DOCSLIB.ORG

Relative Orientation of Close-Packed ,8-Pleated Sheets in Proteins

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Proc. Nati Acad. Sci. USA Vol. 78, No. 7, pp. 4146-4150, July 1981 Biochemistry Relative orientation of close-packed ,8-pleated sheets in proteins (twisted ,3sheets/protein secondary and tertiary structure) CYRUS CHOTHIA*t AND JOEL JANINt *William Ramsay, Ralph Foster and Christopher Ingold Laboratories, University College London, Gower Street, London WC1E 6BT, England; tMedical Research Council Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, England; and *Unit6 de Biochimie Cellulaire, Department de Biochimie et Gen~tique Moleculaire, Institut Pasteur, 28 rue du Docteur Roux, 75724 Paris Cedex 15, France Communicated by Max F. Perutz, March 30, 1981 ABSTRACT When (3-pleated sheets pack face to face in pro- curve from left to right, while those that point down curve from teins, the angle between the strand directions ofthe two (3-sheets right to left. is observed to be near -30°. We propose a simple model for f3- Fig. 2 b and c illustrates some of the effects of twisting two sheet-to-(-sheet packing in concanavalin A, plastocyanin, y-crys- ,(3pleated sheets that are packed together. We show, on the left tallin, superoxide dismutase, prealbumin, and the immunoglobin ofthe figures, two hypothetical untwisted (3-sheets packed with fragment VREI. This model shows how the observed relative ori- their rows of side chains aligned: those in the top (-sheet are entation of two packed (3-sheets is a consequence of (i) the rows parallel to those in the bottom. On the right of the figures we of side chains at the interface being approximately aligned and show what happens when these packed (3-sheets are twisted (ii) the (3-sheet having a right-handed twist. The special amino acid about an axis that is in the plane oftheir interface. We see that composition ofresidues at the (-sheet-to-(3-sheet interfaces makes for the twisted close-packed (3-sheets: (i) The ends of the side the contact surfaces essentially smooth and hydrophobic. chains that form contacts across the interface maintain their alignment. (ii) The main chains of the two sheets now point in The three-dimensional structure of most globular proteins can different directions. be described as an assembly of close-packed a-helices and (& As one looks down the twist axis, the main chains ofthe top pleated sheets. It is therefore important to understand the rules ,(3sheet go from left to right, while those ofthe bottom go from that govern the packing ofsecondary structure (1-3). We have right to left (Fig. 2b). As one looks from the top (Fig. 2c), the previously analyzed the packing ofa-helices on a-helices (4) and main chains make an angle, which is negative because it rep- on (3-sheets (5). In this paper, we concentrate on (3sheet-to-,(- resents a left-handed screw rotation. This angle, fQ, would be sheet packing in a family ofproteins that are formed by two (3- -350 if the top and bottom main chains are 10 A apart and the pleated sheets packed face to face. twist along the chains is 40/A. Concanavalin A (6), plastocyanin (7), 'y-crystallin (8), super- This angle of -35° would be increased or decreased by the oxide dismutase (9), prealbumin (10), and the immunoglobulin rows of residues being not exactly aligned, by there being a domains (11) are representative members of this family, which different amount of twist, or both. If the side chains are not also contains the coat proteins of tomato bushy stunt virus (12) exactly aligned, and if the direction ofthe twist axis is closer to and of southern bean mosaic virus (13), azurin (14), and other the direction of the strands in one (-sheet than to that in the proteins. All these proteins have two (8-pleated sheets packed other, the extent of the twist in the two (3-sheets will differ face to face and oriented so that the direction of the strands in somewhat. one sheet is at an angle ofabout -300 to that in the other sheet, Viewed from the end, the twisted (3-pleated sheets in pro- the minus sign indicating a left-handed screw rotation (Fig. 1). jection take a cylindrical shape that Fig. 2b shows clearly. Note We show here that this characteristic orientation is a natural that the "barrel effect" (17) is observed even in the absence of consequence of the right-handed twist of normal (pleated a continuous network of hydrogen bonds joining the two (3- sheets and allows the close packing of side chains between the sheets into a single one. In our model, top and bottom strands two (3sheets in contact. In other proteins formed by (3pleated have their side chains pointing to each other and their main sheets, such as trypsin or staphylococcal nuclease, the (3-pleated chains cannot hydrogen bond. The actual shape ofthe structure sheets are packed with the strand directions making an angle is not a "barrel" or a cylinder, but a twisted prism with a rec- ofabout 900 (1). The principles applying to these structures are tangular cross-section that rotates around the twist axis. different and will be discussed elsewhere. The model described here supersedes a previous model of A model for pacldng twisted (3-pleated sheets Chothia et al. (1). In the remainder ofthe paper, we shall discuss The P-pleated sheets observed in protein structures are not flat its relevance to real protein structures. but twisted in a right-handed direction (16). In this section we describe the effects of introducing this twist into an idealized ,B-Sheet-to-p-sheet packing in six proteins model of two (3-sheets packed face to face (Fig. 2). This model We used computer graphics to examine the detailed atomic will be useful in understanding the structural descriptions pre- structures of the six proteins or protein subunits illustrated in sented in later sections. Fig. 1: concanavalin A, plastocyanin, y-crystallin domain I, su- Fig. 2a illustrates one effect of the right-handed twist on a peroxide dismutase, prealbumin, and immunoglobulin frag- single polypeptide chain. On twisting ofthe main chain, the side ment VREI. For each protein, we determined the twist and the chains that point up curve in the opposite direction to those that relative orientation ofthe (B-sheets and we analyzed the packing point down. As one looks down the chain, those that point up of amino acid residues at the (3-sheet to (3-sheet interfaces. P-Sheet Twist. The twist is conveniently expressed by the The publication costs ofthis article were defrayed in part by page charge dihedral angle between pairs of adjacent strands. This angle is payment. This article must therefore be hereby marked "advertise- negative for all but three of44 pairs ofstrands in the twelve (& ment" in accordance with 18 U. S. C. §1734 solely to indicate this fact. sheets, indicating a normal right-handed twist of the strands, 4146 Downloaded by guest on October 1, 2021 Biochemistry: Chothia and janin Proc. Natl. Acad. Sci. USA 78 (1981) 4147 7 2S 1472 747 2 & 243~~~S C 2 042A 38 57 PIG e-Cry r S3 Sod Pro VREI FIG. 1. The 3-pleated sheets in concanavalin A (Con A), plastocyanin (Pla), y-crystallin (-,Cry), superoxide dismutase (Sod), prealbumin (Pra), and the immunoglobulin fragment VREI. *, Ca atoms in the "top" -3-sheet; 0, Ca atoms in the "bottom" 3sheet. Atomic coordinates are from the Cambridge Protein Data Bank (15), except for plastocyanin and 'tycrystallin, gifts of J. M. Guss, H. C. Freeman, and T. Blundell (7, 8). which leads to a left-handed twist of the 3-sheets viewed from middle, and the sections in Fig. 4c, through the back. A com- the edge. The mean ofthe distribution (Fig. 3) is -17° and the plete representation of the structures would involve stacking standard deviation is 9°. The distribution is not significantly a number of such sections on top of each other. different from that found in a/P proteins, in which the mean We see in Fig. 4 that: (i) The description of the prealbumin dihedral angle between adjacent strands is - 19° with a standard and VREI structures as made oftwo ,(3pleated sheets packed face deviation of 70 (4). to face is essentially correct. Residues not belonging to the (3 The dihedral angle between the right-most and left-most sheets are marked in dotted lines. They make little contribution strands of each (3-sheet is a measure of its total twist. It varies to the internal packing. (ii) The (3sheet-to-,3sheet interface is between -30° over six strands in the top (-sheet of concanav- made up of side chains pointing directly to each other. In each alin A and -65° over four strands for the top (3-sheet of preal- section, a line may be drawn that separates one (3-sheet from narrow distribution the other. Side chains from either side hardly cross this line. bumin (Table 1). Thus, despite the rather Thus, intercalation of side chain from top and bottom is the in Fig. 3, the (3-sheets can differ widely in their total twist. exception rather than the rule in these interfaces. In the six Relative Orientation of the (Sheets. Relative orientation sections shown, it occurs once with Trp-35 in VREI (Fig. 4b), is fixed by the 0 angle of Fig. 2c. The values found for the six which fits its side chain into a hole in the bottom (3-sheet. Yet, proteins are listed in Table 1. The values are in the range -30° the interface is not a plane. Due to the twist, the dividing line + 150.
Recommended publications
  • Meeting Review: Bioinformatics and Medicine – from Molecules To

    Meeting Review: Bioinformatics and Medicine – from Molecules To

    Comparative and Functional Genomics Comp Funct Genom 2002; 3: 270–276. Published online 9 May 2002 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.178 Feature Meeting Review: Bioinformatics And Medicine – From molecules to humans, virtual and real Hinxton Hall Conference Centre, Genome Campus, Hinxton, Cambridge, UK – April 5th–7th Roslin Russell* MRC UK HGMP Resource Centre, Genome Campus, Hinxton, Cambridge CB10 1SB, UK *Correspondence to: Abstract MRC UK HGMP Resource Centre, Genome Campus, The Industrialization Workshop Series aims to promote and discuss integration, automa- Hinxton, Cambridge CB10 1SB, tion, simulation, quality, availability and standards in the high-throughput life sciences. UK. The main issues addressed being the transformation of bioinformatics and bioinformatics- based drug design into a robust discipline in industry, the government, research institutes and academia. The latest workshop emphasized the influence of the post-genomic era on medicine and healthcare with reference to advanced biological systems modeling and simulation, protein structure research, protein-protein interactions, metabolism and physiology. Speakers included Michael Ashburner, Kenneth Buetow, Francois Cambien, Cyrus Chothia, Jean Garnier, Francois Iris, Matthias Mann, Maya Natarajan, Peter Murray-Rust, Richard Mushlin, Barry Robson, David Rubin, Kosta Steliou, John Todd, Janet Thornton, Pim van der Eijk, Michael Vieth and Richard Ward. Copyright # 2002 John Wiley & Sons, Ltd. Received: 22 April 2002 Keywords: bioinformatics;
  • Assessing Sequence Comparison Methods with Reliable Structurally Identified Distant Evolutionary Relationships

    Assessing Sequence Comparison Methods with Reliable Structurally Identified Distant Evolutionary Relationships

    Proc. Natl. Acad. Sci. USA Vol. 95, pp. 6073–6078, May 1998 Biochemistry Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships STEVEN E. BRENNER*†‡,CYRUS CHOTHIA*, AND TIM J. P. HUBBARD§ *MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, United Kingdom; and §Sanger Centre, Wellcome Trust Genome Campus, Hinxton, Cambs CB10 1SA, United Kingdom Communicated by David R. Davies, National Institute of Diabetes, Bethesda, MD, March 16, 1998 (received for review November 12, 1997) ABSTRACT Pairwise sequence comparison methods have Sequence comparison methodologies have evolved rapidly, been assessed using proteins whose relationships are known so no previously published tests has evaluated modern versions reliably from their structures and functions, as described in of programs commonly used. For example, parameters in the SCOP database [Murzin, A. G., Brenner, S. E., Hubbard, T. BLAST (1) have changed, and WU-BLAST2 (2)—which produces & Chothia C. (1995) J. Mol. Biol. 247, 536–540]. The evalua- gapped alignments—has become available. The latest version tion tested the programs BLAST [Altschul, S. F., Gish, W., of FASTA (3) previously tested was 1.6, but the current release Miller, W., Myers, E. W. & Lipman, D. J. (1990). J. Mol. Biol. (version 3.0) provides fundamentally different results in the 215, 403–410], WU-BLAST2 [Altschul, S. F. & Gish, W. (1996) form of statistical scoring. Methods Enzymol. 266, 460–480], FASTA [Pearson, W. R. & The previous reports also have left gaps in our knowledge. Lipman, D. J. (1988) Proc. Natl. Acad. Sci. USA 85, 2444–2448], For example, there has been no published assessment of and SSEARCH [Smith, T.
  • A Novel and Effective Scoring Scheme for Structure Classification And

    A Novel and Effective Scoring Scheme for Structure Classification And

    A novel and effective scoring scheme for structure classification and pairwise similarity measurement Rezaul Karim∗, Md. Momin Al Aziz†,Swakkhar Shatabda‡ and M. Sohel Rahman∗ ∗Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology †Department of Computer Science, University of Manitoba, Canada ‡Department of Computer Science and Engineering, United International University Abstract—Protein tertiary structure defines its functions, clas- However, a major drawback of these approaches with re- sification and binding sites. Similar structural characteristics gards to the global structure similarity comparison is that these between two proteins often lead to the similar characteristics are much sensitive to local structure dissimilarity. Furthermore, thereof. Determining structural similarity accurately in real these approaches need to find an optimal alignment before time is a crucial research issue. In this paper, we present computing the score despite that often there are no remarkable a novel and effective scoring scheme that is dependent on alignment possible. Also finding proper alignment between two novel features extracted from protein alpha carbon distance matrices. Our scoring scheme is inspired from pattern recog- protein structures is computationally expensive if the size of nition and computer vision. Our method is significantly better the protein is large [9]. In spite of these inherent limitations than the current state of the art methods in terms of fam- and drawbacks, there exist a number of such methods in the ily match of pairs of protein structures and other statistical literature and the most notable ones among these are DALI measurements. The effectiveness of our method is tested on [10], [11], CE [12], TM Align [13], [14] and SP Align [15].
  • Transformer Neural Networks for Protein Prediction Tasks

    Transformer Neural Networks for Protein Prediction Tasks

    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.15.153643; this version posted June 16, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. TRANSFORMING THE LANGUAGE OF LIFE:TRANSFORMER NEURAL NETWORKS FOR PROTEIN PREDICTION TASKS Ananthan Nambiar ∗ Maeve Heflin∗ Department of Bioengineering Department of Computer Science Carl R. Woese Inst. for Genomic Biol. Carl R. Woese Inst. for Genomic Biol. University of Illinois at Urbana-Champaign University of Illinois at Urbana-Champaign Urbana, IL 61801 Urbana, IL 61801 [email protected] Simon Liu∗ Sergei Maslov Department of Computer Science Department Bioengineering Carl R. Woese Inst. for Genomic Biol. Department of Physics University of Illinois at Urbana-Champaign Carl R. Woese Inst. for Genomic Biol. Urbana, IL 61801 University of Illinois at Urbana-Champaign Urbana, IL 61801 Mark Hopkinsy Anna Ritzy Department of Computer Science Department of Biology Reed College Reed College Portland, OR 97202 Portland, OR 97202 June 16, 2020 ABSTRACT The scientific community is rapidly generating protein sequence information, but only a fraction of these proteins can be experimentally characterized. While promising deep learning approaches for protein prediction tasks have emerged, they have computational limitations or are designed to solve a specific task. We present a Transformer neural network that pre-trains task-agnostic sequence representations. This model is fine-tuned to solve two different protein prediction tasks: protein family classification and protein interaction prediction.
  • Predicting RNA Binding Proteins Using Discriminative Methods

    Predicting RNA Binding Proteins Using Discriminative Methods

    Predicting RNA binding proteins using discriminative methods by Shweta Bhandare M.S. Department of Interdisciplinary Telecommunications, University of Colorado at Boulder 2003 B.E., College of Engineering, University of Goa, 1995 A thesis submitted to the Faculty of the Graduate School of the University of Colorado in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science 2017 This thesis entitled: Predicting RNA binding proteins using discriminative methods written by Shweta Bhandare has been approved for the Department of Computer Science Prof. Robin Dowell Dr. Debra S. Goldberg Prof. Larry E. Hunter Dr. Daniel Weaver Date The final copy of this thesis has been examined by the signatories, and we find that both the content and the form meet acceptable presentation standards of scholarly work in the above mentioned discipline. iii Bhandare, Shweta (Ph.D., Computer Science) Predicting RNA binding proteins using discriminative methods Thesis directed by Prof. Robin Dowell and Dr. Debra S. Goldberg This thesis examines the role of computational methods in identifying the motifs utilized by RNA-binding proteins (RBPs). RBPs play an important role in post-transcriptional regulation and identify their targets in a highly specific fashion through recognition of primary sequence and/or secondary structure hence making the prediction a complex problem. I applied the existing k-spectrum kernel method to a support vector machine and verified the published binding sites of two RBPs: Human antigen R (HuR) and Tristetraprolin (TTP). These RBPs exhibit opposing effects to the bound messenger RNA (mRNA) transcript but have simi- lar binding preferences.
  • Wolfson Review Wolfson The

    Wolfson Review Wolfson The

    2012 – 2013 2013 No.37 – 2012 The Wolfson Review Wolfson The THE Wolfson Review 2012 – 2013 2013 No.37 – 2012 Wolfson College Barton Road Cambridge CB3 9BB www.wolfson.cam.ac.uk Upon 50 Years by John McClenahen (1986), Press Fellow The College is stone and mortar, and wood and glass. The College is ideas, great and small. The College is books and the Internet. Published in 2013 by Wolfson College, Cambridge The College is gates, gardens, paths, Barton Road, Cambridge CB3 9BB courts, and plaques, and a sundial. © Wolfson College, 2013 The College is Lee Library, the Dining Hall, and Bredon House. The College is students, and tutors, and Fellows. The College is fellowship, principles, and ritual. And the College, this College, Wolfson College, Cambridge, is much more. For this remarkable College is a diverse universe, ever expanding. From this College, in their diversity, those who study, guide, and reside here seek knowledge and truth in myriad ways. From this College, this special place, those who study, guide, and reside here seek to create, to find, to explore, to challenge, and to validate. Now and forever may their efforts – all our efforts wherever we are – Cover photograph Coloured primary hypothalamic neuronal culture, labelled ring true to the diversity and distinguishing humanness of this College, with MAP2, GFAP and Dapi under microscope, part of our young College in this ancient University. Wolfson Fellow Giles Yeo’s research into the brain control of food intake. Image created by Dr Brian Lam and Mr Joseph Polex-Wolf from the Yeo laboratory. The paper used for the Review contains material sourced from responsibly managed forests, certified in accordance with the Forestry Stewardship Council, and is printed using vegetable based inks.
  • New Tools for Old Organelles

    New Tools for Old Organelles

    New tools for old organelles Bioinformatics for comparative cell biology Marc Gouw Dissertation presented to obtain the Ph.D degree in Bioinformatics Instituto de Tecnologia Química e Biológica António Xavier | Universidade Nova de Lisboa Oeiras, July, 2015 New tools for old organelles Bioinformatics for comparative cell biology Marc Gouw Dissertation presented to obtain the Ph.D degree in Evolutionary Biology Instituto de Tecnologia Química e Biológica António Xavier | Universidade Nova de Lisboa Research work coordinated by: Oeiras July, 2015 Acknowledgments This thesis and the years of work and inspiration put into it would not have been possible without the help of many people. First of all, I just generally want to thank everyone for being so great. Especially those who in any way contributed to making the past few years in Portugal and at the IGC such a wonderful experience: without all of you, my life and this thesis would have turned out very different indeed. A special thanks to all of the past and present members of the Computational Genomics Laboratory, who have always been kind enough to be unforgivingly critical of my work, and equally enthusiastic to go enjoy a beer on a Friday afternoon. And especially Beatriz Ferreira Gomes and Yoan Diekmann, who were essential to my PhD project: You have been truly inspiring collogues and close friends. A lot of this work would not have been possible without you. The epic mtoc-explorer.org, and indeed this thesis would also not have been possible without Renato Alves. I blame you, your programming genius and incredible patience for teaching me so much.
  • 1975.Txt 1. Computer Simulation of Protein Folding Michael Levitt

    1975.Txt 1. Computer Simulation of Protein Folding Michael Levitt

    1975.txt 1. Computer simulation of protein folding Michael Levitt* & Arieh Warshel*† †Department of Chemical Physics, Weizmann Institute of Science, Rehovoth, Israel *Present Address: MRC Laboratory of Molecular Biology, Hills Road, Cambridge, UK. A new and very simple representation of protein conformations has been used together with energy minimisation and thermalisation to simulate protein folding. Under certain conditions, the method succeeds in 'renaturing' bovine pancreatic trypsin inhibitor from an open-chain conformation into a folded conformation close to that of the native molecule. References 1.Anfinsen, C. B., Science, 181, 223–230 (1973). 2.Schulz, G. E., Barry, C. D., Friedman, J., Chou, P. Y., Fasman, G. D., Finkelstein, A. V., Lim, V. I., Ptitsyn, O. B., Kabat, E. A., Wu, T. T., Levitt, M., Robson, B., Nagano, K., Nature, 250, 140–142 (1974). 3.Scheraga, H. A., in Current topics in biochemistry (edit. by Anfinsen, C. B., and Schechter, A. N.), 1–42 (Academic, New York, 1974). 4.Ptitsyn, O. B., Vestnik Akad. Nauk S.S.S.R., 5, 57–68 (1973). 5.Flory, P. J. in Statistical Mechanics of Chain Molecules, 248–306 (Wiley, New York, 1969). 6.Hill, T. L., in Statistical Mechanics, 15–17 (McGraw-Hill, New York, 1956). 7.Nozaki, Y., and Tanford, C., J. biol. Chem., 246, 2211–2217 (1971). 8.Simon, E. M., Biopolymers, 10, 973–989 (1971). 9.Huber, R., Kukla, D., Ruhlmann, A., and Steigemann, W., Cold Spring Harbor Symp. quant. Biol., 36, 141–148 (1971). 10.Creighton, T. E., J. molec. Biol., 87, 603–624 (1974). 11.Phillips, D. C., in British Biochemistry, Past and Present (edit.
  • Weighted Graphlets and Deep Neural Networks for Protein Structure

    Weighted Graphlets and Deep Neural Networks for Protein Structure

    Weighted graphlets and deep neural networks for protein structure classification Hongyu Guo1, Khalique Newaz2,3,4, Scott Emrich5, Tijana Milenković2,3,4, and Jun Li1,* 1Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN 46556, USA 2Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA 3Eck Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA 4Interdisciplinary Center for Network Science & Applications, University of Notre Dame, Notre Dame, IN 46556, USA 5Department of Electrical Engineering & Computer Science, University of Tennessee at Knoxville, TN 37996, USA *To whom correspondence should be addressed. Tel: +1 574 631 3429; Fax: +1 574 631 4822; Email: [email protected] Abstract As proteins with similar structures often have similar functions, analysis of protein structures can help predict protein functions and is thus important. We consider the problem of protein structure classification, which computationally classifies the struc- tures of proteins into pre-defined groups. We develop a weighted network that depicts the protein structures, and more importantly, we propose the first graphlet-based mea- sure that applies to weighted networks. Further, we develop a deep neural network (DNN) composed of both convolutional and recurrent layers to use this measure for classification. Put together, our approach shows dramatic improvements in performance arXiv:1910.02594v1 [stat.ML] 7 Oct 2019 over existing graphlet-based approaches on 36 real datasets. Even comparing with the state-of-the-art approach, it almost halves the classification error. In addition to protein structure networks, our weighted-graphlet measure and DNN classifier can potentially be applied to classification of other weighted networks in computational biology as well as in other domains.
  • Multi-Domain Protein Families and Domain Pairs: Comparison with Known Structures and a Random Model of Domain Recombination

    Multi-Domain Protein Families and Domain Pairs: Comparison with Known Structures and a Random Model of Domain Recombination

    Journal of Structural and Functional Genomics 4: 67–78, 2003. 67 © 2003 Kluwer Academic Publishers. Printed in the Netherlands. Multi-domain protein families and domain pairs: comparison with known structures and a random model of domain recombination Gordana Apic1, Wolfgang Huber2 & Sarah A. Teichmann1* 1MRC Laboratory of Molecular Biology, Hills Road, Cambridge CB2 2QH, UK and 2DKFZ (German Cancer Research Center), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; * To whom correspondence should be addressed at [email protected] Received 26 November 2002; Accepted in final form 11 March 2003 Key words: structural genomics, multi-domain proteins, recombination, domain combinations Abstract There is a limited repertoire of domain families in nature that are duplicated and combined in different ways to form the set of proteins in a genome. Most proteins in both prokaryote and eukaryote genomes consist of two or more domains, and we show that the family size distribution of multi-domain protein families follows a power law like that of individual families. Most domain pairs occur in four to six different domain architectures: in isolation and in combinations with different partners. We showed previously that within the set of all pairwise domain combinations, most small and medium-sized families are observed in combination with one or two other families, while a few large families are very versatile and combine with many different partners. Though this may appear to be a stochastic pattern, in which large families have more combination partners by virtue of their size, we establish here that all the domain families with more than three members in genomes are duplicated more frequently than would be expected by chance considering their number of neighbouring domains.
  • Signature of Author

    Signature of Author

    An Analysis of Representations for Protein Structure Prediction by Barbara K. Moore Bryant S.B., S.M., Massachusetts Institute of Technology (1986) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY September 1994 ( Massachusetts Institute of Technology 1994 SignatureofAuthor ............. ,...... ........-.. .. Department of Electrical Engineering and Computer Science September 2, 1994 Certified by... ..... -- Patrick. H. Winston Professor, Electrical Engineering and Computer Science This Supervisor %-"ULfrtifioAvL.Lv--k &y ho, . Tomas o) no-Perez and Computer Science L! Thp.i. .,nPrvi.qnr Accepted by .. ... , a. ,- · F' drY................... / Frederic~) R. Morgenthaler Departmental /Committee on Graduate Students Iv:r tfF:r _ ;: ! --.. t.8r'(itr i-7etFr An Analysis of Representations for Protein Structure Prediction by Barbara K. Moore Bryant Submitted to the Department of Electrical Engineering and Computer Science on September 2, 1994, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract Protein structure prediction is a grand challenge in' the fields of biology and computer science. Being able to quickly determine the structure of a protein from its amino acid sequence would be extremely useful to biologists interested in elucidating the mechanisms of life and finding ways to cure disease. In spite of a wealth of knowl- edge about proteins and their structure, the structure prediction problem has gone unsolved in the nearly forty years since the first determination of a protein structure by X-ray crystallography. In this thesis, I discuss issues in the representation of protein structure and se- quence for algorithms which perform structure prediction.
  • Emma E. Hill 461B Koshland Hall, #3102 Brenner

    Emma E. Hill 461B Koshland Hall, #3102 Brenner

    Emma E. Hill 461B Koshland Hall, #3102 Tel: +1 510 642-9614 Brenner Research Group, Fax: +1 413 280-7813 University of California, Berkeley http://compbio.berkeley.edu Berkeley, CA 94720-3102 [email protected] Education and Research 2002-present Post-Doctoral Researcher with Professor Steven E. Brenner, University of California, Berkeley. 1998-2002 Ph.D. in Biological Sciences with Dr. Cyrus Chothia, F.R.S. MRC Laboratory of Molecular Biology, University of Cambridge, U.K. Thesis: Evolution of Protein Families: Genome Sequences and Three Dimensional Structures. 1995-1998* B.Sc. (hons) Genetics. The University of Manchester, School of Biological Sciences, U.K. *1996-1997 ERASMUS program (one semester spent studying in France as part of B.Sc.) Université Paul Sabatier, Toulouse, France Research Interests Using genome sequence and structural genomic data to discover the functions of important proteins, and analyze protein families: ♦ For protein families directly or indirectly linked to medical disorders, we can perform structural, sequence and functional analyses to understand how mutation and/or evolution have altered the proteins and how this impacts different organisms. ♦ We can detect functionally and/or structurally important residues in proteins of clinical interest, by producing accurate structural alignments. These help (i) us to understand how a certain protein fold is maintained despite sequence divergence and (ii) give vital clues as to functionally and/or structurally important residues for that protein. ♦ Determining ancient evolutionary relationships between proteins based upon structure and sequence will permit a better understanding of molecular evolution. ♦ Deducing potential model organisms suitable for the study of proteins of medical import can be done by comparing protein repertoires within and between genomes and correlating these with known phylogenetic relationships.