An electronic environment and contact direction sensitive scoring function for predicting affinities of protein-ligand complexes in Contour(®).

Contour(®) is a computational structure-based drug design technology that grows drug-like molecules by assembling context sensitive fragments in well-defined binding pockets. The grown molecules are scored by a novel empirical scoring function developed using high-resolution crystal structures of diverse classes of protein-ligand complexes and associated experimental binding affinities. An atomic model bearing features of the valence bond and VSEPR theories embodying their molecular electronic environment has been developed for non-covalent intermolecular interactions. On the basis of atomic hybridization and polarization states, each atom is modeled by features representing electron lone pairs, p-orbitals, and polar and non-polar hydrogens. A simple formal charge model was used to differentiate between polar and non-polar atoms. The interaction energy and the desolvation contribution of the protein-ligand association energy is computed as a linear sum of pair-wise interactions and desolvation terms. The pair-wise interaction energy captures short-range positive electrostatic interactions via hydrogen bonds, electrostatic repulsion of like charges, and non-bond contacts. The desolvation energy is estimated by calculating the energy required to desolvate interaction surfaces of the protein and the ligand in the complex. The scoring function predicts binding energies of a diverse set of protein-ligand complexes used for training with a correlation coefficient of 0.61. It also performs equally well in predicting association energies of a diverse validation set of protein-ligand complexes with a correlation coefficient of 0.57, which is equivalent to or better than 12 other scoring functions tested against this set including X-Score, GOLD, and DrugScore.

Structure-based design technology contour and its application to the design of renin inhibitors.

It is well-known that the structure-based design approach has had a measurable impact on the drug discovery process in identifying novel and efficacious therapeutic agents for a variety of disease targets. The de novo design approach has inherent potential to generate novel molecules that best fit into a protein binding site when compared to all of the computational methods applied to structure-based design. In its initial attempts, this approach did not achieve much success due to technical hurdles. More recently, the algorithmic advancements in the methodologies and clever strategies developed to design drug-like molecules have improved the success rate. We describe a state-of-the-art structure-based design technology called Contour and provide details of the algorithmic enhancements we have implemented. Contour was designed to create novel drug-like molecules by assembling synthetically viable fragments in the protein binding site using a high-resolution crystal structure of the protein. The technology consists of a sophisticated growth algorithm and a novel scoring function based on a directional model. The growth algorithm generates molecules by dynamically selecting only those fragments from the fragment library that are complementary to the binding site, and assembling them by sampling the conformational space for each attached fragment. The scoring function embodying the essential elements of the binding interactions aids in the rank ordering of grown molecules and helps identify those that have high probability of exhibiting activity against the protein target of interest. The application of Contour to identify inhibitors against human renin enzyme eventually leading to the clinical candidate VTP-27,999 will be discussed here.