PT  - JOURNAL ARTICLE
AU  - Rajeswari Appadurai
AU  - Jaya Krishna Koneru
AU  - Massimiliano Bonomi
AU  - Paul Robustelli
AU  - Anand Srivastava
TI  - Demultiplexing the heterogeneous conformational ensembles of intrinsically disordered proteins into structurally similar clusters
AID  - 10.1101/2022.11.11.516231
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.11.11.516231
4099  - http://biorxiv.org/content/early/2022/12/10/2022.11.11.516231.short
4100  - http://biorxiv.org/content/early/2022/12/10/2022.11.11.516231.full
AB  - 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.Competing Interest StatementThe authors have declared no competing interest.