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Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters

Rajeswari Appadurai, Jaya Krishna Koneru, Massimiliano Bonomi, Paul Robustelli, Anand Srivastava
doi: https://doi.org/10.1101/2022.11.11.516231
Rajeswari Appadurai
1Molecular Biophysics Unit, Indian Institute of Science Bangalore, C. V. Raman Road, Bangalore, Karnataka 560012, India
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Jaya Krishna Koneru
2Dartmouth College, Department of Chemistry, Hanover, NH, 03755, USA
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Massimiliano Bonomi
3Structural Bioinformatics Unit, Department of Structural Biology and Chemistry. CNRS UMR 3528, C3BI, CNRS USR 3756, Institut Pasteur, Paris, France
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Paul Robustelli
2Dartmouth College, Department of Chemistry, Hanover, NH, 03755, USA
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Anand Srivastava
1Molecular Biophysics Unit, Indian Institute of Science Bangalore, C. V. Raman Road, Bangalore, Karnataka 560012, India
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  • For correspondence: anand@iisc.ac.in
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Abstract

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into “structurally similar” clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and a C-terminal fragment of α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered sub-states within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information and provide interpretable visualizations of the conformational heterogeneity within each ensemble and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.

Significance Grouping heterogeneous conformations of IDPs into “structurally similar” clusters facilitates a clearer understanding of the properties of IDP conformational ensembles and provides insights into ”structural ensemble: function” relationships. In this work, we provide a unique approach for clustering IDP ensembles efficiently using a non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), to create clusters with structurally similar IDP conformations. We show how this can be used for meaningful biophysical analyses such as understanding the binding mechanisms of IDPs such as α-synuclein and Amyloid β42 with small drug molecules.

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Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Polished the draft further, and added kinetics data and transition probabilities analyses.

  • https://github.com/codesrivastavalab/tSNE-IDPClustering

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 10, 2022.
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Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters
Rajeswari Appadurai, Jaya Krishna Koneru, Massimiliano Bonomi, Paul Robustelli, Anand Srivastava
bioRxiv 2022.11.11.516231; doi: https://doi.org/10.1101/2022.11.11.516231
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Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters
Rajeswari Appadurai, Jaya Krishna Koneru, Massimiliano Bonomi, Paul Robustelli, Anand Srivastava
bioRxiv 2022.11.11.516231; doi: https://doi.org/10.1101/2022.11.11.516231

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