Basith Shaherin, Manavalan Balachandran, Lee Gwang
Department of Physiology, Ajou University School of Medicine, Suwon 16499 Republic of Korea.
Department of Integrative Biotechnology, College of Biotechnology and Bioengineering, Sungkyunkwan University, Suwon 16419 Republic of Korea.
Methods. 2025 Feb;234:264-274. doi: 10.1016/j.ymeth.2025.01.003. Epub 2025 Jan 10.
Pancreatic α-amylase breaks down starch into isomaltose and maltose, which are further hydrolyzed by α-glucosidase in the intestine into monosaccharides, rapidly raising blood sugar levels and contributing to type 2 diabetes mellitus (T2DM). Synthetic inhibitors of carbohydrate-digesting enzymes are used to manage T2DM but may harm organ function over time. Bioactive peptides offer a safer alternative, avoiding such adverse effects. Computational methods for predicting antidiabetic peptides (ADPs) can significantly reduce the time and cost of experimental testing. While machine learning (ML) has been applied to identify ADPs, advancements in data analysis and algorithms continue to drive progress in the field. To address this, we developed AntiT2DMP-Pred, the first ML-based tool specifically designed for predicting type 2 antidiabetic peptides (T2ADPs). This tool employs a feature fusion strategy, combining ten highly discriminative feature descriptors chosen from a pool of 32 descriptors and eight ML algorithms, tested across a range of baseline models. AntiT2DMP-Pred demonstrated excellent performance, surpassing both baseline and feature-optimized models, with an accuracy (ACC) and Matthews' correlation coefficient (MCC) of 0.976 and 0.953 on the training dataset, and an ACC and MCC of 0.957 and 0.851 on the independent dataset. The web server (https://balalab-skku.org/AntiT2DMP-Pred) is freely accessible, enabling researchers worldwide to utilize it in their experimental workflows and contribute to the discovery and understanding of T2ADPs, ultimately supporting peptide-based therapeutic development for diabetes management.
胰腺α-淀粉酶将淀粉分解为异麦芽糖和麦芽糖，这些物质在肠道中被α-葡萄糖苷酶进一步水解为单糖，从而迅速升高血糖水平，进而导致2型糖尿病（T2DM）。碳水化合物消化酶的合成抑制剂被用于治疗T2DM，但随着时间的推移可能会损害器官功能。生物活性肽提供了一种更安全的替代方法，可避免此类不良反应。预测抗糖尿病肽（ADP）的计算方法可以显著减少实验测试的时间和成本。虽然机器学习（ML）已被应用于识别ADP，但数据分析和算法的进步仍在推动该领域的发展。为了解决这个问题，我们开发了AntiT2DMP-Pred，这是第一个专门设计用于预测2型抗糖尿病肽（T2ADP）的基于ML的工具。该工具采用特征融合策略，结合了从32个描述符库中选出的10个高度有区分力的特征描述符和8种ML算法，并在一系列基线模型上进行了测试。AntiT2DMP-Pred表现出优异的性能，超过了基线模型和特征优化模型，在训练数据集上的准确率（ACC）和马修斯相关系数（MCC）分别为0.976和0.953，在独立数据集上的ACC和MCC分别为0.957和0.851。该网络服务器（https://balalab-skku.org/AntiT2DMP-Pred）可免费访问，使世界各地的研究人员能够在其实验工作流程中使用它，为T2ADP的发现和理解做出贡献，最终支持基于肽的糖尿病管理治疗开发。
