Nazari Simin, Abdelrasoul Amira
Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, Saskatchewan, S7N 5A9, Canada.
Department of Chemical and Biological Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, Saskatchewan, S7N 5A9, Canada.
Sci Rep. 2025 Jan 28;15(1):3474. doi: 10.1038/s41598-024-83674-z.
Membrane incompatibility poses significant health risks, including severe complications and potential fatality. Surface modification of membranes has emerged as a pivotal technology in the membrane industry, aiming to improve the hemocompatibility and performance of dialysis membranes by mitigating undesired membrane-protein interactions, which can lead to fouling and subsequent protein adsorption. Affinity energy, defined as the strength of interaction between membranes and human serum proteins, plays a crucial role in assessing membrane-protein interactions. These interactions may trigger adverse reactions, potentially harmful to patients. Researchers often rely on trial-and-error approaches to enhance membrane hemocompatibility by reducing these interactions. This study focuses on developing machine learning algorithms that accurately and rapidly predict affinity energy between novel chemical structures of membrane materials and human serum proteins, based on a molecular docking dataset. Various membrane materials with distinct characteristics, chemistry, and orientation are considered in conjunction with different proteins. A comparative analysis of linear regression, K-nearest neighbors regression, decision tree regression, random forest regression, XGBoost regression, lasso regression, and support vector regression is conducted to predict affinity energy. The dataset, comprising 916 records for both training and test segments, incorporates 12 parameters extracted from data points and involves six different proteins. Results indicate that random forest (R² = 0.8987, MSE = 0.36, MAE = 0.45) and XGBoost (R² = 0.83, MSE = 0.49, MAE = 0.49) exhibit comparable predictive performance on the training dataset. However, random forest outperforms XGBoost on the testing dataset. Seven machine learning algorithms for predicting affinity energy are analyzed and compared, with random forest demonstrating superior predictive accuracy. The application of machine learning in predicting affinity energy holds significant promise for researchers and professionals in hemodialysis. These models, by enabling early interventions in hemodialysis membranes, could enhance patient safety and optimize the care of hemodialysis patients.
膜不相容性带来重大健康风险，包括严重并发症和潜在死亡。膜表面改性已成为膜行业的一项关键技术，旨在通过减轻不期望的膜 - 蛋白质相互作用来改善透析膜的血液相容性和性能，这种相互作用会导致污染及随后的蛋白质吸附。亲和能定义为膜与人血清蛋白之间相互作用的强度，在评估膜 - 蛋白质相互作用中起关键作用。这些相互作用可能引发不良反应，对患者有潜在危害。研究人员通常依靠试错方法来通过减少这些相互作用来提高膜的血液相容性。本研究专注于开发机器学习算法，该算法基于分子对接数据集准确快速地预测膜材料新化学结构与人血清蛋白之间的亲和能。结合不同蛋白质考虑了具有不同特性、化学性质和取向的各种膜材料。对线性回归、K近邻回归、决策树回归、随机森林回归、XGBoost回归、套索回归和支持向量回归进行了比较分析以预测亲和能。该数据集包括训练和测试部分的916条记录，包含从数据点提取的12个参数，并涉及六种不同蛋白质。结果表明，随机森林（R² = 0.8987，均方误差 = 0.36，平均绝对误差 = 0.45）和XGBoost（R² = 0.83，均方误差 = 0.49，平均绝对误差 = 0.49）在训练数据集上表现出可比的预测性能。然而，在测试数据集上随机森林优于XGBoost。分析并比较了七种预测亲和能的机器学习算法，随机森林显示出卓越的预测准确性。机器学习在预测亲和能方面的应用对血液透析领域的研究人员和专业人员具有重大前景。这些模型通过在血液透析膜中实现早期干预，可以提高患者安全性并优化血液透析患者的护理。
