In Silico Insights into the Impact of Deleterious SNPs on the Functioning and Dynamics of TP53 and Its Targeted Therapy by COTI-2 and PRIMA1 in Breast Cancer Patients

Abstract

Breast cancer is the most frequent malignancy in women globally and has a high death
rate. In Pakistan, one in every nine women is at risk. Tumor protein 53 (TP53) is a
transcription factor that suppresses tumors by regulating gene expression in response to
stress. In 40-60% of breast tumors, mutations in TP53 contribute to tumor growth,
medication resistance, and a poor prognosis. This study uses an in silico approach to find
deleterious missense SNPs in TP53 and assess their effects on protein dynamics.
Deleterious TP53 variants were screened through SNP prediction tools. Four of the 25
detected deleterious variants (Q16K, L130Q, P223R, and R273I) were chosen based on
two criteria: pathogenicity threshold (>80%) and their presence in functionally significant
domains. Structural stability studies using I-Mutant2.0 and MUPro revealed that L130Q
is the most destabilizing variant, showing consistently lower stability across both tools.
By contrast, R273I is predicted to be stabilizing in MUPro and Dynamut, whereas IMutant
considers it destabilizing. Molecular docking analysis was performed using CBdock
to explore mutant TP53 interactions with small-molecule inhibitors COTI-2 and
PRIMA. This study reveals the structural instability caused by TP53 mutations and their
impact on drug interactions. To restore TP53 activity and improve the effectiveness of
TP53-targeted inhibitors in breast cancer treatment, future research should investigate
targeted therapeutic approaches.