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相关概念视频

Transcription Factors02:16

Transcription Factors

82.2K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
82.2K
Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
5.0K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
7.1K
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

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General Transcription Factors01:30

General Transcription Factors

6.7K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
6.7K
Combinatorial Gene Control02:33

Combinatorial Gene Control

9.5K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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相关实验视频

Updated: Jan 14, 2026

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy
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将基于物理的蛋白质-DNA能量学与机器学习相结合,以预测可解释的转录因子-DNA结合.

Carmen Al Masri1, Jin Yu2

  • 1Department of Physics and Astronomy, University of California, Irvine, California 92697, United States.

Journal of chemical information and modeling
|October 24, 2025
PubMed
概括

这项研究结合了基于物理的模拟和机器学习,以预测转录因子如何与DNA结合. 这种新方法准确地预测了结合 afinities,提供了对基因调节和疾病机制的见解.

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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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相关实验视频

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Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物物理学的生物物理.
  • 基因组学就是基因组学.

背景情况:

  • 转录因子 (TFs) 通过与特定的DNA序列结合来调节基因表达.
  • TF-DNA结合亲和力和特异性的改变与癌症和发育障碍等疾病有关.
  • 准确预测TF-DNA相互作用对于理解基因调节和疾病至关重要.

研究的目的:

  • 开发一个结合基于物理的模拟和机器学习 (ML) 的计算框架,用于预测蛋白质-DNA结合的亲和性和特异性.
  • 提高TF-DNA结合预测的准确性和可解释性.
  • 调查TF-DNA结合亲和力和特异性的关键分子决定因素.

主要方法:

  • 结合了全原子分子动力学 (MD) 模拟和分子力学-一般化出生表面积 (MMGBSA) 计算.
  • 使用机器学习模型 (神经网络,随机森林,支持矢量机器) 进行预测.
  • 利用基因组背景蛋白结合微阵列 (gcPBM) 的高质量实验数据进行模型培训和验证.

主要成果:

  • 在预测DNA结合亲缘关系时,获得了大约0.73的皮尔森相关性和0.4的平均绝对误差,超过了传统的MMGBSA.
  • 确定了TF-DNA界面互补性和疏水性相互作用作为结合的关键决定因素.
  • 突出了对TF-DNA界面结的进一步物理特征的需要,以实现序列依赖.

结论:

  • 开发的基于物理的ML框架为准确和可解释的蛋白质-DNA相互作用预测提供了强大的方法.
  • 这种方法有可能对TF-DNA结合进行可扩展的预测,进步我们对基因调节和疾病的理解.
  • 这些发现为改善针对TF-DNA相互作用的诊断和治疗铺平了道路.