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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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...
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Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

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Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
Although the genome of each species varies greatly from each other, a few sequences are highly conserved. Such conserved...
3.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

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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...
12.4K
Sampling Plans01:23

Sampling Plans

157
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
157

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相关实验视频

Updated: May 17, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

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对于多目标蛋白质序列设计的帕雷托最佳采样.

Jiaqi Luo1, Kerr Ding1, Yunan Luo1

  • 1School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30308, USA.

iScience
|March 31, 2025
PubMed
概括
此摘要是机器生成的。

使用机器学习,MosPro高效地设计具有所需属性的蛋白质序列. 这种生成方法可以在广的搜索空间中寻找新的功能性蛋白质设计.

关键词:
行为神经科学 行为神经科学认知神经科学是一种认知神经科学.神经科学是一个神经科学.

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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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:

  • 计算生物学是一种计算生物学.
  • 蛋白质工程是一种蛋白质工程.
  • 机器学习是机器学习.

背景情况:

  • 监督机器学习 (ML) 在从序列中预测蛋白质特性方面表现出色.
  • 设计具有特定属性的蛋白质序列 (反向问题) 是由于大搜索空间和复杂的健身景观而具有挑战性的.

研究的目的:

  • 介绍MosPro,一种高效的ML算法,用于属性引导的蛋白质序列设计.
  • 为了解决蛋白质设计中未被充分探索的反向问题.

主要方法:

  • 框架序列设计作为离散采样问题.
  • 使用预训练的可微分ML模型来预测序列属性.
  • 向高属性序列塑造一个概率分布.
  • 在多属性序列设计中使用帕雷托优化.

主要成果:

  • 莫斯普罗有效地从构建的分布中取样序列.
  • 巴雷托优化成功地提出了针对多个属性的优化序列.
  • 对实验性健身场景的评估证实了MosPro能够平衡多个设计需求的能力.

结论:

  • 莫斯普罗证明了有效和可控制的功能性蛋白质设计的巨大潜力.
  • 生成式机器学习为解决复杂的蛋白质工程挑战提供了强大的工具.