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相关概念视频

Protein Organization01:24

Protein Organization

6.6K
Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
6.6K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

93
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
93
Protein and Protein Structure02:15

Protein and Protein Structure

79.7K
Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
79.7K
Protein and Protein Structures02:15

Protein and Protein Structures

10.6K
10.6K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.9K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
10.9K
Introduction to Structures01:30

Introduction to Structures

1.1K
A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
There are three main...
1.1K

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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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S-PLM:通过序列和结构之间的对比学习实现结构意识的蛋白质语言模型.

Duolin Wang, Mahdi Pourmirzaei, Usman L Abbas

    bioRxiv : the preprint server for biology
    |August 23, 2023
    PubMed
    概括

    我们开发了S-PLM,这是一种新的3D结构感知蛋白质语言模型 (PLM),它集成了蛋白质序列和结构数据. 这种方法提高了蛋白质功能预测和设计能力,超出了传统的仅序列模型.

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    Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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    科学领域:

    • 计算生物学 计算生物学
    • 结构生物信息学 结构生物信息学
    • 机器学习在生物学中的应用

    背景情况:

    • 大型蛋白质语言模型 (PLM) 正在彻底改变蛋白质研究,有助于功能确定和蛋白质设计.
    • 目前的PLM主要依赖于蛋白质序列,经常忽视关键的3D结构信息.
    • 这种遗漏限制了PLM在结构依赖的生物和工程应用中的预测能力.

    研究的目的:

    • 介绍S-PLM,一个3D结构感知蛋白质语言模型.
    • 通过整合序列和3D结构数据来增强蛋白质的表示学习.
    • 通过利用全面的结构见解来改善蛋白质功能预测和设计.

    主要方法:

    • 开发了S-PLM,使用多视图对比学习来对准蛋白质序列和3D结构表示.
    • 在AlphaFold预测结构上使用Swin-Transformer嵌入结构信息.
    • 融合了ESM2的基于序列的嵌入式结构嵌入式,并提供了适应的轻量级调整工具.

    主要成果:

    • 与仅序列的PLM相比,S-PLM在蛋白质聚类和分类任务中表现出更高的性能.
    • 该模型通过使用使用序列和结构输入的最先进方法实现了具有竞争力的性能.
    • 轻量级的调工具有助于S-PLM适应各种蛋白质性质预测任务.

    结论:

    • 将3D结构信息集成到蛋白质语言模型中,大大提高了它们的预测能力.
    • S-PLM提供了一种强大而可适应的框架,用于推进蛋白质研究和工程.
    • 开发的S-PLM及其相关工具是研究界公开使用的.