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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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...
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Conserved Binding Sites01:49

Conserved Binding Sites

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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.
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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Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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Protein Families02:47

Protein Families

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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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...
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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相关实验视频

Updated: May 13, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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进化:一个基于人工智能的蛋白质突变预测和进化阶段探索的网络平台.

Satyam Sangeet1,2, Anushree Sinha1, Madhav B Nair1

  • 1Department of Chemical Sciences, Indian Institute of Science Education and Research, Kolkata, West Bengal 741246, India.

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概括

EVOLVE是一个新的网络工具,使用机器学习和统计力学来预测蛋白质突变位置及其集体效应. 它通过分析突变来帮助识别潜在的关注变异,帮助生物物理化学研究.

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科学领域:

  • 生物物理化学 生物物理化学
  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质工程.

背景情况:

  • 从序列数据中预测蛋白质结构-功能关系至关重要.
  • 识别突变部位是了解蛋白质行为的关键.
  • 分析大型序列空间带来了计算挑战.

研究的目的:

  • 开发一个网络工具,EVOLVE,用于探索潜在的突变站点及其集体行为.
  • 为了应对蛋白质研究中大序列空间分析的挑战.
  • 提供一种识别潜在关注变异的方法.

主要方法:

  • 统计力学引导的机器学习算法的集成.
  • 使用统计力学来确定热点的突变的计算.
  • 应用阶段过渡概念的统计力学来量化波动.

主要成果:

  • 进化精确地预测了可能的突变地点及其功能后果.
  • 对病毒蛋白序列的验证证实了该工具的预测能力.
  • 已启用对关注变种 (VOC) 和监测变种 (VUM) 的定量识别.

结论:

  • EVOLVE提供了一个用户友好的平台,用于分析蛋白质突变部位.
  • 该工具利用先进的计算方法来增强生物物理洞察力.
  • 进化有助于理解蛋白质进化和识别显著的病毒变异.