Evolutionary Sequence Modeling for Discovery of Peptide Hormones

There are currently a large number of “orphan” G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development.Author Summary: Peptide hormones, or neuropeptides, are made up of a string of amino acids ranging from approximately 3 to 50 residues. These peptides are processed from a larger protein called a prohormone and activate a class of proteins called G-protein-coupled receptors (GPCRs). Neuropeptides signal neurons and other cells leading to changes in cellular biochemistry and potentially gene expression. There are a number of “orphan” GPCRs, i.e., receptors that have been discovered either by genomic sequence or by cloning, in which its respective peptide hormone is unknown. We have devised a computational method that models patterns in protein sequence simultaneously with evolutionary differences across species in order to identify previously unknown peptide hormones. We have used this computational methodology to identify a previously unknown putative prohormone that contains up to four potential neuropeptides, and we have characterized this prohormone with respect to location in rat brain and various human tissues. This computational technique will be useful for the identification of additional neuropeptides and help to characterize orphan GPCRs. Because roughly half of all pharmaceuticals act through activation or inhibition of GPCRs, this technique should lead to the identification of additional pharmaceutical targets and ultimately clinically used drugs.