# Quantum chemical profiling of protein mutations via fragment-based DFT

## TL;DR

This paper introduces a computational pipeline that combines genomics and quantum chemistry to analyze the electronic properties of TP53 protein mutations in breast cancer.

## Contribution

A novel pipeline integrating WXS data with quantum chemical analysis to study dysfunctional TP53 missense mutations.

## Key findings

- 28 representative TP53 missense mutants from TCGA-BRCA were analyzed using quantum chemical methods.
- The pipeline enables large-scale analysis of electronic properties like NPA, ESP, and HOMO/LUMO.
- The approach allows hypothesis generation about protein dysfunction through electronic descriptors.

## Abstract

Missense mutations have been extensively studied in tumor-suppressing antigens (TP53) to understand oncogenesis within malignant epithelial cells. Using Whole Exome Sequencing (WXS), missense mutations can be profiled into protein sequences to identify the most common variants in tumor samples. Since most mutations arise randomly, it is necessary to isolate those that produce dysfunctional proteins within large cohorts. Using threading and generative algorithms such as AlphaFold and ColabFold, large cohorts of WXS information can be converted into computationally analyzable structures. By evaluating both high- and low-confidence regions in these structures, these antigens can be studied en masse using pipelines that generate analytical inputs for quantum chemistry analysis. We created a pipeline that processed whole-exome sequencing (WXS) data and selected 28 representative TP53 missense mutants from the TCGA-BRCA cohort for quantum-chemical feasibility analysis. These structures were systematically cleaned using tools such as OpenBabel and AmberTools, and each was prepared for Natural Population Analysis (NPA), Electrostatic Potential (ESP) calculations, and Highest and Lowest Occupied Molecular Orbital (HOMO/LUMO) evaluation within Q-Chem. Using this pipeline, population genomics can be integrated with chemoinformatics to analyze electron density concentrations and generate hypothesis-generating electronic descriptors associated with protein dysfunction. By modifying the generated inputs, additional analyses such as Fukui orbitals, chemical shifts, and Raman shifts can also be performed. This provides a computational means to probe electronic properties not readily accessible at scale using experimental techniques.