A network-based representation of protein fold space Spencer Bliven Qualifying Examination6/6/2011

Overview 1. Background & Motivation 2. Preliminary Research 3. Proposed Future Research

Fold Space What protein folds are possible? Discrete or Continuous? Both? Neither? What portion of fold space is utilized by nature? Long debated questions. Why? Understanding of structure-function relationship Protein design/engineering Protein evolution Classification

Previous Work Orengo, Flores, Taylor, Thornton. Protein Eng (1993) vol. 6 (5) pp Holm and Sander. J Mol Biol (1993) vol. 233 (1) pp Holm and Sander. Science (1996) vol. 273 (5275) pp Shindyalov and Bourne. Proteins (2000) vol. 38 (3) pp Hou, Sims, Zhang, Kim. PNAS (2003) vol. 100 (5) pp Taylor. Curr Opin Struct Biol (2007) vol. 17 (3) pp Sadreyev et al. Curr Opin Struct Biol (2009) vol. 19 (3) pp α α+β β α/β

Why can we do better? More structures Sampling of globular folds “saturated” Few novel folds being discovered Geometric arguments for saturation of small protein folds Recent all-vs-all computation Cluster sequence to 40% identity 17,852 representative (updated weekly) 189 million FATCAT rigid-body alignments entGrowthChart.do?content=total&se qid=100 Accessed 5/31/2011

Structural Similarity Graph Nodes: PDB chains, non-redundant to 40% Edges: FATCAT-rigid alignments “Significant” edges: p<0.001 Length > 25 Coverage > 50 Hierarchically cluster to reduce complexity in visualization a b a/b a+b Multi Membrane Small

Agreement with SCOP Classp<10 -6 Foldp<10 -7 Superfamilyp<10 -10

Continuity Grishin. J Struct Biol (2001) vol. 134 (2-3) pp Skolnick claims ≤ 7 intermediates between any proteins We observe network diameter=15 Can find interesting paths

C4C4 C5C5 C6C6 C7C7 Symmetry Beta Propellers

Symmetry Functionally important Protein evolution (e.g. beta-trefoil) DNA binding Allosteric regulation Cooperativity Widespread (~20% of proteins) Focus of algorithmic work FGF-1Lee & Blaber. PNAS 2011 TATA Binding Protein 1TGH Hemoglobin 4HHB

Cross-class example 3GP6.A PagP, modifies lipid A f.4.1 (transmembrane beta- barrel) 1KT6.A Retinol-binding protein b.60.1 (Lipocalins)

Summary of Preliminary Research Calculated all-vs-all alignment Prlić A, Bliven S, Rose PW, Bluhm WF, Bizon C, Godzik A, Bourne PE. Pre-calculated protein structure alignments at the RCSB PDB website. Bioinformatics (2010) vol. 26 (23) pp Built network of significant alignments Approximately matches SCOP classifications Improved structural alignment algorithms Identify symmetry, circular permutations, topology independent alignments Discussed more in report

Future Research Improve the network 1. Improve all-vs-all comparison algorithm 2. Tune parameters during graph generation Annotate the network & draw biological inferences 3. Annotate nodes with functional information 4. Compare with other networks Create new networks 5. Enhance structural comparison algorithms

1. Improve all-vs-all comparison algorithm Need domain decomposition Use Combinatorial Extension (CE)

2. Tune parameters during graph generation Don’t use p-values Shouldn’t compare p-values, statistically* Not normalized by secondary structure Not accurate due to multiple testing problem Use TM-score RMSD, normalized to the alignment length Determine optimal thresholds for determining “significance” For instance, train an SVG * Technically ok here, since one-to-one with the FATCAT score

FATCAT p-value by Class Perform poorly on all-alpha in “twilight zone” Terrible on membrane proteins Probably reflects non- structural considerations in SCOP assignment

(Dis)agreement with SCOP by Class

3. Annotate nodes with functional information SCOP/CATH classifications GO terms Metal binding Ligand binding Symmetry a b a/b a+b Multi Membrane Small

4. Compare with other networks Define other types of network over the set of protein representatives Protein-protein interactions Co-expression Correlate to the structural similarities Structural similarity Protein-protein interaction

5. Enhance structural comparison algorithms Improve automated pseudo-symmetry detection Find topology-independent relationships C3C3

Summary Fold space as network Improve network creation Annotate network with functional information Improve structural similarity detection

Remaining Challenges Short Term: Hierarchical clustering amplifies errors Bias towards short, helical alignments Better metric of clustering accuracy Correct p-value calculation (remove secondary structure bias), or use TM-value as threshold Long Term Including more functional characteristics (metal ions, GO terms, HDX profiles) Use other types of similarity to construct graph

Acknowledgments Bourne Lab Philip Bourne Andreas Prlić Lab & PDB members Qualifying Exam Committee Ruben Abagyan Patricia Jennings Andy McCammon Collaborators Philippe Youkharibache Jean-Pierre Changeux Rotation Advisors Pavel Pevzner Philip Bourne José Onuchic & Pat Jennings Mike MacCoss Virgil Woods