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Computational design of ligand-binding membrane receptors with high selectivity.

ABSTRACT: Accurate modeling and design of protein-ligand interactions have broad applications in cell biology, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein-homology-modeling, ligand-docking protein-design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced-fit modes of ligand binding. In structure modeling tests, we blindly predicted, with near-atomic accuracy, ligand conformations bound to G-protein-coupled receptors (GPCRs) that have rarely been identified using traditional approaches. We also quantitatively predicted the binding selectivity of diverse ligands to structurally uncharacterized GPCRs. We then applied this technique to design functional human dopamine receptors with novel ligand-binding selectivity. Most blindly predicted ligand-binding specificities closely agreed with experimental validations. Our method should prove useful in ligand discovery approaches and in reprogramming the ligand-binding profile of membrane receptors that remain difficult to crystallize.

SUBMITTER: Feng X

PROVIDER: S-EPMC5478435 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Computational design of ligand-binding membrane receptors with high selectivity.

Feng Xiang X Ambia Joaquin J Chen Kuang-Yui M KM Young Melvin M Barth Patrick P

Nature chemical biology 20170501 7

Accurate modeling and design of protein-ligand interactions have broad applications in cell biology, synthetic biology and drug discovery but remain challenging without experimental protein structures. Here we developed an integrated protein-homology-modeling, ligand-docking protein-design approach that reconstructs protein-ligand binding sites from homolog protein structures in the presence of protein-bound ligand poses to capture conformational selection and induced-fit modes of ligand binding ...[more]

PMID: 28459439

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Project description:Apicomplexan parasites are the causative agents of globally prevalent diseases including malaria and toxoplasmosis. These obligate intracellular pathogens have evolved a sophisticated host cell invasion strategy that relies on a parasite-host cell junction anchored by interactions between apical membrane antigens (AMAs) on the parasite surface and rhoptry neck 2 (RON2) proteins discharged from the parasite and embedded in the host cell membrane. Key to formation of the AMA1-RON2 complex is displacement of an extended surface loop on AMA1 called the DII loop. While conformational flexibility of the DII loop is required to expose the mature RON2 binding groove, a definitive role of this substructure has not been elucidated. To establish a role of the DII loop in Toxoplasma gondii AMA1, we engineered a form of the protein where the mobile portion of the loop was replaced with a short Gly-Ser linker (TgAMA1?DIIloop). Isothermal titration calorimetry measurements with a panel of RON2 peptides revealed an influential role for the DII loop in governing selectivity. Most notably, an Eimeria tenella RON2 (EtRON2) peptide that showed only weak binding to TgAMA1 bound with high affinity to TgAMA1?DIIloop. To define the molecular basis for the differential binding, we determined the crystal structure of TgAMA1?DIIloop in complex with the EtRON2 peptide. When analyzed in the context of existing AMA1-RON2 structures, spatially distinct anchor points in the AMA1 groove were identified that, when engaged, appear to provide the necessary traction to outcompete the DII loop. Collectively, these data support a model where the AMA1 DII loop serves as a structural gatekeeper to selectively filter out ligands otherwise capable of binding with high affinity in the AMA1 apical groove. These data also highlight the importance of considering the functional implications of the DII loop in the ongoing development of therapeutic intervention strategies targeting the AMA1-RON2 invasion complex.

Dataset Information

Computational design of ligand-binding membrane receptors with high selectivity.

Publications

Computational design of ligand-binding membrane receptors with high selectivity.

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