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Research on extremostable proteins has seen immense growth in the past decade owing to their industrial importance. Basic research of attributes related to extreme-stability requires further exploration. Modern mechanistic approaches to engineer such proteins in vitro will have more impact in industrial biotechnology economy. Developing a priori knowledge about the mechanism behind extreme-stability will nurture better understanding of pathways leading to protein molecular evolution and folding. This review is a vivid compilation about all classes of extremostable proteins and the attributes that lead to myriad of adaptations divulged after an extensive study of 6495 articles belonging to extremostable proteins. Along with detailing on the rationale behind extreme-stability of proteins, emphasis has been put on modern approaches that have been utilized to render proteins extremostable by protein engineering. It was understood that each protein shows different approaches to extreme-stability governed by minute differences in their biophysical properties and the milieu in which they exist. Any general rule has not yet been drawn regarding adaptive mechanisms in extreme environments. This review was further instrumental to understand the drawback of the available 14 stabilizing mutation prediction algorithms. Thus, this review lays the foundation to further explore the biophysical pleiotropy of extreme-stable proteins to deduce a global prediction model for predicting the effect of mutations on protein stability.

Protein stability is affected at different hierarchies – gene, RNA, amino acid sequence and structure. Gene is the first level which contributes via varying codon compositions. Codon selectivity of an organism differs with normal and extremophilic milieu. The present work attempts at detailing the codon usage pattern of six extremophilic classes and their harmony. Homologous gene datasets of thermophile-mesophile, psychrophile-mesophile, thermophile-psychrophile, acidophile-alkaliphile, halophile-nonhalophile and barophile-nonbarophile were analysed for filtering statistically significant attributes. Relative abundance analysis, 1–9 scale ranking, nucleotide compositions, attribute weighting and machine learning algorithms were employed to arrive at findings. AGG in thermophiles and barophiles, CAA in mesophiles and psychrophiles, TGG in acidophiles, GAG in alkaliphiles and GAC in halophiles had highest preference. Preference of GC-rich and G/C-ending codons were observed in halophiles and barophiles whereas, a decreasing trend was reflected in psychrophiles and alkaliphiles. GC-rich codons were found to decrease and G/C-ending codons increased in thermophiles whereas, acidophiles showed equal contents of GC-rich and G/C-ending codons. Codon usage patterns exhibited harmony among different extremophiles and has been detailed. However, the codon attribute preferences and their selectivity of extremophiles varied in comparison to non-extremophiles. The finding can be instrumental in codon optimization application for heterologous expression of extremophilic proteins.

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SARS‐CoV‐2 has been responsible for causing 6,218,308 deaths globally till date and has garnered worldwide attention. The lack of effective preventive and therapeutic drugs against SARS‐CoV‐2 has further worsened the scenario and has bolstered research in the area. The N‐terminal and C‐terminal RNA binding domains (NTD and CTD) of SARS‐CoV‐2 nucleocapsid protein represent attractive therapeutic drug targets. Naturally occurring compounds are an excellent source of novel drug candidates due to their structural diversity and safety. Ten major bioactive compounds were identified in ethanolic extract (s) of Cinnamomum zeylanicum , Cinnamomum tamala , Origanum vulgare , and Petroselinum crispum using HPLC and their cytotoxic potential was determined against cancer and normal cell lines by MTT assay to ascertain their biological activity in vitro . To evaluate their antiviral potential, the binding efficacy to NTD and CTD of SARS‐CoV‐2 nucleocapsid protein was determined using in silico biology tools. In silico assessment of the phytocomponents revealed that most of the phytoconstituents displayed a druglike character with no predicted toxicity. Binding affinities were in the order apigenin > catechin > apiin toward SARS‐CoV‐2 nucleocapsid NTD. Toward nucleocapsid CTD, the affinity decreased as apigenin > cinnamic acid > catechin. Remdesivir displayed lesser affinity with NTD and CTD of SARS‐CoV‐2 nucleocapsid proteins than any of the studied phytoconstituents. Molecular dynamics (MD) simulation results revealed that throughout the 100 ns simulation, SARS‐CoV‐2 nucleocapsid protein NTD‐apigenin complex displayed greater stability than SARS‐CoV‐2 nucleocapsid protein NTD‐cinnamic acid complex. Hence, apigenin, catechin, apiin and cinnamic acid might prove as effective prophylactic and therapeutic candidates against SARS‐CoV‐2, if examined further in vitro and in vivo . Practical applications Ten major bioactive compounds were identified in the extract(s) of four medicinally important plants viz . Cinnamomum zeylanicum , Cinnamomum tamala , Origanum vulgare and Petroselinum crispum using HPLC and their biological activity was also evaluated against cancer and normal cell lines. Interestingly, while all extract(s) wielded significant cytotoxicity against cancer cells, no significant toxicity was found against normal cells. The outcome of the results prompted evaluation of the antiviral potential of the ten bioactive compounds using in silico biology tools. The present study emphasizes on the application of computational approaches to understand the binding interaction and efficacy of the ten bioactive compoun...

Wastewater surveillance is a cost-effective tool for monitoring SARS-CoV-2 transmission in a community. However, challenges remain with regard to interpretating such studies, not least in how to compare SARS-CoV-2 levels between different-sized wastewater treatment plants. Viral faecal indicators, including crAssphage and pepper mild mottle virus, have been proposed as population biomarkers to normalise SARS-CoV-2 levels in wastewater. However, as these indicators exhibit variability between individuals and may not be excreted by everyone, their utility as population biomarkers may be limited. Coprostanol, meanwhile, is a bacterial metabolite of cholesterol which is excreted by all individuals. In this study, composite influent samples were collected from a large- and medium-sized wastewater treatment plant in Dublin, Ireland and SARS-CoV-2 N1, crAssphage, pepper mild mottle virus, HF183 and coprostanol levels were determined. SARS-CoV-2 N1 RNA was detected and quantified in all samples from both treatment plants. Regardless of treatment plant size, coprostanol levels exhibited the lowest variation in composite influent samples, while crAssphage exhibited the greatest variation. Moreover, the strongest correlations were observed between SARS-CoV-2 levels and national and Dublin COVID-19 cases when levels were normalised to coprostanol. This work demonstrates the usefulness of coprostanol as a population biomarker for wastewater surveillance studies.

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Aim:To study the major histocompatibility complex (MHC) Class II DRB1 gene polymorphism in Rohilkhandi goat using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and nucleotide sequencing techniques.Materials and Methods:DNA was isolated from 127 Rohilkhandi goats maintained at sheep and goat farm, Indian Veterinary Research Institute, Izatnagar, Bareilly. A 284 bp fragment of exon 2 of DRB1 gene was amplified and digested using BsaI and TaqI restriction enzymes. Population genetic parameters were calculated using Popgene v 1.32 and SAS 9.0. The genotypes were then sequenced using Sanger dideoxy chain termination method and were compared with related breeds/species using MEGA 6.0 and Megalign (DNASTAR) software.Results:TaqI locus showed three and BsaI locus showed two genotypes. Both the loci were found to be in Hardy–Weinberg equilibrium (HWE), however, population genetic parameters suggest that heterozygosity is still maintained in the population at both loci. Percent diversity and divergence matrix, as well as phylogenetic analysis revealed that the MHC Class II DRB1 gene of Rohilkhandi goats was found to be in close cluster with Garole and Scottish blackface sheep breeds as compared to other goat breeds included in the sequence comparison.Conclusion:The PCR-RFLP patterns showed population to be in HWE and absence of one genotype at one locus (BsaI), both the loci showed excess of one or the other homozygote genotype, however, effective number of alleles showed that allelic diversity is present in the population. Sequence comparison of DRB1 gene of Rohilkhandi goat with other sheep and goat breed assigned Rohilkhandi goat in divergence with Jamanupari and Angora goats.