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Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
Computational Protein Design. 1. Challenges in Protein Engineering
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Computational Protein Design. 1. Challenges in Protein Engineering

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  • 1. Computational Protein Design 1. Challenges in Protein Engineering Pablo Carbonell pablo.carbonell@issb.genopole.fr iSSB, Institute of Systems and Synthetic Biology Genopole, University d’Évry-Val d’Essonne, France mSSB: December 2010Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 1 / 40
  • 2. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 2 / 40
  • 3. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 3 / 40
  • 4. Protein Engineering Protein engineering is a technology that alters protein structures in order to improve their properties in applications such as pharmaceuticals, green chemistry and biofuels. The main challenge is to build more accurate models to predict which substitutions are the best candidates to insert in the parent protein in order to enhance the desired property. Both experimental data and in silico predictions can contribute to the model. Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 4 / 40
  • 5. Protein Engineering Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 5 / 40
  • 6. The Protein Engineering Cycle Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 6 / 40
  • 7. Computational Protein Design in the Engineering Cycle Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 7 / 40
  • 8. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 8 / 40
  • 9. Locating the SubstitutionsHow to select the best residues to mutate in theparent protein? If detailed structural information on the parent enzyme is available, a rational approach can be applied to the design When partial information on structure is available, a semi-rational approach is used If there is no information available, then a random search is used Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 9 / 40
  • 10. Choosing the Right Strategy Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 10 / 40
  • 11. Additivity and Cooperativity Effects Additivity of the effects of substitutions is rarely seen when screening mutants In order to avoid dead ends, typically a screening strategy is designed based on building libraries with simultaneous mutations, in order to find cooperativity effects Testing for simultaneous mutations comes at the cost of a larger screening Natural evolution, however, has favored single-step mutations beneficial, although neutral drift in this case has probably allowed Additivity/cooperativity experiments searching for high affinity for a larger search in the sequence space antibody variants. [Chodorge et al., 2008] Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 11 / 40
  • 12. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 12 / 40
  • 13. Types of Protein Interactions Protein-ligand binding Protein-nucleotide (drug-target, enzyme-substrate) (DNA/RNA) binding) Protein-peptide interaction Protein-protein interaction Protein-Protein interactions Protein-protein complexes homo-oligomeric hetero-oligomeric non-obligate obligate (weak and strong) transient permanentAdapted from [Perkins et al., 2010] [Nooren and Thornton, 2003] Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 13 / 40
  • 14. Protein Specificity and Promiscuity Multispecificity : broad partner specificity (multiple substrates, proteins, ligands) Small molecule ligand : similar chemical structure, usually with stereoselectivity Proteins or peptides : structural similar motifs rather than sequence motifs Promiscuity : the ability to participate in a function other than the native one (moonlighting) Allostery : regulation of the protein by binding of some ligand (the effector) at the allosteric site Conformational selection Lock and key Induced fit [Boehr et al., 2009] [Fischer, 1894] [Koshland, 1958] Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 14 / 40
  • 15. Protein Specificity and Promiscuity: The Case of PPIs PPI : any physical binding between proteins that occur in vivo in the cell PPI screening methods still have some limitations Y2H : high FP-rate TAP-MS : limited scalability single-interface multi-interface Luminiscence-based methods, proteome chips, co-immunoprecipitation / MS, real-time analysis (3rd generation DNA-seq) Transient and PTM-dependent interactions are often missed Biological context : developmental stage, co-localization, protein modifications, presence of cofactors, presence of other binding partners Protein hubs : highly connected proteins, related to essentiality, robustness, modularity, evolvability. Party [Kim et al., 2006] and date hubs: under debate Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 15 / 40
  • 16. Data Sources Enzymatic activity BRENDA: experimental parameters KEGG, MetaCyc: metabolic networks Catalytic Site Atlas: catalytic sites Data validation and prediction GeneMANIA: lists of genes with functionally similar or shared properties STRING: based on genomic context, HT experiments, co-expression, literature ComPASS : assign confidence to an interaction detected by MS Primary PPI databases DIP, BioGRID, IntAct, MINT Common languages: PSICQUIC: expression, co-localization, genetic, metabolic, signaling pathways, experimental data, SBML Building the network: Cytoscape Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 16 / 40
  • 17. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 17 / 40
  • 18. Overview of Protein Engineering TechnologyFrom a need to adjust enzyme properties for industrial processes ...... to the challenge of generating novel proteins for therapeutic and biomedicalapplicationsGoals: Increased catalytic function related to the parent Altered specificity, stereospecificity, or affinity to interacting partners Increased stability A paradigm shift in the last 2 Property Parameters decades: Thermostability T50 PCR and recombinant gene Catalytic activity kcat , KM , kcat /KM technologies (kcat /KM )A /(kcat /KM )B Binding specificity Kd , KI Recreation of evolution in the Ka = 1/Kd lab Binding affinity ∆G = −RT ln 1/Kd Computer algorithms Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 18 / 40
  • 19. Goal 1. Increasing the Thermostability Thermostability quantifies the ability of protein’s secondary and tertiary structures to withstand high temperatures, avoiding denaturation. Thermostability is typically measured experimentally by T50 , the temperature at which 50% of the proteins are inactivated in 10 minutes. Increasing the thermostability can be considered the first step in protein engineering, in order to make the protein tolerant to a greater range of amino acid substitutions. Main design techniques: Sequence-based design: comparison through multiple alignments Structure-based approach: assumes that a more rigid protein will be more stable at high temperatures Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 19 / 40
  • 20. Goal 2. Increasing the Catalytic ActivityHow to quantify enzyme activity? Michaelis-Menten model of kinetics k1 E +S ES E +P (1) k−1 k2 d[ES] = k1 [E][S] − [ES](k−1 + k2 ) (2) dt d[P] = k2 [ES] (3) dt k2 is also known as kcat or turnover rate (in more complex cases kcat is function of several rates) kcat alone is not enough, we need to quantify the affinity of the enzyme to the substrate Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 20 / 40
  • 21. Enzyme KineticsAssumptions First assumption: the concentration of the substrate-bound enzyme [ES] is approximately constant compared with the rate of change of the concentration of substrate [S] and product [P]: d[ES] = k1 [E][S] − [ES](k−1 + k2 ) ≈ 0 (4) dt Second assumption: the total concentration of enzyme [E]0 does not change with time: [E]0 = [E] + [ES] ≈ const (5) Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 21 / 40
  • 22. The Michaelis constant KM 0 = k1 [S]([E]0 − [ES]) − [ES](k−1 + k2 ) (6) k1 [S][E]0 = k1 [S][ES] + [ES](k−1 + k2 ) (7) k−1 + k2 [S][E]0 = [S][ES] + [ES] (8) k1 (9) KM : Michaelis constant k−1 + k2 KM = (10) k1 Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 22 / 40
  • 23. The Michaelis Constant KM and the steady-state flux Rate of product formation (flux): d[P] [S] = v = k2 [ES] = k2 [E]0 (11) dt KM + [S] vmax [S] 1 v = = vmax (12) KM + [S] 1 + KM [S] KM can be measured as the concentration of substrate [S] that corresponds to a product formation yield half of the maximum: vmax v = (13) 2 Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 23 / 40
  • 24. Determining KM from the concentration curve Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 24 / 40
  • 25. Evaluating Enzyme Efficiency kcat /KM is often used as a specificity constant to compare relative enzyme rates of reaction of pairs of substrates transformed by an enzyme. For an enzyme acting simultaneously on two substrates SA , SB at rates vA , vB A A vA kcat /KM [SA ] = B B (14) vB kcat /KM [SB ] At [SA ] = [SB ], kcat /KM provides a measure of substrate promiscuity efficiency Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 25 / 40
  • 26. Goal 3. Protein Binding Affinity and SpecificityProteins can bind to different partners: Protein-ligand binding: interaction with a small molecule, such as drug-target or enzyme-substrate Protein-nucleotide (DNA/RNA) binding: in transcription regulation, promoters, etc. Protein-protein interaction: Permanent or obligated: in multi-units proteins, it could have a structural or functional role Transient: in signaling, transport, and regulation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 26 / 40
  • 27. 3.1. Protein Binding Affinity Dissociation constant k1 A+B AB (15) k−1 d[AB] = k1 [A][B] − k−1 [AB] (16) dt In equilibrium: 0 = k1 [A][B] − k−1 [AB] (17) k−1 [A][B] kd = = (18) k1 [AB] Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 27 / 40
  • 28. 3.1. Protein Binding Affinity Affinity constant 1 ka = (19) kd In antibodies: kforward Ab + Ag AbAg (20) kback Binding free energy 1 ∆G = −RT ln ka = −RT ln (21) kd Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 28 / 40
  • 29. Simplified Thermodynamics of an Enzymatic Reaction[Jonas and Hollfelder, in Protein Engineering Handbook, (2009)] Ground-state binding (KM ) Transition-state binding (Ktx ) Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 29 / 40
  • 30. 3.2. Protein Binding SpecificityThese concepts are central to modern protein design, in applications such as drugdesign, biosynthesis and degradation Binding specificity to some partner is determined by comparing either kcat /KM , ka , or kd for all partners KI : inhibition constant. When an inhibitor competes with a ligand Multispecificity : the protein has broad partner specificity : multiple substrates, proteins, or ligands Small molecule ligand : similar chemical structure, usually with stereoselectivity Proteins or peptides : structural similar motifs rather than sequence motifs Promiscuity : the ability to participate n a function other than the native one Allostery : regulation of a protein by binding of some ligand (the effector) Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 30 / 40
  • 31. Thermodynamics of a Reaction with 2 Competing Substrates[Desari and Miller, in Protein Engineering Handbook, (2009)] Specificity reflects differences in the absolute heights of the transition states Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 31 / 40
  • 32. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 32 / 40
  • 33. Introducing the Substitutions Site-directed (saturation) mutagenesis 1 Cloning the DNA of interest into a plasmid vector 2 The plasmid DNA is denatured to produce single strands 3 A synthetic oligonucleotide with desired mutation (point mutation, deletion, or insertion) is annealed to the target region 4 Extending the mutant oligonucleotide using a plasmid DNA strand as the template 5 The heteroduplex is propagated by transformation in E. coli. Error-prone PCR Modifications of standard PCR methods, designed to alter and enhance the natural error rate of the polymerase Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 33 / 40
  • 34. Outline1 The Protein Design Cycle2 Locating the Substitutions3 Types of Protein Interactions4 Engineering Protein Activity5 Introducing the Substitutions6 Screening and Library Creation Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 34 / 40
  • 35. Recombination and DNA-shuffling A natural approach to making multiple mutations is recombination Circular permutation: to alter protein topology DNA-shuffling: to perform functional domain or motif shuffling in vitro Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 35 / 40
  • 36. Recombinant Protein Folding E. coli is a typically first choice for expressing a heterologous protein However, numerous recombinant proteins fail to fold into soluble form when expressed in E. coliSome misfolding-related issues Multidomains proteins usually require the assistance of folding modulators such as chaperones as/or foldases The environment (crowding, pH, osmolarity, etc.) Post-translational modifications such as disulfide bond formation or glycoslylation (usually confined to extra-cytoplasmic compartments) Two possible outcomes for a misfolded protein: Insoluble aggregation into inclusion bodies Degradation: proteolysis E. coli expressing human leptin as inclusion body Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 36 / 40
  • 37. Directed Evolution A remarkable property of proteins is their evolvability: they can adapt under pressure of selection by changing their behavior, function or even fold Inspired by natural evolution, directed evolution uses iterative rounds of random mutation and artificial selection or screening to discover protein variants with novel functionalitiesAn iterative process: Identifying a good starting sequence, usually containing some level of latent promiscuity Creation of a library of variants Selecting variants with improved function (mutation and screening) Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 37 / 40
  • 38. From Natural Enzymes to Protein Engineeringto Computational Protein Design Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 38 / 40
  • 39. Computational Protein Design 1. Challenges in Protein Engineering Pablo Carbonell pablo.carbonell@issb.genopole.fr iSSB, Institute of Systems and Synthetic Biology Genopole, University d’Évry-Val d’Essonne, France mSSB: December 2010Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 39 / 40
  • 40. Bibliography IDavid D. Boehr, Ruth Nussinov, and Peter E. Wright. The role of dynamic conformational ensembles in biomolecular recognition. Nature chemical biology, 5 (11):789–796, November 2009. ISSN 1552-4469. doi: 10.1038/nchembio.232. URL http://dx.doi.org/10.1038/nchembio.232.Matthieu Chodorge, Laurent Fourage, Gilles Ravot, Lutz Jermutus, and Ralph Minter. In vitro DNA recombination by L-Shuffling during ribosome display affinity maturation of an anti-Fas antibody increases the population of improved variants. Protein Engineering Design and Selection, 21(5):343–351, May 2008. doi: 10.1093/protein/gzn013. URL http://dx.doi.org/10.1093/protein/gzn013.Philip M. Kim, Long J. Lu, Yu Xia, and Mark B. Gerstein. Relating three-dimensional structures to protein networks provides evolutionary insights. Science (New York, N.Y.), 314(5807):1938–1941, December 2006. ISSN 1095-9203. doi: 10.1126/science.1136174. URL http://dx.doi.org/10.1126/science.1136174.D. E. Koshland. Application of a Theory of Enzyme Specificity to Protein Synthesis. Proceedings of the National Academy of Sciences of the United States of America, 44(2):98–104, February 1958. ISSN 0027-8424. URL http://view.ncbi.nlm.nih.gov/pubmed/16590179].Irene M. Nooren and Janet M. Thornton. Diversity of protein-protein interactions. The EMBO journal, 22(14):3486–3492, July 2003. ISSN 0261-4189. doi: 10.1093/emboj/cdg359. URL http://dx.doi.org/10.1093/emboj/cdg359.James R. Perkins, Ilhem Diboun, Benoit H. Dessailly, Jon G. Lees, and Christine Orengo. Transient Protein-Protein Interactions: Structural, Functional, and Network Properties. Structure, 18(10):1233–1243, October 2010. ISSN 09692126. doi: 10.1016/j.str.2010.08.007. URL http://dx.doi.org/10.1016/j.str.2010.08.007. Pablo Carbonell (iSSB) Computational Protein Design mSSB: December 2010 40 / 40

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