Browsing by Subject "Proteins"

Now showing 1 - 10 of 43
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    Accurate single-sequence prediction of solvent accessible surface area using local and global features
    (Wiley Blackwell (John Wiley & Sons), 2014-11) Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej; Department of Biochemistry & Molecular Biology, IU School of Medicine
    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org.
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    Addressing Intersite Coupling Unlocks Large Combinatorial Chemical Spaces for Alchemical Free Energy Methods
    (American Chemical Society, 2022) Hayes, Ryan L.; Vilseck, Jonah Z.; Brooks, Charles L., III.; Biochemistry and Molecular Biology, School of Medicine
    Alchemical free energy methods are playing a growing role in molecular design, both for computer-aided drug design of small molecules and for computational protein design. Multisite λ dynamics (MSλD) is a uniquely scalable alchemical free energy method that enables more efficient exploration of combinatorial alchemical spaces encountered in molecular design, but simulations have typically been limited to a few hundred ligands or sequences. Here, we focus on coupling between sites to enable scaling to larger alchemical spaces. We first discuss updates to the biasing potentials that facilitate MSλD sampling to include coupling terms and show that this can provide more thorough sampling of alchemical states. We then harness coupling between sites by developing a new free energy estimator based on the Potts models underlying direct coupling analysis, a method for predicting contacts from sequence coevolution, and find it yields more accurate free energies than previous estimators. The sampling requirements of the Potts model estimator scale with the square of the number of sites, a substantial improvement over the exponential scaling of the standard estimator. This opens up exploration of much larger alchemical spaces with MSλD for molecular design.
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    Aldosterone-induced proteins in primary cultures of rabbit renal cortical collecting system
    (1996-10) Bindels, Rend J.M.; Engbersen, A.M.T.; Hartog, A.; Blazer-Yost, Bonnie
    Primary cultures of immunodissected cells from rabbit kidney connecting tubule and cortical collecting duct were used to study aldosterone's action on transcellular Na+ flux. Incubation with 10(-7) M aldosterone stimulated transcellular Na+ transport which was detected as an increase in benzamil-sensitive short-circuit current. The stimulatory response was consistently noted after 2 h of incubation and stabilized after 6 h. 2D-PAGE was used to identify proteins which were induced concurrently with the increase in transcellular Na+ flux after an aldosterone incubation of 15 h. Three aldosterone-induced proteins (AIPs; M(r) = 100, 70-77 and 46-50 kDa) were found in the membrane and microsomal fractions. Two of these appeared to have more than one isoform. A single heterogeneous AIP (M(r) = 77 kDa) was detected in the soluble fraction.
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    Carbonyl Posttranslational Modification Associated With Early-Onset Type 1 Diabetes Autoimmunity
    (American Diabetes Association, 2022) Yang, Mei-Ling; Connolly, Sean E.; Gee, Renelle J.; Lam, TuKiet T.; Kanyo, Jean; Peng, Jian; Guyer, Perrin; Syed, Farooq; Tse, Hubert M.; Clarke, Steven G.; Clarke, Catherine F.; James, Eddie A.; Speake, Cate; Evans-Molina, Carmella; Arvan, Peter; Herold, Kevan C.; Wen, Li; Mamula, Mark J; Medicine, School of Medicine
    Inflammation and oxidative stress in pancreatic islets amplify the appearance of various posttranslational modifications to self-proteins. In this study, we identified a select group of carbonylated islet proteins arising before the onset of hyperglycemia in NOD mice. Of interest, we identified carbonyl modification of the prolyl-4-hydroxylase β subunit (P4Hb) that is responsible for proinsulin folding and trafficking as an autoantigen in both human and murine type 1 diabetes. We found that carbonylated P4Hb is amplified in stressed islets coincident with decreased glucose-stimulated insulin secretion and altered proinsulin-to-insulin ratios. Autoantibodies against P4Hb were detected in prediabetic NOD mice and in early human type 1 diabetes prior to the onset of anti-insulin autoimmunity. Moreover, we identify autoreactive CD4+ T-cell responses toward carbonyl-P4Hb epitopes in the circulation of patients with type 1 diabetes. Our studies provide mechanistic insight into the pathways of proinsulin metabolism and in creating autoantigenic forms of insulin in type 1 diabetes.
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    Chemical modification of proteins
    (1963) Dennen, David Warren
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    Coiled-coil domain containing 42 (Ccdc42) is necessary for proper sperm development and male fertility in the mouse
    (Elsevier, 2016-04-15) Pasek, Raymond C.; Malarkey, Erik; Berbari, Nicolas F.; Sharma, Neeraj; Kesterson, Robert A.; Tres, Laura L.; Kierszenbaum, Abraham L.; Yoder, Bradley K.; Department of Biology, School of Science
    Spermiogenesis is the differentiation of spermatids into motile sperm consisting of a head and a tail. The head harbors a condensed elongated nucleus partially covered by the acrosome-acroplaxome complex. Defects in the acrosome-acroplaxome complex are associated with abnormalities in sperm head shaping. The head-tail coupling apparatus (HTCA), a complex structure consisting of two cylindrical microtubule-based centrioles and associated components, connects the tail or flagellum to the sperm head. Defects in the development of the HTCA cause sperm decapitation and disrupt sperm motility, two major contributors to male infertility. Here, we provide data indicating that mutations in the gene Coiled-coil domain containing 42 (Ccdc42) is associated with malformation of the mouse sperm flagella. In contrast to many other flagella and motile cilia genes, Ccdc42 expression is only observed in the brain and developing sperm. Male mice homozygous for a loss-of-function Ccdc42 allele (Ccdc42(KO)) display defects in the number and location of the HTCA, lack flagellated sperm, and are sterile. The testes enriched expression of Ccdc42 and lack of other phenotypes in mutant mice make it an ideal candidate for screening cases of azoospermia in humans.
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    COMPARATIVE ANALYSIS OF THE DISCORDANCE BETWEEN THE GLOBAL TRANSCRIPTIONAL AND PROTEOMIC RESPONSE OF THE YEAST SACCHAROMYCES CEREVISIAE TO DELETION OF THE F-BOX PROTEIN, GRR1
    (2010-05) Heyen, Joshua William; Goebl, Mark, 1958-; Roach, Peter J.; Clemmer, David E.; Wang, Mu; Chen, Jake
    The Grr1 (Glucose Repression Resistant) protein in Saccharomyces cerevisiae is an F-box protein for the E3 ubiquitin ligase protein complex known as the SCFGrr1 (Skp, Cullin, F-box). F-box proteins serve as substrate receptors for this complex and in this capacity Grr1 serves to promote the ubiquitylation and subsequent proteasomal degradation of a number of intracellular protein substrates. Substrates of SCFGrr1 include the G1-S phase cyclins, Cln1 and Cln2, the Cdc42 effectors and cell polarity proteins, Gic1 and Gic2, the FCH-bar domain protein, Hof1, required for cytokinesis, the meiosis activating serine/threonine protein kinase, Ime2, the transcriptional regulators of glucose transporters, Mth1 and Std1, and the mitochondrial retrograde response inhibitor Mks1. Stabilization of these substrates lead to pleiotrophic phenotypic defects in grr1Δ strains including resistance to glucose repression, accumulation of grr1Δ cells in G2 and M phase of the cell cycle, sensitivity to osmotic stress, and resistance to divalent cations. However, many of these phenotypes are not reflected at the gene expression level. We conducted a quantitative genomic vii and proteomic comparison of 914 loci in a grr1Δ and wild-type strain grown to early log-phase in glucose media. These loci encompassed 16.7% of the Saccharomyces proteome of which 22.3% exhibited discordance between gene and protein expression. GO process enrichment analysis revealed that discordant loci were enriched in the processes of “trafficking”, “mitosis”, and “carbon/energy” metabolism. Here we show that these instances of discordance are biologically relevant and in fact reflect phenotypes of grr1Δ strains not evident at the transcriptional level. Additionally, through combined biochemical and network analysis of discordant loci among “carbon and energy metabolism” we were able to not only construct a model for central carbon metabolism in grr1Δ strains but also were able to elucidate a novel molecular event that may serve to regulate glucose repression of genes needed for respiration in response to changes in glucose concentration.
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    Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry
    (American Chemical Society, 2024-06-04) Jiang, Yuming; Rex, Devasahayam Arokia Balaya; Schuster, Dina; Neely, Benjamin A.; Rosano, Germán L.; Volkmar, Norbert; Momenzadeh, Amanda; Peters-Clarke, Trenton M.; Egbert, Susan B.; Kreimer, Simion; Doud, Emma H.; Crook, Oliver M.; Yadav, Amit Kumar; Vanuopadath, Muralidharan; Hegeman, Adrian D.; Mayta, Martín L.; Duboff, Anna G.; Riley, Nicholas M.; Moritz, Robert L.; Meyer, Jesse G.; Biochemistry and Molecular Biology, School of Medicine
    Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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    Critical assessment of protein intrinsic disorder prediction
    (Springer Nature, 2021) Necci, Marco; Piovesan, Damiano; CAID Predictors; DisProt Curators; Tosatto, Silvio C. E.; Biochemistry and Molecular Biology, School of Medicine
    Intrinsically disordered proteins, defying the traditional protein structure–function paradigm, are a challenge to study experimentally. Because a large part of our knowledge rests on computational predictions, it is crucial that their accuracy is high. The Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment was established as a community-based blind test to determine the state of the art in prediction of intrinsically disordered regions and the subset of residues involved in binding. A total of 43 methods were evaluated on a dataset of 646 proteins from DisProt. The best methods use deep learning techniques and notably outperform physicochemical methods. The top disorder predictor has Fmax = 0.483 on the full dataset and Fmax = 0.792 following filtering out of bona fide structured regions. Disordered binding regions remain hard to predict, with Fmax = 0.231. Interestingly, computing times among methods can vary by up to four orders of magnitude.