Structure prediction and analysis of neuraminidase sequence variants

被引:2
|
作者
Thayer, Kelly M. [1 ,2 ]
机构
[1] Vassar Coll, Dept Chem, 124 Raymond Ave, Poughkeepsie, NY 12604 USA
[2] Wesleyan Univ, Dept Chem, Hall Atwater Labs, Middletown, CT 06459 USA
关键词
Protein folding; active learning; biophysical methods; computational biology; computers in research and teaching; laboratory exercises; molecular graphics and representations; molecular visualization; protein design; virology; MOLECULAR-DYNAMICS SIMULATIONS; INFLUENZA-VIRUS; PANDEMIC INFLUENZA; H1N1; NEURAMINIDASE; SUSCEPTIBILITY; INHIBITOR; PROTEINS; BINDING; MODELS; ACID;
D O I
10.1002/bmb.20963
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Analyzing protein structure has become an integral aspect of understanding systems of biochemical import. The laboratory experiment endeavors to introduce protein folding to ascertain structures of proteins for which the structure is unavailable, as well as to critically evaluate the quality of the prediction obtained. The model system used is the highly mutable influenza virus protein neuraminidase, which is the key target in the development of therapeutics. In light of recent pandemics, understanding how mutations confer drug resistance, which translates at the molecular level to understanding how different sequence variants differ, constitutes an area of great interest because of the ramifications in public health. This lab targets upper level undergraduate biochemistry students, and aims to introduce tools to be used to explore protein folding and protein visualization in the context of the neuraminidase case study. Students proceed to critically evaluate the folded models by comparison with crystallographic structures. When validity is established, they fold a neuraminidase sequence for which a structure is not available. Through structural alignment and visual inspection of the 150 loop, students gain molecular insight into two possible conformations of the protein, which are actively being studied. Folding the third chosen sequence mimics a true research environment in allowing students to generate a structure from a sequence for which a structure was not previously available, and to assess whether their particular variant has an open or closed loop. From this vantage, they are then challenged to speculate about the connection between loop conformation and drug susceptibility. (c) 2016 by The International Union of Biochemistry and Molecular Biology, 44(4):361-376, 2016.
引用
收藏
页码:361 / 376
页数:16
相关论文
共 50 条
  • [1] The Dundee Resource for Sequence Analysis and Structure Prediction
    MacGowan, Stuart A.
    Madeira, Fabio
    Britto-Borges, Thiago
    Warowny, Mateusz
    Drozdetskiy, Alexey
    Procter, James B.
    Barton, Geoffrey J.
    PROTEIN SCIENCE, 2020, 29 (01) : 277 - 297
  • [2] SEQUENCE AND STRUCTURE ALIGNMENT OF PARAMYXOVIRUS HEMAGGLUTININ-NEURAMINIDASE WITH INFLUENZA-VIRUS NEURAMINIDASE
    COLMAN, PM
    HOYNE, PA
    LAWRENCE, MC
    JOURNAL OF VIROLOGY, 1993, 67 (06) : 2972 - 2980
  • [3] Structure-Function Analysis of Two Variants of Mumps Virus Hemagglutinin-Neuraminidase Protein
    Santos-Lopez, Gerardo
    Scior, Thomas
    del Transito Borraz-Argueello, Maria
    Vallejo-Ruiz, Veronica
    Herrera-Camacho, Irma
    Tapia-Ramirez, Jose
    Reyes-Leyva, Julio
    BRAZILIAN JOURNAL OF INFECTIOUS DISEASES, 2009, 13 (01): : 24 - 34
  • [4] Prediction of functional sites by analysis of sequence and structure conservation
    Panchenko, AR
    Kondrashov, F
    Bryant, S
    PROTEIN SCIENCE, 2004, 13 (04) : 884 - 892
  • [5] Servers for sequence-structure relationship analysis and prediction
    Dosztányi, Z
    Magyar, C
    Tusnády, GE
    Cserzo, M
    Fiser, A
    Simon, I
    NUCLEIC ACIDS RESEARCH, 2003, 31 (13) : 3359 - 3363
  • [6] Sequence-based prediction of variants' effects
    Rusk, Nicole
    NATURE METHODS, 2018, 15 (07) : 571 - 571
  • [7] Better prediction of functional effects for sequence variants
    Maximilian Hecht
    Yana Bromberg
    Burkhard Rost
    BMC Genomics, 16
  • [8] Sequence-based prediction of variants’ effects
    Nicole Rusk
    Nature Methods, 2018, 15 : 571 - 571
  • [9] Better prediction of functional effects for sequence variants
    Hecht, Maximilian
    Bromberg, Yana
    Rost, Burkhard
    BMC GENOMICS, 2015, 16
  • [10] RDNAnalyzer: A tool for DNA secondary structure prediction and sequence analysis
    Afzal, Muhammad
    Shahid, Ahmad Ali
    Shehzadi, Abida
    Nadeem, Shahid
    Husnain, Tayyab
    BIOINFORMATION, 2012, 8 (14) : 687 - 690