ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update).

Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.

Perfect Match Genomic Landscape strategy: Refinement and customization of reference genomes.

When addressing a genomic question, having a reliable and adequate reference genome is of utmost importance. This drives the necessity to refine and customize reference genomes (RGs). Our laboratory has recently developed a strategy, the Perfect Match Genomic Landscape (PMGL), to detect variation between genomes [K. Palacios-Flores et al.Genetics 208, 1631-1641 (2018)]. The PMGL is precise and sensitive and, in contrast to most currently used algorithms, is nonstatistical in nature. Here we demonstrate the power of PMGL to refine and customize RGs. As a proof-of-concept, we refined different versions of the Saccharomyces cerevisiae RG. We applied the automatic PMGL pipeline to refine the genomes of microorganisms belonging to the three domains of life: the archaea Methanococcus maripaludis and Pyrococcus furiosus; the bacteria Escherichia coli, Staphylococcus aureus, and Bacillus subtilis; and the eukarya Schizosaccharomyces pombe, Aspergillus oryzae, and several strains of Saccharomyces paradoxus. We analyzed the reference genome of the virus SARS-CoV-2 and previously published viral genomes from patients' samples with COVID-19. We performed a mutation-accumulation experiment in E. coli and show that the PMGL strategy can detect specific mutations generated at any desired step of the whole procedure. We propose that PMGL can be used as a final step for the refinement and customization of any haploid genome, independently of the strategies and algorithms used in its assembly.

A hijack mechanism of Indian SARS-CoV-2 isolates for relapsing contemporary antiviral therapeutics.

Coronavirus disease (COVID-19) rapidly expands to a global pandemic and its impact on public health varies from country to country. It is caused by a new virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is imperative for relapsing current antiviral therapeutics owing to randomized genetic drift in global SARS-CoV-2 isolates. A molecular mechanism behind the emerging genomic variants is not yet understood for the prioritization of selective antivirals. The present computational study was aimed to repurpose existing antivirals for Indian SARS-CoV-2 isolates by uncovering a hijack mechanism based on structural and functional characteristics of protein variants. Forty-one protein mutations were identified in 12 Indian SARS-CoV-2 isolates by analysis of genome variations across 460 genome sequences obtained from 30 geographic sites in India. Two unique mutations such as W6152R and N5928H found in exonuclease of Surat (GBRC275b) and Gandhinagar (GBRC239) isolates. We report for the first time the impact of folding rate on stabilizing/retaining a sequence-structure-function-virulence link of emerging protein variants leading to accommodate hijack ability from current antivirals. Binding affinity analysis revealed the effect of point mutations on virus infectivity and the drug-escaping efficiency of Indian isolates. Emodin and artinemol suggested herein as repurposable antivirals for the treatment of COVID-19 patients infected with Indian isolates. Our study concludes that a protein folding rate is a key structural and evolutionary determinant to enhance the receptor-binding specificity and ensure hijack ability from the prevalent antiviral therapeutics.

A genetic barcode of SARS-CoV-2 for monitoring global distribution of different clades during the COVID-19 pandemic.

OBJECTIVE: The SARS-CoV-2 pathogen has established endemicity in humans. This necessitates the development of rapid genetic surveillance methodologies to serve as an adjunct with existing comprehensive, albeit though slower, genome sequencing-driven approaches. METHODS: A total of 21,789 complete genomes were downloaded from GISAID on May 28, 2020 for analyses. We have defined the major clades and subclades of circulating SARS-CoV-2 genomes. A rapid sequencing-based genotyping protocol was developed and tested on SARS-CoV-2-positive RNA samples by next-generation sequencing. RESULTS: We describe 11 major mutations which defined five major clades (G614, S84, V251, I378 and D392) of globally circulating viral populations. The clades can specifically identify using an 11-nucleotide genetic barcode. An analysis of amino acid variation in SARS-CoV-2 proteins provided evidence of substitution events in the viral proteins involved in both host entry and genome replication. CONCLUSION: Globally circulating SARS-CoV-2 genomes could be classified into 5 major clades based on mutational profiles defined by an 11-nucleotide barcode. We have successfully developed a multiplexed sequencing-based, rapid genotyping protocol for high-throughput classification of major clade types of SARS-CoV-2 in clinical samples. This barcoding strategy will be required to monitor decreases in genetic diversity as treatment and vaccine approaches become widely available.