Ligand-induced conformational changes: improved predictions of ligand binding conformations and affinities.
A computational docking strategy using multiple conformations of the target protein is discussed and evaluated. A series of low molecular weight, competitive, nonpeptide protein tyrosine phosphatase inhibitors are considered for which the x-ray crystallographic structures in complex with protein tyrosine phosphatase 1B (PTP1B) are known. To obtain a quantitative measure of the impact of conformational changes induced by the inhibitors, these were docked to the active site region of various structures of PTP1B using the docking program FlexX. Firstly, the inhibitors were docked to a PTP1B crystal structure cocrystallized with a hexapeptide. The estimated binding energies for various docking modes as well as the RMS differences between the docked compounds and the crystallographic structure were calculated. In this scenario the estimated binding energies were not predictive inasmuch as docking modes with low estimated binding energies corresponded to relatively large RMS differences when aligned with the corresponding crystal structure. Secondly, the inhibitors were docked to their parent protein structures in which they were cocrystallized. In this case, there was a good correlation between low predicted binding energy and a correct docking mode. Thirdly, to improve the predictability of the docking procedure in the general case, where only a single target protein structure is known, we evaluate an approach which takes possible protein side-chain conformational changes into account. Here, side chains exposed to the active site were considered in their allowed rotamer conformations and protein models containing all possible combinations of side-chain rotamers were generated. To evaluate which of these modeled active sites is the most likely binding site conformation for a certain inhibitor, the inhibitors were docked against all active site models. The receptor rotamer model corresponding to the lowest estimated binding energy is taken as the top candidate. Using this protocol, correct inhibitor binding modes could successfully be discriminated from proposed incorrect binding modes. Moreover, the ranking of the estimated ligand binding energies was in good agreement with experimentally observed binding affinities.
Frimurer
Thomas M
TM
7TM Pharma, Rønnegade 2, DK-2100 Copenhagen Ø, Denmark.
Peters
Günther H
GH
Iversen
Lars F
LF
Andersen
Henrik S
HS
Møller
Niels Peter H
NP
Olsen
Ole H
OH
D001665:Binding Sites; D003198:Computer Simulation; D003461:Crystallography / Q000379:methods*; D004789:Enzyme Activation; D004791:Enzyme Inhibitors / Q000737:chemistry*; D006801:Humans; D008024:Ligands; D046911:Macromolecular Substances; D008958:Models, Molecular; D009842:Oligopeptides / Q000737:chemistry*; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D021122:Protein Subunits; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1; D017027:Protein Tyrosine Phosphatases / Q000037:antagonists & inhibitors* / Q000737:chemistry*; D011786:Quality Control
C479342:DADEpYL-NH2; D004791:Enzyme Inhibitors; D008024:Ligands; D046911:Macromolecular Substances; D009842:Oligopeptides; D021122:Protein Subunits; C516607:PTPN1 protein, human; D054562:Protein Tyrosine Phosphatase, Non-Receptor Type 1; D017027:Protein Tyrosine Phosphatases