Exploring the dynamics of flagellar dynein within the axoneme with Fluctuating Finite Element Analysis

Abstract

Flagellar dyneins are the molecular motors responsible for producing the propagating bending motions of cilia and flagella. They are located within a densely packed and highly organised super-macromolecular cytoskeletal structure known as the axoneme. Using the mesoscale simulation technique Fluctuating Finite Element Analysis (FFEA), which represents proteins as viscoelastic continuum objects subject to explicit thermal noise, we have quantified the constraints on the range of molecular conformations that can be explored by dynein-c within the crowded architecture of the axoneme. We subsequently assess the influence of crowding on the 3D exploration of microtubule-binding sites, and specifically on the axial step length. Our calculations combine experimental information on the shape, flexibility and environment of dynein-c from three distinct sources; negative stain electron microscopy, cryo-electron microscopy (cryo-EM) and cryo-electron tomography (cryo-ET). Our FFEA simulations show that the super-macromolecular organisation of multiple protein complexes into higher-order structures can have a significant influence on the effective flexibility of the individual molecular components, and may, therefore, play an important role in the physical mechanisms underlying their biological function.

A new visual design language for biological structures in a cell

“…Studies have used FFEA (fluctuating finite element analysis) to represent proteins as continuum viscoelastic solids, essentially a volumetric rendering using an irregular tetrahedron (Richardson et al, 2014(Richardson et al, , 2020Lee et al, 2019;Hanson et al, 2021). Tetrahedra also have been used in modeling biological systems, such as multiscale mechano-biological finite element modeling of oncoplastic breast surgery (Vavourakis et al, 2016) and multiphysics whole-heart modeling (Gerach et al, 2021).…”

From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules

“…The absence of an atomistic level of detail clearly sets a dramatically large lower bound to the length scales achievable by FFEA; on the other hand, this method represents a promising opportunity for pushing the analysis of biological systems to truly meso-to macroscopic scales. Originally applied to the prediction of the dynamics of globular proteins close to their native states ( Oliver et al, 2013 ), FFEA was later employed to analyze the behavior of complex macromolecular systems such as molecular chaperones ( Solernou et al, 2018 ), conformational transition of molecular motors ( Richardson et al, 2014 ; Hanson et al, 2015 ; Richardson et al, 2020 ; Hanson et al, 2021 ), and the effect of the application of stretching and torsional forces on the structural stability of antibodies ( van der Heijden et al, 2020 ).…”

Intelligent Resolution: Integrating Cryo-EM with AI-driven Multi-resolution Simulations to Observe the SARS-CoV-2 Replication-Transcription Machinery in Action

“…Therefore, FFEA uses a unique "top-down" rather than the more conventional "bottom-up" coarse-graining strategy, by introducing nanoscale thermal uctuations into macroscopic continuum mechanics equations. FFEA also includes functionality to exert external forces on proteins, to connect proteins together with harmonic springs and to represent conformational changes between distinct protein conformational states, for example between the pre-and post-powerstroke states of molecular motors (Richardson et al, 2020). FFEA has been used to successfully model diverse biological systems, including the rotary ATPase motor (Richardson et al, 2014), axonemal (Richardson et al, 2020) and cytoplasmic (Hanson et al, 2021) dynein motors, and protein antibodies subjected to external forces (van der Heijden et al, 2020).…”