We present a new force field parameter set for simulating alkanes. Its functional form and parameters are chosen to make it directly compatible with the AMBER94/99/12 family of force fields implemented in the available software. The proposed parameterization enables universal description of both the conformational and thermodynamic properties of linear, branched, and cyclic alkanes. Such unification is achieved by using two essential principles: (1) reduction of the Lennard-Jones radius for all sp3 carbons to 1.75Å; (2) separate optimization of Lennard-Jones well depths for carbons with different degree of substitution. The new parameter set may prove to be optimal for description of alkyl residues in a broad range of biomolecules, from amino acids to lipids with their extended linear tails.

A molecular mechanics force field of the AMBER/OPLS family for perfluoroalkanes, noble gases, and their mixtures with alkanes has been proposed. We had to abandon the traditional Lorentz-Berthelot combining rules for the Lennard-Jones potential to be able to uniformly describe these substance classes and their mixtures. Instead, the Waldman-Hagler rules developed for noble gases were used for all of these elements except hydrogen. Hydrogen is considered to be a particular substance to which the usual Lorentz-Berthelot rules are applied. The proposed rules have little effect on the organic chemistry of H, C, N, and O elements but make it compliant with the chemistry of heavy elements. Because of assigning a relatively high partial charge of -0.37e to fluorine atoms, the new force field reproduces the mutual insolubility of higher liquid alkanes and perfluoroalkanes.

Motivation Local protein structure is usually described via classifying each peptide to a unique class from a set of pre-defined structures. These classifications may differ in the number of structural classes, the length of peptides, or class attribution criteria. Most methods that predict the local structure of a protein from its sequence first rely on some classification and only then proceed to the 3D conformation assessment. However, most classification methods rely on homologous proteins’ existence, unavoidably lose information by attributing a peptide to a single class or suffer from a suboptimal choice of the representative classes. Results To alleviate the above challenges, we propose a method that constructs a peptide’s structural representation from the sequence, reflecting its similarity to several basic representative structures. For 5-mer peptides and 16 representative structures, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Our prediction method does not utilize information about protein homologues but relies only on the amino acids’ physicochemical properties and the resolved structures’ statistics. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions under various geometric constraints.

MotivationLocal protein structure is usually described via classifying each peptide to a unique element from a set of pre-defined structures. These so-called structural alphabets may differ in the number of structures or the length of peptides. Most methods that predict the local structure of a protein from its sequence rely on this kind of classification. However, since all peptides assigned to the same class are indistinguishable, such an approach may not be sufficient to model protein folding with high accuracy.ResultsWe developed a method that predicts the structural representation of a peptide from its sequence. For 5-mer peptides, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Importantly, our prediction method does not utilize information about protein homologues but only physicochemical properties of the amino acids and the statistics of the structures, but relies on a comprehensive feature-generation procedure based only on the protein sequence and the statistics of resolved structures. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions for that under various geometric constraints.AvailabilityThe online implementation of the method is provided freely at http://pbpred.eimb.ruContactmilch@eimb.ru or vmilchev@uni-koeln.deSupplementary informationSupplementary data are available online at http://pbpred.eimb.ru/S/index.html