A Clue for Generating a New Leucine-Rich Repeat Gene in Maize
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Evolution of Biomolecular Structure Class II Trna-Synthetases and Trna
University of Illinois at Urbana-Champaign Luthey-Schulten Group Theoretical and Computational Biophysics Group Evolution of Biomolecular Structure Class II tRNA-Synthetases and tRNA MultiSeq Developers: Prof. Zan Luthey-Schulten Elijah Roberts Patrick O’Donoghue John Eargle Anurag Sethi Dan Wright Brijeet Dhaliwal September 25, 2006. A current version of this tutorial is available at http://www.scs.uiuc.edu/˜schulten/tutorials/evolution/ CONTENTS 2 Contents 1 Introduction 4 1.1 The MultiSeq Bioinformatic Analysis Environment . 4 1.2 Aminoacyl-tRNA Synthetases: Role in translation . 4 1.3 Getting Started . 7 1.3.1 Requirements . 7 1.3.2 Copying the tutorial files . 7 1.3.3 Configuring MultiSeq . 7 1.3.4 Configuring BLAST for MultiSeq . 10 1.4 The Aspartyl-tRNA Synthetase/tRNA Complex . 12 1.4.1 Loading the structure into MultiSeq . 12 1.4.2 Selecting and highlighting residues . 13 1.4.3 Domain organization of the synthetase . 14 1.4.4 Nearest neighbor contacts . 14 2 Evolutionary Analysis of AARS Structures 17 2.1 Loading Molecules . 17 2.2 Multiple Structure Alignments . 18 2.3 Structural Conservation Measure: Qres . 19 2.4 Structure Based Phylogenetic Analysis . 21 2.4.1 Limitations of sequence data . 21 2.4.2 Structural metrics look further back in time . 23 3 Complete Evolutionary Profile of AspRS 26 3.1 BLASTing Sequence Databases . 26 3.1.1 Importing the archaeal sequences . 26 3.1.2 Now the other domains of life . 27 3.2 Organizing Your Data . 28 3.3 Finding a Structural Domain in a Sequence . 29 3.4 Aligning to a Structural Profile using ClustalW . -
Genome Sequence of the Progenitor of the Wheat D Genome Aegilops Tauschii Ming-Cheng Luo1*, Yong Q
OPEN LETTER doi:10.1038/nature24486 Genome sequence of the progenitor of the wheat D genome Aegilops tauschii Ming-Cheng Luo1*, Yong Q. Gu2*, Daniela Puiu3*, Hao Wang4,5,6*, Sven O. Twardziok7*, Karin R. Deal1, Naxin Huo1,2, Tingting Zhu1, Le Wang1, Yi Wang1,2, Patrick E. McGuire1, Shuyang Liu1, Hai Long1, Ramesh K. Ramasamy1, Juan C. Rodriguez1, Sonny L. Van1, Luxia Yuan1, Zhenzhong Wang1,8, Zhiqiang Xia1, Lichan Xiao1, Olin D. Anderson2, Shuhong Ouyang2,8, Yong Liang2,8, Aleksey V. Zimin3, Geo Pertea3, Peng Qi4,5, Jeffrey L. Bennetzen6, Xiongtao Dai9, Matthew W. Dawson9, Hans-Georg Müller9, Karl Kugler7, Lorena Rivarola-Duarte7, Manuel Spannagl7, Klaus F. X. Mayer7,10, Fu-Hao Lu11, Michael W. Bevan11, Philippe Leroy12, Pingchuan Li13, Frank M. You13, Qixin Sun8, Zhiyong Liu8, Eric Lyons14, Thomas Wicker15, Steven L. Salzberg3,16, Katrien M. Devos4,5 & Jan Dvořák1 Aegilops tauschii is the diploid progenitor of the D genome of We conclude therefore that the size of the Ae. tauschii genome is about hexaploid wheat1 (Triticum aestivum, genomes AABBDD) and 4.3 Gb. an important genetic resource for wheat2–4. The large size and To assess the accuracy of our assembly, sequences of 195 inde- highly repetitive nature of the Ae. tauschii genome has until now pendently sequenced and assembled AL8/78 BAC clones8, which precluded the development of a reference-quality genome sequence5. contained 25,540,177 bp in 2,405 unordered contigs, were aligned to Here we use an array of advanced technologies, including ordered- Aet v3.0. Five contigs failed to align and six extended partly into gaps, clone genome sequencing, whole-genome shotgun sequencing, accounting for 0.25% of the total length of the contigs. -
The Architecture of the Protein Domain Universe
The architecture of the protein domain universe Nikolay V. Dokholyan Department of Biochemistry and Biophysics, The University of North Carolina at Chapel Hill, School of Medicine, Chapel Hill, NC 27599 ABSTRACT Understanding the design of the universe of protein structures may provide insights into protein evolution. We study the architecture of the protein domain universe, which has been found to poses peculiar scale-free properties (Dokholyan et al., Proc. Natl. Acad. Sci. USA 99: 14132-14136 (2002)). We examine the origin of these scale-free properties of the graph of protein domain structures (PDUG) and determine that that the PDUG is not modular, i.e. it does not consist of modules with uniform properties. Instead, we find the PDUG to be self-similar at all scales. We further characterize the PDUG architecture by studying the properties of the hub nodes that are responsible for the scale-free connectivity of the PDUG. We introduce a measure of the betweenness centrality of protein domains in the PDUG and find a power-law distribution of the betweenness centrality values. The scale-free distribution of hubs in the protein universe suggests that a set of specific statistical mechanics models, such as the self-organized criticality model, can potentially identify the principal driving forces of molecular evolution. We also find a gatekeeper protein domain, removal of which partitions the largest cluster into two large sub- clusters. We suggest that the loss of such gatekeeper protein domains in the course of evolution is responsible for the creation of new fold families. INTRODUCTION The principles of molecular evolution remain elusive despite fundamental breakthroughs on the theoretical front 1-5 and a growing amount of genomic and proteomic data, over 23,000 solved protein structures 6 and protein functional annotations 7-9. -
Documentation and Localization of Force-Mediated Filamin a Domain
ARTICLE Received 29 May 2014 | Accepted 10 Jul 2014 | Published 14 Aug 2014 DOI: 10.1038/ncomms5656 Documentation and localization of force-mediated filamin A domain perturbations in moving cells Fumihiko Nakamura1, Mia Song1, John H. Hartwig1 & Thomas P. Stossel1 Endogenously and externally generated mechanical forces influence diverse cellular activities, a phenomenon defined as mechanotransduction. Deformation of protein domains by application of stress, previously documented to alter macromolecular interactions in vitro, could mediate these effects. We engineered a photon-emitting system responsive to unfolding of two repeat domains of the actin filament (F-actin) crosslinker protein filamin A (FLNA) that binds multiple partners involved in cell signalling reactions and validated the system using F-actin networks subjected to myosin-based contraction. Expressed in cultured cells, the sensor-containing FLNA construct reproducibly reported FLNA domain unfolding strikingly localized to dynamic, actively protruding, leading cell edges. The unfolding signal depends upon coherence of F-actin-FLNA networks and is enhanced by stimulating cell contractility. The results establish protein domain distortion as a bona fide mechanism for mechanotransduction in vivo. 1 Translational Medicine Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02445, USA. Correspondence and requests for materials should be addressed to F.N. (email: [email protected]). NATURE COMMUNICATIONS | 5:4656 | DOI: 10.1038/ncomms5656 -
Lipid-Targeting Pleckstrin Homology Domain Turns Its Autoinhibitory Face Toward the TEC Kinases
Lipid-targeting pleckstrin homology domain turns its autoinhibitory face toward the TEC kinases Neha Amatyaa, Thomas E. Walesb, Annie Kwonc, Wayland Yeungc, Raji E. Josepha, D. Bruce Fultona, Natarajan Kannanc, John R. Engenb, and Amy H. Andreottia,1 aRoy J. Carver Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, IA 50011; bDepartment of Chemistry and Chemical Biology, Northeastern University, Boston, MA 02115; and cInstitute of Bioinformatics and Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602 Edited by Natalie G. Ahn, University of Colorado Boulder, Boulder, CO, and approved September 17, 2019 (received for review May 3, 2019) The pleckstrin homology (PH) domain is well known for its phos- activation loop phosphorylation site are also controlled by noncatalytic pholipid targeting function. The PH-TEC homology (PHTH) domain domains (17). In addition to the N-terminal PHTH domain, the within the TEC family of tyrosine kinases is also a crucial component TEC kinases contain a proline-rich region (PRR) and Src ho- of the autoinhibitory apparatus. The autoinhibitory surface on the mology 3 (SH3) and Src homology 2 (SH2) domains that impinge PHTH domain has been previously defined, and biochemical investi- on the kinase domain to alter the conformational ensemble and gations have shown that PHTH-mediated inhibition is mutually thus the activation status of the enzyme. A crystal structure of the exclusive with phosphatidylinositol binding. Here we use hydrogen/ BTK SH3-SH2-kinase fragment has been solved (10) showing that deuterium exchange mass spectrometry, nuclear magnetic resonance the SH3 and SH2 domains of BTK assemble onto the distal side of (NMR), and evolutionary sequence comparisons to map where and the kinase domain (the surface opposite the activation loop), how the PHTH domain affects the Bruton’s tyrosine kinase (BTK) stabilizing the autoinhibited form of the kinase in a manner similar domain. -
Pleckstrin Homology Domains and the Cytoskeleton
FEBS 25627 FEBS Letters 513 (2002) 71^76 View metadata, citation and similar papers at core.ac.uk brought to you by CORE Minireview provided by Elsevier - Publisher Connector Pleckstrin homology domains and the cytoskeleton Mark A. Lemmona;Ã, Kathryn M. Fergusona, Charles S. Abramsb;Ã aDepartment of Biochemistry and Biophysics, University of Pennsylvania School of Medicine, 809C Stellar-Chance Laboratories, 422 Curie Boulevard, Philadelphia, PA 19104-6059, USA bDepartment of Medicine, University of Pennsylvania School of Medicine, 912 BRB II/III, 421 Curie Boulevard, Philadelphia, PA 19104-6160, USA Received 20 October 2001; revised 30 October 2001; accepted 14 November 2001 First published online 6 December 2001 Edited by Gianni Cesareni and Mario Gimona some 27 di¡erent proteins contain a total of 36 PH domains, Abstract Pleckstrin homology (PH) domains are 100^120 amino acid protein modules best known for their ability to bind making the PH domain the 17th most common yeast domain phosphoinositides. All possess an identical core L-sandwich fold [6]. The sequence characteristics used to identify PH domains and display marked electrostatic sidedness. The binding site for appear to de¢ne a particular protein fold that has now been phosphoinositides lies in the center of the positively charged face. seen in the X-ray crystal structures and/or nuclear magnetic In some cases this binding site is well defined, allowing highly resonance (NMR) structures of some 13 di¡erent PH domains specific and strong ligand binding. In several of these cases the [7^12]. Each of these PH domains possesses an almost iden- PH domains specifically recognize 3-phosphorylated phospho- tical core L-sandwich structure (described below), despite pair- inositides, allowing them to drive membrane recruitment in wise sequence identities between PH domains that range from response to phosphatidylinositol 3-kinase activation. -
A New Census of Protein Tandem Repeats and Their Relationship with Intrinsic Disorder
G C A T T A C G G C A T genes Article A New Census of Protein Tandem Repeats and Their Relationship with Intrinsic Disorder Matteo Delucchi 1,2 , Elke Schaper 1,2,† , Oxana Sachenkova 3,‡, Arne Elofsson 3 and Maria Anisimova 1,2,* 1 ZHAW Life Sciences und Facility Management, Applied Computational Genomics, 8820 Wädenswil, Switzerland; [email protected] 2 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland 3 Science of Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 106 91 Stockholm, Sweden * Correspondence: [email protected]; Tel.: +41-(0)58-934-5882 † Present address: Carbon Delta AG, 8002 Zürich, Switzerland. ‡ Present address: Vildly AB, 385 31 Kalmar, Sweden. Received: 9 March 2020; Accepted: 1 April 2020; Published: 9 April 2020 Abstract: Protein tandem repeats (TRs) are often associated with immunity-related functions and diseases. Since that last census of protein TRs in 1999, the number of curated proteins increased more than seven-fold and new TR prediction methods were published. TRs appear to be enriched with intrinsic disorder and vice versa. The significance and the biological reasons for this association are unknown. Here, we characterize protein TRs across all kingdoms of life and their overlap with intrinsic disorder in unprecedented detail. Using state-of-the-art prediction methods, we estimate that 50.9% of proteins contain at least one TR, often located at the sequence flanks. Positive linear correlation between the proportion of TRs and the protein length was observed universally, with Eukaryotes in general having more TRs, but when the difference in length is taken into account the difference is quite small. -
Chapter 6 Protein Structure and Folding
Chapter 6 Protein Structure and Folding 1. Secondary Structure 2. Tertiary Structure 3. Quaternary Structure and Symmetry 4. Protein Stability 5. Protein Folding Myoglobin Introduction 1. Proteins were long thought to be colloids of random structure 2. 1934, crystal of pepsin in X-ray beam produces discrete diffraction pattern -> atoms are ordered 3. 1958 first X-ray structure solved, sperm whale myoglobin, no structural regularity observed 4. Today, approx 50’000 structures solved => remarkable degree of structural regularity observed Hierarchy of Structural Layers 1. Primary structure: amino acid sequence 2. Secondary structure: local arrangement of peptide backbone 3. Tertiary structure: three dimensional arrangement of all atoms, peptide backbone and amino acid side chains 4. Quaternary structure: spatial arrangement of subunits 1) Secondary Structure A) The planar peptide group limits polypeptide conformations The peptide group ha a rigid, planar structure as a consequence of resonance interactions that give the peptide bond ~40% double bond character The trans peptide group The peptide group assumes the trans conformation 8 kJ/mol mire stable than cis Except Pro, followed by cis in 10% Torsion angles between peptide groups describe polypeptide chain conformations The backbone is a chain of planar peptide groups The conformation of the backbone can be described by the torsion angles (dihedral angles, rotation angles) around the Cα-N (Φ) and the Cα-C bond (Ψ) Defined as 180° when extended (as shown) + = clockwise, seen from Cα Not -
Phytosiderophore Release in Aegilops Tauschii and Triticum Species Under Zinc and Iron Deficiencies
Journal of Experimental Botany, Vol. 52, No. 358, pp. 1093±1099, May 2001 Phytosiderophore release in Aegilops tauschii and Triticum species under zinc and iron deficiencies I. Tolay1, B. Erenoglu1,2,V.RoÈ mheld2, H.J. Braun3 and I. Cakmak4,5 1 Cukurova University, Department of Soil Science and Plant Nutrition, 01330 Adana, Turkey 2 UniversitaÈ t Hohenheim, Institut fuÈ r PflanzenernaÈ hrung %330), 70593 Stuttgart, Germany 3 CIMMYT, POB 39, Emek 06511, Ankara, Turkey 4 Sabanci University, Faculty of Engineering and Natural Sciences, 81474 Tuzla, Istanbul, Turkey Received 2 August 2000; Accepted 27 November 2000 Abstract of PS release was around 14 mmol *30 plants)À1 *3 h)À1. Diploid wheats and Ae. tauschii accessions Using three diploid *Triticum monococcum, AA), behaved similarly in their capacity to release PS three tetraploid *Triticum turgidum, BBAA), two hexa- and intermediate between tetraploid and hexa- ploid *Triticum aestivum and Triticum compactum, ploid wheats regarding the PS release capacity. All BBAADD) wheats and two Aegilops tauschii *DD) Triticum and Aegilops species released more PS genotypes, experiments were carried out under under Fe than Zn deficiency, particularly when the controlled environmental conditions in nutrient solu- rate of PS release was expressed per unit dry weight tion *i) to study the relationships between the rates of roots. On average, the rates of PS release under Fe of phytosiderophore *PS) release from the roots and deficiency were 3.0, 5.7, 8.4, and 16 mmol *30 plants)À1 the tolerance of diploid, tetraploid, and hexaploid *3 h)À1 for Ae. tauschii, diploid, tetraploid and wheats and Ae. -
Deep Learning of Protein Structural Classes: Any Evidence for an ‘Urfold?
Deep Learning of Protein Structural Classes: Any Evidence for an ‘Urfold? 1st Menuka Jaiswal 1st Saad Saleem 1st Yonghyeon Kweon 2nd Eli J Draizen School of Data Science School of Data Science School of Data Science Department of Biomedical Engineering University of Virginia University of Virginia University of Virginia University of Virginia Charlottesville Charlottesville Charlottesville Charlottesville [email protected] [email protected] [email protected] [email protected] 2nd Stella Veretnik 2nd Cameron Mura 2nd Philip E Bourne Department of Biomedical Engineering Department of Biomedical Engineering School of Data Science University of Virginia University of Virginia University of Virginia Charlottesville Charlottesville Charlottesville [email protected] [email protected] [email protected] Abstract—Recent computational advances in the accurate pre- (“point mutations” or ‘substitutions’) to larger-scale changes diction of protein three-dimensional (3D) structures from amino such as reorganization of entire segments of a polypeptide acid sequences now present a unique opportunity to decipher chain. Such changes are critical because they influence 3D the interrelationships between proteins. This task entailsbut is not equivalent toa problem of 3D structure comparison and structure, and protein function stems from 3D structure [2]. classification. Historically, protein domain classification has been Indeed, our ability to elucidate protein function and evolution a largely manual and subjective activity, relying upon various is intimately linked to our knowledge of protein structure. heuristics. Databases such as CATH represent significant steps Equally important, interrelationships between structures define towards a more systematic (and automatable) approach, yet there a map of the protein universe [3]. Thus, it is paramount still remains much room for the development of more scalable and quantitative classification methods, grounded in machine to have a robust classification system for categorically orga- learning. -
Predicting Protein Domain Boundary from Sequence Alone Using Stacked Bidirectional LSTM
Pacific Symposium on Biocomputing 2019 DeepDom: Predicting protein domain boundary from sequence alone using stacked bidirectional LSTM Yuexu Jiang, Duolin Wang, Dong Xu Department of Electrical Engineering and Computer Science, Bond Life Sciences Center, University of Missouri, Columbia, Missouri 65211, USA Email: [email protected] Protein domain boundary prediction is usually an early step to understand protein function and structure. Most of the current computational domain boundary prediction methods suffer from low accuracy and limitation in handling multi-domain types, or even cannot be applied on certain targets such as proteins with discontinuous domain. We developed an ab-initio protein domain predictor using a stacked bidirectional LSTM model in deep learning. Our model is trained by a large amount of protein sequences without using feature engineering such as sequence profiles. Hence, the predictions using our method is much faster than others, and the trained model can be applied to any type of target proteins without constraint. We evaluated DeepDom by a 10-fold cross validation and also by applying it on targets in different categories from CASP 8 and CASP 9. The comparison with other methods has shown that DeepDom outperforms most of the current ab-initio methods and even achieves better results than the top-level template-based method in certain cases. The code of DeepDom and the test data we used in CASP 8, 9 can be accessed through GitHub at https://github.com/yuexujiang/DeepDom. Keywords: protein domain; domain boundary prediction; deep learning; LSTM. 1. Introduction Protein domains are conserved parts on protein sequences and structures that can evolve, function, and exist independently of the rest of the protein chain. -
Insights Into the Molecular Architecture of the 26S Proteasome
Insights into the molecular architecture of the 26S proteasome Stephan Nickella, Florian Becka, Sjors H. W. Scheresb, Andreas Korineka, Friedrich Fo¨ rstera,c, Keren Laskerc,d, Oana Mihalachea, Na Suna, Istva´ n Nagya, Andrej Salic,Ju¨ rgen M. Plitzkoa, Jose-Maria Carazob, Matthias Manna, and Wolfgang Baumeistera,1 aMax Planck Institute of Biochemistry, D-82152 Martinsried, Germany; bCentro Nacional de Biotecnologı´a–ConsejoSuperior de Investigaciones Cientı´ficas, Cantoblanco, 28049, Madrid, Spain; cDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158-2330; and dBlavatnik School of Computer Science, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 69978, Israel Communicated by Alexander Varshavsky, California Institute of Technology, Pasadena, CA, May 12, 2009 (received for review March 9, 2009) Cryo-electron microscopy in conjunction with advanced image analysis was used to analyze the structure of the 26S proteasome and to elucidate its variable features. We have been able to outline the boundaries of the ATPase module in the ‘‘base’’ part of the regulatory complex that can vary in its position and orientation relative to the 20S core particle. This variation is consistent with the ‘‘wobbling’’ model that was previously proposed to explain the role of the regulatory complex in opening the gate in the ␣-rings of the core particle. In addition, a variable mass near the mouth of the ATPase ring has been identified as Rpn10, a multiubiquitin receptor, by correlating the electron microscopy data with quan- titative mass spectrometry. ATPase ͉ cryo-electron microscopy ͉ mass spectrometry ͉ protein degradation ͉ AAA-ATPase Fig.