MDTOMO: Continuous conformational variability analysis in cryo electron subtomogram data using flexible fitting based on Molecular Dynamics simulations

Abstract

Cryo electron tomography (cryo-ET) allows observing macromolecular complexes in their native environment. The common routine of subtomogram averaging (STA) allows obtaining the three-dimensional (3D) structure of abundant macromolecular complexes, and can be coupled with discrete classification to reveal conformational heterogeneity of the sample. However, the number of complexes extracted from cryo-ET data is usually small, which restricts the discrete-classification results to a small number of enough populated states and, thus, results in a largely incomplete conformational landscape. Alternative approaches are currently being investigated to explore the continuity of the conformational landscapes that in situ cryo-ET studies could provide. In this article, we present MDTOMO, a method for analyzing continuous conformational variability in cryo-ET subtomograms based on Molecular Dynamics (MD) simulations. MDTOMO allows obtaining an atomic-scale model of conformational variability and the corresponding free-energy landscape, from a given set of cryo-ET subtomograms. The article presents the performance of MDTOMO on a synthetic ABC exporter dataset and an in situ SARS-CoV-2 spike dataset. MDTOMO allows analyzing dynamic properties of molecular complexes to understand their biological functions, which could also be useful for structure-based drug discovery.

Computational biophysics meets cryo‐EM revolution in the search for the functional dynamics of biomolecular systems

“…81 An extension of MDSPACE for cryo-ET called MDTOMO has also shown promising results. 200 As previously discussed, the progress made in the computational characterization of physics-based structural dynamics and artificial intelligence methods, has been mutually beneficial for the generation of highly accurate tools designed to predict pathogenicity and uncover functional mechanisms. 201 In the near future, the continuous development of these methods promises to further elucidate the complex nature of biological systems and potentially aid in the design of therapeutic interventions.…”

AFMfit : Deciphering conformational dynamics in AFM data using fast nonlinear NMA and FFT-based search

“…The problem of reconstructing the continuous flexibility of biomolecules, as opposed to discrete states, from heterogeneous 2D views has been widely studied in cryo-EM and has undergone extensive developments in recent years [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. In particular, continuous-state approaches emerged, which intend to treat each single molecule (designated as a particle in the cryo-EM field) as a different conformation and try to place each particle onto a continuous conformational space.…”

“…This space typically contains 10 5 to 10 6 data points corresponding to each particle and gives an overall understanding of the conformational distribution in the sample. Some recent methods use model-based approaches that infer molecular flexibility using computational simulations from an initial atomic model, including MD simulations [21,22,29], the normal mode analysis (NMA) [17,27,28], and Zernike polynomials [25].…”