文献类型：期刊文献

英文题名：PMMVar: Leveraging Multi-level Protein Structures for Enhanced Coding Variant Pathogenicity Prediction

作者：Chen, Yijia[1,2]; Chen, Yiwen[3]; Nie, Shanling[4]; Yang, Hai[1]

机构：[1] East China University of Science and Technology, Department of Computer Science and Engineering, Shanghai, China; [2] Shanghai Key Laboratory of Computer Software Evaluating and Testing, Shanghai, China; [3] National University of Singapore, Center for Continuing and Lifelong Education, Singapore; [4] The University of Sydney, Faculty of Engineering, Australia

年份：2023

起止页码：269

外文期刊名：Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

收录：EI(收录号：20240715560606)

语种：英文

摘要：Genomic variants, which can disrupt cellular functions, present a challenge in distinguishing deleterious from benign instances. While assessing genome-wide functional impacts, many current algorithms neglect protein tertiary structure of coding region variants due to limitations in protein structural prediction. This study introduces PMMVar, an advanced multimodal deep convolutional network, which adeptly integrates protein tertiary structures with conservation properties from ESM-2, supplemented by other protein structural sequences. PMMVar achieves outstanding performance on the latest clinical variant datasets, NCBI ClinVar (2023), and the Mendelian variant dataset, surpassing existing benchmarks. Ablation analyses validate the significance of protein multi-level structures in enhancing the model's accuracy. Overall, our findings spotlight the essential role of multi-level protein structures in pathogenicity predictions and their potential to discern deleterious genomic variants effectively. ? 2023 IEEE.