The document discusses protein structure prediction. It begins by reviewing protein structure, including primary, secondary, tertiary, and quaternary structure. It then describes the building blocks of proteins, amino acids, and how their properties allow formation of regular secondary structures like alpha helices and beta sheets. The document outlines different types of secondary structure and how their patterns of hydrogen bonding influence 3D structure. It concludes by describing six classes of protein structure defined by their arrangements of alpha helices and beta sheets.

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Lecture 14:
Protein Structure Prediction
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

2. Review of Proteins
• Proteins: polypeptides with a three
dimensional structure
•
• Primary structure – sequence of amino
acids constituting polypeptide chain
• Secondary structure – local organization of
polypeptide chain into secondary structures
such as α helices and β sheets
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

3. Review of Proteins
• Tertiary structure –three dimensional
arrangements of amino acids as they react to
one another due to polarity and interactions
between side chains
• Quaternary structure – Interaction of several
protein subunits
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

4. Protein Structure
• Proteins: chains of amino acids joined by
peptide bonds
• Amino Acids:
– Polar (separate positive and negatively charged
regions)
– free C=O group (CARBOXYL), can act as
hydrogen bond acceptor
– free NH group (AMINYL), can act as hydrogen
bond donor
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

5. Protein Structure
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

6. Protein Structure
• Many confirmations possible due to the
rotation around the Alpha-Carbon (Cα)
atom
• Confirmational changes lead to
differences in three-dimensional
structure of protein
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

7. Protein Structure
• Polypeptide chain has pattern of N-Cα-C
repeated
• Angle between aminyl group and Cα is
PHI (φ) angle; angle between Cα and
carboxyl group is PSI (ψ) angle
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

8. Protein Structure
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

9. Differences between A.A.’s
• Difference between 20 amino acids is the R
side chains
• Amino acids can be separated based on the
chemical properties of the side chains:
– Hydrophobic
– Charged
– Polar
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

10. Differences between A.A.’s
• Hydrophobic: Alanine(A), Valine(V),
phenylalanine (Y), Proline (P), Methionine
(M), isoleucine (I), and Leucine(L)
• Charged: Aspartic acid (D), Glutamic Acid
(E), Lysine (K), Arginine (R)
• Polar: Serine (S), Theronine (T), Tyrosine (Y);
Histidine (H), Cysteine (C), Asparagine (N),
Glutamine (Q), Tryptophan (W)
•
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

11. Secondary Structure
• Image source: http://www.ebi.ac.uk/microarray/biology_intro.html
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

12. Secondary Structures
• Core of each protein made up of regular
secondary structures
• Regular patterns of hydrogen bonds are
formed between neighboring amino acids
• Amino acids in secondary structures have
similar φ and ψ angles
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

13. Secondary Structures
• Structures act to neutralize the polar groups
on each amino acid
• Secondary structures tightly packed in protein
core and a hydrophobic environment
• Each amino acid side group has a limited
space to occupy -- therefore a limited number
of possible interactions
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

14. Types of Secondary
Structures
• α Helices
• β Sheets
• Loops
• Coils
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

15. α Helix
• Most abundant secondary
structure
• 3.6 amino acids per turn
• Hydrogen bond formed
between every fourth reside
• Average length: 10 amino
acids, or 3 turns
• Varies from 5 to 40 amino acids
Image source: http://www.hhmi.princeton.edu/sw/2002/psidelsk/scavengerhunt.htm; http://www4.ocn.ne.jp/~bio/biology/protein.htm
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

16. α Helix
• Normally found on the surface of protein
cores
• Interact with aqueous environment
– Inner facing side has hydrophobic amino
acids
– Outer-facing side has hydrophilic amino
acids
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

17. α Helix
• Every third amino acid tends to be
hydrophobic
• Pattern can be detected computationally
• Rich in alanine (A), gutamic acid (E), leucine
(L), and methionine (M)
• Poor in proline (P), glycine (G), tyrosine (Y),
and serine (S)
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

18. β Sheet
Image source: http://broccoli.mfn.ki.se/pps_course_96/ss_960723_12.html;
http://www4.ocn.ne.jp/~bio/biology/protein.htm
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

19. β Sheet
• Hydrogen bonds between 5-10
consecutive amino acids in one portion
of the chain with another 5-10 farther
down the chain
• Interacting regions may be adjacent
with a short loop, or far apart with other
structures in between
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

20. β Sheet
• Directions:
– Same: Parallel Sheet
– Opposite: Anti-parallel Sheet
– Mixed: Mixed Sheet
• Pattern of hydrogen bond formation in
parallel and anti-parallel sheets is
different
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

21. β Sheet
• Slight counterclockwise rotation
• Alpha carbons (as well as R side
groups) alternate above and below the
sheet
• Prediction difficult, due to wide range of
φ and ψ angles
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

22. Interactions in Helices and
Sheets
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

23. Loop
• Regions between α helices and β
sheets
• Various lengths and three-dimensional
configurations
• Located on surface of the structure
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

24. Loop
• Hairpin loops: complete turn in the
polypeptide chain, (anti-parallel β sheets)
• More variable sequence structure
• Tend to have charged and polar amino acids
• Frequently a component of active sites
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

25. Coil
• Region of secondary structure that is
not a helix, sheet, or loop
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

26. Secondary Structure
• Image source: http://www.ebi.ac.uk/microarray/biology_intro.html
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

27. 6 Classes of Protein Structure
1) Class α: bundles of α helices connected by
loops on surface of proteins
2) Class β: antiparallel β sheets, usually two
sheets in close contact forming sandwich
3) Class α/β: mainly parallel β sheets with
intervening α helices; may also have mixed β
sheets (metabolic enzymes)
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

28. 6 Classes of Protein Structure
4) Class α+ β: mainly segregated α helices and
antiparallel β sheets
5) Multidomain (α and β) proteins more than
one of the above four domains
6) Membrane and cell-surface proteins and
peptides excluding proteins of the immune
system
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

29. α Class Protein (hemoglobin)
• http://www.rcsb.org/pdb/cgi/explore.cgi?job=graphics;pdbId=3hhb;page=;pid=&opt=show&size=250
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

30. β Class Protein (T-Cell CD8)
• http://www.rcsb.org/pdb/cgi/explore.cgi?job=graphics;pdbId=1cd8;page=;pid=&opt=show&size=500
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

31. α/ β Class Protein
(tryptohan synthase)
• http://www.rcsb.org/pdb/cgi/explore.cgi?job=graphics;pdbId=2wsy;page=;pid=&opt=show&size=500
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

32. α+β Class Protein
(1RNB)
• http://www.rcsb.org/pdb/cgi/explore.cgi?job=graphics;pdbId=1rnb;page=;pid=&opt=show&size=500
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

33. Membrane Protein (10PF)
• http://www.rcsb.org/pdb/cgi/explore.cgi?job=graphics;pdbId=1opf;page=;pid=&opt=show&size=500
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

34. Protein Structure Databases
• Databases of three dimensional structures of
proteins, where structure has been solved
using X-ray crystallography or nuclear
magnetic resonance (NMR) techniques
• Protein Databases:
– PDB
– SCOP
– Swiss-Prot
– PIR
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

35. Protein Structure Databases
• Most extensive for 3-D structure is the
Protein Data Bank (PDB)
• Current release of PDB (April 8, 2003)
has 20,622 structures
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

36. Partial PDB File
ATOM 1 N VAL A 1 6.452 16.459 4.843 7.00 47.38 3HHB 162
ATOM 2 CA VAL A 1 7.060 17.792 4.760 6.00 48.47 3HHB 163
ATOM 3 C VAL A 1 8.561 17.703 5.038 6.00 37.13 3HHB 164
ATOM 4 O VAL A 1 8.992 17.182 6.072 8.00 36.25 3HHB 165
ATOM 5 CB VAL A 1 6.342 18.738 5.727 6.00 55.13 3HHB 166
ATOM 6 CG1 VAL A 1 7.114 20.033 5.993 6.00 54.30 3HHB 167
ATOM 7 CG2 VAL A 1 4.924 19.032 5.232 6.00 64.75 3HHB 168
ATOM 8 N LEU A 2 9.333 18.209 4.095 7.00 30.18 3HHB 169
ATOM 9 CA LEU A 2 10.785 18.159 4.237 6.00 35.60 3HHB 170
ATOM 10 C LEU A 2 11.247 19.305 5.133 6.00 35.47 3HHB 171
ATOM 11 O LEU A 2 11.017 20.477 4.819 8.00 37.64 3HHB 172
ATOM 12 CB LEU A 2 11.451 18.286 2.866 6.00 35.22 3HHB 173
ATOM 13 CG LEU A 2 11.081 17.137 1.927 6.00 31.04 3HHB 174
ATOM 14 CD1 LEU A 2 11.766 17.306 .570 6.00 39.08 3HHB 175
ATOM 15 CD2 LEU A 2 11.427 15.778 2.539 6.00 38.96 3HHB 176
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

37. Description of PDB File
• second column: amino acid position in the
polypeptide chain
• fourth column: current amino acid
• Columns 7, 8, and 9: x, y, and z coordinates
(in angstroms)
• The 11th column: temperature factor -- can be
used as a measurement of uncertainty
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

38. Protein Structure
Classification Databases
• Structural Classification of proteins
(SCOP)
• based on expert definition of structural
similarities
• SCOP classifies by class, family, superfamily,
and fold
• http://scop.mrc-lmb.cam.ac.uk/scop/
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

39. Protein Structure
Classification Databases
• Classification by class, architecture,
topology, and homology (CATH)
• Classifies proteins into hierarchical levels by
class
• a/B and a+B are considered to be a single
class
• http://www.biochem.ucl.ac.uk/bsm/cath/
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

40. Protein Structure
Classification Databases
• Molecular Modeling Database (MMDB)
• structures from PDB categorized into
structurally related groups using the VAST
• looks for similar arrangements of secondary
structural elements
• http://www.ncbi.nlm.nih.gov/Entrez
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

41. Protein Structure
Classification Databases
• Spatial Arrangement of Backbone
Fragments (SARF)
• categorized on structural similarities,
similar to the MMDB
• http://www-lmmb.ncifcrf.gov/~nicka/sarf2.html
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

42. Visualization of Proteins
• A number of programs convert atomic
coordinates of 3-d structures into views of the
molecule
• allow the user to manipulate the molecule by
rotation, zooming, etc.
• Critical in drug design -- yields insight into
how the protein might interact with ligands at
active sites
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

43. Visualization of Proteins
• Most popular program for viewing 3-
dimensional structures is Rasmol
Rasmol: http://www.umass.edu/microbio/rasmol/
Chime: http://www.umass.edu/microbio/chime/
Cn3D: http://www.ncbi.nlm.nih.gov/Structure/
Mage: http://kinemage.biochem.duke.edu/website/kinhome.html
Swiss 3D viewer: http://www.expasy.ch/spdbv/mainpage.html
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

44. Alignment of Protein Structure
• Three-dimensional structure of one protein
compared against three-dimensional
structure of second protein
• Atoms fit together as closely as possible to
minimize the average deviation
• Structural similarity between proteins does
not necessarily mean evolutionary
relationship
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

45. Alignment of Protein Structure
• Positions of atoms in three-dimensional
structures compared
• Look for positions of secondary
structural elements (helices and
strands) within a protein domain
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

46. Alignment of Protein Structure
• Distances between carbon atoms
examined to determine degree
structures may be superimposed
• Side chain information can be
incorporated
– Buried; visible
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

47. SSAP
• Secondary Structure Alignment
Program
• Incorporates double dynamic
programming to produce a structural
alignment between two proteins
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

48. Steps in SSAP
• 1) Calculate vectors from Cβ of one amino
acid to set of nearby amino acids
– Vectors from two separate proteins compared
– Difference (expressed as an angle) calculated,
and converted to score
• 2) Matrix for scores of vector differences
from one protein to the next is computed.
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

49. Steps in SSAP
• 3) Optimal alignment found using
global dynamic programming, with a
constant gap penalty
• 4) Next amino acid residue
considered, optimal path to align this
amino acid to the second sequence
computed
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

50. Steps in SSAP
• 5) Alignments transferred to
summary matrix
– If paths cross same matrix position, scores
are summed
– If part of alignment path found in both
matrices, evidence of similarity
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

51. Steps in SSAP
• 6) Dynamic programming alignment
is performed for the summary matrix
– Final alignment represents optimal
alignment between the protein structures
– Resulting score converted so it can be
compared to see how closely related two
structures are
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

52. Distance Matrix Approach
• Uses graphical procedure similar to dot
plots
• Identifies atoms that lie most closely
together in three-dimensional structure
• Two sequences with similar structure
can have dot plots superimposed
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

53. Distance Matrix Approach
• Values in distance matrix represent distance
between the Cα atoms in the three
dimensional structure
• positions of closest packing atoms marked
with a dot to highlight regions of interest
• Similar groups superimposed as closely as
possible by minimizing sum of atomic
distances
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

54. DALI
• Distance Alignment Tool (DALI)
• Uses distance matrix method to align protein
structures
• Assembly step uses Monte Carlo simulation
to find submatrices that can be aligned
• Existing structures that have been compared
are organized into the FSSP database
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

55. Fast Structural Similarity
Search
• Compare types and arrangements of
secondary structures within two proteins
• If elements similarly arranged, three-
dimensional structures are similar
• VAST and SARF are programs that use
these fast methods
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

56. Structural Motifs Based on
Sequence Analysis
• Some structural elements can be
determined by looking at sequence
composition
– zinc finger motifs
– leucine zippers
– coiled-coil structures
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

57. Zinc Finger Motifs
• Found by looking at
order and spacing of
cysteine and
histidine residues
• Typical zinc finger
motifs are
composed of two
cysteines followed Image source: www.bmb.psu.edu/faculty/tan/lab/
by two histidines tanlab_gallery_protdna.html
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

58. Leucine Zippers
• Found by looking for
two antiparallel alpha
helices held together
• Interactions between
hydrophobic leucine
residues found every
seventh position in helix Image source: ww2.mcgill.ca/biology/undergra/
c200a/sec3-5.htm
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

59. Transmembrane Proteins
• traverse back and forth
through alpha helices
• Typical length: 20-30
residues
• Transmembrane alpha
helices have hydrophobic
residues on the inside
facing portions, and
hydrophilic residues on the
outside Image source:
http://www.northwestern.edu/neurobiology/faculty/pinto2/pinto_12big.jpg
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

60. Membrane Prediction
Programs
• PHDhtm: employs neural network approach;
neural network trained to recognize sequence
patterns and variations of helices in
transmembrane proteins of known structures
• Tmpred: functions by searching a protein
against a sequence scoring matrix obtained
by aligning the sequences of all known
transmembrane alpha helix regions
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

61. Distance Matrix Approach
• Uses graphical procedure similar to dot
plots
• Identifies atoms that lie most closely
together in three-dimensional structure
• Two sequences with similar structure
can have dot plots superimposed
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

62. Distance Matrix Approach
• Values in distance matrix represent distance
between the Cα atoms in the three
dimensional structure
• positions of closest packing atoms marked
with a dot to highlight regions of interest
• Similar groups superimposed as closely as
possible by minimizing sum of atomic
distances
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

63. DALI
• Distance Alignment Tool (DALI)
• Uses distance matrix method to align protein
structures
• Assembly step uses Monte Carlo simulation
to find sub-matrices that can be aligned
• Existing structures that have been compared
are organized into the FSSP database
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

64. Fast Structural Similarity
Search
• Compare types and arrangements of
secondary structures within two proteins
• If elements similarly arranged, three-
dimensional structures are similar
• VAST and SARF are programs that use
these fast methods
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

65. Structural Motifs Based on
Sequence Analysis
• Some structural elements can be
determined by looking at sequence
composition
– zinc finger motifs
– leucine zippers
– coiled-coil structures
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

66. Zinc Finger Motifs
• Found by looking at
order and spacing of
cysteine and
histidine residues
• Typical zinc finger
motifs are
composed of two
cysteines followed Image source: www.bmb.psu.edu/faculty/tan/lab/
by two histidines tanlab_gallery_protdna.html
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

67. Leucine Zippers
• Found by looking for
two antiparallel alpha
helices held together
• Interactions between
hydrophobic leucine
residues found every
seventh position in helix Image source: ww2.mcgill.ca/biology/undergra/
c200a/sec3-5.htm
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

68. Transmembrane Proteins
• traverse back and forth
through alpha helices
• Typical length: 20-30
residues
• Transmembrane alpha
helices have hydrophobic
residues on the inside
facing portions, and
hydrophilic residues on the
outside Image source:
http://www.northwestern.edu/neurobiology/faculty/pinto2/pinto_12big.jpg
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

69. Membrane Prediction
Programs
• PHDhtm: employs neural network approach;
neural network trained to recognize sequence
patterns and variations of helices in
transmembrane proteins of known structures
• Tmpred: functions by searching a protein
against a sequence scoring matrix obtained
by aligning the sequences of all known
transmembrane alpha helix regions
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

70. Chou-Fasman Method
• based on analyzing frequency of amino acids in
different secondary structures
– A, E, L, and M strong predictors of alpha helices
– P and G are predictors in the break of a helix
• Table of predictive values created for alpha helices,
beta sheets, and loops
• Structure with greatest overall prediction value
greater than 1 used to determine the structure
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

71. GOR Method
• Improves upon the Chou-Fasman method
• Assumes amino acids surrounding the central amino
acid influence secondary structure central amino acid
is likely to adopt
• Scoring matrices used in GOR method, incorporates
information theory and Bayesian statistics
• Mount, p450-451
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

72. Neural Network Models
• Programs trained to recognize amino acid
patterns located in known secondary
structures
• distinguish these patterns from patterns not
located in structures
• PHD and NNPREDICT use neural networks
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

73. Nearest-neighbor
• machine learning method
• secondary structure confirmation of an amino
acid calculated by identifying sequences of
known structures similar to the query by
looking at the surrounding amino acids
• Nearest-neighbor programs include include
PSSP, Simpa96, SOPM, and SOPMA
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

74. Prediction of 3d Structures
• Threading is most Robust technique
• Time consuming
• Requires knowledge of protein structure
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

75. Threading
• Searches for structures with similar folds
without sequence similarity
• Threading takes a sequence with unknown
structure and threads it through the
coordinates of a target protein whose
structure has been solved
– X-ray crystallography
– NMR imaging
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

76. Threading
• Considered position by position subject
to predetermined constraints
• Thermodynamic calculations made to
determine most energetically favorable
and confirmationally stable alignment
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

77. Environmental Template
• Environment of each amino acid in each
known structural core is determined
– secondary structure
– area of side chain buried by closeness to
other atoms
– types of nearby side chains
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

78. Environmental Template
• Each position classified into one of 18
types
– 6 representing increasing levels of residue
burial
– three classes of secondary structure (alpha
helices, beta sheets, and loops).
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

79. Upcoming Seminars
• Topic TBA
– Rafael Irizarry, Johns Hopkins University
• Friday, 4/23/2004
• 8:30 AM – 9:30 AM
• LOCATION: K-Building Room 2036 (HSC
Campus)
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka

80. Presentations
• 4:45 – 5:00 Richard Jones
• 5:00 – 5:15 Steven Xu
• 5:15 – 5:30 Olutola Iyun
• 5:30 – 5:45 Frank Baker
• 5:45 – 6:00 Guanghui Lan
• 6:00 – 6:15 Tim Hardin
• 6:15 – 6:30 Satish Bollimpalli & Ravi
Gundlapalli
CECS 694-02 Introduction to Bioinformatics University of Louisville Spring 2004 Dr. Eric Rouchka