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相关概念视频

Protein Folding01:25

Protein Folding

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Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
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Protein Folding Quality Check in the RER01:29

Protein Folding Quality Check in the RER

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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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Forced Transdifferentiation01:28

Forced Transdifferentiation

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Transdifferentiation, also known as lineage reprogramming, was first discovered by Selman and Kafatos in 1974 in silkmoths. They observed that the moths’ cuticle-producing cells transformed into salt-producing cells. Many such cases of natural transdifferentiation occur in organisms. In humans, pancreatic alpha cells can become beta cells. In newts, the loss of the eye’s lens causes the pigmented epithelial cells to transdifferentiate into the lens cells.
Artificial...
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Divergence and Curl01:15

Divergence and Curl

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The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the...
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Singularity Functions for Shear01:26

Singularity Functions for Shear

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In structural analysis, singularity functions are crucial in simplifying the representation of shear forces in beams under discontinuous loading. These functions describe discontinuous  variations in shear force across a beam with varying loads by using a single mathematical expression, regardless of the complexity of the loading conditions. The singularity functions are derived from creating a free-body diagram of the beam and then making conceptual cuts at specific points to examine the...
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Assembly of Cytoskeletal Filaments01:18

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Cytoskeletal filaments are polymeric forms of smaller protein subunits. However, individual cytoskeletal filaments may easily disassemble or associate with other similar filaments to form rigid structures. Microfilaments, made of actin monomers, rely on actin-binding proteins to form bundles and create networks of individual actin filaments. Microtubules rely on microtubule-associated proteins (MAPs) to form sturdy cylindrical structures. However, the proteins involved in forming complex...
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RNA Secondary Structure Prediction Using High-throughput SHAPE
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JAX-RNAfold:可扩展的可分化的折叠.

Ryan K Krueger1, Max Ward2

  • 1School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, United States.

Bioinformatics (Oxford, England)
|April 25, 2025
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概括
此摘要是机器生成的。

通过改进可微分折叠算法,JAX-RNAfold可以实现大规模的RNA设计. 这个新的软件可扩展到1,250个核酸,克服了先前对复杂RNA结构预测和优化的限制.

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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.
  • 预测RNA结构的预测

背景情况:

  • 微分折叠是RNA设计的新方法,利用梯度下降优化概率序列表示.
  • 现有的方法面临内存限制,限制RNA序列长度在50个核酸以下.

研究的目的:

  • 为了介绍JAX-RNAfold,一个软件包显著增强可分化的RNA折叠算法.
  • 为了实现可扩展的RNA设计,使用单个GPU对最多1,250个核酸序列进行测量.

主要方法:

  • 开发了一个大大改进的可微分折叠算法,在JAX中实现.
  • 优化了算法,以减少与区分预期的分区函数相关的内存开销.
  • 将算法打包到一个开源软件中,JAX-RNAfold,可以作为Python包安装.

主要成果:

  • JAX-RNAfold在单个GPU上展示了1250个核酸的可扩展性,比以前的方法增加了25倍.
  • 该软件允许将可差分折叠集成到深度学习管道中.
  • 方便复杂的RNA设计任务,包括具有可适应目标功能的mRNA设计.

结论:

  • JAX-RNAfold克服了先前可微分RNA折叠技术的可扩展性限制.
  • 该软件为计算机生物学中先进的RNA设计和分析提供了强大的工具.
  • 能够为RNA序列和结构优化中的深度学习应用提供新的可能性.