GPCR homology model template selection benchmarking: Global versus local similarity measures

Abstract

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development. GPCR ligand interaction studies often have a starting point with either crystal structures or comparative models. The majority of GPCR do not have experimentally-characterized 3-dimensional structures, so comparative modeling is a good structure-based starting point. Comparative modeling is a widely used method for generating models of proteins with unknown structures by analogy to crystallized proteins that are expected to exhibit structural conservation. Traditionally, comparative modeling template selection is based on global sequence identity and shared function. However high sequence identity localized to the ligand binding pocket may produce better models to examine protein-ligand interactions. This in silico benchmark study examined the performance of a global versus local similarity measure applied to comparative modeling template selection for 6 previously crystallized, class A GCPR (CXCR4, FFAR1, NOP, P2Y12, OPRK, and M1) with the long-term goal of optimizing GPCR ligand identification efforts. Comparative models were generated from templates selected using both global and local similarity measures. Similarity to reference crystal structures was reflected in RMSD values between atom positions throughout the structure or localized to the ligand binding pocket. Overall, models deviated from the reference crystal structure to a similar degree regardless of whether the template was selected using a global or local similarity measure. Ligand docking simulations were performed to assess relative performance in predicting protein-ligand complex structures and interaction networks. Calculated RMSD values between ligand poses from docking simulations and crystal structures indicate that models based on locally selected templates give docked poses that better mimic crystallographic ligand positions than those based on globally-selected templates in five of the six benchmark cases. However, protein model refinement strategies in advance of ligand docking applications are clearly essential as the average RMSD between crystallographic poses and poses docked into local template models was 9.7 Å and typically less than half of the ligand interaction sites are shared between the docked and crystallographic poses. These data support the utilization of local similarity measures to guide template selection in protocols using comparative models to investigate ligand-receptor interactions.

BIO-GATS: A Tool for Automated GPCR Template Selection Through a Biophysical Approach for Homology Modeling

“…The best benchmarked modeling pair choices, as well as pairs which did not perform well, were considered for the analysis. The performance of the templates was ranked as good or bad, in published studies, on the basis of good ligand enrichment in VLS ( Perry et al, 2015 ; Loo et al, 2018 ; Jaiteh et al, 2020 ), local and global (RMSD) from crystal structures ( Castleman et al, 2019 ), and both ligand enrichment and RMSD from the crystal structure ( Shahaf et al, 2016 ). Researchers have compared varied parameters in these studies among the target-template pairs, including global sequence identity, TM-wise sequence identity, local sequence identity (identity within the binding pocket), model refinement through molecular dynamics and/or induced-fit docking, and the ligand binding site plasticity.…”

“…Also, sequence-structure correlation is not always implied according to the published studies, for instance, the model of P2Y 12 R based on P2Y 12 R- PAR1_HUMAN pair (sequence identity: 23%) was closer to the P2Y 12 R crystal structure in comparison with the model based on the P2Y 12 R- OPRK_HUMAN pair (sequence identity: 28%) ( Castleman et al, 2019 ). We note that the St scores reported here correctly rank PAR1_HUMAN as the best template over the other three templates ( Table 1 ), without model building and VLS.…”

“…Numerous benchmarking studies have been conducted by incorporating global and local similarity measures to select the appropriate template for GPCRs. Models based on local similarity measures have produced better results in virtual screening experiments ( Castleman et al, 2019 ; Szwabowski et al, 2020 ). Multiple studies have shown that sequence identity above 30% could result in good GPCR homology models (within 3 Å) ( Shahaf et al, 2016 ; Loo et al, 2018 ; Jaiteh et al, 2020 ).…”

Importance of Homology Modeling for Predicting the Structures of GPCRs

“…First step in developing homology models is the alignment of fingerprint motifs that are common among the family which are then are extrapolated to assign coordinates for the entire helical bundle. On the basis of databases of loop conformations and based on the specific application loop regions are either ignored or modeled accordingly [35]. As the template and query sequences used in homology modeling both belong to the GPCR family, the seven transmembrane (TM) helixes were properly transformed in the models according to that of the template structure.…”

Orphan G protein-coupled receptor, GPR37L1: pharmacological toolbox empty once again

“…The CoINPocket methodology was also used to identify new ligand pairings in the evolutionarily unrelated protein lysine methyltransferase protein family 13 . Beyond the search for pharmacological similarities, the GPCR-CoINPocket approach helped improve template selection for accurate GPCR homology modeling 14 and delineate the common activation mechanism in Class A GPCRs 15 . These examples illustrate the breadth of impact of GPCR-CoINPocket at the intersection of computational and experimental biology.…”