Impact of the Conformational Variability of Oligopeptides on the Computational Prediction of Their CD Spectra.

Although successful in the structural determination of ordered biomolecules, the spectroscopic investigation of oligopeptides in solution is hindered by their complex and rapidly changing conformational ensemble. The measured circular dichroism (CD) spectrum of an oligopeptide is an ensemble average over all microstates, severely limiting its interpretation, in contrast to ordered biomolecules. Spectral deconvolution methods to estimate the secondary structure contributions in the ensemble are still mostly based on databases of larger ordered proteins. Here, we establish how the interpretation of CD spectra of oligopeptides can be enhanced by the ability to compute the same observable from a set of atomic coordinates. Focusing on two representative oligopeptides featuring a known propensity toward an α-helical and ß-hairpin motif, respectively, we compare and cross-validate the structural information coming from deconvolution of the experimental CD spectra, sequence-based de novo structure prediction, and molecular dynamics simulations based on enhanced sampling methods. We find that small conformational variations can give rise to significant changes in the CD signals. While for the simpler conformational landscape of the α-helical peptide de novo structure prediction can already give reasonable agreement with the experiment, an extended ensemble of conformers needs to be considered for the ß-hairpin sequence.

Effects of ulapualide A and synthetic macrolide analogues on actin dynamics and gene regulation.

Several marine macrolide toxins act as potent and specific actin-severing molecules. Recent elucidation of their stereochemistries and modes of interaction with actin has allowed the syntheses of bioactive analogues. Here we used synthetic analogues in a structure-function analysis of ulapualide A, a trisoxazole-based macrolide. Ulapualide A harboured potent actin-depolymerising activity both in cells and in vitro. Its synthetic diastereoisomer was three orders of magnitude less active than the natural toxin and synthetic macrolide fragments lacked actin-capping/ severing activity altogether. Modulation of serum response factor (SRF)-dependent gene expression, as described for other actin-binding toxins, was also examined. Specific changes in response to ulapualide A were not observed, primarily due to its profound effects on cytoskeletal integrity and cell adhesion. Several synthetic fragments of ulapualide A also had no effect on SRF-dependent gene expression. However, inhibition was observed with a molecule corresponding to the extended aliphatic side chain of halichondramide, a structurally related macrolide. These findings indicate that side-chain derivatives of trisoxazole-based macrolides may serve to uncouple gene-regulatory events from actin dynamics.

The evolutionary landscape of functional model proteins.

To study the distinct influences of structure and function on evolution, we propose a minimalist model for proteins with binding pockets, called functional model proteins, based on a shifted-HP model on a two-dimensional square lattice. These model proteins are not maximally compact and contain an empty lattice site surrounded by at least three nearest neighbors, thus providing a binding pocket. Functional model proteins possess a unique native state, cooperative folding and tolerance to mutation. Due to the explicit functionality in these models (by design), we have been able to explore their fitness or evolutionary landscapes, as characterized by the size and distribution of homologous families and by the complexity of the inter-relatedness of the functional model proteins. Mindful that these minimalist models are highly idealized and two-dimensional, functional model proteins should nevertheless provide a useful means for exploring the constraints of maintaining structure and function on the evolution of proteins.

Do active site conformations of small ligands correspond to low free-energy solution structures?

We compare the low free energy structures of ten small, polar ligands in solution to their conformations in their respective receptor active sites. The solution conformations are generated by a systematic search and the free energies of representative structures are computed with a continuum solvation model. Based on the values of torsion angles, we find little similarity between low energy solution structures of small ligands and their active site conformations. However, in nine out of ten cases, the positions of 'anchor points' (key atoms responsible for tight binding) in the lowest energy solution structures are very similar to the positions of these atoms in the active site conformations. A metric that more closely captures the essentials of binding supports the basic premise underlying pharmacophore mapping, namely that active site conformations of small flexible ligands correspond to their low energy structures in solution. This work supports the efforts of building pharmacophore models based on the information present in solution structures of small isolated ligands.

Predicting ligand binding energies.

Computer-aided drug design is a burgeoning discipline, in part, because of the fundamental role that molecular recognition plays in chemistry and biology, and partly due to the obvious commercial implications. This review focuses on methods rather than applications and a selection of novel techniques for predicting ligand binding energies that have recently emerged in the field, ranging from statistical analyses of highly reduced descriptions of ligands (with no information about the receptor) to statistical mechanical methods requiring atomic detailed knowledge of both ligand and receptor are presented.

Predicting leucine zipper structures from sequence.

The leucine zipper structure is adopted by one family of the coiled coil proteins. Leucine zippers have a characteristic leucine repeat: Leu-X6-Leu-X6-Leu-X6-Liu (where X may be any residue). However, many sequences have the leucine repeat, but do not adopt the leucine zipper structure (we shall refer to these as non-zippers). We have found and analyzed residue pair patterns that allow one to identify correctly 90% of leucine zippers and 97% of non-zippers. Simpler analyses, based on the frequency of occurrence of residues at certain positions, specify, at most, 65% of zippers and 80-90% of non-zippers. Both short and long patterns contribute to the successful discrimination of leucine zippers from non-zippers. A number of these patterns involve hydrophobic residues that would be placed on the solvent-exposed surface of the helix, were the sequence to adopt a leucine zipper structure. Thus, an analysis of protein sequences has allowed us to improve discrimination between leucine zippers and non-zippers, and has provided some further insight into the physical factors influencing the leucine zipper structure.

Nonlinear quantitative structure-activity relationship for the inhibition of dihydrofolate reductase by pyrimidines.

A novel method for quantitative structure-activity relationship (QSAR) analysis is presented. The method, which does not assume any particular functional form for the QSAR, develops nonlinear relationships between parameters describing a set of molecules and the activity of the molecules. For the QSAR of the inhibition of Escherichia coli dihydrofolate reductase by 2,4-diamino-5-(substituted benzyl)pyrimidines, the method compares favorably to other nonlinear methods. Cross-validation trials demonstrate that the predictive ability is as accurate as other methods, and the method is simpler and faster than neural network and machine-learning methods. Consequently, its implementation is much easier, and interpretation of the generated QSAR is more straightforward.

Molecular dynamics simulations of isolated helices of myoglobin.

The apo form of myoglobin has two non-native stable states that have been experimentally characterized. Investigation of these states has suggested possible folding pathways for myoglobin. We have performed molecular dynamics simulations on solvated isolated helices of myoglobin to investigate the relationship between the intrinsic stabilities of the isolated helices and the structure and folding pathway of apomyoglobin. Analyses of hydrogen bonding and fluctuations from simulations at 298 and 368 K are used to explore the relative stabilities of the helices of myoglobin. The ordering observed is A approximately G approximately H > B > E > F, which mirrors both the experimental equilibrium and kinetic data available for apomyoglobin. The experimental observation that a subdomain comprising helices A, G, and H is an important early intermediate and our result that these helices are the most stable suggest that the intrinsically more stable helices form early in the folding process and that this significantly influences the folding pathway.

Helicity, circular dichroism and molecular dynamics of proteins.

In protein unfolding studies, reduction in circular dichroism (CD) at 222 nm has been interpreted as loss of helicity. Estimates of the helicity of a protein from its CD spectrum are calibrated by reference to X-ray crystal structures, based on the assumption that the mean residue ellipticity at 222 nm is directly proportional to the number of residues in a helix. We have examined various influences on the CD at 222 nm, using molecular dynamics simulations to provide the structural detail required. We have found that the fragmentation of long helices, without a reduction in the number of helical residues, significantly reduces the mean residue ellipticity at 222 nm. The dynamical motion of the protein and the precise conformation of helical residues also play an important role. We discuss the implications of these factors to the interpretation of CD for the partial unfolding of apomyoglobin.

Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines.

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.

One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR)--the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives--has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.

Prediction of structural and functional features of protein and nucleic acid sequences by artificial neural networks.

The applications of artificial neural networks to the prediction of structural and functional features of protein and nucleic acid sequences are reviewed. A brief introduction to neural networks is given, including a discussion of learning algorithms and sequence encoding. The protein applications mostly involve the prediction of secondary and tertiary structure from sequence. The problems in nucleic acid analysis tackled by neural networks are the prediction of translation initiation sites in Escherichia coli, the recognition of splice junctions in human mRNA, and the prediction of promoter sites in E. coli. The performance of the approach is compared with other current statistical methods.

Prediction of ATP/GTP-binding motif: a comparison of a perceptron type neural network and a consensus sequence method [corrected].

Neural networks have been applied to a number of protein structure problems. In some applications their success has not been substantiated by a comparison with the performance of a suitable alternative statistical method on the same data. In this paper, a two-layer feed-forward neural network has been trained to recognize ATP/GTP-binding [corrected] local sequence motifs. The neural network correctly classified 78% of the 349 sequences used. This was much better than a simple motif-searching program. A more sophisticated statistical method was developed, however, which performed marginally better (80% correct classification) than the neural network. The neural network and the statistical method performed similarly on sequences of varying degrees of homology. These results do not imply that neural networks, especially those with hidden layers, are not useful tools, but they do suggest that two-layer networks in particular should be carefully tested against other statistical methods.