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