利用卷积神经网络预测氨基酸序列的磷酸化位点

Yanchun Zeng, Yuzi Li, Lambert Yan
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引用次数: 0

摘要

本研究项目的重点是通过人类蛋白质的氨基酸序列来预测磷酸化的位置。信号通过受体进入细胞,这一过程被称为信号级联,可能会改变细胞的状态。信号级联的一个基本元素是蛋白激酶,它是一种给目标蛋白一个磷酸基团的酶。它们可以激活或使蛋白质失活,或者作为传递给其他蛋白质的信号。通过卷积神经网络(CNN),我们的模型旨在为特定人类蛋白质序列的磷酸化模式提供合理的预测,从而为未来的实验提供基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Phosphorylation Sites in Amino Acid Sequences Using Convolutional Neural Networks
The focus of this research project is to predict the location of phosphorylation on human proteins by their amino acid sequences. Signals are sent into cells through receptors during a process called signaling cascade, which may alter the state of cells. One essential element of signaling cascade is protein kinases, which are enzymes that give the target protein a phosphate group. They can activate, deactivate proteins or act as signals to be passed onto other proteins. Through the Convolutional Neural Network (CNN), our model aims to provide a plausible prediction of the phosphorylation pattern of a given human protein sequence, on which future experiments can be based.
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