Browsing by Subject "PROTEIN-STRUCTURE"

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  • Domanska, Ausra; Flatt, Justin Wayne; Jukonen, Joonas; Geraets, James; Butcher, Sarah Jane (2019)
    Human parechovirus 3 (HPeV3) infection is associated with sepsis characterized by significant immune activation and subsequent tissue damage in neonates. Strategies to limit infection have been unsuccessful due to inadequate molecular diagnostic tools for early detection and the lack of a vaccine or specific antiviral therapy. Toward the latter, we present a 2.8-angstrom-resolution structure of HPeV3 in complex with fragments from a neutralizing human monoclonal antibody, AT12-015, using cryo-electron microscopy (cryo-EM) and image reconstruction. Modeling revealed that the epitope extends across neighboring asymmetric units with contributions from capsid proteins VP0, VP1, and VP3. Antibody decoration was found to block binding of HPeV3 to cultured cells. Additionally, at high resolution, it was possible to model a stretch of RNA inside the virion and, from this, identify the key features that drive and stabilize protein-RNA association during assembly. IMPORTANCE Human parechovirus 3 (HPeV3) is receiving increasing attention as a prevalent cause of sepsis-like symptoms in neonates, for which, despite the severity of disease, there are no effective treatments available. Structural and molecular insights into virus neutralization are urgently needed, especially as clinical cases are on the rise. Toward this goal, we present the first structure of HPeV3 in complex with fragments from a neutralizing monoclonal antibody. At high resolution, it was possible to precisely define the epitope that, when targeted, prevents virions from binding to cells. Such an atomic-level description is useful for understanding host-pathogen interactions and viral pathogenesis mechanisms and for finding potential cures for infection and disease.
  • Pandurangan, Arun Prasad; Shakeel, Shabih; Butcher, Sarah Jane; Topf, Maya (2014)
  • Holm, Liisa; Laakso, Laura M. (2016)
    The Dali server (http://ekhidna2.biocenter.helsinki.fi/dali) is a network service for comparing protein structures in 3D. In favourable cases, comparing 3D structures may reveal biologically interesting similarities that are not detectable by comparing sequences. The Dali server has been running in various places for over 20 years and is used routinely by crystallographers on newly solved structures. The latest update of the server provides enhanced analytics for the study of sequence and structure conservation. The server performs three types of structure comparisons: (i) Protein Data Bank (PDB) search compares one query structure against those in the PDB and returns a list of similar structures; (ii) pairwise comparison compares one query structure against a list of structures specified by the user; and (iii) all against all structure comparison returns a structural similarity matrix, a dendrogram and a multidimensional scaling projection of a set of structures specified by the user. Structural superimpositions are visualized using the Java-free WebGL viewer PV. The structural alignment view is enhanced by sequence similarity searches against Uniprot. The combined structure-sequence alignment information is compressed to a stack of aligned sequence logos. In the stack, each structure is structurally aligned to the query protein and represented by a sequence logo.
  • Howard, Sasha R.; Guasti, Leonardo; Ruiz-Babot, Gerard; Mancini, Alessandra; David, Alessia; Storr, HelenL; Metherell, Lousie A.; Sternberg, Michael J. E.; Cabrera, Claudia P.; Warren, Helen R.; Barnes, Michael R.; Quinton, Richard; de Roux, Nicolas; Young, Jacques; Guiochon-Mantel, Anne; Wehkalampi, Karoliina; Andre, Valentina; Gothilf, Yoav; Cariboni, Anna; Dunkel, Leo (2016)
    Early or late pubertal onset affects up to 5% of adolescents and is associated with adverse health and psychosocial outcomes. Self-limited delayed puberty (DP) segregates predominantly in an autosomal dominant pattern, but the underlying genetic background is unknown. Using exome and candidate gene sequencing, we have identified rare mutations in IGSF10 in 6 unrelated families, which resulted in intracellular retention with failure in the secretion of mutant proteins. IGSF10 mRNA was strongly expressed in embryonic nasal mesenchyme, during gonadotropin-releasing hormone (GnRH) neuronal migration to the hypothalamus. IGSF10 knockdown caused a reduced migration of immature GnRH neurons invitro, and perturbed migration andextension of GnRH neurons in a gnrh3:EGFP zebrafish model. Additionally, loss-of-function mutations in IGSF10 were identified in hypothalamic amenorrhea patients. Our evidence strongly suggests that mutations in IGSF10 cause DP in humans, and points to a common genetic basis for conditions of functional hypogonadotropic hypogonadism (HH). While dysregulation of GnRH neuronal migration is known to cause permanent HH, this is the first time that this has been demonstrated as a causal mechanism in DP. Synopsis Self-limited delayed puberty (DP) has strong familial inheritance, but the underlying genetic determinants are unknown. IGSF10 deficiency is found to affect embryonic GnRH neuronal migration and results in DP in humans. Pathogenic mutations in IGSF10 are found in patients with self-limited delayed puberty. IGSF10 is a gene of previously unclear function with no known human mutations. IGSF10 is expressed within the nasal mesenchyme during fetal development, in a pattern similar to known chemokines that direct migrational GnRH neurons to the hypothalamus. Knockdown of IGSF10 led to a reduced migration of GnRH neurons invitro and in a transgenic zebrafish model. IGSF10 loss-of-function mutations were also identified in patients with hypothalamic amenorrhea, suggesting an overlapping genetic and mechanistic basis between different types of functional hypogonadotropic hypogonadism, including DP and hypothalamic amenorrhea.
  • Xu, Yingying; Puranen, Santeri; Corander, Jukka; Kabashima, Yoshiyuki (2018)
    We propose an efficient procedure for significance determination in high-dimensional dependence learning based on surrogate data testing, termed inverse finite-size scaling (IFSS). The IFSS method is based on our discovery of a universal scaling property of random matrices which enables inference about signal behavior from much smaller scale surrogate data than the dimensionality of the original data. As a motivating example, we demonstrate the procedure for ultra-high-dimensional Potts models with order of 1010 parameters. IFSS reduces the computational effort of the data-testing procedure by several orders of magnitude, making it very efficient for practical purposes. This approach thus holds considerable potential for generalization to other types of complex models.
  • Puranen, Santeri; Pesonen, Maiju; Pensar, Johan; Xu, Ying Ying; Lees, John A.; Bentley, Stephen D.; Croucher, Nicholas J.; Corander, Jukka (2018)
    The potential for genome-wide modelling of epistasis has recently surfaced given the possibility of sequencing densely sampled populations and the emerging families of statistical interaction models. Direct coupling analysis (DCA) has previously been shown to yield valuable predictions for single protein structures, and has recently been extended to genome-wide analysis of bacteria, identifying novel interactions in the co-evolution between resistance, virulence and core genome elements. However, earlier computational DCA methods have not been scalable to enable model fitting simultaneously to 10(4)-10(5) polymorphisms, representing the amount of core genomic variation observed in analyses of many bacterial species. Here, we introduce a novel inference method (SuperDCA) that employs a new scoring principle, efficient parallelization, optimization and filtering on phylogenetic information to achieve scalability for up to 10(5) polymorphisms. Using two large population samples of Streptococcus pneumoniae, we demonstrate the ability of SuperDCA to make additional significant biological findings about this major human pathogen. We also show that our method can uncover signals of selection that are not detectable by genome-wide association analysis, even though our analysis does not require phenotypic measurements. SuperDCA, thus, holds considerable potential in building understanding about numerous organisms at a systems biological level.