O-85 Predicting the effect of phytoconstituents from Cocoa on human health

Methods: Cocoa’s phytoconstituents were identified from the literature and aligned with unique identifiers in PubChem. Molecular modelling was performed in ChEMBL to predict target proteins and return standardized values indicating the strength of the predicted interactions. The R programming language utilized the package PCSF and prior knowledge from STRING to identify affected protein networks. Utilizing the protein database UniProt and the gene annotation database GO, the R package clusterProfiler extracted biological processes that were significantly (p<0.0001) altered.

Results: Twenty-six phytoconstituents had unique identifiers. ChEMBL generated 1,247 unique phytoconstituentprotein interactions with a mean (±SD) standard value of 5.46±1.08. 98.9% were single proteins, three hundred (24.0%) were G-protein-coupled receptors (GPCRs), 59 (4.7%) were protein kinases, 30 (2.4%) were nuclear receptors and 849 (68.1%) were classed as “other”. The PCSF algorithm generated a network containing 112 Terminal Nodes, 132 Edges and 14 Steiner Nodes. ClusterProfiler refined this into subnetworks, mapped the target proteins to UniProt and using the GO database of gene annotations, returned 10 significantly (p<0.0001) altered biological processes with GO Terms. GO Terms relate the action of gene products and the biological processes involved. These were one-carbon metabolic process, dopamine uptake, synaptic transmission, dopaminergic catecholamine uptake, estrogen metabolic process, catecholamine transport, dopamine uptake involved in synaptic transmission, catecholamine uptake involved in synaptic transmission, norepinephrine transport and extracellular matrix disassembly. Seven of these processes imply effects on synaptic function and cognition and may be associated with the pathophysiology of neurodegenerative disorders.

Conclusion: Phytoconstituents found in Cocoa may impact biological processes that promote cognitive function. The bioinformatic procedures employed represent steps that can evolve into bioinformatic “pipelines” to query the effect of phytoconstituents on biological processes in human health.