Applications of sequence coevolution in membrane protein biochemistry

Molecular characterization of Rft1, an ER membrane protein associated with congenital disorder of glycosylation RFT1-CDG

The oligosaccharide needed for protein N-glycosylation is assembled on a lipid carrier via a multi-step pathway. Synthesis is initiated on the cytoplasmic face of the endoplasmic reticulum (ER) and completed on the luminal side after transbilayer translocation of a heptasaccharide lipid intermediate. More than 30 Congenital Disorders of Glycosylation (CDGs) are associated with this pathway, including RFT1-CDG which results from defects in the membrane protein Rft1. Rft1 is essential for the viability of yeast and mammalian cells and was proposed as the transporter needed to flip the heptasaccharide lipid intermediate across the ER membrane. However, other studies indicated that Rft1 is not required for heptasaccharide lipid flipping in microsomes or unilamellar vesicles reconstituted with ER membrane proteins, nor is it required for the viability of at least one eukaryote. It is therefore not known what essential role Rft1 plays in N-glycosylation. Here, we present a molecular characterization of human Rft1, using yeast cells as a reporter system. We show that it is a multi-spanning membrane protein located in the ER, with its N and C-termini facing the cytoplasm. It is not N-glycosylated. The majority of RFT1-CDG mutations map to highly conserved regions of the protein. We identify key residues that are important for Rft1's ability to support N-glycosylation and cell viability. Our results provide a necessary platform for future work on this enigmatic protein.

Molecular characterization of Rft1, an ER membrane protein associated with congenital disorder of glycosylation RFT1-CDG

The oligosaccharide needed for protein N-glycosylation is assembled on a lipid carrier via a multi-step pathway. Synthesis is initiated on the cytoplasmic face of the endoplasmic reticulum (ER) and completed on the luminal side after transbilayer translocation of a heptasaccharide lipid intermediate. More than 30 Congenital Disorders of Glycosylation (CDGs) are associated with this pathway, including RFT1-CDG which results from defects in the membrane protein Rft1. Rft1 is essential for the viability of yeast and mammalian cells and was proposed as the transporter needed to flip the heptasaccharide lipid intermediate across the ER membrane. However, other studies indicated that Rft1 is not required for heptasaccharide lipid flipping in microsomes or unilamellar vesicles reconstituted with ER membrane proteins, nor is it required for the viability of at least one eukaryote. It is therefore not known what essential role Rft1 plays in N-glycosylation. Here, we present a molecular characterization of human Rft1, using yeast cells as a reporter system. We show that it is a multi-spanning membrane protein located in the ER, with its N and C-termini facing the cytoplasm. It is not N-glycosylated. The majority of RFT1-CDG mutations map to highly conserved regions of the protein. We identify key residues that are important for Rft1's ability to support N-glycosylation and cell viability. Our results provide a necessary platform for future work on this enigmatic protein.

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .

Evolutionary coupling analysis guides identification of mistrafficking-sensitive variants in cardiac K+ channels: Validation with hERG

Loss of function (LOF) mutations of voltage sensitive K+ channel proteins hERG (Kv11.1) and KCNQ1 (Kv7.1) account for the majority of instances of congenital Long QT Syndrome (cLQTS) with the dominant molecular phenotype being a mistrafficking one resulting from protein misfolding. We explored the use of Evolutionary Coupling (EC) analysis, which identifies evolutionarily conserved pairwise amino acid interactions that may contribute to protein structural stability, to identify regions of the channels susceptible to misfolding mutations. Comparison with published experimental trafficking data for hERG and KCNQ1 showed that the method strongly predicts "scaffolding" regions of the channel membrane domains and has useful predictive power for trafficking phenotypes of individual variants. We identified a region in and around the cytoplasmic S2-S3 loop of the hERG Voltage Sensor Domain (VSD) as susceptible to destabilising mutation, and this was confirmed using a quantitative LI-COR ® based trafficking assay that showed severely attenuated trafficking in eight out of 10 natural hERG VSD variants selected using EC analysis. Our analysis highlights an equivalence in the scaffolding structures of the hERG and KCNQ1 membrane domains. Pathogenic variants of ion channels with an underlying mistrafficking phenotype are likely to be located within similar scaffolding structures that are identifiable by EC analysis.

Conformational dynamics of the membrane enzyme LspA upon antibiotic and substrate binding

Lipoprotein signal peptidase (LspA) is an aspartyl protease that cleaves the transmembrane helix signal peptide of lipoproteins as part of the lipoprotein-processing pathway. Members of this pathway are excellent targets for the development of antibiotic therapeutics because they are essential in Gram-negative bacteria, are important for virulence in Gram-positive bacteria, and may not develop antibiotic resistance. Here, we report the conformational dynamics of LspA in the apo state and bound to the antibiotic globomycin determined using molecular dynamics simulations and electron paramagnetic resonance. The periplasmic helix fluctuates on the nanosecond timescale and samples unique conformations in the different states. In the apo state, the dominant conformation is the most closed and occludes the charged active site from the lipid bilayer. With antibiotic bound there are multiple binding modes with the dominant conformation of the periplasmic helix in a more open conformation. The different conformations observed in both bound and apo states indicate a flexible and adaptable active site, which explains how LspA accommodates and processes such a variety of substrates.

Sampling alternative conformational states of transporters and receptors with AlphaFold2

Equilibrium fluctuations and triggered conformational changes often underlie the functional cycles of membrane proteins. For example, transporters mediate the passage of molecules across cell membranes by alternating between inward- and outward-facing states, while receptors undergo intracellular structural rearrangements that initiate signaling cascades. Although the conformational plasticity of these proteins has historically posed a challenge for traditional de novo protein structure prediction pipelines, the recent success of AlphaFold2 (AF2) in CASP14 culminated in the modeling of a transporter in multiple conformations to high accuracy. Given that AF2 was designed to predict static structures of proteins, it remains unclear if this result represents an underexplored capability to accurately predict multiple conformations and/or structural heterogeneity. Here, we present an approach to drive AF2 to sample alternative conformations of topologically diverse transporters and G-protein-coupled receptors that are absent from the AF2 training set. Whereas models of most proteins generated using the default AF2 pipeline are conformationally homogeneous and nearly identical to one another, reducing the depth of the input multiple sequence alignments by stochastic subsampling led to the generation of accurate models in multiple conformations. In our benchmark, these conformations spanned the range between two experimental structures of interest, with models at the extremes of these conformational distributions observed to be among the most accurate (average template modeling score of 0.94). These results suggest a straightforward approach to identifying native-like alternative states, while also highlighting the need for the next generation of deep learning algorithms to be designed to predict ensembles of biophysically relevant states.

Unique features of different classes of G-protein-coupled receptors revealed from sequence coevolutionary and structural analysis

G-protein-coupled receptors (GPCRs) are the largest family of human membrane proteins and represent the primary targets of about one third of currently marketed drugs. Despite the critical importance, experimental structures have been determined for only a limited portion of GPCRs and functional mechanisms of GPCRs remain poorly understood. Here, we have constructed novel sequence coevolutionary models of the A and B classes of GPCRs and compared them with residue contact frequency maps generated with available experimental structures. Significant portions of structural residue contacts were successfully detected in the sequence-based covariational models. "Exception" residue contacts predicted from sequence coevolutionary models but not available structures added missing links that were important for GPCR activation and allosteric modulation. Moreover, we identified distinct residue contacts involving different sets of functional motifs for GPCR activation, such as the Na⁺ pocket, CWxP, DRY, PIF, and NPxxY motifs in the class A and the HETx and PxxG motifs in the class B. Finally, we systematically uncovered critical residue contacts tuned by allosteric modulation in the two classes of GPCRs, including those from the activation motifs and particularly the extracellular and intracellular loops in class A GPCRs. These findings provide a promising framework for rational design of ligands to regulate GPCR activation and allosteric modulation.

Structure and Sequence-based Computational Approaches to Allosteric Signal Transduction: Application to Electromechanical Coupling in Voltage-gated Ion Channels

Allosteric signaling underlies the function of many biomolecules, including membrane proteins such as ion channels. Experimental methods have enabled specific quantitative insights into the coupling between the voltage sensing domain (VSD) and the pore gate of voltage-gated ion channels, located tens of Ångström apart from one another, as well as pinpointed specific residues and domains that participate in electromechanical signal transmission. Nevertheless, an overall atomic-level resolution picture is difficult to obtain from these methods alone. Today, thanks to the cryo-EM resolution revolution, we have access to high resolution structures of many different voltage-gated ion channels in various conformational states, putting a quantitative description of the processes at the basis of these changes within our close reach. Here, we review computational methods that build on structures to detect and characterize allosteric signaling and pathways. We then examine what has been learned so far about electromechanical coupling between VSD and pore using such methods. While no general theory of electromechanical coupling in voltage-gated ion channels integrating results from all these methods is available yet, we outline the types of insights that could be achieved in the near future using the methods that have not yet been put to use in this field of application.

The γ-Protocadherins Interact Physically and Functionally with Neuroligin-2 to Negatively Regulate Inhibitory Synapse Density and Are Required for Normal Social Interaction

Cell adhesion molecules (CAMs) are key players in the formation of neural circuits during development. The γ-protocadherins (γ-Pcdhs), a family of 22 CAMs encoded by the Pcdhg gene cluster, are known to play important roles in dendrite arborization, axon targeting, and synapse development. We showed previously that multiple γ-Pcdhs interact physically with the autism-associated CAM neuroligin-1, and inhibit the latter's ability to promote excitatory synapse maturation. Here, we show that γ-Pcdhs can also interact physically with the related neuroligin-2, and inhibit this CAM's ability to promote inhibitory synapse development. In an artificial synapse assay, γ-Pcdhs co-expressed with neuroligin-2 in non-neuronal cells reduce inhibitory presynaptic maturation in contacting hippocampal axons. Mice lacking the γ-Pcdhs from the forebrain (including the cortex, the hippocampus, and portions of the amygdala) exhibit increased inhibitory synapse density and increased co-localization of neuroligin-2 with inhibitory postsynaptic markers in vivo. These Pcdhg mutants also exhibit defective social affiliation and an anxiety-like phenotype in behavioral assays. Together, these results suggest that γ-Pcdhs negatively regulate neuroligins to limit synapse density in a manner that is important for normal behavior.

AlphaFold2 predicts the inward-facing conformation of the multidrug transporter LmrP

As part of the CASP competition, the protein structure prediction algorithm AlphaFold2 generated multiple models of the proton/drug antiporter LmrP. Previous distance restraints from double electron-electron resonance spectroscopy, a technique which reports distance distributions between spin labels attached to proteins, suggest that one of the lower-ranked models may have captured a conformation that has so far eluded experimental structure determination.

Structures and General Transport Mechanisms by the Major Facilitator Superfamily (MFS)

The major facilitator superfamily (MFS) is the largest known superfamily of secondary active transporters. MFS transporters are responsible for transporting a broad spectrum of substrates, either down their concentration gradient or uphill using the energy stored in the electrochemical gradients. Over the last 10 years, more than a hundred different MFS transporter structures covering close to 40 members have provided an atomic framework for piecing together the molecular basis of their transport cycles. Here, we summarize the remarkable promiscuity of MFS members in terms of substrate recognition and proton coupling as well as the intricate gating mechanisms undergone in achieving substrate translocation. We outline studies that show how residues far from the substrate binding site can be just as important for fine-tuning substrate recognition and specificity as those residues directly coordinating the substrate, and how a number of MFS transporters have evolved to form unique complexes with chaperone and signaling functions. Through a deeper mechanistic description of glucose (GLUT) transporters and multidrug resistance (MDR) antiporters, we outline novel refinements to the rocker-switch alternating-access model, such as a latch mechanism for proton-coupled monosaccharide transport. We emphasize that a full understanding of transport requires an elucidation of MFS transporter dynamics, energy landscapes, and the determination of how rate transitions are modulated by lipids.

A Conserved Motif in Intracellular Loop 1 Stabilizes the Outward-Facing Conformation of TmrAB

The ATP binding cassette (ABC) family of transporters moves small molecules (lipids, sugars, peptides, drugs, nutrients) across membranes in nearly all organisms. Transport activity requires conformational switching between inward-facing and outward-facing states driven by ATP-dependent dimerization of two nucleotide binding domains (NBDs). The mechanism that connects ATP binding and hydrolysis in the NBDs to conformational changes in a substrate binding site in the transmembrane domains (TMDs) is currently an outstanding question. Here we use sequence coevolution analyses together with biochemical characterization to investigate the role of a highly conserved region in intracellular loop 1 we define as the GRD motif in coordinating domain rearrangements in the heterodimeric peptide exporter from Thermus thermophilus, TmrAB. Mutations in the GRD motif alter ATPase activity as well as transport. Disulfide crosslinking, evolutionary trace, and evolutionary coupling analysis reveal that these effects are likely due to the destabilization of a network in which the GRD motif in TmrA bridges residues of the Q-loop, X-loop, and ABC motif in the NBDs to residues in the TmrAB peptide substrate binding site, thus providing an avenue for conformational coupling. We further find that disruption of this network in TmrA versus TmrB has different functional consequences, hinting at an intrinsic asymmetry in heterodimeric ABC transporters extending beyond that of the NBDs. These results support a mechanism in which the GRD motifs help coordinate a transition to an outward open conformation, and each half of the transporter likely plays a different role in the conformational cycle of TmrAB.

ACES: A co-evolution simulator generates co-varying protein and nucleic acid sequences

Sequence-specific and consequential interactions within or between proteins and/or RNAs can be predicted by identifying co-evolution of residues in these molecules. Different algorithms have been used to detect co-evolution, often using biological data to benchmark a methods ability to discriminate against indirect co-evolution. Such a benchmark is problematic, because not all the interactions and evolutionary constraints underlying real data can be known a priori. Instead, sequences generated in silico to simulate co-evolution would be preferable, and can be obtained using aCES, the software tool presented here. Conservation and co-evolution constraints can be specified for any residue across a number of molecules, allowing the user to capture a complex, realistic set of interactions. Resulting alignments were used to benchmark several co-evolution detection tools for their ability to separate signal from background as well as discriminating direct from indirect signals. This approach can aid in refinement of these algorithms. In addition, systematic tuning of these constraints sheds new light on how they drive co-evolution between residues. Better understanding how to detect co-evolution and the residue interactions they predict can lead to a wide range of insights important for synthetic biologists interested in engineering new, orthogonal interactions between two macromolecules.

Evolutionary history of ATP-binding cassette proteins

ATP-binding cassette (ABC) proteins are found in every sequenced genome and evolved deep in the phylogenetic tree of life. ABC proteins form one of the largest homologous protein families, with most being involved in substrate transport across biological membranes, and a few cytoplasmic members regulating in essential processes like translation. The predominant ABC protein classification scheme is derived from human members, but the increasing number of fully sequenced genomes permits to reevaluate this paradigm in the light of the evolutionary history the ABC-protein superfamily. As we study the diversity of substrates, mechanisms, and physiological roles of ABC proteins, knowledge of the evolutionary relationships highlights similarities and differences that can be attributed to specific branches in protein divergence. While alignments and trees built on natural sequence variation account for the evolutionary divergence of ABC proteins, high-throughput experiments and next-generation sequencing creating experimental sequence variation are instrumental in identifying functional constraints. The combination of natural and experimentally produced sequence variation allows a broader and more rational study of the function and physiological roles of ABC proteins.

Decoding the Functional Evolution of an Intramembrane Protease Superfamily by Statistical Coupling Analysis

How evolution endowed membrane enzymes with specific abilities, and then tuned them to the needs of different cells, is poorly understood. We examined whether statistical coupling analysis (SCA) can be applied to rhomboid proteases, the most widely distributed membrane proteins, to identify amino acid "sectors" that evolved independently to acquire a specific function. SCA revealed three coevolving residue networks that form two sectors. Sector 1 determines substrate specificity, but is paradoxically scattered across the protein, consistent with dynamics driving rhomboid-substrate interactions. Sector 2 is hierarchically composed of a subgroup that maintains the catalytic site, and another that maintains the overall fold, forecasting evolution of rhomboid pseudoproteases. Changing only sector 1 residues of a "recipient" rhomboid converted its substrate specificity and catalytic efficiency to that of the "donor." While used only twice over a decade ago, SCA should be generally applicable to membrane proteins, and our sector grafting approach provides an efficient strategy for designing enzymes.

Improved inference of intermolecular contacts through protein-protein interaction prediction using coevolutionary analysis

MOTIVATION

Predicting residue-residue contacts between interacting proteins is an important problem in bioinformatics. The growing wealth of sequence data can be used to infer these contacts through correlated mutation analysis on multiple sequence alignments of interacting homologs of the proteins of interest. This requires correct identification of pairs of interacting proteins for many species, in order to avoid introducing noise (i.e. non-interacting sequences) in the analysis that will decrease predictive performance.

RESULTS

We have designed Ouroboros, a novel algorithm to reduce such noise in intermolecular contact prediction. Our method iterates between weighting proteins according to how likely they are to interact based on the correlated mutations signal, and predicting correlated mutations based on the weighted sequence alignment. We show that this approach accurately discriminates between protein interaction versus non-interaction and simultaneously improves the prediction of intermolecular contact residues compared to a naive application of correlated mutation analysis. This requires no training labels concerning interactions or contacts. Furthermore, the method relaxes the assumption of one-to-one interaction of previous approaches, allowing for the study of many-to-many interactions.

AVAILABILITY AND IMPLEMENTATION

Source code and test data are available at www.bif.wur.nl/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Co-evolution in the Jungle: From Leafcutter Ant Colonies to Chromosomal Ends

Biological entities are multicomponent systems where each part is directly or indirectly dependent on the others. In effect, a change in a single component might have a consequence on the functioning of its partners, thus affecting the fitness of the entire system. In this article, we provide a few examples of such complex biological systems, ranging from ant colonies to a population of amino acids within a single-polypeptide chain. Based on these examples, we discuss one of the central and still challenging questions in biology: how do such multicomponent consortia co-evolve? More specifically, we ask how telomeres, nucleo-protein complexes protecting the integrity of linear DNA chromosomes, originated from the ancestral organisms having circular genomes and thus not dealing with end-replication and end-protection problems. Using the examples of rapidly evolving topologies of mitochondrial genomes in eukaryotic microorganisms, we show what means of co-evolution were employed to accommodate various types of telomere-maintenance mechanisms in mitochondria. We also describe an unprecedented runaway evolution of telomeric repeats in nuclei of ascomycetous yeasts accompanied by co-evolution of telomere-associated proteins. We propose several scenarios derived from research on telomeres and supported by other studies from various fields of biology, while emphasizing that the relevant answers are still not in sight. It is this uncertainty and a lack of a detailed roadmap that makes the journey through the jungle of biological systems still exciting and worth undertaking.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

The peptide transporter 1a of the zebrafish Danio rerio, an emerging model in nutrigenomics and nutrition research: molecular characterization, functional properties, and expression analysis

Background

Peptide transporter 1 (PepT1, alias Slc15a1) mediates the uptake of dietary di/tripeptides in all vertebrates. However, in teleost fish, more than one PepT1-type transporter might function, due to specific whole genome duplication event(s) that occurred during their evolution leading to a more complex paralogue gene repertoire than in higher vertebrates (tetrapods).

Results

Here, we describe a novel di/tripeptide transporter in the zebrafish (Danio rerio), i.e., the zebrafish peptide transporter 1a (PepT1a; also known as Solute carrier family 15 member a1, Slc15a1a), which is a paralogue (78% similarity, 62% identity at the amino acid level) of the previously described zebrafish peptide transporter 1b (PepT1b, alias PepT1; also known as Solute carrier family 15 member 1b, Slc15a1b). Also, we report a basic analysis of the pept1a (slc15a1a) mRNA expression levels in zebrafish adult tissues/organs and embryonic/early larval developmental stages. As assessed by expression in Xenopus laevis oocytes and two-electrode voltage clamp measurements, zebrafish PepT1a, as PepT1b, is electrogenic, Na⁺-independent, and pH-dependent and functions as a low-affinity system, with K_0.5 values for Gly-Gln at − 60 mV of 6.92 mmol/L at pH 7.6 and 0.24 mmol/L at pH 6.5 and at − 120 mV of 3.61 mmol/L at pH 7.6 and 0.45 mmol/L at pH 6.5. Zebrafish pept1a mRNA is highly expressed in the intestine and ovary of the adult fish, while its expression in early development undergoes a complex trend over time, with pept1a mRNA being detected 1 and 2 days post-fertilization (dpf), possibly due to its occurrence in the RNA maternal pool, decreasing at 3 dpf (~ 0.5-fold) and increasing above the 1–2 dpf levels at 4 to 7 dpf, with a peak (~ 7-fold) at 6 dpf.

Conclusions

We show that the zebrafish PepT1a-type transporter is functional and co-expressed with pept1b (slc15a1b) in the adult fish intestine. Its expression is also confirmed during the early phases of development when the yolk syncytial layer is present and yolk protein resorption processes are active. While completing the missing information on PepT1-type transporters function in the zebrafish, these results open to future investigations on the similar/differential role(s) of PepT1a/PepT1b in zebrafish and teleost fish physiology.

Interaction specificity of clustered protocadherins inferred from sequence covariation and structural analysis

Clustered protocadherins, a large family of paralogous proteins that play important roles in neuronal development, provide an important case study of interaction specificity in a large eukaryotic protein family. A mammalian genome has more than 50 clustered protocadherin isoforms, which have remarkable homophilic specificity for interactions between cellular surfaces. A large antiparallel dimer interface formed by the first 4 extracellular cadherin (EC) domains controls this interaction. To understand how specificity is achieved between the numerous paralogs, we used a combination of structural and computational approaches. Molecular dynamics simulations revealed that individual EC interactions are weak and undergo binding and unbinding events, but together they form a stable complex through polyvalency. Strongly evolutionarily coupled residue pairs interacted more frequently in our simulations, suggesting that sequence coevolution can inform the frequency of interaction and biochemical nature of a residue interaction. With these simulations and sequence coevolution, we generated a statistical model of interaction energy for the clustered protocadherin family that measures the contributions of all amino acid pairs at the interface. Our interaction energy model assesses specificity for all possible pairs of isoforms, recapitulating known pairings and predicting the effects of experimental changes in isoform specificity that are consistent with literature results. Our results show that sequence coevolution can be used to understand specificity determinants in a protein family and prioritize interface amino acid substitutions to reprogram specific protein-protein interactions.

The ghrelin O-acyltransferase structure reveals a catalytic channel for transmembrane hormone acylation

Integral membrane proteins represent a large and diverse portion of the proteome and are often recalcitrant to purification, impeding studies essential for understanding protein structure and function. By combining co-evolutionary constraints and computational modeling with biochemical validation through site-directed mutagenesis and enzyme activity assays, we demonstrate here a synergistic approach to structurally model purification-resistant topologically complex integral membrane proteins. We report the first structural model of a eukaryotic membrane-bound O-acyltransferase (MBOAT), ghrelin O-acyltransferase (GOAT), which modifies the metabolism-regulating hormone ghrelin. Our structure, generated in the absence of any experimental structural data, revealed an unanticipated strategy for transmembrane protein acylation with catalysis occurring in an internal channel connecting the endoplasmic reticulum lumen and cytoplasm. This finding validated the power of our approach to generate predictive structural models for other experimentally challenging integral membrane proteins. Our results illuminate novel aspects of membrane protein function and represent key steps for advancing structure-guided inhibitor design to target therapeutically important but experimentally intractable membrane proteins.

Structural and Evolutionary Insights Point to Allosteric Regulation of TRP Ion Channels

The familiar pungent taste of spicy food, the refreshing taste of mint, and many other physiological phenomena are mediated by transient receptor potential (TRP) ion channels. TRP channels are a superfamily of ion channels that are sensitive to diverse chemical and physical stimuli and play diverse roles in biology. In addition to chemical regulation, some family members also sense common physical stimuli, such as temperature or pressure. Since their discovery and cloning in the 1990s and 2000s, understanding the molecular mechanisms governing TRP channel function and polymodal regulation has been a consistent but challenging goal. Until recently, a general lack of high-resolution TRP channel structures had significantly limited a molecular understanding of their function. In the past few years, a flood of TRP channel structures have been released, made possible primarily by advances in cryo-electron microscopy (cryo-EM). The boon of many structures has unleashed unparalleled insight into TRP channel architecture. Substantive comparative studies between TRP structures provide snapshots of distinct states such as ligand-free, stabilized by chemical agonists, or antagonists, partially illuminating how a given channel opens and closes. However, the now ∼75 TRP channel structures have ushered in surprising outcomes, including a lack of an apparent general mechanism underlying channel opening and closing among family members. Similarly, the structures reveal a surprising diversity in which chemical ligands bind TRP channels. Several TRP channels are activated by temperature changes in addition to ligand binding. Unraveling mechanisms of thermosensation has proven an elusive challenge to the field. Although some studies point to thermosensitive domains in the transmembrane region of the channels, results have sometimes been contradictory and difficult to interpret; in some cases, a domain that proves essential for thermal sensitivity in one context can be entirely removed from the channel without affecting thermosensation in another context. These results are not amenable to simple interpretations and point to allosteric networks of regulation within the channel structure. TRP channels have evolved to be fine-tuned for the needs of a species in its environmental niche, a fact that has been both a benefit and burden in unlocking their molecular features. Functional evolutionary divergence has presented challenges for studying TRP channels, as orthologs from different species can give conflicting experimental results. However, this diversity can also be examined comparatively to decipher the basis for functional differences. As with structural biology, untangling the similarities and differences resulting from evolutionary pressure between species has been a rich source of data guiding the field. This Account will contextualize the existing biochemical and functional data with an eye to evolutionary data and couple these insights with emerging structural biology to better understand the molecular mechanisms behind chemical and physical regulation of TRP channels.

Structural models of the NaPi-II sodium-phosphate cotransporters

Progress towards understanding the molecular mechanisms of phosphate homeostasis through sodium-dependent transmembrane uptake has long been stymied by the absence of structural information about the NaPi-II sodium-phosphate transporters. For many other coupled transporters, even those unrelated to NaPi-II, internal repeated elements have been revealed as a key feature that is inherent to their function. Here, we review recent structure prediction studies for NaPi-II transporters. Attempts to identify structural templates for NaPi-II transporters have leveraged the structural repeat perspective to uncover an otherwise obscured relationship with the dicarboxylate-sodium symporters (DASS). This revelation allowed the prediction of three-dimensional structural models of human NaPi-IIa and flounder NaPi-IIb, whose folds were evaluated by comparison with available biochemical data outlining the transmembrane topology and solvent accessibility of various regions of the protein. Using these structural models, binding sites for sodium and phosphate were proposed. The predicted sites were tested and refined based on detailed electrophysiological and biochemical studies and were validated by comparison with subsequently reported structures of transporters belonging to the AbgT family. Comparison with the DASS transporter VcINDY suggested a conformational mechanism involving a large, two-domain structural change, known as an elevator-like mechanism. These structural models provide a foundation for further studies into substrate binding, conformational change, kinetics, and energetics of sodium-phosphate transport. We discuss future opportunities, as well as the challenges that remain.