Concept: Protein structure
Vitamin E is a fat-soluble vitamin with antioxidant properties. Tocopherols are the predominant form of vitamin E found in the diet and in supplements and have garnered interest for their potential cancer therapeutic and preventive effects, such as the dephosphorylation of Akt, a serine/threonine kinase with a pivotal role in cell growth, survival, and metabolism. Dephosphorylation of Akt at Ser(473) substantially reduces its catalytic activity and inhibits downstream signaling. We found that the mechanism by which α-tocopherol and γ-tocopherol facilitate this site-specific dephosphorylation of Akt was mediated through the pleckstrin homology (PH) domain-dependent recruitment of Akt and PHLPP1 (PH domain leucine-rich repeat protein phosphatase, isoform 1) to the plasma membrane. We structurally optimized these tocopherols to obtain derivatives with greater in vitro potency and in vivo tumor-suppressive activity in two prostate xenograft tumor models. Binding affinities for the PH domains of Akt and PHLPP1 were greater than for other PH domain-containing proteins, which may underlie the preferential recruitment of these proteins to membranes containing tocopherols. Molecular modeling revealed the structural determinants of the interaction with the PH domain of Akt that may inform strategies for continued structural optimization. By describing a mechanism by which tocopherols facilitate the dephosphorylation of Akt at Ser(473), we provide insights into the mode of antitumor action of tocopherols and a rationale for the translational development of tocopherols into novel PH domain-targeted Akt inhibitors.
Bioluminescence methodologies have been extraordinarily useful due to their high sensitivity, broad dynamic range, and operational simplicity. These capabilities have been realized largely through incremental adaptations of native enzymes and substrates, originating from luminous organisms of diverse evolutionary lineages. We engineered both an enzyme and substrate in combination to create a novel bioluminescence system capable of more efficient light emission with superior biochemical and physical characteristics. Using a small luciferase subunit (19 kDa) from the deep sea shrimp Oplophorus gracilirostris, we have improved luminescence expression in mammalian cells ∼2.5 million-fold by merging optimization of protein structure with development of a novel imidazopyrazinone substrate (furimazine). The new luciferase, NanoLuc, produces glow-type luminescence (signal half-life >2 h) with a specific activity ∼150-fold greater than that of either firefly (Photinus pyralis) or Renilla luciferases similarly configured for glow-type assays. In mammalian cells, NanoLuc shows no evidence of post-translational modifications or subcellular partitioning. The enzyme exhibits high physical stability, retaining activity with incubation up to 55 °C or in culture medium for >15 h at 37 °C. As a genetic reporter, NanoLuc may be configured for high sensitivity or for response dynamics by appending a degradation sequence to reduce intracellular accumulation. Appending a signal sequence allows NanoLuc to be exported to the culture medium, where reporter expression can be measured without cell lysis. Fusion onto other proteins allows luminescent assays of their metabolism or localization within cells. Reporter quantitation is achievable even at very low expression levels to facilitate more reliable coupling with endogenous cellular processes.
Nature has shaped the make up of proteins since their appearance, [Formula: see text]3.8 billion years ago. However, the fundamental drivers of structural change responsible for the extraordinary diversity of proteins have yet to be elucidated. Here we explore if protein evolution affects folding speed. We estimated folding times for the present-day catalog of protein domains directly from their size-modified contact order. These values were mapped onto an evolutionary timeline of domain appearance derived from a phylogenomic analysis of protein domains in 989 fully-sequenced genomes. Our results show a clear overall increase of folding speed during evolution, with known ultra-fast downhill folders appearing rather late in the timeline. Remarkably, folding optimization depends on secondary structure. While alpha-folds showed a tendency to fold faster throughout evolution, beta-folds exhibited a trend of folding time increase during the last [Formula: see text]1.5 billion years that began during the “big bang” of domain combinations. As a consequence, these domain structures are on average slow folders today. Our results suggest that fast and efficient folding of domains shaped the universe of protein structure. This finding supports the hypothesis that optimization of the kinetic and thermodynamic accessibility of the native fold reduces protein aggregation propensities that hamper cellular functions.
Single-molecule techniques facilitate analysis of mechanical transitions within nucleic acids and proteins. Here, we describe an integrated fluorescence and magnetic tweezers instrument that permits detection of nanometer-scale DNA structural rearrangements together with the application of a wide range of stretching forces to individual DNA molecules. We have analyzed the force-dependent equilibrium and rate constants for telomere DNA G-quadruplex (GQ) folding and unfolding, and have determined the location of the transition state barrier along the well-defined DNA-stretching reaction coordinate. Our results reveal the mechanical unfolding pathway of the telomere DNA GQ is characterized by a short distance (<1 nm) to the transition state for the unfolding reaction. This mechanical unfolding response reflects a critical contribution of long-range interactions to the global stability of the GQ fold, and suggests that telomere-associated proteins need only disrupt a few base pairs to destabilize GQ structures. Comparison of the GQ unfolded state with a single-stranded polyT DNA revealed the unfolded GQ exhibits a compacted non-native conformation reminiscent of the protein molten globule. We expect the capacity to interrogate macromolecular structural transitions with high spatial resolution under conditions of low forces will have broad application in analyses of nucleic acid and protein folding.
The recent elucidation of the X-ray structure of several class A GPCRs clearly indicates that the amphipathic helix 8 (H8) is a conserved structural domain in most crystallized GPCRs. Very little is known about the presence and the possible role of an analogous H8 domain in the distantly related class C GPCRs. In this study, we investigated the structural properties for the H8 domain of the mGluR2 receptor, a class C GPCR, by applying extended molecular dynamics simulations. Our study indicates that the amphipathic H8 adopts membrane-sensitive conformational states, which depend on the membrane composition. Cholesterol-rich membranes stabilize the helical structure of H8 whereas cholesterol-depleted membranes induce a disruption of H8. The observed link between membrane cholesterol levels and H8 conformational states suggests that H8 behaves as a sensor of cholesterol concentration.
The vast majority of membrane proteins are anchored to biological membranes through hydrophobic α-helices. Sequence analysis of high-resolution membrane protein structures show that ionizable amino acid residues are present in transmembrane ™ helices, often with a functional and/or structural role. Here, using as scaffold the hydrophobic TM domain of the model membrane protein glycophorin A (GpA), we address the consequences of replacing specific residues by ionizable amino acids on TM helix insertion and packing, both in detergent micelles and in biological membranes. Our findings demonstrate that ionizable residues are stably inserted in hydrophobic environments, and tolerated in the dimerization process when oriented toward the lipid face, emphasizing the complexity of protein-lipid interactions in biological membranes.
Heat shock protein information resource (HSPIR) is a concerted database of six major heat shock proteins (HSPs), namely, Hsp70, Hsp40, Hsp60, Hsp90, Hsp100 and small HSP. The HSPs are essential for the survival of all living organisms, as they protect the conformations of proteins on exposure to various stress conditions. They are a highly conserved group of proteins involved in diverse physiological functions, including de novo folding, disaggregation and protein trafficking. Moreover, their critical role in the control of disease progression made them a prime target of research. Presently, limited information is available on HSPs in reference to their identification and structural classification across genera. To that extent, HSPIR provides manually curated information on sequence, structure, classification, ontology, domain organization, localization and possible biological functions extracted from UniProt, GenBank, Protein Data Bank and the literature. The database offers interactive search with incorporated tools, which enhances the analysis. HSPIR is a reliable resource for researchers exploring structure, function and evolution of HSPs.
[Background] Protein domain ranking is a fundamental task in structural biology. Most protein domain ranking methods rely on the pairwise comparison of protein domains while neglecting the global manifold structure of the protein domain database. Recently, graph regularized ranking that exploits the global structure of the graph defined by the pairwise similarities has been proposed. However, the existing graph regularized ranking methods are very sensitive to the choice of the graph model and parameters, and this remains a difficult problem for most of the protein domain ranking methods. [Results] To tackle this problem, we have developed the Multiple Graph regularized Ranking algorithm, MultiG-Rank. Instead of using a single graph to regularize the ranking scores, MultiG-Rank approximates the intrinsic manifold of protein domain distribution by combining multiple initial graphs for the regularization. Graph weights are learned with ranking scores jointly and automatically, by alternately minimizing an objective function in an iterative algorithm. Experimental results on a subset of the ASTRAL SCOP protein domain database demonstrate that MultiG-Rank achieves a better ranking performance than single graph regularized ranking methods and pairwise similarity based ranking methods. [Conclusion] The problem of graph model and parameter selection in graph regularized protein domain ranking can be solved effectively by combining multiple graphs. This aspect of generalization introduces a new frontier in applying multiple graphs to solving protein domain ranking applications.
BACKGROUND: Allostery is one of the most powerful and common ways of regulation of protein activity. However,for most allosteric proteins identified to date the mechanistic details of allosteric modulation are notyet well understood. Uncovering common mechanistic patterns underlying allostery would allow notonly a better academic understanding of the phenomena, but it would also streamline the design ofnovel therapeutic solutions. This relatively unexplored therapeutic potential and the putativeadvantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drugresearch is receiving at present. A first step to harness the regulatory potential and versatility ofallosteric sites, in the context of drug-discovery and design, would be to detect or predict theirpresence and location. In this article, we describe a simple computational approach, based on theeffect allosteric ligands exert on protein flexibility upon binding, to predict the existence and positionof allosteric sites on a given protein structure. RESULTS: By querying the literature and a recently available database of allosteric sites, we gathered 213allosteric proteins with structural information that we further filtered into a non-redundant set of 91proteins. We performed normal-mode analysis and observed significant changes in protein flexibilityupon allosteric-ligand binding in 70% of the cases. These results agree with the current view thatallosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligandbinding. Furthermore, we implemented an approach that achieves 65% positive predictive value inidentifying allosteric sites within the set of predicted cavities of a protein (stricter parameters set,0.22 sensitivity), by combining the current analysis on dynamics with previous results on structuralconservation of allosteric sites. We also analyzed four biological examples in detail, revealing thatthis simple coarse-grained methodology is able to capture the effects triggered by allosteric ligandsalready described in the literature. CONCLUSIONS: We introduce a simple computational approach to predict the presence and position of allosteric sitesin a protein based on the analysis of changes in protein normal modes upon the binding of acoarse-grained ligand at predicted cavities. Its performance has been demonstrated using a newlycurated non-redundant set of 91 proteins with reported allosteric properties. The software developedin this work is available upon request from the authors.
Protein structure alignment is a fundamental problem in computational structure biology. Many programs have been developed for automatic protein structure alignment, but most of them align two protein structures purely based upon geometric similarity without considering evolutionary and functional relationship. As such, these programs may generate structure alignments which are not very biologically meaningful from the evolutionary perspective. This paper presents a novel method DeepAlign for automatic pairwise protein structure alignment. DeepAlign aligns two protein structures using not only spatial proximity of equivalent residues (after rigid-body superposition), but also evolutionary relationship and hydrogen-bonding similarity. Experimental results show that DeepAlign can generate structure alignments much more consistent with manually-curated alignments than other automatic tools especially when proteins under consideration are remote homologs. These results imply that in addition to geometric similarity, evolutionary information and hydrogen-bonding similarity are essential to aligning two protein structures.