Three Dimensional Structure Modeling and Ramachandran Plot Analysis of Autographa Californica Nucleopolyhdro Viral Protein
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Biomolecules
CHAPTER 3 Biomolecules 3.1 Carbohydrates In the previous chapter you have learnt about the cell and 3.2 Fatty Acids and its organelles. Each organelle has distinct structure and Lipids therefore performs different function. For example, cell membrane is made up of lipids and proteins. Cell wall is 3.3 Amino Acids made up of carbohydrates. Chromosomes are made up of 3.4 Protein Structure protein and nucleic acid, i.e., DNA and ribosomes are made 3.5 Nucleic Acids up of protein and nucleic acids, i.e., RNA. These ingredients of cellular organelles are also called macromolecules or biomolecules. There are four major types of biomolecules— carbohydrates, proteins, lipids and nucleic acids. Apart from being structural entities of the cell, these biomolecules play important functions in cellular processes. In this chapter you will study the structure and functions of these biomolecules. 3.1 CARBOHYDRATES Carbohydrates are one of the most abundant classes of biomolecules in nature and found widely distributed in all life forms. Chemically, they are aldehyde and ketone derivatives of the polyhydric alcohols. Major role of carbohydrates in living organisms is to function as a primary source of energy. These molecules also serve as energy stores, 2021-22 Chapter 3 Carbohydrade Final 30.018.2018.indd 50 11/14/2019 10:11:16 AM 51 BIOMOLECULES metabolic intermediates, and one of the major components of bacterial and plant cell wall. Also, these are part of DNA and RNA, which you will study later in this chapter. The cell walls of bacteria and plants are made up of polymers of carbohydrates. -
Itcontents 9..22
INTERNATIONAL TABLES FOR CRYSTALLOGRAPHY Volume F CRYSTALLOGRAPHY OF BIOLOGICAL MACROMOLECULES Edited by MICHAEL G. ROSSMANN AND EDDY ARNOLD Advisors and Advisory Board Advisors: J. Drenth, A. Liljas. Advisory Board: U. W. Arndt, E. N. Baker, S. C. Harrison, W. G. J. Hol, K. C. Holmes, L. N. Johnson, H. M. Berman, T. L. Blundell, M. Bolognesi, A. T. Brunger, C. E. Bugg, K. K. Kannan, S.-H. Kim, A. Klug, D. Moras, R. J. Read, R. Chandrasekaran, P. M. Colman, D. R. Davies, J. Deisenhofer, T. J. Richmond, G. E. Schulz, P. B. Sigler,² D. I. Stuart, T. Tsukihara, R. E. Dickerson, G. G. Dodson, H. Eklund, R. GiegeÂ,J.P.Glusker, M. Vijayan, A. Yonath. Contributing authors E. E. Abola: The Department of Molecular Biology, The Scripps Research W. Chiu: Verna and Marrs McLean Department of Biochemistry and Molecular Institute, La Jolla, CA 92037, USA. [24.1] Biology, Baylor College of Medicine, Houston, Texas 77030, USA. [19.2] P. D. Adams: The Howard Hughes Medical Institute and Department of Molecular J. C. Cole: Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge Biophysics and Biochemistry, Yale University, New Haven, CT 06511, USA. CB2 1EZ, England. [22.4] [18.2, 25.2.3] M. L. Connolly: 1259 El Camino Real #184, Menlo Park, CA 94025, USA. F. H. Allen: Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge [22.1.2] CB2 1EZ, England. [22.4, 24.3] K. D. Cowtan: Department of Chemistry, University of York, York YO1 5DD, U. W. Arndt: Laboratory of Molecular Biology, Medical Research Council, Hills England. -
Density Estimation for Protein Conformation Angles Using a Bivariate Von Mises Distribution and Bayesian Nonparametrics
Density Estimation for Protein Conformation Angles Using a Bivariate von Mises Distribution and Bayesian Nonparametrics Kristin P. LENNOX, David B. DAHL, Marina VANNUCCI, and Jerry W. TSAI Interest in predicting protein backbone conformational angles has prompted the development of modeling and inference procedures for bivariate angular distributions. We present a Bayesian approach to density estimation for bivariate angular data that uses a Dirichlet process mixture model and a bivariate von Mises distribution. We derive the necessary full conditional distributions to fit the model, as well as the details for sampling from the posterior predictive distribution. We show how our density estimation method makes it possible to improve current approaches for protein structure prediction by comparing the performance of the so-called ‘‘whole’’ and ‘‘half’’ position dis- tributions. Current methods in the field are based on whole position distributions, as density estimation for the half positions requires techniques, such as ours, that can provide good estimates for small datasets. With our method we are able to demonstrate that half position data provides a better approximation for the distribution of conformational angles at a given sequence position, therefore providing increased efficiency and accuracy in structure prediction. KEY WORDS: Angular data; Density estimation; Dirichlet process mixture model; Torsion angles; von Mises distribution. 1. INTRODUCTION and Ohlson 2002; Lovell et al. 2003; Rother, Sapiro, and Pande 2008), but they behave poorly for these subdivided datasets. Computational structural genomics has emerged as a pow- This is unfortunate, because these subsets provide structure erful tool for better understanding protein structure and func- prediction that is more accurate, as it utilizes more specific tion using the wealth of data from ongoing genome projects. -
Structure Validation Article
14 STRUCTURAL QUALITY ASSURANCE Roman A. Laskowski The experimentally determined three-dimensional (3D) structures of proteins and nucleic acids represent the knowledge base from which so much understanding of biological processes has been derived over the last three decades of the twentieth cen- tury. Individual structures have provided explanations of specific biochemical functions and mechanisms, while comparisons of structures have given insights into general principles governing these complex molecules, the interactions they make, and their biological roles. The 3D structures form the foundation of structural bioinformatics; all structural analyses depend on them and would be impossible without them. Therefore, it is crucial to bear in mind two important truths about these structures, both of which result from the fact that they have been determined experimentally. The first is that the result of any experiment is merely a model that aims to give as good an explanation for the experimental data as possible. The term structure is commonly used, but you should realize that this should be correctly read as model. As such the model may be an accurate and meaningful representation of the molecule, or it may be a poor one. The quality of the data and the care with which the experiment has been performed will determine which it is. Independently performed experiments can arrive at very similar models of the same molecule; this suggests that both are accurate representations, that they are good models. The second important truth is that any experiment, however carefully performed, will have errors associated with it. These errors come in two distinct varieties: sys- tematic and random. -
Assessment of the Model Refinement Category in CASP12 Ladislav
Assessment of the model refinement category in CASP12 1, * 1, * 1, * Ladislav Hovan, Vladimiras Oleinikovas, Havva Yalinca, Andriy 2 1 1, 3 Kryshtafovych, Giorgio Saladino, and Francesco Luigi Gervasio 1Department of Chemistry, University College London, London WC1E 6BT, United Kingdom 2Genome Center, University of California, Davis, California 95616, USA 3Institute of Structural and Molecular Biology, University College London, London WC1E 6BT, United Kingdom. ∗ Contributed equally. Correspondence: Francesco Luigi Gervasio, Department of Chemistry, University College London, London WC1E 6BT, United Kingdom. Email:[email protected] ABSTRACT We here report on the assessment of the model refinement predictions submitted to the 12th Experiment on the Critical Assessment of Protein Structure Prediction (CASP12). This is the fifth refinement experiment since CASP8 (2008) and, as with the previous experiments, the predictors were invited to refine selected server models received in the regular (non- refinement) stage of the CASP experiment. We assessed the submitted models using a combination of standard CASP measures. The coefficients for the linear combination of Z-scores (the CASP12 score) have been obtained by a machine learning algorithm trained on the results of visual inspection. We identified 8 groups that improve both the backbone conformation and the side chain positioning for the majority of targets. Albeit the top methods adopted distinctively different approaches, their overall performance was almost indistinguishable, with each of them excelling in different scores or target subsets. What is more, there were a few novel approaches that, while doing worse than average in most cases, provided the best refinements for a few targets, showing significant latitude for further innovation in the field. -
Chapter 13 Protein Structure Learning Objectives
Chapter 13 Protein structure Learning objectives Upon completing this material you should be able to: ■ understand the principles of protein primary, secondary, tertiary, and quaternary structure; ■use the NCBI tool CN3D to view a protein structure; ■use the NCBI tool VAST to align two structures; ■explain the role of PDB including its purpose, contents, and tools; ■explain the role of structure annotation databases such as SCOP and CATH; and ■describe approaches to modeling the three-dimensional structure of proteins. Outline Overview of protein structure Principles of protein structure Protein Data Bank Protein structure prediction Intrinsically disordered proteins Protein structure and disease Overview: protein structure The three-dimensional structure of a protein determines its capacity to function. Christian Anfinsen and others denatured ribonuclease, observed rapid refolding, and demonstrated that the primary amino acid sequence determines its three-dimensional structure. We can study protein structure to understand problems such as the consequence of disease-causing mutations; the properties of ligand-binding sites; and the functions of homologs. Outline Overview of protein structure Principles of protein structure Protein Data Bank Protein structure prediction Intrinsically disordered proteins Protein structure and disease Protein primary and secondary structure Results from three secondary structure programs are shown, with their consensus. h: alpha helix; c: random coil; e: extended strand Protein tertiary and quaternary structure Quarternary structure: the four subunits of hemoglobin are shown (with an α 2β2 composition and one beta globin chain high- lighted) as well as four noncovalently attached heme groups. The peptide bond; phi and psi angles The peptide bond; phi and psi angles in DeepView Protein secondary structure Protein secondary structure is determined by the amino acid side chains. -
Wefold: a Coopetition for Protein Structure Prediction George A
proteins STRUCTURE O FUNCTION O BIOINFORMATICS WeFold: A coopetition for protein structure prediction George A. Khoury,1 Adam Liwo,2 Firas Khatib,3,15 Hongyi Zhou,4 Gaurav Chopra,5,6 Jaume Bacardit,7 Leandro O. Bortot,8 Rodrigo A. Faccioli,9 Xin Deng,10 Yi He,11 Pawel Krupa,2,11 Jilong Li,10 Magdalena A. Mozolewska,2,11 Adam K. Sieradzan,2 James Smadbeck,1 Tomasz Wirecki,2,11 Seth Cooper,12 Jeff Flatten,12 Kefan Xu,12 David Baker,3 Jianlin Cheng,10 Alexandre C. B. Delbem,9 Christodoulos A. Floudas,1 Chen Keasar,13 Michael Levitt,5 Zoran Popovic´,12 Harold A. Scheraga,11 Jeffrey Skolnick,4 Silvia N. Crivelli ,14* and Foldit Players 1 Department of Chemical and Biological Engineering, Princeton University, Princeton 2 Faculty of Chemistry, University of Gdansk, Gdansk, Poland 3 Department of Biochemistry, University of Washington, Seattle 4 Center for the Study of Systems Biology, School of Biology, Georgia Institute of Technology, Atlanta 5 Department of Structural Biology, School of Medicine, Stanford University, Palo Alto 6 Diabetes Center, School of Medicine, University of California San Francisco (UCSF), San Francisco 7 School of Computing Science, Newcastle University, Newcastle, United Kingdom 8 Laboratory of Biological Physics, Faculty of Pharmaceutical Sciences at Ribeir~ao Preto, University of S~ao Paulo, S~ao Paulo, Brazil 9 Institute of Mathematical and Computer Sciences, University of S~ao Paulo, S~ao Paulo, Brazil 10 Department of Computer Science, University of Missouri, Columbia 11 Baker Laboratory of Chemistry and Chemical Biology, Cornell University, Ithaca 12 Department of Computer Science and Engineering, Center for Game Science, University of Washington, Seattle 13 Departments of Computer Science and Life Sciences, Ben Gurion University of the Negev, Israel 14 Department of Computer Science, University of California, Davis, Davis 15 Department of Computer and Information Science, University of Massachusetts Dartmouth, Dartmouth ABSTRACT The protein structure prediction problem continues to elude scientists. -
Stretch and Twist of HEAT Repeats Leads to Activation of DNA-PK Kinase
Combined ManuscriptbioRxiv preprint File doi: https://doi.org/10.1101/2020.10.19.346148; this version posted October 21, 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-NC-ND 4.0 International license. Chen, et al., 2020 Stretch and Twist of HEAT Repeats Leads to Activation of DNA-PK Kinase Xuemin Chen1, Xiang Xu1,*,&, Yun Chen1,*,#, Joyce C. Cheung1,%, Huaibin Wang2, Jiansen Jiang3, Natalia de Val4, Tara Fox4, Martin Gellert1 and Wei Yang1 1 Laboratory of Molecular Biology and 2 Laboratory of Cell and Molecular Biology, NIDDK, and 3 Laboratory of Membrane Proteins and Structural Biology, NHLBI, National Institutes of Health, Bethesda, MD 20892. 4 Cancer Research Technology Program Frederick National Laboratory for Cancer Research, Leidos Biomedical Research Inc., Frederick, MD 21701, USA. * These authors contributed equally. & Current address: [email protected] # Current address: [email protected] % Current address: [email protected] Running title: Slinky-like HEAT-repeat Movement Leads to DNA-PK Activation Keyword: DNA-PKcs, Ku70, Ku80, PIKKs, DNA-end binding Correspondence: Wei Yang ([email protected]) Martin Gellert ([email protected]) 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.10.19.346148; this version posted October 21, 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-NC-ND 4.0 International license. -
Prediction of Amino Acid Side Chain Conformation Using a Deep Neural
Prediction of amino acid side chain conformation using a deep neural network Ke Liu 1, Xiangyan Sun3, Jun Ma3, Zhenyu Zhou3, Qilin Dong4, Shengwen Peng3, Junqiu Wu3, Suocheng Tan3, Günter Blobel2, and Jie Fan1, Corresponding author details: Jie Fan, Accutar Biotechnology, 760 Parkside Ave, Room 213 Brooklyn, NY 11226, USA. [email protected] 1. Accutar Biotechnology, 760 parkside Ave, Room 213, Brooklyn, NY 11226, USA. 2. Laboratory of Cell Biology, Howard Hughes Medical Institute, The Rockefeller University, New York, New York 10065, USA 3. Accutar Biotechnology (Shanghai) Room 307, No. 6 Building, 898 Halei Rd, Shanghai, China 4. Fudan University 220 Handan Rd, Shanghai, China Summary: A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy. Amino acid side chain conformation prediction is essential for protein homology modeling and protein design. Current widely-adopted methods use physics-based energy functions to evaluate side chain conformation. Here, using a deep neural network architecture without physics-based assumptions, we have demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods. More strikingly, the prediction method presented here is robust enough to identify individual conformational outliers from high resolution structures in a protein data bank without providing its structural factors. We envisage that our amino acid side chain predictor could be used as a quality check step for future protein structure model validation and many other potential applications such as side chain assignment in Cryo-electron microscopy, crystallography model auto-building, protein folding and small molecule ligand docking. -
4.0-Е Resolution Cryo-EM Structure of the Mammalian Chaperonin Tric
4.0-Å resolution cryo-EM structure of the mammalian chaperonin TRiC/CCT reveals its unique subunit arrangement Yao Conga, Matthew L. Bakera, Joanita Jakanaa, David Woolforda, Erik J. Millerb, Stefanie Reissmannb,2, Ramya N. Kumarb, Alyssa M. Redding-Johansonc, Tanveer S. Batthc, Aindrila Mukhopadhyayc, Steven J. Ludtkea, Judith Frydmanb, and Wah Chiua,1 aNational Center for Macromolecular Imaging, Verna and Marrs McLean Department of Biochemsitry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030; bDepartment of Biology and BioX Program, Stanford University, Stanford, CA 94305; and cPhysical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 Communicated by Michael Levitt, Stanford University School of Medicine, Stanford, CA, December 7, 2009 (received for review October 21, 2009) The essential double-ring eukaryotic chaperonin TRiC/CCT (TCP1- consists of eight distinct but related subunits sharing 27–39% ring complex or chaperonin containing TCP1) assists the folding sequence identity (Fig. S1) (13, 17). In contrast, bacterial (18) of ∼5–10% of the cellular proteome. Many TRiC substrates cannot and archael chaperonins (19) only have 1–3 different types of be folded by other chaperonins from prokaryotes or archaea. These subunits, and for those archaea with three types of subunits, it unique folding properties are likely linked to TRiC’s unique hetero- is unclear whether in the natural organism they form homo- or oligomeric subunit organization, whereby each ring consists of heterooligomeric chaperonins (20). The divergence of TRiC sub- eight different paralogous subunits in an arrangement that re- units occurred early in the evolution of eukaryotes, because all mains uncertain. Using single particle cryo-EM without imposing eukaryotes sequenced to date carry genes for all eight subunits; symmetry, we determined the mammalian TRiC structure at 4.7- orthologs of the various subunits across species are more similar Å resolution. -
Methods for the Refinement of Protein Structure 3D Models
International Journal of Molecular Sciences Review Methods for the Refinement of Protein Structure 3D Models Recep Adiyaman and Liam James McGuffin * School of Biological Sciences, University of Reading, Reading RG6 6AS, UK; [email protected] * Correspondence: l.j.mcguffi[email protected]; Tel.: +44-0-118-378-6332 Received: 2 April 2019; Accepted: 7 May 2019; Published: 1 May 2019 Abstract: The refinement of predicted 3D protein models is crucial in bringing them closer towards experimental accuracy for further computational studies. Refinement approaches can be divided into two main stages: The sampling and scoring stages. Sampling strategies, such as the popular Molecular Dynamics (MD)-based protocols, aim to generate improved 3D models. However, generating 3D models that are closer to the native structure than the initial model remains challenging, as structural deviations from the native basin can be encountered due to force-field inaccuracies. Therefore, different restraint strategies have been applied in order to avoid deviations away from the native structure. For example, the accurate prediction of local errors and/or contacts in the initial models can be used to guide restraints. MD-based protocols, using physics-based force fields and smart restraints, have made significant progress towards a more consistent refinement of 3D models. The scoring stage, including energy functions and Model Quality Assessment Programs (MQAPs) are also used to discriminate near-native conformations from non-native conformations. Nevertheless, there are often very small differences among generated 3D models in refinement pipelines, which makes model discrimination and selection problematic. For this reason, the identification of the most native-like conformations remains a major challenge. -
NIH Public Access Author Manuscript Proteins
NIH Public Access Author Manuscript Proteins. Author manuscript; available in PMC 2009 May 11. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Proteins. 2008 June ; 71(4): 1637±1646. doi:10.1002/prot.21845. Using Quantum Mechanics to Improve Estimates of Amino Acid Side Chain Rotamer Energies P. Douglas Renfrew, Glenn Butterfoss, and Brian Kuhlman Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, Email: [email protected] Abstract Amino acid side chains adopt a discrete set of favorable conformations typically referred to as rotamers. The relative energies of rotamers partially determine which side chain conformations are more often observed in protein structures and accurate estimates of these energies are important for predicting protein structure and designing new proteins. Protein modelers typically calculate side chain rotamer energies by using molecular mechanics (MM) potentials or by converting rotamer probabilities from the protein database (PDB) into relative free energies. One limitation of the knowledge-based energies is that rotamer preferences observed in the PDB can reflect internal side chain energies as well as longer-range interactions with the rest of the protein. Here, we test an alternative approach for calculating rotamer energies. We use three different quantum mechanics (QM) methods (second order Moller-Plesset (MP2), density functional theory (DFT) energy calculation using the B3LYP functional, and Hartree-Fock) to calculate the energy of amino acid rotamers in a dipeptide model system, and then use these pre-calculated values in side chain placement simulations. Energies were calculated for over 35,000 different conformations of leucine, isoleucine and valine dipeptides with backbone torsion angles from the helical and strand regions of the Ramachandran plot.