Scott Hollingsworth
Mentor:
In Collaboration With:
(Department of Biochemistry & Biophysics, Oregon State University)
Dr. P. Andrew Karplus (Department Of Biochemistry & Biophysics, OSU)
Dr. Weng-Keen Wong (Department Of Computer Science, OSU)
Dr. Donald Berkholz (Department of Biochemistry and Molecular Biology, Mayo Clinic)
Dr. Dale Tronrud (Department of Biochemistry & Biophysics, OSU)

Each protein has an
individual structure

Structure flows from
function

Understand structure,
understand function
Ptr Tox A

Phi & Psi (φ, ψ)
 Phi and psi describe the
conformation of the planar
peptide (amino acid) in
regards to other peptides
 One amino acid – two angles
φ
ψ
Ramachandran Plot
Voet, Voet & Pratt Biochemistry
(Upcoming 4th Edition)

310 Helix
Use of Protein Geometry Database
(PGD) to identify linear group
existence (i.e. α-helix, β-sheet, πhelix…)
 Simple repeating structures
 Methods: manual searches
 Hollingsworth et al. 2009. “On the
occurrence of linear groups in proteins.”
Protein Sci. 18:1321-25
α-Helix

Linear groups are only part of the picture
 Not all common protein motifs are repeating structures
 Many have changing conformations

Goal of this research:
 Identify all common motifs in proteins

Too complex for manual searches
 Enter machine learning

Form of artificial intelligence

Can identify clusters within a dataset
 Cluster – significant grouping of data points

Visual example…
Topographical map of Oregon
Data value: Elevation
Mt. Hood
(11,239 Feet)
Mt. Jefferson
(10,497 Feet)
Three Sisters
(10,358-10,047 Feet)
Highest points (Individual peaks)
Topographical map of Oregon
Data value: Elevation
Highest points (Individual peaks)
Topographical map of Oregon
Data value: Elevation
TUALATIN
HILLS
OCHOCO
STRAWBER RIES
PAULINA
MTS
MAHOGANY
MTS
JACKASS
MTS
SISKIYOUS
( KALAMAT H )
HART MTN
TROUT CREEK
MTS
Mountain ranges (Broad patterns)
φ
ψ
Similar approach with our data
2-Dimensional Example
α-helix
β
Abundance
PII
αL
ψ
Similar approach with our data
2-Dimensional Example
φ

Complications…

Our Data: 4-dimensional dataset
 4D to 2D distance conversions

What has and hasn’t been observed?
 No definitive source
 Abundance / Peak Heights

Machine learning programs can identify both
previously documented and unknown
common motifs and their abundances

1) Create and prep datasets with
resolution of at least 1.2Å or higher,
1.75Å or higher

2) Run cuevas

3) Analyze identified clusters
 Automated process using Python to
remove bias

4) Analyze context of motifs
2D-visual example of cuevas clustering

Goal: Definitive list of the
most common protein motifs
 In order of abundance

“Everest” Method
 Locate “highest” peak first
▪ Bad pun : “Mt. Alpha-rest”
 Locate second highest peak
 Locate third…….

Identifying motifs
 Search for peaks while looking
for ranges

Results:
 Definitive list of common protein
motifs in order of abundance
 The list…
Points Per
Circle r=10 Degree2
5644
247
173
147
125
117
88
55
51
43
40
36
35
34
31
31
30
29
24
20
20
20
19
17
15
14
11
10
9
9
8
8
7
6
6
6
6
6
18.07
0.7909
0.5540
0.4707
0.4003
0.3747
0.2818
0.1761
0.1633
0.1377
0.1281
0.1153
0.1121
0.1089
0.0993
0.0993
0.0961
0.0929
0.0769
0.0640
0.0640
0.0640
0.0608
0.0544
0.0480
0.0448
0.0352
0.0320
0.0288
0.0288
0.0256
0.0256
0.0224
0.0192
0.0192
0.0192
0.0192
0.0192
φi
ψi
φi+1
ψi+1
-63.4
-125.5
-69.9
-65.5
-70.4
-57.2
-88.3
-88.1
-91.8
93.5
-133.9
-82.4
54.9
-122.3
-136.1
65.3
82.6
56.7
78
-78.3
-96.6
50.5
-69.9
-129.1
53.7
-87.6
76.3
78.8
-138.5
92.8
-107.6
84.6
-85.8
-102.4
-77.9
83.8
57.1
-128.3
-42
132.4
157.4
-21.4
153.6
131
-2
1.3
-1.9
-0.1
164.3
-26.8
38.3
119.6
70.4
28.3
5.6
-133.5
0.5
116
0.9
49.9
-32.3
80.8
48
61
-169.3
171.1
165.7
165.9
16.8
8.1
71.8
-9
-8.6
-166.3
44.5
98.7
-64
-118
-61
-90.3
-60.4
82.4
-64.7
87.9
-58.4
-71.7
-62.2
-146.3
84.5
52.7
-65
-67.2
-103.1
-73.7
-67.5
-89.1
-133.8
-61.2
-129.8
-70.3
-118.9
-140.3
-61.4
-69.3
57.7
-62.5
80
-143
-83.1
92.6
86.7
-121.9
-152.5
56.7
-40.6
130.2
-36.3
1.5
143
-0.6
136.9
5.7
-42.5
146
-34.1
152.1
0.8
41
-19
140.8
137.5
-10.7
-43.1
-31.1
156.3
148.3
73.1
141.9
126.6
149.5
138.3
-29.6
-137.8
-35.7
-177
169.3
163.5
163.3
174.2
132.1
158.8
-133.3
Residue
i
i+1
α
β
PII
α
PII
PII
δ
δ
δ
δL
β
δ
αL
β
ζ
αL
δL
PII`
δL
PII
δ
αL
α
ζ
αL
γ`
PII`
PII`
β
ε
δ
δL
γ`
δ
δ
PII`
αL
ζ
α
β
α
δ
PII
δL
PII
δL
α
PII
α
β
δL
αL
α
PII
β
δ
α
δ
β
PII
ζ
PII
β
β
PII
α
PII`
α
PII`
β
PII
ε
ε
β
β
PII`
Cluster Size
Motif Name
1
1
1
1
1
1
1
1
1
1
1
2
1
1
1
1
1
3
1
1
1
1
2
1
1
1
2
1
2
1
1
1
3
4
1
1
1
1
α-helix / 310-helix
β-strand
PII- Helix N-Cap / Capping Box
Type I Turn#
PII
Type II Turn
Type I Turn Cap
Schellman Motif
Reverse Type I Turn
Reverse Type II Turn
βα Turn
Classic Beta Bulge‡
Type I` Turn
β → αL
ζ → αP†
G1 Beta Bulge
δL → β
Type II` Turn
δL → α
Type VIa1 Turn (S)
Classic Beta Bulge (S)
Wide Beta Bulge (S)
α → ζ†
ζ → PII
αL → β (S)
γ` Turn
PII` → PII
PII` → α (S)
β → PII`
ε→α
Reverse Type II` Turn
δL → β
γ` → PII
δ→ε
δ → ε (S)
PII` → β
αL → β
ζ → PII`
New Motif
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X

Motif “shapes”

Results:
 Each motif analyzed by plotting
 New insight into each motif’s
of each motif range
 Understand the shape of the
cluster/motif
structure
 Context
 Comparisons
Example Cluster Shape
Type II Vs. Type II`
Type II Vs. Type II`
Hairpin turns
180° Turn
Two Residues
Defined as mirror
images of each other
φ
Distributions show
differences between the
two structures
Nearly four years in the
making…
ψ

The results go on…
 Motif analysis
▪ Viral forming of “Pangea”
 Range and peak method sections
▪ Adapting cuevas for our data
▪ Python automation
▪ Identification of 310 Helix & Type I Turn
 6D, 8D, 10D and 12D clustering
▪ Full helix caps, loops, halfturns…

For full story, a manuscript for publication is being prepared:
 Hollingsworth et al. “The protein parts list: motif identification
through the application of machine learning.”(Unpublished)

Cuevas was successful in identifying both documented and
undocumented motifs
 Previously described: Linear groups, helix caps, β-turns (& reverses),
β-bulges, α-turns, loops, helix bends, π-structures…
 Numerous new motifs
 Successful from 4D through 20D

Results form the “Protein Parts” List
 Comprehensive list of all common protein motifs found in proteins
•
•
•
•
•
•
Dr. P. Andrew Karplus
Dr. Weng-Keen Wong
Dr. Donald Berkholz
Dr. Dale Tronrud
Dr. Kevin Ahern
Howard Hughes Medical
Institute