Iterative Machine Learning for Classification and Discovery of Single-Molecule Unfolding Trajectories from Force Spectroscopy Data

We report the application of machine learning techniques to expedite classification and analysis of protein unfolding trajectories from force spectroscopy data. Using kernel methods, logistic regression, and triplet loss, we developed a workflow called Forced Unfolding and Supervised Iterative Online (FUSION) learning where a user classifies a small number of repeatable unfolding patterns encoded as images, and a machine is tasked with identifying similar images to classify the remaining data. We tested the workflow using two case studies on a multidomain XMod-Dockerin/Cohesin complex, validating the approach first using synthetic data generated with a Monte Carlo algorithm and then deploying the method on experimental atomic force spectroscopy data. FUSION efficiently separated traces that passed quality filters from unusable ones, classified curves with high accuracy, and identified unfolding pathways that were undetected by the user. This study demonstrates the potential of machine learning to accelerate data analysis and generate new insights in protein biophysics

Reorientation of multiple cells on cyclically stretched substrates.

<p>(a) Alignment of 100 individual cells adhered to a substrate which is subjected to a 10% stretch at 1 Hz. The stretch is applied along the horizontal direction and from left to right, the order parameters corresponding to particular time points are: , and . (b, c) Dynamic evolution of the order parameter representing instantaneous cell orientation of 100 cells on the cyclically stretched substrate at different values of straining frequency, amplitude and rotational diffusivity. Each error bar reflects the standard deviation (SD) of 10 independent sets of simulation.</p

The viscoelastic model of a contracting filament.

<p>The structure consisits of a linear spring of stiffness , a dashpot of viscous coefficient in series, and a parallel module of contraction force .</p

Model description.

<p>(a) Illustration of a spindle shaped cell adhered to a substrate subjected to cyclic stretch. The stress fibers (SFs) are largely along the long axis of the cell, anchored at focal adhesions (FAs) near the poles. (b) Schematic drawing of focal adhesions in cell-substrate contact based on specific binding between receptors and complementary ligands. Actin filaments anchor into an adhesion plaque that connects substrate via receptor-ligand bond clusters.</p

The compliance of the bond-substrate system represented by the effective spring constant <i>K</i>.

<p>The receptors actually bind to specific head groups of certain adhesion molecules, such as fibronectin, coated on the substrate surface.</p

Long-time average filament density as a function of the cell orientation angle under different values of stretching amplitude (stretch frequency: 1 Hz).

<p>Long-time average filament density as a function of the cell orientation angle under different values of stretching amplitude (stretch frequency: 1 Hz).</p

Long-time average filament density as a function of the cell orientation angle under a 10% stretch at different frequencies.

<p>(a) . (b) where strain stiffening is not present as the substrate is stretched. (c, d) Effects of (c) strain stiffening and (d) substrate rigidity on for low frequencies (0.05 Hz and 0.001 Hz, respectively).</p

Effects of Poisson's ratio on cell reorientation.

<p>(a) The effective stretching strain acting on each SF as a function of cell orientation angle , influenced by Poisson's ratio of substrate materials. (b, c, d) Long-time average filament density as a function of the cell orientation angle for slow kinetic process () and (b) , (c) and (d) without strain stiffening effects, respectively.</p

Evolutions of the bond and filament densities.

<p>(a) A relatively stiff substrate of . (b) A relatively soft substrate of .</p