Enhanced interpretation of 935 hotspot and non-hotspot RAS variants using evidence-based structural bioinformatics

Swarnendu Tripathi; Nikita R Dsouza; Angela J Mathison; Elise Leverence; Raul Urrutia; Michael T Zimmermann

doi:10.1016/j.csbj.2021.12.007

Enhanced interpretation of 935 hotspot and non-hotspot RAS variants using evidence-based structural bioinformatics

Comput Struct Biotechnol J. 2021 Dec 11:20:117-127. doi: 10.1016/j.csbj.2021.12.007. eCollection 2022.

Authors

Swarnendu Tripathi^{1

2}, Nikita R Dsouza¹, Angela J Mathison^{3

4}, Elise Leverence³, Raul Urrutia^{3

4

5}, Michael T Zimmermann^{1

6

5}

Affiliations

¹ Bioinformatics Research and Development Laboratory, Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI 53226, USA.
² Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA.
³ Genomic Sciences and Precision Medicine Center (GSPMC), Medical College of Wisconsin, Milwaukee, WI 53226, USA.
⁴ Division of Research, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
⁵ Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
⁶ Clinical and Translational Sciences Institute, Medical College of Wisconsin, Milwaukee, WI 53226, USA.

Abstract

In the current study, we report computational scores for advancing genomic interpretation of disease-associated genomic variation in members of the RAS family of genes. For this purpose, we applied 31 sequence- and 3D structure-based computational scores, chosen by their breadth of biophysical properties. We parametrized our data by assembling a numerically homogenized experimentally-derived dataset, which when use in our calculations reveal that computational scores using 3D structure highly correlate with experimental measures (e.g., GAP-mediated hydrolysis R_Spearman = 0.80 and RAF affinity R_spearman = 0.82), while sequence-based scores are discordant with this data. Performing all-against-all comparisons, we applied this parametrized modeling approach to the study of 935 RAS variants from 7 RAS genes, which led us to identify 4 groups of mutations according to distinct biochemical scores within each group. Each group was comprised of hotspot and non-hotspot KRAS variants, indicating that poorly characterized variants could functionally behave like pathogenic mutations. Combining computational scores using dimensionality reduction indicated that changes to local unfolding propensity associate with changes in enzyme activity by genomic variants. Hence, our systematic approach, combining methodologies from both clinical genomics and 3D structural bioinformatics, represents an expansion for interpreting genomic data, provides information of mechanistic value, and that is transferable to other proteins.

Keywords: Data interpretation; Functional genomics; Genomics; Protein science; RAS mutation; Structural bioinformatics.