Dataset Information

Modeling Structural Coordination and Ligand Binding in Zinc Proteins with a Polarizable Potential.

ABSTRACT: As the second most abundant cation in human body, zinc is vital for the structures and functions of many proteins. Zinc-containing matrix metalloproteinases (MMPs) have been widely investigated as potential drug targets in a range of diseases ranging from cardiovascular disorders to cancers. However, it remains a challenge in theoretical studies to treat zinc in proteins with classical mechanics. In this study, we examined Zn(2+) coordination with organic compounds and protein side chains using a polarizable atomic multipole based electrostatic model. We find that polarization effect plays a determining role in Zn(2+) coordination geometry in both matrix metalloproteinase (MMP) complexes and in zinc-finger proteins. In addition, the relative binding free energies of selected inhibitors binding with MMP13 have been estimated and compared with experimental results. While not directly interacting with the small molecule inhibitors, the permanent and polarizing field of Zn(2+) exerts a strong influence on the relative affinities of the ligands. The simulation results also reveal the polarization effect on binding is ligand dependent and thus difficult to be incorporated into fixed-charge models implicitly.

SUBMITTER: Zhang J

PROVIDER: S-EPMC3383645 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Modeling Structural Coordination and Ligand Binding in Zinc Proteins with a Polarizable Potential.

Zhang Jiajing J Yang Wei W Piquemal Jean-Philip JP Ren Pengyu P

Journal of chemical theory and computation 20120102 4

As the second most abundant cation in human body, zinc is vital for the structures and functions of many proteins. Zinc-containing matrix metalloproteinases (MMPs) have been widely investigated as potential drug targets in a range of diseases ranging from cardiovascular disorders to cancers. However, it remains a challenge in theoretical studies to treat zinc in proteins with classical mechanics. In this study, we examined Zn(2+) coordination with organic compounds and protein side chains using ...[more]

PMID: 22754403

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Project description:S100 proteins are a family of small, EF-hand containing calcium-binding signaling proteins that are implicated in many cancers. While the majority of human S100 proteins share 25-65% sequence similarity, S100A7 and its recently identified paralog, S100A15, display 93% sequence identity. Intriguingly, however, S100A7 and S100A15 serve distinct roles in inflammatory skin disease; S100A7 signals through the receptor for advanced glycation products (RAGE) in a zinc-dependent manner, while S100A15 signals through a yet unidentified G-protein coupled receptor in a zinc-independent manner. Of the seven divergent residues that differentiate S100A7 and S100A15, four cluster in a zinc-binding region and the remaining three localize to a predicted receptor-binding surface.To investigate the structural and functional consequences of these divergent clusters, we report the X-ray crystal structures of S100A15 and S100A7D24G, a hybrid variant where the zinc ligand Asp24 of S100A7 has been substituted with the glycine of S100A15, to 1.7 Å and 1.6 Å resolution, respectively. Remarkably, despite replacement of the Asp ligand, zinc binding is retained at the S100A15 dimer interface with distorted tetrahedral geometry and a chloride ion serving as an exogenous fourth ligand. Zinc binding was confirmed using anomalous difference maps and solution binding studies that revealed similar affinities of zinc for S100A15 and S100A7. Additionally, the predicted receptor-binding surface on S100A7 is substantially more basic in S100A15 without incurring structural rearrangement.Here we demonstrate that S100A15 retains the ability to coordinate zinc through incorporation of an exogenous ligand resulting in a unique zinc-binding site among S100 proteins. The altered surface chemistry between S100A7 and S100A15 that localizes to the predicted receptor binding site is likely responsible for the differential recognition of distinct protein targets. Collectively, these data provide novel insight into the structural and functional consequences of the divergent surfaces between S100A7 and S100A15 that may be exploited for targeted therapies.

Project description:BackgroundModels that are capable of reliably predicting binding affinities for protein-ligand complexes play an important role the field of structure-guided drug design.MethodsHere, we begin by applying the computational geometry technique of Delaunay tessellation to each set of atomic coordinates for over 1400 diverse macromolecular structures, for the purpose of deriving a four-body statistical potential that serves as a topological scoring function. Next, we identify a second, independent set of three hundred protein-ligand complexes, having both high-resolution structures and known dissociation constants. Two-thirds of these complexes are randomly selected to train a predictive model of binding affinity as follows: two tessellations are generated in each case, one for the entire complex and another strictly for the isolated protein without its bound ligand, and a topological score is computed for each tessellation with the four-body potential. Predicted protein-ligand binding affinity is then based on an empirically derived linear function of the difference between both topological scores, one that appropriately scales the value of this difference.ResultsA comparison between experimental and calculated binding affinity values over the two hundred complexes reveals a Pearson's correlation coefficient of r = 0.79 with a standard error of SE = 1.98 kcal/mol. To validate the method, we similarly generated two tessellations for each of the remaining protein-ligand complexes, computed their topological scores and the difference between the two scores for each complex, and applied the previously derived linear transformation of this topological score difference to predict binding affinities. For these one hundred complexes, we again observe a correlation of r = 0.79 (SE = 1.93 kcal/mol) between known and calculated binding affinities. Applying our model to an independent test set of high-resolution structures for three hundred diverse enzyme-inhibitor complexes, each with an experimentally known inhibition constant, also yields a correlation of r = 0.79 (SE = 2.39 kcal/mol) between experimental and calculated binding energies.ConclusionsLastly, we generate predictions with our model on a diverse test set of one hundred protein-ligand complexes previously used to benchmark 15 related methods, and our correlation of r = 0.66 between the calculated and experimental binding energies for this dataset exceeds those of the other approaches. Compared with these related prediction methods, our approach stands out based on salient features that include the reliability of our model, combined with the rapidity of the generated predictions, which are less than one second for an average sized complex.

Dataset Information

Modeling Structural Coordination and Ligand Binding in Zinc Proteins with a Polarizable Potential.

Publications

Modeling Structural Coordination and Ligand Binding in Zinc Proteins with a Polarizable Potential.

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