We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the four- or five-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine-learning-based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal-mode prediction, high-temperature molecular dynamics, dihedral torsional profile prediction, and even bond breaking. We also demonstrate the smoothness, transferability, and extrapolation capabilities of ACE on a new challenging benchmark data set comprised of a potential energy surface of a flexible druglike molecule.
flii 6-fliJ molecular motor assists with unfolding in the type iii secretion export apparatus Jiri Kucera & eugene M. terentjev ✉ the role of rotational molecular motors of the Atp synthase class is integral to the metabolism of cells. Yet the function of flii 6-fliJ complex, a homolog of the F 1 ATPase motor, within the flagellar export apparatus remains unclear. We use a simple two-state model adapted from studies of linear molecular motors to identify key features of this motor. the two states are the 'locked' ground state where the FliJ coiled coil filament experiences angular fluctuations in an asymmetric torsional potential, and a 'free' excited state in which FliJ undergoes rotational diffusion. Michaelis-Menten kinetics was used to treat transitions between these two states, and obtain the average angular velocity of the unloaded FliJ filament within the FliI 6 stator: ω max ≈ 9.0 rps. The motor was then studied under external counter torque conditions in order to ascertain its maximal power output: P max ≈ 42 k B T/s (or 102 kW/mol), and the stall torque: G stall ≈ 3 k B T/rad (or 0.01 nN•nm/rad). Two modes of action within the flagellar export apparatus are proposed, in which the motor performs useful work either by continuously 'grinding' through the resistive environment of the export gate, or by exerting equal and opposite stall force on it. In both cases, the resistance is provided by flagellin subunits entering the flagellar export channel prior to their unfolding. We therefore propose that the function of the flii 6-fliJ complex is to lower the energy barrier, and therefore assist in unfolding of the flagellar proteins before feeding them into the transport channel. With the advance of imaging techniques, our view of living matter and of its fundamental units, the cells, has changed dramatically. These micron-sized 'bags of chemicals' turned out to be run by complex yet physically describable networks of proteins, lipids, and carbohydrates. The immense number of processes occurring in a cell at any given moment would be unattainable in such a packed environment without a considerable level of organization and reaction catalysis. In general, this is achieved by proteins-long chains of amino acids that come in various sizes and shapes when folded in solution. Their functionality originates from polarity and hydrophobicity of different aminoacids, and is responsible for the unique self-assembly and the resulting properties that range from simple structural support to powerful catalysers. One such class of proteins are the molecular motors 1-3. These large molecular complexes are responsible for organised powered movement within cells and can be characterised by the following properties. They consume energy (usually chemical energy stored in molecules of ATP, or in electrochemical gradient of ions across membranes) and transform it into mechanical work. The energy input is crucial to drive the system out of equilibrium. Another requirement to achieve directed motion is the presence of asymmetry (or br...
We demonstrate that accurate linear force fields can be built using the Atomic Cluster Expansion (ACE) framework for molecules. Our model is built from body ordered symmetric polynomials which makes it a natural extension of traditional molecular mechanics force fields, and the large number of free parameters allows sufficient flexibility that it reaches the accuracy typical of recently proposed machine learning based approaches. We test our model on the MD17 and ISO17 data sets and also on a larger, more flexible molecule, and compare to leading machine learning models as well as refitted empirical force fields. We show that the linear body ordered ACE model has excellent transferability for properties beyond raw energy and force RMSE, both for molecular dynamics at different temperatures and for configurations very far from the training set including dihedral scans and even bond breaking.
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