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.

Classifying soft self-assembled materials via unsupervised machine learning of defects.

Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven "defectometer" to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them.

Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects.

Supramolecular fibers composed of monomers that self-assemble directionally via noncovalent interactions are ubiquitous in nature, and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a nontrivial challenge. Here, we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional vs. nondirectional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or from all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies.

A Data-Driven Dimensionality Reduction Approach to Compare and Classify Lipid Force Fields.

Molecular dynamics simulations of all-atom and coarse-grained lipid bilayer models are increasingly used to obtain useful insights for understanding the structural dynamics of these assemblies. In this context, one crucial point concerns the comparison of the performance and accuracy of classical force fields (FFs), which sometimes remains elusive. To date, the assessments performed on different classical potentials are mostly based on the comparison with experimental observables, which typically regard average properties. However, local differences of the structure and dynamics, which are poorly captured by average measurements, can make a difference, but these are nontrivial to catch. Here, we propose an agnostic way to compare different FFs at different resolutions (atomistic, united-atom, and coarse-grained), by means of a high-dimensional similarity metrics built on the framework of Smooth Overlap of Atomic Position (SOAP). We compare and classify a set of 13 FFs, modeling 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayers. Our SOAP kernel-based metrics allows us to compare, discriminate, and correlate different FFs at different model resolutions in an unbiased, high-dimensional way. This also captures differences between FFs in modeling nonaverage events (originating from local transitions), for example, the liquid-to-gel phase transition in dipalmitoylphosphatidylcholine (DPPC) bilayers, for which our metrics allows us to identify nucleation centers for the phase transition, highlighting some intrinsic resolution limitations in implicit versus explicit solvent FFs.

Swarm-CG: Automatic Parametrization of Bonded Terms in MARTINI-Based Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization.

We present Swarm-CG, a versatile software for the automatic iterative parametrization of bonded parameters in coarse-grained (CG) models, ideal in combination with popular CG force fields such as MARTINI. By coupling fuzzy self-tuning particle swarm optimization to Boltzmann inversion, Swarm-CG performs accurate bottom-up parametrization of bonded terms in CG models composed of up to 200 pseudo atoms within 4-24 h on standard desktop machines, using default settings. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of complex molecular systems interesting for bio- and nanotechnology. Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity, and size. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Demonstration data are available at: www.github.com/GMPavanLab/SwarmCG.

Correction: Facile synthesis of stable, water soluble, dendron-coated gold nanoparticles.

Correction for 'Facile synthesis of stable, water soluble, dendron-coated gold nanoparticles' by Alan E. Enciso, et al., Nanoscale, 2017, 9, 3128-3132.

Site accessibility tailors DNA cleavage by restriction enzymes in DNA confined monolayers.

Density-tunable nanografted monolayers (NAMs) of short oligonucleotide sequences on gold surfaces show novel properties that make them suitable for advanced biosensing applications, and in particular to study the effects of crowding and confinement on biomolecular interactions. Here, combining atomic force microscopy nanolithography, topography measurements and coarse-grained molecular dynamics simulations, we investigated restriction enzyme reaction mechanisms within confined DNA brushes highlighting the role played by the DNA sequence conformation and restriction site position along the chain, respectively, in determining the accessibility of the enzyme, and its consequent cleavage efficiency.

Facile synthesis of stable, water soluble, dendron-coated gold nanoparticles.

Upon reduction with sodium borohydride, diazonium tetrachloroaurate salts of triazine dendrons yield dendron-coated gold nanoparticles connected by a gold-carbon bond. These robust nanoparticles are stable in water and toluene solutions for longer than one year and present surface groups that can be reacted to change surface chemistry and manipulate solubility. Molecular modeling was used to provide insight on the hydration of the nanoparticles and their observed solubilties.

Pressure-induced conformation transition of o-phenylene solvated in bulk hydrocarbons.

The conformational behavior of o-phenylene 8-mers and 10-mers solvated in a series of linear alkane solvents by means of classical molecular dynamics and first-principles calculations was studied. Irrespective of the solvent used, we find that at ambient pressure the molecule sits in the well-defined close-helical arrangement previously observed in light polar solvents. However, for pressures greater than 50 atm, and for tetradecane or larger solvent molecules, our simulations predict that o-phenylene undergoes a conformational transition to an uncoiled, extended geometry with a 35% longer head-to-tail distance and a much larger overlap between its lateral aromatic ring groups. The free energy barrier for the transition was studied as a function of pressure and temperature for both solute molecules in butane and hexadecane. Gas-phase density functional theory-based nudged elastic band calculations on 8-mer and 10-mer o-phenylene were used to estimate how the pressure-induced transition energy barrier changes with solute length. Our results indicate that a sufficiently large solvent molecule size is the key factor enabling a configuration transition upon pressure changes and that longer solute molecules associate with higher conformation transition energy barriers. This suggests the possibility of designing systems in which a solute molecule can be selectively "activated" by a controlled conformation transition achieved at a predefined set of pressure and temperature conditions.

Structural and energetic basis for hybridization limits in high-density DNA monolayers.

High-density monolayers (HDMs) of single-strand (ss) DNA are important nanoscale platforms for the fabrication of sensors and for mechanistic studies of enzymes on surfaces. Such systems can be used, for example, to monitor gene expression, and for the construction of more complex nanodevices via selective hybridization with the complementary oligos dissolved in solution. In this framework, controlling HDM hybridization is essential to control the final properties. Different studies demonstrate that at the typical density of ≈10(13) molecules per cm(2) no more than ≈30-40% of the HDM ssDNA is successfully hybridized. Until now, however, the origin of the HDM hybridization limit has remained unclear. In this work, molecular dynamics (MD) simulations of HDM systems with variable hybridization reveal that, independently of other experimental parameters, the effective hybridization for a HDM of this density is intrinsically limited by molecular and electrostatic crowding. A detailed structural analysis of the HDM model shows good agreement with our atomic force microscopy (AFM) experiments, and provides further insight into the steric hindrance behaviour and time-resolved surface topography of these nanostructured systems. The explicit relationship proposed between structural crowding and limited HDM hybridization offers a rationale to control the final properties of HDM-based nanodevices.

Generation-dependent molecular recognition controls self-assembly in supramolecular dendron-virus complexes.

In this work molecular dynamics simulation identifies a clear link between the dendron-virus multivalent molecular recognition and the nature of the consequent self-assembly. Data demonstrate how a weak hydrophobic association is transformed in an electrostatic self-assembly, orders of magnitude stronger, depending on the dendron generation used to assemble the viruses. This opens a new frontier in the engineering of hierarchical self-assemblies, potentially enabling the control of the supramolecular properties by acting at the single-molecule level.