Effective Local and Secondary Protein Structure Prediction by Combining a Neural Network-Based Approach with Extensive Feature Design and Selection without Reliance on Evolutionary Information.

Protein structure prediction continues to pose multiple challenges despite outstanding progress that is largely attributable to the use of novel machine learning techniques. One of the widely used representations of local 3D structure-protein blocks (PBs)-can be treated in a similar way to secondary structure classes. Here, we present a new approach for predicting local conformation in terms of PB classes solely from amino acid sequences. We apply the RMSD metric to ensure unambiguous future 3D protein structure recovery. The selection of statistically assessed features is a key component of the proposed method. We suggest that ML input features should be created from the statistically significant predictors that are derived from the amino acids' physicochemical properties and the resolved structures' statistics. The statistical significance of the suggested features was assessed using a stepwise regression analysis that permitted the evaluation of the contribution and statistical significance of each predictor. We used the set of 380 statistically significant predictors as a learning model for the regression neural network that was trained using the PISCES30 dataset. When using the same dataset and metrics for benchmarking, our method outperformed all other methods reported in the literature for the CB513 nonredundant dataset (for the PBs, Q16 = 81.01%, and for the DSSP, Q3 = 85.99% and Q8 = 79.35%).

Structural coordinates: A novel approach to predict protein backbone conformation.

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.

A new AMBER-compatible force field parameter set for alkanes.

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.

Modification of the CHARMM force field for DMPC lipid bilayer.

The CHARMM force field for DMPC lipids was modified in order to improve agreement with experiment for a number of important properties of hydrated lipid bilayer. The modification consists in introduction of a scaling factor 0.83 for 1-4 electrostatic interactions (between atoms separated by three covalent bonds), which provides correct transgauche ratio in the alkane tails, and recalculation of the headgroup charges on the basis of HF/6-311(d,p) ab-initio computations. Both rigid TIP3P and flexible SPC water models were used with the new lipid model, showing similar results. The new model in a 75 ns simulation has shown a correct value of the area per lipid at zero surface tension, as well as good agreement with the experiment for the electron density, structure factor, and order parameters, including those in the headgroup part of lipids.

New six-site acetonitrile model for simulations of liquid acetonitrile and its aqueous mixtures.

A new six site flexible acetonitrile molecular model is developed. The AMBER force field was used for description of intramolecular parameters, the atomic charges were calculated from a high level ab initio theory and finally the Lennard-Jones parameters were tuned to fit the experimental density and evaporation heat. The obtained in this way model reproduces correctly densities of water-acetonitrile mixtures as well as provides qualitative description of the dielectric permittivity and self-diffusion coefficients.