一个基于蛋白质动态的深度学习模型增强了对健身和经验的预测
Ngoc Huynh1,2,3, I Can Kazan1,4, Jin Lu1,4
1Center for Biological Physics, Arizona State University, Tempe, AZ 85287.
概括
预测多个突变如何影响蛋白质功能是很困难的. 这项研究引入了一种使用蛋白质动态的深度学习框架,以准确地建模这些复杂的相互作用,优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 生物物理学的生物物理.
- 机器学习是机器学习.
背景情况:
- 通过深度学习,评估单个突变对蛋白质功能的影响是可行的.
- 预测多个突变 (epistasis) 的综合效应仍然是一个重大挑战.
研究的目的:
- 开发一种深度学习框架,用于预测受多种突变影响的蛋白质功能.
- 将蛋白质动态,特别是非对称动态合指数 (DCI) 纳入预测模型.
主要方法:
- 使用不对称的动态合指数 (DCI) 构建了一个神经网络架构.
- 训练了一种全质图神经网络 (GNN),将残留物与它们的动态影响物联系起来.
- 该模型在四种蛋白质的深度突变扫描数据集上进行了评估.
主要成果:
- 在预测表观相互作用方面,GNN模型的表现优于现有的方法,尽管它没有在实验表观数据上接受训练.
- 该模型对新型的计算设计的TEM-1β-乳糖酶变体表现出高的预测准确性.
结论:
- 开发的GNN框架有效地模拟了复杂的多变异效应和表现.
- 这种方法提高了对影响蛋白质功能的突变的评估,即使是远离活性部位的突变.
相关概念视频
Epistasis Analysis
5.6K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.6K
Physiological Pharmacokinetic Models: Assumption with Protein Binding
209
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
209
Protein Dynamics in Living Cells
2.6K
Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
2.6K
Epistasis
50.1K
In addition to multiple alleles at the same locus influencing traits, numerous genes or alleles at different locations may interact and influence phenotypes in a phenomenon called epistasis. For example, rabbit fur can be black or brown depending on whether the animal is homozygous dominant or heterozygous at a TYRP1 locus. However, if the rabbit is also homozygous recessive at a locus on the tyrosinase gene (TYR), it will have an unshaded coat that appears white, regardless of its TYRP1...
50.1K
Model Approaches for Pharmacokinetic Data: Physiological Models
246
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
246
Mechanistic Models: Compartment Models in Individual and Population Analysis
244
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
244


