Development and Validation of a Novel Protein−Ligand Fingerprint To Mine Chemogenomic Space: Application to G Protein-Coupled Receptors and Their Ligands

Abstract

The present study introduces a novel low-dimensionality fingerprint encoding both ligand and target properties which is suitable to mine protein-ligand chemogenomic space. Whereas ligand properties have been represented by standard descriptors, protein cavities are encoded by a fixed length bit string describing pharmacophoric properties of a definite number of binding site residues. In order to simplify the cavity fingerprint, the concept was applied here to a unique family of targets (G protein-coupled receptors) with a homogeneous cavity description. Particular attention was given to set up data sets of really diverse protein-ligand pairs covering as exhaustively as possible both ligand and target spaces. Several machine learning classification algorithms were trained on two sets of roughly 200000 receptor-ligand fingerprints with a different definition of inactive decoys. Cross-validated models show excellent precision (>0.9) in distinguishing true from false pairs with a particular preference for support vector machine classifiers. When applied to two external test sets of GPCR ligands, the most predictive models were not those performing the best in the previous cross-validation. The ability to recover true GPCR ligands (ligand prediction mode) or true GPCRs (receptor prediction mode) depends on multiple parameters: the molecular complexity of the ligands, the chemical space from which ligand decoys are selected to generate false protein-ligand pairs, and the target space under consideration. In most cases, predicting ligands is easier than predicting receptors. Although receptor profiling is possible, it probably requires a more detailed description of the ligand-binding site. Noteworthy, protein-ligand fingerprints outperform the corresponding ligand fingerprints in mining the GPCR-ligand space. Since they can be applied to a much larger number of receptors than ligand-based fingerprints, protein-ligand fingerprints represent a novel and promising way to directly screen protein-ligand pairs in chemogenomic applications.

Identifying mechanism-of-action targets for drugs and probes

“…In the teeth of current opinion, we found that few drugs lack reasonable primary targets, at least among domain experts. The ability of chemoinformatics methods to rapidly identify targets, and to predict those which can be experimentally confirmed, supports the idea that this and related methods (11,12,(46)(47)(48) will be useful in target identification for drugs that emerge from nontarget-based methods. Even in this molecular era of drug discovery, there remain new investigational drugs whose molecular targets are unclear, restricting their optimization and broad use in disease.…”

From Laptop to Benchtop to Bedside: Structure-based Drug Design on Protein Targets

“…Examples include the similarity ensemble approach developed by Keiser et al [346], three-dimensional shape based approach for identifying diverse targets [358], structural similarities of molecular scaffolds to map drug promiscuity GPCRs by encoding both, ligand descriptors and protein pharmacophoric properties, in a low dimensional fingerprint for chemogenomic screening applications [172]. Text mining approaches [361] and semantic framework based methods [362] have been applied to map and predict polypharmacology using widely available biochemical information contained in publicly available resources.…”

“…1b) [172]. Their method combines standard fingerprint representations of ligand with a protein "cavity fingerprint (CavFP)" representing pharmacophore features of protein binding site residues.…”

“…PLFP model was trained by known protein-ligand pairs, and in GPCR case, this combined model outperformed other corresponding ligand-fingerprint-only models in mining chemogenomic space. While ligand prediction (ligand screening) performed better than receptor prediction (target profiling), PLFP was already robust in terms of the high recall and precision, short hit list size and low hit rank in ligand screening [172]. This approach can be utilized in future polypharmacology design to directly predict chemogenomic protein-ligand pairs.…”

In silico structure‐activity‐relationship (SAR) models from machine learning: a review

“…A novel yet critical requirement is on explicit target-ligand complex/relationship representation (i.e., representing the binding event/relationship between biologicals and chemicals). In chemogenomics-based methods, the complexes are represented/modeled by protein-ligand fingerprints [Weill and Rognan, 2009], protein and ligand descriptor concatenation [Bock and Gough, 2005], protein space and ligand space tensor product and kernel fusion [Jacob and Vert, 2008], among others.…”