Computational engineering of bionanostructures Ram Samudrala University of Washington

1. Computational engineering of bionanostructures Ram Samudrala University of Washington How can we analyse, design, & engineer peptides capable of specific binding properties and activities?

2. A comprehensive computational approach • Sequence-based informatics • - analyse sequence patterns responsible for binding specificity • within experimentally characterised binders by creating • specialised similarity matrices • Structure-based informatics • - analyse structural patterns within experimental characterised • binders by performing de novo simulations both in the • presence and absence of substrate • Computational design • - use de novo protocol to predict structures of the best • candidate peptides or peptide assemblies, with validation by • further experiment

3. Sequence-based informatics • Create specialised similarity matrices by optimising the alignment • scores such that strong, moderate, and weak binders for a given • inorganic substrate cluster together – determines sequences patterns: Ersin Emre Oren (Sarikaya group)

4. Protein folding not unique mobile inactive expanded irregular spontaneous self-organisation (~1 second) Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… Protein sequence …-L-K-E-G-V-S-K-D-… one amino acid Unfolded protein

5. Protein folding not unique mobile inactive expanded irregular spontaneous self-organisation (~1 second) unique shape precisely ordered stable/functional globular/compact helices and sheets Native biologically relevant state Gene …-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-… Protein sequence …-L-K-E-G-V-S-K-D-… one amino acid Unfolded protein

6. Structure-based informatics: De novo prediction of protein structure sample conformational space such that native-like conformations are found select hard to design functions that are not fooled by non-native conformations (“decoys”) astronomically large number of conformations 5 states/100 residues = 5100 = 1070

7. Semi-exhaustive segment-based folding Make random moves to optimise what is observed in known structures generate … … Find the most protein-like structures minimise … … all-atom pairwise interactions, bad contacts compactness, secondary structure, consensus of generated conformations filter EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK

8. CASP prediction for T215 5.0 Å Cα RMSD for all 53 residues Ling-Hong Hung/Shing-Chung Ngan

9. CASP prediction for T281 4.3 Å Cα RMSD for all 70 residues Ling-Hong Hung/Shing-Chung Ngan

10. CASP prediction for T138 4.6 Å Cα RMSD for 84 residues

11. CASP prediction for T146 5.6 Å Cα RMSD for 67 residues

12. CASP prediction for T170 4.8 Å Cα RMSD for all 69 residues

13. Structure-based informatics • Make predictions of peptides without the presence of substrates using • de novo protocol • Make predictions of peptides in the presence of substrates using • physics-based force-fields such as GROMACS • Analyse for similarity of structures (local and global) as well as • common contact patterns between atoms in amino acids – the • structural similarities and patterns give us the structural patterns • responsible for folding and inorganic substrate binding • Perform higher-order simulations that involve many copies of a • single or multiple peptides to generate sequences with specific • stabilities and inorganic binding properties – larger assemblies • for more controlled binding

14. Computational design • Select the most promising candidate peptides generated from the • sequence- and structure-based informatics for further simulation • and design • Simulations can be performed to ensure that active sites and/or • topologies found in nature are grafted onto these peptides • Experimental validation – synthesise peptides and check for • binding activity • Main goal here is to help with rational design of inorganic • binding peptides and focus experimental efforts in a more • optimal manner • A good framework to obtain knowledge obtained experimentally • with state of the protein structure prediction methodologies

15. Grafting of biological active sites onto engineered peptides TIM barrel proteins 2246 with known structure hydrolase ligase lyase oxidoreductase transferase

16. Acknowledgements Samudrala group: Aaron Chang Chuck Mader David Nickle Ekachai Jenwitheesuk Gong Cheng Jason McDermott Jeremy Horst Sarikaya group: Ersin Emre Oren Kai Wang Ling-Hong Hung Michal Guerquin Shing-Chung Ngan Stewart Moughon Tianyun Lu Zach Frazier National Institutes of Health National Science Foundation Searle Scholars Program (Kinship Foundation) Puget Sound Partners in Global Health UW Advanced Technology Initiative in Infectious Diseases http://bioverse.compbio.washington.edu http://protinfo.compbio.washington.edu