Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) ...

Collision-induced dissociation mass spectrometry (CID-MS) is an important tool in analytical chemistry for the structural elucidation of unknown compounds. The theoretical prediction of the CID spectra plays a critical role in supporting and accelerating this process. To this end, we adapt the recently developed QCxMS2 program originally designed for the calculation of electron ionization (EI) spectra to enable the computation of CID-MS. To account for the fragmentation conditions characteristic of CID within the automated reaction network discovery approach of QCxMS2 we adapted the internal energy distribution to match the experimental conditions. This distribution can be adjusted via a single parameter to approximate various activation settings, thereby eliminating the need for explicit simulations of the collisional process. We evaluate our approach on a test set of 13 organic molecules with diverse functional groups, compiled specifically for this study. All reference spectra were recorded consistently under the same measurement conditions, including both CID and higher-energy collisional dissociation (HCD) modes. Overall, QCxMS2 achieves a good average entropy similarity score (ESS) of 0.687 for the HCD spectra and 0.773 for the CID spectra. The direct comparison to experimental data demonstrates that the QCxMS2 approach, even without explicit modeling of collisions, is generally capable of computing both CID and HCD spectra with reasonable accuracy and robustness. This highlights its potential as a valuable tool for integration into structure elucidation workflows in analytical mass spectrometry.

Protein folding remains a formidable challenge despite significant advances, particularly in sequence-to-structure prediction. Accurately capturing thermodynamics and intermediates via simulations dem...

The Martini model is a coarse-grained force field allowing simulations of biomolecular systems as well as a range of materials including different types of nanomaterials of technological interest. Recently, a new version of the force field (version 3) has been released that includes new parameters for lipids, proteins, carbohydrates, and a number of small molecules, but not yet carbon nanomaterials. Here, we present new Martini models for three major types of carbon nanomaterials: fullerene, carbon nanotubes, and graphene. The new models were parametrized within the Martini 3 framework, and reproduce semiquantitatively a range of properties for each material. In particular, the model of fullerene yields excellent solid-state properties and good properties in solution, including correct trends in partitioning between different solvents and realistic translocation across lipid membranes. The models of carbon nanotubes reproduce the atomistic behavior of nanotube porins spanning lipid bilayers. The model of graphene reproduces structural and elastic properties, as well as trends in experimental adsorption enthalpies of organic molecules. All new models can be used in large-scale simulations to study the interaction with the wide variety of molecules already available in the Martini 3 force field, including biomolecular and synthetic systems.

Senior Computational Biophysicist and Chemist, Software Engineer, and Medical Statistician. I build novel chemical software to solve protein-disease models. - acnash

This Collection aims to highlight research that advances our understanding of electronic structure and chemical dynamics, as well as the application of ...

Gaussian Accelerated Molecular Dynamics (GaMD) is a computational method for both unconstrained enhanced sampling and free energy calculations of biomolecules. It works by adding a harmonic boost p...

Why automated virtual screening and docking remains hard and why expertise remains essential

Classical empirical force fields have dominated biomolecular simulations for over 50 years. Although widely used in drug discovery, crystal structure prediction, and biomolecular dynamics, they generally lack the accuracy and transferability required for first-principles predictive modeling. In this paper, we introduce MACE-OFF, a series of short-range transferable force fields for organic molecules created using state-of-the-art machine learning technology and first-principles reference data computed with a high level of quantum mechanical theory. MACE-OFF demonstrates the remarkable capabilities of short-range models by accurately predicting a wide variety of gas- and condensed-phase properties of molecular systems. It produces accurate, easy-to-converge dihedral torsion scans of unseen molecules as well as reliable descriptions of molecular crystals and liquids, including quantum nuclear effects. We further demonstrate the capabilities of MACE-OFF by determining free energy surfaces in explicit solvent as well as the folding dynamics of peptides and nanosecond simulations of a fully solvated protein. These developments enable first-principles simulations of molecular systems for the broader chemistry community at high accuracy and relatively low computational cost.