Engineering Salt Bridge Networks between Transmembrane Helices Confers Thermostability in G-Protein-Coupled Receptors

Sequence coevolution and structure stabilization modulate olfactory receptor expression

Olfactory receptors (ORs) belong to class A G-protein coupled receptors (GPCRs) and are activated by a variety of odorants. To date, there is no three-dimensional structure of an OR available. One of the major bottlenecks in obtaining purified protein for structural studies of ORs is their poor expression in heterologous cells. To design mutants that enhance expression and thereby enable protein purification, we first identified computable physical properties that recapitulate OR and class A GPCR expression and further conducted an iterative computational prediction-experimental test cycle and generated human OR mutants that express as high as biogenic amine receptors for which structures have been solved. In the process of developing the computational method to recapitulate the expression of ORs in membranes, we identified properties, such as amino acid sequence coevolution, and the strength of the interactions between intracellular loop 1 (ICL1) and the helix 8 region of ORs, to enhance their heterologous expression. We identified mutations that are directly located in these regions as well as other mutations not located in these regions but allosterically strengthen the ICL1-helix 8 enhance expression. These mutants also showed functional responses to known odorants. This method to enhance heterologous expression of mammalian ORs will facilitate high-throughput "deorphanization" of ORs, and enable OR purification for biochemical and structural studies to understand odorant-OR interactions.

Destabilization of the Alzheimer's amyloid-β protofibrils by THC: A molecular dynamics simulation study

Alzheimer's disease is a leading cause of dementia in the elderly population for which there is no cure at present. Deposits of neurotoxic plaques are found in the brains of patients which are composed of fibrils of the amyloid-β peptide. Molecules which can disrupt these fibrils have gained attention as potential therapeutic agents. Δ-tetrahydrocannabidiol (THC) is a cannabinoid, which can bind to the receptors in the brain, and has shown promise in reducing the fibril content in many experimental studies. In our present study, by employing all atom molecular dynamics simulations, we have investigated the mechanism of the interaction of the THC molecules with the amyloid-β protofibrils. Our results show that the THC molecules disrupt the protofibril structure by binding strongly to them. The driving force for the binding was the hydrophobic interactions with the hydrophobic residues in the fibrils. As a result of these interactions, the tight packing of the hydrophobic core of the protofibrils was made loose, and salt bridges, which were important for stability were disrupted. Hydrogen bonds between the chains of the protofibrils which are important for stability were disrupted, as a result of which the β-sheet content was reduced. The destabilization of the protofibrils by the THC molecules leads to the conclusion that THC molecules may be considered for the therapy in treating Alzheimer's disease.

Enhancing the binding of the β-sheet breaker peptide LPFFD to the amyloid-β fibrils by aromatic modifications: A molecular dynamics simulation study

Alzheimer's is a fatal neurodegenerative disease for which there is no cure at present. The disease is characterized by the presence of plaques in the brains of a patient, which are composed mainly of aggregates of the amyloid-β peptide in the form of β-sheet fibrils. Here, we investigated the possibility of exploiting the superior binding ability of aromatic amino acids to a particular model of the amyloid-β fibrils. which is a difficult target for drug design. The β-sheet breaker peptide LPFFD was modified with aromatic amino acids and its binding to these fibrils was studied. We found that the orientation and the electrostatic complementarity of the modified peptide with respect to the fibrils played a crucial role in determining whether its binding was improved by the aromatic amino acids. The modified LPFFD peptides were able to bind to those fibril residues. which are important in the aggregation of amyloid-β peptides and thus can potentially inhibit the further aggregation of the amyloid-beta peptides by blocking their interactions. We found that the tryptophan modified LPFFD peptides had the best binding affinities. In most cases, the aromatic amino acids in the N-terminus of the modified peptides made more contacts with the fibrils than those in the C-terminus. We also found that increasing the aromatic content did not significantly improve the binding of the LPFFD peptide to the fibrils. Our study can serve as a basis for the design of novel peptide-based drugs for Alzheimer's disease in which aromatic interactions play an important role.

A universal allosteric mechanism for G protein activation

G proteins play a central role in signal transduction and pharmacology. Signaling is initiated by cell-surface receptors, which promote guanosine triphosphate (GTP) binding and dissociation of Gα from the Gβγ subunits. Structural studies have revealed the molecular basis of subunit association with receptors, RGS proteins, and downstream effectors. In contrast, the mechanism of subunit dissociation is poorly understood. We use cell signaling assays, molecular dynamics (MD) simulations, and biochemistry and structural analyses to identify a conserved network of amino acids that dictates subunit release. In the presence of the terminal phosphate of GTP, a glycine forms a polar network with an arginine and glutamate, putting torsional strain on the subunit binding interface. This "G-R-E motif" secures GTP and, through an allosteric link, discharges the Gβγ dimer. Replacement of network residues prevents subunit dissociation regardless of agonist or GTP binding. These findings reveal the molecular basis of the final committed step of G protein activation.

Structural instability and divergence from conserved residues underlie intracellular retention of mammalian odorant receptors

Mammalian odorant receptors are a diverse and rapidly evolving set of G protein-coupled receptors expressed in olfactory cilia membranes. Most odorant receptors show little to no cell surface expression in nonolfactory cells due to endoplasmic reticulum retention, which has slowed down biochemical studies. Here we provide evidence that structural instability and divergence from conserved residues of individual odorant receptors underlie intracellular retention using a combination of large-scale screening of odorant receptors cell surface expression in heterologous cells, point mutations, structural modeling, and machine learning techniques. We demonstrate the importance of conserved residues by synthesizing consensus odorant receptors that show high levels of cell surface expression similar to conventional G protein-coupled receptors. Furthermore, we associate in silico structural instability with poor cell surface expression using molecular dynamics simulations. We propose an enhanced evolutionary capacitance of olfactory sensory neurons that enable the functional expression of odorant receptors with cryptic mutations.

Machine Learning for Prioritization of Thermostabilizing Mutations for G-Protein Coupled Receptors

Although the three-dimensional structures of G-protein coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence-, structure-, and dynamics-based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available data set. A blind prediction for thermostable mutations of the complement factor C5a receptor 1 retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.

Computational design for thermostabilization of GPCRs

GPCR superfamily is the largest clinically relevant family of targets in human genome; however, low thermostability and high conformational plasticity of these integral membrane proteins make them notoriously hard to handle in biochemical, biophysical, and structural experiments. Here, we describe the recent advances in computational approaches to design stabilizing mutations for GPCR that take advantage of the structural and sequence conservation properties of the receptors, and employ machine learning on accumulated mutation data for the superfamily. The fast and effective computational tools can provide a viable alternative to existing experimental mutation screening and are poised for further improvements with expansion of thermostability datasets for training the machine learning models. The rapidly growing practical applications of computational stability design streamline GPCR structure determination and may contribute to more efficient drug discovery.