The Relationship Between Protein Structure and Function: a Comprehensive Survey with Application to the Yeast Genome Hedi Hegyi and Mark Gerstein*
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Allostery in the Ferredoxin Protein Motif Does Not Involve a Conformational Switch
Allostery in the ferredoxin protein motif does not involve a conformational switch Rachel Nechushtaia, Heiko Lammertb, Dorit Michaelia, Yael Eisenberg-Domovicha, John A. Zurisc, Maria A. Lucac, Dominique T. Capraroc, Alex Fisha, Odelia Shimshona, Melinda Royc, Alexander Schugb,d, Paul C. Whitforde, Oded Livnaha, José N. Onuchicb,1, and Patricia A. Jenningsc,1 aLife Science Institute and The Wolfson Centre for Applied Structural Biology, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel; bCenter for Theoretical Biological Physics and the Department of Physics, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0375; cDepartment of Chemistry and Biochemistry, University of California at San Diego, 9500 Gilman Drive, La Jolla, CA 92093- 0375; dDepartment of Chemistry, Umeå University, Umeå, Sweden; and eLos Alamos National Laboratory, Theoretical Biology and Biophysics, MS K710, Los Alamos, NM 87545 Contributed by José N. Onuchic, December 28, 2010 (sent for review December 11, 2010) Regulation of protein function via cracking, or local unfolding substantial theoretical (9–13) and experimental (14, 15) work and refolding of substructures, is becoming a widely recognized has aimed at revealing the details of the functionally relevant mechanism of functional control. Oftentimes, cracking events are transition-state ensembles. These investigations have shown localized to secondary and tertiary structure interactions between that rearrangements may involve a degree of cracking, or loca- domains that control the optimal position for catalysis and/or the lized unfolding and refolding, which serves to reduce the asso- formation of protein complexes. Small changes in free energy ciated free-energy barriers (16, 17). -
1 SUPPLEMENTAL DATA Figure S1. Poly I:C Induces IFN-Β Expression
SUPPLEMENTAL DATA Figure S1. Poly I:C induces IFN-β expression and signaling. Fibroblasts were incubated in media with or without Poly I:C for 24 h. RNA was isolated and processed for microarray analysis. Genes showing >2-fold up- or down-regulation compared to control fibroblasts were analyzed using Ingenuity Pathway Analysis Software (Red color, up-regulation; Green color, down-regulation). The transcripts with known gene identifiers (HUGO gene symbols) were entered into the Ingenuity Pathways Knowledge Base IPA 4.0. Each gene identifier mapped in the Ingenuity Pathways Knowledge Base was termed as a focus gene, which was overlaid into a global molecular network established from the information in the Ingenuity Pathways Knowledge Base. Each network contained a maximum of 35 focus genes. 1 Figure S2. The overlap of genes regulated by Poly I:C and by IFN. Bioinformatics analysis was conducted to generate a list of 2003 genes showing >2 fold up or down- regulation in fibroblasts treated with Poly I:C for 24 h. The overlap of this gene set with the 117 skin gene IFN Core Signature comprised of datasets of skin cells stimulated by IFN (Wong et al, 2012) was generated using Microsoft Excel. 2 Symbol Description polyIC 24h IFN 24h CXCL10 chemokine (C-X-C motif) ligand 10 129 7.14 CCL5 chemokine (C-C motif) ligand 5 118 1.12 CCL5 chemokine (C-C motif) ligand 5 115 1.01 OASL 2'-5'-oligoadenylate synthetase-like 83.3 9.52 CCL8 chemokine (C-C motif) ligand 8 78.5 3.25 IDO1 indoleamine 2,3-dioxygenase 1 76.3 3.5 IFI27 interferon, alpha-inducible -
Structural Genomics Analysis: Characteristics of Atypical, Typical, and Horizontally Transferred Folds
Structural Genomics Analysis: Characteristics of atypical, typical, and horizontally transferred folds Hedi Hegyi, Jimmy Lin, Dov Greenbaum, & Mark Gerstein Department of Molecular Biophysics & Biochemistry 266 Whitney Avenue, Yale University PO Box 208114, New Haven, CT 06520 (203) 432-6105, FAX (203) 432-5175 [email protected] (Revised version, 092101) ABSTRACT We carried out a structural-genomics analysis of the folds and structural superfamilies in the first 20 completely sequenced genomes, focusing on the patterns of fold usage and trying to identify structural characteristics of typical and atypical folds. We assigned folds to sequences using PSI-blast, run with a systematic protocol to reduce the amount of computational overhead. On average, folds could be assigned to about a fourth of the ORFs in the genomes and about a fifth of the amino acids in the proteomes. More than 80% of all the folds in the SCOP structural classification were identified in one of the 20 organisms, with worm and E. coli having the largest number of distinct folds. Folds are particularly effective at comprehensively measuring levels of gene duplication, as they group together even very remote homologues. Using folds, we find the average level of duplication varies depending on the complexity of the organism, ranging from 2.4 in M. genitalium to 32 for the worm -- values significantly higher than those observed based purely on sequence similarity. We rank the common folds in the 20 organisms, finding that the top three are the P-loop NTP hydrolase, the ferrodoxin fold, and the TIM-barrel, and discuss in detail the many factors that affect and bias these rankings. -
Proteins Eugene Shakhnovich Harvard University Boulder, CO July 25-27 2012 Outline
Proteins Eugene Shakhnovich Harvard University Boulder, CO July 25-27 2012 Outline Lecture 1. Introduction: Principles of structural organization of proteins and Protein Universe. Concepts of convergent and divergent molecular evolution. Lecture 2. Protein folding – Experiment and Theory and Simulations Lecture 3: Bridging scales: From Protein Biophysics to Population Genetics (‘’From Crick to Darwin and back’’) Marriage made in heaven: genomics and protein folding/evolution Proteins are folded on various scales As of now we know hundreds of thousands of sequences (Swissprot) and a few thousand of Structures (protein data bank) Proteins are tightly packed Structural organization of globular proteins Fold, Folding Pattern and Packing Simplified models of protein structures. a), A detailed fold describing positions of secondary structures in the protein hain and in space (see also Fig. 13-1e). (b), The folding pattern of the protein chain with omitted details of loop pathways, the size and exact orientation of α-helices (shown as cylinders) and β-strands (shown as arrows). (c), Packing: a stack of structural segments with no loop shown and omitted details of size, orientation and direction of α-helices and β-strands (which are therefore presented as ribbons rather than as arrows). Similarities at the fold level, differences at the detailed structural level Two close relatives: horse hemoglobin α and horse hemoglobin β (both possessing a heme shown as a wire model with iron in the center). Find similarities and differences. (For tips: they are highly similar although have some differences in details of loop conformations, in details of orientation of some helices, and in one additional helical turn available in the β globin, on the right). -
©Copyright 2014 Javier Castellanos I
©Copyright 2014! Javier Castellanos $i Iterative Multistate Negative Design of protein folds Javier Castellanos ! A dissertation! submitted in partial fulfillment of the! requirements for the degree of Doctor of Philosophy University of Washington! 2014 Reading Committee:! David Baker, Chair! Barry Stoddard! Wim Hol Program Authorized to Offer Degree:! Biochemistry $ii University of Washington Abstract Iterative Multistate Negative Design of protein folds! Javier Castellanos Chair of Supervisory Committee:! Professor David Baker! Department of Biochemistry One of the challenges of computational de novo protein design is to find a protein sequence that adopts a desired conformation disfavoring alternative low energy conformations. A protein adopts a specific fold when the interactions that stabilize the native state collectively outweigh the interactions present in all the possible alternative states. To stabilize a protein in a specific fold one can either reduce the energy of the protein in the target conformation(positive design) or increase the energy of the alternative conformations(negative design). Here we describe a new protocol that simultaneously stabilizes the target state and destabilizes the possible alternative low energy states of de novo design proteins. The new protocol performs a contact analysis of the alternative states $i to identify residues that are participating undesired contacts and then performs a multi state sequence optimization of those positions. After iterations of sampling,analysis and optimization the protocol -
Sampling of Structure and Sequence Space of Small Protein Folds
bioRxiv preprint doi: https://doi.org/10.1101/2021.03.10.434454; this version posted March 11, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Sampling of Structure and Sequence Space of Small Protein Folds Linsky T*1,2, Noble K*3, Tobin A*3, Crow R*4, Lauren Carter2, Urbauer J 5, Baker D1,2,6, Strauch EM3,7# *Contributed equally #Corresponding author 1Department of Biochemistry, University of Washington, Seattle, WA 98195, USA. 2Institute for Protein Design, University of Washington, Seattle, WA 98195, USA. 3Department of Pharmaceutical anD BiomeDical Sciences, University of Georgia, Athens, GA 30602, USA. 4Department of Microbiology, University of Washington, Seattle, WA 98195, USA. 5Department of Chemistry, University of Georgia, Athens, GA 30602, USA. 6 HowarD Hughes MeDical Institute, University of Washington, Seattle, WA 98195, USA 7Institute of Bioinformatics, University of Georgia, Athens, GA 30602, USA. Nature only samples a small fraction in sequence space, yet many more amino acid combinations can fold into stable proteins. Furthermore, small structural variations in a single fold, which may only be a few amino acids different from the next homolog, define their molecular function. Hence, to design proteins with novel molecular functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling high shape diversity. We designed and evaluated about 30,000 de novo protein designs of 7 different folds. -
Mechanistic Evaluation of Inhibitors Against Mycobacterium Tuberculosis Shikimate Kinase
Transition from classical methods to new strategies: Mechanistic evaluation of inhibitors against Mycobacterium tuberculosis shikimate kinase by Ngolui René Fuanta A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama August 4, 2018 Keywords: Kinetics, spectrometry, tuberculosis, spectrofluorimetry, dissociation constants Copyright 2018 by Ngolui René Fuanta. Approved by Douglas Goodwin, Chair, Associate Professor, Chemistry and Biochemistry Holly Ellis, Professor, Chemistry and Biochemistry Chris Easley, Professor, Chemistry and Biochemistry Steve Mansoorabadi, Assistant Professor, Chemistry and Biochemistry Angela Calderón, Associate Professor, Drug Discovery and Development Abstract Tuberculosis (TB) still stands as one of the leading causes of mortality resulting from an infectious agent. In part, this stems from the emergence of multidrug and extensive drug-resistant strains of tubercle bacilli, demonstrating the pressing need for the development of novel anti tubercular agents. Interestingly, the shikimate pathway, found in plants, bacteria, fungi and algae, is essential for the survival of Mycobacterium tuberculosis. This pathway produces precursors to aromatic amino acids and other aromatic cellular metabolites. Fortunately, it has no mammalian analogue. This makes the enzymes of this pathway suitable targets for the development of novel anti-microbial agents. The research presented in this dissertation focuses on Mycobacterium tuberculosis shikimate kinase (MtSK). Our objectives were to develop LC- MS-based screening and characterization of potential anti tubercular agents – natural, synthetic or semi synthetic, develop fluorescence methods for rapid mechanistic evaluation of MtSK inhibitors and strategies for screening out promiscuous inhibitors. Using mass spectrometry, we characterized a group of marine derived compounds, manzamine alkaloids. -
Hnrnp K Co-Immunoprecipitated Transcripts Regulated by BDNF
Table 1-5 - hnRNP K co-immunoprecipitated transcripts regulated by BDNF Gene Symbol Description Fold change LOC302228 PREDICTED: Rattus norvegicus similar to Spindlin-like protein 2 (SPIN-2) (LOC302228), mRNA [XM_229842] 0,085 Tuba3a Rattus norvegicus tubulin, alpha 3A (Tuba3a), mRNA [NM_001040008] 0,204 Fabp12 Rattus norvegicus fatty acid binding protein 12 (Fabp12), mRNA [NM_001134614] 0,215 Slc12a8 Rattus norvegicus solute carrier family 12 (potassium/chloride transporters), member 8 (Slc12a8), mRNA [NM_153625] 0,222 Nog Rattus norvegicus noggin (Nog), mRNA [NM_012990] 0,225 0 Unknown 0,233 Gpx2 Rattus norvegicus glutathione peroxidase 2 (Gpx2), mRNA [NM_183403] 0,251 Ltb Rattus norvegicus lymphotoxin beta (TNF superfamily, member 3) (Ltb), mRNA [NM_212507] 0,255 Ahrr Rattus norvegicus aryl-hydrocarbon receptor repressor (Ahrr), mRNA [NM_001024285] 0,271 0 Uncharacterized protein [Source:UniProtKB/TrEMBL;Acc:D3ZUC5] [ENSRNOT00000046312] 0,273 Megf8 PREDICTED: Rattus norvegicus multiple EGF-like-domains 8 (Megf8), mRNA [XM_341803] 0,274 Pdcl2 PREDICTED: Rattus norvegicus phosducin-like 2 (Pdcl2), mRNA [XM_001076368] 0,284 Mepe Rattus norvegicus matrix extracellular phosphoglycoprotein (Mepe), mRNA [NM_024142] 0,289 Galr3 Rattus norvegicus galanin receptor 3 (Galr3), mRNA [NM_019173] 0,290 LOC685685 PREDICTED: Rattus norvegicus similar to S100 calcium-binding protein, ventral prostate (LOC685685), mRNA [XM_001064819] 0,306 Sec16b Rattus norvegicus SEC16 homolog B (S. cerevisiae) (Sec16b), mRNA [NM_053571] 0,314 Fam20a Rattus norvegicus -
Detecting Internally Symmetric Protein Structures Changhoon Kim1,2, Jodi Basner1, Byungkook Lee1*
Kim et al. BMC Bioinformatics 2010, 11:303 http://www.biomedcentral.com/1471-2105/11/303 RESEARCH ARTICLE Open Access Detecting internally symmetric protein structures Changhoon Kim1,2, Jodi Basner1, Byungkook Lee1* Abstract Background: Many functional proteins have a symmetric structure. Most of these are multimeric complexes, which are made of non-symmetric monomers arranged in a symmetric manner. However, there are also a large number of proteins that have a symmetric structure in the monomeric state. These internally symmetric proteins are interesting objects from the point of view of their folding, function, and evolution. Most algorithms that detect the internally symmetric proteins depend on finding repeating units of similar structure and do not use the symmetry information. Results: We describe a new method, called SymD, for detecting symmetric protein structures. The SymD procedure works by comparing the structure to its own copy after the copy is circularly permuted by all possible number of residues. The procedure is relatively insensitive to symmetry-breaking insertions and deletions and amplifies positive signals from symmetry. It finds 70% to 80% of the TIM barrel fold domains in the ASTRAL 40 domain database and 100% of the beta-propellers as symmetric. More globally, 10% to 15% of the proteins in the ASTRAL 40 domain database may be considered symmetric according to this procedure depending on the precise cutoff value used to measure the degree of perfection of the symmetry. Symmetrical proteins occur in all structural classes and can have a closed, circular structure, a cylindrical barrel-like structure, or an open, helical structure. Conclusions: SymD is a sensitive procedure for detecting internally symmetric protein structures.