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2D binary QSAR modeling of LPA3 receptor antagonism.

ABSTRACT: A structurally diverse dataset of 119 compounds was used to develop and validate a 2D binary QSAR model for the LPA(3) receptor. The binary QSAR model was generated using an activity threshold of greater than 15% inhibition at 10 microM. The overall accuracy of the model on the training set was 82%. It had accuracies of 55% for active and 91% for inactive compounds, respectively. The model was validated using an external test set of 10 compounds. The accuracy on the external test set was 60% overall, identifying three out of seven actives and all three inactive compounds. This model was combined with similarity searching to rapidly screen libraries and select 14 candidate LPA(3) antagonists. Experimental assays confirmed 13 of these (93%) met the 15% inhibition threshold defining actives. The successful application of the model to select candidates for screening demonstrates the power of this binary QSAR model to prioritize compound selection for experimental consideration.

SUBMITTER: Fells JI

PROVIDER: S-EPMC2873117 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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2D binary QSAR modeling of LPA3 receptor antagonism.

Fells James I JI Tsukahara Ryoko R Liu Jianxiong J Tigyi Gabor G Parrill Abby L AL

Journal of molecular graphics & modelling 20100307 8

A structurally diverse dataset of 119 compounds was used to develop and validate a 2D binary QSAR model for the LPA(3) receptor. The binary QSAR model was generated using an activity threshold of greater than 15% inhibition at 10 microM. The overall accuracy of the model on the training set was 82%. It had accuracies of 55% for active and 91% for inactive compounds, respectively. The model was validated using an external test set of 10 compounds. The accuracy on the external test set was 60% ove ...[more]

PMID: 20356772

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Project description:Ordered two-dimensional arrays such as S-layers1,2 and designed analogues3-5 have intrigued bioengineers6,7, but with the exception of a single lattice formed with flexible linkers8, they are constituted from just one protein component. Materials composed of two components have considerable potential advantages for modulating assembly dynamics and incorporating more complex functionality9-12. Here we describe a computational method to generate co-assembling binary layers by designing rigid interfaces between pairs of dihedral protein building blocks, and use it to design a p6m lattice. The designed array components are soluble at millimolar concentrations, but when combined at nanomolar concentrations, they rapidly assemble into nearly crystalline micrometre-scale arrays nearly identical to the computational design model in vitro and in cells without the need for a two-dimensional support. Because the material is designed from the ground up, the components can be readily functionalized and their symmetry reconfigured, enabling formation of ligand arrays with distinguishable surfaces, which we demonstrate can drive extensive receptor clustering, downstream protein recruitment and signalling. Using atomic force microscopy on supported bilayers and quantitative microscopy on living cells, we show that arrays assembled on membranes have component stoichiometry and structure similar to arrays formed in vitro, and that our material can therefore impose order onto fundamentally disordered substrates such as cell membranes. In contrast to previously characterized cell surface receptor binding assemblies such as antibodies and nanocages, which are rapidly endocytosed, we find that large arrays assembled at the cell surface suppress endocytosis in a tunable manner, with potential therapeutic relevance for extending receptor engagement and immune evasion. Our work provides a foundation for a synthetic cell biology in which multi-protein macroscale materials are designed to modulate cell responses and reshape synthetic and living systems.

Dataset Information

2D binary QSAR modeling of LPA3 receptor antagonism.

Publications

2D binary QSAR modeling of LPA3 receptor antagonism.

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