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SARS-CoV-2 is the causative agent of the COVID-19 pandemic that has proven to be a menacing nemesis to public health infrastructure across the globe. Since its advent in December 2019, the RNA virus has exhibited a high rate of mutation (~8.066 × 10−4 nucleotides per site per year) and the accumulation of mutations in key elements of its genome has led to the emergence of distinct SARS-CoV-2 variants such as α, β, γ, δ, ο, λ, and µ, which showed varying levels of infectivity and spreadability. The structural changes induced by non-synonymous mutations in major drug targets such as Spike protein and Main-protease (Mpro) of the virus are known to alter the binding of potential inhibitors designed against it. In this study, we have analyzed the effects of non-synonymous mutations in major targets of SARS-CoV-2 in response to potential peptide inhibitors, wherein we have screened 12 peptide inhibitors reported to have antiviral properties from three distinct classes (hACE2 receptor-based de-novo designed inhibitors, Antimicrobial) against Receptor Binding Domain (RBD) region and 5 antimicrobial peptides against Mpro of SARS-CoV-2 variants using molecular docking and simulation approaches. The mutational landscape of RBD among SARS-CoV-2 variants had a total of 21 non-synonymous mutations across 18 distinct sites. Among these, 14 mutations were present in the RBM region directly interacting with the hACE-2 receptor. However, Only 3 non-synonymous mutations were observed in Mpro among the 7 variants analyzed with one each in Beta, Omicron, and Lambda variants. We found that LCB1 - a de novo-synthesized peptide has the highest binding affinity to RBD despite non-synonymous mutations in variants and engages key residues of RBD-hACE2 interaction such as K417, E484, N487, and N501. Similarly, an antimicrobial peptide; 2JOS, was identified against Mpro with high binding affinity as it interacts with key residues in dimerization sites such as E166 and F140 crucial for viral replication. Molecular Dynamics simulations affirm the stability of RBD-LCB1 and Mpro-2JOS complexes with an average RMSD of 1.902 and 2.476 respectively. We ascertain that LCB1 and 2JOS peptides are promising inhibitors to combat emerging variants of SARS-CoV-2 and thus warrant further investigations using in-vitro and in-vivo analysis.

SARS-CoV-2, the causative agent of COVID-2019 pandemic is an RNA virus prone to mutations. Information on mutations within the circulating strains of the virus is pivotal to understand disease spread and dynamics. Here, we analyse the mutations associated with 2,954 globally reported high quality genomes of SARS-CoV-2 with special emphasis on genomes of viral strains from India. Molecular phylogenetic analysis suggests that SARS-CoV-2 strains circulating in India form five distinct phyletic clades designated R1-R5. These clades categorize into the previously reported S, G as well as a new unclassified subtype. A detailed analysis of gene encoding the Spike (S) protein in the strains across the globe shows non-synonymous mutations on 54 amino acid residues. Among these, we pinpoint 4 novel mutations in the region that interacts with human ACE2 receptor (RBD). Further in silico molecular docking analyses suggest that these RBD mutations could alter the binding affinity of S-protein with ACE2 that may lead to changes in SARS-CoV-2 infectivity. Strikingly, one of these RBD mutations (S438F) is unique to a subset within the R4 clade suggesting intrinsic S-protein variations in strains currently circulating in India. Together, our findings reveal a unique pattern of SARS-CoV-2 evolution that may alert vaccine and therapeutic development.

The idea that language history is best visualized by a branching tree has been controversially discussed in the linguistic world and many alternative theories have been proposed. The reluctance of many scholars to accept the tree as the natural metaphor for language history was due to conflicting signals in linguistic data: many resemblances would simply not point to a unique tree. Despite these observations, the majority of automatic approaches applied to language data has been based on the tree model, while network approaches have rarely been applied. Due to the specific sociolinguistic situation in China, where very divergent varieties have been developing under the roof of a common culture and writing system, the history of the Chinese dialects is complex and intertwined. They are therefore a good test case for methods which no longer take the family tree as their primary model. Here we use a network approach to study the lexical history of 40 Chinese dialects. In contrast to previous approaches, our method is character-based and captures both vertical and horizontal aspects of language history. According to our results, the majority of characters in our data (about 54%) cannot be readily explained with the help of a given tree model. The borrowing events inferred by our method do not only reflect general uncertainties of Chinese dialect classification, they also reveal the strong influence of the standard language on Chinese dialect history.

Due to the specific sociolinguistic situation in China where very divergent linguistic varieties for a long time have been "roofed" by a common culture and a common writing system, the development of the Chinese dialects reflects a 'complex and intertwined evolu- tionary history' (Hamed and Wang 2006: 31). Chinese dialects are therefore a good test case for methods which no longer take the family tree as their primary evolutionary model. In this talk, we shall introduce and discuss the implications of network approaches by applying them to a recently compiled dataset covering translations of 180 concepts for 40 different di- atopic varieties of Chinese (Hóu 2004). Apart from the fact that cognacy information can be extracted from the data, this dataset has the great advantage of giving full phonetic records of all dialect words. This makes it possible to apply methods which work on cognate sets (Nelson-Sathi et al. 2011) as well as methods for computational sequence comparison (List 2010).