Elucidating the Role of Wildtype and Variant FGFR2 Structural Dynamics in (Dys)Function and Disorder

Area of Science:

• Molecular biology
• Structural biology
• Genetics

Background:

• Fibroblast growth factor receptor 2 (FGFR2) mutations are linked to human disorders, often causing constitutive protein activation.
• Existing analyses of FGFR2 mutations use static structures, neglecting crucial intrinsic dynamics.

Purpose of the Study:

• To capture and analyze the intrinsic structural dynamics of the FGFR2 tyrosine kinase domain.
• To correlate these dynamics with functional regions and disease types.
• To predict structures of variants lacking experimental data.

Main Methods:

• Utilized experimentally resolved FGFR2 tyrosine kinase domain structures.
• Applied machine learning techniques to capture intrinsic structural dynamics.
• Integrated predicted structures for variants with no available experimental data.

Main Results:

• Characterized the intrinsic structural dynamics of the FGFR2 tyrosine kinase domain.
• Correlated specific structural dynamics with functional regions and disease phenotypes.
• Provided insights into the impact of mutations on FGFR2 (dys)function.

Conclusions:

• Machine learning-enhanced analysis of structural dynamics is valuable for understanding FGFR2 mutations.
• This approach reveals how mutations impact protein function and contribute to disease.
• The study highlights the importance of dynamic structural information in disease-associated gene analysis.