Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

8.1K
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...
8.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

From in silico screening to mechanism: computational advances in developing anti-allergic agents for food allergy.

Critical reviews in food science and nutrition·2026
Same author

Mechanistic Insights into Pancreatic Lipase Inhibition by Pea-Derived Peptides: Integrating Process Optimization, Activity Assays, Docking, and Molecular Dynamics.

Foods (Basel, Switzerland)·2026
Same author

Interpretable machine learning and molecular simulations identify natural pancreatic lipase inhibitors and hydrophobic hotspot residues.

Bioorganic chemistry·2026
Same author

Effects of an AI-enhanced BOPPPS teaching model in nursing courses: a meta-analysis of randomized controlled trials.

Frontiers in medicine·2026
Same author

Polyoxovanadate Building Units in Metal-Organic Frameworks: Coordination Connectivity and Selective Oxidation of Aromatic Thioether.

Inorganic chemistry·2026
Same author

Sleep regulation in <i>Drosophila</i>: a review of neural circuits and genetics.

Frontiers in neuroscience·2026

相关实验视频

Updated: Jan 9, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

2.5K

HemPepPred:基于机器学习和蛋白质语言模型衍生特征的分离活性量的预测.

Xiang Li1, Wanting Zhao1, Xiao Liang1

  • 1Key Laboratory of Biorheological Science and Technology, Ministry of Education, College of Bioengineering, Chongqing University, Chongqing 400044, China.

Foods (Basel, Switzerland)
|December 11, 2025
PubMed
概括

预测血溶性对于药物安全至关重要. 一个新的回归框架整合了蛋白质语言模型和氨基酸特征,提高了设计的准确性和可解释性.

关键词:
组合学习组合学习这是一种血溶性.

更多相关视频

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

15.5K
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

9.9K

相关实验视频

Last Updated: Jan 9, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

2.5K
A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes
07:59

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

15.5K
Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope
08:09

Peptide Scanning-assisted Identification of a Monoclonal Antibody-recognized Linear B-cell Epitope

Published on: March 24, 2017

9.9K

科学领域:

  • 生物化学 生物化学
  • 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:

  • 精确预测血溶性对于安全性评估和治疗设计至关重要.
  • 现有的预测模型在准确性和可解释性方面存在局限性.

研究的目的:

  • 开发一种先进的回归框架,用于预测血溶性活性.
  • 为了提高血溶性预测的准确性和可解释性.

主要方法:

  • 整合蛋白质语言模型嵌入 (ESM2_t33) 与手工制作的氨基酸描述器.
  • 采用三个阶段的特征选择策略:差异过,F测试排名和相互信息分析.
  • 使用随机森林,极端随机树木,梯度提升,XGBoost和回归构建一个集合模型.

主要成果:

  • 整体模型在测试组中实现了0.57的确定系数 (R2) 和0.76的相关系数 (R).
  • 该模型在预测血溶性度 (HC50) 值方面表现优于之前的方法.
  • 沙普利值分析和Calibrated_Explanation算法提供了特征贡献和样本特定的解释.

结论:

  • 拟议的框架显著提高了血溶性预测的准确性和可解释性.
  • 开发的工具HemPepPred为合理的设计和安全评估提供了一个实用的平台.
  • 这种方法促进了更安全,更有效的治疗性的开发.