Biology subjects

Filion, L.

Publications and source records attributed to Filion, L..

4 recordsLinked to original sources

bioRxiv2024

Accurate Drift-Invariant Single-Molecule Force Calibration Using the Hadamard Variance

Single-molecule force spectroscopy (SMFS) techniques play a pivotal role in unraveling the mechanics and conformational transitions of biological macromolecules under external forces. Among these techniques, multiplexed magnetic tweezers (MTs) are particularly well suited to probe very small forces, [≤]1 pN, critical for studying non-covalent interactions and regulatory conformational changes at the single-molecule level. However, to apply and measure such small forces, a reliable and accurate force calibration procedure is crucial. Here, we introduce a new approach to calibrate MTs based on thermal motion using the Hadamard variance (HV). To test our method, we develop a bead-tether Brownian dynamics simulation that mimics our experimental system and compare the performance of the HV method against two established techniques: power spectral density (PSD) and Allan variance (AV) analyses. Our analysis includes an assessment of each methods ability to mitigate common sources of additive noise, such as white and pink noise, as well as drift, which often complicate experimental data analysis. Our findings demonstrate that the HV method exhibits overall similar or even higher precision and accuracy, yielding lower force estimation errors across a wide range of signal-to-noise ratios (SNR) and drift speeds compared to the PSD and AV methods. Notably, the HV method remains robust against drift, maintaining consistent uncertainty levels across the entire studied SNR and drift speed spectrum. We also explore the HV method using experimental MT data, where we find overall smaller force estimation errors compared to PSD and AV approaches. Overall, the HV method offers a robust method for achieving sub-pN resolution and precision in multiplexed MT measurements. Its potential extends to other SMFS techniques, presenting exciting opportunities for advancing our understanding of mechano-sensitivity and force generation in biological systems. Therefore, we provide a well-documented Python implementation of the HV method as an extension to the Tweezepy package. Statement of Signi[fi]canceSingle-molecule force spectroscopy techniques are vital for studying the mechanics and conformations of bio-macromolecules under external forces. Multiplexed magnetic tweezers (MTs) excel in applying forces [≤] 1 pN, which are critical for examining non-covalent interactions and regulatory changes at the single-molecule level. Precise and reliable force calibration is essential for these measurements. In this study, we present a new force calibration method for multiplexed MTs using Hadamard variance (HV) based on thermal motion. The HV method shows similar or even higher precision and accuracy to established techniques like power spectral density and Allan variance. Most significantly, it is drift-invariant, maintaining consistent performance across varying experimental conditions. This robustness against drift ensures reliable force application and measurements at sub-pN resolution.

biophysics↗

bioRxiv2024

Protoxylem microtubule patterning requires ROP pattern co-alignment, realistic microtubule-based nucleation, and sufficient microtubule flexibility.

The development of the water transporting xylem tissue in plants involves an intricate interplay of Rho-of-Plants (ROP) proteins and cortical microtubules to generate highly functional secondary cell wall patterns, such as the ringed or spiral patterns in early-developing protoxylem. We study the requirements of protoxylem microtubule band formation with simulations in CorticalSim, extended to include finite microtubule persistence length and a novel algorithm for microtubule-based nucleation. We find that microtubule flexibility is required to facilitate pattern formation for all realistic degrees of mismatch between array and pattern orientation. At the same time, flexibility leads to more density loss, both from collisions and the microtubule-hostile gap regions, making it harder to maintain microtubule bands. Microtubule-dependent nucleation helps to counteract this effect by gradually shifting nucleation from the gap regions to the bands as microtubules disappear from the gaps. Our results reveal the main mechanisms required for efficient protoxylem band formation.

cell biology↗

bioRxiv2024

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning

Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction ([≥]1 kbp) are challenging to address experimentally or by all-atom molecular dynamics simulations, making coarse-grained simulations a natural approach. Here we present a simple and generic coarse-grained model for the DNA-protein and protein-protein interactions, and investigate the role of the latter in the protein-induced compaction of DNA. Our approach models the DNA as a discrete worm-like chain. The proteins are treated in the grand-canonical ensemble and the protein-DNA binding strength is taken from experimental measurements. Protein-DNA interactions are modeled as an isotropic binding potential with an imposed binding valency, without specific assumptions about the binding geometry. To systematically and quantitatively classify DNA-protein complexes, we present an unsupervised machine learning pipeline that receives a large set of structural order parameters as input, reduces the dimensionality via principal component analysis, and groups the results using a Gaussian mixture model. We apply our method to recent data on the compaction of viral genome-length DNA by HIV integrase and we find that protein-protein interactions are critical to the formation of looped intermediate structures seen experimentally. Our methodology is broadly applicable to DNA-binding proteins and to protein-induced DNA compaction and provides a systematic and quantitative approach for analyzing their mesoscale complexes. SIGNIFICANCEDNA is central to the storage and transmission of genetic information and is frequently compacted and condensed by interactions with proteins. Their size and dynamic nature make the resulting complexes difficult to probe experimentally and by all-atom simulations. We present a simple coarse-grained model to explore [~]kbp DNA interacting with proteins of defined valency and concentration. Our analysis uses unsupervised learning to define conformational states of the DNA-protein complexes and pathways between them. We apply our simulations and analysis to the compaction of viral genome-length DNA by HIV integrase. We find that protein-protein interactions are critical to account for the experimentally observed intermediates and our simulated complexes are in good agreement with experimental observations.

biophysics↗

bioRxiv2022

Nucleation complex behaviour is critical for cortical microtubule array homogeneity and patterning

Plant cell walls are versatile materials that can adopt a wide range of mechanical properties through controlled deposition of cellulose fibrils. Wall integrity requires a sufficiently homogeneous fibril distribution to cope effectively with wall stresses. Additionally, specific conditions, such as the negative pressure in water transporting xylem vessels, may require more complex wall patterns, e.g., bands in protoxylem. The orientation and patterning of cellulose fibrils is guided by dynamic cortical microtubules. New microtubules are predominantly nucleated from parent microtubules causing positive feedback on local microtubule density with the potential to yield highly inhomogeneous patterns. Inhomogeneity indeed appears in all current cortical array simulations that include microtubule-based nucleation, suggesting that plant cells must possess an as-yet unknown balancing mechanism to prevent it. Here, in a combined simulation and experimental approach, we show that the naturally limited local recruitment of nucleation complexes to microtubules can counter the positive feedback, whereas local tubulin depletion cannot. We observe that nucleation complexes are preferentially inserted at microtubules. By incorporating our experimental findings in stochastic simulations, we find that the spatial behaviour of nucleation complexes delicately balances the positive feedback, such that differences in local microtubule dynamics - as in developing protoxylem - can quickly turn a homogeneous array into a patterned one. Our results provide insight into how the plant cytoskeleton is wired to meet diverse mechanical requirements and greatly increase the predictive power of computational cell biology studies. Significance statementThe plant cortical microtubule array is an established model system for self-organisation, with a rich history of complementary experiments, computer simulations, and analytical theory. Understanding how array homogeneity is maintained given that new microtubules nucleate from existing microtubules has been a major hurdle for using mechanistic (simulation) models to predict future wall structures. We overcome this hurdle with detailed observations of the nucleation process from which we derive a more "natural" nucleation algorithm. With this algorithm, we enable various new lines of quantitative, mechanistic research into how cells dynamically control their cell wall properties. At a mechanistic level, moreover, this work relates to the theory on cluster coexistence in Turing-like continuum models and demonstrates its relevance for discrete stochastic entities.

plant biology↗