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Membrane proteins must balance the sequence constraints associated with folding and function against the hydrophobicity required for solvation within the bilayer. We recently found the expression and maturation of rhodopsin are limited by the hydrophobicity of its seventh transmembrane domain (TM7), which contains polar residues that are essential for function. On the basis of these observations, we hypothesized that rhodopsin's expression should be less tolerant of mutations in TM7 relative to those within hydrophobic TM domains. To test this hypothesis, we used deep mutational scanning to compare the effects of 808 missense mutations on the plasma membrane expression of rhodopsin in HEK293T cells. Our results confirm that a higher proportion of mutations within TM7 (37%) decrease rhodopsin's plasma membrane expression relative to those within a hydrophobic TM domain (TM2, 25%). These results in conjunction with an evolutionary analysis suggest solvation energetics likely restricts the evolutionary sequence space of polar TM domains.
Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).
Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.
Copyright © 2019 Biophysical Society. Published by Elsevier Inc. All rights reserved.
Computational methods that produce accurate protein structure models from limited experimental data, for example, from nuclear magnetic resonance (NMR) spectroscopy, hold great potential for biomedical research. The NMR-assisted modeling challenge in CASP13 provided a blind test to explore the capabilities and limitations of current modeling techniques in leveraging NMR data which had high sparsity, ambiguity, and error rate for protein structure prediction. We describe our approach to predict the structure of these proteins leveraging the Rosetta software suite. Protein structure models were predicted de novo using a two-stage protocol. First, low-resolution models were generated with the Rosetta de novo method guided by nonambiguous nuclear Overhauser effect (NOE) contacts and residual dipolar coupling (RDC) restraints. Second, iterative model hybridization and fragment insertion with the Rosetta comparative modeling method was used to refine and regularize models guided by all ambiguous and nonambiguous NOE contacts and RDCs. Nine out of 16 of the Rosetta de novo models had the correct fold (global distance test total score > 45) and in three cases high-resolution models were achieved (root-mean-square deviation < 3.5 å). We also show that a meta-approach applying iterative Rosetta + NMR refinement on server-predicted models which employed non-NMR-contacts and structural templates leads to substantial improvement in model quality. Integrating these data-assisted refinement strategies with innovative non-data-assisted approaches which became possible in CASP13 such as high precision contact prediction will in the near future enable structure determination for large proteins that are outside of the realm of conventional NMR.
© 2019 Wiley Periodicals, Inc.
Advances over the past 25 years have revealed much about how the structural properties of membranes and associated proteins are linked to the thermodynamics and kinetics of membrane protein (MP) folding. At the same time biochemical progress has outlined how cellular proteostasis networks mediate MP folding and manage misfolding in the cell. When combined with results from genomic sequencing, these studies have established paradigms for how MP folding and misfolding are linked to the molecular etiologies of a variety of diseases. This emerging framework has paved the way for the development of a new class of small molecule "pharmacological chaperones" that bind to and stabilize misfolded MP variants, some of which are now in clinical use. In this review, we comprehensively outline current perspectives on the folding and misfolding of integral MPs as well as the mechanisms of cellular MP quality control. Based on these perspectives, we highlight new opportunities for innovations that bridge our molecular understanding of the energetics of MP folding with the nuanced complexity of biological systems. Given the many linkages between MP misfolding and human disease, we also examine some of the exciting opportunities to leverage these advances to address emerging challenges in the development of therapeutics and precision medicine.
Membrane proteins are prone to misfolding and degradation within the cell, yet the nature of the conformational defects involved in this process remain poorly understood. The earliest stages of membrane protein folding are mediated by the Sec61 translocon, a molecular machine that facilitates the lateral partitioning of the polypeptide into the membrane. Proper membrane integration is an essential prerequisite for folding of the nascent chain. However, the marginal energetic drivers of this reaction suggest the translocon may operate with modest fidelity. In this work, we employed biophysical modeling in conjunction with quantitative biochemical measurements in order to evaluate the extent to which cotranslational folding defects influence membrane protein homeostasis. Protein engineering was employed to selectively perturb the topological energetics of human rhodopsin, and the expression and cellular trafficking of engineered variants were quantitatively compared. Our results reveal clear relationships between topological energetics and the efficiency of rhodopsin biogenesis, which appears to be limited by the propensity of a polar transmembrane domain to achieve its correct topological orientation. Though the polarity of this segment is functionally constrained, we find that its topology can be stabilized in a manner that enhances biogenesis without compromising the functional properties of rhodopsin. Furthermore, sequence alignments reveal this topological instability has been conserved throughout the course of evolution. These results suggest that topological defects significantly contribute to the inefficiency of membrane protein folding in the cell. Additionally, our findings suggest that the marginal stability of rhodopsin may represent an evolved trait.
Mutations that induce loss of function (LOF) or dysfunction of the human KCNQ1 channel are responsible for susceptibility to a life-threatening heart rhythm disorder, the congenital long QT syndrome (LQTS). Hundreds of mutations have been identified, but the molecular mechanisms responsible for impaired function are poorly understood. We investigated the impact of 51 KCNQ1 variants with mutations located within the voltage sensor domain (VSD), with an emphasis on elucidating effects on cell surface expression, protein folding, and structure. For each variant, the efficiency of trafficking to the plasma membrane, the impact of proteasome inhibition, and protein stability were assayed. The results of these experiments combined with channel functional data provided the basis for classifying each mutation into one of six mechanistic categories, highlighting heterogeneity in the mechanisms resulting in channel dysfunction or LOF. More than half of the KCNQ1 LOF mutations examined were seen to destabilize the structure of the VSD, generally accompanied by mistrafficking and degradation by the proteasome, an observation that underscores the growing appreciation that mutation-induced destabilization of membrane proteins may be a common human disease mechanism. Finally, we observed that five of the folding-defective LQTS mutant sites are located in the VSD S0 helix, where they interact with a number of other LOF mutation sites in other segments of the VSD. These observations reveal a critical role for the S0 helix as a central scaffold to help organize and stabilize the KCNQ1 VSD and, most likely, the corresponding domain of many other ion channels.
While great progress has been made, only 10% of the nearly 1,000 integral, α-helical, multi-span membrane protein families are represented by at least one experimentally determined structure in the PDB. Previously, we developed the algorithm BCL::MP-Fold, which samples the large conformational space of membrane proteins de novo by assembling predicted secondary structure elements guided by knowledge-based potentials. Here, we present a case study of rhodopsin fold determination by integrating sparse and/or low-resolution restraints from multiple experimental techniques including electron microscopy, electron paramagnetic resonance spectroscopy, and nuclear magnetic resonance spectroscopy. Simultaneous incorporation of orthogonal experimental restraints not only significantly improved the sampling accuracy but also allowed identification of the correct fold, which is demonstrated by a protein size-normalized transmembrane root-mean-square deviation as low as 1.2 Å. The protocol developed in this case study can be used for the determination of unknown membrane protein folds when limited experimental restraints are available.
Copyright © 2018 Elsevier Ltd. All rights reserved.
Contractile actomyosin bundles, stress fibers, are crucial for adhesion, morphogenesis, and mechanosensing in nonmuscle cells. However, the mechanisms by which nonmuscle myosin II (NM-II) is recruited to those structures and assembled into functional bipolar filaments have remained elusive. We report that UNC-45a is a dynamic component of actin stress fibers and functions as a myosin chaperone in vivo. UNC-45a knockout cells display severe defects in stress fiber assembly and consequent abnormalities in cell morphogenesis, polarity, and migration. Experiments combining structured-illumination microscopy, gradient centrifugation, and proteasome inhibition approaches revealed that a large fraction of NM-II and myosin-1c molecules fail to fold in the absence of UNC-45a. The remaining properly folded NM-II molecules display defects in forming functional bipolar filaments. The C-terminal UNC-45/Cro1/She4p domain of UNC-45a is critical for NM-II folding, whereas the N-terminal tetratricopeptide repeat domain contributes to the assembly of functional stress fibers. Thus, UNC-45a promotes generation of contractile actomyosin bundles through synchronized NM-II folding and filament-assembly activities.
© 2017 Lehtimäki et al.
Prediction of protein tertiary structures from amino acid sequence and understanding the mechanisms of how proteins fold, collectively known as "the protein folding problem," has been a grand challenge in molecular biology for over half a century. Theories have been developed that provide us with an unprecedented understanding of protein folding mechanisms. However, computational simulation of protein folding is still difficult, and prediction of protein tertiary structure from amino acid sequence is an unsolved problem. Progress toward a satisfying solution has been slow due to challenges in sampling the vast conformational space and deriving sufficiently accurate energy functions. Nevertheless, several techniques and algorithms have been adopted to overcome these challenges, and the last two decades have seen exciting advances in enhanced sampling algorithms, computational power and tertiary structure prediction methodologies. This review aims at summarizing these computational techniques, specifically conformational sampling algorithms and energy approximations that have been frequently used to study protein-folding mechanisms or to de novo predict protein tertiary structures. We hope that this review can serve as an overview on how the protein-folding problem can be studied computationally and, in cases where experimental approaches are prohibitive, help the researcher choose the most relevant computational approach for the problem at hand. We conclude with a summary of current challenges faced and an outlook on potential future directions.
Increasing evidence supports the hypothesis that soluble misfolded protein assemblies contribute to the degeneration of postmitotic tissue in amyloid diseases. However, there is a dearth of reliable nonantibody-based probes for selectively detecting oligomeric aggregate structures circulating in plasma or deposited in tissues, making it difficult to scrutinize this hypothesis in patients. Hence, understanding the structure-proteotoxicity relationships driving amyloid diseases remains challenging, hampering the development of early diagnostic and novel treatment strategies. We report peptide-based probes that selectively label misfolded transthyretin (TTR) oligomers circulating in the plasma of TTR hereditary amyloidosis patients exhibiting a predominant neuropathic phenotype. These probes revealed that there are much fewer misfolded TTR oligomers in healthy controls, in asymptomatic carriers of mutations linked to amyloid polyneuropathy, and in patients with TTR-associated cardiomyopathies. The absence of misfolded TTR oligomers in the plasma of cardiomyopathy patients suggests that the tissue tropism observed in the TTR amyloidoses is structure-based. Misfolded oligomers decrease in TTR amyloid polyneuropathy patients treated with disease-modifying therapies (tafamidis or liver transplant-mediated gene therapy). In a subset of TTR amyloid polyneuropathy patients, the probes also detected a circulating TTR fragment that disappeared after tafamidis treatment. Proteomic analysis of the isolated TTR oligomers revealed a specific patient-associated signature composed of proteins that likely associate with the circulating TTR oligomers. Quantification of plasma oligomer concentrations using peptide probes could become an early diagnostic strategy, a response-to-therapy biomarker, and a useful tool for understanding structure-proteotoxicity relationships in the TTR amyloidoses.
Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.