Homology Modeling (Comparative Modeling) Structure Prediction
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NIH Public Access Author Manuscript Proteins
NIH Public Access Author Manuscript Proteins. Author manuscript; available in PMC 2015 February 01. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Proteins. 2014 February ; 82(0 2): 208–218. doi:10.1002/prot.24374. One contact for every twelve residues allows robust and accurate topology-level protein structure modeling David E. Kim, Frank DiMaio, Ray Yu-Ruei Wang, Yifan Song, and David Baker* Department of Biochemistry, University of Washington, Seattle 98195, Washington Abstract A number of methods have been described for identifying pairs of contacting residues in protein three-dimensional structures, but it is unclear how many contacts are required for accurate structure modeling. The CASP10 assisted contact experiment provided a blind test of contact guided protein structure modeling. We describe the models generated for these contact guided prediction challenges using the Rosetta structure modeling methodology. For nearly all cases, the submitted models had the correct overall topology, and in some cases, they had near atomic-level accuracy; for example the model of the 384 residue homo-oligomeric tetramer (Tc680o) had only 2.9 Å root-mean-square deviation (RMSD) from the crystal structure. Our results suggest that experimental and bioinformatic methods for obtaining contact information may need to generate only one correct contact for every 12 residues in the protein to allow accurate topology level modeling. Keywords protein structure prediction; rosetta; comparative modeling; homology modeling; ab initio prediction; contact prediction INTRODUCTION Predicting the three-dimensional structure of a protein given just the amino acid sequence with atomic-level accuracy has been limited to small (<100 residues), single domain proteins. -
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). -
Atomic Modeling of Proteins Via Small Angle X-Ray Scattering (SAXS)
Atomic modeling of proteins via Small Angle X-ray Scattering (SAXS) Susan Tsutakawa SIBYLS Beamline. Advanced Light Source Synchrotron, Lawrence Berkeley Natl Lab Six Take Home Messages on What can SAXS do for you? X-ray Scattering by Small Angle X-ray Scattering electrons provides distances measures all electron pair between electrons. distances in a protein in solution Atomic models can be quantitatively compared with SAXS data Atomic models are more SAXS can validate protein powerful than shape structure predictions because they can be tested. SAXS can reveal protein conformations occurring in solution at the atomic level If I could ask for any scientific app, what would it be? Accurate and reliable protein structure prediction For proteins with no known orthologs, structure predictions currently are not reliable. Amino Acid Protein Prediction Structure Prediction Sequence Server SIBYLS SAXS data I propose that Beamline input of SAXS data 12.3.1 can improve protein structure algoriths. As in crystallography, SAXS uses elastic scattering of X-rays, where the X-rays are scattered by an electron without a change in energy. Scattered X- rays constructively or destructively combine with each other. The X-ray scattering provides information on the distance between electrons. In SAXS, the intramolecular distances In Crystallography, these electrons are are constant; the scattering is related to each in crystallographic coherent, and the amplitudes are symmetry. added. SAXS is a distance method, measuring all electron pair distances. Scattering Curve Electron Pair Distance Histogram Fourier dmax Intensity (q) Intensity SAXS sample 30 ul q (Å-1) Protein 1-3 mg/ml Exact Buffer Distance information can validate an atomic model – as exemplified by validation of the May, 1953 model of DNA by fiber diffraction GeneticalImplications of the structure of Deoxyribonucleic Acid WatsonJ.D. -
Comparative Protein Structure Modeling of Genes and Genomes
P1: FPX/FOZ/fop/fok P2: FHN/FDR/fgi QC: FhN/fgm T1: FhN January 12, 2001 16:34 Annual Reviews AR098-11 Annu. Rev. Biophys. Biomol. Struct. 2000. 29:291–325 Copyright c 2000 by Annual Reviews. All rights reserved COMPARATIVE PROTEIN STRUCTURE MODELING OF GENES AND GENOMES Marc A. Mart´ı-Renom, Ashley C. Stuart, Andras´ Fiser, Roberto Sanchez,´ Francisco Melo, and Andrej Sˇali Laboratories of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, Rockefeller University, 1230 York Ave, New York, NY 10021; e-mail: [email protected] Key Words protein structure prediction, fold assignment, alignment, homology modeling, model evaluation, fully automated modeling, structural genomics ■ Abstract Comparative modeling predicts the three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more pro- teins of known structure (templates). The prediction process consists of fold assign- ment, target–template alignment, model building, and model evaluation. The number of protein sequences that can be modeled and the accuracy of the predictions are in- creasing steadily because of the growth in the number of known protein structures and because of the improvements in the modeling software. Further advances are nec- essary in recognizing weak sequence–structure similarities, aligning sequences with structures, modeling of rigid body shifts, distortions, loops and side chains, as well as detecting errors in a model. Despite these problems, it is currently possible to model with useful accuracy significant parts of approximately one third of all known protein sequences. The use of individual comparative models in biology is already rewarding and increasingly widespread. -
FORCE FIELDS for PROTEIN SIMULATIONS by JAY W. PONDER
FORCE FIELDS FOR PROTEIN SIMULATIONS By JAY W. PONDER* AND DAVIDA. CASEt *Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, 51. Louis, Missouri 63110, and tDepartment of Molecular Biology, The Scripps Research Institute, La Jolla, California 92037 I. Introduction. ...... .... ... .. ... .... .. .. ........ .. .... .... ........ ........ ..... .... 27 II. Protein Force Fields, 1980 to the Present.............................................. 30 A. The Am.ber Force Fields.............................................................. 30 B. The CHARMM Force Fields ..., ......... 35 C. The OPLS Force Fields............................................................... 38 D. Other Protein Force Fields ....... 39 E. Comparisons Am.ong Protein Force Fields ,... 41 III. Beyond Fixed Atomic Point-Charge Electrostatics.................................... 45 A. Limitations of Fixed Atomic Point-Charges ........ 46 B. Flexible Models for Static Charge Distributions.................................. 48 C. Including Environmental Effects via Polarization................................ 50 D. Consistent Treatment of Electrostatics............................................. 52 E. Current Status of Polarizable Force Fields........................................ 57 IV. Modeling the Solvent Environment .... 62 A. Explicit Water Models ....... 62 B. Continuum Solvent Models.......................................................... 64 C. Molecular Dynamics Simulations with the Generalized Born Model........ -
Structural Bioinformatics
Structural Bioinformatics Sanne Abeln K. Anton Feenstra Centre for Integrative Bioinformatics (IBIVU), and Department of Computer Science, Vrije Universiteit, De Boelelaan 1081A, 1081 HV Amsterdam, Netherlands December 4, 2017 arXiv:1712.00425v1 [q-bio.BM] 1 Dec 2017 2 Abstract This chapter deals with approaches for protein three-dimensional structure prediction, starting out from a single input sequence with unknown struc- ture, the `query' or `target' sequence. Both template based and template free modelling techniques are treated, and how resulting structural models may be selected and refined. We give a concrete flowchart for how to de- cide which modelling strategy is best suited in particular circumstances, and which steps need to be taken in each strategy. Notably, the ability to locate a suitable structural template by homology or fold recognition is crucial; without this models will be of low quality at best. With a template avail- able, the quality of the query-template alignment crucially determines the model quality. We also discuss how other, courser, experimental data may be incorporated in the modelling process to alleviate the problem of missing template structures. Finally, we discuss measures to predict the quality of models generated. Structural Bioinformatics c Abeln & Feenstra, 2014-2017 Contents 7 Strategies for protein structure model generation5 7.1 Template based protein structure modelling . .7 7.1.1 Homology based Template Finding . .7 7.1.2 Fold recognition . .8 7.1.3 Generating the target-template alignment . 10 7.1.4 Generating a model . 11 7.1.5 Loop or missing substructure modelling . 12 7.2 Template-free protein structure modelling . -
Ten Quick Tips for Homology Modeling of High-Resolution Protein 3D Structures
PLOS COMPUTATIONAL BIOLOGY EDUCATION Ten quick tips for homology modeling of high- resolution protein 3D structures 1,2 1,2 1,2 Yazan HaddadID , Vojtech AdamID , Zbynek HegerID * 1 Department of Chemistry and Biochemistry, Mendel University in Brno, Brno, Czech Republic, 2 Central European Institute of Technology, Brno University of Technology, Brno, Czech Republic * [email protected] Author summary The purpose of this quick guide is to help new modelers who have little or no background in comparative modeling yet are keen to produce high-resolution protein 3D structures a1111111111 for their study by following systematic good modeling practices, using affordable personal a1111111111 computers or online computational resources. Through the available experimental 3D- a1111111111 structure repositories, the modeler should be able to access and use the atomic coordinates a1111111111 for building homology models. We also aim to provide the modeler with a rationale a1111111111 behind making a simple list of atomic coordinates suitable for computational analysis abiding to principles of physics (e.g., molecular mechanics). Keeping that objective in mind, these quick tips cover the process of homology modeling and some postmodeling computations such as molecular docking and molecular dynamics (MD). A brief section OPEN ACCESS was left for modeling nonprotein molecules, and a short case study of homology modeling Citation: Haddad Y, Adam V, Heger Z (2020) Ten is discussed. quick tips for homology modeling of high- resolution protein 3D structures. PLoS Comput Biol 16(4): e1007449. https://doi.org/10.1371/ journal.pcbi.1007449 Introduction Editor: Francis Ouellette, University of Toronto, Protein 3D-structure folding from a simple sequence of amino acids was seen as a very difficult CANADA problem in the past. -
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. -
Small Angle X-Ray Scattering Experiments of Monodisperse Samples Close to the Solubility Limit
Small angle x-ray scattering experiments of monodisperse samples close to the solubility limit Erik W. Martina, Jesse B. Hopkinsb, Tanja Mittaga1 a Department of Structural Biology, St. Jude Children’s Research Hospital, Memphis TN b The Biophysics Collaborative Access Team (BioCAT), Department of Biological Sciences, Illinois Institute of Technology, Chicago, IL 1Corresponding author: email address: [email protected] Abstract The condensation of biomolecules into biomolecular condensates via liquid-liquid phase separation (LLPS) is a ubiquitous mechanism that drives cellular organization. To enable these functions, biomolecules have evolved to drive LLPS and facilitate partitioning into biomolecular condensates. Determining the molecular features of proteins that encode LLPS will provide critical insights into a plethora of biological processes. Problematically, probing biomolecular dense phases directly is often technologically difficult or impossible. By capitalizing on the symmetry between the conformational behavior of biomolecules in dilute solution and dense phases, it is possible to infer details critical to phase separation by precise measurements of the dilute phase thus circumventing complicated characterization of dense phases. The symmetry between dilute and dense phases is found in the size and shape of the conformational ensemble of a biomolecule – parameters that small-angle x-ray scattering (SAXS) is ideally suited to probe. Recent technological advances have made it possible to accurately characterize samples of intrinsically disordered protein regions at low enough concentration to avoid interference from intermolecular attraction, oligomerization or aggregation, all of which were previously roadblocks to characterizing self-assembling proteins. Herein, we describe the pitfalls inherent to measuring such samples, the details required for circumventing these issues and analysis methods that place the results of SAXS measurements into the theoretical framework of LLPS. -
Exercise 6: Homology Modeling
Workshop in Computational Structural Biology (81813), Spring 2019 Exercise 6: Homology Modeling Homology modeling, sometimes referred to as comparative modeling, is a commonly-used alternative to ab-initio modeling (which requires zero knowledge about the native structure, but also overlooks much of our understanding of structural diversity). Homology modeling leverages the massive amount of structural knowledge available today to guide our search of the native structure. Practically, it assumes that sequence similarity implies structural similarity, i.e. assumes a protein homologous to our query protein cannot be very different from it. Therefore, we can roughly "thread" the query sequence onto the template (homologue) structure, and exploit existing knowledge to guide our modeling. Contents Homology modeling with Rosetta 1 Overview 1 Template Selection 1 Loop Modeling 2 Running the full simulation 4 Bonus section! Homology modeling with automated/pre-calculated servers 6 MODBASE 6 I-Tasser 6 Homology modeling with Rosetta Overview In this section, we will model the structure of the Erbin PDZ domain, using homologue template structures, and analyze the quality of the models. The structure of this protein has been solved (PDB ID 1MFG), so we will be able to compare prediction with experiment. As taught in class, classical homology modeling in Rosetta is composed of four phases: 1. Template selection 2. Template-query alignment 3. Rebuilding loop regions 4. Full-atom refinement of the model Our model protein is the Erbin PDZ domain: Erbin contains a class-I PDZ domain that binds to the C-terminal region of the receptor tyrosine kinase ErbB2. PDB ID 1MFG is the crystal structure of the human Erbin PDZ bound to the peptide EYLGLDVPV corresponding to the C-terminal residues of human ErbB2. -
Solution Small Angle X-Ray Scattering : Fundementals and Applications in Structural Biology
The First NIH Workshop on Small Angle X-ray Scattering and Application in Biomolecular Studies Open Remarks: Ad Bax (NIDDK, NIH) Introduction: Yun-Xing Wang (NCI-Frederick, NIH) Lectures: Xiaobing Zuo, Ph.D. (NCI-Frederick, NIH) Alexander Grishaev, Ph.D. (NIDDK, NIH) Jinbu Wang, Ph.D. (NCI-Frederick, NIH) Organizer: Yun-Xing Wang (NCI-Frederick, NIH) Place: NCI-Frederick campus Time and Date: 8:30am-5:00pm, Oct. 22, 2009 Suggested reading Books: Glatter, O., Kratky, O. (1982) Small angle X-ray Scattering. Academic Press. Feigin, L., Svergun, D. (1987) Structure Analysis by Small-angle X-ray and Neutron Scattering. Plenum Press. Review Articles: Svergun, D., Koch, M. (2003) Small-angle scattering studies of biological macromolecules in solution. Rep. Prog. Phys. 66, 1735- 1782. Koch, M., et al. (2003) Small-angle scattering : a view on the properties, structures and structural changes of biological macromolecules in solution. Quart. Rev. Biophys. 36, 147-227. Putnam, D., et al. (2007) X-ray solution scattering (SAXS) combined with crystallography and computation: defining accurate macromolecular structures, conformations and assemblies in solution. Quart. Rev. Biophys. 40, 191-285. Software Primus: 1D SAS data processing Gnom: Fourier transform of the I(q) data to the P(r) profiles, desmearing Crysol, Cryson: fits of the SAXS and SANS data to atomic coordinates EOM: fit of the ensemble of structural models to SAXS data for disordered/flexible proteins Dammin, Gasbor: Ab initio low-resolution structure reconstruction for SAXS/SANS data All can be obtained from http://www.embl-hamburg.de/ExternalInfo/Research/Sax/software.html MarDetector: 2D image processing Igor: 1D scattering data processing and manipulation SolX: scattering profile calculation from atomic coordinates Xplor/CNS: high-resolution structure refinement GASR: http://ccr.cancer.gov/staff/links.asp?profileid=5546 Part One Solution Small Angle X-ray Scattering: Basic Principles and Experimental Aspects Xiaobing Zuo (NCI-Frederick) Alex Grishaev (NIDDK) 1. -
Bridging the Solution Divide: Comprehensive Structural Analyses of Dynamic RNA, DNA, and Protein Assemblies by Small-Angle X-Ray
Available online at www.sciencedirect.com Bridging the solution divide: comprehensive structural analyses of dynamic RNA, DNA, and protein assemblies by small-angle X-ray scattering Robert P Rambo1 and John A Tainer1,2 Small-angle X-ray scattering (SAXS) is changing how we macromolecules with functional flexibility and intrinsic perceive biological structures, because it reveals dynamic disorder, which occurs in functional regions and interfaces macromolecular conformations and assemblies in solution. [1,2]. Therefore, as structural biology evolves, it will SAXS information captures thermodynamic ensembles, need to provide structural insights into larger macromol- enhances static structures detailed by high-resolution ecular assemblies that display dynamics, flexibility, and methods, uncovers commonalities among diverse disorder. macromolecules, and helps define biological mechanisms. SAXS-based experiments on RNA riboswitches and ribozymes Solution structures from X-ray scattering and on DNA–protein complexes including DNA–PK and p53 For noncoding functional RNAs and intrinsically disor- discover flexibilities that better define structure–function dered proteins, defining their shapes and conformational relationships. Furthermore, SAXS results suggest space in solution marks a critical step toward understand- conformational variation is a general functional feature of ing their functional roles. Facilitating this goal are ab initio macromolecules. Thus, accurate structural analyses will bead-modeling algorithms for interpreting small-angle X- require a comprehensive approach that assesses both ray scattering (SAXS) data. These ab initio models flexibility, as seen by SAXS, and detail, as determined by X-ray represent low-resolution shapes and contribute signifi- crystallography and NMR. Here, we review recent SAXS cantly to interpretation of flexible systems in solution, computational tools, technologies, and applications to nucleic particularly for protein–DNA complexes.