DOCSLIB.ORG

Protein Structure Prediction Rachel

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Protein Structure Prediction Rachel From: Nurul Affiah Binte Sahrin Sent: Tuesday, November 7, 2017 9:07 AM To: Ting Ai Hua Cc: Rachel Seah Mei Hui Subject: FW: RE: FW: Re: Fw: 巜物理学报》软物质专题英文版 Dear Ai Hua, Please kindly refer to the email below. I will come down later and explain to u in detailed Name of Journal: 物理学报 Article title: 水的奇异性质与液液相变 December 11, 2017 9:5 IJMPB S021797921840009X page 1 Thanks & Regards, Affiah Sahrin(Ms) Journal Publishing Administrator World Scientific Publishing Company 5 Toh Tuck Link Singapore 596224 From: Rachel Seah Mei Hui Monday, 6 November, 2017 3:46 PM International Journal of ModernSent: Physics B Vol. 32 (2018) 1840009 (17 pages)To: Nurul Affiah Binte Sahrin <[email protected]> c World Scientific PublishingSubject: Company FW: RE: FW: Re: Fw: 巜物理学报》软物质专题英文版 DOI: 10.1142/S021797921840009X Dear Affiah, Could we try implementing the new layout that you suggested, but add to the back of the citation: Please cite the original DOI. Also, put in brackets the Chinese name of the journal 物理学报 and the Chinese title of the article? Do let me know if you need help with that. Let’s do a sample layout before we typeset all the papers. Thanks Best regards, Protein structure prediction Rachel Haiyou发件人： Deng Rachel∗, YaSeah Jia Meiy and Hui Yang Zhangz;x 发送时间： 2017-11-06∗ 10:39:05 收件人： StevenCollege Shi Hong of Science, Bing - EXT 抄送：Huazhong Agricultural University, 主题：Wuhan RE: FW: 430070, Re: Fw: P.巜物理学报》软物质专题英文版 R. China yCollegeDear Steven, of Physical Science and Technology, Central China Normal University, I do agreeWuhan with their 430079, recommendation P. R. China to add the Chinese citation, although their case seems to be slightly different because it appears that they are one journal which published both the English and Chinese versions. But hopefully that will be fine for the APS editor. I’ll check with the production team again. zDepartmentThanks! of Computational Medicine and Bioinformatics, BestUniversity regards, of Michigan, Ann Arbor, MI 45108, USA Rachel [email protected] From: AcceptedSteven Shi Hongbing 26 September [mailto:[email protected] 2017 ] Published 11 December 2017 Sent: Monday, November 6, 2017 10:13 AM To: Rachel Seah Mei Hui <[email protected]> Translated fromSubject: Acta PhysicaRe: FW: Re: Sinica Fw: 巜物理学报》软物质专题英文版 ( ), 2016, 65(17): 178701. Please reference the original doi:10.7498/aps.65.178701 (⺮ⓑૅৈߣ௄ࡹ). Dear Rachel, Predicting 3D structureThank of protein you fromfor your its email amino and acid thank sequence you for is onethe advice. of the most I have impor- also asked the editor of APS for their suggestion to the different DOI. They have consulted some other editorial office with experience for two language version of 1 journal, and agreed 2 different DOIs are necessary. tant unsolved problemsBelow in biophysics is their suggestion and computational and I think biology.it will be Thiseasy to paper do since attempts very litter change needed. Please let me know your comments. to give a comprehensive introduction of the most recent effort and progress on protein 关于DOI号，我咨询了同时刊登中英文的一本期刊，他们采用的办法也是两个DOI号，但是统计引用的时候因为是同一本期刊，不受影响。为方便读者，我的建议是，在脚注《物理学报》的引文信息后面，括号注明 in Chinese，这样，愿意看中文版的读者知道这个是中文的，直接看中文，愿意看英文的就直接看译文。请你们确定一 structure prediction. Following the general flowchart of structure prediction, related con- Int. J. Mod. Phys. B Downloaded from www.worldscientific.com 下。 cepts and methods are presented and discussed. Moreover, brief introductions are made to several widely-used predictionAnd the pointer methods URL link and on the the community-wide original DOI number critical is good, assessment please keep it. of protein structure prediction (CASP) experiments. by UNIVERSITY OF MICHIGAN ANN ARBOR on 03/06/18. For personal use only. Thank you and best regards Keywords: Protein structure prediction; homology modeling; ab initio prediction; struc- ture refinement. Steven PACS numbers: 87.14.E-, 82.30.-b 2017-11-06 Steven Shi Hongbing 1. Introduction 发件人： Rachel Seah Mei Hui Since the latter half of发送时间： 20th century,2017-11-06 a 09:36:10 growing number of researchers from 收件人： Steven Shi Hong Bing - EXT diverse academic backgrounds抄送： are devoted to bio-related researches. Protein, as one of the most widespread主题： and FW: complicated Re: Fw: 巜物理学报》软物质专题英文版 macromolecules within life organisms, attracts a great deal of attentions. Proteins differ from one another primarily in file:////AIHUA/aihua/FWREFW~1.HTM[11/7/2017 1:16:01 PM] xCorresponding author. 1840009-1 December 11, 2017 9:5 IJMPB S021797921840009X page 2 H. Deng, Y. Jia & Y. Zhang their sequence of amino acids, which usually results in different spatial shape and structure and therefore different biological functionalities in cells. However, so for little is known about how protein folds into the specific three-dimensional structure from its one-dimensional sequence. In comparison with the genetic code by which a triple-nucleotide codon in a nucleic acid sequence specifies a single amino acid in protein sequence, the relationship between protein sequence and its steric structure is called the second genetic code (or folding code).1 The various advanced experimental techniques to determine protein sequence and structure have been developed in the last several decades. The number of pro- teins depositing in both UniProt2 and Protein Data Bank (PDB)3 keeps almost an exponential growth, especially in last two decades. It's much easier to obtain protein sequences than to obtain their structures. With the development of advanced DNA sequencing technology, the sequences of proteins have been rapidly accumulated. The UniProt/TrEMBL database contains currently more than 85 million of protein sequences. On the structure side, X-ray crystallography and NMR spectroscopy are currently the two major experimental techniques for protein structure determina- tion. Both of them are, however, time- and manpower-consuming, and have their own technical limitations for different protein targets. As of April 2017, the number of protein structures in PDB increases to ∼ 120;000, which counts however only < 0:2% of the protein sequences in the UniProt. The computational methods for predicting protein structure from its amino acid sequence spring up like mushrooms since the end of 20th century. The research article by Anfinsen in 19734 demonstrated that all the information a protein needs to fold properly is encoded in its amino acid sequence (called Anfinsen’s dogma). From the physical point of view, the amino acid sequence determines the basic molecular composition of the protein, and its native structure corresponds to the most stable conformation with the lowest free energy. Although we know that the protein folding process is governed by various physical laws, it is very hard to give Int. J. Mod. Phys. B Downloaded from www.worldscientific.com such complicated macromolecule (including its interaction with surrounding solvent molecules) an accurate physical description. On the other hand, the huge amount of conformational space needed to search through is also a crucial issue remaining to by UNIVERSITY OF MICHIGAN ANN ARBOR on 03/06/18. For personal use only. be solved on the basis of the current computing power. Many different approaches have been proposed, including the employment of coarse-grained physical model and optimized conformational search algorithms, to address these issues. There is a basic observation that similar sequences from the same evolutionary family often adopt similar protein structures, which forms the foundation of homol- ogy modeling. So far it is the most accurate way to predict protein structure by taking its homologous structure in PDB as template. With the rapid growth of PDB database, an increasing proportion of target proteins can be predicted via homol- ogy modeling. When no structure with obvious sequence similarity to the target protein can be found in PDB, it is still possible to find out proteins with struc- tural similarity to the target protein.5 The method to identify template structures from the PDB is called threading or fold recognition,6{8 which actually matches the 1840009-2 December 11, 2017 9:5 IJMPB S021797921840009X page 3 Protein structure prediction Fig. 1. The general flowchart of protein structure prediction. target sequence to homologous and distant-homologous structures based on some algorithm and take the best matches as structural template. The basic premise for threading to work is that protein structure is highly conservative in evolution and the number of unique structural folds are limited in nature.9,10 Both homology modeling (based on sequence comparison) and threading meth- ods (based on fold-recognition) can be called template-based structure prediction methods.11 Unlike homology modeling and threading methods, ab initio method aims to build structure from the first principles of physics which does not rely on any previously solved structure. The development of ab initio method is also the exploration of the second genetic code. However, successful ab initio methods are very rare and there are still many problems and challenges waiting to be con- quered. Currently Nearly all protein structure prediction methods make use of the structural information from previously solved structures to some extent. Therefore, \template-free"12 is often used for naming the methods that do not belong to the category of homology modeling and threading. Although the detailed prediction processes involved in different prediction methods vary widely, the fundamental steps are consistent, including conformation Int. J. Mod. Phys. B Downloaded from www.worldscientific.com initialization, conformational search, structure selection, all-atom structure recon- struction and structure refinement (as shown in Fig. 1). In this paper, we will in- by UNIVERSITY OF MICHIGAN ANN ARBOR on 03/06/18. For personal use only. troduce these steps in turn, including some well-known methods or tools commonly used in each step. We will also present a short overview of the critical assessment of protein structure prediction (CASP) experiments13,14 and some discussions about the current difficulties and future directions of protein structure prediction.
Load more

Recommended publications
  • Homology Modeling and Analysis of Structure Predictions of the Bovine Rhinitis B Virus RNA Dependent RNA Polymerase (Rdrp)
    Int. J. Mol. Sci. 2012, 13, 8998-9013; doi:10.3390/ijms13078998 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Homology Modeling and Analysis of Structure Predictions of the Bovine Rhinitis B Virus RNA Dependent RNA Polymerase (RdRp) Devendra K. Rai and Elizabeth Rieder * Foreign Animal Disease Research Unit, United States Department of Agriculture, Agricultural Research Service, Plum Island Animal Disease Center, Greenport, NY 11944, USA; E-Mail: [email protected] * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-631-323-3177; Fax: +1-631-323-3006. Received: 3 May 2012; in revised form: 3 July 2012 / Accepted: 11 July 2012 / Published: 19 July 2012 Abstract: Bovine Rhinitis B Virus (BRBV) is a picornavirus responsible for mild respiratory infection of cattle. It is probably the least characterized among the aphthoviruses. BRBV is the closest relative known to Foot and Mouth Disease virus (FMDV) with a ~43% identical polyprotein sequence and as much as 67% identical sequence for the RNA dependent RNA polymerase (RdRp), which is also known as 3D polymerase (3Dpol). In the present study we carried out phylogenetic analysis, structure based sequence alignment and prediction of three-dimensional structure of BRBV 3Dpol using a combination of different computational tools. Model structures of BRBV 3Dpol were verified for their stereochemical quality and accuracy. The BRBV 3Dpol structure predicted by SWISS-MODEL exhibited highest scores in terms of stereochemical quality and accuracy, which were in the range of 2Å resolution crystal structures. The active site, nucleic acid binding site and overall structure were observed to be in agreement with the crystal structure of unliganded as well as template/primer (T/P), nucleotide tri-phosphate (NTP) and pyrophosphate (PPi) bound FMDV 3Dpol (PDB, 1U09 and 2E9Z).
    [Show full text]
  • Dnpro: a Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations Xiao Zhou and Jianlin Cheng*
    DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations Xiao Zhou and Jianlin Cheng* Computer Science Department University of Missouri Columbia, MO 65211, USA *Corresponding author: [email protected] a function from the data to map the input information Abstract—A single amino acid mutation can have a significant regarding a protein and its mutation to the energy change impact on the stability of protein structure. Thus, the prediction of without the need of approximating the physics underlying protein stability change induced by single site mutations is critical mutation stability. This data-driven formulation of the and useful for studying protein function and structure. Here, we problem makes it possible to apply a large array of machine presented a new deep learning network with the dropout technique learning methods to tackle the problem. Therefore, a wide for predicting protein stability changes upon single amino acid range of machine learning methods has been applied to the substitution. While using only protein sequence as input, the overall same problem. E Capriotti et al., 2004 [2] presented a neural prediction accuracy of the method on a standard benchmark is >85%, network for predicting protein thermodynamic stability with which is higher than existing sequence-based methods and is respect to native structure; J Cheng et al., 2006 [1] developed comparable to the methods that use not only protein sequence but also tertiary structure, pH value and temperature. The results MUpro, a support vector machine with radial basis kernel to demonstrate that deep learning is a promising technique for protein improve mutation stability prediction; iPTREE based on C4.5 stability prediction.
    [Show full text]
  • 11: Catchup II Machine Learning and Real-World Data (MLRD)
    11: Catchup II Machine Learning and Real-world Data (MLRD) Ann Copestake Lent 2019 Last session: HMM in a biological application In the last session, we used an HMM as a way of approximating some aspects of protein structure. Today: catchup session 2. Very brief sketch of protein structure determination: including gamification and Monte Carlo methods (and a little about AlphaFold). Related ideas are used in many very different machine learning applications . What happens in catchup sessions? Lecture and demonstrated session scheduled as in normal session. Lecture material is non-examinable. Time for you to catch-up in demonstrated sessions or attempt some starred ticks. Demonstrators help as usual. Protein structure Levels of structure: Primary structure: sequence of amino acid residues. Secondary structure: highly regular substructures, especially α-helix, β-sheet. Tertiary structure: full 3-D structure. In the cell: an amino acid sequence (as encoded by DNA) is produced and folds itself into a protein. Secondary and tertiary structure crucial for protein to operate correctly. Some diseases thought to be caused by problems in protein folding. Alpha helix Dcrjsr - Own work, CC BY 3.0, https://commons.wikimedia.org/w/index.php?curid=9131613 Bovine rhodopsin By Andrei Lomize - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=34114850 found in the rods in the retina of the eye a bundle of seven helices crossing the membrane (membrane surfaces marked by horizontal lines) supports a molecule of retinal, which changes structure when exposed to light, also changing the protein structure, initiating the visual pathway 7-bladed propeller fold (found naturally) http://beautifulproteins.blogspot.co.uk/ Peptide self-assembly mimic scaffold (an engineered protein) http://beautifulproteins.blogspot.co.uk/ Protein folding Anfinsen’s hypothesis: the structure a protein forms in nature is the global minimum of the free energy and is determined by the animo acid sequence.
    [Show full text]
  • Advances in Rosetta Protein Structure Prediction on Massively Parallel Systems
    UC San Diego UC San Diego Previously Published Works Title Advances in Rosetta protein structure prediction on massively parallel systems Permalink https://escholarship.org/uc/item/87g6q6bw Journal IBM Journal of Research and Development, 52(1) ISSN 0018-8646 Authors Raman, S. Baker, D. Qian, B. et al. Publication Date 2008 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Advances in Rosetta protein S. Raman B. Qian structure prediction on D. Baker massively parallel systems R. C. Walker One of the key challenges in computational biology is prediction of three-dimensional protein structures from amino-acid sequences. For most proteins, the ‘‘native state’’ lies at the bottom of a free- energy landscape. Protein structure prediction involves varying the degrees of freedom of the protein in a constrained manner until it approaches its native state. In the Rosetta protein structure prediction protocols, a large number of independent folding trajectories are simulated, and several lowest-energy results are likely to be close to the native state. The availability of hundred-teraflop, and shortly, petaflop, computing resources is revolutionizing the approaches available for protein structure prediction. Here, we discuss issues involved in utilizing such machines efficiently with the Rosetta code, including an overview of recent results of the Critical Assessment of Techniques for Protein Structure Prediction 7 (CASP7) in which the computationally demanding structure-refinement process was run on 16 racks of the IBM Blue Gene/Le system at the IBM T. J. Watson Research Center. We highlight recent advances in high-performance computing and discuss future development paths that make use of the next-generation petascale (.1012 floating-point operations per second) machines.
    [Show full text]
  • Chapter 1 Ab Initio Protein Structure Prediction
    Chapter 1 Ab Initio Protein Structure Prediction Jooyoung Lee, Peter L. Freddolino and Yang Zhang Abstract Predicting a protein’s structure from its amino acid sequence remains an unsolved problem after several decades of efforts. If the query protein has a homolog of known structure, the task is relatively easy and high-resolution models can often be built by copying and refining the framework of the solved structure. However, a template-based modeling procedure does not help answer the questions of how and why a protein adopts its specific structure. In particular, if structural homologs do not exist, or exist but cannot be identified, models have to be con- structed from scratch. This procedure, called ab initio modeling, is essential for a complete solution to the protein structure prediction problem; it can also help us understand the physicochemical principle of how proteins fold in nature. Currently, the accuracy of ab initio modeling is low and the success is generally limited to small proteins (<120 residues). With the help of co-evolution based contact map predictions, success in folding larger-size proteins was recently witnessed in blind testing experiments. In this chapter, we give a review on the field of ab initio structure modeling. Our focus will be on three key components of the modeling algorithms: energy function design, conformational search, and model selection. Progress and advances of several representative algorithms will be discussed. Keyword Protein structure prediction Ab initio folding Contact prediction Force field Á Á Á J. Lee School of Computational Sciences, Korea Institute for Advanced Study, Seoul 130-722, Korea P.L.
    [Show full text]
  • The PSIPRED Protein Analysis Workbench
    Nucleic Acids Research, 2019 1 doi: 10.1093/nar/gkz297 The PSIPRED Protein Analysis Workbench: 20 years Downloaded from https://academic.oup.com/nar/advance-article-abstract/doi/10.1093/nar/gkz297/5480136 by Medical Research Council, [email protected] on 22 May 2019 on Daniel W.A. Buchan* and David T. Jones UCL Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK Received February 01, 2019; Revised April 02, 2019; Editorial Decision April 11, 2019; Accepted April 15, 2019 ABSTRACT of the previous step or steps. The complete data analysis task can then be defined by the input data, the required The PSIPRED Workbench is a web server offering a outputs and the dependency graph of data transformations range of predictive methods to the bioscience com- that take the data from input to output. Often, data analy- munity for 20 years. Here, we present the work we sis tasks decomposed into steps in this manner are regarded have completed to update the PSIPRED Protein Anal- as pipelines or workflows. A range of workflow manage- ysis Workbench and make it ready for the next 20 ment tools, both proprietary and open source, are readily years. The main focus of our recent website upgrade available. Such as Microsoft Azure, Spotify’s Luigi, Twitter work has been the acceleration of analyses in the Storm, HADOOP and so forth. In the Open Source space face of increasing protein sequence database size. Apache Taverna (https://taverna.incubator.apache.org/)isa We additionally discuss any new software, the new user-centric tool for specify workflows which can access a hardware infrastructure, our webservices and web heterogenous set of data processing web services.
    [Show full text]
  • A Molecular Modeling Approach to Identify Potential
    biomolecules Article A Molecular Modeling Approach to Identify Potential Antileishmanial Compounds Against the Cell Division Cycle (cdc)-2-Related Kinase 12 (CRK12) Receptor of Leishmania donovani Emmanuel Broni 1 , Samuel K. Kwofie 1,2,* , Seth O. Asiedu 3 , Whelton A. Miller III 4,5,6 and Michael D. Wilson 3,4 1 Department of Biomedical Engineering, School of Engineering Sciences, College of Basic & Applied Sciences, University of Ghana, Legon, Accra LG 77, Ghana; [email protected] 2 West African Center for Cell Biology of Infectious Pathogens, Department of Biochemistry, Cell and Molecular Biology, College of Basic and Applied Sciences, University of Ghana, Legon, Accra LG 54, Ghana 3 Department of Parasitology, Noguchi Memorial Institute for Medical Research (NMIMR), College of Health Sciences (CHS), University of Ghana, Legon, Accra LG 581, Ghana; [email protected] (S.O.A.); [email protected] (M.D.W.) 4 Department of Medicine, Loyola University Medical Center, Maywood, IL 60153, USA; [email protected] 5 Department of Molecular Pharmacology and Neuroscience, Loyola University Medical Center, Maywood, IL 60153, USA 6 Department of Chemical and Biomolecular Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, IL 19104, USA Citation: Broni, E.; Kwofie, S.K.; * Correspondence: skkwofi[email protected]; Tel.: +233-203-797922 Asiedu, S.O.; Miller, W.A., III; Abstract: The huge burden of leishmaniasis caused by the trypanosomatid protozoan parasite Leishmania Wilson, M.D. A Molecular Modeling Approach to Identify Potential is well known. This illness was included in the list of neglected tropical diseases targeted for elimination Antileishmanial Compounds Against by the World Health Organization.
    [Show full text]
  • A Simple and Efficient Statistical Potential for Scoring Ensembles of Protein Structures
    A simple and efficient statistical potential for scoring ensembles of protein SUBJECT AREAS: BIOINFORMATICS structures COMPUTATIONAL BIOLOGY Pilar Cossio1, Daniele Granata1, Alessandro Laio1, Flavio Seno2 & Antonio Trovato2 STRUCTURAL BIOLOGY PROTEINS 1SISSA - Scuola Internazionale Superiore di Studi Avanzati, via Bonomea 256, I-34136 Trieste, Italy, 2Dipartimento di Fisica e Astronomia-Universita’ di Padova e CNISM-Unita’ di Padova, Via Marzolo 8, I-35131 Padova. Received 5 January 2012 In protein structure prediction it is essential to score quickly and reliably large sets of models by selecting the Accepted ones that are closest to the native state. We here present a novel statistical potential constructed by Bayesian 14 March 2012 analysis measuring a few structural observables on a set of 500 experimental protein structures. Even though employing much less parameters than current state-of-the-art methods, our potential is capable of Published discriminating with an unprecedented reliability the native state in large sets of misfolded models of the 3 April 2012 same protein. We also introduce the new idea that thermal fluctuations cannot be neglected for scoring models that are very similar to each other. In these cases, the best structure can be recognized only by comparing the probability distributions of our potential over short finite temperature molecular dynamics simulations starting from the competing models. Correspondence and requests for materials should be addressed to nowledge based potentials (KBP)1–4 are energy functions derived from databases of known protein con- A.L. ([email protected]) formations that empirically aim to capture the most relevant aspects of the physical chemistry of protein structure and function.
    [Show full text]
  • Intro Key Concepts Med Chem1
    Introduction This volume is part of Elsevier’s Learning Trends series. Elsevier Science & Technology Books provides this series of free digital volumes to support and encourage learning and development across the sciences. Titles include content excerpts focused on a central theme to give the reader an introduction to new ideas and information on that topic. This volume in Chemistry Learning Trends introduces readers to a key chapter from the 4th edition of Camille Wermuth’s Practice of Medicinal Chemistry and highlights the interdisciplinary nature of medicinal chemistry. The succeeding articles, from the ScienceDirect Reference Module in Chemistry, Molecular Sciences and Chemical Engineering, will introduce readers to important themes and valuable methods raised in this chapter. Thank you for being a part of the Elsevier community! Table of Contents 1) The Practice of Medicinal Chemistry 4thEdition – Chapter 3 Drug Targets, Target Identification, Validation and Screening by Walter MM Van den Broeck 2) Medicinal and Pharmaceutical Chemistry by Timmerman 3) Perspectives in Drug Discovery by W.T. Comer 4) LIQUID CHROMATOGRAPHY | Affinity Chromatography by D.S. Hage 5) Microarrays by D. Amaratunga H. Göhlmann & P.J. Peeters 6) Systems Biology by L. Coulier, S. Wopereis, C. Rubingh, H. Hendriks, M. Radonjić & R.H. Jellema 7) Comparative Modeling of Drug Target Proteins by B. Webb, N. Eswar, H. Fan, N. Khuri, U. Pieper, G.Q. Dong & A. Sali CHAPTER 3 Drug Targets, Target Identification, Validation, and Screening Walter M.M. Van den Broeck Janssen Infectious Diseases BVBA, Beerse, Belgium OUTLINE I. Introduction 45 C. Haploinsufficiency Profiling in Yeast 58 D. Analysis of Resistant Mutants 59 II.
    [Show full text]
  • Crystallographic Molecular Replacement Using an in Silico-Generated Search Model of SARS-Cov-2 ORF8
    bioRxiv preprint doi: https://doi.org/10.1101/2021.01.05.425441; this version posted January 5, 2021. 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-NC-ND 4.0 International license. Crystallographic molecular replacement using an in silico-generated search model of SARS-CoV-2 ORF8 Thomas G. Flower 1 and James H. Hurley1,2 1 Department of Molecular and Cell Biology and California Institute for Quantitative Biosciences, University of California, Berkeley, Berkeley, CA 94720 2 Corresponding author: J. H. H. [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/2021.01.05.425441; this version posted January 5, 2021. 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-NC-ND 4.0 International license. Abstract The majority of crystal structures are determined by the method of molecular replacement (MR). The range of application of MR is limited mainly by the need for an accurate search model. In most cases, pre-existing experimentally determined structures are used as search models. In favorable cases, ab initio predicted structures have yielded search models adequate for molecular replacement. The ORF8 protein of SARS-CoV-2 represents a challenging case for MR using an ab initio prediction because ORF8 has an all b-sheet fold and few orthologs.
    [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]
  • Protein Structure Prediction and Model Quality Assessment
    REVIEWS Drug Discovery Today Volume 14, Numbers 7/8 April 2009 Reviews INFORMATICS Protein structure prediction and model quality assessment Andriy Kryshtafovych and Krzysztof Fidelis Protein Structure Prediction Center, Genome Center, University of California Davis, Davis, CA 95616, USA Protein structures have proven to be a crucial piece of information for biomedical research. Of the millions of currently sequenced proteins only a small fraction is experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. Half a century has passed since it was shown that the amino acid sequence of a protein determines its shape, but a method to translate the sequence code reliably into the 3D structure still remains to be developed. This review summarizes modern protein structure prediction techniques with the emphasis on comparative modeling, and describes the recent advances in methods for theoretical model quality assessment. In the late 1950s Anfinsen and coworkers suggested that an amino communities. Several reviews have been published recently on acid sequence carries all the information needed to guide protein various aspects of structure modeling [2–14]. With the improve- folding into a specific spatial shape of a protein [1]. In turn, protein ment of prediction techniques, protein models are becoming structure can provide important insights into understanding the genuinely useful in biology and medicine, including drug design. biological role of protein molecules in cellular processes. Thus, in There are numerous examples where in silico models were used to principle it should be possible to form a hypothesis concerning the infer protein function, hint at protein interaction partners and function of a protein just from its amino acid sequence, using the binding site locations, design or improve novel enzymes or anti- structure of a protein as a stepping stone toward reaching this goal.
    [Show full text]