BCB 444/544
Lecture 21
Protein Structure Visualization, Classification &
Comparison
 Secondary Structure Prediction
#21_Oct10
BCB 444/544 F07 ISU Dobbs #21 - Protein Secondary Structure Prediction
10/10/07
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Required Reading
(before lecture)
Mon Oct 8 - Lecture 20
Protein Secondary Structure Prediction
• Chp 14 - pp 200 - 213
Wed Oct 10 - Lecture 21
Protein Tertiary Structure Prediction
• Chp 15 - pp 214 - 230
Thurs Oct 11 & Fri Oct 12 - Lab 7 & Lecture 22
Protein Tertiary Structure Prediction
• Chp 15 - pp 214 - 230
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Assignments & Announcements
ALL: HomeWork #3
√Due: Mon Oct 8 by 5 PM
• HW544: HW544Extra #1
√Due: Task 1.1 - Mon Oct 1 by noon
Due: Task 1.2 & Task 2 - Fri Oct 12 by 5 PM
• 444 "Project-instead-of-Final" students should also submit:
• HW544Extra #1
• √Due: Task 1.1 - Mon Oct 8 by noon
• Due: Task 1.2 - Fri Oct 12 by 5 PM
<Task 2 NOT required for BCB444 students>
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Seminars this Week - Thurs:
BCB List of URLs for Seminars related to Bioinformatics:
http://www.bcb.iastate.edu/seminars/index.html
• Oct 11 Thurs
• Dr. Klaus Schulten (Univ of Illinois) - Baker Center Seminar
The Computational Microscope 2:10 PM in E164 Lagomarcino
http://www.bioinformatics.iastate.edu/seminars/abstracts/2007_2008/
Klaus_Schulten_Seminar.pdf
• Dr. Dan Gusfield (UC Davis) - Computer Science Colloquium
ReCombinatorics: Combinatorial Algorithms for Studying
History of Recombination in Populations 3:30 PM in Howe Hall
Auditorium
http://www.cs.iastate.edu/~colloq/new/gusfield.shtml
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Seminars this Week - Fri:
BCB List of URLs for Seminars related to Bioinformatics:
http://www.bcb.iastate.edu/seminars/index.html
• Oct 12 Fri
• Dr. Edward Yu (Physics/BBMB, ISU) - BCB Faculty Seminar
TBA: "Structural Biology" (see URL below) 2:10 PM in 102 Sci
http://webdev.its.iastate.edu/webnews/data/site_gdcb_dept_seminars/30/webne
wsfilefield_abstract/Dr.-Ed-Yu.pdf
• Dr. Srinivas Aluru (ECprE, ISU) - GDCB Seminar
Consensus Genetic Maps: A Graph Theoretic Approach
4:10 PM in 1414 MBB
http://webdev.its.iastate.edu/webnews/data/site_gdcb_dept_seminars/35/web
newsfilefield_abstract/Dr.-Srinivas-Aluru.pdf
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Chp 12 - Protein Structure Basics
SECTION V
STRUCTURAL BIOINFORMATICS
Xiong: Chp 12 Protein Structure Basics
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Amino Acids
Peptide Bond Formation
Dihedral Angles
Hierarchy
Secondary Structures
Tertiary Structures
• Determination of Protein 3-Dimensional Structure
• Protein Structure DataBank (PDB)
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Protein Structure & Function
• Protein structure - primarily determined by sequence
• Protein function - primarily determined by structure
• Globular proteins: compact hydrophobic core & hydrophilic surface
• Membrane proteins: special hydrophobic surfaces
• Folded proteins are only marginally stable
• Some proteins do not assume a stable "fold" until they bind to
something = Intrinsically disordered
 Predicting protein structure and function can be very hard
-- & fun!
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6 Main Classes of Protein Structure
1) a-Domains
Bundles of helices connected by loops
2) -Domains
Mainly antiparallel sheets, usually 2 sheets forming sandwich
3) aDomains
Mainly parallel sheets with intervening helices, mixed sheets
4) aDomains
Mainly segregated helices and sheets
5)
Multidomain (a  
Containing domains from more than one class
6)
Membrane & cell-surface proteins
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Protein Structure Databases
PDB - Protein Data Bank
http://www.rcsb.org/pdb/
(RCSB) - THE protein structure database
MMDB - Molecular Modeling Database
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=Structure
(NCBI Entrez) - has "added" value
MSD - Molecular Structure Database
http://www.ebi.ac.uk/msd
Especially good for interactions & binding sites
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PDB (RCSB) - recently "remediated"
http://www.rcsb.org/pdb
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Structure at NCBI
http://www.ncbi.nlm.nih.gov/Structure
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MMDB at NCBI
http://www.ncbi.nlm.nih.gov/Structure/MMDB/mmdb.shtml
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MMDB: Molecular Modeling Data Base
• Derived from PDB structure records
• "Value-added" to PDB records includes:
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Integration with other ENTREZ databases & tools
Conversion to parseable ASN.1 data description language
Data also available in mmCIF & XML (also true for PDB now)
Correction of numbering discrepancies in structure vs sequence
Validation
Explicit chemical graph information (covalent bonds)
• Integrated tool for identifying structural neighbors
Vector Alignment Search Tool (VAST)
http://www.ncbi.nlm.nih.gov/Structure/VAST/vastsearch.html
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MSD: Molecular Structure Database
http://www.ebi.ac.uk/msd/
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wwPDB: World Wide PDB
http://www.wwpdb.org
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Experimental Determination of 3D
Structure
2 Major Methods to obtain high-resolution structures
1. X-ray Crystallography (most PDB structures)
2. Nuclear Magnetic Resonance (NMR) Spectroscopy
Note Advantages & Limitations of each method
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(See your lecture notes & textbook)
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For more info: http://en.wikipedia.org/wiki/Protein_structure
3. Other methods (usually lower resolution, at present):
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Electron Paramagnetic Resonance (EPR - also called ESR, EMR)
Electron microscopy (EM)
Cryo-EM
Scanning Probe Microscopies (AFM - Atomic Force Microscopy)
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http://www.uweb.engr.washington.edu/research/tutorials/SPM.pdf
Circular Dichroism (CD), several other spectroscopic methods
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Chp 13 - Protein Structure Visualization,
Comparison & Classification
SECTION V
STRUCTURAL BIOINFORMATICS
Xiong: Chp 13
Protein Structure Visualization, Comparison &
Classification
• Protein Structural Visualization
• Protein Structure Comparison
• Protein Structure Classification
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Protein Structure Visualization
RASMOL & decendents: PyMol, MolMol
http://www.umass.edu/microbio/rasmol/index2.htm
Cn3D - esp. good for structural alignments
http://www.biosino.org/mirror/www.ncbi.nlm.nih.gov/Structure/cn3d/
CHIME (Protein Explorer)
http://www.umass.edu/microbio/chime/getchime.htm
MolviZ.Org
http://www.umass.edu/microbio/chime
Deep View = Swiss-PDB Viewer
http://www.expasy.org/spdbv
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PyMol
http://pymol.sourceforge.net/
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Cn3D
http://www.ncbi.nlm.nih.gov/Structure/CN3D/cn3d.shtml
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Cn3D : Displaying 3' Structures
Chloroquine
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Cn3D: Structural Alignments
NADH
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Protein Explorer (Chime)
http://www.umass.edu/microbio/chime/pe_beta/pe/protexpl
/frntdoor.htm
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Protein Structure Comparison Methods
We will skip this for now
3 Basic Approaches for Aligning Structures:
1. Intermolecular -
2. Intramolecular 3. Combined •
DALI/FSSP (most commonly used)
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Fully automated structure alignments
DALI server http://www.ebi.ac.uk/dali/index.html
DALI Database (fold classification)
http://ekhidna.biocenter.helsinki.fi/dali/start
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Protein Structure Classification
• SCOP = Structural Classification of Proteins
Levels reflect both evolutionary and structural relationships
http://scop.mrc-lmb.cam.ac.uk/scop
• CATH = Classification by Class, Architecture,Topology & Homology
http://cathwww.biochem.ucl.ac.uk/latest/
• DALI - (recently moved to EBI & reorganized)
DALI Database (fold classification)
http://ekhidna.biocenter.helsinki.fi/dali/start
Each method has strengths & weaknesses….
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SCOP - Structure Classification
http://scop.mrc-lmb.cam.ac.uk/scop/
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
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CATH - Structure Classification
http://www.cathdb.info/latest/index.html
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
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Chp 14 - Secondary Structure Prediction
SECTION V
STRUCTURAL BIOINFORMATICS
Xiong: Chp 14
Protein Secondary Structure Prediction
• Secondary Structure Prediction for Globular Proteins
• Secondary Structure Prediction for Transmembrane
Proteins
• Coiled-Coil Prediction
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Secondary Structure Prediction
Has become highly accurate in recent years (>85%)
• Usually 3 (or 4) state predictions:
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H = a-helix
E = -strand
C = coil (or loop)
(T = turn)
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Secondary Structure Prediction Methods
• 1st Generation methods
Ab initio - used relatively small dataset of structures available
Chou-Fasman - based on amino acid propensities (3-state)
GOR - also propensity-based (4-state)
• 2nd Generation methods
based on much larger datasets of structures now available
GOR II, III, IV, SOPM
• 3rd Generation methods
Homology-based & Neural network based
PHD, PSIPRED, SSPRO, PROF, HMMSTR
• Meta-Servers
combine several different methods
Consensus & Ensemble based
JPRED, PredictProtein
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Secondary Structure Prediction Servers
Prediction Evaluation?
• Q3 score - % of residues correctly predicted (3-state)
in cross-validation experiments
Best results? Meta-servers
• http://expasy.org/tools/
(scroll for 2' structure prediction)
• http://www.russell.embl-heidelberg.de/gtsp/secstrucpred.html
• JPred www.compbio.dundee.ac.uk/~www-jpred
• PredictProtein http://www.predictprotein.org/
Rost, Columbia
Best individual programs? ??
• CDM
• GOR V
http://gor.bb.iastate.edu/cdm/
Sen…Jernigan, ISU
http://gor.bb.iastate.edu/ Kloczkowsky…Jernigan, ISU
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Consensus Data Mining (CDM)
• Developed by Jernigan Group at ISU
• Basic premise: combination of 2 complementary methods can
enhance performance by harnessing distinct advantages of both
methods; combines FDM & GOR V:
• FDM - Fragment Data Mining - exploits availability of sequencesimilar fragments in the PDB, which can lead to highly accurate
prediction - much better than GOR V - for such fragments, but such
fragments are not available for many cases
• GOR V - Garnier, Osguthorpe, Robson V - predicts secondary
structure of less similar fragments with good performance; these are
protein fragments for which FDM method cannot find suitable
structures
• For references & additional details: http://gor.bb.iastate.edu/cdm/
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Secondary Structure Prediction:
for Different Types of Proteins/Domains
For Complete proteins:
Globular Proteins - use methods previously described
Transmembrane (TMM) Proteins - use special methods
(next slides)
For Structural Domains: many under development:
Coiled-Coil Domains (Protein interaction domains)
Zinc Finger Domains (DNA binding domains),
others…
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SS Prediction for Transmembrane Proteins
Transmembrane (TM) Proteins
• Only a few in the PDB - but ~ 30% of cellular proteins are
membrane-associated !
• Hard to determine experimentally, so prediction important
• TM domains are relatively 'easy' to predict!
Why? constraints due to hydrophobic environment
2 main classes of TM proteins:
a- helical
- barrel
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SS Prediction for TM a-Helices
a-Helical TM domains:
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Helices are 17-25 amino acids long (span the membrane)
Predominantly hydrophobic residues
Helices oriented perpendicular to membrane
Orientation can be predicted using "positive inside" rule
Residues at cytosolic (inside or cytoplasmic) side of TM helix, near
hydrophobic anchor are more positively charged than those on lumenal
(inside an organelle in eukaryotes) or periplasmic side (space between
inner & outer membrane in gram-negative bacteria)
• Alternating polar & hydrophobic residues provide clues to
interactions among helices within membrane
Servers?
• TMHMM or HMMTOP - 70% accuracy - confused by hydrophobic
signal peptides (short hydrophobic sequences that target proteins to
the endoplasmic reticulum, ER)
•
Phobius - 94% accuracy - uses distinct HMM models for TM helices
& signal peptide sequences
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SS Prediction for TM -Barrels
-Barrel TM domains:
• -strands are amphipathic (partly hydrophobic, partly
hydrophilic)
• Strands are 10 - 22 amino acids long
• Every 2nd residue is hydrophobic, facing lipid bilayer
• Other residues are hydrophilic, facing "pore" or opening
Servers? Harder problem, fewer servers…
TBBPred - uses NN or SVM (more on these ML methods later)
Accuracy ?
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Prediction of Coiled-Coil Domains
Coiled-coils
• Superhelical protein motifs or domains, with two or more
interacting a-helices that form a "bundle"
• Often mediate inter-protein (& intra-protein) interactions
'Easy' to detect in primary sequence:
• Internal repeat of 7 residues (heptad)
• 1 & 4 = hydrophobic (facing helical interface)
• 2,3,5,6,7 = hydrophilic (exposed to solvent)
• Helical wheel representation - can be used manually detect
these, based on amino acid sequence
Servers?
Coils, Multicoil - probability-based methods
2Zip - for Leucine zippers = special type of CC in TFs:
characterized by Leu-rich motif: L-X(6)-L-X(6)-L-X(6)-L
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Chp 15 - Tertiary Structure Prediction
SECTION V
STRUCTURAL BIOINFORMATICS
Xiong: Chp 15
Protein Tertiary Structure Prediction
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Methods
Homology Modeling
Threading and Fold Recognition
Ab Initio Protein Structural Prediction
CASP
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