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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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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.
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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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pLM4CPPs:基于蛋白质语言模型的预测器,用于细胞透.

Nandan Kumar1, Zhenjiao Du1, Yonghui Li1

  • 1Department of Grain Science and Industry, Kansas State University, Manhattan, Kansas 66506, United States.

Journal of chemical information and modeling
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概括
此摘要是机器生成的。

本研究介绍了pLM4CCPs,这是一种新的深度学习模型,使用先进的蛋白质语言模型准确预测细胞透 (CPP). 新方法显著提高了预测准确度,有助于药物输送研究.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 酸科学 酸科学

背景情况:

  • 细胞透 (CPPs) 对于药物输送和细胞内向至关重要,因为它们具有膜透性.
  • 准确预测CPP对于降低实验成本和加速验证至关重要.
  • 现有的预测方法需要改进,以提高可靠性和性能.

研究的目的:

  • 评估各种预训练的蛋白质语言模型 (pLMs) 在表示CPP的有效性.
  • 为CPP分类开发一种新的,高性能的深度学习模型.
  • 建立一个可靠的计算工具来识别潜在的CPP.

主要方法:

  • 评估了来自各种pLM的嵌,包括ESM变体,ProtT5和ProtBERT.
  • 开发了pLM4CCPs,这是一种使用卷积神经网络 (CNN) 进行二进制CPP分类的深度学习架构.
  • 从多个模型中集成预测,以创建共识分类,以提高可靠性.

主要成果:

  • pLM4CCPs在准确度,MCC和灵敏度方面超过了现有的最先进的CPP预测模型.
  • 在个别的pLM中,ESM-1280和ProtT5-XL BFD表现最好.
  • pLM4CCPs模型显示了显著的改进:精度为4.9-5.5%,MCC为9.3-10.2%,灵敏度为14.1-19.6%.

结论:

  • 开发的pLM4CCPs模型为CPP预测提供了卓越的性能.
  • 从多个pLM中利用共识预测可以提高分类可靠性.
  • 可访问的Web服务器和源代码促进了功能建模的更广泛的研究应用.