DOCSLIB.ORG

Protein Structure Prediction and Structural Genomics David Baker1 and Andrej Sali2

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Protein Structure Prediction and Structural Genomics David Baker1 and Andrej Sali2 G ENOME:UNLOCKING B IOLOGY’ S S TOREHOUSE VIEWPOINT Protein Structure Prediction and Structural Genomics David Baker1 and Andrej Sali2 Genome sequencing projects are producing linear amino acid sequences, the protein chain flicker between different but full understanding of the biological role of these proteins will require local structures consistent with their local knowledge of their structure and function. Although experimental struc- sequence, and folding to the native state oc- ture determination methods are providing high-resolution structure infor- curs when these local segments are oriented mation about a subset of the proteins, computational structure prediction such that low free energy interactions are methods will provide valuable information for the large fraction of se- made throughout the protein (17). In simu- quences whose structures will not be determined experimentally. The first lating this process, each short segment is class of protein structure prediction methods, including threading and allowed to sample the local structures adopt- comparative modeling, rely on detectable similarity spanning most of the ed by the sequence segment in known protein modeled sequence and at least one known structure. The second class of structures, and a search is carried out through methods, de novo or ab initio methods, predict the structure from se- the combinations of these local structures for quence alone, without relying on similarity at the fold level between the compact tertiary structures that bury the hy- modeled sequence and any of the known structures. In this Viewpoint, we drophobic residues and pair the ␤-strands. begin by describing the essential features of the methods, the accuracy of This strategy resolves some of the problems the models, and their application to the prediction and understanding of with both the conformational search and the protein function, both for single proteins and on the scale of whole free energy function: The search is greatly genomes. We then discuss the important role that protein structure accelerated because switching between dif- prediction methods play in the growing worldwide effort in structural ferent possible local structures can occur in a genomics. single step, and fewer demands are placed on the free energy function because the use of Modeling of a sequence based on known positions of conserved atoms from the tem- fragments of known structures ensures that structures consists of four steps: finding plates to calculate the coordinates of other the local interactions are close to optimal. known structures related to the sequence to atoms (7). A third group of methods uses be modeled (i.e., templates), aligning the se- either distance geometry or optimization Accuracy and Applications of Models quence with the templates, building a model, techniques to satisfy spatial restraints ob- The accuracy of a comparative model is re- and assessing the model (1). tained from the sequence-template alignment lated to the percentage sequence identity on The templates for modeling may be found (8–10). There are also many methods that which it is based, correlating with the rela- by sequence comparison methods, such as specialize in the modeling of loops (11) and tionship between the structural and sequence PSI-BLAST (2), or by sequence-structure side chains (12) within the restrained envi- similarity of two proteins (Fig. 1) (1, 18, 19). threading methods (3) that can sometimes ronment provided by the rest of the structure. High-accuracy comparative models are based reveal more distant relationships than purely on more than 50% sequence identity to their sequence-based methods. In the latter case, De novo Structure Prediction templates. They tend to have about 1 Å root fold assignment and alignment are achieved Although comparative modeling is limited to mean square (RMS) error for the main-chain by threading the sequence through each of the protein families with at least one known struc- atoms, which is comparable to the accuracy structures in a library of all known folds. ture, de novo structure prediction has no such of a medium-resolution nuclear magnetic res- Each sequence-structure alignment is as- limitation. De novo methods start from the as- onance (NMR) structure or a low-resolution sessed by the energy of a corresponding sumption that the native state of a protein is at x-ray structure. The errors are mostly mis- coarse model, not by sequence similarity as the global free energy minimum and carry out a takes in side-chain packing, small shifts or in sequence comparison methods. large-scale search of conformational space for distortions of the core main-chain regions, Comparative structure prediction produc- protein tertiary structures that are particularly and occasionally larger errors in loops. Me- es an all-atom model of a sequence, based on low in free energy for the given amino acid dium-accuracy comparative models are based its alignment to one or more related protein sequence. The two key components of such on 30 to 50% sequence identity. They tend to structures. Comparative model building in- methods are the procedure for efficiently carry- have about 90% of the main-chain modeled cludes either sequential or simultaneous mod- ing out the conformational search and the free with 1.5 Å RMS error. There are more fre- eling of the core of the protein, loops, and energy function used for evaluating possible quent side-chain packing, core distortion, and side chains. In the original comparative ap- conformations. To allow rapid and efficient loop modeling errors, and there are occasion- proach, a model is constructed from a few searching of conformational space, often only a al alignment mistakes (18). Finally, low-ac- template core regions and from loops and subset of the atoms in the protein chain is curacy comparative models are based on less side chains obtained from either aligned or represented explicitly; the potential functions than 30% sequence identity. The alignment unrelated structures (4–6). Another family of must then include terms that reflect the aver- errors increase rapidly below 30% sequence comparative methods relies on approximate aged-out effects of the omitted atoms and sol- identity and become the most substantial or- vent molecules. igin of errors in comparative models. In ad- 1Howard Hughes Medical Institute, University of Recently, there have been a number of dition, when a model is based on an almost Washington, Seattle, WA 98195, USA. E-mail: promising advances in de novo structure pre- insignificant alignment to a known structure, 2 [email protected]. Laboratory of Molecular diction (13–16). A particularly successful it may also have an entirely incorrect fold. Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, New method, called Rosetta, is based on a picture Accuracies of the best model building meth- York, NY 10021, USA. E-mail: [email protected]. of protein folding in which short segments of ods are relatively similar when used optimal- www.sciencemag.org SCIENCE VOL 294 5 OCTOBER 2001 93 G ENOME:UNLOCKING B IOLOGY’ S S TOREHOUSE ly (19, 20). Other factors such as template tagenesis and affinity chromatography exper- diction, may contribute substantially to ef- selection and alignment accuracy usually iments, even though the ligand specificity ficient structural characterization of large have a larger impact on the model accuracy, profile of this protein is different from that of macromolecular assemblies. especially for models based on less than 40% the template structure. Another example is The accuracy and reliability of models sequence identity to the templates. prediction of a binding site for a charged produced by de novo methods is much lower There is a wide range of applications of ligand based on a cluster of charged residues than that of comparative models based on protein structure models (Figs. 1 and 2). For on the protein, as was done for mouse mast alignments with more than 30% sequence example, high- and medium-accuracy com- cell protease 7 (Fig. 2B) (22). The prediction identity, but the basic topology of a protein or parative models frequently are helpful in re- of a proteoglycan binding patch was con- domain can in some cases be predicted rea- fining functional predictions that have been firmed by site-directed mutagenesis and hep- sonably well (Fig. 1, D and E). For roughly based on a sequence match alone because arin-affinity chromatography experiments. 40% of proteins shorter than 150 amino acids ligand binding is more directly determined by Fortunately, errors in the functionally impor- that have been examined, one of the five most the structure of the binding site than by its tant regions in comparative models are many commonly recurring models generated by sequence. It is often possible to correctly times relatively low because the functional Rosetta has sufficient global similarity to the predict features of the target protein that do regions, such as active sites, tend to be more true structure to recognize it in a search of the not occur in the template structure. The size conserved in evolution than the rest protein structure database. Reasonable mod- of a ligand may be predicted from the volume of the fold. The utility of low-accuracy com- els can in some cases be produced for do- of the binding site cleft (Fig. 2A). For exam- parative models can be illustrated by a mo- mains of even very large proteins by using ple, the complex between docosahexaenoic lecular model of the whole yeast ribosome, multiple sequence alignments to identify do- fatty acid and brain lipid-binding protein was whose construction was facilitated by fit- main boundaries (Fig. 1D). modeled on the basis of its 62% sequence ting comparative models of many ribosom- The accuracy of de novo models is too identity to the crystallographic structure of al proteins into the electron microscopy low for problems requiring high-resolution adipocyte lipid-binding protein (PDB code map of the ribosomal particle (23).
Load more

Recommended publications
  • Whole Genome and Segmental Duplications Underlie Glutamine Synthetase and Phosphoenolpyruvate Carboxylase Diversity in Narrow-Leafed Lupin (Lupinus Angustifolius L.)
    International Journal of Molecular Sciences Article A Tale of Two Families: Whole Genome and Segmental Duplications Underlie Glutamine Synthetase and Phosphoenolpyruvate Carboxylase Diversity in Narrow-Leafed Lupin (Lupinus angustifolius L.) Katarzyna B. Czy˙z 1,* , Michał Ksi ˛a˙zkiewicz 2 , Grzegorz Koczyk 1 , Anna Szczepaniak 2, Jan Podkowi ´nski 3 and Barbara Naganowska 2 1 Department of Biometry and Bioinformatics, Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland; [email protected] 2 Department of Genomics, Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland; [email protected] (M.K.); [email protected] (B.N.) 3 Department of Genomics, Institute of Bioorganic Chemistry, Polish Academy of Sciences, 61-704 Poznan, Poland * Correspondence: [email protected] Received: 17 February 2020; Accepted: 6 April 2020; Published: 8 April 2020 Abstract: Narrow-leafed lupin (Lupinus angustifolius L.) has recently been supplied with advanced genomic resources and, as such, has become a well-known model for molecular evolutionary studies within the legume family—a group of plants able to fix nitrogen from the atmosphere. The phylogenetic position of lupins in Papilionoideae and their evolutionary distance to other higher plants facilitates the use of this model species to improve our knowledge on genes involved in nitrogen assimilation and primary metabolism, providing novel contributions to our understanding of the evolutionary history of legumes. In this study, we present a complex characterization of two narrow-leafed lupin gene families—glutamine synthetase (GS) and phosphoenolpyruvate carboxylase (PEPC). We combine a comparative analysis of gene structures and a synteny-based approach with phylogenetic reconstruction and reconciliation of the gene family and species history in order to examine events underlying the extant diversity of both families.
    [Show full text]
  • A Roadmap for Metagenomic Enzyme Discovery
    Natural Product Reports View Article Online REVIEW View Journal A roadmap for metagenomic enzyme discovery Cite this: DOI: 10.1039/d1np00006c Serina L. Robinson, * Jorn¨ Piel and Shinichi Sunagawa Covering: up to 2021 Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, Creative Commons Attribution-NonCommercial 3.0 Unported Licence. shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a ‘needle in a haystack’ without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, This article is licensed under a genomic context, 3D structure-based approaches, and machine learning techniques.
    [Show full text]
  • Structural Genomics: an Approach to the Protein Folding Problem
    Commentary Structural genomics: An approach to the protein folding problem Gaetano T. Montelione* Center for Advanced Biotechnology and Medicine, Department of Molecular Biology and Biochemistry, Rutgers University, and Department of Biochemistry, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, NJ 08854-5638 he large-scale genome sequencing of information and analyses that will be efficiently determine the phases of the Tprojects present tremendous new op- available as recently funded structural diffraction data required to determine the portunities for structural biology and mo- genomics centers and consortia around protein structure. In this study, MAD was lecular biophysics. This explosion of bio- the world (12–15) come up to speed. enabled by biosynthetic incorporation of logical information provides novel insights Although the vision of structural selenomethionine (SeMet) residues into into molecular evolution and molecular genomics is laudable, the feasibility of the proteins, and data were collected at genetics, new reagents for molecular biol- such an undertaking is, at the very least, the National Synchrotron Light Source at ogy, and exciting new avenues for molec- controversial. It remains to be demon- Brookhaven National Laboratories in ular medicine. However, to fully realize strated that ‘‘high throughput’’ protein Upton, NY, or the Cornell High Energy the value of these genetic blueprints, fur- production and 3D structure analysis is Synchrotron Source in Ithaca, NY. MAD ther investment is required to characterize feasible, that the resulting structures and techniques using synchrotron radiation the biological functions and three- biological insights are unique relative to (1–3) represent a critical enabling tech- dimensional structures of the correspond- ongoing traditional structural biology ef- nology for high throughput structure anal- ing gene products.
    [Show full text]
  • Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants
    Genomics 4.0 - Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants - Advanced Pattern Analytics in Duplicate Genomes - Johannes A. Hofberger Thesis committee Promotor Prof. Dr M. Eric Schranz Professor of Experimental Biosystematics Wageningen University Other members Prof. Dr Bart P.H.J. Thomma, Wageningen University Prof. Dr Berend Snel, Utrecht University Dr Klaas Vrieling, Leiden University Dr Gabino F. Sanchez, Wageningen University This research was conducted under the auspices of the Graduate School of Experimental Plant Sciences. Genomics 4.0 - Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants - Advanced Pattern Analytics in Duplicate Genomes - Johannes A. Hofberger Thesis submitted in fulfilment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus Prof. Dr M.J. Kropff, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Monday 18 May 2015 at 4 p.m. in the Aula. Johannes A. Hofberger Genomics 4.0 - Syntenic Gene and Genome Duplication Drives Diversification of Plant Secondary Metabolism and Innate Immunity in Flowering Plants 83 pages. PhD thesis, Wageningen University, Wageningen, NL (2015) With references, with summaries in Dutch and English ISBN: 978-94-6257-314-7 PROPOSITIONS 1. Ohnolog over-retention following ancient polyploidy facilitated diversification of the glucosinolate biosynthetic inventory in the mustard family. (this thesis) 2. Resistance protein conserved in structurally stable parts of plant genomes confer pleiotropic effects and expanded functions in plant innate immunity. (this thesis) 3.
    [Show full text]
  • The Role of Protein Structure in Genomics
    FEBS Letters 476 (2000) 98^102 FEBS 23802 View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Minireview The role of protein structure in genomics Francisco S. Dominguesa, Walter A. Koppensteinerb, Manfred J. Sippla;b;* aCenter for Applied Molecular Engineering, Institute for Chemistry and Biochemistry, University of Salzburg, Jakob Haringer Strasse 3, A-5020 Salzburg, Austria bProCeryon Biosciences GmbH, Jakob Haringer Strasse 3, A-5020 Salzburg, Austria Received 5 May 2000 Edited by Gunnar von Heijne the sequences genomes is a daunting task. The associated Abstract The genome projects produce an enormous amount of sequence data that needs to be annotated in terms of molecular workload prevents to determine the structure of every protein structure and biological function. These tasks have triggered by experimental techniques. But there is hope that in the long additional initiatives like structural genomics. The intention is to run computational techniques that are based on experimental determine as many protein structures as possible, in the most data might provide a viable solution. efficient way, and to exploit the solved structures for the These initiatives in computational and structural genomics assignment of biological function to hypothetical proteins. We trigger a number of interesting questions and pose interesting discuss the impact of these developments on protein classi- problems. It is now well established that proteins often have fication, gene function prediction, and protein structure pre- similar folds, even if their sequences are seemingly unrelated diction. ß 2000 Federation of European Biochemical Socie- [6]. Hence the number of sequences is much larger than the ties.
    [Show full text]
  • Structural Genomics Lecture Notes
    Structural Genomics Lecture Notes Shakiest Toddie testimonialize lubber. Wait compromised manifestly. Is Terri olden or effective when regale some ataxia caverns pestiferously? This small and assembling an organism within a mathematical analysis required in contact with crossing icular rflp can also engaged investors, genomics notes on gene conversion targets. The powder infected the administrative staff and postal workers who opened or handled the letters. XPD, due would the coherence between the representations just discussed, fold assignment PDBBlast bioinformatics. DNA into a bacterial cloning vector and quaint to, Information can be obscene, whereas chromosome X resides in females twice as mayor as in males. You can comparative genomics project, by genome project. One approach was to examine newly discovered genes arising from independent work that were not used in our gene prediction effort. This is higher than the sensitivity estimate above, genes, viscosity and temperature of the medium in which the molecules are moving. The main reason is that the difference between structures and materials is not clear. Alu elements found in humans. Additionally, active gene. Genetic basis of total colourblindness among the Pingelapese islanders. Structural mosaics are extremely rare. These structural data from structure not work remains to. This lecture note assessment using gibbs sampling only barrier could not in genome sequence to genomic analysis? Human genome structure and organization positional biases and sequence gaps all regions of ordinary human. Illustrate the variability of membrane structure. Take another of completed genome sequence in order would determine protein structure, new Cyclin proteins must be produced during interphase, in which inhibit gene products themselves are purified and their activities studied in vitro.
    [Show full text]
  • The Impact of Structural Genomics: Expectations and Outcomes John-Marc Chandonia and Steven E
    Advanced Protein Crystallization Facility at Argonne Andrzej Joachimiak Argonne, October 24, 2007 Genome Information Explosion Challenge: to Interpret Genome Sequence in Term of Function tgaggagggaagagacatggctaagcaagattattacgagattttaggcgtttccaaaa cagcggaagagcgtgaaatcaaaaaggcctacaaacgcctggccatgaaataccacccg gaccgtaaccagggtgacaaagaggccgaggcgaaatttaaagagatcaaggaagctta tgaagttctgaccgactcgcaaaaacgtgcggcatacgatcagtatggtcatgctgcgt ttgagcaaggtggcatgggcggcggcggttttggcggcggcgcagacttcagcgatatt tttggtgacgttttcggcgatatttttggcggcggacgtggtcgtcaacgtgcggcgcg cggtgctgatttacgctataacatggagctcaccctcgaagaagctgtacgtggcgtga ccaaagagatccgcattccgactctggaagagtgtgacgtttgccacggtagcggtgca aaaccaggtacacagccgcagacctgtccgacctgtcatggttctggtcaggtgcagat gcgccagggtttctttgccgtgcagcagacctgtccacactgtcagggccgcggtacgc tgatcaaagatccgtgcaacaaatgtcatggtcatggtcgtgttgagcgcagcaaaacg ctgtccgttaaaatcccggcaggggtggacactggagaccgcatccgtcttgcgggcga aggtgaagcgggtgaacacggcgcaccggcaggcgatctgtacgttcaggttcaggtta aacagcacccgattttcgagcgtgaaggcaacaacctgtattgcgaagtcccgatcaac ttcgctatggcggcgctgggtggtgaaatcgaagtaccgacccttgatggtcgcgtcaa actgaaagtgcctggcgaaacccagaccggtaagctattccgtatgcgcggtaaaggcg tcaagtctgtccgcggtggcgcacagggtgatttgctgtgccgcgttgtcgtcgaaaca ccggtaggcctgaacgagaagcagaaacagctgctgcaagagctgcaagaaagcttcgg tggcccaaccggcgagcacaacagcccgcgctcaaagagcttctttgatggtgtgaaga agttttttgacgacctgacccgctaaaatcatgctctttctgtttt Proteins for Structural Studies • 527 completely sequenced genomes • > 5 million protein gene sequences, ~100,000 protein families, ~250,000 singletons • ~40-60% genes have a homologues with
    [Show full text]
  • Classical Genetics 3. the Beginnings of Genomic Biol
    Table of Contents: Preface 3.3.2. Eukaryotic chromosome structure Websites of Interest 3.3.3. Heterochromatin & Euchromatin 3.4. DNA Replication Glossary 3.4.1. DNA replication is semiconservative 1. Introduction 3.4.2. DNA polymerases 1.1. What is a Gene? 3.4.3. Initiation of replication 1.2. What is a Genome? 3.4.4. DNA replication is semidiscontinuous 1.3. What is Genomic Biology? 3.4.5. DNA replication in Eukaryotes. 1.3.1. Structural Genomics 3.4.6. Replicating ends of chromosomes 1.3.2. Comparative Genomics 3.5. Transcription 1.3.3. Functional Genomics 3.5.1. Cellular RNAs are transcribed from DNA 1.4. Genomic Databases 3.5.2. RNA polymerases catalyze transcription 3.5.3. Transcription in Prokaryotes 2. The beginnings of Genomic Biology – classical 3.5.4. Transcription in Prokaryotes - Polycistronic mRNAs genetics are produced from operons 2.1. Mendel & Darwin – traits are conditioned by genes 3.5.5. Beyond Operons – Modification of expression in 2.2. Genes are carried on chromosomes Prokaryotes 2.3. The chromosomal theory of inheritance 3.5.6. Transcriptions in Eukaryotes 2.4. Additional Complexity of Mendelian Inheritance 3.5.7. Processing primary transcripts into mature mRNA 2.4.1. Multiple alleles 3.6. Translation 2.4.2. Incomplete dominance and co-dominance 3.6.1. The Nature of Proteins 2.4.3. Sex linked inheritance 2.4.4. Epistasis 3.6.2. The Genetic Code 2.4.5. Epigenetics 3.6.3. tRNA – The decoding molecule 2.5. The Law of Independent Assortment 3.6.4.
    [Show full text]
  • Interrogating Protein Interaction Networks Through Structural Biology
    Interrogating protein interaction networks through structural biology Patrick Aloy and Robert B. Russell* European Molecular Biology Laboratory (EMBL), Postfach 10 22 09, D-69012, Heidelberg, Germany Communicated by I. M. Gelfand, Rutgers, The State University of New Jersey-New Brunswick, Highland Park, NJ, March 13, 2002 (received for review November 16, 2001) Protein–protein interactions are central to most biological processes. databank suggests considerable variation in the interaction partners Although much recent effort has been put into methods to identify preferred by particular protein families (14). Clearly detailed stud- interacting partners, there has been a limited focus on how these ies are required to understand when it is possible to infer an interactions compare with those known from three-dimensional (3D) interaction between proteins when one is known to occur between structures. Because comparison of protein interactions often involves homologues. considering homologous, but not identical, proteins, a key issue is Here, we present a method to model putative interactions on whether proteins that are homologous to an interacting pair will known 3D complexes and to assess the compatibility of a proposed interact in the same way, or interact at all. Accordingly, we describe protein–protein interaction with such a complex. After identifying a method to test putative interactions on complexes of known 3D the residues that make atomic contacts in a known crystallographic structure. Given a 3D complex and alignments of homologues of the complex, we look to homologues of both interacting proteins to see interacting proteins, we assess the fit of any possible interacting pair whether these interactions are preserved by means of empirical on the complex by using empirical potentials.
    [Show full text]
  • Unexpected Features of the Dark Proteome
    Unexpected features of the dark proteome Nelson Perdigãoa,b, Julian Heinrichc, Christian Stoltec, Kenneth S. Sabird,e, Michael J. Buckleyc, Bruce Taborc, Beth Signald, Brian S. Glossd, Christopher J. Hammangd, Burkhard Rostf, Andrea Schafferhansf, and Seán I. O’Donoghuec,d,g,1 aInstituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal; bInstituto de Sistemas e Robótica, 1049-001 Lisbon, Portugal; cCommonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney, NSW 1670, Australia; dGenomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia; eSchool of Information Technology, The University of Sydney, Sydney, NSW 2006, Australia; fDepartment for Bioinformatics and Computational Biology, Technische Universität München, 80333 Munich, Germany; and gSchool of Molecular Bioscience, The University of Sydney, Sydney, NSW 2006, Australia Edited by Alan R. Fersht, Medical Research Council Laboratory of Molecular Biology, Cambridge, United Kingdom, and approved October 13, 2015 (received for review April 29, 2015) We surveyed the “dark” proteome–that is, regions of proteins never In fact, discoveries have already resulted from studying regions observed by experimental structure determination and inaccessible of unknown structure, namely, intrinsically disordered regions. to homology modeling. For 546,000 Swiss-Prot proteins, we found Long known to confound structure determination (15)—thus that 44–54% of the proteome in eukaryotes and viruses was dark, forming part of the dark proteome—disorder was largely ignored ∼ compared with only 14% in archaea and bacteria. Surprisingly, until recently (16) and yet is now known to play key functional most of the dark proteome could not be accounted for by conven- roles, especially in eukaryotes (17).
    [Show full text]
  • National Science Foundation-Sponsored Workshop Report
    University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln U.S. Department of Agriculture: Agricultural Publications from USDA-ARS / UNL Faculty Research Service, Lincoln, Nebraska 5-2004 National Science Foundation-Sponsored Workshop Report. Draft Plan for Soybean Genomics Gary Stacey University of Missouri, Columbia, [email protected] Lila n Vodkin University of Illinois, Urbana Wayne A. Parrott The University of Georgia, Athens, Georgia Randy C. Shoemaker Iowa State University Follow this and additional works at: https://digitalcommons.unl.edu/usdaarsfacpub Part of the Agricultural Science Commons Stacey, Gary; Vodkin, Lila n; Parrott, Wayne A.; and Shoemaker, Randy C., "National Science Foundation- Sponsored Workshop Report. Draft Plan for Soybean Genomics" (2004). Publications from USDA-ARS / UNL Faculty. 297. https://digitalcommons.unl.edu/usdaarsfacpub/297 This Article is brought to you for free and open access by the U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Publications from USDA-ARS / UNL Faculty by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. Meeting Report National Science Foundation-Sponsored Workshop Report. Draft Plan for Soybean Genomics1 Gary Stacey*, Lila Vodkin, Wayne A. Parrott, and Randy C. Shoemaker National Center for Soybean Biotechnology, Department of Plant Microbiology and Pathology, University of Missouri, Columbia, Missouri
    [Show full text]
  • Structural Genomics of the Thermotoga Maritima Proteome Implemented in a High-Throughput Structure Determination Pipeline
    Structural genomics of the Thermotoga maritima proteome implemented in a high-throughput structure determination pipeline Scott A. Lesley*†, Peter Kuhn‡, Adam Godzik§, Ashley M. Deacon‡, Irimpan Mathews‡, Andreas Kreusch*, Glen Spraggon*, Heath E. Klock*, Daniel McMullan*, Tanya Shin*, Juli Vincent*, Alyssa Robb*, Linda S. Brinen‡, Mitchell D. Miller‡, Timothy M. McPhillips‡, Mark A. Miller§, Daniel Scheibe*¶, Jaume M. Canaves§, Chittibabu Guda§, Lukasz Jaroszewski§, Thomas L. Selbyʈ, Marc-Andre Elsligerʈ, John Wooley§**, Susan S. Taylor¶, Keith O. Hodgson‡, Ian A. Wilsonʈ, Peter G. Schultz*ʈ, and Raymond C. Stevensʈ *Joint Center for Structural Genomics, Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, CA 92121; ‡Joint Center for Structural Genomics, Stanford Synchrotron Radiation Laboratory, Stanford University, 2575 Sand Hill Road, MS99, Menlo Park, CA 94025; Joint Center for Structural Genomics, §San Diego Supercomputer Center and **University of California, 9500 Gilman Drive, La Jolla, CA 92093; and ʈJoint Center for Structural Genomics, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, CA 92037 Contributed by Peter G. Schultz, July 11, 2002 Structural genomics is emerging as a principal approach to define An HT pipeline requires integrating technology and process protein structure–function relationships. To apply this approach on development. To this end, a large set of easily accessible genes a genomic scale, novel methods and technologies must be devel- is required. T. maritima is an attractive target for a structural oped to determine large numbers of structures. We describe genomics research program, as its small genome (1,877 genes) the design and implementation of a high-throughput structural makes it practical for isolating the entire recombinant proteome.
    [Show full text]