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Jonathan D Hirst

Publications and source records attributed to Jonathan D Hirst.

15 recordsLinked to original sources
PubMed2005

Lattice models of peptide aggregation: evaluation of conformational search algorithms.

We present a series of conformational search calculations on the aggregation of short peptide fragments that form fibrils similar to those seen in many protein mis-folding diseases. The proteins were represented by a face-centered cubic lattice model with the conformational energies calculated using the Miyazawa-Jernigan potential. The searches were performed using algorithms based on the Metropolis Monte Carlo method, including simulated annealing and replica exchange. We also present the results of searches using the tabu search method, an algorithm that has been used for many optimization problems, but has rarely been used in protein conformational searches. The replica exchange algorithm consistently found more stable structures then the other algorithms, and was particularly effective for the octamers and larger systems.

Algorithms↗
PubMed2005

Population dynamics simulations of functional model proteins.

In order to probe the fundamental principles that govern protein evolution, we use a minimalist model of proteins to provide a mapping from genotype to phenotype. The model is based on physically realistic forces of protein folding and includes an explicit definition of protein function. Thus, we can find the fitness of a sequence from its ability to fold to a stable structure and perform a function. We study the fitness landscapes of these functional model proteins, that is, the set of all sequences mapped on to their corresponding fitnesses and connected to their one mutant neighbors. Through population dynamics simulations we directly study the influence of the nature of the fitness landscape on evolution. Populations are observed to move to a steady state, the distribution of which can often be predicted prior to the population dynamics simulations from the nature of the fitness landscape and a quantity analogous to a partition function. In this paper, we develop a scheme for predicting the steady-state population on a fitness landscape, based on the nature of the fitness landscape, thereby obviating the need for explicit population dynamics simulations and providing some insight into the impact on molecular evolution of the nature of fitness landscapes. Poor predictions are indicative of fitness landscapes that consist of a series of weakly connected sublandscapes.

Computer Simulation↗
PubMed2005

Protein secondary structure prediction with dihedral angles.

We present DESTRUCT, a new method of protein secondary structure prediction, which achieves a three-state accuracy (Q3) of 79.4% in a cross-validated trial on a nonredundant set of 513 proteins. An iterative set of cascade-correlation neural networks is used to predict both secondary structure and psi dihedral angles, with predicted values enhancing the subsequent iteration. Predictive accuracies of 80.7% and 81.7% are achieved on the CASP4 and CASP5 targets, respectively. Our approach is significantly more accurate than other contemporary methods, due to feedback and a novel combination of structural representations.

Amino Acid Sequence↗
PubMed2004

A sequential molecular mechanics/quantum mechanics study of the electronic spectra of amides.

We report gas-phase electronic spectra of formamide, N-methyformamide, acetamide, and N-methylacetamide at 300 K calculated using a combination of classical molecular dynamics and time-dependent density functional theory (TDDFT). In comparison to excitation energies computed using the global minima structures, the valence npi* and pi(nb)pi* states show a significant red-shift of 0.1-0.35 eV, while smaller shifts are found for the n3s and pi(nb)3s Rydberg states. In this work, we have identified the physical origin of these shifts arising from variations of the molecular structure. We present simple relationships between key geometrical parameters and spectral shifts. Consequently, electronic spectra can be generated directly from ground-state structures, without additional quantum chemical calculations. The electronic spectrum of formamide in aqueous solution is computed using TDDFT using an explicit solvent model. This provides a quantitative determination of the condensed-phase spectrum. In general, this study shows that temperature effects can change the predicted excitation energies significantly and demonstrates how electronic spectra at elevated temperatures can be computed in a computationally efficient way.

Amides↗
PubMed2004

First-principles calculations of protein circular dichroism in the near ultraviolet.

Electronic transitions in aromatic side chains are responsible for the characteristics of proteins in the near UV. We present the first systematic study of a large number of proteins focused on the accurate calculation of near-UV circular dichroism (CD) spectra. We report new parameter sets derived from ab initio calculations for benzene, phenol, and indole that describe the valence electronic transitions to the (1)L(b), (1)L(a), (1)B(b), and (1)B(a) states in the side chains of amino acids phenylalanine, tyrosine, and tryptophan. CD spectra were calculated, using the matrix method with the new side-chain parameters, for 30 proteins whose CD spectra and crystal structures have been made publicly available. The new parameter sets are fully self-consistent and yield near-UV spectra better than those obtained using previous parameter sets. The mean absolute errors for computed wild-type spectra in the near UV are reduced by a factor of approximately 2. A similiar reduction is found for the near-UV spectra (and difference spectra) of mutants involving aromatic amino acids. Empirical modifications to model vibronic coupling in the side-chain chromophore of phenylalanine offer no overall improvement. Protein CD calculations from first principles coupled with atomic-level modeling enhance the utility and interpretability of CD measurements in the near UV.

Amino Acid Sequence↗
PubMed2004

Calculations of protein circular dichroism from first principles.

Understanding the relationship between the amino acid sequence of a protein and its unique, compact 3D structure is one of the grand challenges in molecular biophysics. One particularly exciting approach is time-resolved electronic circular dichroism (CD) spectroscopy, which offers resolution on a nanosecond (or faster) time scale, although it does not provide the spatial resolution of techniques like X-ray crystallography or NMR. The thrust of our work is to underpin fast time scale spectroscopic studies of protein folding with a stronger theoretical foundation. Ultimately, we seek to use molecular dynamics simulations to study the influence of conformational dynamics and conformational transitions on the electronic CD spectra of proteins. We discuss how improved quantum chemical models of individual chromophores, including aromatic sidechains, can be incorporated into calculations of the electronic structure of proteins and their CD.

Bacterial Proteins↗
PubMed2004

Predicting protein secondary structure by cascade-correlation neural networks.

The back-propagation neural network algorithm is a commonly used method for predicting the secondary structure of proteins. Whilst popular, this method can be slow to learn and here we compare it with an alternative: the cascade-correlation architecture. Using a constructive algorithm, cascade-correlation achieves predictive accuracies comparable to those obtained by back-propagation, in shorter time.

Algorithms↗
PubMed2003

Molecular dynamics simulations of a helicase.

Helicases are ubiquitous enzymes involved in nucleic acid metabolism. The PcrA DNA helicase is an essential bacterial protein involved in rolling circle plasmid replication and DNA repair. Recent crystal structures of PcrA bound to DNA indicate that a flexible loop mediates a functionally important rigid-body-domain rotation. In this study, we report stochastic boundary molecular dynamics simulations focused on this region for wild-type and mutants designed to increase the rigidity of the region. Residues in the region that were helix-disfavoring, such as glycine, threonine, and others, were mutated to alanine. The simulated dynamics, analyzed with a variety of measures of structure and mobility, indicate that a few point mutations will substantially increase helix formation in this region. Subnanosecond stochastic boundary molecular dynamics simulations at several temperatures offer a rapid protocol for assessing large numbers of mutants and provides a novel strategy for the design of experiments to test the role of this flexible loop region in the function of PcrA.

Amino Acid Sequence↗
PubMed2003

Theoretical studies of time-resolved spectroscopy of protein folding.

Recently, we have made significant improvements in the accuracy of calculations of the circular dichroism of proteins from first principles. The quality of these calculations (especially at 220 nm, a key wavelength, where the intensity of the band correlates well with the helical content of polypeptides) has given us confidence to use such calculations to analyse nanosecond molecular dynamics simulations of the folding of polypeptides. We use this combined approach to explore the influence of dynamics on the circular dichroism spectroscopy of polypeptides. We apply it to equilibrium molecular dynamics simulations of two beta-sheet proteins with similar structures, but differing circular dichroism spectra. We analyse a molecular dynamics simulation of the acid-unfolding of myoglobin. For both alpha-helical and beta-sheet conformations, we find that changes in dihedral angles of 30 degrees can change intensities of bands in circular dichroism spectra by up to 5000 degree cm2 dmol(-1). Thus, in isolation, moderate differences in circular dichroism spectra cannot be interpreted uniquely in terms of conformational changes. Examination of individual structures allows us to dissect the influence of conformation on the calculated circular dichroism spectra. Our results are aimed at providing a deeper understanding of the optical properties of proteins. An atomic level connection between molecular dynamics simulations and optical spectroscopy is increasingly desirable as theoretical and experimental studies begin to probe protein folding events reliably on the nanosecond timescale.

Circular Dichroism↗
PubMed2002

Stabilizing interactions between aromatic and basic side chains in alpha-helical peptides and proteins. Tyrosine effects on helix circular dichroism.

Here we investigate the structures and energetics of interactions between aromatic (Phe or Tyr) and basic (Lys or Arg) amino acids in alpha-helices. Side chain interaction energies are measured using helical peptides, by quantifying their helicities with circular dichroism at 222 nm and interpreting the results with Lifson-Roig-based helix/coil theory. A difficulty in working with Tyr is that the aromatic ring perturbs the CD spectrum, giving an incorrect helicity. We calculated the effect of Tyr on the CD at 222 nm by deriving the intensities of the bands directly from the electronic and magnetic transition dipole moments through the rotational strengths corresponding to each excited state of the polypeptide. This gives an improved value of the helix preference of Tyr (from 0.48 to 0.35) and a correction to the helicity for the peptides containing Tyr. We find that Phe-Lys, Lys-Phe, Phe-Arg, Arg-Phe, and Tyr-Lys are all stabilizing by -0.10 to -0.18 kcal.mol-1 when placed i, i + 4 on the surface of a helix in aqueous solution, despite the great difference in polarity between these residues. Interactions between these side chains have previously been attributed to cation-pi bonds. A survey of protein structures shows that they are in fact predominantly hydrophobic interactions between the CH2 groups of Lys or Arg and the aromatic rings.

Amino Acid Sequence↗
PubMed2002

Application of non-parametric regression to quantitative structure-activity relationships.

Several non-parametric regressors have been applied to modelling quantitative structure-activity relationship (QSAR) data. Performances were benchmarked against multilinear regression and the nonlinear method of smoothing splines. Variable selection was explored through systematic combinations of different variables and combinations of principal components. For the training set examined--539 inhibitors of the tyrosine kinase, Syk--the best two-descriptor model had a 5-fold cross-validated q2 of 0.43. This was generated by a multi-variate Nadaraya-Watson kernel estimator. A subsequent, independent, test set of 371 similar chemical entities showed the model had some predictive power. Other approaches did not perform as well. A modest increase in predictive ability can be achieved with three descriptors, but the resulting model is less easy to visualise. We conclude that non-parametric regression offers a potentially powerful approach to identifying predictive, low-dimensional QSARs.

Databases, Factual↗
PubMedDate not supplied

Inhibition of the tyrosine kinase, Syk, analyzed by stepwise nonparametric regression.

A set of 538 inhibitors of the tyrosine kinase, Syk, including purines, pyrimidines, indoles, imidazoles, pyrazoles, and quinazolines, has been analyzed using a stepwise nonparametric regression (SNPR) algorithm, which has been developed for QSAR studies of pharmacological data. The algorithm couples stepwise descriptor selection with flexible, nonparametric, kernel regression, to generate structure-activity relationships. A further 371 molecules have been used as a test set to evaluate the models generated. Descriptors were selected using an internal monitoring set, and models were assessed using 10% of the principal (538-compound) data set, selected randomly, as an external validation set. The best model had a Q(2) of 0.46 for the external validation set. Test set predictions were significantly less accurate, partly due to the higher mean activity of the test molecules. However at a more coarse-grain level the SNPR models classified active molecules accurately, giving good enrichments. The data sets are difficult to model accurately and SNPR performs better than multilinear regression and a neural network analysis. In the additive implementation of SNPR multidimensional models are considered as a sum of single dimensional regressions. This makes the resultant models easily interpretable. For example, in the most predictive SNPR models, there is a clear nonlinear relationship between hydrophobicity (AlogP98) and inhibitory activity.

Enzyme Inhibitors↗
PubMedDate not supplied

On the stability of CoMFA models.

Abrupt, smooth, and box methods for the calculation of electrostatic and steric field values in the comparative molecular field analysis (CoMFA) 3D QSAR technique are assessed on three diverse data sets of medicinal chemistry interest. While the standard CoMFA settings are robust to small changes in the position of the lattice, superior results may sometimes be obtained by use of only one field. However, if only the electrostatic field is used, then sometimes large differences between models are apparent. This appears to be due to a lack of column dropping, and these difficulties can be remedied by use of the box method.

Journal Article↗
PubMedDate not supplied

Exploring phase-transfer catalysis with molecular dynamics and 3D/4D quantitative structure-selectivity relationships.

Quantitative Structure-Selectivity Relationships (QSSR) are developed for a library of 40 phase-transfer asymmetric catalysts, based around quaternary ammonium salts, using Comparative Molecular Field Analysis (CoMFA) and closely related variants. Due to the flexibility of these catalysts, we use molecular dynamics (MD) with an implicit Generalized Born solvent model to explore their conformational space. Comparison with crystal data indicates that relevant conformations are obtained and that, furthermore, the correct biphenyl twist conformation is predicted, as illustrated by the superiority of the resulting model (leave-one-out q(2) = 0.78) compared to a random choice of low-energy conformations for each catalyst (average q(2) = 0.22). We extend this model by incorporating the MD trajectory directly into a 4D QSSR and by Boltzmann-weighting the contribution of selected minimized conformations, which we refer to as '3.5D' QSSR. The latter method improves on the predictive ability of the 3D QSSR (leave-one-out q(2) = 0.83), as confirmed by repeated training/test splits.

Catalysis↗