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Enhanced computational strategies for categorizing HER2 kinase domain variants of uncertain significance through
Tamizhini Loganathan1, C George Priya Doss2
1Laboratory of Integrative Genomics, Department of Integrative Biology, School of Bio Sciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India.
Abstract:
The HER2 (human epidermal growth factor receptor 2) kinase domain plays a pivotal role in receptor-mediated signaling and is frequently mutated across various cancer types. Variants of uncertain significance (VUS) within this domain pose significant challenges for clinical interpretation. In this study, a comprehensive computational analysis was conducted on 97 HER2 kinase domain variants comprising 25 pathogenic and 72 VUS entries using a panel of 13 predictive algorithms, including PredictSNP, PolyPhen-2, SIFT, and CADD. A subset of 32 variants (11 pathogenic and 21 VUS) was prioritized for in-depth structural and functional evaluation, based on concordant deleterious predictions from ≥ 9 algorithms. Conservation profiling via ConSurf and Align-GVGD revealed that these variants predominantly localize to highly conserved and functionally critical residues, underscoring their potential biological relevance. Thermodynamic stability profiling using I-Mutant indicated that the majority of prioritized variants exerted destabilizing effects on the protein's tertiary structure, consistent with their pathogenic annotations. Extended molecular dynamics simulations (200 ns) revealed noticeable deviations in root mean square displacement (RMSD), root mean square fluctuation (RMSF), and solvent-accessible surface area (SASA), accompanied by reductions in intramolecular hydrogen bonding and global structural compactness, highlighting conformational destabilization. Stereochemical integrity, assessed through Ramachandran plot analysis, was compromised in specific variants. At the same time, perturbations in hydrophobic core interactions and secondary structure elements further underscored disruptions in core packing and local conformational stability. Notably, the R816P variant demonstrated minimal perturbation to structural dynamics, highlighting the necessity of integrating time-resolved simulations with sequence-based pathogenicity predictions.
Insights
Computational analysis of 97 HER2 kinase domain variants, including pathogenic and uncertain significance types, reveals significant structural and functional impacts. This aids in interpreting HER2 variants in cancer.
Area of Science:
- Oncology
- Genomics
- Structural Biology
Background:
- The HER2 (human epidermal growth factor receptor 2) kinase domain is crucial for cancer signaling and frequently harbors mutations.
- Variants of Uncertain Significance (VUS) in HER2 present diagnostic challenges.
- Accurate interpretation of HER2 variants is vital for targeted cancer therapy.
Purpose of the Study:
- To computationally analyze HER2 kinase domain variants, distinguishing pathogenic from VUS.
- To evaluate the structural and functional impact of these variants.
- To improve the clinical interpretation of HER2 VUS.
Main Methods:
- Utilized 13 predictive algorithms for 97 HER2 kinase domain variants (25 pathogenic, 72 VUS).
- Prioritized 32 variants for detailed analysis based on concordant predictions.
- Employed conservation profiling (ConSurf, Align-GVGD), thermodynamic stability (I-Mutant), and molecular dynamics simulations (200 ns).
Main Results:
- Variants were predominantly located in conserved, functionally critical residues.
- Most variants destabilized protein structure and reduced intramolecular hydrogen bonding.
- Molecular dynamics revealed conformational destabilization, altered RMSD, RMSF, and SASA.
- Specific variants compromised stereochemical integrity and hydrophobic core interactions.
Conclusions:
- Computational and structural analyses effectively differentiate pathogenic HER2 variants from VUS.
- Destabilizing effects on protein structure correlate with pathogenicity.
- Integrating time-resolved simulations with sequence-based predictions is essential for accurate variant interpretation.
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