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Rotamer-free protein sequence design based on deep learning and self-consistency

A Publisher Correction to this article was published on 02 August 2022

This article has been updated

A preprint version of the article is available at Research Square.


Several previously proposed deep learning methods to design amino acid sequences that autonomously fold into a given protein backbone yielded promising results in computational tests but did not outperform conventional energy function-based methods in wet experiments. Here we present the ABACUS-R method, which uses an encoder–decoder network trained using a multitask learning strategy to predict the sidechain type of a central residue from its three-dimensional local environment, which includes, besides other features, the types but not the conformations of the surrounding sidechains. This eliminates the need to reconstruct and optimize sidechain structures, and drastically simplifies the sequence design process. Thus iteratively applying the encoder–decoder to different central residues is able to produce self-consistent overall sequences for a target backbone. Results of wet experiments, including five structures solved by X-ray crystallography, show that ABACUS-R outperforms state-of-the-art energy function-based methods in success rate and design precision.

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Fig. 1: An overview of the ABACUS-R method.
Fig. 2: Performance of Modeleval in computational tests.
Fig. 3: Results of overall sequence design for natural backbones.
Fig. 4: Results of experimental analysis of designed proteins.
Fig. 5: Variable sidechain types and sidechain packing have been designed by ABACUS-R.

Data availability

The following data are available from Zenodo63: complete lists of proteins for training and testing the models; the amino acid sequences designed for the 100 targets by Modeleval; the amino acid sequences and DNA sequences of the experimentally examined proteins. The experimentally solved protein structures have been deposited in the PDB under accession codes: 7VQL (1r26-A3, 10.2210/pdb7VQL/pdb); 7VQV (1r26-A6, 10.2210/pdb7VQV/pdb); 7VQW (1r26-A7, 10.2210/pdb7VQW/pdb); 7VTY(1cy5-A7, 10.2210/pdb7VTY/pdb); 7VU4(1r26-B4, 10.2210/pdb7VU4/pdb). Source Data are provided with this paper.

Code availability

The source code is available from Code Ocean64 at

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This work was supported by the National Key R&D Program of China (grant no. 2018YFA0900703 to H.Y.L. and 2018YFA090 1600 to Q.C.), National Natural Science Foundation of China (grant no. 21773220 to H.Y.L., 31971175 and 32171411 to Q.C.), the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Project (grant no. TSBICIP-PTJS-001 to H.Y.L.), and Youth Innovation Promotion Association, Chinese Academy of Sciences (grant no. 2017494 to Q.C.). We thank the members of staff from BL19U1 and BL02U1 beamlines of National Facility for Protein Science in Shanghai (NFPS) and of Shanghai Synchrotron Radiation Facility for assistance during crystallographic data collection. We thank M. Lv and Y. Yun for their assistance with X-ray diffraction data collection and processing.

Author information

Authors and Affiliations



H.Y.L., H.Q.L, Y.F.L. and W.L.W. conceived the computational framework. Q.C., L.Z. and Y.F.L. designed the experimental study. Y.F.L. and W.L.W. wrote the computer programs and performed the calculations under the supervision of H.Y.L. and H.Q.L. L.Z. performed experimental analyses under the supervision of Q.C. and H.Y.L. M.Z., C.C.W. and F.D.L. analyzed the crystallographic data. J.H.Z. collected and processed NMR data. Y.F.L., W.L.W. and H.Y.L. wrote the paper with input from all of the other authors.

Corresponding authors

Correspondence to Houqiang Li, Quan Chen or Haiyan Liu.

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Competing interests

H.Y.L. Q.C., H.Q.L., Y.F.L. and W.L.W. have filed patent application (no. 202210091553.7) relating to rotamer-free protein seuqence design in the name of University of Science and Technology of China. The other authors declare no competing interests.

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Peer review information

Nature Computational Science thanks Jue Wang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Handling editor: Jie Pan, in collaboration with the Nature Computational Science team. Peer reviewer reports are available.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–21, Tables 1–14 and references.

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Supplementary Data 1

Raw data for Supplementary Figs. 1a–d, 2a,b, 3a,c, 5c, 6a, 7 and 11, and protein lists.

Source data

Source Data Fig. 2

Raw data of recovery rate of Modeleval for single residues in a test set.

Source Data Fig. 3

Intermediate results for designing the overall sequences for 100 targets during self-consistency iteration. Overall sequence recovery rate and Rosetta energy for ABACUS-R-designed sequences. ABACUS-R-designed sequences for 100 targets.

Source Data Fig. 4

Raw data for three fast protein liquid chromatography experiments, three 1H NMR spectra, three DSC spectra, one HSQC spectrum and two crystal structures for three ABACUS-R designed sequences.

Source Data Fig. 5

PDB files and validation reports of X-ray structures, including those shown in this figure.

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Liu, Y., Zhang, L., Wang, W. et al. Rotamer-free protein sequence design based on deep learning and self-consistency. Nat Comput Sci 2, 451–462 (2022).

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