Computational Method to Identify Mutational Epistasis in Proteins

Invention Description
Viral evolution, particularly rapid viral evolution, can lead to the emergence of variants of concern. In order to develop effective antiviral strategies, it is essential to understand the molecular mechanisms driving these adaptations. Some research has focused on identifying adaptive mutations and how they impact protein structure and function. However, there is still much to be discovered about the role of protein dynamics in viral evolution.
 
Researchers at Arizona State University have developed a novel computational tool which can be used to quantify epistatic interactions in a virus to predict new viral variants for the development of effective antiviral therapies. Utilizing advanced metrics such as the dynamic flexibility index (DFI), dynamic coupling index (DCI), and a novel epistasis metric termed EpiScore, this tool assesses how mutations interact non-additively to influence protein behavior. It provides mechanistic insights into the impact of candidate adaptive polymorphisms and variants of concern on viral evolution and protein functionality.
 
This tool offers a predictive platform for antiviral strategy development, including drug discovery, diagnostics, and vaccine design.
 
Potential Applications
  • Pharmaceutical research for antiviral drug discovery
  • Vaccine design and optimization by targeting dynamic mutational hotspots
  • Diagnostic development for rapidly identifying impactful viral mutations
  • Biotech companies focused on protein engineering and evolutionary studies
  • Academic and clinical research to study viral mutation effects on infectivity and immunity
Benefits and Advantages
  • Quantifies complex mutational interactions using EpiScore, revealing epistatic effects otherwise undetectable
  • Incorporates dynamic protein metrics (DFI, DCI) to analyze structural flexibility and coupling in detail
  • Enables mechanistic understanding of how specific mutations affect viral protein function and evolution
  • Facilitates prediction of viral characteristics and behavior based on mutation dynamics
  • Supports drug and vaccine development by identifying functionally significant mutational patterns
  • Uses validated computational simulations paired with statistical rigor for reliable insights
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Patent Information:
Direct Link:
https://canberra-ip.technologypublisher.com/tech/Computational_Method_to_Iden tify_Mutational_Epistasis_in_Proteins
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For Information, Contact:
Jovan Heusser
Director of Licensing and Business Development
Skysong Innovations
jovan.heusser@skysonginnovations.com