Homology Modeling and Optimized Expression of Truncated IK Protein, Tik, As an Anti-Inflammatory Peptide
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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). -
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. -
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. -
Homology Modeling
25 HOMOLOGY MODELING Elmar Krieger, Sander B. Nabuurs, and Gert Vriend The ultimate goal of protein modeling is to predict a structure from its sequence with an accuracy that is comparable to the best results achieved experimentally. This would allow users to safely use rapidly generated in silico protein models in all the contexts where today only experimental structures provide a solid basis: structure-based drug design, analysis of protein function, interactions, antigenic behavior, and rational design of proteins with increased stability or novel functions. In addition, protein modeling is the only way to obtain structural information if experimental techniques fail. Many proteins are simply too large for NMR analysis and cannot be crystallized for X-ray diffraction. Among the three major approaches to three-dimensional (3D) structure prediction described in this and the following two chapters, homology modeling is the easiest one. It is based on two major observations: 1. The structure of a protein is uniquely determined by its amino acid sequence (Epstain, Goldberger, and Anfinsen, 1963). Knowing the sequence should, at least in theory, suffice to obtain the structure. 2. During evolution, the structure is more stable and changes much slower than the associated sequence, so that similar sequences adopt practically identical structures, and distantly related sequences still fold into similar structures. This relationship was first identified by Chothia and Lesk (1986) and later quantified by Sander and Schneider (1991). Thanks to the exponential growth of the Protein Data Bank (PDB), Rost (1999) could recently derive a precise limit for this rule, shown in Figure 25.1. -
Homology Modeling and Docking Studies of a 9-Fatty Acid Desaturase
Homology modeling and docking studies of a 19-fatty acid desaturase from a Cold-tolerant Pseudomonas sp. AMS8 Lawal Garba1,2,3, Mohamad Ariff Mohamad Yussoff4, Khairul Bariyyah Abd Halim4, Siti Nor Hasmah Ishak1, Mohd Shukuri Mohamad Ali1,5, Siti Nurbaya Oslan1,5 and Raja Noor Zaliha Raja Abd. Rahman1,2 1 Enzyme and Microbial Technology Research Centre, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia 2 Department of Microbiology, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia 3 Department of Microbiology, Faculty of Science, Gombe State University, Gombe, Gombe State, Nigeria 4 Department of Biotechnology, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang Darul Makmur, Malaysia 5 Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia ABSTRACT Membrane-bound fatty acid desaturases perform oxygenated desaturation reactions to insert double bonds within fatty acyl chains in regioselective and stereoselective manners. The 19-fatty acid desaturase strictly creates the first double bond between C9 and 10 positions of most saturated substrates. As the three-dimensional structures of the bacterial membrane fatty acid desaturases are not available, relevant information about the enzymes are derived from their amino acid sequences, site-directed mutagenesis and domain swapping in similar membrane-bound desaturases. The cold-tolerant Pseudomonas sp. AMS8 was found to produce high amount of monounsaturated fatty acids at low temperature. Subsequently, an active 19-fatty acid desaturase was isolated and functionally expressed in Escherichia coli. In this paper we report homology modeling and docking studies of a 19-fatty acid desaturase from a Cold- Submitted 8 May 2017 tolerant Pseudomonas sp. -
A Thesis Entitled Homology-Based Structural Prediction of the Binding
A Thesis entitled Homology-based Structural Prediction of the Binding Interface Between the Tick-Borne Encephalitis Virus Restriction Factor TRIM79 and the Flavivirus Non-structural 5 Protein. by Heather Piehl Brown Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Biomedical Science _________________________________________ R. Travis Taylor, PhD, Committee Chair _________________________________________ Xiche Hu, PhD, Committee Member _________________________________________ Robert M. Blumenthal, PhD, Committee Member _________________________________________ Amanda Bryant-Friedrich, PhD, Dean College of Graduate Studies The University of Toledo December 2016 Copyright 2016, Heather Piehl Brown This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of Homology-based Structural Prediction of the Binding Interface Between the Tick-Borne Encephalitis Virus Restriction Factor TRIM79 and the Flavivirus Non-structural 5 Protein. by Heather P. Brown Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Biomedical Sciences The University of Toledo December 2016 The innate immune system of the host is vital for determining the outcome of virulent virus infections. Successful immune responses depend on detecting the specific virus, through interactions of the proteins or genomic material of the virus and host factors. We previously identified a host antiviral protein of the tripartite motif (TRIM) family, TRIM79, which plays a critical role in the antiviral response to flaviviruses. The Flavivirus genus includes many arboviruses that are significant human pathogens, such as tick-borne encephalitis virus (TBEV) and West Nile virus (WNV). -
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.