Template-based modeling of insect odorant receptors outperforms AlphaFold3 for ligand binding predictions

Abstract

Insects rely on odorant receptors (ORs) to detect and respond to volatile environmental cues, so the ORs are attracting increasing interest as potential targets for pest control. However, experimental analysis of their structures and functions faces significant challenges. Computational methods such as template-based modeling (TBM) and AlphaFold3 (AF3) could facilitate the structural characterisation of ORs. This study first showed that both models accurately predicted the structural fold of MhOR5, a jumping bristletail OR with known experimental 3D structures, although accuracy was higher in the extracellular region of the protein and binding mode of their cognate ligands with TBM. The two approaches were then compared for their ability to predict the empirical binding evidence available for OR-odorant complexes in two economically important fruit fly species, Bactrocera dorsalis and B. minax. Post-simulation analyses including binding affinities, complex and ligand stability and receptor-ligand interactions (RLIs) revealed that TBM performed better than AF3 in discriminating between binder and non-binder complexes. TBM's superior performance is attributed to hydrophobicity-based helix-wise multiple sequence alignment (MSA) between available insect OR templates and the ORs for which the binding data were generated. This MSA identified conserved residues and motifs which could be used as anchor points for refining the alignments.

Keywords: Bactrocera; Molecular docking; Molecular dynamics; Receptor-ligand interactions; Transmembrane proteins.

Fig. 1

Superimposition of the experimental 7LID structure on the TBM and AF3 models. (a) Side and extracellular views of 7LID (salmon color) superimposed on the TBM model (blue). (b) Side and extracellular views of 7LID (salmon) superimposed on the AF3 model (green). Images were generated with Chimera. (c) Helix-wise superimposition of 7LID (salmon), MhOR5_AF3 (green), and MhOR5_TBM (blue). (d) Structure-based sequence alignment of 7LID, MhOR5_AF3 and MhOR5_HM generated with Geneious version 2023.2 and highlighted according to hydrophobicity, from hydrophobic (red) to hydrophilic (blue).

Fig. 2

Performance of TBM and AF3 in predicting the binding of EOL and DEET by MhOR5. (a) 2D structures of EOL (above) and (b) DEET (below). (c) Comparison of ICM docking scores and post-dynamics binding free energy calculations (kcal/mol) using EOL and DEET. Differences between experimental and modelled EOL conformations (above) in 7LID (pink), TBM_EOL (blue), and AF3_EOL (green) and (e) differences in DEET conformations (below) in 7LIG (pink), TBM_DEET (blue), and AF3_DEET (green). (f) RLIs of MhOR5_TBM with EOL and DEET were more similar than those of MhOR5_AF3 to 7LID and 7LIG. Elaborated post-docking and post-simulation (PS) RLIs are illustrated in Figures S4 and S5 respectively and docking RLIs are shown in Figure S6.

Fig. 3

Sequence logo plots for the seven helices of B. dorsalis and B. minax ORs based on their helix-wise MSAs. Colors represent the chemical nature of amino acid side chains. Y-axis scaling has been adjusted based on conservation. Highly conserved residues are highlighted in yellow.

Fig. 4

Experimental EC₅₀ values taken from the literature for functionally characterized B. dorsalis and B. minax ORs.

Fig. 5

Comparison of post-dynamics energy and stability values and RLIs produced by TBM and AF3 in the empirical binder and non-binder cohorts. (a) Linear discriminant analyses of the five energy and stability, and RLI variables showed TBM models differentiated binders from non-binders while (b) AF3 models showed some overlaps. (c) Heat maps of normalized values for each energy and stability variable for the TBM and AF3 post-dynamics complexes show that each variable contributed to the resolution between the binders and non-binders. Values <= − 0.25 (pink), > = 0.25 (orange), and intermediate (gray). (d) Heat maps of RLI frequencies show many differences between TBM and AF3 binders and non-binders. Discriminatory sites are boxed red. Detailed RMSD plots for the various complexes and ligands are shown in Figures S17–S20 and energy values are detailed in Table S10.

Fig. 6

In silico workflow to compare the ability of TBM and AF3 to reproduce the experimental structural and functional data using molecular docking and simulation. Post-dynamics binding affinities were assessed in terms of estimates of GB (generalized Born) and PB (Poisson–Boltzmann) energy values, CSD and LSD stability values (expressed as root means square deviations) for the OR-ligand complex and ligand, respectively, in the last 10ns of MD and RLIs (post-simulation receptor ligand interactions).