First-principles molecular structure search with a genetic algorithm

The identification of low-energy conformers for a given molecule is a fundamental problem in computational chemistry and cheminformatics. We assess here a conformer search that employs a genetic algorithm for sampling the low-energy segment of the conformation space of molecules. The algorithm is designed to work with first-principles methods, facilitated by the incorporation of local optimization and blacklisting conformers to prevent repeated evaluations of very similar solutions. The aim of the search is not only to find the global minimum, but to predict all conformers within an energy window above the global minimum. The performance of the search strategy is: (i) evaluated for a reference data set extracted from a database with amino acid dipeptide conformers obtained by an extensive combined force field and first-principles search and (ii) compared to the performance of a systematic search and a random conformer generator for the example of a drug-like ligand with 43 atoms, 8 rotatable bonds and 1 cis/trans bond

The Conformational Space of a Flexible Amino Acid at Metallic Surfaces

In interfaces between inorganic and biological materials relevant for technological applications, the general challenge of structure determination is exacerbated by the high flexibility of bioorganic components, chemical bonding, and charge rearrangement at the interface. In this paper, we investigate a chemically complex building block, namely, the arginine (Arg) amino-acid interfaced with Cu, Ag and Au (111) surfaces. We investigate how the environment changes the accessible conformational space of this amino acid, by building and analyzing a database of thousands of structures optimized with the PBE functional including screened pairwise van der Waals interactions. When in contact with metallic surfaces, the accessible space for Arg is dramatically reduced, while the one for Arg-H$^+$ is instead increased if compared to the gas-phase. This is explained by the formation of strong bonds between Arg and the surfaces and by their absence and charge screening on Arg-H$^+$ upon adsorption. We also observe protonation-dependent stereoselective binding of the amino acid to the metal surfaces: Arg adsorbs with its chiral C$_\alpha$H center pointing H away from the surfaces while Arg-H$^+$ adsorbs with H pointing toward the surface

Kinetically Trapped Liquid-State Conformers of a Sodiated Model Peptide Observed in the Gas Phase

We investigate the peptide AcPheAla5LysH+, a model system for studying helix formation in the gas phase, in order to fully understand the forces that stabilize the helical structure. In particular, we address the question of whether the local fixation of the positive charge at the peptide's C-terminus is a prerequisite for forming helices by replacing the protonated C-terminal Lys residue by Ala and a sodium cation. The combination of gas-phase vibrational spectroscopy of cryogenically cooled ions with molecular simulations based on density-functional theory (DFT) allows for detailed structure elucidation. For sodiated AcPheAla6, we find globular rather than helical structures, as the mobile positive charge strongly interacts with the peptide backbone and disrupts secondary structure formation. Interestingly, the global minimum structure from simulation is not present in the experiment. We interpret that this is due to high barriers involved in re-arranging the peptide-cation interaction that ultimately result in kinetically trapped structures being observed in the experiment.Comment: 28 pages, 10 figure

System-specific parameter optimization for non-polarizable and polarizable force fields

The accuracy of classical force fields (FFs) has been shown to be limited for the simulation of cation-protein systems despite their importance in understanding the processes of life. Improvements can result from optimizing the parameters of classical FFs or by extending the FF formulation by terms describing charge transfer and polarization effects. In this work, we introduce our implementation of the CTPOL model in OpenMM, which extends the classical additive FF formula by adding charge transfer (CT) and polarization (POL). Furthermore, we present an open-source parameterization tool, called FFAFFURR that enables the (system specific) parameterization of OPLS-AA and CTPOL models. The performance of our workflow was evaluated by its ability to reproduce quantum chemistry energies and by molecular dynamics simulations of a Zinc finger protein.Comment: 62 pages and 25 figures (including SI), manuscript to be submitted soo

How Cations Change Peptide Structure

Specific interactions between cations and proteins have a strong impact on peptide and protein structure. We here shed light on the nature of the underlying interactions, especially regarding the effects on the polyamide backbone structure. To do so, we compare the conformational ensembles of model peptides in isolation and in the presence of either Li+ or Na+ cations by state-of-the-art density-functional theory (including van der Waals effects) and gas-phase infrared spectroscopy. These monovalent cations have a drastic effect on the local backbone conformation of turn-forming peptides, by disruption of the H bonding networks and the resulting severe distortion of the backbone conformations. In fact, Li+ and Na+ can even have different conformational effects on the same peptide. We also assess the predictive power of current approximate density functionals for peptide-cation systems and compare to results from established protein force fields as well as to high-level quantum chemistry (CCSD(T)).Comment: 30 pages, 7 figure

Mapping and classifying molecules from a high-throughput structural database

High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from computational searches, as well as the agglomeration of data of heterogeneous provenance leads to considerable challenges when it comes to navigating the database, representing its structure at a glance, understanding structure-property relations, eliminating duplicates and identifying inconsistencies. Here we present a case study, based on a data set of conformers of amino acids and dipeptides, of how machine-learning techniques can help addressing these issues. We will exploit a recently-developed strategy to define a metric between structures, and use it as the basis of both clustering and dimensionality reduction techniques-showing how these can help reveal structure-property relations, identify outliers and inconsistent structures, and rationalise how perturbations (e.g. binding of ions to the molecule) affect the stability of different conformers