A search for specificity in DNA-drug interactions

Computational solvent mapping in structure-based drug design

Over the past two decades, solvent mapping has emerged as a useful tool for identifying hot spots within binding sites on proteins for drug-like molecules and suggesting properties of potential binders. While the experimental technique requires solving multiple crystal structures of a protein in different solvents, computational solvent mapping allows for fast analysis of a protein for potential binding sites and their druggability. Recent advances in genomics, systems biology and interactomics provide a multitude of potential targets for drug development and solvent mapping can provide useful information to help prioritize targets for drug discovery projects. Here, we review various approaches to computational solvent mapping, highlight some key advances and provide our opinion on future directions in the field.

Wavelet Approximation of GRID Fields: Application to Quantitative Structure-Activity Relationships

Molecular interaction fields such as those computed by the GRID program are widely used in applications such as virtual screening, molecular docking and 3D-QSAR modelling. They characterise molecules according to their favourable interaction sites and therefore enable predictions to be made on how molecules might interact. The fields are, however, comprised of a very large number of data points which presents difficulties for many applications. For example, there are likely to be high degrees of correlation between the variables which can lead to misleading results in 3D-QSAR. We describe the use of wavelet methods for approximating such data into a much smaller number of variables. We present a number of validation experiments, including use of the approximated GRIDs in 3D-QSAR, and demonstrate that wavelet approximation at high levels of data compression preserves the information content in GRID fields while significantly reducing computational requirements.

Rational design, synthesis, biophysical and antiproliferative evaluation of fluorenone derivatives with DNA G-quadruplex binding properties

Molecular modeling studies carried out with experimental DNA models with the sequence d[AG(3)(T(2)AG(3))(3)] suggest that the introduction of a net positive charge onto the side chain of a series of fluorenone carboxamides can improve G-quadruplex binding. The terminal morpholino moiety was replaced with a novel N-methylmorpholinium cation starting from two 4-carboxamide compounds. A different substitution on the fluorenone ring was also investigated and submitted to the same quaternarization process. All compounds were analyzed for their DNA binding properties by competition dialysis methods. In vitro antiproliferative tests were carried out against two different tumor cell lines. Docking experiments were conducted by including all four known human repeat unit G-quadruplex DNA sequences (27 experimentally determined conformations) against the most active fluorenone derivatives. The results of theoretical, biophysical, and in vitro experiments indicate two novel derivatives as lead compounds for the development of a new generation of G-quadruplex ligands with greater potency and selectivity.

Computational approaches to identifying and characterizing protein binding sites for ligand design

Given the three-dimensional structure of a protein, how can one find the sites where other molecules might bind to it? Do these sites have the properties necessary for high affinity binding? Is this protein a suitable target for drug design? Here, we discuss recent developments in computational methods to address these and related questions. Geometric methods to identify pockets on protein surfaces have been developed over many years but, with new algorithms, their performance is still improving. Simulation methods show promise in accounting for protein conformational variability to identify transient pockets but lack the ease of use of many of the (rigid) shape-based tools. Sequence and structure comparison approaches are benefiting from the constantly increasing size of sequence and structure databases. Energetic methods can aid identification and characterization of binding pockets, and have undergone recent improvements in the treatment of solvation and hydrophobicity. The "druggability" of a binding site is still difficult to predict with an automated procedure. The methodologies available for this purpose range from simple shape and hydrophobicity scores to computationally demanding free energy simulations.

Structure-based chemogenomics: analysis of protein family landscapes

Analysis of the three-dimensional structures of protein ligand complexes provides valuable insight into both the common interaction patterns within a target family and the discriminating features between the different members of a target family. Knowledge of the common interaction patterns helps to design target family focused chemical libraries for hit finding, while the discriminating features can be exploited to optimize the selectivity profile of a lead compound against particular member of a target family. Herein, we review the computational tools which have been developed to analyze crystal structures of members of a target family.

Matrix Metalloproteinase Target Family Landscape: A Chemometrical Approach to Ligand Selectivity Based on Protein Binding Site Analysis

To gain insight into the structural determinants for the matrix metalloproteinase (MMP) family, we characterized the binding sites of 56 MMP structures and one TACE (tumor necrosis factor alpha converting enzyme) structure using molecular interaction fields (MIFs). These MIFs were produced by two approaches: the GRID force field and the knowledge-based potential DrugScore. The subsequent statistical analysis using consensus principal component analysis (CPCA) for the entire binding site and each subpockets revealed both approaches to encode similar information about discriminating regions. However, the relative importance of the probes varied between both approaches. The CPCA models provided the following ranking of the six subpockets based on the opportunity for selective interactions with different MMPs: S1' > S2, S3, S3' > S1, S2'. The interpretation of these models agreed with experimental binding modes inferred from crystal structures or docking.

Structural classification of protein kinases using 3D molecular interaction field analysis of their ligand binding sites: target family landscapes

Protein kinases are critical components of signaling pathways and trigger various biological events. Several members of this superfamily are interesting targets for novel therapeutic approaches. All known eukaryotic protein kinases exhibit a conserved catalytic core domain with an adenosine 5'-triphosphate (ATP) binding site, which often is targeted in drug discovery programs. However, as ATP is common to kinases and other proteins, specific protein-ligand interactions are crucial prerequisites for valuable ATP site-directed ligands. In the present study, a set of 26 X-ray structures of eukaryotic protein kinases were classified into subfamilies with similar protein-ligand interactions in the ATP binding site using a chemometrical approach based on principal component analysis (PCA) and consensus PCA. This classification does not rely on protein sequence similarities, as descriptors are derived from three-dimensional (3D) binding site information only computed using GRID molecular interaction fields. The resulting classification, which we refer to as "target family landscape", lead to the identification of common binding pattern and specific interaction sites for particular kinase subfamilies. Moreover, those findings are in good agreement with experimental selectivity profiles for a series of 2,6,9-substituted purines as CDK inhibitors. Their interpretation in structural terms unveiled favorable substitutions toward selective CDK inhibitors and thus allowed for a rational design of specific ligands with minimized side effects. Additional 3D-quantitative structure-activity relationship (QSAR) analyses of a larger set of CDK-directed purines lead to the identification of essential structural requirements for affinity in this CDK ATP binding site. The combined interpretation of 3D-QSAR and the kinase target family landscape provides a consistent view to protein-ligand interactions, which are both favorable for affinity and selectivity in this important subfamily.

Molecular modelling in structural biology

Molecular modelling is a powerful methodology for analysing the three dimensional structure of biological macromolecules. There are many ways in which molecular modelling methods have been used to address problems in structural biology. It is not widely appreciated that modelling methods are often an integral component of structure determination by NMR spectroscopy and X-ray crystallography. In this review we consider some of the numerous ways in which modelling can be used to interpret and rationalise experimental data and in constructing hypotheses that can be tested by experiment. Genome sequencing projects are producing a vast wealth of data describing the protein coding regions of the genome under study. However, only a minority of the protein sequences thus identified will have a clear sequence homology to a known protein. In such cases valuable three-dimensional models of the protein coding sequence can be constructed by homology modelling methods. Threading methods, which used specialised schemes to relate protein sequences to a library of known structures, have been shown to be able to identify the likely protein fold even in cases where there is no clear sequence homology. The number of protein sequences that cannot be assigned to a structural class by homology or threading methods, simply because they belong to a previously unidentified protein folding class, will decrease in the future as collaborative efforts in systematic structure determination begin to develop. For this reason, modelling methods are likely to become increasingly useful in the near future. The role of the blind prediction contests, such as the Critical Assessment of techniques for protein Structure Prediction (CASP), will be briefly discussed. Methods for modelling protein-ligand and protein-protein complexes are also described and examples of their applications given.

The effect of anions on azide binding to myoglobin: an unusual functional modulation

The effect of increasing concentrations of several anions on the azide (N(-)(3)) binding properties of sperm whale and horse ferric myoglobin has been studied. Surprisingly, a number of anions may act as heterotropic effectors, decreasing the affinity of myoglobins for N(-)(3), in the following order: ClO(-)(4)=I(-)>Br(-)>Cl(-) and SO(2-)(4), which mirrors the increase in their charge density. The largest effects were measured using ClO(-)(4) and I(-), which produce a 4-fold and 8-fold reduction of the N(-)(3) binding affinity in horse and sperm whale myoglobins, respectively. A dissociation equilibrium constant (K(d)) ranging from 150 to 250 mM was estimated for ClO(-)(4) and I(-) binding to myoglobins. In order to analyse the molecular mechanism producing the reduction of the N(-)(3) binding affinity to ferric myoglobin, the potential anionic binding sites within ferric myoglobin were investigated by a molecular modelling study using the program Grid. Analysis of the theoretical results suggests two particularly favourable binding sites: the first, next to the distal side of the haem, whose occupancy might alter the electrostatic potential surrounding the bound N(-)(3); the second, involving residues of helices B and G which are far from the haem iron atom, thus implying a long range effect on the bound N(-)(3). Based on the evidence that no significant conformational changes are found in the three-dimensional structures of N(-)(3)-free and N(-)(3)-bound myoglobin and on previous results on N(-)(3) binding to ferric myoglobin mutants in CD3 positions, we favour the first hypothesis, suggesting that the functional heterotropic modulation of monomeric myoglobin is mainly depending on a decrease of the positive charge density induced by the binding of anions to the haem distal side.

GRID/CPCA: a new computational tool to design selective ligands

We present a computational procedure aimed at understanding enzyme selectivity and guiding the design of drugs with respect to selectivity. It starts from a set of 3D structures of the target proteins characterized by the program GRID. In the multivariate description proposed, the variables are organized and scaled in a different way than previously published methodologies. Then, consensus principal component analysis (CPCA) is used to analyze the GRID descriptors, allowing the straightforward identification of possible modifications in the ligand to improve its selectivity toward a chosen target. As an important new feature the computational method is able to work with more than two target proteins and with several 3D structures for each protein. Additionally, the use of a 'cutout tool' allows to focus on the important regions around the active site. The method is validated for a total number of nine structures of the three homologous serine proteases thrombin, trypsin, and factor Xa. The regions identified by the method as being important for selectivity are in excellent agreement with available experimental data and inhibitor structure-activity relationships.

Chemometric rationalization of the structural and physicochemical basis for selective cyclooxygenase-2 inhibition: toward more specific ligands

The discovery that proinflammatory prostaglandins are produced by cyclooxygenase-2 (COX-2), an inducible isoform of the constitutive cyclooxygenase-1 (COX-1), opened a new frontier in the treatment of inflammatory diseases, because the selective inhibition of COX-2 can lead to therapeutically effective compounds which do not have the common side effects of classical non-steroidal antiinflammatory drugs (NSAIDs). Different crystallographic structures of both free COX-1 and COX-2 as well as complexes with inhibitors have been solved. Because of the great similarity between the two enzymes, it is difficult to detect the most important structural and physicochemical features that would be useful for designing inhibitors with an improved selectivity. In this paper we describe the application of a chemometric procedure to the study of COX-2 selective inhibition. This method, developed to reveal the most suitable regions of isoenzymes for the design of selective ligands, also has a very practical utility. GRID multivariate characterization of the enzymes and subsequent Principal Component Analysis (PCA) of the descriptor variables allow the identification of chemical groups that could be added to a core template structure to increase ligand selectivity.

Affinity and selectivity of matrix metalloproteinase inhibitors: a chemometrical study from the perspective of ligands and proteins

A novel strategy to understand affinity and selectivity for enzyme inhibitors using information from ligands and target protein 3D structures is described. It was applied to 2-arylsulfonyl-1,2,3, 4-tetrahydro-isoquinoline-3-carboxylates and -hydroxamates as inhibitors of the matrix metalloproteinases MMP-3 (stromelysin-1) and MMP-8 (human neutrophil collagenase). As the first step, consistent and predictive 3D-QSAR models were derived using CoMFA, CoMSIA, and GRID/Golpe approaches, leading to the identification of binding regions where steric, electronic, or hydrophobic effects are important for affinity. These models were validated using multiple analyses using two or five randomly chosen cross-validation groups and randomizations of biological activities. Second, 3D-QSAR models were derived based on the affinity ratio IC(50)(MMP-8)/IC(50)(MMP-3), allowing the identification of key ligand determinants for selectivity toward one of both enzymes. In addition to this ligands' view, the third step encompasses a chemometrical approach based on principal component analysis (PCA) of multivariate GRID descriptors to uncover the major differences between both protein binding sites with respect to their GRID probe interaction pattern. The resulting information, based on the accurate knowledge of the target protein 3D structures, led to a consistent picture in good agreement with experimentally observed differences in selectivity toward MMP-8 or MMP-3. The interpretation of all three classes of statistical models leads to detailed SAR information for MMP inhibitors, which is in agreement with available data for binding site topologies, ligand affinities, and selectivities. Thus the combined chemical analyses provide guidelines and accurate activity predictions for designing novel, selective MMP inhibitors.

Structure-affinity relationships for the binding of actinomycin D to DNA

Molecular models of the complexes between actinomycin D and 14 different DNA hexamers were built based on the X-ray crystal structure of the actinomycin-d(GAAGCTTC)2 complex. The DNA sequences included the canonical GpC binding step flanked by different base pairs, nonclassical binding sites such as GpG and GpT, and sites containing 2,6-diamino-purine. A good correlation was found between the intermolecular interaction energies calculated for the refined complexes and the relative preferences of actinomycin binding to standard and modified DNA. A detailed energy decomposition into van der Waals and electrostatic components for the interactions between the DNA base pairs and either the chromophore or the peptidic part of the antibiotic was performed for each complex. The resulting energy matrix was then subjected to principal component analysis, which showed that actinomycin D discriminates among different DNA sequences by an interplay of hydrogen bonding and stacking interactions. The structure-affinity relationships for this important antitumor drug are thus rationalized and may be used to advantage in design of novel sequence-specific DNA-binding agents.

Wavelets and molecular structure

The wavelet method offers possibilities for display, editing, and topological comparison of proteins at a user-specified level of detail. Wavelets are a mathematical tool that first found application in signal processing. The multiresolution analysis of a signal via wavelets provides a hierarchical series of "best' lower-resolution approximations. B-spline ribbons model the protein fold, with one control point per residue. Wavelet analysis sets limits on the information required to define the winding of the backbone through space, suggesting a recognizable fold is generated from a number of points equal to 1/4 or less the number of residues. Wavelets applied to surfaces and volumes show promise in structure-based drug design.