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Papers

Generative De Novo Protein Design with Global Context

2022-04-21 · Cheng Tan, Zhangyang Gao, Jun Xia, Bozhen Hu, Stan Z. Li

The linear sequence of amino acids determines protein structure and function. Protein design, known as the inverse of protein structure prediction, aims to obtain a novel protein sequence that will fold into the defined structure. Recent works on computational protein design have studied designing sequences for the desired backbone structure with local positional information and achieved competitive performance. However, similar local environments in different backbone structures may result in different amino acids, indicating that protein structure's global context matters. Thus, we propose the Global-Context Aware generative de novo protein design method (GCA), consisting of local and global modules. While local modules focus on relationships between neighbor amino acids, global modules explicitly capture non-local contexts. Experimental results demonstrate that the proposed GCA method outperforms state-of-the-arts on de novo protein design. Our code and pretrained model will be released.

📄 PDF Abstract BibTeX arXiv:2204.10673

Code (1)

chengtan9907/gca-generative-protein-design 공식 구현 pytorch

Tasks

Protein DesignProtein Structure Prediction

Methods 이 논문이 사용한 방법론

GCA 설명 없음
AWARE We propose to theoretically and empirically examine the effect of incorporating weighting schemes into walk-aggregating GNNs. To this end, we propose a simple, interpretable, and…

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