computational engineering of bionanostructures ram samudrala university of washington how can we...
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Computational engineering of bionanostructuresRam Samudrala
University of Washington
How can we analyse, design, & engineerpeptides capable of specific binding
properties and activities?
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A comprehensive computational approach
• Sequence-based informatics- analyse sequence patterns responsible for binding specificitywithin experimentally characterised binders by creatingspecialised similarity matrices
• Structure-based informatics- analyse structural patterns within experimental characterisedbinders by performing de novo simulations both in the presence and absence of substrate
• Computational design- use de novo protocol to predict structures of the bestcandidate peptides or peptide assemblies, with validation by further experiment
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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)
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Protein folding
…-L-K-E-G-V-S-K-D-…
…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…
one amino acid
Gene
Protein sequence
Unfolded protein
Native biologicallyrelevant state
spontaneous self-organisation (~1 second)
not uniquemobileinactive
expandedirregular
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Protein folding
…-L-K-E-G-V-S-K-D-…
…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…
one amino acid
Gene
Protein sequence
Unfolded protein
Native biologicallyrelevant state
spontaneous self-organisation (~1 second)
unique shapeprecisely orderedstable/functionalglobular/compacthelices and sheets
not uniquemobileinactive
expandedirregular
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Structure-based informatics: De novo prediction of protein structure
astronomically large number of conformations5 states/100 residues = 5100 = 1070
select
hard to design functionsthat are not fooled by
non-native conformations(“decoys”)
sample conformational space such thatnative-like conformations are found
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Semi-exhaustive segment-based foldingEFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
generateMake random moves to optimisewhat is observed in known structures
… …
minimiseFind the most protein-like structures
… …
filter all-atom pairwise interactions, bad contactscompactness, secondary structure,consensus of generated conformations
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CASP prediction for T2155.0 Å Cα RMSD for all 53 residues
Ling-Hong Hung/Shing-Chung Ngan
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Ling-Hong Hung/Shing-Chung Ngan
CASP prediction for T2814.3 Å Cα RMSD for all 70 residues
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CASP prediction for T1384.6 Å Cα RMSD for 84 residues
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CASP prediction for T1465.6 Å Cα RMSD for 67 residues
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CASP prediction for T1704.8 Å Cα RMSD for all 69 residues
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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
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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
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oxidoreductase transferase
hydrolase ligase
lyase
Grafting of biological active sites onto engineered peptides
TIM barrelproteins
2246 withknown structure
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Acknowledgements
Samudrala group:
Aaron ChangChuck MaderDavid NickleEkachai JenwitheesukGong Cheng Jason McDermottJeremy Horst
Sarikaya group:
Ersin Emre Oren
National Institutes of HealthNational Science Foundation
Searle Scholars Program (Kinship Foundation)Puget Sound Partners in Global Health
UW Advanced Technology Initiative in Infectious Diseases
http://bioverse.compbio.washington.eduhttp://protinfo.compbio.washington.edu
Kai WangLing-Hong HungMichal GuerquinShing-Chung NganStewart MoughonTianyun LuZach Frazier