Structural Mapping of Oncogenic Driver Proteins for Computational Drug Discovery — Cancer Research Dataset v1.0

Abstract

This dataset contains curated amino acid sequences and disorder annotations for 14 oncogenic driver proteins, including tumor suppressors (p53, BRCA1, PTEN), oncogenes (KRAS, BRAF, c-Myc, ABL1), hormone receptors (Androgen Receptor, Estrogen Receptor alpha, Progesterone Receptor), and metastasis-associated targets (E-cadherin/CDH1, Vimentin). Each entry includes UniProt accession numbers, DisProt disorder classifications, disease associations, and full-length sequences suitable for structural prediction, molecular docking, and virtual screening pipelines. This resource is intended to accelerate computational approaches to targeted cancer therapeutics.

Plain Language Summary

Cancer occurs when certain proteins in our cells malfunction due to genetic mutations, causing uncontrolled cell growth. This dataset provides the detailed molecular sequences of 14 of the most important cancer-related proteins. By making this structural data freely available, we enable researchers worldwide to study exactly how these proteins are shaped and how mutations alter their function — essential first steps in designing precision medicines that can specifically target cancer cells while minimizing side effects.

Related Resources

• Source Code: GitHub — Nexus Resonance Codex / Protein-Folding
• Author Profile: ORCID — James Paul Trageser
• Author: @jtrag on X