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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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Conservation of Protein Domains02:26

Conservation of Protein Domains

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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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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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相关实验视频

Updated: May 30, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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使用蛋白质语言模型进行可解释基化域特异性预测.

Abhinav K Adduri1, Andrew T McNutt2, Caleb N Ellington1

  • 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.

bioRxiv : the preprint server for biology
|January 27, 2025
PubMed
概括

我们开发了MASPR,这是一种机器学习工具,用于从微生物基因组中预测自然产品结构. 这种方法加速了新型抗生素,抗真菌药物和抗癌化合物的发现.

关键词:
药物发现 药物发现 药物发现生成式建模生成式建模机器学习 机器学习自然产品 自然产品蛋白质语言模型

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

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相关实验视频

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 非核糖体 (NRPs) 和多基 (PKs) 等自然产品是重要的药物来源.
  • 传统的发现方法是缓慢的,经常重新发现已知的化合物.
  • 基因组挖掘提供了一种高通量方法来识别新型自然产品.

研究的目的:

  • 开发一种机器学习方法,用于预测参与NRP和NRP-PK生物合成的腺化 (A) 域的基质特异性.
  • 创建一个高效的算法,从基因组数据中预测混合NRP-PK自然产品结构.
  • 为了确定孤儿自然产品的潜在生物合成基因集群.

主要方法:

  • 利用蛋白质语言模型开发MASPR,以准确地预测A域基质特异性.
  • 实施MASPR用于零射击分类,预测新型基板.
  • 开发了集成MASPR的Seq2Hybrid算法,以预测微生物基因组中的混合NRP-PK结构.

主要成果:

  • 与现有方法相比,MASPR显示出更高的准确性和概括性.
  • 马斯普成功地预测了其训练数据中不存在的基板.
  • 通过Seq2Hybrid,可以提出生物合成基因集群的建议,用于孤儿天然产品Octaminomycin A,Dityromycin,SW-163B和JBIR-39.

结论:

  • MASPR为A域基质预测提供了一个准确和可解释的方法.
  • Seq2Hybrid加速了新型混合NRP-PK天然产品的发现.
  • 这种基因组挖掘策略增强了识别有价值的生物活性化合物的能力.