strategies for the discovery and engineering of enzymes...

Strategies for the discovery and engineering of enzymes forbiocatalysisTimo Davids, Marlen Schmidt, Dominique Bottcher andUwe T Bornscheuer

Available online at www.sciencedirect.com

Protein engineering is the most important method to overcome

the limitations of natural enzymes as biocatalysts. The past few

years have seen a tremendous increase in novel concepts to

facilitate the design of mutant libraries for focused directed

evolution mostly guided by advanced bioinformatic tools. In

addition, advanced high-throughput methods were developed

using, for example, FACS analysis or microfluidic systems.

These achievements significantly facilitate the tailor-made

design of enzymes to make them suitable for industrial

applications.

Address

Institute of Biochemistry, Department of Biotechnology and Enzyme

Catalysis, University of Greifswald, Felix-Hausdorff-Str. 4, 17487

Greifswald, Germany

Corresponding author: Bornscheuer, Uwe T (uwe.bornscheuer@uni-

greifswald.de)

Current Opinion in Chemical Biology 2013, 17:215–220

This review comes from a themed issue on Biocatalysis and

Biotransformation

Edited by Nicholas J Turner and Matthew D Truppo

For a complete overview see the Issue and the Editorial

Available online 21st March 2013

1367-5931/$ – see front matter, # 2013 Elsevier Ltd. All rights

reserved.

http://dx.doi.org/10.1016/j.cbpa.2013.02.022

IntroductionEnzymes have found numerous applications as biocata-

lysts for synthetic organic chemistry due to their high

stereoselectivity required for the creation of chiral com-

pounds, but also for targets where mild reaction con-

ditions are important, such as in the conversion of

labile compounds, in protection/deprotection steps or

where chemoselectivity and regioselectivity are needed.

Furthermore, engineered enzymes became useful for the

pretreatment of renewable resources as starting materials

such as in the degradation of lignocellulose to make

biofuels of the 2nd or 3rd generation. As recently pointed

out in our review [1��] ‘‘in the past, an enzyme-based processwas designed around the limitations of the enzyme; today, theenzyme is engineered to fit the process specifications’’, biocata-

lysis became a mature technology through several waves

of innovations. Initially only wild-type enzymes could be

used, but the advent of recombinant DNA technology

enabled the cloning and functional expression of the

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enzyme of interest. First protein engineering methods

using rational protein design or directed evolution (by

error-prone PCR or gene shuffling in combination with

high-throughput screening and iterative cycles of im-

provement) allowed alteration of the properties of the

enzyme to meet limitations in their application to a

reasonable extent. Novel tools of protein engineering

open up new avenues to achieve the goal of industrially

useful biocatalysts significantly faster and even allow to

create enzyme variants where the wild-type had no

activity at all as demonstrated for a transaminase to make

the drug sitagliptin [2]. Numerous examples of successful

protein engineering can be found in books [3–5] and

recent reviews [1��,6–8,9��,10,11�]. Further examples

for the application of various enzymes in biocatalysis

can be found elsewhere in this issue. This contribution

covers recent developments published mostly in 2010–2012 focusing on novel concepts for enzyme discovery, for

the creation of mutant libraries and identification of hits,

and bioinformatic tools to guide protein engineering

strategies (Figure 1). The de novo computational design

of enzymes is outside of the scope of this review and

covered in the contribution by Hilvert and co-authors in

this issue [12].

Novel tools for enzyme discoveryThe metagenome approach is nowadays a well-estab-

lished approach to find novel proteins [13]. The limitation

that most microbes cannot be grown under standard

laboratory conditions is overcome here by extracting

the complete genomic DNA from an environmental

sample followed by either first, sequencing and explora-

tion of the genetic information through identification of

open reading frames encoding enzymes and their func-

tional annotation or second, by cloning and functional

expression in metagenome libraries, which are then

screened with high-throughput assays for novel enzyme

activities (Figure 1). In a recent contribution, the com-

pany BRAIN identified a surprisingly large molecular

diversity for the well-known serine protease Subtilisin

Carlsberg [14��]. In only four soil samples they were able

to find 94 sequences of this subtilisin bearing 38

mutations, equivalent to 2–8 amino acid exchanges per

variant. 51 unique protein variants could be functionally

expressed in Bacillus subtilis, which also differed in their

functionality. Hence, their discovery of coexisting gene

variants is a potent source for novel enzyme variants. This

method thus represents an alternative to directed


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216 Biocatalysis and Biotransformation

Figure 1

Starting Material

metagenomic library

gene(s) of interest

information about hotspots

gene of interest

protein structure

structural information andprediction of key motifs

define transition state for thedesired reaction

propose an active site able tostabilize that transition state

QM/MM modeling

accomodate the active siteinto an existing scaffold

ROSETTA algorithm experimental verification

library of novel enzymesnovel protein sequencesdatabase search

gene1: ..HIYQ..PRAHQFgene2: ..YIRP..RMGVRPgene3: ..FVER..GVRGTRgene4: ..FVEL..GVRGSR.

Dire

cted

Evo

lutio

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emi-r

atio

nal D

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nR

atio

nal D

esig

n

Pro

tein

Eng

inee

ring

Dat

abas

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earc

hde

nov

o D

esig

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cloning and sequencing orfunctional annotation

non-homologous mutagenesis• epPCR• SeSaM

homologous mutagenesis• gene shuffling

saturation mutagenesis• ISM• CASTing

Computational tools• HotSpot Wizard• QSAR• SCHEMA• “small but smart”

library of novel enzymes ornovel protein sequences

screening

gene librarymutagenesis

mutagenesis

computer supportedpredictions and mutagenesis

gene library

specific point mutations

chimeric genes

gene variant

determination of specificactivity, enantioselectivity,

etc.

Sea

rch

for

Exi

stin

g E

nzym

es

Realization Result Screening

Current Opinion in Chemical Biology

Overview of concepts used to identify or create enzymes with desired properties.

Current Opinion in Chemical Biology 2013, 17:215–220 www.sciencedirect.com

Strategies for the discovery and engineering of enzymes for biocatalysis Davids et al. 217

evolution concepts relying only on random mutagenesis

as the metagenome-based microdiversity concept gave a

broad set of well expressed active variants.

The enormous progress in sequencing technology in

combination with metagenome libraries has led to an

exponential increase in the number of sequence data

(currently approx. 20 million sequences). However, the

annotation of the putative protein function is performed

automatically and consequently can lead to misleading

interpretations. In a recent example, we took advantage

of these errors while trying to identify (R)-selective

amine transaminases (ATA) for which no protein

sequence was reported in the literature at the time of

our study. ATA are pyridoxal-50-phosphate (PLP) de-

pendent enzymes catalyzing the synthesis of chiral

amines from ketones (in contrast to amino acid trans-

aminases that convert a-keto acids to a-amino acids)

and only (S)-selective ATA were known. Using a soph-

isticated search algorithm — based on distinct amino

acid motifs of known amino acid transaminases — an

alignment of >5000 protein sequences from public

database of PLP-dependent transaminases identified

21 new protein sequences (equivalent to a hit rate of

0.5%) [15]. For 17 proteins it could be confirmed that

they all are true amine transaminases having the pre-

dicted (R)-enantiopreference and these were then used

in the asymmetric synthesis of a set of 12 chiral (R)-

amines [16]. Another still only scarcely explored source

for novel enzymes is the Brookhaven protein structure

database (www.pdb.org), which contains numerous

proteins, where the 3D-structure has been deposited,

but where the proteins were never biochemically

characterized. We recently identified four (S)-selective

ATA in the pdb and could functionally assign them by

biochemical characterization [17].

Concepts guided by computational toolsMany computational tools such as HotSpot Wizard [18],

ProSAR [19] and SCHEMA have been established to

analyze enzyme 3D-structures or homology models to

guide the design of mutations to alter enzyme properties

[20]. The recently proposed ASRA (adaptive substituent

reordering algorithm) is an alternative to conventional

quantitative structure–activity relationship (QSAR)

methods to enable the identification of enzyme variants

in focused mutant libraries having desired properties. In

combination with iterative saturation mutagenesis (ISM)

[21] the most enantioselective mutants of an epoxide

hydrolase from Aspergillus niger could be predicted [22].

Using SCHEMA, a structure-guided computational

algorithm, it was possible to recombine multiple low

identity proteins to obtain chimeric enzymes with

improved thermostability or substrate specificity [23].

In a very recent example this technique was used to

recombine human arginase I and II to catalytically active

chimeras [24].


Semirational design/focused directedevolutionSemirational methods, based on the knowledge derived

from biochemical and structural data, combine the advan-

tages of rational and random tools to create small focused

libraries, which is especially advantageous if no high-

throughput assay system is available. Methods of choice

for the semirational approach are site-saturation mutagen-

esis methods like ISM or CASTing [25]. Using these

techniques the substrate specificity of an amylosucrase

from Neisseria polysaccharea (a-transglucosidase) was

recently altered after screening 20,000 variants. The best

mutant showed 400-fold enhanced catalytic efficiency

toward the donor substrate sucrose and the non-natural

acceptor substrate allyl 2-acetamido-2-deoxy-alpha-D-

glucopyranoside [26]. The regioselectivity and enantios-

electivity of a P450-BM3 monooxygenase toward cyclo-

hexene-1-carboxylic acid methyl ester was altered via

saturation mutagenesis at 24 single-residue sites and

some two-residue sites. Only the two-residue sites

yielded (R)-selective mutants with >95%ee [27].

Saturation mutagenesis at more than one amino acid

residue simultaneously creates a huge number of variants

and due to the degeneracy of the genetic code the amino

acid distribution in the resulting library is unbalanced.

Several concepts were developed to reduce the library

size: one can decrease it by using NDT or NDK codons

with the disadvantage that not all proteinogenic amino

acids are covered. An alternative is to use chemically

synthesized dinucleotide or trinucleotide phosphorami-

dites [28] to allow a fully controlled randomization, but

this method is still not generally used due to costs and

availability of starting materials. The codon search algor-

ithm CoFinder [29��] allows to determine the optimal

primer mixture for the desired amino acid distribution. A

very recent example for reducing codon redundancy has

been called 22c-trick [30]. Here, a mixture of three

oligonucleotides (two degenerate) with NDT (12 codons)

VHG (9 codons) and one TGG primer resulted in a codon

to amino acid ratio of 22:20. This mixture contains no stop

codons and only two redundant codons for valine and

leucine. A quite similar approach using four primers (with

NDT, VMA, ATG and TGG codons) was also reported

[31].

Structure-guided approachesThis concept combines structural information with

protein sequences. The well established ‘consensus

approach’ is based on the theory that most abundant

amino acids at each position in a set of homologous

enzymes contribute more than average to protein per-

formance than the nonconsensus amino acids [32]. Com-

parison of sequences within large enzyme families can

then identify conserved amino acids followed by their

insertion into the starting protein. The Bommarius group

combined this method with the B-FIT analysis (B-Factor


http://www.pdb.org/


Iterative Test) to improve the thermostability of an a-

amino ester hydrolase from Xanthomonas campestris and

obtained a quadruple mutant, which was 7 8C more

thermostable and 1.3-fold more active than the wild-type

[33]. In another example the thermostability of an endo-

glucanase from Clostridium thermocellum could be

improved by this method too. The sequence alignment

of 18 glycoside hydrolases with 30–60% homology yielded

three more thermostable mutants with similar or higher

activity compared to the wild-type [34].

In another approach, the 3DM-software was successfully

used to guide protein engineering of enzymes from the a/

b-hydrolase fold superfamily [35]. 3DM is a commercial

structure-based sequence alignment and analysis tool,

which goes beyond the consensus approach as it not only

enables to identify the distribution of amino acids within

a protein superfamily, but can also analyze connectivity

between residues as it is constructed from protein struc-

ture alignments. In a subset of the a/b-hydrolase fold

enzyme superfamily, 1751 structurally related esterases

served as basis to create ‘small, but smart’ libraries [36].

The 3DM analysis enabled to design libraries to cover

only amino acids frequently occurring at a given position

in this superfamily and hence led to a substantial

reduction in the library size to be created and screened

in the laboratory. Targeting four residues in the active site

region of an esterase from Pseudomonas fluorescens (PFE)

improved the enantioselectivity toward a chiral carboxylic

acid from E = 3 to E = 80 and also improved activity up to

240-fold [37]. In combination with the B-FIT method,

the 3DM-analysis was also used to increase the thermo-

activity of PFE by 8 8C [38]. Rational design in combi-

nation with the B-FIT method and the 3DM database for

the glucosidase family 13 containing 5092 sequences

helped to create mutants of a sucrose phosphorylase from

Bifidobacterium adolescentis with more than doubled half-

life at 60 8C [39]. In the 3DM database for methyltrans-

ferases 140 sequences of the subfamily were aligned

and — supported by a homology model — the analysis

led to the identification of the putative cofactor binding

site and catalytic residues, which could be confirmed by

site-directed mutagenesis [40].

Tunnel software toolsAs many enzymes have buried active sites, predictions for

changing substrate specificity, enantioselectivity or

regioselectivity are difficult without detailed knowledge

about the shape and size of the tunnels enabling substrate

and product entry or release. Also the solvent access can

be important. Nowadays geometry-based softwares like

CAVER [41], MOLE [42] and POREWALKER [43] help

to understand these relationships, but have the limitation

to calculate primarily static structures. An extensive over-

view about these different software tools can be found in a

current excellent review [44��]. CAVER could be suc-

cessfully applied to engineer the tunnel in a haloalkane


dehalogenase from Sphingobium japonicum leading to a

mechanism change in product release [45]. The same

software was used to gain insights into the role of sub-

strate protonation and the inhibitor interactions in the

human monoamine oxidase A [46]. With the latest version

CAVER 3.0 it is now also possible to include protein

dynamics into these calculations and to analyze multiple

different access pathways using large collections of

protein structures [47]. Analysis of tunnel residues and

their mutation enabled to substantially improve the sol-

vent stability of the haloalkane dehalogenase DhaA from

Rhodococcus rhodochrous [48].

New methods for mutagenesisIn the past decades numerous mutagenesis methods

beside error-prone PCR and gene shuffling have been

described [49], but still few new tools are reported. Most

molecular biology methods only insert point mutations,

but the Tawfik group recently described a method

dubbed TRINS (tandem repeat insertions), in which they

describe how defined sequences of variables length (3–150 bp) can be randomly introduced into the gene of

interest. Although TRINS is limited to insertion-by-

duplication using distinct short sequences, it allows to

identify regions in a protein, which are not accessible by

standard protocols as shown experimentally for variants of

a TEM-1 b-lactamase [50].

Tools for high-throughput screeningOnce a mutant library has been created, the desired

variants must be identified for which various high-

throughput screening (HTS) methods have been pub-

lished, usually tailor-designed for the specific problem of

interest [51]. By far the highest throughput is possible by

fluorescence activated cell sorting (FACS) [52,53] or

using microfluidic devices [54].

Agresti et al. presented an ultra-HTS platform in which

aqueous droplets dispersed in oil as picoliter-volume

reaction vessels are used and thousands can be screened

per second. After two rounds of evolution 108 variants of a

horseradish peroxidase were analyzed within 10 h leading

to a variant with 10-fold higher catalytic efficiency com-

pared to the wild-type. The authors claimed that their

device reduced the assay costs 1-million-fold since the

total reaction volume was <150 ml and the assay was

1000-fold faster than a FACS system [55].

A flow cytometry-based screening system for directed

evolution of a protease applying in vitro compartmenta-

lization (IVC) was developed by the Schwaneberg group.

A high mutational load error-prone PCR-based mutant

library of Subtilisin Carlsberg was expressed in an extra-

cellular protease-deficient Bacillus subtilis strain and

screened in double emulsions using a fluorogenic peptide

substrate for increased resistance toward a protease

inhibitor. After three rounds of iterative sorting an


Strategies for the discovery and engineering of enzymes for biocatalysis Davids et al. 219

enzyme variant containing six mutations was found exhi-

biting a 160% increased resistance compare to the wild-

type [56].

Another example for protease engineering using FACS

uses a so-called counter-selection approach to eliminate

unwanted enzyme variant and this enabled to alter the

substrate specificity of the outer membrane endopepti-

dase OmpT [53]. We have recently developed a FACS-

based system in which highly enantioselective variants of

the esterase PFE were selected from a mutant library in

Escherichia coli by coupling one enantiomer of the sub-

strate to the carbon source glycerol and the other enan-

tiomer to a toxic compound. An E. coli showing better

viability could hence be sorted out by the FACS and

analyzed for PFE mutants having higher selectivities

[52].

ConclusionsThe various novel tools covered in this review substan-

tially facilitate the discovery and engineering of enzymes.

Especially the combination of bioinformatic tools to

guide library design helps to achieve the engineering

target much faster and with less effort in terms of labor

and consumables reducing development times from years

to months. These achievements are not only useful for

biocatalyst development, but also facilitate synthetic

biology and metabolic engineering projects.

AcknowledgementWe thank the German Research Foundation (DFG, Grant Bo1862/6-1) forfinancial support.

References and recommended readingPapers of particular interest, published within the period of review,have been highlighted as:

� of special interest�� of outstanding interest

1.��

Bornscheuer UT, Huisman GW, Kazlauskas RJ, Lutz S, Moore J,Robins K: Engineering the third wave in biocatalysis. Nature2012, 485:185-194.

In this review the authors describe the major developments that proteinengineering has been experienced during the past ten years fastening theprocess of tailoring a biocatalyst for industrial application. New conceptsare highlighted and remaining challenges are discussed.

2. Savile CK, Janey JM, Mundorff EC, Moore JC, Tam S, Jarvis WR,Colbeck JC, Krebber A, Fleitz FJ, Brands J et al.: Biocatalyticasymmetric synthesis of chiral amines from ketones appliedto sitagliptin manufacture. Science 2010, 329:305-309.

3. Buchholz K, Kasche V, Bornscheuer UT: Biocatalysts and EnzymeTechnology. edn 2. Weinheim: Wiley-VCH; 2012.

4. Lutz S, Bornscheuer UT (Eds): Protein Engineering Handbook, vol.3. Weinheim: Wiley-VCH; 2012.

5. May O, Groger H, Drauz K (Eds): Enzyme Catalysis in OrganicSynthesis. Weinheim: VCH; 2012.

6. Behrens GA, Hummel A, Padhi SK, Schatzle S, Bornscheuer UT:Discovery and protein engineering of biocatalysts for organicsynthesis. Adv Synth Catal 2011, 353:2191-2215.

7. Kazlauskas RJ, Bornscheuer UT: Finding better proteinengineering strategies. Nat Chem Biol 2009, 5:526-529.


8. Reetz MT: Laboratory evolution of stereoselective enzymes: aprolific source of catalysts for asymmetric reactions. AngewChem Int Ed 2011, 50:138-174.

9.��

Steiner K, Schwab H: Recent advances in rational approachesfor enzyme engineering. Comput Struct Biotechnol J 2012,2:e201209010.

An excellent review providing a detailed overview about up-to-date meth-ods for protein engineering alongside with various successful examples.

10. Strohmeier GA, Pichler H, May O, Gruber-Khadjawi M:Application of designed enzymes in organic synthesis. ChemRev 2011, 111:4141-4164.

11.�

Wenda S, Illner S, Mell A, Kragl U: Industrial biotechnology —the future of green chemistry? Green Chem 2011, 13:3007-3047.

This review addresses the question about efficiency of biocatalytic routesand their importance for greener industrial processes.

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13. Lorenz P, Eck J: Metagenomics, industrial applications. Nat RevMicrobiol 2005, 3:510-516.

14.��

Gabor E, Niehaus F, Aehle W, Eck J: Zooming in onmetagenomics: molecular microdiversity of SubtilisinCarlsberg in soil. J Mol Biol 2012, 418:16-20.

In this article, the authors took advantage of spontaneous natural muta-tions, which are estimated to produce 600 new enzyme variants per day ina 100 g soil sample of which 4–5 are claimed beneficial to the micro-diversity. The enormous potential of this concept is demonstrated experi-mentally for the protease Subtilisin.

15. Hohne M, Schatzle S, Jochens H, Robins K, Bornscheuer UT:Rational assignment of key motifs for function guides in silicoenzyme identification. Nat Chem Biol 2010, 6:807-813.

16. Schatzle S, Steffen-Munsberg F, Thontowi A, Hohne M, Robins K,Bornscheuer UT: Enzymatic asymmetric synthesis ofenantiomerically pure aliphatic, aromatic and arylaliphaticamines with (R)-selective amine transaminases. Adv SynthCatal 2011, 353:2439-2445.

17. Steffen-Munsberg F, Vickers C, Thontowi A, Schatzle S,Tumlirsch T, Svedendahl Humble M, Land H, Berglund P,Bornscheuer UT, Hohne M: Connecting unexplored proteincrystal structures to enzymatic function. ChemCatChem 2013,5:150-153.

18. Pavelka A, Chovancova E, Damborsky J: HotSpot Wizard: a webserver for identification of hot spots in protein engineering.Nucleic Acids Res 2009, 37:W376-W383.

19. Fox RJ, Davis SC, Mundorff EC, Newman LM, Gavrilovic V, Ma SK,Chung LM, Ching C, Tam S, Muley S et al.: Improving catalyticfunction by ProSAR-driven enzyme evolution. Nat Biotechnol2007, 25:338-344.

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21. Reetz MT, Carballeira F D, Vogel A: Iterative saturationmutagenesis on the basis of B factors as a strategy forincreasing protein thermostability. Angew Chem Int Ed 2006,45:7745-7751.

22. Feng XJ, Sanchis J, Reetz MT, Rabitz H: Enhancing theefficiency of directed evolution in focused enzyme libraries bythe adaptive substituent reordering algorithm. Chem Eur J2012, 18:5646-5654.

23. Heinzelman P, Komor R, Kanaan A, Romero P, Yu XL, Mohler S,Snow C, Arnold FH: Efficient screening of fungalcellobiohydrolase class I enzymes for thermostabilizingsequence blocks by SCHEMA structure-guidedrecombination. Protein Eng Des Sel 2010, 23:871-880.

24. Romero PA, Stone E, Lamb C, Chantranupong L, Krause A,Miklos AE, Hughes RA, Fechtel B, Ellington AD, Arnold FH et al.:SCHEMA-designed variants of human arginase I and II revealsequence elements important to stability and catalysis. ACSSynth Biol 2012, 1:221-228.



25. Reetz MT, Bocola M, Carballeira JD, Zha DX, Vogel A: Expandingthe range of substrate acceptance of enzymes: combinatorialactive-site saturation test. Angew Chem Int Ed 2005,44:4192-4196.

26. Champion E, Guerin F, Moulis C, Barbe S, Tran TH, Morel S,Descroix K, Monsan P, Mourey L, Mulard LA et al.: Applyingpairwise combinations of amino acid mutations for sorting outhighly efficient glucosylation tools for chemo-enzymaticsynthesis of bacterial oligosaccharides. J Am Chem Soc 2012,134:18677-18688.

27. Agudo R, Roiban GD, Reetz MT: Achieving regio- andenantioselectivity of P450-catalyzed oxidative CH activation ofsmall functionalized molecules by structure-guided directedevolution. Chembiochem 2012, 13:1465-1473.

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29.��

van Leeuwen JGE, Wijma HJ, Floor RJ, van der Laan JM,Janssen DB: Directed evolution strategies forenantiocomplementary haloalkane dehalogenases: fromchemical waste to enantiopure building blocks. Chembiochem2012, 13:137-148.

Van Leeuwen et al. applied five rounds of evolution using site-restrictedmutagenesis with sophisticated primer design based on a codon searchalgorithm. After screening of only 5500 clones, they obtained twoenantiocomplementary haloalkane dehalogenase variants given accessto both enantiomers of epichlorohydrin with 90% ee and 97% ee,respectively.

30. Kille S, Acevedo-Rocha CG, Parra LP, Zhang Z-G, Opperman DJ,Reetz MT, Acevedo JP: Reducing codon redundancy andscreening effort of combinatorial protein libraries created bysaturation mutagenesis. ACS Synth Biol 2013, 2:83-92.

31. Tang LX, Gao H, Zhu XC, Wang X, Zhou M, Jiang RX:Construction of ‘‘small-intelligent’’ focused mutagenesislibraries using well-designed combinatorial degenerateprimers. Biotechniques 2012, 52:149-158.

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35. Kuipers RKP, Joosten HJ, van Berkel WJ, Leferink NG, Rooijen E,Ittmann E, van Zimmeren F, Jochens H, Bornscheuer U, Vriend Get al.: 3DM: systematic analysis of heterogeneous superfamilydata to discover protein functionalities. Proteins 2010, 78:2101-2113.

36. Kourist R, Jochens H, Bartsch S, Kuipers R, Padhi SK, Gall M,Bottcher D, Joosten HJ, Bornscheuer UT: The alpha/beta-hydrolase fold 3DM database (ABHDB) as a tool for proteinengineering. Chembiochem 2010, 11:1635-1643.

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39. Cerdobbel A, De Winter K, Aerts D, Kuipers R, Joosten HJ,Soetaert W, Desmet T: Increasing the thermostability ofsucrose phosphorylase by a combination of sequence- andstructure-based mutagenesis. Protein Eng Des Sel 2011,24:829-834.

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41. Petrek M, Otyepka M, Banas P, Kosinova P, Koca J, Damborsky J:CAVER: a new tool to explore routes from protein clefts,pockets and cavities. BMC Bioinformatics 2006, 7:316.

42. Petrek M, Kosinova P, Koca J, Otyepka M: MOLE: a Voronoidiagram-based explorer of molecular channels, pores, andtunnels. Structure 2007, 15:1357-1363.

43. Pellegrini-Calace M, Maiwald T, Thornton JM: PoreWalker: anovel tool for the identification and characterization ofchannels in transmembrane proteins from their three-dimensional structure. PLoS Comput Biol 2009, 5(7):e1000440.

44.��

Brezovsky J, Chovancova E, Gora A, Pavelka A, Biedermannova L,Damborsky J: Software tools for identification, visualizationand analysis of protein tunnels and channels. Biotechnol Adv2012, 31:38-49.

This review provides an excellent overview about software available foranalysis of protein tunnels and channels. The individual tools are criticallycompared concerning their capabilities or drawbacks and examples oftheir applications are given.

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