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相关概念视频

LTR Retrotransposons03:08

LTR Retrotransposons

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LTR retrotransposons are class I transposable elements with long terminal repeats flanking an internal coding region. These elements are less abundant in mammals compared to other class I transposable elements. About 8 percent of human genomic DNA comprises LTR retrotransposons. Some of the common examples of LTR retrotransposons are Ty elements in yeast and Copia elements in Drosophila.
The internal coding region of LTR retrotransposons and their mechanism of transposition closely resembles a...
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Conservative Site-specific Recombination and Phase Variation02:53

Conservative Site-specific Recombination and Phase Variation

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Because the DNA segments are cut and reorganized in a direction-specific manner, site-specific recombination has emerged as an efficient genetic engineering technique. Flippase and Cyclization recombinases or Flp and Cre, respectively, are two members of the tyrosine recombinase family derived from bacteriophages, that are used to mediate site-specific DNA insertions, deletions, and targeted expression of proteins in mammalian cell lines.
The recognition sites for Cre recombinase called LoxP...
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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使用新型LSTM编码解码模型生成潜在的RET特定抑制剂.

Lu Liu1, Xi Zhao1, Xuri Huang1

  • 1Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Changchun 130061, China.

International journal of molecular sciences
|February 24, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新的分子生成模型,以创建特定的RET激酶抑制剂,解决现有的多激酶药物的挑战. 这种人工智能驱动的方法可以快速识别癌症治疗的有前途的候选药物.

关键词:
在RET的基础上,RET是RET.深度学习是一种深度学习.编码器解码器网络长时间的短期记忆 (LSTM)分子动力学模拟模拟虚拟选 虚拟选 虚拟选

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Direct Injection of a Lentiviral Vector Highlights Multiple Motor Pathways in the Rat Spinal Cord
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Direct Injection of a Lentiviral Vector Highlights Multiple Motor Pathways in the Rat Spinal Cord
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科学领域:

  • 在瘤学瘤学.
  • 计算化学计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 受体氨酸激酶RET对神经系统发育至关重要.
  • 异常的RET激活驱动甲状腺癌和非小细胞肺癌等癌症.
  • 现有的RET抑制剂通常是多酶,缺乏特异性.

研究的目的:

  • 开发一种用于制造RET特异性抑制剂的新型分子生成模型.
  • 为了克服当前多酶抑制剂的局限性.
  • 为了加速发现向癌症治疗方法.

主要方法:

  • 利用基于片段的药物设计 (FBDD) 和长期短期记忆 (LSTM) 编码器-解码器模型.
  • 训练模型以获得高分子组装精度 (98.4%).
  • 员工转移学习以生成特定于RET的分子库,然后进行虚拟选,分子动力学和结合能量的计算.

主要成果:

  • 创建了一个特定于RET的候选分子库.
  • 确定了五种新的RET抑制剂候选者.
  • 两个分子表现出有利的结合亲缘关系,合成能力和高选择性.

结论:

  • 开发的模型有效地产生了新的,受体特定的分子.
  • 这种由人工智能驱动的方法提供了一种快速有效的方法来发现潜在的候选药物.
  • 这些已识别的分子显示出作为选择性RET抑制剂用于癌症治疗的前景.