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Unifying structural descriptors for biological and bioinspired nanoscale complexes

Nature Computational Science volume 2, pages 243–252 (2022)Cite this article

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Abstract

Biomimetic nanoparticles are known to serve as nanoscale adjuvants, enzyme mimics and amyloid fibrillation inhibitors. Their further development requires better understanding of their interactions with proteins. The abundant knowledge about protein–protein interactions can serve as a guide for designing protein–nanoparticle assemblies, but the chemical and biological inputs used in computational packages for protein–protein interactions are not applicable to inorganic nanoparticles. Analysing chemical, geometrical and graph-theoretical descriptors for protein complexes, we found that geometrical and graph-theoretical descriptors are uniformly applicable to biological and inorganic nanostructures and can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the machine-learning algorithms trained on protein–protein interactions to inorganic nanoparticles and found a nearly exact match between experimental and predicted interaction sites with proteins. These findings can be extended to other organic and inorganic nanoparticles to predict their assemblies with biomolecules and other chemical structures forming lock-and-key complexes.

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Fig. 1: The concept of the distance matrix of a protein complex and the introduction of descriptors.
Fig. 2: Analysis of descriptors.
Fig. 3: Construction of distance-based feature matrices for prediction of protein complexes.
Fig. 4: Prediction of protein–protein complexes with the DNN model trained on the GE + GT descriptors.
Fig. 5: The prediction of complexes between protein and nanocarbons.

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Data availability

Our Code Ocean Capsule66 contains all the associated data for PPI training and PPI/protein–NP interaction testing. The source data for Figs. 1–5 and Table 1 are provided with this paper.

Code availability

All Python codes associated with this study are deposited in the Code Ocean capsule66 at https://doi.org/10.24433/CO.7800040.v1.

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Acknowledgements

We thank the University of Michigan College of Engineering for support through the BlueSky Initiative and the University of Michigan for access to the HPC resources of the Great Lakes Cluster. Support from the Vannevar Bush DoD Fellowship to N.A.K. (‘Engineered Chiral Ceramics’ ONR N000141812876, ONR COVID-19 Newton Award ‘Pathways to Complexity with ‘Imperfect’ Nanoparticles’ HQ00342010033 and AFOSR FA9550-20-1-0265 ‘Graph Theory Description of Network Material’) is gratefully acknowledged. X.X. and P.B. gratefully acknowledge the support by the National Science Foundation Career award under grant number CPS/CNS-1453860, the NSF awards under grant numbers CCF-1837131, MCB-1936775, CNS-1932620 and CMMI-1936624, the Okawa Foundation research award, the Defense Advanced Research Projects Agency (DARPA) Young Faculty Award and DARPA Director Award under grant number N66001-17-1-4044, a 2021 USC Stevens Center Technology Advancement Grant (TAG) award, an Intel faculty award and a Northrop Grumman grant. The views, opinions, and/or findings contained in this article are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied by the Defense Advanced Research Projects Agency, the Department of Defense or the National Science Foundation.

Author information

Author notes

  1. These authors contributed equally: Minjeong Cha, Emine Sumeyra Turali Emre.

Authors and Affiliations

  1. Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA

    Minjeong Cha & Nicholas A. Kotov

  2. Biointerfaces Institute, University of Michigan, Ann Arbor, MI, USA

    Minjeong Cha, Emine Sumeyra Turali Emre, Ji-Young Kim, J. Scott VanEpps & Nicholas A. Kotov

  3. Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

    Emine Sumeyra Turali Emre, Ji-Young Kim, Angela Violi & Nicholas A. Kotov

  4. Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA

    Xiongye Xiao & Paul Bogdan

  5. Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

    J. Scott VanEpps & Nicholas A. Kotov

  6. Program in Macromolecular Science and Engineering, University of Michigan, Ann Arbor, MI, USA

    J. Scott VanEpps & Nicholas A. Kotov

  7. Department of Emergency Medicine, University of Michigan, Ann Arbor, MI, USA

    J. Scott VanEpps

  8. Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, MI, USA

    J. Scott VanEpps

  9. Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA

    Angela Violi

  10. Biophysics Program, University of Michigan, Ann Arbor, MI, USA

    Angela Violi

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Contributions

N.A.K conceived the project. M.C., E.S.T.E. and N.A.K. designed the descriptor sets and the workflow. E.S.T.E. collected and curated the protein complex dataset. M.C. analysed the protein complex data and computed the CH, GE and GT descriptors. J.-Y.K. contributed to OPD index calculation. X.X. and P.B. contributed to the computation of MFD in GT features. M.C., X.X. and P.B. designed and trained the DNN model and carried out comparative studies of different ML models. M.C. visualized the analysed data. M.C., E.S.T.E. and N.A.K co-wrote the paper. All authors contributed to data analysis, discussion and writing.

Corresponding author

Correspondence to Nicholas A. Kotov.

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The authors declare no competing interests.

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Nature Computational Science thanks Ning Gu, Häkkinen Hannu and Açelya Yilmazer for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–28, Tables 1–6, Sections 1–3 and References.

Supplementary Data 1

Source data for Table 1.

Source data

Source Data Fig. 1.

Source data for distance matrix of protein 1ma9, chain A and B. Source data for computed feature values per protein (1ma9, chain A) residues.

Source Data Fig. 2.

Source data for correlation plot (a,b). Source data for distribution plot (c–e).

Source Data Fig. 3.

Source data for ML performance plot. Tenfold validation for true-positive rate data for Fig. 3b are included in the folder Fig3b_DNN_TPR_SOURCE.

Source Data Fig. 4.

Source data for true and predicted interface residue number (A and B indicate each chain of a protein in a protein complex).

Source Data Fig. 5.

Source data for predicted interface residues for protein and NPs.

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Cha, M., Emre, E.S.T., Xiao, X. et al. Unifying structural descriptors for biological and bioinspired nanoscale complexes. Nat Comput Sci 2, 243–252 (2022). https://doi.org/10.1038/s43588-022-00229-w

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