LOSCHMIDT LABORATORIES Protein Engineering Molecular Biotechnology Lecture #4 Michal Vašina 16/10/2024 Outline 1. Proteins in biotechnology 2. Aims of protein engineering 3. Main strategies ► Directed evolution ► Rational design ► Machine learning ► Semi-rational design Introduction Proteins in Biotechnology ► key problem -availability of optimal protein for specific process ►traditional biotechnology - adapt process ► modern biotechnology - adapt protein Available catalyst Available catalyst Adapted catalyst How to get new protein? Classical Screening ► screening culture collections ► polluted and extreme environment Environmental Gene Libraries ► metagenomic DNA Database mining ► gene databases ► (meta)genome sequencing projects ► numerous uncharacterized proteins Query Sequence MSAIKMHECOSNLGSEPOFRSGDETSQKARFERLPPADTRYVISETKDKL ENZYME MINER Selection Table Similarity Network lSo^ATOrÍes ^' Prote'ns 'n Biotechnology ► New proteins Hon et a l. Nucleic Acids Research 2020, link https://loschmidt.chemi.muni.cz/enzymeminer/ How to get new protein? If suitable protein does not exist in nature? ► Protein Engineering lSo^ATOWES 1' Proteins in Biotechnology ► New proteins How to get new protein? Protein Directed Rational Machine Discovery Evolution Design Learning Protein Engineering at a glance ► use of genetic manipulations to alter the coding sequence of a gene and thus modify the properties of the protein ► General engineering cycle: Design-build-test-learn AIMS AND APPLICATIONS ► technological - optimization of the protein to be suitable in particular technology process ► scientific - desire to understand what elements of proteins contribute to folding, stability and function 2. Aims of Protein Engineering ► Overview What shall we improve? structural properties of proteins ► stability (temperature, solvents) ►tolerance to pH, salt ► Resolve the atomic structure (to understand function) functional properties of proteins ► substrate specificity and selectivity ► kinetic properties (e.g., Km, kcaV K) ► Inhibition by small molecules (drugs) ► protein-protein or protein-DNA interactions Target properties Stability Structure Thermostability Folding Temperature Activity Optimal T/pH In vivo / In vitro Temperature Substrates X-ray NMR, Cryo-EM crystallography SAXS Kinetics [S]/[l] [Substrate] Steady-state Transcient-state [Inhibitor] LOSCHMIDT LABORATORIES 2. Aims of Protein Engineering ► Target properties vašina etai. Biotech.Adv.2023,unk 8 Overview of strategies Enzyme Discovery Ii Stability Directed A Evolution Rational [F\ Design Machine Learning Biochemical Characterization Structure Activity Kinetics [s] / [I] L/So^ATOrÍes 2' ^'ms °f Pr°tein Engineering ► Target properties Main strategies RATIONAL DESIGN 1. Computer aided design DIRECTED EVOLUTION 1. not applied fa 2. Site-directed mutagenesis Individual mutated gene 3. Transformation 4. Protein expression 5. Protein purification 6. not applied IMPROVED ENZYME Constructed mutant enzyme 7. Biochemical testing 2. Random mutagenesis Library of mutated genes (>10.000 clones) 3. Transformation 4. Protein expression 5. nor applied 6. Screening and selection - stability - selectivity affinity activity flB yBHG$ <%Mtf 4mE$ WW WW Selected mutant enzymes LOSCHMIDT LABORATORIES 3. Main strategies ► Overview DALLE 3 10 Main strategies RATIONAL DESIGN DIRECTED EVOLUTION Directed Evolution ► emerged during mid-1990s ► inspired by natural evolution ► "laboratory evolution" ► requires outside intelligence, not blind chance ► does not take millions of years, but happens rapidly The Nobel Prize in Chemistry 2018 Frances H. Arnold Prize share: 1/2 The Nobel Prize in Chemistry 2018 was divided, one half awarded to Frances H. Arnold "for the directed evolution of enzymes", https://www.nobelprize.org/prizes/chemistry/2018/summary/ LOSCHMIDT LABORATORIES 3. Main strategies ► Directed Evolution 12 Directed Evolution 1. not applied ► evolution in test tube comprises two steps ► random mutagenesis building mutant library (diversity) ► screening and selection identification of desired biocatalyst ► prerequisites for directed evolution ► gene encoding protein of interest ► method to create mutant library ► suitable expression system ► screening or selection system IMPROVED ENZYME 2. Random mutagenesis 4, Library of mutated genes (>10,000 clones) 3. Transformation 4. Protein expression 5. not applied 6. Screening and selection - stability - selectivity - affinity activity 7. Biochemical testing Selected mutant enzymes LOSCHMIDT LABORATORIES 3. Main strategies ► Directed Evolution 13 Methods to create mutant libraries ► technology to generate large diversity ► Non-recombining one parent gene -> variants with point mutations ft ft ft ► Recombining (also „sexual mutagenesis") several parental homologous genes -> chimeras lSo^ATOrIes ^' ^a'n strategies ► Directed Evolution ► Mutagenesis 14 Non-recombining mutagenesis ► UV irradiation or chemical mutagens (traditional) ► mutator strains - lacks DNA repair mechanism mutations during replication (e.g., Epicurian co//XL1-Red) ► error-prone polymerase chain reaction (ep-PCR) ► gene amplified in imperfect copying process (e.g., unbalanced deoxyribonucleotides concentrations, high Mg2+concentration, Mn2+, low annealing temperatures) ► 1 to 20 mutations per 1,000 base pairs ► site-saturation mutagenesis ► randomization of single or multiple codons ► degenerate primers (NNN for complete randomization) ► other methods ► insertion/deletions (InDel) ► cassette mutagenesis (region mutagenesis) Error-prone mutagenesis Low-fidelity polymerase GOI ATG - -TAA Site saturation mutagenesis •NNN- GOI - 3. Main strategies ► Directed Evolution ► Mutagenesis vidaietai./?c5c/>e/nfi/o/.2023,iink Recombining mutagenesis ► DNA shuffling ►fragmentation step ► random reassembly of segments DNA-shuffling ► StEP - staggered extension process ► simpler then shuffling, no fragmentation ► random reannealing combined with limited primer extension ► other methods shuffling of genes with lower homology down to 70% (e.g., RACHITT, ITCHY, SCRATCHY) GOIs DNase I Annealing Extension Shuffled gene StEP Primers bind denatured template = Brief polymerase-catalysed extension Short fragments are synthesized Fragments randomly prime the templates and are extended further Full-length genes LOSCHMIDT LABORATORIES 3. Main strategies ► Directed Evolution ► Mutagenesis vidai et ai./?csc/>e/n 0/0/. 2023, link 16 Screening and selection ► most critical step of direct evolution ► isolation of positive mutants hiding in library ► genotype to phenotype linkage is crucial ► High-throughput screening experimental testing of variants one by one ► Direct selection applying selective pressure to the library 2 3. Main strategies ► Directed Evolution ► Screening (Ultra)-High throughput screening ► Golden rule: „You get what you screen for!" ► agar plate (pre)screening ► microtiter plates screening ► 96-, 384- or 1536-well formats ► robot assistance (colony picker, liquid handler) ► 104 libraries ►volume 10-100 uL ► microfluidic systems ► water in oil emulsions (up to 10 kHz) ► FACS sorting (108 events/hour) ► 109 libraries ►volume 1 - 10 pL LOSCHMIDT LABORATORIES 3. Main strategies ► Directed Evolution ► Screening Nozzle llp_ m Poinl c'/ analysis Deflection plale —*l f • 18 Experimental throughput is critical STANDARD DESIGN ► Random mutagenesis (2-3 positions) ► Library of 104 clones ADVANCED DESIGN ► Random mutagenesis (5-7 positions) ► Library of > 106clones volume: 100' (j,L assays/day: 103 volume: 10' pL assays/day: 107 I ► Microfluidics Lecture 7 LOSCHMIDT LABORATORIES 3. Main strategies ► Directed Evolution ► Screening 19 Direct selection ► not generally applicable (mutant libraries >106 variants) ► link between genotype and phenotype ► display technologies ► ribosome, phage display ►yeast, bacteria display ► life-or-death assay ► auxotrophic strain ►toxicity based selection The Nobel Prize in Chemistry 2018 LOSCHMIDT LABORATORIES The Nobel Prize in Chemistry 2018 was divided, the other half jointly to George P. Smith and Sir Gregory P. Winter "for the phage display of peptides and antibodies" George P. Smith Prize share: r/4 Sir Gregory P. Winter Prize share: 1/4 Phage plasmid https://www.nobelprize.org/prizes/chemistry/2018/summary/ Phage display library Ab DNA library Recombinant phagemid CP ® © Antibody Production Drug Discovery Diagnostics JUL Eluting High affinity mAb 1 2-3 Rounds Affinity screening JUL Binding and Wash 3. Main strategies ► Directed Evolution ► Screening Weak affinity Abs 20 Success story #1 directed evolution of enantioselectivity ► lipase from P. aeruginosa (E-value improved from 1.1 into 51) ► spectrophotometric screening of (R)- and (S)-nitrophenyl esters ► 40,000 variants screened ►the best mutant contains six amino acid substitutions H20 CH3 rac-i R = ^-GSH,7 R' = ^NQ2C6H4 P, aeruginosa lipase 0 CH3 (S)-2 ON 0 ÖH3 [R)-2 OH 50 CD Si 20 o 1« 10,000 clones ) 3. Transformation 4. Protein expression 5. not applied 6. Screening and selection - stability - selectivity - affinity - activity m VBf Vap Wfr vBjr Selected mutant enzymes DALLE 3 24 Main strategies RATIONAL DESIGN DIRECTED EVOLUTION Rational design introduction I. Computer aided design 2. Site-directed mutagenesis Individual mutated gene 3. Transformation 4. Protein expression 5. Protein purification 6. not applied IMPROVED ENZYME ► emerged around 1980s as the original protein engineering approach ► knowledge based - combining theory and experiment ► protein engineering cycle: „learn-design-build-test-learn" ► difficulty in prediction of mutation effects on protein property ► c/e novo design most challenging Constructed mutant enzyme 7. Biochemical testing LABORATORIES 3' ^a'n strategies ► Rational design 26 Principals of rational design I. Computer aided design 2. Site-directed mutagenesis Individual mutated gene 3. Transformation 4. Protein expression 5. Protein purification 6. not applied IMPROVED ENZYME ► rational design comprises: ► design - understanding of protein functionality ► experiment - construction and testing of mutants ► prerequisites for rational design: ► gene encoding protein of interest ► 3D structure (e.g., X-ray, NMR) or sequence alignment ► computational methods and capacity ► site-directed mutagenesis techniques ► efficient expression system ► biochemical assay to test mutants Constructed mutant enzyme 7. Biochemical testing LOSCHMIDT LABORATORIES 3. Main strategies ► Rational design 27 Bioinformatics-based design ► Sequence homology approach ► homologous wild-type sequences alignment ► identifying amino acid residues responsible for differences ► design - combination of positive mutation from all parental proteins ►Ancestral reconstruction ► construction of phylogenetic tree ► design - nods prediction by consensus approach m.ad Q-MLPV IM.ao IM.ao h I ao hi.ao h I ao I . a H I ao I : h i ao I- i ao I : .". 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Main strategies ► Rational design ► Approaches 28 Bioinformatics-based design ► Sequence homology approach ► homologous wild-type sequences alignment ► identifying amino acid residues responsible for differences ► design - combination of positive mutation from all parental proteins ►Ancestral reconstruction ► construction of phylogenetic tree ► design - nods prediction by consensus approach #FIREPi O ASH i SEQUENCE SET Of SEQUENCES ^^Hfe JMUMMMM LOSCHMIDT LABORATORIES Musil et al. BriefBioinform2020, link https://loschmidt.chemi.muni.cz/fireprotasr/ 3. Main strategies ► Rational design ► Approaches 29 Bioinformatika Bi5000/Bi5000c ► Období: podzim ► Rozsah: přednáška 2 hodiny/týden, cvičení 2 hodiny/týden ► Vyučující: prof. Mgr. Jiří Damborský, Dr., prof. RNDr. Roman Pantůček, Ph.D., ► Osnova: ► bioinformatické databáze a jejich prohledávání ► analýza nukleotidových a proteinových sekvencí ► hledání a identifikace genů ► analýza a před poveď struktury proteinů Structure-based design ► prediction of enzyme function from structure alone is challenging ► protein structure: experimental (X-ray crystallography, NMR), computational (AlfaFold models, homology models!) ► molecular modelling ► molecular docking ► molecular dynamics ► quantum mechanics/molecular mechanics (QM/MM) Strukturní biologie Bi9410/Bi9410c ► Období: podzim ► Rozsah: přednáška 2 hodiny/týden, cvičení 2 hodiny/týden ► Vyučující: doc. Mgr. David Bednář, Ph.D. ► Osnova: ► struktura, stabilita a dynamika biologických makromolekul ► makromolekulami interakce a komplexy ► stanovenia před poveď struktury, identifikace důležitých oblastí ► stanovenívlivu mutace na strukturu a funkci proteinu ► aplikace v biologickém výzkumu, návrhu léčiv a biokatalyzátorů Gene of interest construction ► site-directed mutagenesis ► introducing point mutations ► multi site-directed mutagenesis ► gene synthesis ► commercial service 1. Mutant Strand Synthesis Perform thermal cycling to: • Denature DNA template • Anneal mutagenic primers containing desired mutation • Extend and incorporate primers with high-fidelityDNA polymerase 2. Dpn\ Digestion of Template Digest parental methylated and hemimethylated DNA with Dpn I ► codon optimization 3. Transformation Transform mutated molecule into competent cells for nick repair I GENE ART AGenScript THE GENE OF YOUR CHOICE M. M W B* _ aWmm ^^^V Make Research E; 5V LABORATORIES ^' ^a'n strategies ► Rational design ► Mutagenesis 33 Rational design targets Rational design Activity Temperature Organic solvents Oxidizing agents Substrate scope Reactivity Specificity selectivity ► Salt bridges & H-bonds ► Disulfidebonds ► Rigid vs. flexible regions ► Solvent-exposed vs. buried residues ► Polar vs. hydrophobic residues Conserved features Regio ] Stereo ► Active-site residues ► Tunnels/cavities inside ► Ligand/substrate binding residues LOSCHMIDT LABORATORIES 3. Main strategies ► Rational design ► Examples Success story #2 Stabilizing already stabilized enzyme DhaA115: Tm = 73.3 °C (previously by FireProt) 1 Introduction of disulfide bridges ► no increase in 7"„ m 2. Automated platforms FireProt and PROSS ► FireProt: best Tm = 77.0 °C ► PROSS : best Tm = 78 A °C 3. Further stability increase by manual curation ► FireProt: best Tm = 79.3 °C ► PROSS : best Tm= 80.9 °C 4. Automated curation by machine Learning ► MutCompute 7"m=81.7°C LOSCHMIDT LABORATORIES 35 30 O 25 o a a. TO ^E 20 15 >- CO Í 10 5 0 Computational enzyme stabilization Pushing the limits J. DhaA115 o-►<> o o oo O" O o OCX oo o o o CURATION -► manual w 0 ► automated DhaAwt 0o - o o - future o Previous stabilization Rational Design FireProt PROSS PROSS+ MutCompute 3. Main strategies ► Rational design ► Examples Kunka, et a I. ACS Cata 12023, link 35 Targets for Machine Learning Designing molecules ► Design mutations/protein variants ► Design drugs/ligands to bind proteins Predictions ► Structure prediction (AlphaFold2,...) ► Sequence from structure (find binding proteins, RF Diffusion) ► Function from sequence ► Al in Life Sciences Lecture 6 Design mutations Structure from Sequence Design drugs/ligands Sequence from Structure Predict function lSo^ATOrIes ^' ^a'n strategies ► Machine Learning 36 AI in Biology, Chemistry, and Bioengineering Bi9680En ► Období: podzim ► Rozsah: přednáška 2 hodiny/týden ► Vyučující: Dr. Stanislav Mazurenko ► Osnova: ► modern bio-challenges: drug design, DNA interpretation, protein engineering ►types of AI algorithms and workflow for designing predictors ► clustering algorithms, random forests, artificial neural networks ►features, databases, and predictors used in applications 37 Nobel Prize in Chemistry 2024 THE NOBEL PRIZE IN CHEMISTRY 2024 David Baker 'for computational protein design" Demis John AA. Hassabis Jumper "for protein structure prediction" 2016: New nanomaterials where up to 120 proteins spontaneously link together. DeepMind 2021: Nanoparticles [yellowl with proteins imitating influenza virus on the surface (greenl that can be used as a vaccine for influenza. Successful in animal models. 2022: Proteins that function as a type of molecular rotor. 2024: Geometrically shaped proteins that can change their shape due to external influences. Could be used for producing tiny sensors. 100 AlphaFold2- AlphaFoId[ THE ROYAL SWEDISH ACADEMY OF SCIENCES -- Year 2006 2008 2010 2012 2014 2016 2018 2020 CASP 7 8 9 10 11 12 13 14 LOSCHMIDT LABORATORIES 3. Main strategies ► Machine Learning https://www.nobelprize.org/prizes/chemistry/2024/ 38 Main strategies RATIONAL DESIGN 1. Computer aided design 2. Site-directed mutagenesis Individual mutated gene 3. Transformation 4. Protein expression 5. Protein purification 6. not applied DIRECTED EVOLUTION IMPROVED ENZYME Constructed mutant enzyme 7. Biochemical testing SEMI-RATIONAL DESIGN 2. Random mutagenesis Library of mutated genes ( >10,000 clones ) 3. Transformation 4. Protein expression 5. not applied 6. Screening and selection - stability - selectivity - affinity - activity TM* TM7 TWrf WW WW Selected mutant enzymes LA^O^ATOrÍeS 3' Main stratešies * Semi-rational design Success story #3: Degrading a toxic pollutant ► conversion of 1,2,3-trichloropropane by DhaA from Rhodococcus erythropolis Y2 LOSCHMIDT LABORATORIES 3. Main strategies ► Semi-rational design ► Example 40 First round: Directed evolution ► conversion of 1,2,3-trichloropropane by DhaA from Rhodococcus erythropolis Y2 ► Directed Evolution - importance of access pathways Second round guided by structural insights ► conversion of 1,2,3-trichloropropane by DhaA from Rhodococcus erythropolis Y2 ► Directed Evolution - importance of access pathways ► Semi-rational Design - hot spots in access tunnels ► library of 5,300 clones screened Results Accessible solvent Active site Proteinové inženýrství BÍ7410 ► Období: jaro ► Rozsah: přednáška 2 hodiny/týden ► Vyučující: Mgr. Michal Vašina, Ph.D., ► Osnova: ► strukturně-funkčnívztahy proteinů ► metody exprese a purifikace rekombinantních proteinů ► metody strukturní a funkční analýzy proteinů ► racionálnídesign, semi-racionálnídesign a řízená evoluce ► příklady využití proteinového inženýrství doc. Mgr. David Bednář, Ph.D. LOSCHAAIDT LABORATORIES Advertisement Multidisciplinary in protein research Combine multiple strategies Protein Discovery • • • 11 LOSCHMIDT LABORATORIES Directed Evolution Summary Rational Design Machine Learning 46 Reading ► Lutz, S. 2010: Beyond directed evolution - semi-rational protein engineering and design. CurrOpin BiotechnoL 21(6): 734-743 (link) ► Computational enzyme redesign and Computational de novo enzyme design (page 5-7) NTH Public Access k Author Manuscript thor manuscripl; available in PMC 2011 December 1. > > 0) o I Published in final edited form as: CurrOpin BiotechnoL 2010 December ; 21(6): 734-743. doi: 10.1016/j.copbio.2010.08.011. Beyond directed evolution - semi-rational protein engineering and design Stefan Lutz Department of Chemistry, Emory University, 1515 Dickey Drive, Atlanta, GA. 30322 Abstract Over the las: two decades, directed evolution has transformed the field of protein engineering. The advances in understanding protein structure and function, in no insignificant part a result of directed evolution studies, arc increasingly empowering scientists and engineers to device more effective methods for manipulating and tailoring biocatalysts. Abandoning large combinatorial libraries, the LOSCHAAIDT LABORATORIES