Annotation of Allosteric Compounds to Enhance Bioactivity Modeling for Class A GPCRs

Proteins
often have both orthosteric and allosteric binding sites.
Endogenous ligands, such as hormones and neurotransmitters, bind to
the orthosteric site, while synthetic ligands may bind to orthosteric
or allosteric sites, which has become a focal point in drug discovery.
Usually, such allosteric modulators bind to a protein noncompetitively
with its endogenous ligand or substrate. The growing interest in allosteric
modulators has resulted in a substantial increase of these entities
and their features such as binding data in chemical libraries and
databases. Although this data surge fuels research focused on allosteric
modulators, binding data is unfortunately not always clearly indicated
as being allosteric or orthosteric. Therefore, allosteric binding
data is difficult to retrieve from databases that contain a mixture
of allosteric and orthosteric compounds. This decreases model performance
when statistical methods, such as machine learning models, are applied.
In previous work we generated an allosteric data subset of ChEMBL
release 14. In the current study an improved text mining approach
is used to retrieve the allosteric and orthosteric binding types from
the literature in ChEMBL release 22. Moreover, convolutional deep
neural networks were constructed to predict the binding types of compounds
for class A G protein-coupled receptors (GPCRs). Temporal split validation
showed the model predictiveness with Matthews correlation coefficient
(MCC) = 0.54, sensitivity allosteric = 0.54, and sensitivity orthosteric
= 0.94. Finally, this study shows that the inclusion of accurate binding
types increases binding predictions by including them as descriptor
(MCC = 0.27 improved to MCC = 0.34; validated for class A GPCRs, trained
on all GPCRs). Although the focus of this study is mainly on class
A GPCRs, binding types for all protein classes in ChEMBL were obtained
and explored. The data set is included as a supplement to this study,
allowing the reader to select the compounds and binding types of interest.