Prediction of Inter-residue Multiple Distances and Exploration of Protein Multiple Conformations by Deep Learning

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. PP; pp. 1 - 9
• Main Authors: Zhang, Fujin, Li, Zhangwei, Zhao, Kailong, Zhao, Pengxin, Zhang, Guijun
• Format: Journal Article
• Language: English
• Published: IEEE 10.06.2024
• Subjects: attention mechanism; Deep learning; Feature extraction; Multiple conformations; multiple distances prediction; Periodic structures; Predictive models; Proteins; Tensors; Training
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2024.3411825

AlphaFold2 has achieved a major breakthrough in end-to-end prediction for static protein structures. However, protein conformational change is considered to be a key factor in protein biological function. Inter-residue multiple distances prediction is of great significance for research on protein multiple conformations exploration. In this study, we proposed an inter-residue multiple distances prediction method, DeepMDisPre, based on an improved network which integrates triangle update, axial attention and ResNet to predict multiple distances of residue pairs. We built a dataset which contains proteins with a single structure and proteins with multiple conformations to train the network. We tested DeepMDisPre on 114 proteins with multiple conformations. The results show that the inter-residue distance distribution predicted by DeepMDisPre tends to have multiple peaks for flexible residue pairs than for rigid residue pairs. On two cases of proteins with multiple conformations, we modeled the multiple conformations relatively accurately by using the predicted inter-residue multiple distances. In addition, we also tested the performance of DeepMDisPre on 279 proteins with a single structure. Experimental results demonstrate that the average contact accuracy of DeepMDisPre is higher than that of the comparative method. In terms of static protein modeling, the average TM-score of the 3D models built by DeepMDisPre is also improved compared with the comparative method. The executable program is freely available at https://github.com/iobio-zjut/DeepMDisPre .