Computational chemistry in drug lead discovery and design

The main contributions of our group during the last 15 years developing and using biomolecular simulation tools in drug lead discovery and design, in close collaboration with experimental researchers, are presented. Special emphasis has been given to methodological improvements in the following areas: (1) target homology modeling incorporating knowledge about known ligands to accurately characterize the binding site; (2) designing alternative strategies to account for protein flexibility in high-throughput docking; (3) development of stochastic- and normal-mode-based methods to de novo design structurally diverse protein conformers; (4) development and validation of quantum mechanical semi-empirical linear-scaling calculations to correctly estimate ligand binding free energy. Several successful cases of computer-aided drug discovery are also presented, especially our recent work on viral targets.     

Representing receptor flexibility in ligand docking through relevant normal modes

Inspired by the current representation of the ligand-receptor binding process, a normal-mode-based methodology is presented to incorporate receptor flexibility in ligand docking and virtual screening. However, the systematic representation of the deformation space grows geometrically with the number of modes, and furthermore, midscale loop rearrangements like those found in protein kinase binding pockets cannot be accounted for with the first lowest-frequency modes. We thus introduced a measure of relevance of normal modes on a given region of interest and showed that only very few modes in the low-frequency range are necessary and sufficient to describe loop flexibility in cAMP-dependent protein kinase. We used this approach to generate an ensemble of representative receptor backbone conformations by perturbing the structure along a combination of relevant modes. Each ensemble conformation is complexed with known non-native binders to optimize the position of the binding-pocket side chains through a full flexible docking procedure. The multiple receptor conformations thus obtained are used in a small-scale virtual screening using receptor ensemble docking. We evaluated this algorithm on holo and apo structures of cAMP-dependent protein kinase that exhibit backbone rearrangements on two independent loop regions close to the binding pocket. Docking accuracy is improved, since the ligands considered in the virtual screening docked within 1.5 A to at least one of the structures. The discrimination between binders and nonbinders is also enhanced, as shown by the improvement of the enrichment factor. This constitutes a new step toward the systematic integration of flexible ligand-flexible receptor docking tools in structure-based drug discovery.     

Exo-exo and endo-endo vicinal proton spin-spin coupling constants in norbornane and norbornene. An IPPP-CLOPPA analysis

The IPPP-CLOPPA method is used to study the origin of the difference between exo-exo and endo-endo vicinal interproton couplings in norbornane and norbornene. The main pathway defining such couplings is found to be determined by the corresponding C? H bonds and antibonds. For this reason they can be considered to be transmitted through space. Contributions from excitations involving MOs of bridge 7 and the C? H endo and exo bonds and antibonds are found to be negligible in both compounds. Even though bridges 5–6 and 7 are not included in the coupling pathway, they influence the structure of endo and exo C? H bonds, and thus cause the nonequivalence of endo-endo and exo-exo couplings. Results obtained with the NNBI method are compared to those of IPPP-CLOPPA.     

Detecting functional magnetic resonance imaging activation in white matter: Interhemispheric transfer across the corpus callosum

Background  

It is generally believed that activation in functional magnetic resonance imaging (fMRI) is restricted to gray matter. Despite this, a number of studies have reported white matter activation, particularly when the corpus callosum is targeted using interhemispheric transfer tasks. These findings suggest that fMRI signals may not be neatly confined to gray matter tissue. In the current experiment, 4 T fMRI was employed to evaluate whether it is possible to detect white matter activation. We used an interhemispheric transfer task modelled after neurological studies of callosal disconnection. It was hypothesized that white matter activation could be detected using fMRI.     

Ligand and decoy sets for docking to G protein-coupled receptors

We compiled a G protein-coupled receptor (GPCR) ligand library (GLL) for 147 targets, selecting for each ligand 39 decoy molecules, collected in the GPCR Decoy Database (GDD). Decoys were chosen ensuring a ligand-decoy similarity of six physical properties, while enforcing ligand-decoy chemical dissimilarity. The performance in docking of the GDD was evaluated on 19 GPCRs, showing a marked decrease in enrichment compared to bias-uncorrected decoy sets. Both the GLL and GDD are freely available for the scientific community.     

Computational and experimental studies of the interaction between phospho-peptides and the C-terminal domain of BRCA1

The C-terminal domain of BRCA1(BRCT) is involved in the DNA repair pathway by recognizing the pSXXF motif in interacting proteins. It has been reported that short peptides containing this motif bind to BRCA1(BRCT) in the micromolar range with high specificity. In this work, the binding of pSXXF peptides has been studied computationally and experimentally in order to characterize their interaction with BRCA1(BRCT). Elucidation of the contacts that drive the protein–ligand interaction is critical for the development of high affinity small-molecule BRCA1 inhibitors. Molecular dynamics (MD) simulations revealed the key role of threonine at the peptide P+2 position in providing structural rigidity to the ligand in the bound state. The mutation at P+1 had minor effects. Peptide extension at the N-terminal position with the naphthyl amino acid exhibited a modest increase in binding affinity, what could be explained by the dispersion interaction of the naphthyl side-chain with a hydrophobic patch. Three in silico end-point methods were considered for the calculation of binding free energy. The Molecular Mechanics Poisson–Boltzmann Surface Area and the Solvated Interaction Energy methods gave reasonable agreement with experimental data, exhibiting a Pearlman predictive index of 0.71 and 0.78, respectively. The MM-quantum mechanics-surface area method yielded improved results, which was characterized by a Pearlman index of 0.78. The correlation coefficients were 0.59, 0.61 and 0.69, respectively. The ability to apply a QM level of theory within an end-point binding free energy protocol may provide a way for a consistent improvement of accuracy in computer-aided drug design.     

Ligand-steered modeling and docking: A benchmarking study in class A G-protein-coupled receptors

Class A G-protein-coupled receptors (GPCRs) are among the most important targets for drug discovery. However, a large set of experimental structures, essential for a structure-based approach, will likely remain unavailable in the near future. Thus, there is an actual need for modeling tools to characterize satisfactorily at least the binding site of these receptors. Using experimentally solved GPCRs, we have enhanced and validated the ligand-steered homology method through cross-modeling and investigated the performance of the thus generated models in docking-based screening. The ligand-steered modeling method uses information about existing ligands to optimize the binding site by accounting for protein flexibility. We found that our method is able to generate quality models of GPCRs by using one structural template. These models perform better than templates, crude homology models, and random selection in small-scale high-throughput docking. Better quality models typically exhibit higher enrichment in docking exercises. Moreover, they were found to be reliable for selectivity prediction. Our results support the fact that the ligand-steered homology modeling method can successfully characterize pharmacologically relevant sites through a full flexible ligand-flexible receptor procedure.