Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors

One-shot design elevates functional expression levels of a voltage-gated potassium channel

Membrane proteins play critical physiological roles as receptors, channels, pumps, and transporters. Despite their importance, however, low expression levels often hamper the experimental characterization of membrane proteins. We present an automated and web-accessible design algorithm called mPROSS (https://mPROSS.weizmann.ac.il), which uses phylogenetic analysis and an atomistic potential, including an empirical lipophilicity scale, to improve native-state energy. As a stringent test, we apply mPROSS to the Kv1.2-Kv2.1 paddle chimera voltage-gated potassium channel. Four designs, encoding 9-26 mutations relative to the parental channel, were functional and maintained potassium-selective permeation and voltage dependence in Xenopus oocytes with up to 14-fold increase in whole-cell current densities. Additionally, single-channel recordings reveal no significant change in the channel-opening probability nor in unitary conductance, indicating that functional expression levels increase without impacting the activity profile of individual channels. Our results suggest that the expression levels of other dynamic channels and receptors may be enhanced through one-shot design calculations.

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.

Development of enhanced conformational sampling methods to probe the activation landscape of GPCRs

G protein-coupled receptors (GPCRs) make up the largest superfamily of integral membrane proteins and play critical signal transduction roles in many physiological processes. Developments in molecular biology, biophysical, biochemical, pharmacological, and computational techniques aimed at these important therapeutic targets are beginning to provide unprecedented details on the structural as well as functional basis of their pleiotropic signaling mediated by G proteins, β arrestins, and other transducers. This pleiotropy presents a pharmacological challenge as the same ligand-receptor interaction can cause a therapeutic effect as well as an undesirable on-target side-effect through different downstream pathways. GPCRs don't function as simple binary on-off switches but as finely tuned shape-shifting machines described by conformational ensembles, where unique subsets of conformations may be responsible for specific signaling cascades. X-ray crystallography and more recently cryo-electron microscopy are providing snapshots of some of these functionally-important receptor conformations bound to ligands and/or transducers, which are being utilized by computational methods to describe the dynamic conformational energy landscape of GPCRs. In this chapter, we review the progress in computational conformational sampling methods based on molecular dynamics and discrete sampling approaches that have been successful in complementing biophysical and biochemical studies on these receptors in terms of their activation mechanisms, allosteric effects, actions of biased ligands, and effects of pathological mutations. Some of the sampled simulation time scales are beginning to approach receptor activation time scales. The list of conformational sampling methods and example uses discussed is not exhaustive but includes representative examples that have pushed the limits of classical molecular dynamics and discrete sampling methods to describe the activation energy landscape of GPCRs.

An online GPCR structure analysis platform

We present an online, interactive platform for comparative analysis of all available G-protein coupled receptor (GPCR) structures while correlating to functional data. The comprehensive platform encompasses structure similarity, secondary structure, protein backbone packing and movement, residue-residue contact networks, amino acid properties and prospective design of experimental mutagenesis studies. This lets any researcher tap the potential of sophisticated structural analyses enabling a plethora of basic and applied receptor research studies.

A novel high-throughput screen for identifying lipids that stabilise membrane proteins in detergent based solution

Membrane proteins have a range of crucial biological functions and are the target of about 60% of all prescribed drugs. For most studies, they need to be extracted out of the lipid-bilayer, e.g. by detergent solubilisation, leading to the loss of native lipids, which may disturb important protein-lipid/bilayer interactions and thus functional and structural integrity. Relipidation of membrane proteins has proven extremely successful for studying challenging targets, but the identification of suitable lipids can be expensive and laborious. Therefore, we developed a screen to aid the high-throughput identification of beneficial lipids. The screen covers a large lipid space and was designed to be suitable for a range of stability assessment methods. Here, we demonstrate its use as a tool for identifying stabilising lipids for three membrane proteins: a bacterial pyrophosphatase (Tm-PPase), a fungal purine transporter (UapA) and a human GPCR (A_2AR). A_2AR is stabilised by cholesteryl hemisuccinate, a lipid well known to stabilise GPCRs, validating the approach. Additionally, our screen also identified a range of new lipids which stabilised our test proteins, providing a starting point for further investigation and demonstrating its value as a novel tool for membrane protein research. The pre-dispensed screen will be made commercially available to the scientific community in future and has a number of potential applications in the field.

IMPROvER: the Integral Membrane Protein Stability Selector

Identifying stabilising variants of membrane protein targets is often required for structure determination. Our new computational pipeline, the Integral Membrane Protein Stability Selector (IMPROvER) provides a rational approach to variant selection by employing three independent approaches: deep-sequence, model-based and data-driven. In silico tests using known stability data, and in vitro tests using three membrane protein targets with 7, 11 and 16 transmembrane helices provided measures of success. In vitro, individual approaches alone all identified stabilising variants at a rate better than expected by random selection. Low numbers of overlapping predictions between approaches meant a greater success rate was achieved (fourfold better than random) when approaches were combined and selections restricted to the highest ranked sites. The mix of information IMPROvER uses can be extracted for any helical membrane protein. We have developed the first general-purpose tool for selecting stabilising variants of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upalpha$$\end{document}α-helical membrane proteins, increasing efficiency and reducing workload. IMPROvER can be accessed at http://improver.ddns.net/IMPROvER/.

Theoretical identification of thermostabilizing amino acid mutations for G-protein-coupled receptors

Thermostabilization of a membrane proteins, especially G-protein-coupled receptors (GPCRs), is often necessary for biochemical applications and pharmaceutical studies involving structure-based drug design. Here we review our theoretical, physics-based method for identifying thermostabilizing amino acid mutations. Its novel aspects are the following: The entropic effect originating from the translational displacement of hydrocarbon groups within the lipid bilayer is treated as a pivotal factor; a reliable measure of thermostability is introduced and a mutation which enlarges the measure to a significant extent is chosen; and all the possible mutations can be examined with moderate computational effort. It was shown that mutating the residue at a position of N_BW = 3.39 (N_BW is the Ballesteros-Weinstein number) to Arg or Lys leads to the stabilization of significantly many different GPCRs of class A in the inactive state. Up to now, we have been successful in stabilizing several GPCRs and newly solving three-dimensional structures for the muscarinic acetylcholine receptor 2 (M2R), prostaglandin E receptor 4 (EP4), and serotonin 2A receptor (5-HT_2AR) using X-ray crystallography. The subjects to be pursued in future studies are also discussed.

Methodology for Further Thermostabilization of an Intrinsically Thermostable Membrane Protein Using Amino Acid Mutations with Its Original Function Being Retained

We develop a new methodology best suited to the identification of thermostabilizing mutations for an intrinsically stable membrane protein. The recently discovered thermophilic rhodopsin, whose apparent midpoint temperature of thermal denaturation T_m is measured to be ∼91.8 °C, is chosen as a paradigmatic target. In the methodology, we first regard the residues whose side chains are missing in the crystal structure of the wild type (WT) as the "residues with disordered side chains," which make no significant contributions to the stability, unlike the other essential residues. We then undertake mutating each of the residues with disordered side chains to another residue except Ala and Pro, and the resultant mutant structure is constructed by modifying only the local structure around the mutated residue. This construction is based on the postulation that the structure formed by the other essential residues, which is nearly optimized in such a highly stable protein, should not be modified. The stability changes arising from the mutations are then evaluated using our physics-based free-energy function (FEF). We choose the mutations for which the FEF is much lower than for the WT and test them by experiments. We successfully find three mutants that are significantly more stable than the WT. A double mutant whose T_m reaches ∼100 °C is also discovered.

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.

Prediction of Conformation Specific Thermostabilizing Mutations for Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are highly flexible and prone to denaturation during protein extraction in detergents and purification. This poses a huge challenge to purify a conformationally homogeneous solution of GPCRs. Thermostabilizing mutations have been used widely to purify and obtain crystal structures of several GPCRs. However, identifying thermostabilizing mutations for GPCRs remains a tedious and expensive task as they are not transferable even among closely related GPCRs. Additionally, the mutations stabilizing one conformational state of a GPCR do not always stabilize other conformational state(s) of the same GPCR. Previously we developed a computational method, LiticonDesign, for rapid prediction of thermostabilizing mutations for a specific GPCR conformation. In this study, we have used LiticonDesign to predict thermostabilizing mutations for the agonist bound active-intermediate state of the human adenosine receptor (A_2AR) using the structure of the inactive state of the same GPCR and vice versa. Our study shows that the thermostable mutation predictions using LiticonDesign, for an active-intermediate state of a GPCR (A_2AR in our case), requires a homology model that is derived from an active/active-intermediate state GPCR structure as a template. Similarly, the homology models derived from inactive state GPCR conformations are better in predicting the thermostable mutations for the inactive state of A_2AR. Overall, LiticonDesign method is not only efficient in predicting thermostabilizing mutations for a given GPCR sequence but also can recover conformation specific mutations for a state of interest, if a suitable starting structure of desired conformation is chosen.

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.

Structural insights into the subtype-selective antagonist binding to the M2 muscarinic receptor

Human muscarinic receptor, M₂ is one of the five subtypes of muscarinic receptors belonging to the family of G protein-coupled receptors. Muscarinic receptors are targets for multiple neurodegenerative diseases. The challenge has been designing subtype selective ligands against one of the five muscarinic receptors. We report high resolution structures of a thermostabilized mutant M₂ receptor bound to a subtype selective antagonist AF-DX 384 and a non-selective antagonist NMS. The thermostabilizing mutation S110R in M₂ was predicted using a theoretical strategy previously developed in our group. Comparison of the crystal structures and pharmacological properties of the M₂ receptor shows that the Arg in the S110R mutant mimics the stabilizing role of the sodium cation, that is known to allosterically stabilize inactive state(s) of class A GPCRs. Molecular Dynamics simulations reveal that tightening of the ligand-residue contacts in M₂ receptor compared to M₃ receptor leads to subtype selectivity of AF-DX 384.

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

Introduction of specific point mutations has been an effective strategy in enhancing the thermostability of G-protein-coupled receptors (GPCRs). Our previous work showed that a specific residue position on transmembrane helix 6 (TM6) in class A GPCRs consistently yields thermostable mutants. The crystal structure of human chemokine receptor CCR5 also showed increased thermostability upon mutation of two positions, A233D^6.33 and K303E^7.59. With the goal of testing the transferability of these two thermostabilizing mutations in other chemokine receptors, we tested the mutations A237D^6.33 and R307E^7.59 in human CCR3 for thermostability and aggregation properties in detergent solution. Interestingly, the double mutant exhibited a 6-10-fold decrease in the aggregation propensity of the wild-type protein. This is in stark contrast to the two single mutants whose aggregation properties resemble the wild type (WT). Moreover, unlike in CCR5, the two single mutants separately showed no increase in thermostability compared to the wild-type CCR3, while the double-mutant A237D^6.33/R307E^7.59 confers an increase of 2.6 °C in the melting temperature compared to the WT. Extensive all-atom molecular dynamics (MD) simulations in detergent micelles show that a salt bridge network between transmembrane helices TM3, TM6, and TM7 that is absent in the two single mutants confers stability in the double mutant. The free energy surface of the double mutant shows conformational homogeneity compared to the single mutants. An annular n-dodecyl maltoside detergent layer packs tighter to the hydrophobic surface of the double-mutant CCR3 compared to the single mutants providing additional stability. The purification of other C-C chemokine receptors lacking such stabilizing residues may benefit from the incorporation of these two point mutations.

Membrane protein engineering to the rescue

The inherent hydrophobicity of membrane proteins is a major barrier to membrane protein research and understanding. Their low stability and solubility in aqueous environments coupled with poor expression levels make them a challenging area of research. For many years, the only way of working with membrane proteins was to optimise the environment to suit the protein, through the use of different detergents, solubilising additives, and other adaptations. However, with innovative protein engineering methodologies, the membrane proteins themselves are now being adapted to suit the environment. This mini-review looks at the types of adaptations which are applied to membrane proteins from a variety of different fields, including water solubilising fusion tags, thermostabilising mutation screening, scaffold proteins, stabilising protein chimeras, and isolating water-soluble domains.

Computational design of thermostabilizing point mutations for G protein-coupled receptors

Engineering of GPCR constructs with improved thermostability is a key for successful structural and biochemical studies of this transmembrane protein family, targeted by 40% of all therapeutic drugs. Here we introduce a comprehensive computational approach to effective prediction of stabilizing mutations in GPCRs, named CompoMug, which employs sequence-based analysis, structural information, and a derived machine learning predictor. Tested experimentally on the serotonin 5-HT_2C receptor target, CompoMug predictions resulted in 10 new stabilizing mutations, with an apparent thermostability gain ~8.8°C for the best single mutation and ~13°C for a triple mutant. Binding of antagonists confers further stabilization for the triple mutant receptor, with total gains of ~21°C as compared to wild type apo 5-HT_2C. The predicted mutations enabled crystallization and structure determination for the 5-HT_2C receptor complexes in inactive and active-like states. While CompoMug already shows high 25% hit rate and utility in GPCR structural studies, further improvements are expected with accumulation of structural and mutation data.

The trillions of cells in the human body rely on receptors that sit in their cell membranes to communicate with each other. Hundreds of different receptors belong to the G protein-coupled receptor superfamily (called GPCRs for short) and play vital roles in the all organs and bodily systems. Indeed, GPCRs are the targets for almost 40% of therapeutic drugs. As such, deciphering the shape and activity of GPCRs is key to understanding the normal workings of the human biology and could help scientists discover new treatments for various diseases, from depression to high blood pressure to cancer. These receptors, however, are notoriously flimsy and unstable, making them difficult to work with in the laboratory.

Different approaches have been developed to make GPCRs more stable, usually by swapping one or a few of the amino acid building blocks in the protein for other amino acids. Currently, this requires a costly and slow trial-and-error approach in which each amino acid out of 300-400 in the protein is mutated and tested experimentally.

To speed up and reduce the cost of the process, Popov et al. asked if a computer could predict which mutations in the protein would stabilize it, meaning that fewer proteins would actually need to be tested. Four computer algorithms based on four different principles were developed and verified. The first one compares the target GPCR to other closely related receptors, trying to detect variations that cause the instability. The second tries to build in specific stabilizing interactions, or “bridges”, between different parts of the receptor. The third algorithm searches the known structures of other GPCRs for useful mutations. Finally, the fourth one uses accumulated data on the stability of hundreds of mutations in different GPCRs to train a machine learning predictor to recognize stabilizing mutations.

All four algorithms produced useful predictions in a real-life project. Indeed, when combined in one computational tool, named CompoMug, the algorithms made it possible to detect optimal mutations in a human GPCR called 5-HT_2C. This made the protein much easier to work with in the laboratory, and ultimately helped to solve its three-dimensional structure (which was reported in a separate study, published earlier in 2018)

The 5-HT_2C receptor is involved in regulating, among other things, mood and appetite. Details of its structure might therefore help researchers to design new antidepressants and obesity treatments. Moreover, CompoMug is already helping structural biologists to solve the structures of other GPCRs, which will further facilitate many aspects of GPCR drug discovery.

Rapid Bioinformatic Identification of Thermostabilizing Mutations

Ex vivo stability is a valuable protein characteristic but is laborious to improve experimentally. In addition to biopharmaceutical and industrial applications, stable protein is important for biochemical and structural studies. Taking advantage of the large number of available genomic sequences and growth temperature data, we present two bioinformatic methods to identify a limited set of amino acids or positions that likely underlie thermostability. Because these methods allow thousands of homologs to be examined in silico, they have the advantage of providing both speed and statistical power. Using these methods, we introduced, via mutation, amino acids from thermoadapted homologs into an exemplar mesophilic membrane protein, and demonstrated significantly increased thermostability while preserving protein activity.

Investigation of allosteric modulation mechanism of metabotropic glutamate receptor 1 by molecular dynamics simulations, free energy and weak interaction analysis

Metabotropic glutamate receptor 1 (mGlu₁), which belongs to class C G protein-coupled receptors (GPCRs), can be coupled with G protein to transfer extracellular signal by dimerization and allosteric regulation. Unraveling the dimer packing and allosteric mechanism can be of great help for understanding specific regulatory mechanism and designing more potential negative allosteric modulator (NAM). Here, we report molecular dynamics simulation studies of the modulation mechanism of FITM on the wild type, T815M and Y805A mutants of mGlu₁ through weak interaction analysis and free energy calculation. The weak interaction analysis demonstrates that van der Waals (vdW) and hydrogen bonding play an important role on the dimer packing between six cholesterol molecules and mGlu₁ as well as the interaction between allosteric sites T815, Y805 and FITM in wild type, T815M and Y805A mutants of mGlu₁. Besides, the results of free energy calculations indicate that secondary binding pocket is mainly formed by the residues Thr748, Cys746, Lys811 and Ser735 except for FITM-bound pocket in crystal structure. Our results can not only reveal the dimer packing and allosteric regulation mechanism, but also can supply useful information for the design of potential NAM of mGlu₁.

A neural network learning approach for improving the prediction of residue depth based on sequence-derived features

Residue depth is a solvent exposure measure that quantitatively describes the depth of a residue from the protein surface.

Single-Point Mutation with a Rotamer Library Toolkit: Toward Protein Engineering

Protein engineers have long been hard at work to harness biocatalysts as a natural source of regio-, stereo-, and chemoselectivity in order to carry out chemistry (reactions and/or substrates) not previously achieved with these enzymes. The extreme labor demands and exponential number of mutation combinations have induced computational advances in this domain. The first step in our virtual approach is to predict the correct conformations upon mutation of residues (i.e., rebuilding side chains). For this purpose, we opted for a combination of molecular mechanics and statistical data. In this work, we have developed automated computational tools to extract protein structural information and created conformational libraries for each amino acid dependent on a variable number of parameters (e.g., resolution, flexibility, secondary structure). We have also developed the necessary tool to apply the mutation and optimize the conformation accordingly. For side-chain conformation prediction, we obtained overall average root-mean-square deviations (RMSDs) of 0.91 and 1.01 Å for the 18 flexible natural amino acids within two distinct sets of over 3000 and 1500 side-chain residues, respectively. The commonly used dihedral angle differences were also evaluated and performed worse than the state of the art. These two metrics are also compared. Furthermore, we generated a family-specific library for kinases that produced an average 2% lower RMSD upon side-chain reconstruction and a residue-specific library that yielded a 17% improvement. Ultimately, since our protein engineering outlook involves using our docking software, Fitted/Impacts, we applied our mutation protocol to a benchmarked data set for self- and cross-docking. Our side-chain reconstruction does not hinder our docking software, demonstrating differences in pose prediction accuracy of approximately 2% (RMSD cutoff metric) for a set of over 200 protein/ligand structures. Similarly, when docking to a set of over 100 kinases, side-chain reconstruction (using both general and biased conformation libraries) had minimal detriment to the docking accuracy.

How Can Mutations Thermostabilize G-Protein-Coupled Receptors?

Structures of over 30 different G-protein-coupled receptors (GPCRs) have advanced our understanding of cell signaling and have provided a foundation for structure-guided drug design. This exciting progress has required the development of three complementary methods to facilitate GPCR crystallization, one of which is the thermostabilization of receptors by systematic mutagenesis. However, the reason why a particular mutation, or combination of mutations, stabilizes the receptor is not always evident from a static crystal structure. Molecular dynamics (MD) simulations have been used to identify and estimate the energetic factors that affect thermostability through comparing the dynamics of the thermostabilized receptors with structure-based models of the wild-type receptor. The data indicate that receptors are stabilized through a combination of factors, including an increase in receptor rigidity, a decrease in collective motion, reduced stress at specific residues, and the presence of ordered water molecules. Predicting thermostabilizing mutations computationally represents a major challenge for the field.

Stabilization of G protein-coupled receptors by point mutations

G protein-coupled receptors (GPCRs) are flexible integral membrane proteins involved in transmembrane signaling. Their involvement in many physiological processes makes them interesting targets for drug development. Determination of the structure of these receptors will help to design more specific drugs, however, their structural characterization has so far been hampered by the low expression and their inherent instability in detergents which made protein engineering indispensable for structural and biophysical characterization. Several approaches to stabilize the receptors in a particular conformation have led to breakthroughs in GPCR structure determination. These include truncations of the flexible regions, stabilization by antibodies and nanobodies, fusion partners, high affinity and covalently bound ligands as well as conformational stabilization by mutagenesis. In this review we focus on stabilization of GPCRs by insertion of point mutations, which lead to increased conformational and thermal stability as well as improved expression levels. We summarize existing mutagenesis strategies with different coverage of GPCR sequence space and depth of information, design and transferability of mutations and the molecular basis for stabilization. We also discuss whether mutations alter the structure and pharmacological properties of GPCRs.

Large-scale production and protein engineering of G protein-coupled receptors for structural studies

Structural studies of G protein-coupled receptors (GPCRs) gave insights into molecular mechanisms of their action and contributed significantly to molecular pharmacology. This is primarily due to technical advances in protein engineering, production and crystallization of these important receptor targets. On the other hand, NMR spectroscopy of GPCRs, which can provide information about their dynamics, still remains challenging due to difficulties in preparation of isotopically labeled receptors and their low long-term stabilities. In this review, we discuss methods used for expression and purification of GPCRs for crystallographic and NMR studies. We also summarize protein engineering methods that played a crucial role in obtaining GPCR crystal structures.