Structure-Based Modeling and Machine Learning to Study Dynamic Asymmetry of ClpB Disaggregase Nanomachine

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Thaddeus Weidle
Ashan Dayananda
George Stan


By Thaddeus Weidle, Chemistry; Ashan Dayananda, University of Cincinnati

Advisor: George Stan

Presentation ID: 228

Abstract: Following thermodynamic principles, proteins fold into unique, native, conformations, allowing them to interact with substrates or other chemical compounds in a specific manner. Misfolding of a protein can therefore yield defective conformations, which may result in the creation of non-functioning or insoluble biomolecules. To ensure cell survival, the HSP100/Clp family mediates protein quality control via protein degradation or disaggregation. ClpB (Caseinolytic Peptidase B) is a homohexameric protein containing two conserved AAA+ modules (ATPases Associated with diverse cellular Activities) that undergo large conformational changes driven by ATP hydrolysis. Recent biochemical studies showed that these nanomachines exhibit a non-planar organization of the protomers rather than a fully symmetric ring. The study of the structure and function of ClpB is therefore important as a means of understanding the biological mechanisms which may assist in remediation of misfolded proteins. To this end, we performed molecular dynamics simulations using the SMOG2 all-atom, coarse-grained, structure-based model, to elucidate the dynamic asymmetry of ClpB. Coarse-grained modeling carries benefits over "all-atom" modeling. It is not only faster and less computationally intensive, but also gives the benefit of "smoothing" the energy landscape of a simulation. Smoothing allows the simulation to seek the ideal "global energy minima", increasing the accuracy of conformational results. Our Machine learning methods involved Principal Component Analysis (PCA) of the hexamer, which indicated rigid-body domain motions that elucidate pore-opening and torsional events. Consistent with PCA, the Dynamic Cross-Correlation Matrix (DCCM) analysis probed the coupling of both inter and intra subunits of ClpB.

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Category: Digital Futures