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Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles

Nature Computational Science volume 3, pages 393–402 (2023)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Although challenging, the accurate and rapid prediction of nanoscale interactions has broad applications for numerous biological processes and material properties. While several models have been developed to predict the interaction of specific biological components, they use system-specific information that hinders their application to more general materials. Here we present NeCLAS, a general and efficient machine learning pipeline that predicts the location of nanoscale interactions, providing human-intelligible predictions. NeCLAS outperforms current nanoscale prediction models for generic nanoparticles up to 10–20 nm, reproducing interactions for biological and non-biological systems. Two aspects contribute to these results: a low-dimensional representation of nanoparticles and molecules (to reduce the effect of data uncertainty), and environmental features (to encode the physicochemical neighborhood at multiple scales). This framework has several applications, from basic research to rapid prototyping and design in nanobiotechnology.

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Fig. 1: Methods and data.
Fig. 2: Predictive performances of different methods.
Fig. 3: Interactions between molecular tweezers and the 14-3-3σ protein.
Fig. 4: Interaction of PSMα1 and GQDs.
Fig. 5: Predicted and simulated interactions of GQDs.

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Data availability

Additional data are available at Deep Blue Data, an open and permanent data repository maintained by the University of Michigan56. This repository contains all raw files too large to include with the paper, including atomic coordinate files, simulations inputs and outputs, and individual pairwise predictions. The source data for Figs. 1–5 are available with this paper.

Code availability

The code used in this work and the relative documentation is available on Code Ocean57. Public releases of the code can be found at https://gitlab.eecs.umich.edu/violigroup/ml/neclas/-/releases/.

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Acknowledgements

The work was supported by the BlueSky Initiative, funded by The University of Michigan College of Engineering (principal investigator A.V.), the Army Research Office MURI (grant no. W911NF-18-1-0240) (A.V.), and the National Science Foundation Graduate Research Fellowship under grant no. 1256260 (J.C.S.). We thank C. Scott for insightful feedback and discussions on ML and C. Luyet for the help with the all-atom simulation of 6C-g3OH. We acknowledge Advanced Research Computing, a division of Information and Technology Services at the University of Michigan, for computational resources and services provided for the research.

Author information

Authors and Affiliations

  1. Chemical Engineering, University of Michigan, Ann Arbor, MI, USA

    Jacob Charles Saldinger & Angela Violi

  2. Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

    Matt Raymond & Angela Violi

  3. Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA

    Paolo Elvati & Angela Violi

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

    Angela Violi

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Contributions

A.V. and P.E. conceived and supervised the project. J.C.S. and P.E. conceived chemical features and representations. M.R. and J.C.S. designed, trained and tested the machine learning models. J.C.S. designed experiments and created the database. P.E. designed and ran the MD simulations. All authors read, revised and approved the paper.

Corresponding author

Correspondence to Angela Violi.

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Nature Computational Science thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editors: Ananya Rastogi and Fernando Chirigati, in collaboration with the Nature Computational Science team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Notes, Discussion, Tables 1–5 and equations 1–7.

Reporting Summary

Source data

Source Data Fig. 1

Fig1_c_source.csv: first two principal components and bead type, Fig1_def_source.csv: RMSD values for protein–protein and protein–nanoparticle datasets.

Source Data Fig. 2

Fig2_a_source.csv: per-complex AUC values for protein–nanoparticle datasets, Fig2_b_source: per-complex AUC for protein–protein dataset with leave-one-out cross-validation, Fig2_c_source: per-complex AUC for protein–protein dataset on Docking Benchmark Dataset split.

Source Data Fig. 3

Fig3_a_source.csv: mean residue predictions, Fig3_b_source.csv: residue predictions and statistics, Fig3_c_source.csv: feature values.

Source Data Fig. 4

Fig4_b_source.csv: residue interaction predictions and ground truth, Fig4_c_source.csv: interaction predictions.

Source Data Fig. 5

Fig5a_d_source.csv: interaction potentials for internal and external beads of g3OH and g3CHO.

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Saldinger, J.C., Raymond, M., Elvati, P. et al. Domain-agnostic predictions of nanoscale interactions in proteins and nanoparticles. Nat Comput Sci 3, 393–402 (2023). https://doi.org/10.1038/s43588-023-00438-x

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