论文标题
部分可观测时空混沌系统的无模型预测
Identifying Orientation-specific Lipid-protein Fingerprints using Deep Learning
论文作者
论文摘要
对RAS和RAF蛋白的行为与细胞膜中局部脂质环境之间的关系的了解对了解癌症形成的基础机制至关重要。在这项工作中，我们采用深度学习（DL）来学习这种关系，通过根据蛋白质结构域周围的脂质密度来预测RAS和RAS-RAF蛋白复合物的蛋白质定位状态相对于脂质膜的蛋白质（CG）分子动力学（MD）模拟。我们的DL模型可以预测六个蛋白质状态，总体准确性超过80％。这项工作的发现为蛋白质如何调节脂质环境提供了新的见解，这反过来又可以帮助设计新型疗法以调节与癌症发展相关的机制中的这种相互作用。
Improved understanding of the relation between the behavior of RAS and RAF proteins and the local lipid environment in the cell membrane is critical for getting insights into the mechanisms underlying cancer formation. In this work, we employ deep learning (DL) to learn this relationship by predicting protein orientational states of RAS and RAS-RAF protein complexes with respect to the lipid membrane based on the lipid densities around the protein domains from coarse-grained (CG) molecular dynamics (MD) simulations. Our DL model can predict six protein states with an overall accuracy of over 80%. The findings of this work offer new insights into how the proteins modulate the lipid environment, which in turn may assist designing novel therapies to regulate such interactions in the mechanisms associated with cancer development.
