversity amongst virus sequences.