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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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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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Conservation of Protein Domains02:26

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Evolutionary Relationships through Genome Comparisons02:54

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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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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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相关实验视频

Updated: May 26, 2025

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions
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使用修改的微分进化优化算法的优化深森林算法:宿主-病原体蛋白质-蛋白质相互作用预测的一个案例.

Jerry Emmanuel1,2,3, Itunuoluwa Isewon1,2,3, Jelili Oyelade1,2,3

  • 1Department of Computer and Information Sciences, Covenant University, Ota, Nigeria.

Computational and structural biotechnology journal
|February 25, 2025
PubMed
概括

一种新的修改的差异进化 (DE) 方法增强了深森林模型的宿主-病原体蛋白质-蛋白质相互作用预测,提高了准确性和效率.

关键词:
森林深处的森林深处的森林.不同进化的差异进化.这是一个超参数.优化优化 优化优化原菌 (Plasmodium falciparum) 是一种有毒的病毒.蛋白质与蛋白质的相互作用

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 深森林模型提供自适应特征学习,但受到手动超参数调整和低效的困扰.
  • 贝叶斯优化是一种标准的超参数调整方法,通常由演化算法如差异演化 (DE) 增强.
  • 传统的DE方法随机选择矢量,从而在优化中获得次优化解决方案.

研究的目的:

  • 开发一个修改的差异进化 (DE) 获取函数,以在深森林模型中改进超参数优化.
  • 通过使用优化的深森林模型,提高对宿主-病原体蛋白质-蛋白质相互作用的预测.
  • 为了更有效的优化,解决DE中随机向量选择的局限性.

主要方法:

  • 开发了一种经过修改的DE获取函数,使用加权和自适应的捐赠载体技术.
  • 这种优化的DE方法被集成到Deep Forest模型中,用于自动超参数调.
  • 该模型在人类Plasmodium falciparum蛋白序列数据上使用10倍交叉验证进行了评估.

主要成果:

  • 优化的深森林模型实现了89.3%的准确度,85.4%的灵敏度和91.6%的精度.
  • 该模型在所有评估指标中都超过了标准优化方法和其他机器学习模型.
  • 预测了七种新的宿主-病原体相互作用,该模型被部署为web应用程序.

结论:

  • 修改后的DE获取功能显著提高了深森林模型对宿主病原体PPI预测的性能.
  • 开发的方法为复杂的生物预测中超参数优化提供了一种高效和准确的方法.
  • 可访问的Web应用程序促进了优化模型的进一步研究和应用.