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Mutations are changes in the sequence of DNA. These changes can occur spontaneously or they can be induced by exposure to environmental factors. Mutations can be characterized in a number of different ways: whether and how they alter the amino acid sequence of the protein, whether they occur over a small or large area of DNA, and whether they occur in somatic cells or germline cells.
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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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A mutation is a change in the sequence of bases of DNA or RNA in a genome. Some mutations occur during replication of the genome due to errors made by the polymerase enzymes that replicate DNA or RNA. Unlike DNA polymerase, RNA polymerase is prone to errors because it is not capable of “proofreading” its work. Viruses with RNA-based genomes, like HIV, therefore accrue mutations faster than viruses with DNA-based genomes. Because mutation and recombination provide the raw material...
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一个可解释的分子框架来预测癌症驱动器误解突变.

Yan Yang1,2, Weikang Sun1,2, Yang Liu1,2

  • 1Jiangsu Key Laboratory of Drug Discovery and Translational Research for Brain Diseases, School of Basic Medical Sciences, Soochow University, Suzhou, Jiangsu 215123, China.

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概括

预测误解突变的影响是具有挑战性的. 一个新的框架,MutaPheno,利用分子特征准确识别致病和癌症驱动突变,帮助开发向治疗.

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科学领域:

  • 遗传学 是一个遗传学.
  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 误解突变是导致遗传性疾病和癌症的关键因素.
  • 准确预测它们的功能影响,特别是癌症驱动突变,由于数据有限和复杂的瘤发生,很难.
  • 了解诸如致病性,良性,驾驶员和乘客突变等变异类别至关重要.

研究的目的:

  • 使用分子特征系统地描述误解变体.
  • 开发一个可解释的框架 (MutaPheno) 来预测误解突变的功能后果.
  • 评估MutaPheno的性能与现有的癌症驱动突变预测工具相比.

主要方法:

  • 在多个类别中对超过12万个误解变体进行系统的表征.
  • 使用了34个机械基础的分子特征 (结构,功能,物理化学,上下文) 的综合集.
  • 使用随机森林算法开发MutaPheno,对病原和良性变体进行训练.

主要成果:

  • 分子特征有效地区分各种变体类别,显示病原和驱动突变的丰富.
  • MutaPheno准确地预测癌症驱动突变,优于其他工具.
  • 该模型在对新型蛋白质进行测试时显示出强度.

结论:

  • 病原和驱动突变之间存在共享的分子机制.
  • 分子特征对于改善误解变体解释至关重要.
  • MutaPheno提供了一个透明和可通用的工具,用于驱动器发现和向治疗的开发.