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Mechanisms of KCNQ1 channel dysfunction in long QT syndrome involving voltage sensor domain mutations.
Huang H, Kuenze G, Smith JA, Taylor KC, Duran AM, Hadziselimovic A, Meiler J, Vanoye CG, George AL, Sanders CR
(2018) Sci Adv 4: eaar2631
MeSH Terms: Cell Membrane, HEK293 Cells, Humans, KCNQ1 Potassium Channel, Leupeptins, Long QT Syndrome, Loss of Function Mutation, Magnetic Resonance Spectroscopy, Mutant Proteins, Mutation, Proteasome Endopeptidase Complex, Proteasome Inhibitors, Protein Domains, Protein Folding, Protein Structure, Secondary, Proteolysis
Show Abstract · Added March 14, 2018
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.
0 Communities
3 Members
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16 MeSH Terms
Integrated Structural Biology for α-Helical Membrane Protein Structure Determination.
Xia Y, Fischer AW, Teixeira P, Weiner B, Meiler J
(2018) Structure 26: 657-666.e2
MeSH Terms: Algorithms, Binding Sites, Electron Spin Resonance Spectroscopy, Humans, Membrane Proteins, Microscopy, Electron, Models, Molecular, Monte Carlo Method, Nuclear Magnetic Resonance, Biomolecular, Protein Binding, Protein Conformation, alpha-Helical, Protein Folding, Protein Interaction Domains and Motifs, Rhodopsin, Thermodynamics
Show Abstract · Added March 17, 2018
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.
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1 Members
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15 MeSH Terms
UNC-45a promotes myosin folding and stress fiber assembly.
Lehtimäki JI, Fenix AM, Kotila TM, Balistreri G, Paavolainen L, Varjosalo M, Burnette DT, Lappalainen P
(2017) J Cell Biol 216: 4053-4072
MeSH Terms: Actomyosin, Cell Adhesion, Cell Line, Tumor, Cell Movement, Cell Polarity, Gene Expression, Humans, Intracellular Signaling Peptides and Proteins, Myosin Type II, Osteoblasts, Proteasome Endopeptidase Complex, Protein Folding, Protein Isoforms, Stress Fibers, Tetratricopeptide Repeat
Show Abstract · Added March 14, 2018
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.
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1 Members
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15 MeSH Terms
Finding the needle in the haystack: towards solving the protein-folding problem computationally.
Li B, Fooksa M, Heinze S, Meiler J
(2018) Crit Rev Biochem Mol Biol 53: 1-28
MeSH Terms: Algorithms, Animals, Humans, Kinetics, Molecular Dynamics Simulation, Protein Folding, Protein Structure, Tertiary, Proteins, Thermodynamics
Show Abstract · Added March 17, 2018
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.
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1 Members
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9 MeSH Terms
Membrane protein contact and structure prediction using co-evolution in conjunction with machine learning.
Teixeira PL, Mendenhall JL, Heinze S, Weiner B, Skwark MJ, Meiler J
(2017) PLoS One 12: e0177866
MeSH Terms: Algorithms, Amino Acid Sequence, Humans, Machine Learning, Membrane Proteins, Models, Molecular, Protein Folding, Protein Structure, Secondary
Show Abstract · Added March 17, 2018
De novo membrane protein structure prediction is limited to small proteins due to the conformational search space quickly expanding with length. Long-range contacts (24+ amino acid separation)-residue positions distant in sequence, but in close proximity in the structure, are arguably the most effective way to restrict this conformational space. Inverse methods for co-evolutionary analysis predict a global set of position-pair couplings that best explain the observed amino acid co-occurrences, thus distinguishing between evolutionarily explained co-variances and these arising from spurious transitive effects. Here, we show that applying machine learning approaches and custom descriptors improves evolutionary contact prediction accuracy, resulting in improvement of average precision by 6 percentage points for the top 1L non-local contacts. Further, we demonstrate that predicted contacts improve protein folding with BCL::Fold. The mean RMSD100 metric for the top 10 models folded was reduced by an average of 2 Å for a benchmark of 25 membrane proteins.
0 Communities
1 Members
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8 MeSH Terms
Structure of a DNA glycosylase that unhooks interstrand cross-links.
Mullins EA, Warren GM, Bradley NP, Eichman BF
(2017) Proc Natl Acad Sci U S A 114: 4400-4405
MeSH Terms: Anti-Bacterial Agents, Bacterial Proteins, DNA Glycosylases, DNA, Bacterial, Gene Expression Regulation, Bacterial, Gene Expression Regulation, Enzymologic, Models, Molecular, Mutation, Naphthalenes, Peptides, Protein Binding, Protein Conformation, Protein Folding, Streptomyces
Show Abstract · Added August 26, 2019
DNA glycosylases are important editing enzymes that protect genomic stability by excising chemically modified nucleobases that alter normal DNA metabolism. These enzymes have been known only to initiate base excision repair of small adducts by extrusion from the DNA helix. However, recent reports have described both vertebrate and microbial DNA glycosylases capable of unhooking highly toxic interstrand cross-links (ICLs) and bulky minor groove adducts normally recognized by Fanconi anemia and nucleotide excision repair machinery, although the mechanisms of these activities are unknown. Here we report the crystal structure of AlkZ (previously Orf1), a bacterial DNA glycosylase that protects its host by excising ICLs derived from azinomycin B (AZB), a potent antimicrobial and antitumor genotoxin. AlkZ adopts a unique fold in which three tandem winged helix-turn-helix motifs scaffold a positively charged concave surface perfectly shaped for duplex DNA. Through mutational analysis, we identified two glutamine residues and a β-hairpin within this putative DNA-binding cleft that are essential for catalytic activity. Additionally, we present a molecular docking model for how this active site can unhook either or both sides of an AZB ICL, providing a basis for understanding the mechanisms of base excision repair of ICLs. Given the prevalence of this protein fold in pathogenic bacteria, this work also lays the foundation for an emerging role of DNA repair in bacteria-host pathogenesis.
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MeSH Terms
Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.
Li B, Mendenhall J, Nguyen ED, Weiner BE, Fischer AW, Meiler J
(2017) Proteins 85: 1212-1221
MeSH Terms: Algorithms, Amino Acids, Benchmarking, Binding Sites, Membrane Proteins, Models, Molecular, Protein Binding, Protein Conformation, alpha-Helical, Protein Folding, Protein Interaction Domains and Motifs, Protein Structure, Tertiary
Show Abstract · Added April 8, 2017
One of the challenging problems in tertiary structure prediction of helical membrane proteins (HMPs) is the determination of rotation of α-helices around the helix normal. Incorrect prediction of helix rotations substantially disrupts native residue-residue contacts while inducing only a relatively small effect on the overall fold. We previously developed a method for predicting residue contact numbers (CNs), which measure the local packing density of residues within the protein tertiary structure. In this study, we tested the idea of incorporating predicted CNs as restraints to guide the sampling of helix rotation. For a benchmark set of 15 HMPs with simple to rather complicated folds, the average contact recovery (CR) of best-sampled models was improved for all targets, the likelihood of sampling models with CR greater than 20% was increased for 13 targets, and the average RMSD100 of best-sampled models was improved for 12 targets. This study demonstrated that explicit incorporation of CNs as restraints improves the prediction of helix-helix packing. Proteins 2017; 85:1212-1221. © 2017 Wiley Periodicals, Inc.
© 2017 Wiley Periodicals, Inc.
1 Communities
1 Members
0 Resources
11 MeSH Terms
ERAD-icating mutant insulin promotes functional insulin secretion.
Moore DJ
(2017) Sci Transl Med 9:
MeSH Terms: Endoplasmic Reticulum, Endoplasmic Reticulum-Associated Degradation, Insulin, Insulin Secretion, Proinsulin, Protein Folding
Show Abstract · Added January 20, 2017
Overexpression of a chaperone protein liberates functional insulin from a misfolded mutant partner to improve insulin secretion.
Copyright © 2017, American Association for the Advancement of Science.
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1 Members
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6 MeSH Terms
Structural insight for chain selection and stagger control in collagen.
Boudko SP, Bächinger HP
(2016) Sci Rep 6: 37831
MeSH Terms: Amino Acid Sequence, Collagen Type IX, Humans, Models, Molecular, Protein Binding, Protein Domains, Protein Folding, Protein Multimerization, Protein Structure, Secondary
Show Abstract · Added November 2, 2017
Collagen plays a fundamental role in all known metazoans. In collagens three polypeptides form a unique triple-helical structure with a one-residue stagger to fit every third glycine residue in the inner core without disturbing the poly-proline type II helical conformation of each chain. There are homo- and hetero-trimeric types of collagen consisting of one, two or three distinct chains. Thus there must be mechanisms that control composition and stagger during collagen folding. Here, we uncover the structural basis for both chain selection and stagger formation of a collagen molecule. Three distinct chains (α1, α2 and α3) of the non-collagenous domain 2 (NC2) of type IX collagen are assembled to guide triple-helical sequences in the leading, middle and trailing positions. This unique domain opens the door for generating any fragment of collagen in its native composition and stagger.
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1 Members
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9 MeSH Terms
Protocols for Molecular Modeling with Rosetta3 and RosettaScripts.
Bender BJ, Cisneros A, Duran AM, Finn JA, Fu D, Lokits AD, Mueller BK, Sangha AK, Sauer MF, Sevy AM, Sliwoski G, Sheehan JH, DiMaio F, Meiler J, Moretti R
(2016) Biochemistry 55: 4748-63
MeSH Terms: Algorithms, Computational Biology, Internet, Models, Molecular, Protein Binding, Protein Conformation, Protein Folding, Protein Interaction Mapping, Proteins, RNA, Software, User-Computer Interface
Show Abstract · Added April 8, 2017
Previously, we published an article providing an overview of the Rosetta suite of biomacromolecular modeling software and a series of step-by-step tutorials [Kaufmann, K. W., et al. (2010) Biochemistry 49, 2987-2998]. The overwhelming positive response to this publication we received motivates us to here share the next iteration of these tutorials that feature de novo folding, comparative modeling, loop construction, protein docking, small molecule docking, and protein design. This updated and expanded set of tutorials is needed, as since 2010 Rosetta has been fully redesigned into an object-oriented protein modeling program Rosetta3. Notable improvements include a substantially improved energy function, an XML-like language termed "RosettaScripts" for flexibly specifying modeling task, new analysis tools, the addition of the TopologyBroker to control conformational sampling, and support for multiple templates in comparative modeling. Rosetta's ability to model systems with symmetric proteins, membrane proteins, noncanonical amino acids, and RNA has also been greatly expanded and improved.
1 Communities
2 Members
0 Resources
12 MeSH Terms