How to Choose the Suitable Template for Homology Modelling of GPCRs: 5‐HT₇ Receptor as a Test Case

Abstract

G protein-coupled receptors (GPCRs) are a super-family of membrane proteins that attract great pharmaceutical interest due to their involvement in almost every physiological activity, including extracellular stimuli, neurotransmission, and hormone regulation. Currently, structural information on many GPCRs is mainly obtained by the techniques of computer modelling in general and by homology modelling in particular. Based on a quantitative analysis of eighteen antagonist-bound, resolved structures of rhodopsin family "A" receptors - also used as templates to build 153 homology models - it was concluded that a higher sequence identity between two receptors does not guarantee a lower RMSD between their structures, especially when their pair-wise sequence identity (within trans-membrane domain and/or in binding pocket) lies between 25 % and 40 %. This study suggests that we should consider all template receptors having a sequence identity ≤50 % with the query receptor. In fact, most of the GPCRs, compared to the currently available resolved structures of GPCRs, fall within this range and lack a correlation between structure and sequence. When testing suitability for structure-based drug design, it was found that choosing as a template the most similar resolved protein, based on sequence resemblance only, led to unsound results in many cases. Molecular docking analyses were carried out, and enrichment factors as well as attrition rates were utilized as criteria for assessing suitability for structure-based drug design.

A Comparative Study of Modern Homology Modeling Algorithms for Rhodopsin Structure Prediction

“…However, it can still be quite high up to 20% sequence identity. These findings are in accordance with previous studies that considered the comparative modeling of G protein-coupled receptors (GPCRs). − In Figure , this dependence is also visualized by clustering. In this clustering, the distance between each pair of proteins (say A and B) is higher when the structure building algorithm produces models of target A based on template B and vice versa, of lower quality.…”

“…On the other hand, we found that models of good quality, especially in the TM region, can be generated even in the 20–40% region of target-template sequence identity. These findings were earlier reported for the homology modeling of GPCRs. − …”

“…On the other hand, we found that models of good quality, especially in the TM region, can be generated even in the 20–40% region of target-template sequence identity. These findings were earlier reported for the homology modeling of GPCRs. − Medeller protocol sometimes poorly predicts the flexible regions of rhodopsins. However, the TM part of models produced by Medeller was of high quality even when the target-template sequence identity was very low. The choice of the homology modeling methodology depends on the requirements for loops and tails.…”

Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties

“…In efforts to detect novel bioactive ligands, ligand-based techniques, including properties-based and pharmacophore-based tools, are being used more and more for modeling the bioactivity of molecules and for the virtual screening of large chemical databases [ 35 , 36 , 37 , 38 ]. Ligand-based modeling tools use optimization algorithms such as Monte Carlo simulations (MCs), simulated annealing (SA) [ 39 ], genetic algorithms (Gas) [ 40 ], neural networks (NNs) [ 41 ], support vector machines (SVM) [ 42 ], the k-nearest neighbor algorithm (kNN) [ 43 , 44 ], Bayesian classifiers and some combinations thereof (Monte Carlo/ simulated annealing algorithm, MCSA) [ 45 , 46 , 47 , 48 , 49 ]. Distinguishing between active and inactive ligands that are useful for treating a certain disease may be accomplished by using sets of active and inactive chemicals and certain optimization techniques [ 50 , 51 , 52 ].…”