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Point and Frameshift Mutations01:30

Point and Frameshift Mutations

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Point mutations are genetic alterations involving the change of a single nucleotide base pair in DNA. Depending on how the alteration affects protein synthesis, they can lead to various consequences.Point mutations fall into the following types:Silent mutations occur when a nucleotide change does not alter the amino acid sequence due to the redundancy of the genetic code. For instance, changing ACC to ACA still encodes threonine, leaving the protein function unaffected. This occurs because...
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Mutations are heritable changes in an organism’s genome involving alterations in the base sequence of DNA or RNA. These changes can influence cellular processes and phenotypic traits, potentially transforming the unaltered wild type into a mutant form. Such changes, termed forward mutations, are pivotal in shaping the genetic diversity of organisms.RNA viruses exhibit the highest mutation rates due to the absence of robust proofreading mechanisms during genome replication. In contrast,...
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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Probing RNA Structure with Dimethyl Sulfate Mutational Profiling with Sequencing In Vitro and in Cells
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Understanding the mutational frequency in SARS-CoV-2 proteome using structural features.

Puneet Rawat1, Divya Sharma2, Medha Pandey2

  • 1University of Oslo and Oslo University Hospital, Oslo, Norway; Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.

Computers in Biology and Medicine
|June 17, 2022
PubMed
Summary
This summary is machine-generated.

Scientists developed machine learning models to predict SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) mutation sites. This approach aids in the early detection of concerning viral variants and future pandemic prevention.

Keywords:
COVID-19Machine learningMutationProtein mutabilitySARS-CoV-2

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Area of Science:

  • Computational virology and bioinformatics
  • Molecular biology and protein analysis

Background:

  • The prolonged global transmission of SARS-CoV-2 has resulted in diverse, location-specific viral strains.
  • Understanding mutation patterns in SARS-CoV-2 proteins is crucial for tracking viral evolution and predicting variant emergence.

Purpose of the Study:

  • To classify SARS-CoV-2 mutation sites into low and high mutability classes.
  • To develop computational models for predicting mutation hotspots.
  • To assess the utility of physicochemical and structural features in mutation site classification.

Main Methods:

  • Classification of mutation sites based on viral isolate counts.
  • Analysis of physicochemical features (residue type, surface accessibility, bulkiness, stability, sequence conservation) of SARS-CoV-2 proteins.
  • Development of machine learning models to predict low and high mutability sites at varying selection thresholds (5-30%).

Main Results:

  • Physicochemical and structural features effectively differentiate between low and high mutability sites.
  • Machine learning models achieved prediction accuracies ranging from 65% to 77%.
  • Model performance improved with reduced selection thresholds, indicating better predictability for extreme mutation sites.

Conclusions:

  • Physicochemical properties of viral proteins are key indicators of mutation proneness.
  • Developed models can aid in the early detection of SARS-CoV-2 variants of concern.
  • The methodology can be extended to other viruses for enhanced pandemic preparedness.