Scott Hollingsworth (Department of Biochemistry & Biophysics, Oregon State University) Mentor: Dr. P. Andrew Karplus (Department Of Biochemistry & Biophysics, OSU) In Collaboration With: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 Ramachandran Plot Voet, Voet & Pratt Biochemistry (Upcoming 4 th Edition) φ ψ

 Use of Protein Geometry Database (PGD) to identify linear group existence (i.e. α-helix, β-sheet, π- helix…)  Simple repeating structures  Methods: manual searches  Hollingsworth et al “On the occurrence of linear groups in proteins.” Protein Sci. 18: α -Helix 3 10 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 Highest points (Individual peaks) Mt. Hood (11,239 Feet) Mt. Jefferson (10,497 Feet) Three Sisters (10,358-10,047 Feet)

Topographical map of Oregon Data value: Elevation Highest points (Individual peaks)

Topographical map of Oregon Data value: Elevation Mountain ranges (Broad patterns) C A S C A D E S C O A S T R A N G E S I S K I Y O U S ( K A L A M A T H ) B L U E M T S W A L L O W A S S T E E N S S T R A W B E R R I E S O C H O C O M A H O G A N Y M T S J A C K A S S M T S H A R T M T N T U A L A T I N H I L L S T R O U T C R E E K M T S P A U L I N A M T S

Similar approach with our data 2-Dimensional Example φ ψ

Similar approach with our data 2-Dimensional Example α-helix β P II αLαL Abundance φ ψ

 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 PerResidue Circle r=10Degree 2 φiφi ψiψi φ i+1 ψ i+1 ii+1Cluster SizeMotif Name New Motif αα 1 α-helix / helix ββ 1 β-strand P II α 1 PII- Helix N-Cap / Capping Box αδ 1 Type I Turn # P II P II δLδL 1 Type II Turn δP II 1 Type I Turn Cap δδLδL 1 Schellman Motif δα 1 Reverse Type I Turn X δLδL P II 1 Reverse Type II Turn X βα 1 βα Turn δβ 2 Classic Beta Bulge ‡ αLαL δLδL 1 Type I` Turn βαLαL 1 β → α L X ζα 1 ζ → α P † αLαL P II 1 G1 Beta Bulge δLδL β 1 δ L → β X P II `δ 3 Type II` Turn δLδL α 1 δ L → α X P II δ 1 Type VIa1 Turn (S) δβ 1 Classic Beta Bulge (S) αLαL P II 1 Wide Beta Bulge (S) αζ 2 α → ζ † ζP II 1 ζ → P II X αLαL β 1 α L → β (S) X γ`β 1 γ` Turn P II `P II 2 P II ` → P II X P II `α 1 P II ` → α (S) X βP II ` 2 β → P II ` X εα 1 ε → α X δP II ` 1 Reverse Type II` Turn X δLδL β 1 δ L → β X γ`P II 3 γ` → P II X δ ε4 δ → ε X δ ε1 δ → ε (S) X P II `β 1 P II ` → β X αLαL β 1 α L → β X ζP II ` 1 ζ → P II ` X

 Motif “shapes”  Each motif analyzed by plotting of each motif range  Understand the shape of the cluster/motif  Results:  New insight into each motif’s 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 Distributions show differences between the two structures Nearly four years in the making… φ 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 3 10 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