Coevolution of Residues Provides Evidence of a Functional Heterodimer of 5-HT2AR and 5-HT2CR Involving Both Intracellular and Extracellular Domains

Abstract

Serotonin is a neurotransmitter that plays a role in regulating activities such as sleep, appetite, mood and substance abuse disorders; serotonin receptors 5-HT 2A R and 5-HT 2C R are active within pathways associated with substance abuse. It has been suggested that 5-HT 2A R and 5-HT 2C R may form a dimer that affects behavioral processes. Here we study the coevolution of residues in 5-HT 2A R and 5-HT 2C R to identify potential interactions between residues in both proteins. Coevolution studies can detect protein interactions, and since the thus uncovered interactions are subject to evolutionary pressure, they are likely functional. We assessed the significance of the 5-HT 2A R/5-HT 2C R interactions using randomized phylogenetic trees and found the coevolution significant (p-value = 0.01). We also discuss how co-expression of the receptors suggests the predicted interaction is functional. Finally, we analyze how several single nucleotide polymorphisms for the 5-HT 2A R and 5-HT 2C R genes affect their interaction. Our findings are the

Co-evolutionary analysis accurately predicts details of interactions between the Integrator complex subunits

“…However, the applications of DCA methods to large proteins are limited by high number of typically generated false positives. To reduce the number of false positives we have recently proposed to use a local convolution of evolutionary coupling (EC) scores with a kernel fitted to the structure of the proteins (Fongang et al 2019). Here, we will adapt DCA to study interactions of the Integrator complex subunits, which are particularly challenging due to size of some of the subunits.…”

“…DCA algorithms have been shown to produce a large number of false positives, but we recently suggested (Fongang et al 2019) that post-processing of DCA map data, based on the local convolution with Gaussian kernel, may lead to reducing noise in the prediction of most likely interacting residues (Fongang et al 2019). A schematic description of the method is presented in Fig.…”

“…To improve our prediction and avoid false positive in DCA analysis, we use local convolution of ECs, derived from our approach introduced previously in (Fongang et al 2019). Gaussian convolution is applied to local structural elements (here defined by secondary structure) to count the contribution of neighboring residues with an assumption that contacts between proteins occur locally and drive residues evolving within the same structural elements.…”

“…2a). Therefore, we applied our local convolution algorithm (Fongang et al 2019) to the EC map of INTS9/INTS11 with variable parameters describing the variances of the Gaussian kernel and the lengths of stretches of interacting residues ( Fig. 2b-2d).…”

“…Co-evolution of residues in protein sequences has been used for predicting residue-residue interactions (contacts) in small, bacterial proteins for a few decades (Lockless and Ranganathan 1999; Morcos et al 2011;Ochoa et al 2013). As observed by us and others Hopf et al 2014b;Fongang et al 2019), with the rapid increase in number of sequenced animal genomes, these methods also became feasible for protein interactions in metazoans, including humans, and for larger proteins and even protein complexes Naveed et al 2012;Hopf et al 2014b;Kim et al 2014;Ovchinnikov et al 2014;dos Santos et al 2015;Tesileanu et al 2015;Abriata et al 2016;Champeimont et al 2016;Feinauer et al 2016;Lua et al 2016;Neuwald 2016;Yu et al 2016).…”