在氨基酸序列中使用局部对称的碎片模拟毒性
Andrey A Toropov1, Alla P Toropova1, Valentin O Kudyshkin2
1Department of Environmental Health Science, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156, Milan, Italy.
Bio Systems
|August 10, 2025
概括
这项研究引入了一种新方法,使用氨基酸序列和局部对称碎片来预测毒性. 该模型在大型数据集上实现了0.7马修斯相关系数,改善了类毒性预测.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 毒理学 毒理学 毒理学
背景情况:
- 毒性是药物开发和生物研究的关键因素.
- 对类细胞毒性的准确建模对于预测潜在的不良影响至关重要.
- 目前用于预测毒性的方法需要提高预测能力.
研究的目的:
- 开发和验证一个高效的计算方案来建模毒性.
- 调查局部对称碎片对毒性预测模型准确性的影响.
- 探索信息动力学原理在行为建模中的应用.
主要方法:
- 开发了一个数学函数来计算基于构成氨基酸的类细胞毒性.
- 用单符号缩写表示氨基酸序列.
- 通过结合局部对称碎片来增强模型的预测潜力.
- 在一个验证集上使用马修斯相关系数来评估模型性能.
主要成果:
- 拟议的模型有效计算的细胞毒性作为氨基酸组成的函数.
- 包括局部对称碎片显著提高了模型的预测准确性.
- 性能最好的模型在2784个的数据集上实现了0.7的马修斯相关系数.
- 该研究证明了将结构信息 (局部对称性) 纳入基于序列的毒性预测的实用性.
结论:
- 开发的方案提供了一个有效的方法来建模毒性.
- 局部对称碎片是提高毒性模型预测精度的有价值特征.
- 这些发现表明信息动力学在理解和建模生物化学行为的潜在应用.
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
2.0K
05:08Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
Published on: July 8, 2025
357
相关概念视频
01:41Signal Sequences and Sorting Receptors
Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
01:33Peptide Identification Using Tandem Mass Spectrometry
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...
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...