Computational Design of Biological Systems by Automatic Methods - PowerPoint PPT Presentation

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