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Volume 1 Issue 7, July 2021

Efficient protein model refinement with deep learning

Protein refinement methods, which are used to improve the quality of protein structural models, commonly rely on extensive conformational sampling, and therefore, are very time-consuming. In this issue, Xiaoyang Jing and Jinbo Xu propose a method that uses graph neural networks to substantially reduce the time taken to refine protein models, from hours to minutes on a single CPU, while having comparable accuracy with the leading approaches in the field.

SeeXiaoyang Jing and Jinbo XuandPhilip Kim

Image: Kateryna Kon/Science Photo Library. Cover design: Thomas Phillips.

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  • While it is crucial to guarantee the reproducibility of the results reported in a paper, let us also not forget about the importance of making research artifacts reusable for the scientific community.

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  • A graph-neural-network-based framework is proposed for the refinement of protein structure models, substantially improving the efficacy and efficiency of refining protein models when compared with the state-of-the-art approaches.

    • Osama Abdin
    • Philip M. Kim
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    • Houlong Zhuang
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  • Making sense of single-cell data requires various computational efforts such as clustering, visualization and gene regulatory network inference, often addressed by different methods. DeepSEM provides an all-in-one solution.

    • Jun Ding
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