One of the key goals in the postgenomic era is the elucidation of the mechanisms underlying the relationship between genotype and phenotype. In particular, understanding how human genetic and somatic variations are associated with diseases is still an open problem and its solution is a crucial issue for exploiting the possibilities offered by the modern sequencing techniques in the framework of precision and personalized medicine. The increasing amount of data generated by the sequencing initiatives calls for accurate and reliable computational approaches to predict the impact of mutations on the phenotype, and possibly for methods to correlate them with diseases. From the experimental point of view, disease-causing variants are supposed to directly affect protein function, protein stability as well as the kinetics and thermodynamics of protein-protein recognition, and robust validation at the molecular scale is necessary. This approach can be of invaluable help in facing new challenges such as the fast development of effective vaccines.
Progress in computational and experimental research and their interplay in the annotation and interpretation of protein variants to detect pathogenetic variations and analyze their effect will be discussed in this Research Topic. A major theme in this issue is to discuss how the computational and experimental analysis of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in the human genome at a structural and molecular level may help to predict the risk of developing specific diseases, as well as the susceptibility to environmental factors and the personal response to specific drugs.
Authors whose research follows within this area are welcome to contribute to this Research Topic. Original articles and up-to-date reviews will be considered for publication. Areas to be covered in this Research Topic may include, but are not limited to:
• The effects that disease-associated protein variants induce on thermal and thermodynamic stability and 3D structural properties
• The changes induced by disease-associated protein variants on enzyme functions and kinetic properties
• The effects of disease-associated protein variants on the kinetics of protein-protein interactions as well as on protein networks at the system-level
• The changes induced by protein variations on the interactions with specific pharmacological ligands
• The annotation of nsSNP and disease-associated variants in genetic and multifactorial diseases
• The advantages and limitations of current computational methodologies based on the prediction of the impact of protein variations on protein stability and their potential pathogenicity
• Computational approaches to predict the impact of genetic variations on diseases through the analysis of their effects on the protein functions
One of the key goals in the postgenomic era is the elucidation of the mechanisms underlying the relationship between genotype and phenotype. In particular, understanding how human genetic and somatic variations are associated with diseases is still an open problem and its solution is a crucial issue for exploiting the possibilities offered by the modern sequencing techniques in the framework of precision and personalized medicine. The increasing amount of data generated by the sequencing initiatives calls for accurate and reliable computational approaches to predict the impact of mutations on the phenotype, and possibly for methods to correlate them with diseases. From the experimental point of view, disease-causing variants are supposed to directly affect protein function, protein stability as well as the kinetics and thermodynamics of protein-protein recognition, and robust validation at the molecular scale is necessary. This approach can be of invaluable help in facing new challenges such as the fast development of effective vaccines.
Progress in computational and experimental research and their interplay in the annotation and interpretation of protein variants to detect pathogenetic variations and analyze their effect will be discussed in this Research Topic. A major theme in this issue is to discuss how the computational and experimental analysis of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) in the human genome at a structural and molecular level may help to predict the risk of developing specific diseases, as well as the susceptibility to environmental factors and the personal response to specific drugs.
Authors whose research follows within this area are welcome to contribute to this Research Topic. Original articles and up-to-date reviews will be considered for publication. Areas to be covered in this Research Topic may include, but are not limited to:
• The effects that disease-associated protein variants induce on thermal and thermodynamic stability and 3D structural properties
• The changes induced by disease-associated protein variants on enzyme functions and kinetic properties
• The effects of disease-associated protein variants on the kinetics of protein-protein interactions as well as on protein networks at the system-level
• The changes induced by protein variations on the interactions with specific pharmacological ligands
• The annotation of nsSNP and disease-associated variants in genetic and multifactorial diseases
• The advantages and limitations of current computational methodologies based on the prediction of the impact of protein variations on protein stability and their potential pathogenicity
• Computational approaches to predict the impact of genetic variations on diseases through the analysis of their effects on the protein functions