PROTEIN ENGINEERING
7. Rational and semi-rational design
Loschmidt Laboratories
Department of Experimental Biology
Masaryk University, Brno
Outline
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Outline
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Protein engineering
❑ altering protein structure to improve its properties
❑ three main approaches
▪ rational design
▪ directed evolution
▪ semi-rational design
Protein engineering approaches
Protein engineering approaches
Rational design Directed evolution Semi-rational design
high-throughput
screening/selection not essential essential
advantageous
but not essential
structural and/or
functional information
both essential neither essential either is sufficient
sequence space
exploration
low high, random moderate, targeted
probability to obtain
synergistic mutations
moderate low high
❑ worldwide Protein Data Bank (wwPDB)
▪ http://www.wwpdb.org/
▪ central repository of ~160,000 experimental macromolecular
structures
❑ RCSB PDB
▪ https://www.rcsb.org/
❑ PDBe
▪ https://www.ebi.ac.uk/pdbe/
❑ PDBj
▪ https://pdbj.org/
19
Structural information
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Semi-rational design
❑ combine advantages of rational and random approaches
❑ selection of promising target sites (hot-spots) → mutagenesis
→ creation of small “smart” libraries
❑ based on knowledge of protein structure and function
❑ ☺ high-throughput screening usually not needed
❑ ☺ increased chance of obtaining variants with desired
properties
❑  certain knowledge of protein structure-function
relationships is still required, ☺ but not that much
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Identification of hot-spots
❑ hot-spots for engineering catalytic properties
❑ hot-spots for engineering thermostability
Hot-spots for engineering catalytic properties
❑ residues mediating substrate binding, transition-state
stabilization or product release → mutations can improve or
disrupt binding, catalysis or ligand transport
▪ residues involved in protein-ligand interactions
▪ residues located in binding pockets
▪ residues located in access tunnels
→ these residues also include catalytic or other essential residues which
generally should not be mutated!
Analysis of protein-ligand interactions
❑ requires 3D structure of protein-ligand complex
▪ experimental structure (wwPDB)
▪ theoretical model (molecular docking)
Analysis of protein-ligand interactions
❑ schematic diagrams of protein-ligand interactions
LigPlot, LigPlot+ PoseView
Analysis of protein-ligand interactions
❑ inter-atomic contacts between protein and bound ligands
LPC server
Analysis of binding pockets
❑ binding and active sites of enzymes are often associated
with structural pockets and cavities
ligandactive site
pocket
Analysis of binding pockets
❑ binding and active sites of enzymes are often associated
with structural pockets and cavities
Analysis of binding pockets
❑ binding and active sites of enzymes are often associated
with structural pockets and cavities
mutation
Analysis of binding pockets
❑ binding and active sites of enzymes are often associated
with structural pockets and cavities
Analysis of binding pockets
❑ binding and active sites of enzymes are often associated
with structural pockets and cavities
Analysis of binding pockets
❑ binding and active sites of enzymes are often associated
with structural pockets and cavities
▪ most amino acid residues located in these pockets may come into
contact with the ligands during the catalytic cycle
→ one can accurately predict which residues may interact with the
ligand even without precise knowledge of ligand orientation in the
active site
❑ requires 3D structure of protein
❑ software for detection of pockets
▪ CASTp, fPocket, MetaPocket, Caver Analyst…
Analysis of binding pockets
❑ detailed characterization of all pockets in the structure
CASTp
Analysis of access tunnels
❑ buried binding or active sites are connected with bulk
solvent by access tunnels
ligand
active site
pocket
access tunnel
Analysis of access tunnels
❑ buried binding or active sites are connected with bulk
solvent by access tunnels
Analysis of access tunnels
❑ buried binding or active sites are connected with bulk
solvent by access tunnels
mutation
Analysis of access tunnels
❑ buried binding or active sites are connected with bulk
solvent by access tunnels
mutation
Analysis of access tunnels
❑ buried binding or active sites are connected with bulk
solvent by access tunnels
▪ adjusted to permit transport of specific molecules
▪ mutations can speed-up or hinder transport of molecules as well
as allow transport of other molecules
❑ requires 3D structure of protein
❑ software for detection of tunnels
▪ Caver, Mole, HOLE, PoreWalker
Analysis of access tunnels
❑ detailed characteristics of access tunnels
CAVER Analyst 2.0
Hot-spots for engineering thermostability
❑ highly flexible residues – introduction of rigidifying mutations
❑ residues located in access tunnels
❑ residues predicted by systematic in silico saturation
mutagenesis
→ these residues may also include catalytic or other essential
residues which generally should not be mutated!
Identification of highly flexible residues
❑ prediction based on crystallographic B-factors
▪ reflect the degree of thermal motion, and thus the flexibility of
individual residues
❑ requires 3D structure of protein
▪ experimental structure determined by X-ray crystallography (wwPDB)
Identification of highly flexible residues
❑ average B-factor of each residue in the target protein
B-FITTER
Analysis of access tunnels
❑ saturation mutagenesis in tunnel residues has 2× better
chance to significantly improve stability than mutagenesis in
other protein regions (based on computational predictions)
Analysis of access tunnels
❑ Detection of tunnels in proteins and analysis of ligand transport
CAVER Web
Systematic in silico saturation mutagenesis
❑ computational tools for the prediction of effect of amino
acid substitutions on protein stability
▪ each residue in the protein structure is replaced by all other
possible amino acids and the change in folding free energy (ΔΔG)
upon mutation is estimated
▪ positions with a high proportion of stabilizing mutations and/or
low proportion of destabilizing mutations are good candidates for
randomization by experimental saturation mutagenesis
❑ usually requires 3D structure of protein
▪ experimental structure (wwPDB)
▪ theoretical model (homology modeling)
Systematic in silico saturation mutagenesis
❑ fast systematic scan of all possible single-point mutations –
prediction of stability changes upon mutation
❑ sequence optimality score (the sum of all negative ΔΔGs at
a given position) – indicates poorly optimized positions
PoPMuSiC
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Evaluation of hot-spots
❑ hot-spots identified by computational tools can be further
evaluated to prevent replacing indispensable amino acid
residues and to prioritize the hot-spots (i.e., order the hotspots
based on their suitability for mutagenesis)
❑ analysis of evolutionary conservation
❑ prediction of effects of mutations on protein stability or
function
Analysis of evolutionary conservation
❑ residues essential for maintaining structural or functional
properties of a protein tend to be conserved during evolution
▪ conserved residues are generally not recommended as suitable
targets for mutagenesis - their replacement often leads to the loss of
protein function
▪ mutagenesis targeting highly mutable positions provides a significantly
higher proportion of viable variants than random mutagenesis
▪ targeting moderately or highly variable positions, which are expected
to be tolerant to a wide range of substitutions, represents a good
approach for producing efficient smart libraries (i.e., libraries
with a high proportion of correctly folded and active variants)
Analysis of evolutionary conservation
❑ residue conservation can be derived from a multiple
alignment of a set of related proteins (3D structure not
required)
Analysis of evolutionary conservation
❑ residue conservation can be derived from a multiple
alignment of a set of related proteins (3D structure not
required)
Analysis of evolutionary conservation
❑ evolutionary conservation of individual positions in protein
mapped on protein 3D structure
ConSurf
Prediction of mutation effects
❑ computational tools for the prediction of effect of amino
acid substitutions on protein stability or protein function
▪ in silico site-saturation mutagenesis of identified hot-spots – check if
mutations at a given site are likely to be tolerated
▪ many highly destabilizing/deleterious mutations predicted for a
certain position – given site is not a very good target for
mutagenesis
▪ sites with only a few highly destabilizing /deleterious mutations
predicted can still represent promising hot-spots (the amino acids
with potentially destabilizing/deleterious effects can be discarded
from the library by the appropriate selection of degenerate codons)
Prediction of mutation effects
❑ effects on protein stability – usually requires 3D structure
of protein
▪ experimental structure (wwPDB)
▪ theoretical model (homology modeling)
❑ effects on protein function – sequence information often
sufficient
Prediction of mutation effects
❑ prediction of effect of substitutions on protein stability
▪ Evaluation of the change of protein free energy upon mutation
▪ Evaluation of contributions of individual interactions to total
energy
▪ Usually requires structural information
❑ software for prediction of effect of mutation on stability
▪ Rosetta, FoldX, CUPSAT, ERIS
Prediction of mutation effects
❑ prediction of effect of substitutions on protein stability
CUPSAT
Prediction of mutation effects
❑ prediction of effect of substitutions on protein function
▪ Evaluation if a mutation would impair protein function
▪ Hard to describe by physico-chemical properties > machine learning
▪ Usually sequence based calculation
❑ software for prediction of effect of mutation on function
▪ PredictSNP, SIFT, MAPP, PhD-SNP…
Prediction of mutation effects
❑ prediction of effect of substitutions on protein function
PROVEAN
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Selection of substitutions
❑ substitutions introduced using degenerate codons
▪ e.g., NNK (N = A/T/G/C; K = T/G)
Selection of substitutions
❑ all possible substitutions - NNK or NNS degenerate codons
▪ ☺ encode all 20 amino acids with the lowest redundancy and price
(mixture of 32 codons)
▪  redundancy is not completely eliminated (3× Arg, Leu, Ser, 2×
Ala, Gly, Pro, Thr and Val)
Selection of substitutions
❑ all possible substitutions - NNK or NNS degenerate codons
❑ introduction of only selected substitutions using
degenerate codons encoding reduced amino acid alphabets
▪  do not encode all 20 amino acids
▪ ☺ decreased library size → improved screening efficiency
▪ NDT – balanced set of 12 amino acids (12 codons)
Selection of substitutions
❑ all possible substitutions - NNK or NNS degenerate codons
❑ introduction of only selected substitutions using
degenerate codons encoding reduced amino acid alphabets
Selection of reduced amino acid alphabets
❑ introduction of amino acids exhibiting certain properties
▪ VRK – 8 hydrophilic amino acids (12 codons)
▪ NYC – 8 hydrophobic amino acids (8 codons)
▪ KST – 4 small amino acids (4 codons)
▪ ...
Selection of reduced amino acid alphabets
❑ introduction of amino acids exhibiting certain properties
❑ introduction of a balanced set of amino acids
▪ NDT – balanced set of 12 amino acids (12 codons)
Selection of reduced amino acid alphabets
❑ introduction of amino acids exhibiting certain properties
❑ introduction of a balanced set of amino acids
❑ introduction of substitutions existing (at a given site) in
known natural proteins
▪ likely increasing the proportion of viable variants in the resulting
library
▪ can be obtained by analysis of multiple sequence alignment
Selection of reduced amino acid alphabets
❑ introduction of amino acids exhibiting certain properties
❑ introduction of a balanced set of amino acids
❑ introduction of substitutions existing (at a given site) in
known natural proteins
❑ discarding amino acids with potentially destabilizing/
deleterious effects
▪ can be obtained by prediction of effects of mutations on protein
stability or function
HotSpot Wizard
❑ meta-server combining several tools
▪ automatic identification of hot-spots for engineering of enzyme
catalytic properties
▪ prioritization of hot-spots by their mutability
▪ distribution of amino acids at individual positions
HotSpot Wizard
Functional hot-spots Stability hot-spots (flexibility)
Stability hot-spots (evolution)
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Correlated hot-spots
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HotSpot Wizard
HotSpot Wizard
1. protein structure
HotSpot Wizard
2. residues indispensable for protein function: catalytic and
binding residues
HotSpot Wizard
3. functional residues: active site pocket and tunnels
HotSpot Wizard
4. mutability of individual positions of protein
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Design of library
❑ decisions to be made after evaluation and prioritization of
hot-spots:
▪ how many and which positions to target?
▪ should the positions be randomized simultaneously or separately?
▪ should all or only a reduced set of amino acids be introduced at
individual positions?
→ dramatic effect on the size of the resulting library
Design of library – HotSpot Wizard
Design of library – HotSpot Wizard
Design of library – HotSpot Wizard
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling
Mutagenesis and screening
❑ saturation mutagenesis - next lecture ☺
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling→ design of mutations
Rational design
❑ site-specific changes on the target enzyme
❑ few amino-acid substitutions that are predicted to elicit
desired improvements of enzyme function
❑ based on detailed knowledge of protein structure, function
and catalytic mechanism
❑ ☺ relatively simple characterization of constructed variants
❑  complexity of protein structure-function relationships
❑  molecular modeling expertise usually required
❑ Protein engineering approaches
❑ Semi-rational design
▪ identification of hot-spots
▪ evaluation of hot-spots
▪ selection of substitutions
▪ design of library
▪ mutagenesis and screening
❑ Rational design
▪ molecular modeling→ design of mutations
Molecular modeling
❑ “Theoretical or computational technique that provides insight
into the behavior of molecular system.”
A. R. Leach
❑ Applications
▪ Protein stabilization
▪ prediction of protein dynamics
▪ prediction of protein-ligand interactions
▪ prediction of reaction barriers and reaction mechanisms
Molecular modeling
❑ relationship between energy and 3D-structure
▪ potential energy surface
❑ basic methods
▪ molecular mechanics
▪ molecular dynamics
▪ quantum chemistry
▪ molecular docking
❑ Enzymes as biocatalysts
▪ good activity and selectivity in water solution and standard
temperature
▪ for many biotechnological applications, high temperature or
addition of organic solvents are necessary
▪ this conditions can lead to denaturation > importance of stable
proteins
Design of stability
❑ Computational method FireProt
https://loschmidt.chemi.muni.cz/fireprot/
▪ prediction of all single-point mutants
by FoldX, Rosetta, and back-to-consensus
▪ smart filtering based on conservation,
correlation, electrostatic interactions,
and antagonistic effect
▪ final prediction of multiple-point
mutants for gene synthesis
Design of stability
Design of stability
Racionální design stabilnějších enzymů
❑ Stabilization of haloalkane dehalogenase DhaA
▪ In silico prediction of 5,500 mutants
▪ Experimental testing of 5 mutants
❑ Output
▪ 3 more stable mutants
▪ Combined mutant ΔTm = 24°C
Molecular dynamics
❑ successive configurations of system in time
❑ provides information on energetics, amplitudes and time
scales of local motions on atomic level
Molecular dynamics
❑ generates ensemble of structures
▪ more precise calculations of free energies
Molecular docking
❑ predicts structure of receptor (protein) – ligand complex
Molecular docking
❑ Two components procedure
▪ searching – finding the
conformation of ligand in the
active site of the enzyme
▪ scoring – evaluation of the
binding free energy
❑ Docking software
▪ Autodock, Vina, Gold,
Medusa, Rosetta Dock…
Molecular docking
❑ Virtual screening
▪ many compounds against one enzyme
▪ one compound against many enzymes
Quantum chemistry
❑ modeling of reaction
▪ reaction barrier
Quantum chemistry
❑ modeling of reaction
TRITON
Design of mutations
MD of free protein
Docking
MD of complex
QM of complex
Hypothesis
Experiment Investigated system
Knowledge
Design of mutations
❑ identification of functionally important residues
▪ decomposition of energies to individual contribution
▪ flexible residues – functionally important dynamics
▪ residues in contact with ligand
→ further molecular modeling
→ semi-rational design
Design of mutations
❑ design of modified enzymes by in silico screening
▪ study of effects of all relevant mutations
▪ selection and combination of the best mutations
Design of mutations
❑ effect of mutations at molecular level
▪ example: improved activity of tunnel mutant
closed
tunnel
improved
activity
+
PROTEIN ENGINEERING
8. Directed evolution
Loschmidt Laboratories
Department of Experimental Biology
Masaryk University, Brno