Pressure-dependent structure changes in barnase on ligand binding reveal intermediate rate fluctuations.

In this work we measured 1H NMR chemical shifts for the ribonuclease barnase at pressures from 3 MPa to 200 MPa, both free and bound to d(CGAC). Shift changes with pressure were used as restraints to determine the change in structure with pressure. Free barnase is compressed by approximately 0.7%. The largest changes are on the ligand-binding face close to Lys-27, which is the recognition site for the cleaved phosphate bond. This part of the protein also contains the buried water molecules. In the presence of d(CGAC), the compressibility is reduced by approximately 70% and the region of structural change is altered: the ligand-binding face is now almost incompressible, whereas changes occur at the opposite face. Because compressibility is proportional to mean square volume fluctuation, we conclude that in free barnase, volume fluctuation is largest close to the active site, but when the inhibitor is bound, the fluctuations become much smaller and are located mainly on the opposite face. The timescale of the fluctuations is nanoseconds to microseconds, consistent with the degree of ordering required for the fluctuations, which are intermediate between rapid uncorrelated side-chain dynamics and slow conformational transitions. The high-pressure technique is therefore useful for characterizing motions on this relatively inaccessible timescale.

Pressure-dependent 13C chemical shifts in proteins: origins and applications.

Pressure-dependent (13)C chemical shifts have been measured for aliphatic carbons in barnase and Protein G. Up to 200 MPa (2 kbar), most shift changes are linear, demonstrating pressure-independent compressibilities. CH(3), CH(2) and CH carbon shifts change on average by +0.23, -0.09 and -0.18 ppm, respectively, due to a combination of bond shortening and changes in bond angles, the latter matching one explanation for the gamma-gauche effect. In addition, there is a residue-specific component, arising from both local compression and conformational change. To assess the relative magnitudes of these effects, residue-specific shift changes for protein G were converted into structural restraints and used to calculate the change in structure with pressure, using a genetic algorithm to convert shift changes into dihedral angle restraints. The results demonstrate that residual (13)C alpha shifts are dominated by dihedral angle changes and can be used to calculate structural change, whereas (13)C beta shifts retain significant dependence on local compression, making them less useful as structural restraints.

Structural change in a B-DNA helix with hydrostatic pressure.

Study of the effects of pressure on macromolecular structure improves our understanding of the forces governing structure, provides details on the relevance of cavities and packing in structure, increases our understanding of hydration and provides a basis to understand the biology of high-pressure organisms. A study of DNA, in particular, helps us to understand how pressure can affect gene activity. Here we present the first high-resolution experimental study of B-DNA structure at high pressure, using NMR data acquired at pressures up to 200 MPa (2 kbar). The structure of DNA compresses very little, but is distorted so as to widen the minor groove, and to compress hydrogen bonds, with AT pairs compressing more than GC pairs. The minor groove changes are suggested to lead to a compression of the hydration water in the minor groove.

Pressure-induced changes in the solution structure of the GB1 domain of protein G.

The solution structure of the GB1 domain of protein G at a pressure of 2 kbar is presented. The structure was calculated as a change from an energy-minimised low-pressure structure using (1)H chemical shifts. Two separate changes can be characterised: a compression/distortion, which is linear with pressure; and a stabilisation of an alternative folded state. On application of pressure, linear chemical shift changes reveal that the backbone structure changes by about 0.2 A root mean square, and is compressed by about 1% overall. The alpha-helix compresses, particularly at the C-terminal end, and moves toward the beta-sheet, while the beta-sheet is twisted, with the corners closest to the alpha-helix curling up towards it. The largest changes in structure are along the second beta-strand, which becomes more twisted. This strand is where the protein binds to IgG. Curved chemical shift changes with pressure indicate that high pressure also populates an alternative structure with a distortion towards the C-terminal end of the helix, which is likely to be caused by insertion of a water molecule.

New methods for ligand-based virtual screening: use of data fusion and machine learning to enhance the effectiveness of similarity searching.

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.

Virtual screening using binary kernel discrimination: analysis of pesticide data.

This paper discusses the use of binary kernel discrimination (BKD) for identifying potential active compounds in lead-discovery programs. BKD was compared with established virtual screening methods in a series of experiments using pesticide data from the Syngenta corporate database. It was found to be superior to methods based on similarity searching and substructural analysis but inferior to a support vector machine. Similar conclusions resulted from application of the methods to a pesticide data set for which categorical activity data were available.

Virtual screening using binary kernel discrimination: effect of noisy training data and the optimization of performance.

Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualitative activity data are available, to produce a model that can then be applied to the structures of other compounds in order to predict their likely activity. Experiments with the MDL Drug Data Report database show that the optimal value of the smoothing parameter, and hence the predictive power of BKD, is crucially dependent on the number of false positives in the training set. It is also shown that the best results for BKD are achieved using one particular optimization method for the determination of the smoothing parameter that lies at the heart of the method and using the Jaccard/Tanimoto coefficient in the kernel function that is used to compute the similarity between a test set molecule and the members of the training set.

Enhancing the effectiveness of similarity-based virtual screening using nearest-neighbor information.

We test the hypothesis that fusing the outputs of similarity searches based on a single bioactive reference structure and on its nearest neighbors (of unknown activity) is more effective (in terms of numbers of high-ranked active structures) than a similarity search involving just the reference structure. This turbo similarity searching approach provides a simple way to enhance the effectiveness of simulated virtual screening searches of the MDL Drug Data Report database.

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures.

This paper reports a detailed comparison of a range of different types of 2D fingerprints when used for similarity-based virtual screening with multiple reference structures. Experiments with the MDL Drug Data Report database demonstrate the effectiveness of fingerprints that encode circular substructure descriptors generated using the Morgan algorithm. These fingerprints are notably more effective than fingerprints based on a fragment dictionary, on hashing and on topological pharmacophores. The combination of these fingerprints with data fusion based on similarity scores provides both an effective and an efficient approach to virtual screening in lead-discovery programmes.

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures.

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.

Generation of multiple pharmacophore hypotheses using multiobjective optimisation techniques.

Pharmacophore methods provide a way of establishing a structure activity relationship for a series of known active ligands. Often, there are several plausible hypotheses that could explain the same set of ligands and, in such cases, it is important that the chemist is presented with alternatives that can be tested with different synthetic compounds. Existing pharmacophore methods involve either generating an ensemble of conformers and considering each conformer of each ligand in turn or exploring conformational space on-the-fly. The ensemble methods tend to produce a large number of hypotheses and require considerable effort to analyse the results, whereas methods that vary conformation on-the-fly typically generate a single solution that represents one possible hypothesis, even though several might exist. We describe a new method for generating multiple pharmacophore hypotheses with full conformational flexibility being explored on-the-fly. The method is based on multiobjective evolutionary algorithm techniques and is designed to search for an ensemble of diverse yet plausible overlays which can then be presented to the chemist for further investigation.

Generation and display of activity-weighted chemical hyperstructures.

A chemical hyperstructure is a single graph representation of a set of molecules that minimizes the degree of structural redundancy in the data set. This paper describes the use of a genetic algorithm to generate an activity-weighted chemical hyperstructure (AWCH) by sequentially mapping each molecule in the data set to the hyperstructure and then assigning activity and inactivity frequency weights to the nodes and edges of the hyperstructure. Experiments with several data sets demonstrate the level of activity clustering in an AWCH.