ABSTRACT
Intrinsically disordered proteins and protein regions (IDPs/IDRs - hereafter IDRs) are ubiquitous across all domains of life. Unlike their folded domain counterparts, IDRs sample a diverse ensemble of conformations - rapidly interconverting between heterogeneous states. Analogous to how folded proteins adhere to a sequence-structure-function relationship, IDRs follow a sequence-ensemble-function paradigm. While experimental methods to study the conformational properties of IDRs exist, they can be challenging, time-consuming, and often require specialized equipment and expertise. Recent methodological advances in biophysical modeling offer a unique opportunity to explore sequence ensemble relationships; however, these methods are often limited in throughput and require both software and technical expertise. In this work, we integrated rational sequence design, large-scale molecular simulations, and deep learning to develop ALBATROSS, a deep learning model for predicting IDR ensemble dimensions from sequence. ALBATROSS is lightweight, easy to use, and readily accessible as both a locally-installable software package, as well as a point-and-click style interface in the cloud. We first demonstrate the applicability of our predictors by examining the generalizability of sequence-ensemble relationships in IDRs. Then, we leverage the high-throughput nature of our networks to characterize emergent biophysical behavior of local and global IDR ensemble features across the human proteome.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
ASAPBio statement: This preprint follows the ASAPBio philosophy of communicating and sharing new results at the speed at which they emerge. The results and tools presented in this preprint are robust, but we are continuing to iterate on improving the accuracy and stability of the methods presented and expanding the scope of the analyses our new tools enable. As a result, future versions of this manuscript will report differences in training data, predictor accuracy, and new analyses.
https://github.com/holehouse-lab/supportingdata/tree/master/2023/ALBATROSS_2023