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Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability

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  • Peng Xiong

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China)

  • Meng Wang

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China)

  • Xiaoqun Zhou

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China)

  • Tongchuan Zhang

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China)

  • Jiahai Zhang

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China)

  • Quan Chen

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China)

  • Haiyan Liu

    (School of Life Sciences, University of Science and Technology of China, 443 Huangshan Road, Hefei, Anhui 230027, China
    Hefei National Laboratory for Physical Sciences at the Microscales
    Hefei Institutes of Physical Science, Chinese Academy of Sciences)

Abstract

The de novo design of amino acid sequences to fold into desired structures is a way to reach a more thorough understanding of how amino acid sequences encode protein structures and to supply methods for protein engineering. Notwithstanding significant breakthroughs, there are noteworthy limitations in current computational protein design. To overcome them needs computational models to complement current ones and experimental tools to provide extensive feedbacks to theory. Here we develop a comprehensive statistical energy function for protein design with a new general strategy and verify that it can complement and rival current well-established models. We establish that an experimental approach can be used to efficiently assess or improve the foldability of designed proteins. We report four de novo proteins for different targets, all experimentally verified to be well-folded, solved solution structures for two being in excellent agreement with respective design targets.

Suggested Citation

  • Peng Xiong & Meng Wang & Xiaoqun Zhou & Tongchuan Zhang & Jiahai Zhang & Quan Chen & Haiyan Liu, 2014. "Protein design with a comprehensive statistical energy function and boosted by experimental selection for foldability," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms6330
    DOI: 10.1038/ncomms6330
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    Cited by:

    1. Yufeng Liu & Rui Wu & Xinyu Wang & Sheng Wang & Linghui Chen & Fudong Li & Quan Chen & Haiyan Liu, 2025. "Enhancing functional proteins through multimodal inverse folding with ABACUS-T," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    2. Yinglu Cui & Yanchun Chen & Jinyuan Sun & Tong Zhu & Hua Pang & Chunli Li & Wen-Chao Geng & Bian Wu, 2024. "Computational redesign of a hydrolase for nearly complete PET depolymerization at industrially relevant high-solids loading," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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