About this Research Topic
Predictive computational models offer an effective alternative to expensive experimental studies of genetic variations. These models can identify potential mutations linked to disease conditions and the emergence of antimicrobial drug resistance. At the molecular level proteins, via their interactions with other proteins and biomolecules, play an important role in many biological processes. The growing data on protein three-dimensional structure, along with variations observed in sequence data, will enable the development of new computational methods and tools to predict the impact of mutations on protein function, stability and interaction thereby aiding in the understanding of the basic mechanisms that govern normal and mutant disease conditions.
This Research Topic will cover various computational methods and tools that use knowledge-based and machine learning approaches to understand the impact of genetic mutations in the broader context of disease and the emergence of resistance to drugs. Specific topics may include, but not limited to, the analysis and prediction of the impact of mutations on:
• Protein function
• Protein stability and dynamics
• Interaction with other proteins, biomolecules and small molecule ligands
• Intrinsically disordered proteins
• Neurodegenerative disorders including, but not limited to, Alzheimer disease
• Antimicrobial drug resistance in tuberculosis and other neglected diseases
• Disease conditions including but not limited to cancer, inherited and infectious diseases
Original research articles (describing computational tools, databases, methods and its applications), reviews and perspectives are welcome.
Keywords: Mutations, Genetic disorders, Antimicrobial drug resistance, Protein structure, Computational tools and databases
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.