Assessing the pathogenic potential of PIK3CA gene variants in human cancer using a computational approach.

Authors

Rajtilak Detroja, Joydeep Chakraborty, Mandar Kulkarni, Ketankumar Panchal, Neeraj Jain, Mansi Desai

Published in

Journal of biomolecular structure & dynamics. Pages 1-19. Sep 03, 2026. Epub Sep 03, 2026.

Abstract

The phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (p110α) is a key lipid kinase encoded by PIK3CA gene that regulates multiple intracellular signaling pathways. The nsSNPs within PIK3CA can alter protein structure and function, thereby influencing cellular processes and increasing susceptibility to various types of cancers. This study aimed to systematically evaluate the consequences of nsSNPs in PIK3CA, and their potential influence on protein behavior and drug interaction. A comprehensive set of in silico tools-including functional impact predictors, structural stability analyzers, conservation-based algorithms, and protein property assessment tools-was employed to investigate the consequences of nsSNPs. Structural modeling was performed to generate native and mutant p110α structures, followed by molecular docking to evaluate how these variants influence binding affinity with Alpelisib. Ten nsSNPs-G363V, R398C, R555K, C769G, F801C, R808W, R808Q, E849K, E849G, and R992Q-were consistently predicted to be deleterious across all functional prediction platforms and were found to affect evolutionarily conserved residues or domains. Among these, six variants (R398C, C769G, F801C, R808W, R808Q, and R992Q) were further predicted to significantly alter physicochemical protein properties. Docking analyses revealed variant-specific shifts in binding affinity. R555K displaying the highest affinity even higher than native and R398C showing lowest affinity toward Alpelisib. These suggest a potentially altered or dysregulated drug-protein interaction. However, the biological benefit or detriment of this shift warrants further experimental evaluation. The findings of this study may enhances our understanding of how PIK3CA variants contribute to predict disease risk and may support future precision-medicine approaches targeting the PI3K pathway.

PMID:
42692860
Bibliographic data and abstract were imported from PubMed on 04 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.