用公开可用的基于序列的预测器对小蛋白质的新型类别进行整体的in silico开发能力评估
Daniel A M Pais1, Jan-Peter A Mayer2, Karin Felderer2
1Valgenesis Portugal, Lda, R. Castilho 50 4th Floor, 1250-071, Lisbon, Portugal.
Journal of computer-aided molecular design
|August 20, 2024
概括
开发Anticalin®蛋白质的预测模型可以提高药物开发的早期阶段. 这种机器学习方法简化了临床前评估,提高了新型治疗蛋白质的成功率.
科学领域:
- 生物技术是生物技术.
- 蛋白质工程是指蛋白质工程.
- 计算生物学 计算生物学
背景情况:
- 治疗性蛋白质的开发面临着很高的磨损率 (91%) 和相当大的成本.
- 开发能力的早期和广泛评估对于药物成功和供应至关重要.
- 在 silico 预测提供了减少时间和成本的途径,但需要大量的数据集.
研究的目的:
- 开发一种机器学习策略,用于评估Anticalin®蛋白质的可开发性.
- 整合基于知识的方法与序列衍生描述符用于预测建模.
- 为新型治疗性蛋白质候选人建立一个整体的可开发性得分.
主要方法:
- 利用基于知识的机器学习来评估Anticalin®的可开发性.
- 内置的可开发性属性:聚合,电荷变体,免疫性,稳定性等.
- 开发了使用序列衍生的描述符来预测可开发性得分的统计模型.
主要成果:
- 建立了具有低根平均平方误差的新型统计模型,用于预测Anticalin®的可开发性.
- 通过预测来自个别选活动的候选人的可开发性得分来验证模型.
- 证明了序列衍生描述符在预测建模中的有效性.
结论:
- 描述的工作流程可以简化Anticalin®候选药物的临床前开发.
- 这种方法有可能应用于其他治疗性蛋白质支架.
- 预测in silico方法是蛋白质疗法高效创新的关键.
更多相关视频
06:50Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
Published on: January 26, 2024
1.8K
10:21Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
Published on: February 23, 2024
2.4K
相关概念视频
02:04Protein-protein Interfaces
12.5K
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...
12.5K
01:49Conserved Binding Sites
4.2K
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...
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...
4.2K