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Predicting 3D Protein Structure using Homology Modeling

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Title: Predicting 3D Protein Structure using Homology Modeling


1
Predicting 3D Protein Structure using Homology
Modeling
  • By Affan Kayser Diadji Wague Hua Yang
    Jordan Liz Tanjina Nadia Adam Nop Kasarah
    Allen W. Light A.
  • City College Bioinformatics workshop

2
Introduction
  • Proteins play an important roles in
    various diseases. Through analyzing the
    properties and characteristics of proteins, we
    try to find their involvements in diseases.
    Structural analysis of the proteins pave our ways
    to find solutions to certain diseases. In order
    to analyze proteins which structures havent been
    determine yet, we use homology modeling to model
    proteins by using appropriate templates. MOE will
    help us do the homology modeling and be able to
    reveal the significances of the proteins in
    diseases from their structural analysis.

3
What is Homology Modeling?
  • Homology modeling is based on the assumption
    that, if two proteins have similar sequences then
    they will have similar structures. If the
    sequence identity is greater than 90 then the
    predicted structure is practically the same as
    the template structure. If the sequence identity
    is lower than 25 then the predicted structure is
    not reliable. If the sequence identity is in the
    range 50-80 then the homology modeling can
    generate reliable and informative structure.
  • There are many software for homology modeling,
    the general procedure is
  • Search for the template based on sequence
    similarity using Blast (an online software
    provided by the NCBI database)
  • Select the candidate template to make sure the
    E-value of the sequence alignment is less than
    10-4 and sequence identity is greater than 50.
  • Input the target sequence and the template
    sequence and structure into homology modeling
    software to do modeling, for example SWISS MODEL
    (http//swissmodel.expasy.org/SWISS-MODEL.html)
    ,or MOE.
  • Check the accuracy of the generated structure
    model.

4
Sequence Alignment
5
MOE
www.chemcomp.com
  • We are using MOE (molecular
    operating environment,) to do the
    modeling and this software will do all the steps
    for you when you input the sequences to be
    modeled.
  • We all found our protein sequences from the NCBI
    website and save in FASTA format.
  • We copied these sequences to MOE and searched for
    appropriate template.
  • After sequence alignment, we start to model the
    target protein structure based on the template
    structure.
  • Verify the local geometry of the generated model
    using Ramachandran Plot.

6
About TAL1
  • Tumor specific alteration of TAL1 occur in
    patients with T-Cell Acute Lymphoblastic Leukemia
    (T-ALL)
  • This alteration is caused by chromosomal
    rearrangement
  • Alteration of TAL1 may lead to leukemogenesis
  • Related to TAL2 and LYL1. Thus, has an etiologic
    role in leukemia

7
Continue.
  • Contain a) amino acid residues 1-331
  • b) truncated species ( residues 176-331)
  • Both have 1. basic helix-loop-helix motif 2.
    protein dimerization 3. DNA-binding domain that
    were found in transcription factors

As products
subgroup of bHLH protein which is a potential
mediator of T-cell leukemogenesis
8
PDB ID 1MDY As template
  • TAL1 polypeptides do not have intrinsic DNA
    binding activity
  • cant self-associate to form bHLH homodimers
  • May be able to interact with other bHLH protein
    in a stable manner
  • Two heterodimers(TAL1-E12 , TAL1-E47) that
    recognize E-box motif
  • structure model by MOE

Loop
Beta
Helix
E-box motif a element found in various
eukaryotic transcription enhancers
9
Ramachandran Plot
It is a way to visualize dihedral angles f
against ? of amino acid residues in protein
structure
10
JunB
  • Psoriasis is an inflammatory disease of the skin
    and joints
  • JunB is an element of the AP-1 transcription
    factor and is known to coordinate cell
    differentiation, stress responses, cytokine
    expression in various organs and proliferation.
    But when a person develops psoriasis, the
    expression levels of JunB lessen.

11
The structure of. CAG33122 protein using MOE,
PDB 1UAX
12
This is the HIF1 protein structure done by MOE
homology modeling. The PBD code 1P97
13
Protein analysis of HIF1
  • The predicted structure on the previous page
    displays the HIF1 protein, which is the main
    regulator of hypoxia-induced gene expression as
    well as the DNA binding protein. It is a
    heterodimeric transcription factor composed of
    HIF1A.
  • The HIF1 protein expressed by the structure
    concludes that it plays a big role in hypoxia.
    However, even though many proteins are known,
    this protein helps identify what hypoxia is
    really about since it does associate with the
    HIF1A gene.

14
1AQN- Structure modeled by MOE
15
Subtilisin Mutant
  • Acts as an Intramolecular Chaperone
  • Subtilisin-like serine Proteinase
  • It facilitates folding by acting as a template
    for its protease domain, which does not form A
    part of it.
  • There are two Subtilisin

16
Subtilisin Mutant cont.
  • Altered and wild type Subtilisin
  • They have identical amino-acid sequences
  • Differs in thermostability and substrate
    specificities.
  • Through a mutated intramoleculer chaperone
  • An identical polypeptide can fold into an altered
    conformation
  • It also maintains memory of the folding process.

17
LDLR Structure
  • This is the back bone to the structure of the LDL
    receptor protein.
  • This structure was created by Moe. Its the
    template for the LDLR protein.

18
Space Filling Model
  • Space filling molecular models show the relative
    atomic sizes of the atoms of the molecule.
  • This model shows the carbons (gray), hydrogen's
    (light gray), oxygen (blue), carbon monoxide's
    (red), and sulfur (yellow) of the protein.

19
LDL Receptor
  • Knowing the structure of the LDL receptor
    scientists can better understand the effects it
    has on Familial Hypercholesterolemia patients.
  • For example, scientist can find why the LDL
    receptor fails to activate and bind to the
    surface and can learn how to provide LDL
    receptors to FH patients.

20
What Is HER4?
  • HER4 , also known as ErbB4, is able to lower the
    expression level of breast and prostate tumors.
  • ErbB4 is a transmembrane receptor, which is a
    protein that covers the plasma membrane of the
    cell, that controls cell reproduction and
    differentiation.
  • It is apart of the HER family.
  • Has to be enzymatically separated which may
    change the function of its Intercellular
    Domain(ICD) as stated in several reports

21
ABG35747 HER4 MOE Structure
22
Conclusion
  • The predicted structures of our target proteins
    help us analyze the significances of the proteins
    and its involvements in diseases. Thus, we have
    better understandings of the diseases.

23
Further Research
We will continue to create models of
unstructured proteins using modeling softwares
other then MOE to see if they yield similar
results.
24
References
  • http//www.epitomics.com/products/1200-1.htm
  • lhttp//www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db
    proteinval66393602
  • http//www.ncbi.nlm.nih.gov/BLAST/Blast.cgiR
  • http//breast-cancer-research.com/content/8/2/R19
  • http//www.findarticles.com/p/articles/mi_m0DFY/is
    _03_2001/ai_70738971
  • MOE- Molecular Operating Environment
  • http//www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?db
    proteinval31077211
  • http//www.wrongdiagnosis.com/medical/hif1_protein
    .htm
  • http//www.genecards.org/cgi-bin/carddisp.pl?gene
    HIF1A
  • Bacterial lipopolysaccharide induces HIF-1
    activation in human monocytes via p44/42 MAPK and
    NF-kappaB.Frede S, Stockmann C, Freitag P,
    Fandrey J.
  • Wegele H, Muller L, Buchner J. (2004). Hsp70 and
    Hsp90 - a relay team for protein folding. Rev
    Physiol Biochem Pharmacol 1511-44
  • uhttp//www.ncbi.nlm.nih.gov/BLAST/Blast.cgi
  • uhttp//www.ebi.ac.uk/cgi-bin/clustalw/result?tool
    clustalwjobidclustalw-20060809-16204016treendi
    sphidetreetypenewsortbyseqnocoloryes
  • uhttp//www.ncbi.nlm.nih.gov/gquery/gquery.fcgi
  • uhttp//ad2004.com/Biblecodes/Hebrewmatrix/leukemi
    a.html
  • http//www.humpath.com/IMG/jpg/psoriasis_nail_20xp
    asd-2.jpg
  • http//www.nature.com/nature/journal/v437/n7057/fu
    ll/nature03963.html
  • http//www.nycornell.org/dermatology/research/img/
    psoriasis.jpg
  • http//www.chemcomp.com/software-pro.htm

25
Acknowledgements
Special Thanks To
  • Dr. Sat Bhattacharya
  • Dr. Yuying Q. Gosser
  • Joe Wu
  • Harlem Children Society
  • Bioinformatics workshop at City College of New
    York
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