Ingo Muegge

Effect of ligand volume correction on PMF scoring

Recently, a knowledge‐based scoring function has been introduced that estimates the protein‐binding affinity based on the 3D structure of a protein–ligand complex (J Med Chem 1999, 42, 791). A ligand volume correction factor has been proposed and applied to filter out intraligand interactions in this simplified potential approach. Here we evaluate the effect of the ligand volume correction on the predictive power of the PMF scoring function. It is found that the effect of the ligand volume correction is significant on the derived potentials and large on the overall score. However, the effect of the ligand correction on the predictive power of the scoring function appears to be smaller. For a test set containing serine proteases the predictive power of the PMF scoring function does not change with the introduction of the volume correction. For a test set of metalloprotease complexes, the predictive power of the PMF scoring function improves only slightly when the volume correction is applied. For five test sets comprising a total of 225 diverse protein ligand complexes taken from the Brookhaven Protein Data Bank it is found, however, that the introduction of the ligand volume correction consistently improves the correlation between the PMF scores and the measured binding affinities. The effect of the correction factor on docking/scoring experiments is also analyzed using a test set of 61 biphenyl inhibitor‐stromelysin complexes.

A knowledge-based scoring function for protein-ligand interactions: Probing the reference state

Knowledge-based scoring functions have recently emerged as an alternative and very promising way of ranking protein-ligand complexes with known 3D structure according to their binding affinities. Theses implified potential-based approaches use the structural information stored in databases of protein-ligand complexes to derive atom pair interaction potentials also known as potentials of mean force (PMF). The derived PMF depend on the definition of a suitable reference state. The reference states vary among suggested knowledge-based scoring functions. Therefore, we attempt here to shed some light on the influence of different reference state definitions on the predictive power of a knowledge-based scoring function that has been introduced by us very recently [J. Med. Chem., 42 (1999) 791]. It is shown that a reference state that implicitly and more comprehensively accounts for protein and ligand solvation gives the most consistent scoring results for four test sets of diverse protein-ligand complexes taken from the Brookhaven Protein Data Bank. It is also shown that a reference sphere radius of at least 7–8 A is needed to effectively capture solvation effects that are treated implicitly in the scoring function.

Solid-Phase synthesis and investigation of benzofurans as selective estrogen receptor modulators

A library of benzofurans was prepared by solid-phase synthesis methods, and several analogues were identified as potent ligands for the estrogen receptors ER-alpha and ER-beta, with some compounds having selectivity for ER-alpha. Analogues designed to more closely mimic Raloxifene were less effective. Certain benzofurans were effective in a bone pit assay, but were characterized as agonists in a MCF-7 breast tumor cell proliferation assay.

3D‐Quantitative Structure Activity Relationships of Biphenyl Carboxylic Acid MMP‐3 Inhibitors: Exploring Automated Docking as Alignment Method

A series of CoMFA models have been derived from docking-based and atom-based alignments. The statistics of these approaches has been compared to determine whether a docking approach can be employed as an automated alignment tool for the development of 3D-QSAR models. Using a well-characterized training set of 51 biphenyl carboxylic acid MMP-3 inhibitors, the docking-based alignment provided by a DOCK4/PMF-scoring protocol has yielded statistically significant, cross-validated CoMFA models comparable to those derived with a traditional atom-based alignment technique. Field fit minimization has been applied to refine the atom-based and docking-based alignments. The refinement appears to be beneficial for the docking-based approach. For the atom-based alignment, however, field-fit refinement has not resulted in improved CoMFA models. The statistically best CoMFA model has been created by the atom-based alignment that has been found, however, to be inconsistent with the stromelysin crystal structure. The docking alignment refined by field-fit alignment has resulted in a final alignment that is consistent with the crystal structure and only slightly statistically inferior to the atom-based aligned CoMFA model. The results show␣the ability of an automated docking/field-fit alignment technique to provide self-consistent CoMFA alignments.