Huang Pin, Xing Haoming, Zou Xun, Han Qi, Liu Ke, Sun Xiangyan, Wu Junqiu, Fan Jie
College of Life Sciences, Beijing Normal University, Beijing, China.
Accutar Biotechnology Inc., Brooklyn, NY, United States.
Front Mol Biosci. 2021 Sep 20;8:756075. doi: 10.3389/fmolb.2021.756075. eCollection 2021.
We propose a method based on neural networks to accurately predict hydration sites in proteins. In our approach, high-quality data of protein structures are used to parametrize our neural network model, which is a differentiable score function that can evaluate an arbitrary position in 3D structures on proteins and predict the nearest water molecule that is not present. The score function is further integrated into our water placement algorithm to generate explicit hydration sites. In experiments on the OppA protein dataset used in previous studies and our selection of protein structures, our method achieves the highest model quality in terms of F1 score, compared to several previous studies.
我们提出了一种基于神经网络的方法来准确预测蛋白质中的水合位点。在我们的方法中，蛋白质结构的高质量数据用于对我们的神经网络模型进行参数化，该模型是一个可微的评分函数，可以评估蛋白质三维结构中的任意位置，并预测不存在的最近水分子。该评分函数进一步集成到我们的水放置算法中，以生成明确的水合位点。在先前研究中使用的OppA蛋白质数据集以及我们选择的蛋白质结构上进行的实验中，与之前的几项研究相比，我们的方法在F1分数方面实现了最高的模型质量。
