BiochemistryBiophysicsPeer-reviewed journal article
A major disease-related point mutation in spastin alters dramatically the dynamics and allostery of the motor
Shehani KahawatteiD, Amanda C. MackeiD, Carter St. Clair, Ruxandra I. DimaiD
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Abstract
Spastin is a microtubule-severing AAA+ ATPase that is highly expressed in neu-ronal cells and plays a crucial role in axonal growth, branching, and regeneration. This machine oligomerizes into hexamers in the presence of ATP and the microtubule carboxy-terminal tails (CTTs). Conformational changes in spastin hexamers, pow-ered by ATP hydrolysis, apply forces on the microtubule, ultimately leading to the severing of the filament. Mutations disrupt the normal function of spastin, impair-ing its ability to sever microtubules effectively and leading to abnormal microtubule dynamics in neurons characteristic for the set of neurodegenerative disorders called hereditary spastic paraplegias (HSP). Experimental studies have identified the HSP-related R591S (Drosophila melanogaster numbering) mutation as playing a crucial role in spastin. Given its significant role in HSP, we employed a combination of molecular dynamics simulations with machine learning and graph network based approaches to identify and quantify the perturbations caused by the R591S HSP mutation on spastins dynamics and allostery with functional implications. We found that the functional hex-amer, upon the HSP-related mutation, loses the ability to execute the primary motion associated with the severing action. The study of allosteric changes upon the mutation showed that the regions that are most perturbed are those involved in the formation of the inter-protomer contacts. The mutation induces rigidity in the allosteric networks of the motor making it more likely to experience loss of function as any applied per-turbations could not be easily dissipated by passing through a variety of alternative paths as in the wild-type (WT) spastin.
- organism: Drosophila
- assay: protein structure
- research focus: protein folding & dynamics
- approach: deep learning
- approach: graph neural network
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