Department of Bioinformatics and Biostatistics, State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
Peng Cheng Laboratory, Vanke Cloud City Phase I Building 8, Xili Street, Nanshan District, Shenzhen, Guangdong, 518055, P. R. China.
J Chem Inf Model. 2021 Feb 22;61(2):571-586. doi: 10.1021/acs.jcim.0c00488. Epub 2021 Jan 29.
Colorectal cancer is considered one of the leading causes of death that is linked with the Kirsten Rat Sarcoma () harboring codons 13 and 61 mutations. The objective for this study is to search for clinically important codon 61 mutations and analyze how they affect the protein structural dynamics. Additionally, a deep-learning approach is used to carry out a similarity search for potential compounds that might have a comparatively better affinity. Public databases like The Cancer Genome Atlas and Genomic Data Commons were accessed for obtaining the data regarding mutations that are associated with colon cancer. Multiple analysis such as genomic alteration landscape, survival analysis, and systems biology-based kinetic simulations were carried out to predict dynamic changes for the selected mutations. Additionally, a molecular dynamics simulation of 100 ns for all the seven shortlisted codon 61 mutations have been conducted, which revealed noticeable deviations. Finally, the deep learning-based predicted compounds were docked with the 3D conformer, showing better affinity and good docking scores as compared to the already existing drugs. Taking together the outcomes of systems biology and molecular dynamics, it is observed that the reported mutations in the SII region are highly detrimental as they have an immense impact on the protein sensitive sites' native conformation and overall stability. The drugs reported in this study show increased performance and are encouraged to be used for further evaluation regarding the situation that ascends as a result of mutations.
结直肠癌被认为是导致死亡的主要原因之一，其与携带 Kirsten Rat Sarcoma（）密码子 13 和 61 突变的基因有关。本研究的目的是寻找临床上重要的密码子 61 突变，并分析它们如何影响蛋白质结构的动态变化。此外，还采用深度学习方法进行潜在化合物的相似性搜索，这些化合物可能具有更好的亲和力。通过访问公共数据库（如 The Cancer Genome Atlas 和 Genomic Data Commons）获取与结肠癌相关的突变数据。进行了多种分析，如基因组改变景观分析、生存分析和基于系统生物学的动力学模拟，以预测所选突变的动态变化。此外，对七个入选的密码子 61 突变进行了长达 100ns 的分子动力学模拟，结果显示出明显的偏差。最后，对基于深度学习的预测化合物进行了对接，与 3D 构象相比，与现有药物相比，它们具有更好的亲和力和良好的对接评分。综合系统生物学和分子动力学的结果表明，SII 区域报道的突变是高度有害的，因为它们对蛋白质敏感位点的天然构象和整体稳定性有巨大影响。本研究中报道的药物显示出更好的性能，鼓励进一步评估由于突变而导致的情况。
