Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG.

After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ SwarmCG, an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid and bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), which respectively inform on the supra-molecular structure of the lipid bilayer systems and on their submolecular dynamics. In our training sets, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers composed of phosphatidylcholine lipids spanning various tail lengths and degrees of (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ∼80 model parameters within still limited computational budgets, we show that this protocol allows the obtainment of improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine-tuning of the representation and parameters of the models may improve their accuracy and how automatic approaches, such as SwarmCG, may be very useful to this end.

Lessons Learned from Multiobjective Automatic Optimizations of Classical Three-Site Rigid Water Models Using Microscopic and Macroscopic Target Experimental Observables.

The development of accurate water models is of primary importance for molecular simulations. Despite their intrinsic approximations, three-site rigid water models are still ubiquitously used to simulate a variety of molecular systems. Automatic optimization approaches have been recently used to iteratively refine three-site water models to fit macroscopic (average) thermodynamic properties, providing state-of-the-art three-site models that still present some deviations from the liquid water properties. Here, we show the results obtained by automatically optimizing three-site rigid water models to fit a combination of microscopic and macroscopic experimental observables. We use Swarm-CG, a multiobjective particle-swarm-optimization algorithm, for training the models to reproduce the experimental radial distribution functions of liquid water at various temperatures (rich in microscopic-level information on, e.g., the local orientation and interactions of the water molecules). We systematically analyze the agreement of these models with experimental observables and the effect of adding macroscopic information to the training set. Our results demonstrate how adding microscopic-rich information in the training of water models allows one to achieve state-of-the-art accuracy in an efficient way. Limitations in the approach and in the approximated description of water in these three-site models are also discussed, providing a demonstrative case useful for the optimization of approximated molecular models, in general.

Automatic multi-objective optimization of coarse-grained lipid force fields using SwarmCG.

The development of coarse-grained (CG) molecular models typically requires a time-consuming iterative tuning of parameters in order to have the approximated CG models behave correctly and consistently with, e.g., available higher-resolution simulation data and/or experimental observables. Automatic data-driven approaches are increasingly used to develop accurate models for molecular dynamics simulations. However, the parameters obtained via such automatic methods often make use of specifically designed interaction potentials and are typically poorly transferable to molecular systems or conditions other than those used for training them. Using a multi-objective approach in combination with an automatic optimization engine (SwarmCG), here, we show that it is possible to optimize CG models that are also transferable, obtaining optimized CG force fields (FFs). As a proof of concept, here, we use lipids for which we can avail reference experimental data (area per lipid and bilayer thickness) and reliable atomistic simulations to guide the optimization. Once the resolution of the CG models (mapping) is set as an input, SwarmCG optimizes the parameters of the CG lipid models iteratively and simultaneously against higher-resolution simulations (bottom-up) and experimental data (top-down references). Including different types of lipid bilayers in the training set in a parallel optimization guarantees the transferability of the optimized lipid FF parameters. We demonstrate that SwarmCG can reach satisfactory agreement with experimental data for different resolution CG FFs. We also obtain stimulating insights into the precision-resolution balance of the FFs. The approach is general and can be effectively used to develop new FFs and to improve the existing ones.

Exploring the Potential of Benzene-1,3,5-tricarboxamide Supramolecular Polymers as Biomaterials.

The fast dynamics occurring in natural processes increases the difficulty of creating biomaterials capable of mimicking Nature. Within synthetic biomaterials, water-soluble supramolecular polymers show great potential in mimicking the dynamic behavior of these natural processes. In particular, benzene-1,3,5-tricaboxamide (BTA)-based supramolecular polymers have shown to be highly dynamic through the exchange of monomers within and between fibers, but their suitability as biomaterials has not been yet explored. Herein we systematically study the interactions of BTA supramolecular polymers bearing either tetraethylene glycol or mannose units at the periphery with different biological entities. When BTA fibers were incubated with bovine serum albumin (BSA), the protein conformation was only affected by the fibers containing tetraethylene glycol at the periphery (BTA-OEG4). Coarse-grained molecular simulations showed that BSA interacted with BTA-OEG4 fibers rather than with BTA-OEG4 monomers that are present in solution or that may exchange out of the fibers. Microscopy studies revealed that, in the presence of BSA, BTA-OEG4 retained their fiber conformation although their length was slightly shortened. When further incubated with fetal bovine serum (FBS), both long and short fibers were visualized in solution. Nevertheless, in the hydrogel state, the rheological properties were remarkably preserved. Further studies on the cellular compatibility of all the BTA assemblies and mixtures thereof were performed in four different cell lines. A low cytotoxic effect at most concentrations was observed, confirming the suitability of utilizing functional BTA supramolecular polymers as dynamic biomaterials.

Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects.

The application of machine learning in the field of motion capture research is growing rapidly. The purpose of the study is to implement a long-short term memory (LSTM) model able to predict sagittal plane hip joint moment (HJM) across three distinct cohorts (healthy controls, patients and post-operative patients) starting from 3D motion capture and force data. Statistical parametric mapping with paired samples t-test was performed to compare machine learning and inverse dynamics HJM predicted values, with the latter used as gold standard. The results demonstrated favorable model performance on each of the three cohorts, showcasing its ability to successfully generalize predictions across diverse cohorts.

Hip joint contact forces are lower in people with femoroacetabular impingement syndrome during squat tasks.

It remains unknown if hip joint forces during squat tasks are altered in people with femoroacetabular impingement syndrome (FAIS). The aim of this study is to compare hip joint forces between people with FAIS and healthy controls during double leg squat and single leg squat tasks and within limbs during a single leg squat task in people with FAIS. Kinematic and kinetic data were collected in eight people with FAIS and eight healthy matched controls using 3D motion capture and force plates. AnyBody Modeling System was used to perform musculoskeletal simulations to estimate hip joint angles, forces, and moments for all participants. Estimates were postprocessed with AnyPyTools and converted into normalized time series to be compared using a 1D statistical nonparametric mapping (SnPM) approach. SnPM with an independent samples t-test model was used to compare people with FAIS to controls, while a paired samples model was used to compare involved to uninvolved limb in people with FAIS. Patients demonstrated lower proximodistal force compared to controls (p < 0.01) and compared to the uninvolved side (p = 0.01) for single leg squat. The smaller joint contact forces in people with FAIS compared to controls could represent a strategy of reduced muscle forces to avoid pain and symptoms during this high demand task. These findings when combined with imaging data could help assess the severity of FAIS on hip related function during higher demand tasks.