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Computational engineering of bionanostructures
Ram Samudrala
University of Washington
How can we analyse, design, & engineer
peptides capable of specific binding
properties and activities?
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
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
Gene
…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…
Protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
Unfolded protein
spontaneous self-organisation
(~1 second)
Native biologically
relevant state
not unique
mobile
inactive
expanded
irregular
Protein folding
Gene
…-CTA-AAA-GAA-GGT-GTT-AGC-AAG-GTT-…
Protein sequence
…-L-K-E-G-V-S-K-D-…
one amino acid
Unfolded protein
spontaneous self-organisation
(~1 second)
Native biologically
relevant state
not unique
mobile
inactive
expanded
irregular
unique shape
precisely ordered
stable/functional
globular/compact
helices and sheets
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
Semi-exhaustive segment-based folding
EFDVILKAAGANKVAVIKAVRGATGLGLKEAKDLVESAPAALKEGVSKDDAEALKKALEEAGAEVEVK
generate
…
Make random moves to optimise
what is observed in known structures
…
Find the most protein-like structures
minimise
…
…
filter
all-atom pairwise interactions, bad contacts
compactness, secondary structure,
consensus of generated conformations
CASP prediction for T215
5.0 Å Cα RMSD for all 53 residues
Ling-Hong Hung/Shing-Chung Ngan
CASP prediction for T281
4.3 Å Cα RMSD for all 70 residues
Ling-Hong Hung/Shing-Chung Ngan
CASP prediction for T138
4.6 Å Cα RMSD for 84 residues
CASP prediction for T146
5.6 Å Cα RMSD for 67 residues
CASP prediction for T170
4.8 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
Grafting of biological active sites onto engineered peptides
TIM barrel
proteins
2246 with
known structure
hydrolase
ligase
lyase
oxidoreductase
transferase
Acknowledgements
Samudrala group:
Aaron Chang
Chuck Mader
David Nickle
Ekachai Jenwitheesuk
Gong Cheng
Jason McDermott
Jeremy Horst
Sarikaya group:
Kai Wang
Ling-Hong Hung
Michal Guerquin
Shing-Chung Ngan
Stewart Moughon
Tianyun Lu
Zach Frazier
Ersin Emre Oren
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