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相关概念视频

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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机器学习用于抗微生物的识别和设计.

Fangping Wan1,2,3,4,5,6, Felix Wong7,8,9,6, James J Collins7,8,9,10,11

  • 1Machine Biology Group, Departments of Psychiatry and Microbiology, Institute for Biomedical Informatics, Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Nature reviews bioengineering
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概括
此摘要是机器生成的。

人工智能 (AI) 和机器学习 (ML) 加快了抗微生物 (AMP) 的开发. 这些技术解决了设计新抗微生物疗法以打击耐药性的挑战.

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Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
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Semi-automated Biopanning of Bacterial Display Libraries for Peptide Affinity Reagent Discovery and Analysis of Resulting Isolates
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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 抗菌素耐药性需要新的治疗策略.
  • 抗微生物 (AMP) 是有前途的,但面临临临床翻译障碍,如毒性和不稳定性.
  • 传统的抗生素开发是缓慢而具有挑战性的.

研究的目的:

  • 引入AI和ML在抗微生物 (AMP) 开发中的应用.
  • 调查ML方法,以克服AMP的局限性.
  • 突出数据驱动的体设计在临床转化中的机遇.

主要方法:

  • 对当前用于类建模的AI和ML方法的审查.
  • 分析ML在预测生物分子特性和产生新分子方面的作用.
  • 探索用于体设计的数据驱动方法.

主要成果:

  • 人工智能和机器学习在预测性质和设计新分子方面提供了突破性进展.
  • 基于ML的建模可能会减轻传统AMP药物发现的缺点.
  • 数据驱动设计存在新兴的机会,以加速AMP开发.

结论:

  • 人工智能和机器学习是加速开发和临床转化抗菌的强大工具.
  • 通过数据驱动的设计来解决AMP的局限性,对于打击抗菌素耐药性至关重要.
  • 对ML应用的进一步研究可以促进AMP在临床实践中的更广泛采用.