DOCSLIB.ORG

Structure-Based Prediction Reveals Capping Motifs That Inhibit Β-Helix Aggregation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Structure-Based Prediction Reveals Capping Motifs That Inhibit Β-Helix Aggregation Structure-based prediction reveals capping motifs that inhibit β-helix aggregation Allen W. Bryan, Jr.a,b,c, Jennifer L. Starner-Kreinbrinkd, Raghavendra Hosurc, Patricia L. Clarkd,1, and Bonnie Bergerc,e,1 aHarvard-Massachusetts Institute of Technology (MIT) Division of Health Sciences and Technology, 77 Massachusetts Avenue, Cambridge, MA 02139; bWhitehead Institute, 9 Cambridge Center, Cambridge, MA 02139; cComputer Science and Artificial Intelligence Laboratory, 77 Massachusetts Avenue, Cambridge, MA 02139; dDepartment of Chemistry and Biochemistry, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556; and eDepartment of Mathematics, Massachusetts Institute of Technology (MIT), 77 Massachusetts Avenue, Cambridge, MA 02139 Edited* by Susan Lindquist, Whitehead Institute for Biomedical Research, Cambridge, MA, and approved May 13, 2011 (received for review November 23, 2010) The parallel β-helix is a geometrically regular fold commonly found in the proteomes of bacteria, viruses, fungi, archaea, and some vertebrates. β-helix structure has been observed in monomeric units of some aggregated amyloid fibers. In contrast, soluble β- helices, both right- and left-handed, are usually “capped” on each end by one or more secondary structures. Here, an in-depth classi- fication of the diverse range of β-helix cap structures reveals subtle commonalities in structural components and in interactions with the β-helix core. Based on these uncovered commonalities, a toolkit of automated predictors was developed for the two distinct types of cap structures. In vitro deletion of the toolkit-predicted C-terminal cap from the pertactin β-helix resulted in increased aggregation and the formation of soluble oligomeric species. These results suggest that β-helix cap motifs can prevent specific, β-sheet- mediated oligomeric interactions, similar to those observed in BIOPHYSICS AND amyloid formation. COMPUTATIONAL BIOLOGY beta-helix ∣ hidden Markov model ∣ threading ∣ aggregation prediction ∣ beta-sheet oligomerization Fig. 1. Pectin methylesterase, 1GQ8; a typical β-helix. 1GQ8 is a right- arallel β-helices (1–3) are defined by the regular nature of handed β-helix, three-sided, with a single α-helix cap at its N terminus and Ptheir rungs, each of which consists of two or three β-strands a previous-strand visor cap at its C terminus. Inset, the assignment of β-strand arranged in sequential repeats separated by loops of various and turn names in a β-helix rung as seen in residues 167-225 of pectate lyase C β lengths (Fig. 1). Helical stacking of these rungs produces two (2PEC), a similar -helix. or three parallel β-sheets surrounding a central core filled with β inward-facing amino acid side chains. -helices form structurally In this paper we present an in-depth study of β-helix cap struc- and geometrically regular domains, despite the presence of loops tures, describe beta-helix β-helix cap detectors based on our struc- of various lengths, which can themselves include regular struc- β tural results, and report experimental evidence demonstrating a ture. The regularity of the -helix structure persists despite great β disparity in primary sequence (4–6). While right-handed parallel role for cap structures in preventing -helix aggregation. The β β-helices were the first to be described (3), left-handed (4, 7) and available structures in the PDB are classified by -helix cap fold α two-stranded (8) β-helices have now also been identified. Nota- into -helix and visor cap motifs that cross-correlate with estab- bly, β-helices are overrepresented in bacterial Protein Data Bank lished β-helix families. Despite wide variety in both sequence and (PDB) (9) entries but rare in eukaryotic entries (5). structure, these motifs display subtle but consistent themes that Richardson and Richardson (10) noted that β-helices often were revealed by focused modeling using hidden Markov models begin and end with a loop at either end, termed a β-helix cap. This (HMMs) and threading approaches. These models were com- cap is amphipathic: one side, sometimes incorporating charged piled into a prediction toolkit that accurately identifies β-helix residues, is exposed to solvent, while the other side caps the hy- caps from protein sequences with high specificity, even across β β drophobic core of the -helix. Caps thus protect -helix cores superfamilies. In vitro deletion of a toolkit-predicted result, from solvent exposure. Richardson and Richardson, among the C-terminal cap of the pertactin β-helix, is shown to promote others (11), speculated that caps could also prevent aggregation intermolecular interactions and aggregation. of β-helices. Many agglutinative proteins, including prion and amyloid proteins, are suspected to consist of repeating and inde- finitely extendable β-sheets assembled from monomers. Without a mechanism to interrupt formation of potential intermolecular β β Author contributions: A.W.B., J.L.S.-K., P.L.C., and B.B. designed research; A.W.B., hydrogen bonds at the ends of -helices, -helix-forming peptides J.L.S.-K., and R.H. performed research; R.H., P.L.C., and B.B. contributed new reagents/ could associate to form multimeric fibers similar to amyloid. analytic tools; J.L.S.-K. and R.H. rendered figures; A.W.B., J.L.S.-K., R.H., P.L.C., and Thus, disruption of β-helix caps could sequester β-helices into B.B. analyzed data; and A.W.B., J.L.S.-K., P.L.C., and B.B. wrote the paper. aggregate fibrils. However, despite the possible importance of The authors declare no conflict of interest. β-helix caps as preventers of aggregation, and despite interest *This Direct Submission article had a prearranged editor. in β-helices as potential models of prion and aggregative protein Freely available online through the PNAS open access option. assembly (12–15), no survey of the presumably analogous assem- 1To whom correspondence may be addressed. E-mail: [email protected] or [email protected]. — — bly interfaces the known caps and the adjacent structures has This article contains supporting information online at www.pnas.org/lookup/suppl/ been made. doi:10.1073/pnas.1017504108/-/DCSupplemental. www.pnas.org/cgi/doi/10.1073/pnas.1017504108 PNAS ∣ July 5, 2011 ∣ vol. 108 ∣ no. 27 ∣ 11099–11104 Downloaded by guest on September 27, 2021 Results Structural Characterization of β-Helix Caps. In order to understand β-helix cap structures in sufficient detail to make β-helix cap classification possible, clear definitions and characterization of β-helix caps were required. Therefore, a survey of available β-helix structures from the PDB was conducted (see SI Text). For purposes of analysis, the extent of each cap was defined as the minimum continuous set of residues necessary to fulfill three requirements: (i) at least one continuous subset of cap residues maintains van der Waals contact with the hydrophobic core of the β-helix; (ii) at least one continuous subset of cap re- sidues maintains van der Waals contact with at least one strand of the terminal rung of the β-helix such that the cap backbone inter- sects the plane of its β-sheet, and (iii) caps do not begin or end within an element of regular secondary structure. The first two requirements reflect the functions of β-helix caps. Contact with the hydrophobic core adds stability and solubility to the β-helix, while intersecting at least one β-sheet plane provides steric hin- drance against H-bonding of the terminal rung with other pro- teins, especially other monomers or oligomers of the β-helix. The third requirement forced the inclusion of the entirety of each element of secondary structure, ensuring sufficient data for accurate sequence/secondary structure comparison. This survey revealed the vast majority of β-helix caps to follow one of two loose structural patterns. The definitions of these pat- terns, the α-helix and visor caps, are derived from the general definition above and described in more detail in SI Text. The α-helix caps (Fig. 2 A–C) are characterized by two secondary α structures, at least one of which is an -helix, lying approximately Fig. 2. α-helix and visor cap structures. All α-helix caps displayed are N-caps parallel to each other and to β-strand(s) of the adjacent β-helix and all visor caps displayed are C-caps, except for (I), where both N- and rung. In contrast, visor caps (Fig. 2 D–I) have in common a more C-caps are visors. For visibility, other domains and distant portions of the acute angle between structural elements than the angles connect- β-helices have been removed. All images were produced using PyMol ing β-strands in the adjacent β-helix, and a near-perpendicular, (www.pymol.org). (A–C) Representative α-helix N-caps. Structural compo- as opposed to aligned, intersection with the plane of at least nents used in HELIXCAP-HMM prediction are labeled: black labels, AE; red β labels, α-helix; and green labels, first rung. (A) 1BN8 (residues 33–46 shown); one -sheet. Aside from the common patterns of turns and con- – – – tact points, and the presence of at least one α-helix in α-helix (B) 1GQ8 (residues 8 32 shown); (C) 1KQA (residues 22 54 shown). (D I) Re- presentative visor family caps. (D) 1KQA (residues 53–190 shown), a previous- caps, β-helix caps display a wide variety of sequence and structure β – α strand visor on a left-handed -helix; (E) 1JTA (residues 260 340 shown), a diversity, incorporating loops, additional -helices, and short previous-strand visor on a right-handed β-helix; (F) 1G95 (residues 376–441 β-strands. shown), a cross-helix visor on a left-handed β-helix; (G) 1DBG (residues Despite this diversity, models were effectively developed to 379–433 shown), a cross-helix visor on a right-handed β-helix; (H) 2PEC (re- describe the commonalities of each type of cap.
Load more

Recommended publications
  • Progress and Challenges in Protein Structure Prediction Yang Zhang
    Available online at www.sciencedirect.com Progress and challenges in protein structure prediction Yang Zhang Depending on whether similar structures are found in the PDB The crucial problems/efforts in the field of protein struc- library, the protein structure prediction can be categorized into ture prediction include: first, for the sequences of similar template-based modeling and free modeling. Although structures in PDB (especially those of weakly/distant threading is an efficient tool to detect the structural analogs, the homologous relation to the target), how to identify the advancements in methodology development have come to a correct templates and how to refine the template structure steady state. Encouraging progress is observed in structure closer to the native; second, for the sequences without refinement which aims at drawing template structures closer to appropriate templates, how to build models of correct the native; this has been mainly driven by the use of multiple topology from scratch. The progress made along these structure templates and the development of hybrid knowledge- directions was assessed in the recent CASP7 experiment based and physics-based force fields. For free modeling, [5] under the categories of template-based modeling exciting examples have been witnessed in folding small (TBM) and free modeling (FM). Here, I will review proteins to atomic resolutions. However, predicting structures the new progress and challenges in these directions. for proteins larger than 150 residues still remains a challenge, with
    [Show full text]
  • Statistical Inference for Template-Based Protein Structure
    Statistical Inference for template-based protein structure prediction by Jian Peng Submitted to: Toyota Technological Institute at Chicago 6045 S. Kenwood Ave, Chicago, IL, 60637 For the degree of Doctor of Philosophy in Computer Science Thesis Committee: Jinbo Xu (Thesis Supervisor) David McAllester Daisuke Kihara Statistical Inference for template-based protein structure prediction by Jian Peng Submitted to: Toyota Technological Institute at Chicago 6045 S. Kenwood Ave, Chicago, IL, 60637 May 2013 For the degree of Doctor of Philosophy in Computer Science Thesis Committee: Jinbo Xu (Thesis Supervisor) Signature: Date: David McAllester Signature: Date: Daisuke Kihara Signature: Date: Abstract Protein structure prediction is one of the most important problems in computational biology. The most successful computational approach, also called template-based modeling, identifies templates with solved crystal structures for the query proteins and constructs three dimensional models based on sequence/structure alignments. Although substantial effort has been made to improve protein sequence alignment, the accuracy of alignments between distantly related proteins is still unsatisfactory. In this thesis, I will introduce a number of statistical machine learning methods to build accurate alignments between a protein sequence and its template structures, especially for proteins having only distantly related templates. For a protein with only one good template, we develop a regression-tree based Conditional Random Fields (CRF) model for pairwise protein sequence/structure alignment. By learning a nonlinear threading scoring function, we are able to leverage the correlation among different sequence and structural features. We also introduce an information-theoretic measure to guide the learning algorithm to better exploit the structural features for low-homology proteins with little evolutionary information in their sequence profile.
    [Show full text]
  • Deep Learning-Based Advances in Protein Structure Prediction
    International Journal of Molecular Sciences Review Deep Learning-Based Advances in Protein Structure Prediction Subash C. Pakhrin 1,†, Bikash Shrestha 2,†, Badri Adhikari 2,* and Dukka B. KC 1,* 1 Department of Electrical Engineering and Computer Science, Wichita State University, Wichita, KS 67260, USA; [email protected] 2 Department of Computer Science, University of Missouri-St. Louis, St. Louis, MO 63121, USA; [email protected] * Correspondence: [email protected] (B.A.); [email protected] (D.B.K.) † Both the authors should be considered equal first authors. Abstract: Obtaining an accurate description of protein structure is a fundamental step toward under- standing the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the suc- cess of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination Citation: Pakhrin, S.C.; Shrestha, B.; using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress Adhikari, B.; KC, D.B.
    [Show full text]
  • Homology Modeling in the Time of Collective and Artificial Intelligence
    Computational and Structural Biotechnology Journal 18 (2020) 3494–3506 journal homepage: www.elsevier.com/locate/csbj Homology modeling in the time of collective and artificial intelligence ⇑ Tareq Hameduh a, Yazan Haddad a,b, Vojtech Adam a,b, Zbynek Heger a,b, a Department of Chemistry and Biochemistry, Mendel University in Brno, Zemedelska 1, CZ-613 00 Brno, Czech Republic b Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, 612 00 Brno, Czech Republic article info abstract Article history: Homology modeling is a method for building protein 3D structures using protein primary sequence and Received 14 August 2020 utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling Received in revised form 4 November 2020 process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is Accepted 4 November 2020 built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure Available online 14 November 2020 is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Keywords: Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the pro- Homology modeling gress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where Machine learning Protein 3D structure the community is sharing computational resources, whereas RosettaCommons is an example of an initia- Structural bioinformatics tive where a community is sharing a codebase for the development of computational algorithms.
    [Show full text]
  • Template-Based Protein Modeling Using the Raptorx Web Server
    PROTOCOL Template-based protein structure modeling using the RaptorX web server Morten Källberg1–3, Haipeng Wang1,3, Sheng Wang1, Jian Peng1, Zhiyong Wang1, Hui Lu2 & Jinbo Xu1 1Toyota Technological Institute at Chicago, Chicago, Illinois, USA. 2Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, USA. 3These authors contributed equally to this work. Correspondence should be addressed to J.X. ([email protected]). Published online 19 July 2012; doi:10.1038/nprot.2012.085 A key challenge of modern biology is to uncover the functional role of the protein entities that compose cellular proteomes. To this end, the availability of reliable three-dimensional atomic models of proteins is often crucial. This protocol presents a community-wide web-based method using RaptorX (http://raptorx.uchicago.edu/) for protein secondary structure prediction, template-based tertiary structure modeling, alignment quality assessment and sophisticated probabilistic alignment sampling. RaptorX distinguishes itself from other servers by the quality of the alignment between a target sequence and one or multiple distantly related template proteins (especially those with sparse sequence profiles) and by a novel nonlinear scoring function and a probabilistic-consistency algorithm. Consequently, RaptorX delivers high-quality structural models for many targets with only remote templates. At present, it takes RaptorX ~35 min to finish processing a sequence of 200 amino acids. Since its official release in August 2011, RaptorX has processed ~6,000 sequences submitted by ~1,600 users from around the world. INTRODUCTION Proteomes constitute the backbone of cellular function by carrying used by threading methods8,9 such as MUSTER10, SPARKS11,12 and out the tasks encoded in the genes expressed by a given cell type.
    [Show full text]
  • Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition
    International Journal of Molecular Sciences Review Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition Leyi Wei and Quan Zou * School of Computer Science and Technology, Tianjin University, Tianjin 300354, China; [email protected] * Correspondence: [email protected]; Tel.: +86-170-9226-1008 Academic Editor: Salvador Ventura Received: 16 November 2016; Accepted: 11 December 2016; Published: 16 December 2016 Abstract: Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds. Keywords: protein fold recognition; machine learning; computational method 1. Introduction Understanding how proteins adopt their 3D structure remains one of the greatest challenges in science.
    [Show full text]
  • Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles
    Engineering Proteins from Sequence Statistics: Identifying and Understanding the Roles of Conservation and Correlation in Triosephosphate Isomerase DISSERTATION Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University By Brandon Joseph Sullivan B.S. Graduate Program in the Ohio State Biochemistry Program The Ohio State University 2011 Dissertation Committee: Thomas J. Magliery, Advisor Mark P. Foster William C. Ray Copyright by Brandon Joseph Sullivan 2011 Abstract The structure, function and dynamics of proteins are determined by the physical and chemical properties of their amino acids. Unfortunately, the information encapsulated within a position or between positions is poorly understood. Multiple sequence alignments of protein families allow us to interrogate these questions statistically. Here, we describe the characterization of bioinformatically-designed variants of triosephosphate isomerase (TIM). First, we review the state-of-the-art for engineering proteins with increased stability. We examine two methodologies that benefit from the availability of large numbers - high-throughput screening and sequence statistics of protein families. Second, we have deconvoluted what properties are encoded within a position (conservation) and between positions (correlations) by designing TIMs in which each position is the most common amino acid in the multiple sequence alignment. We found that a consensus TIM from a raw sequence database performs the complex isomerization reaction with weak activity as a dynamic molten globule. Furthermore, we have confirmed that the monomeric species is the catalytically active conformation despite being designed from 600+ dimeric proteins. A second consensus TIM from a curated dataset is well folded, has wild-type activity and is dimeric, but it only differs from the raw consensus TIM at 35 nonconserved positions.
    [Show full text]
  • Scaling Ab Initio Predictions of 3D Protein Structures in Microsoft Azure Cloud
    J Grid Computing DOI 10.1007/s10723-015-9353-8 Scaling Ab Initio Predictions of 3D Protein Structures in Microsoft Azure Cloud Dariusz Mrozek · Paweł Gosk · Bozena˙ Małysiak-Mrozek Received: 7 October 2014 / Accepted: 9 October 2015 © The Author(s) 2015. This article is published with open access at Springerlink.com Abstract Computational methods for protein structure tests performed proved good acceleration of predic- prediction allow us to determine a three-dimensional tions when scaling the system vertically and horizon- structure of a protein based on its pure amino acid tally. In the paper, we show the system architecture sequence. These methods are a very important alter- that allowed us to achieve such good results, the native to costly and slow experimental methods, like Cloud4PSP processing model, and the results of the X-ray crystallography or Nuclear Magnetic Reso- scalability tests. At the end of the paper, we try to nance. However, conventional calculations of protein answer which of the scaling techniques, scaling out structure are time-consuming and require ample com- or scaling up, is better for solving such computational putational resources, especially when carried out with problems with the use of Cloud computing. the use of ab initio methods that rely on physical forces and interactions between atoms in a protein. Keywords Bioinformatics · Proteins · 3D protein Fortunately, at the present stage of the development of structure · Protein structure prediction · Tertiary computer science, such huge computational resources structure prediction · Ab initio · Protein structure are available from public cloud providers on a pay- modeling · Cloud computing · Distributed as-you-go basis.
    [Show full text]
  • "Comparative Protein Structure Modeling Using MODELLER". In
    Comparative Protein Structure Modeling UNIT 2.9 Using MODELLER Narayanan Eswar,1 Ben Webb,1 Marc A. Marti-Renom,2 M.S. Madhusudhan,1 David Eramian,1 Min-yi Shen,1 Ursula Pieper,1 and Andrej Sali1 1University of California at San Francisco, San Francisco, California 2Centro de Investigacion´ Pr´ıncipe Felipe (CIPF), Valencia, Spain ABSTRACT Functional characterization of a protein sequence is a common goal in biology, and is usually facilitated by having an accurate three-dimensional (3-D) structure of the studied protein. In the absence of an experimentally determined structure, comparative or homology modeling can sometimes provide a useful 3-D model for a protein that is related to at least one known protein structure. Comparative modeling predicts the 3-D structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (templates). The prediction process consists of fold assignment, target-template alignment, model building, and model evaluation. This unit describes how to calculate comparative models using the program MODELLER and discusses all four steps of comparative modeling, frequently observed errors, and some applications. Modeling lactate dehydrogenase from Trichomonas vaginalis (TvLDH) is described as an example. The download and installation of the MODELLER software is also described. Curr. Protoc. Protein Sci. 50:2.9.1-2.9.31. C 2007 by John Wiley & ! Sons, Inc. Keywords: Modeller protein structure comparative modeling structure prediction rprotein fold r r r Functional characterization of a protein sequence is one of the most frequent problems in biology. This task is usually facilitated by an accurate three-dimensional (3-D) structure of the studied protein.
    [Show full text]
  • Remote Homology Detection in Proteins Using Graphical Models
    Copyright by Noah Manus Daniels 2013 arXiv:1304.6476v1 [cs.CE] 24 Apr 2013 The Dissertation Committee for Noah Manus Daniels certifies that this is the approved version of the following dissertation: Remote Homology Detection in Proteins Using Graphical Models Committee: Prof. Lenore Cowen, Supervisor Prof. Donna Slonim Prof. Benjamin Hescott Prof. Bonnie Berger Prof. Yu-Shan Lin Remote Homology Detection in Proteins Using Graphical Models A dissertation submitted by Noah Manus Daniels, B.S., M.S. In partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science TUFTS UNIVERSITY April 2013 Advisor: Prof. Lenore Cowen For my grandmother Acknowledgments The work described in this dissertation is the result of the advice, contributions, and collaborations of many friends and colleagues. First, I would like to thank my advisor, Professor Lenore Cowen, for many years of mentorship. Her encouragement, patience, and motivation have been es- sential to this work. I thank my dissertation committee members, Professor Donna Slonim, Professor Benjamin Hescott, Professor Yu-Shan Lin, and Professor Bonnie Berger. My collaborators during the course of my doctoral work have also been inspir- ing. I thank Anoop Kumar, Matt Menke, Raghavendra Hosur, Shilpa Nadimpalli, Andrew Gallant, Po-Ru Loh, Michael Baym, Jian Peng, Jisoo Park, and Mengfei Cao for their contributions, be they in code, conversation, or collegiality. I thank Professor Norman Ramsey, Professor Sinaia Nathanson, and Dean Lynne Pepall, along with my GIFT colleagues, for teaching me how to teach. I also thank my teaching assistants, Sarah Nolet, Joel Greenberg, Andrew Pellegrini, and Michael Pietras, for helping me teach, Nathan Ricci for the constant feedback and guest lectures, and Professors Carla Brodley and Diane Souvaine for the opportuni- ties.
    [Show full text]
  • Recent Advances in Solving the Protein Threading Problem Rumen Andonov, Guillaume Collet, Jean-François Gibrat, Antoine Marin, Vincent Poirriez, Nicola Yanev
    Recent Advances in Solving the Protein Threading Problem Rumen Andonov, Guillaume Collet, Jean-François Gibrat, Antoine Marin, Vincent Poirriez, Nicola Yanev To cite this version: Rumen Andonov, Guillaume Collet, Jean-François Gibrat, Antoine Marin, Vincent Poirriez, et al.. Recent Advances in Solving the Protein Threading Problem. [Research Report] PI 1856, 2007, pp.38. inria-00170619 HAL Id: inria-00170619 https://hal.inria.fr/inria-00170619 Submitted on 10 Sep 2007 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. S A RE OI AT LÉ A ES M TÈ SS Y S T E E U IQ T IA M R O F IN N E E H C R E H C RE R P U B L I C A T I O N E D I N T E R N E T U o T I N 1856 T S N I I RECENT ADVANCES IN SOLVING THE PROTEIN THREADING PROBLEM RUMEN ANDONOV , GUILLAUME COLLET , JEAN-FRANÇOIS GIBRAT , ANTOINE MARIN , VINCENT POIRRIEZ , NIKOLA YANEV I R I S A ISSN 1166-8687 CAMPUS UNIVERSITAIRE DE BEAULIEU - 35042 RENNES CEDEX - FRANCE INSTITUT DE RECHERCHE EN INFORMATIQUE ET SYSTÈMES ALÉATOIRES Campus de Beaulieu – 35042 Rennes Cedex – France Tél.
    [Show full text]
  • CASP13 Abstracts.Pdf
    CRITICAL ASSESSMENT OF TECHNIQUES FOR PROTEIN STRUCTURE PREDICTION 13 Thirteenth meeting Riviera Maya, Mexico DECEMBER 1-4, 2018 1 TABLE OF CONTENTS 3DCNN (TS) .......................................................................................................................................................................... 9 PROTEIN MODEL QUALITY ASSESSMENT USING 3D ORIENTED CONVOLUTIONAL NEURAL NETWORK ................................................................ 9 3DCNN (REFINEMENT) ....................................................................................................................................................... 10 REFINEMENT OF PROTEIN MODELS WITH ADDITIONAL CROSS-LINKING INFORMATION USING THE GAUSSIAN NETWORK AND GRADIENT DESCENT .. 10 A7D ................................................................................................................................................................................... 11 DE NOVO STRUCTURE PREDICTION WITH DEEP-LEARNING BASED SCORING.............................................................................................. 11 AIR ..................................................................................................................................................................................... 13 AIR: AN ARTIFICIAL INTELLIGENCE-BASED PROTOCOL FOR PROTEIN STRUCTURE REFINEMENT USING MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION ..........................................................................................................................................................................
    [Show full text]