Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Chen, Jianfang; Gou, Qiaoling; Chen, Xin; Song, Yuanpeng; Zhang, Fuhui; Pu, Xuemei

doi:10.1039/d3cp05050e

Phys. Chem. Chem. Phys.

2024

DOI: 10.1039/d3cp05050e

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Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Jianfang Chen,

Qiaoling Gou,

Xin Chen

et al.

Abstract: Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is...

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Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies

Cecil,

Sogues,

Gurumurthi

et al. 2024

PNAS Nexus

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Nanobody-induced disassembly of Sap S-layers, a two-dimensional paracrystalline protein lattice from Bacillus anthracis, has been presented as a therapeutic intervention for lethal anthrax infections. However, only a subset of existing nanobodies with affinity to Sap exhibit depolymerization activity, suggesting that affinity and epitope recognition are not enough to explain inhibitory activity. In this study, we performed all-atom molecular dynamics simulations of each nanobody bound to the Sap binding site and trained a collection of machine learning classifiers to predict whether each nanobody induces depolymerization. We used feature importance analysis to filter out unnecessary features and engineered remaining features to regularize the feature landscape and encourage learning of the depolymerization mechanism. We find that, while not enforced in training, a gradient-boosting decision tree is able to reproduce the experimental activities of inhibitory nanobodies while maintaining high classification accuracy, whereas neural networks were only able to discriminate between classes. Further feature analysis revealed that inhibitory nanobodies restrain Sap motions toward an inhibitory conformational state described by domain-domain clamping and induced twisting of domains normal to the lattice plane. We believe these motions drive Sap lattice depolymerization and can be used as design targets for improved Sap-inhibitory nanobodies. Finally, we expect our method of study to apply to S-layers that serve as virulence factors in other pathogens, paving the way forward for nanobody therapeutics that target depolymerization mechanisms.

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Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies

Cecil,

Sogues,

Gurumurthi

et al. 2024

PNAS Nexus

View full text Add to dashboard Cite

show abstract

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Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Abstract: Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is...

Cited by 1 publication

References 88 publications

Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies

Molecular dynamics and machine learning stratify motion-dependent activity profiles of S-layer destabilizing nanobodies

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