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 Contact: [e-mail] Related Datasets in This Series Osmotic Stress Response Proteins — Agricultural Genomics Dataset Rare Pediatric Genetic Disorders — Pediatric Research Dataset Neurodegenerative Disease Targets — Neuro Research Dataset摘要 本数据集收录了经人工整理的14种致癌驱动蛋白的氨基酸序列与无序结构注释，涵盖肿瘤抑制蛋白（p53、BRCA1、PTEN）、致癌基因编码蛋白（KRAS、BRAF、c-Myc、ABL1）、激素受体（雄激素受体、雌激素受体α、孕激素受体）以及转移相关靶点（E-钙粘蛋白/CDH1、波形蛋白）。每条数据条目均包含UniProt登录号、DisProt无序分类注释、疾病关联信息，以及适用于结构预测、分子对接与虚拟筛选流程的全长蛋白质序列。本资源旨在推动靶向癌症治疗的计算研究方法发展。 通俗语言摘要 癌症源于细胞内部分蛋白质因基因突变发生功能异常，进而引发细胞不受控增殖。本数据集提供了14种最为关键的癌症相关蛋白质的详细分子序列。通过免费公开此类结构数据，我们可为全球科研人员提供支持，使其能够精准探究这些蛋白质的空间构象，以及基因突变如何改变其功能——这是开发可特异性靶向癌细胞同时最大限度降低副作用的精准药物的必要前置步骤。 相关资源 源代码：GitHub平台——Nexus Resonance Codex / Protein-Folding 作者简介：ORCID——James Paul Trageser 作者账号：X平台@jtrag 联系方式：[e-mail] 本系列相关数据集 渗透压应激响应蛋白——农业基因组数据集 罕见儿科遗传疾病——儿科研究数据集 神经退行性疾病靶点——神经研究数据集