Liu Zhe, Pan Weihao, Li Weihao, Zhen Xuyang, Liang Jisheng, Cai Wenxiang, Xu Fei, Yuan Kai, Lin Guan Ning
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
State Key Laboratory of Functional Materials for Informatics, Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, Shanghai 200050, China.
Biology (Basel). 2022 Oct 3;11(10):1454. doi: 10.3390/biology11101454.
Though AlphaFold2 has attained considerably high precision on protein structure prediction, it is reported that directly inputting coordinates into deep learning networks cannot achieve desirable results on downstream tasks. Thus, how to process and encode the predicted results into effective forms that deep learning models can understand to improve the performance of downstream tasks is worth exploring. In this study, we tested the effects of five processing strategies of coordinates on two single-sequence protein binding site prediction tasks. These five strategies are spatial filtering, the singular value decomposition of a distance map, calculating the secondary structure feature, and the relative accessible surface area feature of proteins. The computational experiment results showed that all strategies were suitable and effective methods to encode structural information for deep learning models. In addition, by performing a case study of a mutated protein, we showed that the spatial filtering strategy could introduce structural changes into HHblits profiles and deep learning networks when protein mutation happens. In sum, this work provides new insight into the downstream tasks of protein-molecule interaction prediction, such as predicting the binding residues of proteins and estimating the effects of mutations.
尽管AlphaFold2在蛋白质结构预测方面已经取得了相当高的精度，但据报道，直接将坐标输入深度学习网络在下游任务中无法取得理想的结果。因此，如何将预测结果处理并编码为深度学习模型能够理解的有效形式以提高下游任务的性能值得探索。在本研究中，我们测试了坐标的五种处理策略对两个单序列蛋白质结合位点预测任务的影响。这五种策略分别是空间滤波、距离图的奇异值分解、计算二级结构特征以及蛋白质的相对可及表面积特征。计算实验结果表明，所有策略都是为深度学习模型编码结构信息的合适且有效的方法。此外，通过对一个突变蛋白进行案例研究，我们表明当蛋白质发生突变时，空间滤波策略可以将结构变化引入HHblits图谱和深度学习网络中。总之，这项工作为蛋白质 - 分子相互作用预测的下游任务提供了新的见解，例如预测蛋白质的结合残基以及估计突变的影响。
