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Abstract Proteins are inherently dynamic, and their conformational ensembles are functionally important in biology. Large-scale motions may govern protein structure–function relationship, and numerous transient but stable conformations of intrinsically disordered proteins (IDPs) can play a crucial role in biological function. Investigating conformational ensembles to understand regulations and disease-related aggregations of IDPs is challenging both experimentally and computationally. In this paper we first introduced an unsupervised deep learning-based model, termed Internal Coordinate Net (ICoN), which learns the physical principles of conformational changes from molecular dynamics (MD) simulation data. Second, we selected interpolating data points in the learned latent space that rapidly identify novel synthetic conformations with sophisticated and large-scale sidechains and backbone arrangements. Third, with the highly dynamic amyloid-β1-42(Aβ42) monomer, our deep learning model provided a comprehensive sampling of Aβ42’s conformational landscape. Analysis of these synthetic conformations revealed conformational clusters that can be used to rationalize experimental findings. Additionally, the method can identify novel conformations with important interactions in atomistic details that are not included in the training data. New synthetic conformations showed distinct sidechain rearrangements that are probed by our EPR and amino acid substitution studies. This approach is highly transferable and can be used for any available data for training. The work also demonstrated the ability for deep learning to utilize learned natural atomistic motions in protein conformation sampling.
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Proteasomal conformation controls unfolding ability
The 26S proteasome is the macromolecular machine responsible for the bulk of protein degradation in eukaryotic cells. As it degrades a ubiquitinated protein, the proteasome transitions from a substrate-accepting conformation (s1) to a set of substrate-processing conformations (s3 like), each stabilized by different intramolecular contacts. Tools to study these conformational changes remain limited, and although several interactions have been proposed to be important for stabilizing the proteasome’s various conformations, it has been difficult to test these directly under equilibrium conditions. Here, we describe a conformationally sensitive Förster resonance energy transfer assay, in which fluorescent proteins are fused to Sem1 and Rpn6, which are nearer each other in substrate-processing conformations than in the substrate-accepting conformation. Using this assay, we find that two sets of interactions, one involving Rpn5 and another involving Rpn2, are both important for stabilizing substrate-processing conformations. Mutations that disrupt these interactions both destabilize substrate-processing conformations relative to the substrate-accepting conformation and diminish the proteasome’s ability to successfully unfold and degrade hard-to-unfold substrates, providing a link between the proteasome’s conformational state and its unfolding ability.
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- Award ID(s):
- 1935596
- PAR ID:
- 10249815
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 118
- Issue:
- 25
- ISSN:
- 0027-8424
- Page Range / eLocation ID:
- Article No. e2101004118
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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