Computational Design of Biological Systems by Automatic Methods Alfonso JARAMILLO Synth-Bio group Programme Epigenomique CNRS-Genopole-UEVE & Ecole Polytechnique http://synth-bio.org
Outline of Talk Molecular Genetics in the post-genomic era Design of molecular parts (I): Macromolecules Design of molecular parts (II): Networks Design of molecular parts (III): Cells
Molecular Genetics in the post-genomic era Can we understand complex genetic systems as a combination Introd of molecular parts? Proteins, RNAs, Genetic circuits, Metabolic circuits, Genomes… Macromol Approach 1: build a list of parts, construct computational models and test their predictions against experimental data. Systems Biology Networks Approach 2: design, construct & validate synthetic systems from molecular parts Synthetic Biology Cells 3
Enabling breakthroughs in a postgenomic era Advances in computing power Introd Internet Genomic sequencing Crystal structures of proteins Macromol High through-put technologies Networks Cells 3
Synthetic Biology Understanding complex genetic systems as a combination of molecular parts Introd Approach: design, construct & validate synthetic systems from molecular parts Problem: Genetic Engineering has been around for more than 30 years Macromol and its technology does not scale with current molecular part lists. Solution: Make biology more engineerable How we could facilitate the engineering of genetic systems? Networks By embracing engineering paradigms Abstraction, Modularization and standardization By developing computational design methods that apply our knowledge Cells 2
Engineering new biological systems Path 1 : the construction of engineered DNA, which allows manipulation at every level of the natural hierarchy. Path 2 : the use of engineered DNA to Introd produce novel nanostructures. Path 3 : the development of nonstandard amino acids and base pairs, which can then be assembled into foldamers and DNA Macromol analogs. Path 4 : the creation of alternative genetic systems. Path 5 : producing minimal genomes (synthetic chromosomes) and transplanting Networks them into prokaryotic hosts. Path 6 : adding new functions to living organisms by manipulating cell machinery. Path 7 : the fusion of proteins to produce assemblies with novel functions. Cells Path 8 : the use of peptide synthesis to create programmable building blocks that can assemble further into functional protein 5 components.
Design principles of SB Decoupling Design & Fabrication Introd Rules insulating design process from details of fabrication Enable parts, device, and system designers to work together VLSI electronics (1970s) Macromol Abstraction Insulate relevant characteristics from overwhelming detail Simple components that can be used in combination From Physics to Electrical Engineering (1900s) Networks Standardization of Components Predictable performance Off-the-shelf Mechanical Engineering (1800s) & the manufacturing revolution (e.g. Henry Cells Ford) 7
Abstraction levels Systems PoPS PoPS PoPS ‘Can I have NOT.1 NOT.2 NOT.3 three inverters?’ Introd ‘Here’s a set of PDP inverters, 1 → N, that each send and receive via a Devices PoPS Macromol PoPS PoPS fungible signal carrier, PoPS.’ NOT.1 ‘I need a few DNA binding proteins.’ Networks ‘Here’s a set of DNA binding proteins, 1 → N, that each Parts recognize a unique cognate DNA site, choose any.’ ‘Get me this DNA.’ Cells Zif268, Paveltich & Pabo c. 1991 DNA ‘Here’s your DNA.’ TAATACGACTCACTATAGGGAGA 8
Off-the-shelf biological parts and devices Introd Promoter RBS Macromol CDS Terminator Notice that for the MIT registry, any Tag combination of parts (e.g. devices and systems) is a part. Primer Networks Operator = BBa_M30109 Cells tetR I13453 B0034 I15008 B0034 I15009 B0015 R0040 B0034 I15010 B0015 9
Cells Networks Macromol Introd Biobricks E X S P 10
Application: hydrogen production Hydrogen is considered the energy carrier of the future Use cyanobacteria for photoproduction of hydrogen: Introd Solar energy is inexpensive Production is clean and sustainable Macromol The problem: Photosynthesis can produce hydrogen (hydrogenase) • Photosynthesis produces oxygen • Networks Oxygen inhibits hydrogen production by hydrogenase! • Options: 1) Use resistent hydrogenase (NiFe), less efficient Cells 2) Use efficient hydrogense (Fe), fight inhibition
Cells Networks Macromol Introd Inhibition X
Cells Networks Macromol Introd X Y Oxygen consumption ?
Fighting inhibition • Use mitochondrion to consume oxygen Introd • Tune photosynthesis so production and consumption match Macromol Networks Cells Mellis, 2004
BioModularH2 project • Abstraction - Parts H 2 production Introd Oxygen consumption - Devices and sensing - Systems Regulation • Specification Macromol • Modularity • Simulation • Optimization Networks Cells Adapted from KEGG
Multi-scale computational design Macromolecules De novo design of proteins: 2 + 2 + ∑ ∑ ∑ DESIGNER E = K b ( b − b o ) K θ ( ) K φ ( 1 + cos( n φ − δ ) ) θ − θ o bonds angles torsions 2 + 2 + PROTDES ∑ ∑ K ϕ ( ) K UB ( r 1,3 − r ) + ϕ − ϕ o 1,3, o impropers Urey − Bradley Biological networks De novo design of: Transcriptional networks GENETDES ASMPARTS RNA networks RNADES Genomic background De novo design of metabolic pathways by retro-biosynthesis DESHARKY Network inference from microarray & proteomics resp. INFERGENE
Multi-scale computational design Macromolecules De novo design of proteins: 2 + 2 + ∑ ∑ ∑ DESIGNER E = K b ( b − b o ) K θ ( ) K φ ( 1 + cos( n φ − δ ) ) θ − θ o bonds angles torsions 2 + 2 + PROTDES ∑ ∑ K ϕ ( ) K UB ( r 1,3 − r ) + ϕ − ϕ o 1,3, o impropers Urey − Bradley Biological networks De novo design of: Transcriptional networks GENETDES ASMPARTS RNA networks RNADES Genomic background De novo design of metabolic pathways by retro-biosynthesis DESHARKY Network inference from microarray & proteomics resp. INFERGENE
How can we design protein structure and function? 19
Protein design: Inverse folding problem Physical model at atomic scale Introd Macromol Networks Cells 20
Protein design: Inverse folding problem Physical model at atomic scale Introd Macromol Unfolded Folded Folding Networks Rotamer library Cells Dipeptide random model partition function 21
Protein design: Inverse folding problem Physical model at atomic scale Introd Score sequences with : Combinatorial optimisation Macromol Unfolded Folded Folding Networks Rotamer library Cells Dipeptide random model partition function 22
Main challenges in protein design The main challenges in protein design require methodological advances. Model unfolded state Introd Syst & Synth Biol 2009 Model folded state Syst & Synth Biol 2009. PROTDES software Macromol Implicit solvation Biophys J. 2005 Side-chain and backbone flexibility Networks Proteins 2009 Combinatorial explosion J Comput Chem 2008 Cells 23
Design & Construction of Parts Introd Macromol Synthetic Protein Scaffolds Networks Cells 23
Design of a New Fold A new topology, not in PDB, was Introd chosen: Macromol Networks Baker’s group, (Science 2003) Cells 24
Designed protein Introd Macromol Blue computationally designed, red x-ray Networks structure RMSD 1.17A Cells 25
Redesign of natural protein domains Core re-design Introd Y3F N N C V39I C D17Q L5V Macromol B1 domain, SH3 Protein G Networks C I30V N Cells Wernisch et al., Ubiquitin JMB 2000 V26L 26
Cells Networks Macromol Introd New Molecular Recognition 27
Design of new sensor proteins Redesign 5 periplasmic binding proteins (PBP) to bind Introd trinitrotoluene (TNT), L-lactate or serotonin in place of the wild-type sugar or amino-acid ligands Hellinga’s group, Macromol (Nature 2003) Networks Cells 28 open closed
Design of new sensor proteins Introd Macromol TNT.R3 Lac.R1 Lac.H1 (Affinity 2 nM) (Affinity 7.4 µ M) (Affinity 1.8 µ M) Networks Hellinga’s group, Stn.A1 (Nature 2003) Cells (Affinity 50 µ M) 29
Designed Binding Site for Vanillin 164E 105N Introd 214D Macromol 90R Networks 15D 16N 235E 89K Cells 103N iGEM-Valencia 2006 http://www.intertech.upv.es/wiki/ 30
Cells Networks Macromol Introd RDX biosensor 31
Design of MHC-I inhibitors Find sequences of 9 residues long binding to MHC- I Introd Minimize the binding energy between the MHC-I and the peptide Peptide from Macromol HTLV-1 Tax complex Designed peptide Networks We designed and characterized 10 peptides: All with binding 131% of reference binding Less than 55% identity with known peptides Cells 3 peptides recognized by the TCR 32
Computational Redesign of Endonuclease Introd Macromol Networks Cells Ashworth et al. Nature 2006 33
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