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. 2023 May 9:21:3002-3009.
doi: 10.1016/j.csbj.2023.05.004. eCollection 2023.

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors

Lorenzo Di Rienzo  1 , Mattia Miotto  1 , Edoardo Milanetti  2   1 , Giancarlo Ruocco  1   2
Affiliations

Computational structural-based GPCR optimization for user-defined ligand: Implications for the development of biosensors

Lorenzo Di Rienzo et al. Comput Struct Biotechnol J. .

Abstract

Organisms have developed effective mechanisms to sense the external environment. Human-designed biosensors exploit this natural optimization, where different biological machinery have been adapted to detect the presence of user-defined molecules. Specifically, the pheromone pathway in the model organism Saccharomyces cerevisiae represents a suitable candidate as a synthetic signaling system. Indeed, it expresses just one G-Protein Coupled Receptor (GPCR), Ste2, able to recognize pheromone and initiate the expression of pheromone-dependent genes. To date, the standard procedure to engineer this system relies on the substitution of the yeast GPCR with another one and on the modification of the yeast G-protein to bind the inserted receptor. Here, we propose an innovative computational procedure, based on geometrical and chemical optimization of protein binding pockets, to select the amino acid substitutions required to make the native yeast GPCR able to recognize a user-defined ligand. This procedure would allow the yeast to recognize a wide range of ligands, without a-priori knowledge about a GPCR recognizing them or the corresponding G protein. We used Monte Carlo simulations to design on Ste2 a binding pocket able to recognize epinephrine, selected as a test ligand. We validated Ste2 mutants via molecular docking and molecular dynamics. We verified that the amino acid substitutions we identified make Ste2 able to accommodate and remain firmly bound to epinephrine. Our results indicate that we sampled efficiently the huge space of possible mutants, proposing such a strategy as a promising starting point for the development of a new kind of S.cerevisiae-based biosensors.

Keywords: Bio-sensors; GPCR; Protein engineering.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

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Graphical abstract
Fig. 1
Fig. 1
Overview of the computational pipeline. a) Cartoon representation of the beta2 adrenoceptor-epinephrine complex (pdb code: 4ldo). After the computation of both the protein and ligand molecular surfaces, epinephrine binding site (EBS) is defined as the set of protein molecular surface points whose distance from any ligand surface point is lower than 3 Å. Shape and hydrophobicity of the extracted pocket are evaluated. b) Cartoon representation of Ste2 protein. The protein patches located in the protein region where pheromone is bound were extracted and characterized with shape and hydrophobicity descriptors. They were compared with the descriptors of EBS. c) Ste2 surface colored according to the local similarity with EBS. d) In the Monte Carlo optimization procedure the dissimilarity between the designed pocket and EBS is minimized, in terms of the shape and hydrophobicity descriptors.
Fig. 2
Fig. 2
Selection of the Ste2 pocket to optimize. a) Density distribution of the Zernike distances between EBS and all the sampled patches on Ste2 surface. b) Same as in panel a) but for the hydrophobic distance. In the central panels, we report the molecular representation of Ste2 colored according to the shape or hydrophobicity dissimilarity, in the left and right boxes respectively. The bottom central figure represents the selected pocket on Ste2 surface.
Fig. 3
Fig. 3
Results of the Monte Carlo procedure. a) Differences in shape, hydrophobicity, and the number of mutations as a function of the Monte Carlo steps for a representative run of the optimization procedure. The orange points represent the mutants satisfying the acceptance condition for the examined descriptor. b) Same as in a) but here points are represented in the 3D space. The red points represent the mutants satisfying at the same time the three conditions. c) Color maps representing the probability of visiting a certain region of the phase space that occurred during the 10 performed MC simulations. Colors range from red to yellow as the occupancy probability increases. White regions correspond to unvisited regions.
Fig. 4
Fig. 4
Analysis of the observed mutations. a) Frequency of mutations for each of the 15 residues involved in the pocket formation of the Ste2 protein during the MC simulation. Each bar of the barplot is subdivided according to the inserted residues. b) Boxplot representation of the distributions of the differences between the hydrophobicity, net charge, size, polarity, and solvation free energy of the WT with respect to the mutated versions of the Ste2 protein.
Fig. 5
Fig. 5
Molecular dynamics-based validation. a) Molecular representation of the docking poses of Ste2-epinephrine complexes obtained with AutoDock of the WT Ste2 (top) and the engineered version of Ste2 (bottom). b) Root Mean Square Deviation (RMSD) as a function of the molecular dynamics simulation time of the epinephrine atoms for three replicas of the WT and optimized systems. c) The boxplots highlight the distributions of RMSD values. d) Percentage of conserved contacts of the epinephrine atoms with the Ste2 protein as a function of the molecular dynamics simulation time for three replicas of the WT and optimized systems. e)The boxplots highlight the distributions of percentages of conserved contacts.
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