Department of Computer Science, University of Miami, 1365 Memorial Drive, Coral Gables, FL 33124, USA.
Computer Information Sciences Department, St. Ambrose University, 518 W. Locust Street, Davenport, IA 52803, USA.
Int J Mol Sci. 2024 Jun 6;25(11):6250. doi: 10.3390/ijms25116250.
Reliable and accurate methods of estimating the accuracy of predicted protein models are vital to understanding their respective utility. Discerning how the quaternary structure conforms can significantly improve our collective understanding of cell biology, systems biology, disease formation, and disease treatment. Accurately determining the quality of multimeric protein models is still computationally challenging, as the space of possible conformations is significantly larger when proteins form in complex with one another. Here, we present EGG (energy and graph-based architectures) to assess the accuracy of predicted multimeric protein models. We implemented message-passing and transformer layers to infer the overall fold and interface accuracy scores of predicted multimeric protein models. When evaluated with CASP15 targets, our methods achieved promising results against single model predictors: fourth and third place for determining the highest-quality model when estimating overall fold accuracy and overall interface accuracy, respectively, and first place for determining the top three highest quality models when estimating both overall fold accuracy and overall interface accuracy.
可靠和准确的预测蛋白质模型准确性的方法对于理解它们各自的用途至关重要。辨别四级结构的构象可以显著提高我们对细胞生物学、系统生物学、疾病形成和治疗的集体理解。准确确定多聚体蛋白质模型的质量仍然具有计算挑战性，因为当蛋白质相互形成复合物时，可能的构象空间显著增大。在这里，我们提出了 EGG（基于能量和图的架构）来评估预测的多聚体蛋白质模型的准确性。我们实现了消息传递和变压器层，以推断预测的多聚体蛋白质模型的整体折叠和界面准确性得分。在与 CASP15 目标进行评估时，我们的方法在预测单模型方面取得了有希望的结果：在估计整体折叠准确性和整体界面准确性时，分别排名第四和第三，在估计整体折叠准确性和整体界面准确性时，排名第一，确定了三个最高质量的模型。
