Bi7430 Molecular Biotechnology
Protein Engineering
Outline
❑ Limitations of proteins in biotechnology processes
❑ Definition and aim of protein engineering
❑ Targeted properties of proteins
❑ Basic approaches in protein engineering
▪ DIRECTED EVOLUTION
▪ RATIONAL DESIGN
▪ SEMI-RATIONAL DESIGN
❑ Examples, application of artificial inteligence
Proteins in biotechnology
Available protein
Available protein
Suitable/adopted protein
❑ key problem -availability of optimal protein for specific process
❑ traditional biotechnology - adapt process
❑ modern biotechnology - adapt protein
H OW TO O BTA I N O P T I M A L P R OT E I N ?
Proteins in biotechnology
❑ classical screening
▪ screening culture collections
▪ polluted and extreme environment
❑ environmental gene libraries
▪ metagenomic DNA
❑ data-base mining
▪ gene databases
▪ (meta)genome sequencing projects
▪ numerous uncharacterised proteins
Proteins in biotechnology
❑ classical screening
▪ screening culture collections
▪ polluted and extreme environment
❑ environmental gene libraries
▪ metagenomic DNA
❑ data-base mining
▪ gene databases
▪ (meta)genome sequencing projects
▪ numerous uncharacterised proteins
❑ PROTEIN ENGINEERING
I F S U I TA B L E P R OT E I N D O E S N OT E X I S T I N N AT U R E ?
Aims of protein engineering
❑ the process of constructing novel protein molecules
by design first principles or altering existing structure
❑ use of genetic manipulations to alter the coding sequence of a
gene and thus modify the properties of the protein
❑ AIMS AND APPLICATIONS
▪ technological - optimisation of the protein to be suitable in
particular technology purpose
▪ scientific - desire to understand what elements of proteins
contribute to folding, stability and function
Targeted properties of proteins
❑ structural properties of proteins
▪ stability (temperature, solvents)
▪ tolerance to pH, salt
▪ resistance to oxidative stress
❑ functional properties of proteins
▪ substrate specificity and selectivity
▪ kinetic properties (e.g., Km, kcat, Ki)
▪ cofactor selectivity
▪ protein-protein or protein-DNA
interactions
Strategies in protein engineering
Improved
protein
Directed evolution
❑ directed evolution techniques emerged during mid -1990s
❑ inspired by natural evolution
❑ this form of "evolution" does not match what Darwin had envisioned
▪ requires outside intelligence, not blind chance
▪ does not take millions of years, but happens rapidly
Improved
protein
Directed evolution
❑ 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
Methods to create mutant libraries
❑ technology to generate large diversity
▪ NON-RECOMBINING
one parent gene -> variants with point mutations
▪ RECOMBINING
several parental homologous genes -> chimeras
Non-recombining mutagenesis
❑ UV irradiation or chemical mutagens (traditional)
❑ mutator strains - lacks DNA repair mechanism
mutations during replication (e.g., Epicurian coli 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 mutation per 1000 base pairs
❑ saturation mutagenesis
▪ randomization of single or multiple codons
▪ gene site saturation mutagenesis
❑ other methods
▪ insertion/deletions (InDel)
▪ cassette mutagenesis (region mutagenesis)
Recombining mutagenesis
❑ also refered to as „sexual mutagenesis“
❑ DNA shuffling
▪ fragmentation step
▪ random reassembly of segments
❑ StEP - staggered extension process
▪ simpler then shuffling
▪ random reannealing combined with
limited primer extension
❑ other methods
shuffling of genes with lower homology down to 70%
(e.g., RACHITT, ITCHY, SCRATCHY)
Screening and selection
❑ most critical step of direct evolution
❑ isolation of positive mutants hiding in library
▪ HIGH THROUGHPUT SCREENING
individual assays of variants one by one
▪ DIRECT SELECTION
display techniques (link between genotype and phenotype)
(Utra)High throughput screening
❑ common methods not applicable
❑ agar plate (pre)screening
❑ microtiter plates screening
▪ 96-, 384- or 1536-well formate
▪ robot assistance
(colony picker, liquid handler)
▪ 10
4
libraries
▪ volume 10 – 100 uL
❑ microfluidic systems (Lesson 6)
▪ water in oil emulsions ( u p t o 1 0 k H z )
▪ FACS sorting (1 0 8 e v e n t s / h o u r )
▪ 10
9
libraries
▪ volume 1 – 10 pL
Direct selection
❑ not generally applicable (mutant libraries >10
6
variants)
❑ link between genotype and phenotype
❑ display technologies
▪ ribosome display
▪ phage display
❑ life-or-death assay
▪ auxotrophic strain
▪ toxicity based selection
Example of Directed evolution
❑ 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
Reetz, M., et al. 2001. Angew. Chem. Int. Ed. 40: 3589-91
Strategies in protein engineering
Improved
protein
Rational design
❑ emerged around 1980s as the original protein engineering approach
❑ knowledge based - combining theory and experiment
❑ protein engineering cycle:
„structure-theory-design-mutation-purification-analysis“
❑ difficulty in prediction of mutation effects on protein property
❑ de novo design most challenging
Principal of rational design
Improved
protein
❑ 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
▪ structure-function relationship
▪ computational methods and capacity
▪ side directed mutagenesis techniques
▪ efficient expression system
▪ biochemical tests
Design
❑ SEQUENCE HOMOLOGY APPROACH
▪ homologous wild-type sequences alignment
▪ identifying amino acid residues responsible for differences
▪ design - combination of possitive mutation from all parental proteins
❑ ANCESTRAL RECONSTRUCTION
▪ construction of phylogenetic tree
▪ design - nods prediction by consensus approach
Design
❑ SEQUENCE HOMOLOGY APPROACH
▪ homologous wild-type sequences alignment
▪ identifying amino acid residues responsible for differences
▪ design - combination of possitive mutation from all parental proteins
❑ ANCESTRAL RECONSTRUCTION
▪ construction of phylogenetic tree
▪ design - nods prediction by consensus approach
Bioinformatika Bi5000
▪ Období: podzim
▪ Rozsah: přednáška 2 hodiny/týden, cvičení 2 hodiny/týden
▪ Vyučující: prof. Mgr. Jiří Damborský, Dr., doc. RNDr. Roman Pantůček, Ph.D.,
▪ Osnova:
▪ bioinformatické databáze a jejich prohledávání
▪ analýza nukleotidových a proteinových sekvencí
▪ hledání a identifikace genů
▪ analýza a předpověď struktury proteinů
Design
❑ STRUCTURE-BASED APPROACH
▪ prediction of enzyme function from structure alone is challenging
▪ protein structure (X-ray crystallography, NMR, homology models!)
▪ molecular modelling
o molecular docking
o molecular dynamics
o quantum mechanics/molecular mechanics (QM/MM)
Strukturní biologie Bi9410
▪ Období: podzim
▪ Rozsah: přednáška 2 hodiny/týden, cvičení 2 hodiny/týden
▪ Vyučující: Mgr. David Bednář
▪ Osnova:
▪ struktura, stabilita a dynamika biologických makromolekul
▪ makromolekulární interakce a komplexy
▪ stanovení a předpověď struktury, identifikace důležitých oblastí
▪ stanovení vlivu mutace na strukturu a funkci proteinu
▪ aplikace v biologickém výzkumu, návrhu léčiv a biokatalyzátorů
Construction
❑ site-directed mutagenesis
▪ introducing point mutations
❑ multi site-directed mutagenesis
❑ gene synthesis
▪ commercial service
▪ codone optimisation
Example of rational design
❑ rational design of protein stability
▪ stability to high temperature, extreme pH, proteases etc.
▪ stabilizing mutations increase strength of weak interactions
o salt bridges and H-bonds
Eijsink et al., Biochem. J. 285: 625-628, 1992
o S-S bonds
Matsumura et al., Nature 342: 291 -293, 1989
o addition of prolines
Watanabe et al., Eur. J. Biochem. 226: 277-283, 1994
o less glycines
Margarit et al., Protein Eng. 5: 543 -550, 1992
o oligomerisation
Dalhus et al., J. Mol. Biol. 318: 707 -721, 2002
Example of rational design
❑ engineering protein to resist boiling
▪ reduced rotational freedom
Ser65Pro, Ala96Pro
▪ introduction of disulfide bridge
Gly8Cys + Asn60Cys
▪ improved internal hydrogen bond
Ala4Thr
▪ filling cavity
Tyr63Phe
Burg, B., et al., 1998. PNAS 95: 2056-2060
Half-lifes
(min.)
80°C 100°C
wild type 17.5 >0.5
mutant stable 170
Strategies in protein engineering
Strategies in protein engineering
Strategies in protein engineering
S E M I R AT I O N A L
D E S I G N
Example of semi-rational design
❑ c o n v e rs i o n o f 1 , 2 , 3 - t r i c h l o ro p ro p a n e
b y D h a A f ro m R h o d o c o c c u s e r y t h r o p o l i s Y 2
Example of semi-rational design
❑ c o n v e rs i o n o f 1 , 2 , 3 - t r i c h l o ro p ro p a n e
b y D h a A f ro m R h o d o c o c c u s e r y t h r o p o l i s Y 2
❑ D I R E C T E D E VO LU T I O N - i m p o r t a n c e o f a c c e s s p a t h w ay s
C176
Y176
F176
Bosma, et al. 2002: AEM 68: 3582-87 Gray, et al. 2003: Adv. Appl. Microbiol. 52: 1-27
Example of semi-rational design
Pavlova, et al. 2009: Nature Chem. Biol. 5: 727-733
❑ c o n v e rs i o n o f 1 , 2 , 3 - t r i c h l o ro p ro p a n e
b y D h a A f ro m R h o d o c o c c u s e r y t h r o p o l i s Y 2
❑ D I R E C T E D E VO LU T I O N - i m p o r t a n c e o f a c c e s s p a t h w ay s
❑ S E M I - R AT I O N A L D E S I G N - h o t s p o t s i n a c c e s s t u n e l s
❑ l i b ra r y o f 5 , 3 0 0 c l o n e s s c re e n e d
Example of semi-rational design
Pavlova, et al. 2009: Nature Chem. Biol. 5: 727-733
Experimental throughput is critical
S T A N D A R D D E S I G N
▪ r a n d o m m u t a g e n e s i s ( 2 - 3 p o s i t i o n s )
▪ l i b r a r y o f 1 0 4 c l o n e s
A D V A N C E D D E S I G N
▪ r a n d o m m u t a g e n e s i s ( 5 - 7 p o s i t i o n s )
▪ l i b r a r y o f > 1 0 6 c l o n e s
v o l u m e : 1 0 ´ p L
a s s a y s / d a y : 1 0 7
v o l u m e : 1 0 0 ´ m L
a s s a y s / d a y : 1 0 3
AI in Protein Engineering
DEEP MUTATIONAL SCANNING
super vised learning
SEQUENCE BASED PREDIC TION
super vised learning
MOLECULAR DYNAMI CS
unsuper vised learning STRUCTURE PREDIC TION
deep learning
ACS Catal. 10, 1210-1223 (2020) – 105
❑ … next week (Lesson 7)
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
Bioinformatics 35: 4986-4993 (2019)
Nucleic Acids Res. 47: W414-W422 (2019)
Tools for protein engineering
Nucleic Acids Res. 48, W356-W362 (2018)
Nucleic Acids Res. 45, W393-W399 (2017)
Brief. Bioinform., bbaa337 (2020)
Bioinformatics 37, 23–28 (2021)
Bioinformatics 34: 3586-3588 (2018)
https://loschmidt.chemi.muni.cz/portal/ www.enantis.com
Proteinové inženýrství Bi7410
▪ Období: jaro
▪ Rozsah: přednáška 1 hodina/týden
▪ Vyučující: doc. Radka Chaloupková, Ph.D.
▪ Osnova:
▪ strukturně-funkční vztahy proteinů
▪ metody exprese a purifikace rekombinantních proteinů
▪ metody strukturní a funkční analýzy proteinů
▪ racionální design, semi-racionální design a řízená evoluce
▪ příklady využití proteinového inženýrství
Reading
❑ L u t z , S . 2 0 1 0 : B e y o n d d i r e c t e d e v o l u t i o n - s e m i - ra t i o n a l
p r o t e i n e n g i n e e r i n g a n d d e s i g n . C u r r O p i n B i o t e c h n o l . 2 1 ( 6 ) :
7 3 4 – 7 4 3
❑ C o m p u ta t i o n a l e n z y m e r e d e s i g n a n d C o m p u ta t i o n a l d e n o v o
e n z y m e d e s i g n ( p a g e 5 - 7 )