12,157 research outputs found
Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics
We present the ProCS method for the rapid and accurate prediction of protein
backbone amide proton chemical shifts - sensitive probes of the geometry of key
hydrogen bonds that determine protein structure. ProCS is parameterized against
quantum mechanical (QM) calculations and reproduces high level QM results
obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is
interfaced with the PHAISTOS protein simulation program and is used to infer
statistical protein ensembles that reflect experimentally measured amide proton
chemical shift values. Such chemical shift-based structural refinements,
starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN
Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and
trans-hydrogen bond (h3JNC') spin-spin coupling constants that are in excellent
agreement with experiment. We show that the structural sensitivity of the
QM-based amide proton chemical shift predictions is needed to refine protein
structures to this agreement. The ProCS method thus offers a powerful new tool
for refining the structures of hydrogen bonding networks to high accuracy with
many potential applications such as protein flexibility in ligand binding.Comment: PLOS ONE accepted, Nov 201
Modelling of potentials for interparticle interactions between methanol molecules
Peculiarities of interparticle interactions between methanol molecules in the
methanol vapor are investigated. The bare potential is considered as a sum of
repulsive, dispersive and electrostatic forces. It is supposed that H-bond is
of electrostatic nature (the irreducible contribution caused by overlapping of
electronic shells is unessential). The dispersive interaction is approximated
with London's formula, the electrostatic interaction is modelled by a multipole
expansion up to dipole-octupole contribution. The multipole moments are assumed
to be equal to their experimental values or to quantum chemical calculations.
The repulsion is modelled by power potential, whose parameters are fitted to
the second virial coefficient and to the parameters of dimers. Along with the
bare potential, the averaged potential of interparticle interaction is
analyzed. It is shown that the repulsive potential has an exponent . The
multipole potential, presented in this paper, is scrupulously compared with the
potential known as the OPLS.Comment: 10 pages, 4 figures, 8 table
Thermodynamics of liquids: standard molar entropies and heat capacities of common solvents from 2PT molecular dynamics
We validate here the Two-Phase Thermodynamics (2PT) method for calculating the standard molar entropies and heat capacities of common liquids. In 2PT, the thermodynamics of the system is related to the total density of states (DoS), obtained from the Fourier Transform of the velocity autocorrelation function. For liquids this DoS is partitioned into a diffusional component modeled as diffusion of a hard sphere gas plus a solid component for which the DoS(υ) → 0 as υ → 0 as for a Debye solid. Thermodynamic observables are obtained by integrating the DoS with the appropriate weighting functions. In the 2PT method, two parameters are extracted from the DoS self-consistently to describe diffusional contributions: the fraction of diffusional modes, f, and DoS(0). This allows 2PT to be applied consistently and without re-parameterization to simulations of arbitrary liquids. We find that the absolute entropy of the liquid can be determined accurately from a single short MD trajectory (20 ps) after the system is equilibrated, making it orders of magnitude more efficient than commonly used perturbation and umbrella sampling methods. Here, we present the predicted standard molar entropies for fifteen common solvents evaluated from molecular dynamics simulations using the AMBER, GAFF, OPLS AA/L and Dreiding II forcefields. Overall, we find that all forcefields lead to good agreement with experimental and previous theoretical values for the entropy and very good agreement in the heat capacities. These results validate 2PT as a robust and efficient method for evaluating the thermodynamics of liquid phase systems. Indeed 2PT might provide a practical scheme to improve the intermolecular terms in forcefields by comparing directly to thermodynamic properties
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods
Feature extraction and dimensionality reduction are important tasks in many
fields of science dealing with signal processing and analysis. The relevance of
these techniques is increasing as current sensory devices are developed with
ever higher resolution, and problems involving multimodal data sources become
more common. A plethora of feature extraction methods are available in the
literature collectively grouped under the field of Multivariate Analysis (MVA).
This paper provides a uniform treatment of several methods: Principal Component
Analysis (PCA), Partial Least Squares (PLS), Canonical Correlation Analysis
(CCA) and Orthonormalized PLS (OPLS), as well as their non-linear extensions
derived by means of the theory of reproducing kernel Hilbert spaces. We also
review their connections to other methods for classification and statistical
dependence estimation, and introduce some recent developments to deal with the
extreme cases of large-scale and low-sized problems. To illustrate the wide
applicability of these methods in both classification and regression problems,
we analyze their performance in a benchmark of publicly available data sets,
and pay special attention to specific real applications involving audio
processing for music genre prediction and hyperspectral satellite images for
Earth and climate monitoring
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Early symptoms and sensations as predictors of lung cancer: a machine learning multivariate model.
The aim of this study was to identify a combination of early predictive symptoms/sensations attributable to primary lung cancer (LC). An interactive e-questionnaire comprised of pre-diagnostic descriptors of first symptoms/sensations was administered to patients referred for suspected LC. Respondents were included in the present analysis only if they later received a primary LC diagnosis or had no cancer; and inclusion of each descriptor required ≥4 observations. Fully-completed data from 506/670 individuals later diagnosed with primary LC (n = 311) or no cancer (n = 195) were modelled with orthogonal projections to latent structures (OPLS). After analysing 145/285 descriptors, meeting inclusion criteria, through randomised seven-fold cross-validation (six-fold training set: n = 433; test set: n = 73), 63 provided best LC prediction. The most-significant LC-positive descriptors included a cough that varied over the day, back pain/aches/discomfort, early satiety, appetite loss, and having less strength. Upon combining the descriptors with the background variables current smoking, a cold/flu or pneumonia within the past two years, female sex, older age, a history of COPD (positive LC-association); antibiotics within the past two years, and a history of pneumonia (negative LC-association); the resulting 70-variable model had accurate cross-validated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background predictors only: 0.652), sensitivity = 84.8% (73.9/76.1%, respectively), specificity = 55.6% (66.7/51.9%, respectively). In conclusion, accurate prediction of LC was found through 63 early symptoms/sensations and seven background factors. Further research and precision in this model may lead to a tool for referral and LC diagnostic decision-making
On the properties of a single OPLS-UA model curcumin molecule in water, methanol and dimethyl sulfoxide. Molecular dynamics computer simulation results
The properties of model solutions consisting of a solute --- single curcumin
molecule in water, methanol and dimethyl sulfoxide solvents have been studied
using molecular dynamics (MD) computer simulations in the isobaric-isothermal
ensemble. The united atom OPLS force field (OPLS-UA) model for curcumin
molecule proposed by us recently [J. Mol. Liq., 2016, 223, 707] in combination
with the SPC/E water, and the OPLS-UA type models for methanol and dimethyl
sulfoxide have been applied. We have described changes of the internal
structure of the solute molecule induced by different solvent media in very
detail. The pair distribution functions between particular fragments of a
solute molecule with solvent particles have been analyzed. Statistical features
of the hydrogen bonding between different species were explored. Finally, we
have obtained a self-diffusion coefficient of curcumin molecules in three model
solvents.Comment: 20 pages, 17 figures, 4 table
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