Marlow, B. | Search bioRxiv

bioRxiv2020

Structural Determinants of Cholesterol Recognition in Helical Integral Membrane Proteins

Cholesterol (CLR) is an integral component of mammalian membranes. It has been shown to modulate membrane dynamics and alter integral membrane protein (IMP) function. However, understanding the molecular mechanisms of these processes is complicated by limited and conflicting structural data: Specifically, in co-crystal structures of CLR-IMP complexes it is difficult to distinguish specific and biologically relevant CLR-IMP interactions from a nonspecific association captured by the crystallization process. The only widely recognized search algorithm for CLR-IMP interaction sites is sequence-based, i.e. searching for the so-called CRAC or CARC motifs. While these motifs are present in numerous IMPs, there is inconclusive evidence to support their necessity or sufficiency for CLR binding. Here we leverage the increasing number of experimental CLR-IMP structures to systematically analyze putative interaction sites based on their spatial arrangement and evolutionary conservation. From this analysis we create three-dimensional representations of general CLR interaction sites that form clusters across multiple IMP classes and classify them as being either specific or nonspecific. Information gleaned from our characterization will eventually enable a structure-based approach for prediction and design of CLR-IMP interaction sites. SIGNIFICANCECLR plays an important role in composition and function of membranes and often surrounds and interacts with IMPs. It is a daunting challenge to disentangle CLRs dual roles as a direct modulator of IMP function through binding or indirect actor as a modulator of membrane plasticity. Only recently studies have delved into characterizing specific CLR-IMP interactions. We build on this previous work by using a combination of structural and evolutionary characteristics to distinguish specific from nonspecific CLR interaction sites. Understanding how CLR interacts with IMPs will underpin future development towards detecting and engineering CLR-IMP interaction sites.

biophysics↗

bioRxiv2019

RosettaGPCR: Multiple Template Homology Modeling of GPCRs with Rosetta

G-protein coupled receptors (GPCRs) represent a significant target class for pharmaceutical therapies. However, to date, only about 10% of druggable GPCRs have had their structures characterized at atomic resolution. Further, because of the flexibility of GPCRs, alternative conformations remain to be modeled, even after an experimental structure is available. Thus, computational modeling of GPCRs is a crucial component for understanding biological function and to aid development of new therapeutics. Previous single- and multi-template homology modeling protocols in Rosetta often generated non-native-like conformations of transmembrane -helices and/or extracellular loops. Here we present a new Rosetta protocol for modeling GPCRs that is improved in two critical ways: Firstly, it uses a blended sequence- and structure-based alignment that now accounts for structure conservation in extracellular loops. Secondly, by merging multiple template structures into one comparative model, the best possible template for every region of a target GPCR can be used expanding the conformational space sampled in a meaningful way. This new method allows for accurate modeling of receptors using templates as low as 20% sequence identity, which accounts for nearly the entire druggable space of GPCRs. A model database of all non-odorant GPCRs is made available at www.rosettagpcr.org. Author SummaryStructure-based drug discovery is among the new technologies driving the development of next generation therapeutics. Inherent to this process is the availability of a protein structure for virtual screening. The most heavily drugged protein family, G-protein coupled receptors (GPCRs), however suffers from a lack of experimental structures that could hinder drug development. Technical challenges prevent the determination of every protein structure, so we turn to computational modeling to predict the structures of the remaining proteins. Again, traditional techniques fail due to the high divergence of this family. Here, we build on available methods specifically for the challenge of modeling GPCRs. This new method outperforms other methods and allows for the ability to accurately model nearly 90% of the entire GPCR family. We therefore generate a model database of all GPCRs (www.rosettagpcr.org) for use in future drug development.

bioinformatics↗