MolAlign: an algorithm for aligning multiple small molecules.

In small molecule drug discovery projects, the receptor structure is not always available. In such cases it is enormously useful to be able to align known ligands in the way they bind in the receptor. Here we shall present an algorithm for the alignment of multiple small molecule ligands. This algorithm takes pre-generated conformers as input, and proposes aligned assemblies of the ligands. The algorithm consists of two stages: the first stage is to perform alignments for each pair of ligands, the second stage makes use of the results from the first stage to build up multiple ligand alignment assemblies using a novel iterative procedure. The scoring functions are improved versions of the one mentioned in our previous work. We have compared our results with some recent publications. While an exact comparison is impossible, it is clear that our algorithm is fast and produces very competitive results.

Training a scoring function for the alignment of small molecules.

A comprehensive data set of aligned ligands with highly similar binding pockets from the Protein Data Bank has been built. Based on this data set, a scoring function for recognizing good alignment poses for small molecules has been developed. This function is based on atoms and hydrogen-bond projected features. The concept is simply that atoms and features of a similar type (hydrogen-bond acceptors/donors and hydrophobic) tend to occupy the same space in a binding pocket and atoms of incompatible types often tend to avoid the same space. Comparison with some recently published results of small molecule alignments shows that the current scoring function can lead to performance better than those of several existing methods.

Structure-based prediction of free energy changes of binding of PTP1B inhibitors.

The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.