Computational engineering of bionanostructures Ram Samudrala University of Washington How can we...
-
Upload
jacob-waters -
Category
Documents
-
view
216 -
download
0
Transcript of Computational engineering of bionanostructures Ram Samudrala University of Washington How can we...
Computational engineering of bionanostructuresRam Samudrala
University of Washington
How can we analyse, design, & engineerpeptides capable of specific binding
properties and activities?
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
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)
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
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
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
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
CASP prediction for T2155.0 Å Cα RMSD for all 53 residues
Ling-Hong Hung/Shing-Chung Ngan
Ling-Hong Hung/Shing-Chung Ngan
CASP prediction for T2814.3 Å Cα RMSD for all 70 residues
CASP prediction for T1384.6 Å Cα RMSD for 84 residues
CASP prediction for T1465.6 Å Cα RMSD for 67 residues
CASP prediction for T1704.8 Å Cα RMSD for all 69 residues
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
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
oxidoreductase transferase
hydrolase ligase
lyase
Grafting of biological active sites onto engineered peptides
TIM barrelproteins
2246 withknown structure
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