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相关概念视频

Size-Exclusion Chromatography01:08

Size-Exclusion Chromatography

1.6K
In size-exclusion chromatography (SEC), also known as molecular-exclusion or gel-permeation chromatography, molecules are separated based on their sizes. This technique is important for separating large molecules such as polymers and biomolecules. The two classes of micron-sized stationary phases encountered in SEC are silica particles and cross-linked polymer resin beads. Both materials are porous, but their pore sizes vary significantly.
Silica particles offer advantages such as rigidity,...
1.6K
Affinity Chromatography01:03

Affinity Chromatography

2.7K
Affinity chromatography is a powerful technique extensively utilized for separating and purifying specific biomolecules from complex mixtures. It capitalizes on the highly selective binding between an analyte and its counterpart, such as antibody-antigen interactions. The counterpart is immobilized on the stationary phase, forming an affinity column. The stationary phase typically consists of solid support, such as agarose or porous glass beads, immobilizing the affinity ligand. The mobile...
2.7K
Antibody Structure01:10

Antibody Structure

65.1K
Overview
Antibodies, also known as immunoglobulins (Ig), are essential players of the adaptive immune system. These antigen-binding proteins are produced by B cells and make up 20 percent of the total blood plasma by weight. In mammals, antibodies fall into five different classes, which each elicits a different biological response upon antigen binding.
The Y-Shaped Structure of Antibodies Consists of Four Polypeptide Chains
Antibodies consist of four polypeptide chains: two identical heavy...
65.1K

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相关实验视频

Updated: Jan 6, 2026

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS
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Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS

Published on: June 20, 2019

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加快抗体发育:基于序列和结构的模型,通过大小排除色谱来预测可发育性质.

A N M Nafiz Abeer1,2, Mehdi Boroumand1, Isabelle Sermadiras3

  • 1Data Science and Modelling, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA.

mAbs
|September 26, 2025
PubMed
概括
此摘要是机器生成的。

这项研究表明,in silico模型可以加快生物制药开发能力选. 机器学习方法,包括蛋白质语言模型和图形神经网络,有效地预测大小排除色谱分析的抗体聚合.

关键词:
抗体结构 抗体结构可开发性属性开发性属性图表神经网络的神经网络蛋白质语言模型尺寸排除色谱法 尺寸排除色谱治疗性抗体治疗性抗体.

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

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Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study
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Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Published on: August 16, 2019

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相关实验视频

Last Updated: Jan 6, 2026

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS
10:00

Characterization of Proteins by Size-Exclusion Chromatography Coupled to Multi-Angle Light Scattering SEC-MALS

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

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Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study
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Activated Cross-linked Agarose for the Rapid Development of Affinity Chromatography Resins - Antibody Capture as a Case Study

Published on: August 16, 2019

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科学领域:

  • 生物制药的发展.
  • 计算生物学是一种计算生物学.
  • 蛋白质工程是一种蛋白质工程.

背景情况:

  • 生物制药开发能力的实验查,例如尺寸排除染色体 (SEC),是资源密集且耗时的.
  • 加快抗体开发过程需要有效的选方法来检测关键的可开发性质.

研究的目的:

  • 探索和比较in silico模型,以加快对生物制药可开发性质的选.
  • 为了确定最有效的计算方法来预测SEC试验中的抗体中的蛋白质聚合倾向.

主要方法:

  • 使用预先计算的序列和预测的结构特征的替代模型的比较.
  • 评估基于序列的方法,利用像ESM-2这样的蛋白质语言模型 (PLM),使用各种微调策略.
  • 通过图形神经网络 (GNN) 将抗体结构信息集成到预测管道中.
  • 应用这些多样化的in silico方法来预测大约1200个免疫球蛋白G (IgG1) 分子的数据集的聚合倾向.

主要成果:

  • 经验评估确定了最有效的in silico方法来预测与SEC试验相关的可开发性质.
  • 通过GNN与PLM一起展示了整合结构信息的潜力.
  • 量化了基于特征的和端到端的PLM方法之间的性能差异.

结论:

  • 在模型中,特别是那些利用序列和结构数据的模型,可以显著加快抗体开发性查.
  • 这项研究为选择最佳计算策略来预测蛋白质聚合提供了宝贵的见解,有助于更快的抗体开发.
  • 这项研究有助于优化早期生物制药查过程,减少实验负担.