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US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes

Nature Methods volume 19, pages 1109–1115 (2022)Cite this article

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Abstract

Structure comparison and alignment are of fundamental importance in structural biology studies. We developed the first universal platform, US-align, to uniformly align monomer and complex structures of different macromolecules—proteins, RNAs and DNAs. The pipeline is built on a uniform TM-score objective function coupled with a heuristic alignment searching algorithm. Large-scale benchmarks demonstrated consistent advantages of US-align over state-of-the-art methods in pairwise and multiple structure alignments of different molecules. Detailed analyses showed that the main advantage of US-align lies in the extensive optimization of the unified objective function powered by efficient heuristic search iterations, which substantially improve the accuracy and speed of the structural alignment process. Meanwhile, the universal protocol fusing different molecular and structural types helps facilitate the heterogeneous oligomer structure comparison and template-based protein–protein and protein–RNA/DNA docking.

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Fig. 1: Four different structure alignment modes of US-align.
Fig. 2: Performance of three oligomeric alignment programs.
Fig. 3: Structure alignments between two protein–RNA complexes from two different bacteria.
Fig. 4: US-align outperforms four control RNA structure alignment programs.
Fig. 5: MSTA RNA alignment by US-align, Matt and MUSTANG.
Fig. 6: Application of US-align to RNA-protein docking.

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Data availability

All data needed to reproduce this work are available at https://doi.org/10.6084/m9.figshare.16725745 under CC BY v.4.0. Source data are provided with this paper.

Code availability

An online webserver and the standalone program of US-align are available at https://zhanggroup.org/US-align. The latest source code of US-align is also available at https://github.com/pylelab/USalign, while the source code for US-align version 20220227 used by this manuscript is included in Supplementary Software. The code was tested on Linux, Windows and Mac OS, where no notable differences in speed across different operating systems were found (Supplementary Fig. 15).

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Acknowledgements

We thank Y. Cao for technical assistance in developing qTMclust and X. Wei for insightful discussions. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI1548562. C.Z. is a Howard Hughes Medical Institute postdoctoral fellow. A.M.P. is a Howard Hughes Medical Institute Investigator. This work is supported in part by the National Human Genome Research Institute (HG011868 to A.M.P.), National Institute of General Medical Sciences (GM136422, OD026825 to Y.Z.), the National Institute of Allergy and Infectious Diseases (AI134678 to Y.Z.) and the National Science Foundation (IIS1901191, DBI2030790, MTM2025426 to Y.Z.).

Author information

Authors and Affiliations

  1. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Chengxin Zhang & Yang Zhang

  2. Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, USA

    Chengxin Zhang

  3. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Chengxin Zhang & Anna Marie Pyle

  4. Yale Combined Program in the Biological and Biomedical Sciences, Yale University, New Haven, CT, USA

    Morgan Shine & Anna Marie Pyle

  5. Department of Chemistry, Yale University, New Haven, CT, USA

    Anna Marie Pyle

  6. Department of Biological Chemistry, University of Michigan, Ann Arbor, MI, USA

    Yang Zhang

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Contributions

Y.Z. conceived the study. C.Z., A.M.P. and Y.Z. designed the experiments. C.Z. developed the method. C.Z. and M.S. drafted the manuscript. All authors revised the manuscript and approved the final version.

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Correspondence to Yang Zhang.

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Peer review information

Nature Methods thanks Ruth Nussinov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Arunima Singh in collaboration with the Nature Methods team.

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Supplementary information

Supplementary Information

Supplementary Texts 1–6, Figs. 1–15 and Tables 1–8.

Reporting Summary

Supplementary Software

US-align version 20220227.

Supplementary Data

Structure coordinate files for Supplementary Fig. 1.

Supplementary Data

Structure coordinate files for Supplementary Fig. 2.

Supplementary Data

Statistical source data for Supplementary Fig. 3

Supplementary Data

Statistical source data for Supplementary Fig. 4

Supplementary Data

Statistical source data for Supplementary Fig. 6.

Supplementary Data

Statistical source data for Supplementary Fig. 7.

Supplementary Data

Statistical source data for Supplementary Fig. 9.

Supplementary Data

Statistical source data for Supplementary Fig. 15.

Source data

Source Data Fig. 2

Statistical source data and structure coordinate files.

Source Data Fig. 3

Structure coordinate files.

Source Data Fig. 4

Statistical source data and structure coordinate files.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 6

Statistical source data and structure coordinate files.

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Zhang, C., Shine, M., Pyle, A.M. et al. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat Methods 19, 1109–1115 (2022). https://doi.org/10.1038/s41592-022-01585-1

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