Department of Biochemistry, Eötvös Loránd University, Pázmány Péter stny 1/c, Budapest H-1117, Hungary.
Nucleic Acids Res. 2024 Jul 5;52(W1):W176-W181. doi: 10.1093/nar/gkae385.
Intrinsically disordered proteins and protein regions (IDPs/IDRs) carry out important biological functions without relying on a single well-defined conformation. As these proteins are a challenge to study experimentally, computational methods play important roles in their characterization. One of the commonly used tools is the IUPred web server which provides prediction of disordered regions and their binding sites. IUPred is rooted in a simple biophysical model and uses a limited number of parameters largely derived on globular protein structures only. This enabled an incredibly fast and robust prediction method, however, its limitations have also become apparent in light of recent breakthrough methods using deep learning techniques. Here, we present AIUPred, a novel version of IUPred which incorporates deep learning techniques into the energy estimation framework. It achieves improved performance while keeping the robustness of the original method. Based on the evaluation of recent benchmark datasets, AIUPred scored amongst the top three single sequence based methods. With a new web server we offer fast and reliable visual analysis for users as well as options to analyze whole genomes in mere seconds with the downloadable package. AIUPred is available at https://aiupred.elte.hu.
无规卷曲蛋白质和蛋白质区域（IDPs/IDRs）在不依赖于单一明确定义构象的情况下发挥着重要的生物学功能。由于这些蛋白质难以进行实验研究，因此计算方法在其特性描述中发挥着重要作用。常用的工具之一是 IUPred 网络服务器，它可用于预测无规区域及其结合位点。IUPred 的基础是一个简单的生物物理模型，仅使用大量源自球状蛋白质结构的有限参数。这使得该方法具有令人难以置信的快速和稳健的预测能力，然而，随着最近使用深度学习技术的突破性方法的出现，其局限性也变得明显。在这里，我们提出了 AIUPred，这是 IUPred 的一个新版本，它将深度学习技术纳入能量估计框架中。它在保持原始方法稳健性的同时，提高了性能。基于对最近基准数据集的评估，AIUPred 在基于单序列的方法中排名前三。我们提供了一个新的网络服务器，为用户提供快速可靠的可视化分析，以及下载包中仅用几秒钟即可分析整个基因组的选项。AIUPred 可在 https://aiupred.elte.hu 上获取。
