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Characterization of the High-Affinity Drug Ligand Binding Site of Mouse Recombinant TSPO.

ABSTRACT: The optimization of translocator protein (TSPO) ligands for Positron Emission Tomography as well as for the modulation of neurosteroids is a critical necessity for the development of TSPO-based diagnostics and therapeutics of neuropsychiatrics and neurodegenerative disorders. Structural hints on the interaction site and ligand binding mechanism are essential for the development of efficient TSPO ligands. Recently published atomic structures of recombinant mammalian and bacterial TSPO1, bound with either the high-affinity drug ligand PK 11195 or protoporphyrin IX, have revealed the membrane protein topology and the ligand binding pocket. The ligand is surrounded by amino acids from the five transmembrane helices as well as the cytosolic loops. However, the precise mechanism of ligand binding remains unknown. Previous biochemical studies had suggested that ligand selectivity and binding was governed by these loops. We performed site-directed mutagenesis to further test this hypothesis and measured the binding affinities. We show that aromatic residues (Y34 and F100) from the cytosolic loops contribute to PK 11195 access to its binding site. Limited proteolytic digestion, circular dichroism and solution two-dimensional (2-D) NMR using selective amino acid labelling provide information on the intramolecular flexibility and conformational changes in the TSPO structure upon PK 11195 binding. We also discuss the differences in the PK 11195 binding affinities and the primary structure between TSPO (TSPO1) and its paralogous gene product TSPO2.

SUBMITTER: Iatmanen-Harbi S

PROVIDER: S-EPMC6470738 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Project description:Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Dataset Information

Characterization of the High-Affinity Drug Ligand Binding Site of Mouse Recombinant TSPO.

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