DOCSLIB.ORG

Deep Learning of Protein Structural Classes: Any Evidence for an ‘Urfold?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. We suspect that re-examining these relationships via nizing protein structures based upon their similarities. Even a Deep Learning (DL) approach may entail a large-scale re- more basically, what does ‘similarity’ mean in this context structuring of classification schemes, improved with respect to (geometrically, chemically, etc.)? And, are there particular the interpretability of distant relationships between proteins. formulations of ‘similarity’ that are more salient than others? Here, we describe our training of DL models on protein domain structures (and their associated physicochemical properties) in Ideally, any system of comparison would also take into account order to evaluate classification properties at CATHs homologous functional propertiesi.e., not just raw geometric shape, but also superfamily (SF) level. To achieve this, we have devised and properties such as acidity/basicity, physicochemical features of applied an extension of image-classification methods and image the surface, “binding pockets”, and so on. segmentation techniques, utilizing a convolutional autoencoder An unprecedented volume of protein structural and func- model architecture. Our DL architecture allows models to learn structural features that, in a sense, ‘define’ different homologous tional data is now available, largely because of exponential SFs. We evaluate and quantify pairwise ‘distances’ between growth in genome sequencing and efforts such as structural SFs by building one model per SF and comparing the loss genomics [4]; alongside these data, novel computational ap- functions of the models. Hierarchical clustering on these distance proaches can now discover subtle signals in sets of sequences matrices provides a new view of protein interrelationshipsa view [5]. These advances have yielded a vast trove of potential that extends beyond simple structural/geometric similarity, and arXiv:2005.08443v1 [q-bio.BM] 18 May 2020 towards the realm of structure/function properties. protein sequences and structures, but the utility of the informa- Index Terms—Autoencoders; CNNs; CATH; deep learning; tion has been limited because the mapping from sequence to protein domain classification; protein structure structure (or ‘fold’) has remained enigmatic; this decades-long grand challenge is known as the “protein folding problem”[6]. I. INTRODUCTION As computational approaches to the folding problem continu- Proteins are key biological macromolecules that consist of ally improve, increasingly we will compute 3D structures from long, unbranched chains of amino acids (AAs) linked via protein sequences de novo. Therefore, we expect to see the peptide bonds [1]; they perform most of the physiological demand for identifying and cataloging new protein structures functions of cellular life (enzymes, receptors, etc.). Different grow at an ever-increasing pace with the rise in the number sequences of the 20 naturally-occurring AAs can fold into of 3D structures, both experimentally determined as well as tertiary structures with variable degrees of geometric similar- computationally predicted (via physics-based simulations [7] ity. At the sequence level, the differences between any two or via Deep Learning approaches [8]). proteins can range from relatively modest single-point edits Historically, efforts to catalog protein structures have been a largely manual and painstaking process, fraught with heuris- chical classification scheme that organizes all known protein tics. There has been a shift in the paradigm since the intro- 3D structures (from the Protein Data Bank [PDB; [21]]) by duction of methodical hierarchical database structures, such their similarity, with implicit inclusion of some evolutionary as CATH, which engender more robust classification schemes information. The PDB houses nearly 180,000 biomolecular into which new protein structures can be incorporated [9]. This structures, determined via experimental means such as X- is critical as we expand our knowledge of known protein struc- ray crystallography, nuclear magnetic resonance (NMR) spec- tures. The CATH database has seen phenomenal growth, going troscopy, and cryo-electron microscopy (cryo-EM). CATH from 53M protein domains classified into 2737 homologous uses both automated and manual methods to parse each SFs in 2016 [10] to a current 95M protein domains classified polypeptide chain in a PDB structure into individual domains. into 6119 SFs. Despite being one of the most comprehensive Domain-level entities are then classified within a structural resources, the CATH database (like any) is not without its hierarchy: Class, Architecture, Topology and Homologous limitations, in terms of its underlying assumptions about the superfamily (see also Fig 1 in[11] for more on this). If there relationships between entities. For instance, it was recently is compelling evidence that two domains are evolutionarily argued that there exists an intermediate level of structural related (i.e., homologous, based on sequence similarity), then granularity that lies between the CATH hierarchys architecture they are classified within the same superfamily. For each do- (A) and topology (T) strata; dubbed the Urfold [11], this main, we obtain 3D structures and corresponding homologous representational level is thought to capture the phenomenon superfamily labels from CATH. Next, we compute a host of “architectural similarity despite topological variability”, of derived properties for each domain in CATH (Draizen et as exhibited by the structure/function similarity in a deeply- al., in prep)—including (i) purely geometric/structural quan- varying collection of proteins that contain a characteristic tities, e.g. secondary structure [21], solvent accessibility [22], small β-barrel (SBB) domain [12]. (ii) physicochemical properties, e.g. hydrophobicity, partial With recent advances in computing power, Deep Learning charges, electrostatic potentials [23])), and (iii) basic chemical methods have begun to be applied to protein structures, descriptors (atom and residue types). As the initial use-cases in terms of predictions, similarity assessment and classifi- that are reported here, we examined three homologous super- cation [13], [14]. However deep neural networks (DNNs), families of particular interest to us: namely, immunoglobulins and specifically 3D CNNs, have not yet seen widespread (2.60.40.10), the SH3 domain (2.30.30.100), and the OB fold use with protein structures. This is likely the case because: (2.40.50.140). Our models were built using 5583, 2834 and (1) there is no single, ‘standard’/canonical orientation across 585 domain structures of Ig, SH3 and OB, respectively. the set of all protein structures [15], which is problematic for CNNs (which are not rotationally invariant), and (2) B. Preprocessing; Further Data Engineering computational demands to train such models are exorbitant We began by considering the aforementioned features for [16]. In this paper, we present a new Deep Learning method each atom in the primary sequence of each domain. Our first to quantify similarities between protein domains using a 3D- step was to ’clean’ the data by eliminating the features that CNN based autoencoder architecture. Our approach treats contained more than 25 percent missing values. Next, we con- protein structures as 3D images, with any associated properties verted continuous-valued numerical features into binary. For (physicochemical, evolutionary, etc.) incorporated as regional example, hydrophobicity values
Load more

Recommended publications
  • 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 .
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Introduction to Bioinformatics (Master Chemoinformatique)
    Introduction to Bioinformatics (Master ChemoInformatique) • Roland Stote • Institut de Génétique et de Biologie Moléculaire et Cellulaire • Biocomputing Group • 03.90.244.730 • [email protected] Biological Function at the Molecular Level 1 • 3.1x109 letters in the DNA code in every one of the 100x1012 cells in the human body. • Humans have between 30,000 and 40,000 genes. • There is approximately 2m of DNA in each cell packed into the nucleosome. • If all the DNA in the human body were put end-to-end, it would reach to the Sun and back more than 600 times What is Bioinformatics? • Bioinformatics is the study of information contained within biological, chemical or medical systems through the use of computers. • Bioinformatics methods are used in a wide variety of fields including basic science, biotechnology, medicine, pharmaceutical development and public health, plus others. • Bioinformatics is continually evolving; new approaches and tools are being developed that allow the researcher to more accurately and efficiently acquire, analyze and present the large amounts of data that are generated in today's research environment. 2 What is Bioinformatics? • Development and application of computerized methods for the study biological information and data (generation of databases) • Analysis and interpretation of these data (software tools) Developing algorithms for text string comparison (sequence alignment and keyword searches) Developing algorithms for pattern matching (data mining, cluster analysis) Algorithms for geometry analysis (docking,visualisation) Physical simulations and model building (molecular dynamics, molecular mechanics, homology modeling) Bioinformatics is situated at the interface of multiple domains of research. 3 Objectives of this module - 20hours 1.
    [Show full text]
  • Tertiary Structure
    Comments Structural motif v sequence motif polyproline (“PXXP”) motif for SH3 binding “RGD” motif for integrin binding “GXXXG” motif within the TM domain of membrane protein Most common type I’ beta turn sequences: X – (N/D/G)G – X Most common type II’ beta turn sequences: X – G(S/T) – X 1 Putting it together Alpha helices and beta sheets are not proteins—only marginally stable by themselves … Extremely small “proteins” can’t do much 2 Tertiary structure • Concerns with how the secondary structure units within a single polypeptide chain associate with each other to give a three- dimensional structure • Secondary structure, super secondary structure, and loops come together to form “domains”, the smallest tertiary structural unit • Structural domains (“domains”) usually contain 100 – 200 amino acids and fold stably. • Domains may be considered to be connected units which are to varying extents independent in terms of their structure, function and folding behavior. Each domain can be described by its fold, i.e. how the secondary structural elements are arranged. • Tertiary structure also includes the way domains fit together 3 Domains are modular •Because they are self-stabilizing, domains can be swapped both in nature and in the laboratory PI3 kinase beta-barrel GFP Branden & Tooze 4 fluorescence localization experiment Chimeras Recombinant proteins are often expressed and purified as fusion proteins (“chimeras”) with – glutathione S-transferase – maltose binding protein – or peptide tags, e.g. hexa-histidine, FLAG epitope helps with solubility, stability, and purification 5 Structural Classification All classifications are done at the domain level In many cases, structural similarity implies a common evolutionary origin – structural similarity without evolutionary relationship is possible – but no structural similarity means no evolutionary relationship Each domain has its corresponding “fold”, i.e.
    [Show full text]
  • Emergence and Evolution of an Interaction Between Intrinsically
    RESEARCH ARTICLE Emergence and evolution of an interaction between intrinsically disordered proteins Greta Hultqvist1*, Emma A˚ berg1, Carlo Camilloni2,3,4, Gustav N Sundell1, Eva Andersson1, Jakob Dogan1,5, Celestine N Chi6, Michele Vendruscolo2*, Per Jemth1* 1Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden; 2Department of Chemistry, University of Cambridge, Cambridge, United Kingdom; 3Department of Chemistry, Technische Universita¨ t Mu¨ nchen, Mu¨ nchen, Germany; 4Institute for Advanced Study, Technische Universita¨ t Mu¨ nchen, Mu¨ nchen, Germany; 5Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden; 6Laboratory of Physical Chemistry, Eidgeno¨ ssische Technische Hochschule Zu¨ rich, Zu¨ rich, Switzerland Abstract Protein-protein interactions involving intrinsically disordered proteins are important for cellular function and common in all organisms. However, it is not clear how such interactions emerge and evolve on a molecular level. We performed phylogenetic reconstruction, resurrection and biophysical characterization of two interacting disordered protein domains, CID and NCBD. CID appeared after the divergence of protostomes and deuterostomes 450–600 million years ago, while NCBD was present in the protostome/deuterostome ancestor. The most ancient CID/NCBD K formed a relatively weak complex ( d~5 mM). At the time of the first vertebrate-specific whole K genome duplication, the affinity had increased ( d~200 nM) and was maintained in further *For correspondence: greta. speciation. Experiments together with molecular modeling using NMR chemical shifts suggest that [email protected] (GH); new interactions involving intrinsically disordered proteins may evolve via a low-affinity complex [email protected] (MV); Per. which is optimized by modulating direct interactions as well as dynamics, while tolerating several [email protected] (PJ) potentially disruptive mutations.
    [Show full text]