EnzymeâPolyelectrolyte Complexes Boost the Catalytic Performance

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Enzyme-Polyelectrolyte Complexes Boost the Catalytic Performance of Enzymes Martin J. Thiele, Mehdi D. Davari, Melanie König, Isabell Hofmann, Niklas Junker, Tayebeh Mirzaei Garakani, Ljubica Vojcic, Joerg Fitter, and Ulrich Schwaneberg ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.8b02935 • Publication Date (Web): 04 Oct 2018 Downloaded from http://pubs.acs.org on October 5, 2018

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ACS Catalysis

Enzyme-Polyelectrolyte Complexes Boost the Catalytic Performance of Enzymes

Martin J. Thiele1§, Mehdi D. Davari1§, Melanie König1, Isabell Hofmann1, Niklas O. Junker2, Tayebeh Mirzaei Garakani1, Ljubica Vojcic1,3, Jörg Fitter2,4, Ulrich Schwaneberg1,5* 1Institute

of Biotechnology, RWTH Aachen University, Worringerweg 3, 52074 Aachen, Germany 2I. Physikalisches Institut (IA), AG Biophysik, RWTH Aachen, Sommerfeldstrasse 14, 52074 Aachen, Germany 3Codexis, 4Institute

Inc., 200 Penobscot Drive, Redwood City, CA 94063 United States

of Complex Systems (ICS-5): Molecular Biophysics, Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

5DWI-Leibniz

Institut für Interaktive Materialien, Forckenbeckstraße 50, 52056 Aachen, Germany

§ MJ. Thiele and M.D. Davari are joint first authors

*Corresponding author: [email protected]

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Abstract Understanding interactions between polymers and enzymes to boost enzymatic activity is of high importance for application of enzymes in multi-component systems such as laundry, food, pharmaceutical or cosmetics. Proteases are widely used in industries and increased performance in presence of polymers has been reported. Boosting of enzymes activity by polymers and understanding of the molecular principles is of high interest in biomedical and biotechnological applications. A molecular understanding of the boosting effect of poly(acrylic acid) (PAA) and poly(Lγ-glutamic acid) (γ-PGA) for a nonspecific subtilisin protease (PDB ID: 1ST3) was generated through biophysical characterization (fluorescence correlation and circular dichroism spectroscopies, isothermal titration calorimetry), molecular dynamics simulations, and protease reengineering (site-saturation mutagenesis). Our study revealed that enthalpically driven interactions via key amino acid residues close to the protease Ca2+ binding sites cause the boosting effect in protease activity. On the molecular level electrostatic interactions results in the formation of proteasepolyelectrolyte complexes. Site-saturation mutagenesis on positions S76, I77, A188, V238, N242 and K245 yielded an increased proteolytic performance against a complex protein mixture (trademark CO-3; up to ~300% and ~70%) in the presence of PAA and γ-PGA. Being able to fine-tune interactions between proteins and negatively charged polymers through integrative use of computational design, protein reengineering and biophysical characterization proved to be an efficient workflow to improve protease performance.

Keywords: protease, protein engineering, directed evolution, polyelectrolytes, enzyme-polyelectrolyte complexes (EPCs), poly(acrylic acid) (PAA) and poly(L-γglutamic acid) (γ-PGA), molecular dynamics simulations


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1. Introduction Boosting of enzymes activity by adding polymers into the reaction environment provides an alternative solution for increasing enzyme activities and is of high interest in many applications. Boosting of activity of industrial enzymes by polymers has been reported for proteases, lipases, amylases, cellulases, and carbonic anhydrases.1-5 Enzyme-polymer complexes improve activity, thermostability, resistance toward nonconventional media and storage stability.3, 6-11 Proteases are hydrolytic enzymes with a wide range of industrial applications (e.g. food production, laundry, textile treatment, pharmaceuticals and clinical diagnostics).12-14 Subtilisin proteases are commonly used in detergents to remove protein stains.15-16 Subtilisins have extensively been reengineered to further improve their properties (catalytic performance, pH resistance, temperature, selectivity or oxidative and/or storage stability).15-18 These success stories prove that protein engineering by directed protein evolution has advanced into a powerful approach to deliver tailor made enzymes that match application demands.19 Boosting protease activity through polymers has been reported for anionic and cationic polyelectrolytes.1, 3-4, 20 The α-chymotrypsin (serine-protease) was reported to have a 7-fold and 18-fold increased affinity toward cationic and anionic substrates (e.g. N-glycyl-L-phenylalanine-pnitroanilide (GPNA) and N-succinyl-Lphenylalanine-p-nitroanilide (SPNA) in the presence of anionic poly(acrylic acids) (PAA) or cationic polyallylamines (PAAm) polyelectrolytes, which thereby enhanced the overall protease activity.1 α-chymotrypsin activity has been increased up to 6.9-fold for anionic substrates by using polymers such as cationic polyamines.20 Anionic polyelectrolytes like poly(γ-glutamic acid) (γ-PGA) have been found to strongly form specific enzyme-polymer complexes (EPCs) with a carbonic anhydrase that enhances its enzyme activity up to 48% 3. Moreover, Lee et al. showed that γ-PGA enhances the activity and stability of carbonic anhydrase, lipase, and α-amylase up to 48%.3 Cationic polyacrylamides (c-PAM) were observed to improve efficiency of amylase and cellulase by enhancing their initial hydrolysis rates (up to 100%).4 c-PAM can act as a catalyst through increased affinity to their substrates.4 The capability of other polymers to maintain activation and stabilization was reported for ten different starch-degrading enzymes (e.g. amylases) for various non-charged synthetic polymers (e.g. polyethylenglycols PEGs and polyvinylalcohols PVAs).2, 21 These polymers functioned


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as stabilizers via noncovalent binding and subsequent folding of the enzyme into a compact barrel structure.2 Polyelectrolytes such as PAA or PAAm interact with proteases and form EPCs, which show enhanced affinity toward complementary charged enzyme substrates due to favorable electrostatic interactions.1 Nevertheless, the influence of polyelectrolytes on enzyme properties (e.g. protein surface charge, activity and flexibility) is complex and often not well understood on a molecular level. In attempt to understand on the molecular level the hyperactivation of enzymes, a broad range of experimental techniques were used to analyse protein–polyelectrolyte interactions. In particular, spectroscopic-, or calorimetric-based techniques such as fluorescence correlation spectroscopy (FCS) and dynamic light scattering (DLS) were utilized to determine the change in hydrodynamic radius of complexes formed through protein-polymer interaction.1,

22-23

Isothermal titration calorimetry (ITC) was used to

analyze the binding enthalpies and the amount of polymers bound to the targeted protein.1, 22 The main objective of this study was to understand the molecular basis underlying protease-polyelectrolyte interactions and to gain knowledge for fine-tuning of favourable interaction between the nonspecific Bacillus Lentus alkaline subtilisin protease (PDB ID: 1ST324) (hereafter called protease 1ST3) and polyelectrolytes. In this work, we have studied the influence of two anionic polyelectrolytes (PAA and γPGA) (differing in the size, structure and hydrophobicity) and demonstrated their boosting effect on protease 1ST3 by using three different enzyme activity assays (i.e. Suc-AAPF-pNA assay, skim-milk assay and solubility assay). Fluorescence Correlation Spectroscopy (FCS), Isothermal Titration Calorimetry (ITC) and Circular dichroism (CD) were applied to analyse systematically interactions between protease 1ST3 and the polyelectrolytes and to understand their driving force. MD simulations were employed in order to identify the interaction sites between protease 1ST3 and the polyelectrolytes PAA and γ-PGA. MD simulations and experimental validation determined that the positions S76, I77, A188, V238, N242 and K245 form interfaces that govern protease-polyelectrolyte interactions in the proximity of the two Ca2+ binding sites and boost protease 1ST3 activity.


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2. Materials and Methods Sodium salt of poly(acrylic acid) (PAA, MW ~ 4500 g mol-1, Acusol 445N, 45% aqueous fully neutralized solution, pH 7.0, Đ~1.3)

25

was purchased from Rohm and Haas Co.

(Philadelphia, USA) and sodium salt of poly(L-γ-glutamic acid) (γ-PGA, cosmetic grade with an average MW ~150.000 g mol-1, fully neutralized solution, pH 7.0, Đ~2) was purchased from Vedan, Enterprise Corporation, Taiwan. The colorimetric artificial substrate Suc-Ala-Ala-Pro-Phe-pNA was purchased from Bachem AG (Bubendorf, Switzerland). Chocolate milk/carbon black protein mixture immobilized on a cotton surface (commercially available as CFT CO-3 stain, Center for test materials (CFT, Vlaardingen) was used in the solubility assay. The protein labeling dye Nhydroxysuccinimidyl(NHS)-ester Dylight 650 was purchased from ThermoFisher Scientific (Waltham, USA). All other chemicals were of analytical-reagent grade or higher quality and were purchased from Sigma-Aldrich (Taufkirchen, Germany) and AppliChem (Darmstadt, Germany).

2.1. Cell culture, expression and purification Expression of the wild type protease and protease variants generated by sitesaturation mutagenesis (SSM) were produced in a protease-deficient Bacillus subtilis DB104 strain (nprR2 nprE18 and ΔaprA3)

26.

For expression buffered LB media (1%

(w/v) tryptone, 0.5% (w/v) yeast extract and 1% (w/v) sodium chloride, 17 mM potassium dihydrogen phosphate and 72 mM dipotassium hydrogen phosphate, pH~8.0) was transferred into a 96-well F-bottom microtiter plate (Greiner, Frickenhausen, Germany) and supplemented with appropriate antibiotics. The volume of 10 μL pre culture (200 μL, 900 rpm, 37 °C, 24 h, and 70 % humidity) was used to inoculate the main culture (200 μL, 900 rpm, 37 °C, 48 h, and 70 % humidity) in a 96well V-bottom microtiter plate supplemented with appropriate antibiotics. The protease fraction was separated from the cells by centrifugation (Eppendorf 5810R; 4 °C, 3,220×g, 20 min) and the obtained protease containing cell culture supernatant (pH ~8.3) was transferred to a new 96-well F-bottom microtiter plate for subsequent screening and activity analysis. Expression of the wild type protease and the finally identified protease variants generated by site-directed mutagenesis (SDM) were produced in a protease-deficient and poly(L-γ-glutamic acid)-deficient B. subtilis DB104.1 strain (provided by Henkel AG & Co. KGaA, Düsseldorf, Germany). For 5 ACS Paragon Plus Environment

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protease production, 10 ml buffered LB media was transferred into Erlenmeyer shaking flask (50 ml) and incubated for 24 h. Afterwards, the OD of 10 ml main culture in Erlenmeyer shaking flask (50 ml) was adjusted to 0.1 and incubated for further 48 h. The protease 1ST3 was purified according the method explained the supporting material (Method M1, Figure S1 in SI).

2.2 Proteolytic activity assays Proteolytic activity of the protease was monitored by using different proteolytic activity assays and substrates varying in complexity, size and chemistry (Figure 1). The solution pH for the assays was kept constant at the identified optimal pH range (Figure S2 in SI) of the protease 1ST3 (pH~8.3) before and after mixing the components. Experimental variations of the data were expressed as means ± SD as indicated. In order to analyze the statistical significance among the different experimental conditions, P values were analyzed with Graph-Pad Prism 6 software using an unpaired Student’s t-test.

2.2.1 Proteolytic Activity: Suc-AAPF-pNA assay The spectrophotometric Suc-AAPF-pNA assay was performed in 96-well F-bottom microtiter plates (Greiner, Frickenhausen, Germany) to determine the proteolytic activity of the expressed protease in the cell culture supernatant towards artificial substrate. The proteolytic activity was determined by the quantification of released chromogenic p-nitroaniline (pNA) from succinyl-L-Ala-L-Ala-L-Pro-L-Phe-p-nitroanilide (Suc-AAPF-pNA) peptide at 410 nm (ɛ410 = 8,800 M-1cm-1) by using a sunrise microtiter plate reader (Tecan Group AG).27 The supernatant of B. subtilis DB104 cells containing protease was first diluted 1:5 with 15°dGH water (pH 8.6, Table S1 in SI) and 25 µl supernatant transferred to 175 µl 15°dGH water supplemented with 25 µl AAPF substrate solution (2.2 mM) and monitored as increase of absorbance per minute at 410 nm by using sunrise microtiter plate reader (Tecan Group AG). The final reaction volume was 225 µl. The increase on absorbance was measured in Absorbance Units per min (AU min-1; at 410 nm; 10 min; 23°C) and final proteolytic activity was calculated as shown below: 6 ACS Paragon Plus Environment

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Proteolytic activity= SampleSubstrate–BlankSubstrate Where SampleSubstrate is the absorbance of protease supernatant in the presence of Suc-AAPF-pNA, while BlankSubstrate is the absorbance of sole Suc-AAPF-pNA.

2.2.2 Proteolytic Activity: Skim-milk assay The spectrophotometric skim-milk assay was performed in a 96-well F-bottom microtiter plates (Greiner, Frickenhausen, Germany) to determine the proteolytic activity of the expressed protease in the cell culture supernatant towards complex substrate. The proteolytic activity was determined by quantifying the clearance of the turbid skim- milk solution over the time. The volume of 190 μL of skim-milk solution (2% (w/v) skim-milk in 15°dGH water (pH 8.3, Table S1) was transferred in each well and protease containing cell culture supernatant was supplemented (1:5 dilution; 10 μL). Protease activity was monitored by the decrease in absorbance at 650 nm across 10 min of incubation at 23°C by using a sunrise microtiter plate reader (Tecan Group AG). The decrease of absorbance was measured in Absorbance Units per min (AU/min at 650, 10 min, 23°C) and final proteolytic activity calculated as shown below: Proteolytic activity= BlankSubstrate– SampleSubstrate Where BlankSubstrate is the absorbance of sole skim-milk solution and SampleSubstrate is the absorbance of protease supernatant in the presence of skim-milk solution.

2.2.3 Solubility assay to measure proteolytic degradation of protein-mixtures from a cotton surface The solubility assay was performed in a 96-well F-bottom microtiter plate (Greiner, Frickenhausen, Germany) filled with round CO-3 cotton pieces (Ø 6 mm) to determine the proteolytic performance of the protease containing cell culture supernatant on a cotton surface. The cell culture supernatant was diluted 1:5 with 15°dGH water (pH 8.6, Table S1) and 80 µl diluted cell culture supernatant transferred into a pre-warmed (40°C) 96-well F-bottom microtiter plate containing CO-3 cotton pieces and 160 µl 15°dGH water. Afterwards, the 96-well F-bottom microtiter plate was placed in a MTP thermo shaker (ELMI Ltd., SkyLine DTS-4 Digital Thermo Shaker, 900 rpm) and incubated for 1 h at 40°C. The solubilized chocolate milk/carbon black protein mixture has an absorbance maximum around 500 nm. After incubation, the protease 7 ACS Paragon Plus Environment

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supernatant in the presence and absence of the polyelectrolytes, the removal of the immobilized chocolate milk/carbon black protein mixture (trademark CO-3) from the cotton surface causes an increase in color intensity. The latter can be monitored by measuring the absorbance increase at 500 nm via UV-vis spectroscopy by using a sunrise microtiter plate reader (Tecan Group AG). Therefore, 180 µl of supernatant was transferred to a new 96-well F-bottom microtiter plate and the absorbance increase at 500 nm measured by using a sunrise microtiter plate reader (Tecan Group AG) to quantify the amount of solubilized CO-3 strain from the cotton surface. The resulting absorbance values were normalized against total protein content in the supernatant. For the characterization of the identified protease variants, 24-well MTP plates (Greiner, Frickenhausen, Germany) filled with round CO-3 cotton pieces (Ø 10 mm) were used with a reaction volume of 480 µl (320 µl 15°dGH water and 160 µl diluted protease cell culture supernatant).

2.2.4. Evaluation of proteolytic performance and boosting Protease activity in the presence of solubilized substrates (Suc-AAPF-pNA, skim-milk) is defined as proteolytic activity. Simultaneous proteolytic activity and solubility activity of the protease on a cotton surface immobilized with a proteineous substrate (CO-3) is regarded as proteolytic performance. For the quantification of the proteolytic boosting (∆PerformanceBoost) of the protease induced by PAA and γ-PGA the following equation was used: ∆PerformanceBoost = PerformanceProtease-Polyelectrolyte complex/ (PerformanceProtease + PerformancePolyelectrolyte)) where

the

total

performance

(PerformanceProtease-Polyelectrolyte performances

of

the

complex)

protease

of

the

protease-polyelectrolyte

complex

was divided by the sum of individual (PerformanceProtease)

and

polyelectrolyte

(PerformancePolyelectrolyte).

2.3. Molecular Modeling The initial coordinates of protease was taken from the X-ray structure of subtilisin BL, Bacillus Lentus alkaline protease, (PDB ID: 1ST3, resolution: 1.4 Å24). All simulations were performed using GROMACS 5.1.2 simulation package28 and parameters from 8 ACS Paragon Plus Environment

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AMBER99SB29 force field for protein. Construction of models and force field parametrization for PAA or γ-PGA are described in detail in the Supporting Information (see Method M8 in SI). To prepare the starting structure for PAA or γ-PGA protease 1ST3 interaction, the protein was centered in a cubic box and PAA or γ-PGA chains were placed on >6 Å distance from protease 1ST3. Then, energy minimization were run to randomize the initial distribution of the PAA or γ-PGA molecules in the box. The initial configurations were then solvated in a box of TIP3P330 water molecules to ensure at least a water layer of 12 Å around the protein and ions concentration were added to reach 15°dGH water hardness. At least one PAA or γ-PGA chain (neutralized with sodium ions) was added to each simulation box. All systems were first energy minimized using steepest descent algorithm with 50000 integration steps or until the maximum force on any atom in the system did not exceed a value of 1000 kJmol-1nm1.

After heating, the systems were equilibrated for 100 ps each at 300 K using velocity

rescale thermostat in NVT ensemble and at 1 bar using Parrinello-Rahman barostat3132

in NPT ensemble. Three independent production runs, 500 ns each, were performed

for every system in NPT ensemble with a time step of 2 fs using a potential-shift cutoff scheme with a cutoff distance of rcut=1.0 nm for the Lennard-Jones interactions. The Particle Mesh Ewald (PME) method33 with a grid spacing of 0.16 nm and a short-range cutoff of rcut=1.0 nm was used for the long-range electrostatic interactions. Bond lengths were held constant by the LINCS algorithm.34-35 The neighbor list was updated every 10 steps using the Verlet neighbor search (VNS) algorithm. Bond lengths were held constant by the LINCS algorithm.34-35 YASARA Structure version 13.9.836 was used for molecular visualizations and GROMCAS tools were used for analysis of MD trajectories. g_MM-PBSA tool37 was used to calculate components of binding energy between polyelectrolytes and protein (polymer-binding affinities) by using MM-PBSA method.38 The electrostatic calculations were performed based on the Poisson Boltzmann (PB) model. The electrostatic potential maps were generated using by Structure version 13.9.836 for Adaptive Poisson Boltzmann Solver (APBS).38 YASARA Structure version 13.9.836 was used to prepare molecular structures for electrostatic calculations.

3. Results


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In order to generate a molecular understanding of the nonspecific protease 1ST3 activity in the presence of the polyelectrolytes PAA and γ-PGA the results are presented as follows. In the first section, proteolytic activity of the protease 1ST3 was monitored in the presence of PAA and γ-PGA by using three different proteolytic activity assays and substrates varying in complexity, size and chemistry (Figure 1).

A

O

O

O

n

N H H

O n

Subtilisin protease

PAA

γ-L-PGA

(MW:~27 kDa, pI:9)

(MW: ~4.5 kDa)

(MW: ~150 kDa)

B

O

Casein submicelle Calcium Protein mixture O + N O

HN

O HO O

N H

H N

O

O

NH

O

N

cotton

O

Suc-AAPF-p-NA

Skim-milk

CO-3

Figure 1: A: Structure of subtilisin protease (PDB ID: 1ST3)24 shown as cartoon model with the catalytic triad (D32, H62, S215) and the oxyanion hole (N153) as red and blue sticks with the structurally important calcium ions Ca-1 (magenta) and Ca-2 (green) as balls and the chemical structures of the polyelectrolytes, i.e. PAA and γ-PGA, respectively. B: Substrates used for the measuring proteolytic performance (Suc-AAPF-pNA, Skim milk, CO-3).

Second section reports on the Fluorescence Correlation Spectroscopy (FCS) to determine the diffusion coefficient of a fluorescently labeled protease in the presence of diluted γ-PGA solutions. The third section provides the results on Isothermal Titration Calorimetry (ITC) to determine the binding affinity, the enthalpic and entropic contributions to binding free energy, and the stoichiometry of formed polyelectrolyteenzyme complexes (PECs). MD simulations performed to elucidate the molecular mode of interaction between protease 1ST3 with PAA or γ-PGA and identified key amino acid residues. Circular dichroism (CD) spectroscopy was applied to study the 10 ACS Paragon Plus Environment

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ACS Catalysis

structure of protease 1ST3 in the presence of PAA and γ-PGA. In the fourth section protease 1ST3 reengineering was performed to confirm and to modulate the interactions in close proximity to the two Ca2+ binding sites of protease 1ST3.

3.1. Anionic polyelectrolytes (PAA and γ-PGA) boost the performance of protease In order to measure protease performance in the presence of polyelectrolytes (PAA and γ-PGA) different proteolytic activity assays with varying complexity of substrates were performed (see Figure 1). The proteolytic degradation of artificial substrate (SucAAPF-pNA) or biologically relevant substrate (skim-milk) or a complex “proteinaceous” mixtures immobilized on the cotton surface (CO-3) were measured in synthetic water containing mono- and divalent ions (denoted as 15°dGH water). By comparison of these assays, it was possible to compare protease performance on an artificial small chemically well-defined substrate, a very complex soluble substrate and an immobilized substrate. For the proteolytic activity and performance measurements, crude supernatant containing expressed and secreted protease was used. The supernatant from B. subtilis contains only secreted and active protease 1ST3 and the supernatant with the inactive protease variant 1ST3 S215Y had no detectable proteolytic activity (Figure S3 in SI). At first, to measure the influence of the polyelectrolytes on the protease 1ST3 activity, the proteolytic activity of a supernatant containing secreted protease 1ST3 was examined by using substrate Suc-AAPF-pNA in the presence of PAA and γ-PGA. Both polyelectrolytes are fully deprotonated and negatively charged in 15°dGH water at pH 8.6. In Figure 2A-B, the measurements showed that the proteolytic activity of protease 1ST3 in the presence of 1.1x10-4 mol L-1 PAA and 3.3x10-6 mol L-1 γ-PGA in water is up to ~1.3-times increased compared to no polyelectrolytes controls. The chosen concentrations of PAA and γ-PGA polyelectrolyte revealed the highest boost on the proteolytic activity (Figure S4 in SI). PAA or γ-PGA alone showed only a very low spontaneous cleavage on the Suc-AAPF-pNA substrate and introduced a negligible error into the boosting measurements (Figure 2A-B). In addition, the degradation of a soluble complex proteinous substrate (skim-milk) in the presence of both polyelectrolytes was analyzed. The measurement showed that 11 ACS Paragon Plus Environment

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the proteolytic activity of the protease 1ST3 in the presence of 1.1x10-4 mol L-1 PAA in water was up to ~1.3-times increased compared to the sole protease performance (Figure 2C). Protease activity toward skim-milk in the presence of 3.3x10-6 mol L-1 γPGA was up to ~1.2-times boosted (Figure 2D), while PAA and γ-PGA displayed also background degradation activity on the skim-milk substrate as shown in Figure 2C-D. This effect can be explained by the fact that negatively charged casein molecules (major component in skim-milk) form electrostatically-bridged casein micelles by calcium phosphate (Ca3(PO4)2)) or by additional calcium chloride (CaCl2) present in the 15°dGH water

39-41.

When the calcium linkers (bridges) are removed by

polyelectrolytes such as PAA or γ-PGA, the micelles become incredibly unstable and the turbidity of skim-milk solutions is consequently decreased. In order to investigate the individual effect of protease 1ST3 in the absence and presence of the PAA and γ-PGA on a cotton surface immobilized with a protein mixture (denoted as CO-3 substrate), a solubility assay in water at 40°C for 1 h was performed (Figure 2E-F). The solubility assay clearly showed that the proteolytic performance of protease 1ST3 on the cotton surface in the presence of 1.1x10-4 mol L-1 PAA is up to ~3-times increased compared to the sole protease performance in water (Figure 2E). Protease performance in the presence of 3.3x10-6 mol L-1 γ-PGA was boosted in water as well but at a lower level (up to ~1.5-times) compared to PAA (Figure 2F). It is notable that the immobilized protein mixture on the cotton surface is barely solubilized by PAA and γ-PGA without the protease (Figure 2E-F).


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ACS Catalysis

A

B

C

D

E

F

Figure 2: Normalized proteolytic activity and performance in the presence of the polyelectrolytes PAA and γ-PGA and three different substrates. A-F: Proteolytic activity of the protease (1:5 dilution) in the presence of 1.1x10-4 mol L-1 PAA or 3.3x10-6 mol L-1 γ-PGA in 15°dGH water. A-B: Proteolytic activity (slope min-1) across 10 min incubation by using AAPF as artificial substrate C-D: Proteolytic activity (slope min-1) across 10 min incubation by using skim-milk as complex substrate E-F: Proteolytic performance (AU after 1 h incubation) by using a complex protein mixture CO-3 as immobilized substrate on a cotton surface. Dashed arrow shows the boosting of the protease activity/performance. All measured absorbance values were normalized against solubility performance/activity of respective aqueous solutions with and without polyelectrolytes. Protease activity toward solubilized substrates is defined as proteolytic activity while a simultaneous proteolytic activity and solubility activity of the protease on a cotton surface immobilized with a proteineous substrate (CO-3) is regarded as proteolytic performance. Results were presented as means ± SD from triplicated samples from 8 representative measurements (n = 8). All P values for the differences between the WT and WT + polyelectrolyte showed P ≥ 0.001. 13 ACS Paragon Plus Environment

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The boosted specific proteolytic performance of semi-purified protease (see Method M1 in SI) was determined in the presence of PAA and γ-PGA and its proteolytic performance analyzed by using the solubility assay (Figure S1). The measured performance of the semi-purified protease 1ST3 in the presence of both polyelectrolytes showed similar boosted proteolytic performance trends compared to the data obtained with the supernatant containing protease (Figure S5). In order to investigate possible interaction between the polyelectrolytes and the purified protease 1ST3, biophysical techniques such as FCS, ITC and CD were applied.

3.2 Fluorescence correlation spectroscopy (FCS) confirms the formation of a protease-polyelectrolyte complex Fluorescence correlation spectroscopy (FCS) was chosen as a method to follow specific interaction between the purified protease and high molecular weight polyelectrolyte γ-PGA (~150 kDa). Firstly, semi-purified protease (see Method M1 in SI) was labeled through lysine residues by crosslinking of amino and amine groups using N-hydroxysuccinimidyl(NHS)-ester Dylight 650 with excitation and emission wavelengths at 652 nm and 672 nm, respectively. Upon labeling, size exclusion chromatography and subsequent proteolytic activity of the labeled protease fraction (see Method M2 in SI) was performed by using Suc-AAPF-pNA assay, which showed similar proteolytic activity compared to the purified non-labeled protease (Figure S6 in SI). Afterwards, the diffusion coefficient of labeled protease (at concentrations in the nano-molar regime) in 15°dGH water and the resulting size of the protein were determined by using FCS. The measured diffusion coefficient (D) was 91.152 ± 3.495 µm2 sec-1 which gives a hydrodynamic radius (RH) of 2.37 ± 0.09 nm as calculated by Stokes-Einstein equation (see Method M3 in SI). This value is in a reasonable relation to the radius of gyration (Rg) of 1.65 nm (Rg = 0.775 RH) which was calculated from MD simulations (Figure S7 in SI). Typically Rg is at least smaller by a factor of 0.775 as compared to RH. In detail, this factor is caused by the deviation from a non-spherical shape of the protein and by the contribution of the hydration layer which is not considered in the calculation of Rg, but contributes to the measured RH value. For measuring specific interaction of the protease and the polyelectrolytes, the change in relative size of the unbound and bound protease has to be significant, to accurately 14 ACS Paragon Plus Environment

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distinguish two different diffusing species with this method. Because PAA (4.5 kDa) was too small and longer PAA chains (≥ 15 kDa) show significantly reduced boosting ability (Figure S8 in SI), only γ-PGA was used for further FCS measurements. The diffusion of the protease in 15°dGH water containing several concentrations of γ-PGA was measured (Figure 3A). The autocorrelation curves of labeled proteases show a pronounced shoulder for γ-PGA concentrations above 1.65x10-5 mol L-1. This indicates an emerging fraction of proteases which diffuses much slower due to long-term binding (at least for a time while the molecules diffuse through the confocal detection volume, i.e. a few milliseconds) of γ-PGA to the protease. A quantitative measure of this binding effect was accomplished by a two-component fit of the data (Table S2 in SI) and by considering the effect of a concurrent increase of the microscopic viscosity with increasing γ-PGA concentrations (Figure S9 in SI). From this analysis we obtain a clearly increased hydrodynamic radius for the protease bound to γ-PGA with RH1 = 7.20 ± 0.45 nm. This value agrees well with the assumption that one γ-PGA molecule is bound to the protease, forming a complex with a molecular mass of approximately 180 kDa. Obviously, the fraction of bound protease increases with rising γ-PGA concentrations (see growing shoulder in the data of Figure 3A). A further indication for the claimed specificity of the in protease-γ-PGA binding is given by a comparative measurement performed with another polymer, polyethylene oxide (PEO, 100 kDa), which at least on a qualitative level represents a convincing negative control. The latter PEO polymer does not show a slow diffusion component caused by protease-polymer binding, typically visible by the pronounced shoulder (Figure 3B).

A

B


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Figure 3: FCS analysis to measure interaction of protease and γ-PGA. A: Normalized autocorrelation curves (G(t)/G(0)) were measured with labeled protease in the absence (black line) and in the presence of 0.33 (1), 1.65 (2), 3.3 (3) and 6.6 (4) x10-5 mol L-1 γ-PGA (see colored lines) in 15°dGH water. With increasing γ-PGA concentrations a fraction of slowly diffusing proteases becomes visible (see shoulder in the autocorrelation curve) B: The comparison of autocorrelation curves between diffusion properties of the sole protease (black line) and the protease dissolved in 3.3x10-4 mol L-1 γ-PGA (orange line) and in 10-4 mol L-1 PEO (violet line).

3.3. ITC confirms formation of protease-polyelectrolyte complexes In order to determine the binding affinity, the enthalpic and entropic contributions to free binding energy, and the stoichiometry of binding between the low molecular weight polyelectrolyte PAA (4.5 kDa, PDI~1.3) to the purified protease, we employed ITC (see Method M4 in SI). ITC has been extensively used in the past to monitor main driving force of interactions of very low affinity proteins and polymers by directly measuring the Gibs free energy (∆G) and enthalpy (∆H) of the interaction as well as entropic (T∆S) contributions derived from the former two values (∆G = ∆H - T∆S).42 Therefore, concentrated PAA solution (5.5x10-4 mol L-1) was injected into a solution with 6x10-5 mol L-1 protease and the resulting isotherms were monitored (Figure S10 A in SI). After titration, the resulting raw heat peaks revealed exothermic isotherms occurred during interaction between the protease and PAA (Figure S10 A in SI). The titration of water into the protease 1ST3 solution revealed only constant peak sizes with low isotherms in comparison to the evaluated binding isotherms of PAA titrated into the protease 1ST3 solution (Figure S11 in SI). In order to elucidate the reaction mechanism, the resulting raw heat peaks from protease-polyelectrolyte interaction were integrated as a function of the molar ratio between the protease and PAA solution (Figure S10B in SI) and the corresponding ΔG, ΔH and -TΔS values were determined from curve-fitting analysis using Origin 7.0 software43 (summarized in Table S3 in SI). Figure 4A presents the binding signature plot calculated from fitted isotherm depicted in Figure S10B in SI, showing that both enthalpic (∆H) and entropic terms (-T∆S) contribute to the overall interaction energy (ΔG) between PAA and the protease. The binding signature plot is considering a stoichiometry (n) of one, which reflects interaction of one PAA chain on the surface of the protease. As shown in Figure 4A,


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the interaction between the protease and PAA is spontaneous (∆G ≤ 0) and enthalpy driven (ΔH ≤ 0), but also composes unfavorable entropy contributions (-T∆S ≥ 0).

A

B -TΔS

ΔG

ΔH

Figure 4: ITC analysis to quantify driving force of interaction between protease and PAA. A: Binding signature plot, calculated from fitted isotherms plotted in Figure S10B in SI, showing the enthalpy (∆H) and entropy (-T∆S) terms contribute to the overall interaction affinity between PAA and the protease. B: Schematic overview representing possible simultaneous processes required for interaction between protease 1ST3 and PAA.

The binding event comprises probably two parallel processes: binding of polyelectrolyte to the enzyme, which is largely enthalpy driven and the entropically disfavored release of counter ions or water molecules associated with the protease (Figure 4B). Considering the binding of two PAA chains on the protease surface a further decrease in the entropic contribution in binding can be observed (Table S3 in SI). It is presumable that binding of two PAA chains might cause a more rigid complex structure in the frozen state compared to binding of one PAA chain to the protease 1ST3 (Table S3 in SI). Nevertheless, the large enthalpy terms suggest that the primary driving force is the electrostatic interaction. In summary, ITC measurements revealed a spontaneous favored electrostatic interaction between protease molecule and PAA chain but also exhibited large disfavored entropy contributions. 3.4. MD simulations identifies PAA and γ-PGA binding sites and reveals that electrostatic interactions drive protease-polyelectrolyte complexation 17 ACS Paragon Plus Environment

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The results obtained from FCS and ITC measurements (see Figure 3 and Figure 4) firmly demonstrated the interaction between PAA and γ-PGA with the protease. In order to understand the structure and dynamics of the protease-polyelectrolytes complex, we have performed all-atom MD simulations of the assembly and dynamics of a polyelectrolyte-enzyme complex of PAA or γ-PGA with the protease 1ST3. We aimed to identify the mode of complexation and amino acids involved in the interaction, i.e. the amino acid residues with the minimum distances less than 0.35 nm (so-called contact residues) to PAA or γ-PGA during simulations. After MD simulations, the trajectories were analyzed to study the protease residues during the interaction with PAA and γ-PGA. We identified residues with frequent contact by using residues contact map. For investigating the role of each amino acid in the interaction of the protease the contact maps for each run were compared (Figure 5 and Figure S14-S17 in SI). Figure 5A shows two snapshots of PAA interacting with the protease and PAA (water molecules are not shown for clarity) during two different production runs. Figure 5B shows the interaction of the protease and γ-PGA. As it is shown in the Figure 5, some amino acid residues have established a side chain interaction with PAA and γ-PGA during the MD simulations. Simulations with different initial structures sampled similar mode for binding. A closer look at the trajectories shows that PAA and γ-PGA are interacting preferentially with the protease mostly via residues close to Ca2+ binding sites (either Ca-1 or Ca-2). These Ca2+ ions are known to affect the structural stability and thermal resistance of subtilisin proteases and are therefore essential for their functionality.24, 44-45


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A

145°

B

145°

> 10% > 30% > 50% > 70%

Figure 5: A: Surface representation of protease 1ST3. Amino acid residues are coloured according to contact frequency along trajectory to PAA or γ-PGA (contact frequency (colour map), less contact frequency = blue, medium contact frequency = yellow, high contact frequency = orange, extreme high contact frequency = red). Interaction sites of the protease with (A) PAA and (B) γ-PGA during the molecular dynamics trajectories. A-B: The interaction of PAA and γ-PGA with amino acid residues close to Ca-2 binding site was shown while in right panel the interaction with amino acid residues close to Ca-1 binding site is shown. Colour code shows the frequency of contact during the molecular dynamics trajectory.

Based on the MD simulations, we have identified the amino acid residues involved in interactions (see Figure S15-S17 in SI). The comparison of contact residues of the protease with PAA and γ-PGA is shown in Figure S15 and S17 in SI and shows that γ-PGA chain covers a larger part of the protease surface than PAA. In addition, the solvent-accessible surface area (SASA) analysis along the trajectories show larger interface between protease and γ-PGA, considering the same number of monomers in a chain of PAA and γ-PGA (see Figures S15 and S17 in SI). Based on the presented calculations we could identify hot spots, which are shown in Figure 6 for subsequent protein engineering to find protease variants with improved properties in the presence of PAA and γ-PGA. 19 ACS Paragon Plus Environment

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Figure 6: Positions that are proposed to be crucial for binding of PAA and γ-PGA to the protease. Amino acid residues (shown as stick) in red are the catalytic triad (D32, H62, S215) and the oxyanion hole (N153). Balls in magenta and green are Ca2+ ions. Violet balls show amino acid residues close to Ca-1 and cyan balls close to Ca-2.

In order to evaluate the effect of interaction between the protease and PAA or γ-PGA on the flexibility of the protease structure we have calculated the Root Mean Square Fluctuations (RMSF) for protease amino acid residues along 500 ns simulations trajectories. Figure S12 in SI shows the RMSF per residue for the protease compared to protease-PAA and protease-γ-PGA complex simulations. As can be seen in this figure, no major effect on the flexibility and structural integrity of protease due to binding to PAA or γ-PGA is visible in this time scale. CD spectroscopy of the protease 1ST3 confirms the conserved secondary structure of protease in the presence of PAA and γPGA (see Figure S13 in SI). In order to determine the driving force for binding of protease and polyelectrolytes in the system, we have analyzed the energetics along simulation trajectories. Figures S14 and S16 in SI show the change in Columbic and Lennard-Jones energies along simulation time. This analysis shows the high electrostatic interaction of protease and polyelectrolytes leading to an attraction of polyelectrolytes to the Ca2+ binding regions. Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations have also been applied to determine binding affinities and the energetics of protease interaction with PAA and γ-PGA. MM/PBSA calculations show that binding of protease by PAA and γ-PGA relies on compensation of the electrostatic and polar solvation energy terms and is stabilized by polar interactions (Figures S14 and S16). Binding free energy calculations obtained through MM/PBSA methodology are in good 20 ACS Paragon Plus Environment

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qualitative agreement with ITC experiments and allow dissection of the energetic terms associated with protease polyelectrolyte PAA and γ-PGA complexes. 3.5.

Engineering

of

protease-polyelectrolyte

interactions

boost

1ST3

performance and confirms MD simulations In order to verify protease-polyelectrolyte contact residues obtained from MD simulations, site saturation mutagenesis (SSM) was performed on all 9 proposed amino acid positions (S76, I77, A166, A188, D191, V238, Q239, N242, K245, shown in Figure 6), which interact dominantly with PAA and γ-PGA. 9 protease SSM libraries (see Method M6, Table S4 in SI) were generated and 180 clones per position screened (in total 1620 clones) in 15°dGH water containing 5.5x10-4 mol L-1 PAA or 1.7x10-5 mol L-1 γ-PGA by using the solubility assay with C0-3 as substrate. Saturation mutagenesis at positions S76, I77, A188, V238, N242 and K245 disclosed protease variants with significant increased boosting in 15°dGH water containing PAA while in the presence of γ-PGA the improvement still appeared at a lower level compared to PAA. Saturation mutagenesis at positions A166, D191, Q239 revealed no protease variants with significant increased or decreased boosting indicating that changing amino acids at these positions does not significantly affect the interaction with the polyelectrolytes and the protease. Afterwards, 15 protease variants with improved proteolytic performance in the presence of PAA or γ-PGA were selected and produced using a high expression vector (see Method M6, Table S5 in SI). The proteolytic performance of the latter protease variants was subsequently measured by using the solubility assay in 24-MTP format plates with larger CO-3 cotton substrate. Performances measurements as shown in Figure 7A-B clearly shows that all selected protease variants (15 in total) are proteolytically improved in presence of PAA or γPGA compared to the WT protease without a significant change in their performances without polyelectrolytes. Figure 7C-D summarizes the improved boosting of the engineered protease variants compared to the wild type (WT) protease 1ST3. As shown in Figure 7C-D, the engineered protease variants revealed an increased boosting of the proteolytic performance in the presence of PAA (up to ~1.5-times additional boosting) or γ-PGA (up to ~1.4-times additional boosting), respectively. Interestingly, substitutions highlighted with an arrow in Figure 7C-D showed an additional proteolytic improvement (≥1.4-times) for PAA and γ-PGA indicating the same 21 ACS Paragon Plus Environment

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amino acid substitutions govern the boosting of the protease performance by PAA and γ-PGA.

A

B

C

D

Figure 7: Proteolytic performance of protease 1ST3 and variants in the presence of the polyelectrolytes PAA and γ-PGA. A-B: Normalized proteolytic performance of the supernatant containing WT protease or protease variants (1:5 dilution, blue circle) performed in a 24-well MTP plate in the presence of 1.1x10-4 mol L-1 PAA (blue circle) or 3.3x10-6 mol L-1 γ-PGA (red circle) in 15°dGH water at 40°C after 1 h incubation. Protease variant S215Y serves as proteolytically inactive control. C-D: Boosting of protease variants compared to the WT protease in the presence of PAA or γ-PGA. Dashed line shows the boosting of the WT protease performance. Arrows indicated substitutions showing improved boost for PAA and γ-PGA. Simultaneous proteolytic activity and solubility activity of the protease on a cotton surface immobilized with a proteineous substrate (CO-3) is regarded as proteolytic performance. Results were presented as means ± SD from duplicate samples from four representative experiments (n = 4). All P values for the differences between the WT and WT + polyelectrolyte showed P ≥ 0.001.


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As shown in Figure 8, the engineering of the proposed positions in the protease by MD simulations clearly revealed that these residues nearby the two Ca2+ binding sites (Ca-1 and Ca-2) are important for interactions with the polyelectrolytes and can be reengineered toward improved protease performance in the presence of PAA and γPGA. S76 K245

I77 A188

N242

V238 180°

Figure 8: Beneficial protease positions (ball in blue) involved mainly in interaction with PAA and γ-PGA. Amino acid residues (shown as stick) in red are the (D32, H62, S215) and the oxyanion hole (N153). Positions indicated as blue balls were confirmed to be crucial for polyelectrolyte binding to protease. Balls in magenta and green are Ca2+ ions.

4. Discussion Understanding of boosting of enzyme performance by polymers is of great importance to increase the industrial applications of enzymes. The integrative approach of colorimetric analysis, biophysical characterization (FCS, ITC and CD), computational modeling and protein reengineering proved to be a promising workflow to gain molecular insights into the interactions that govern the boosting effect and to improve protease performance. We demonstrated that the protease performance is increased up to 30% for Suc-AAPF-pNA and skim-milk substrates and up to 200% for the complex substrate CO-3 in the presence of PAA and γ-PGA. FCS and ITC analysis showed that the increased performance results from the protease-polyelectrolyte complexation without changing the structural integrity of the protease (see Figure S12 in SI). ITC and MD simulations study suggest an electrostatic binding as a mechanism for interaction of PAA and γ-PGA to the protease via surface residues in close proximity of the Ca2+ binding sites. FCS analysis clearly revealed an interaction between one protease molecule and one γ-PGA polyelectrolyte chain, forming EPCs with a molecular mass of approximately 180 kDa. We assume a similar complexation 23 ACS Paragon Plus Environment

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stoichiometry for the low molecular weight PAA while the size determination of the protease-PAA complex by FCS remained challenging. Thermodynamic parameters calculated from isotherms obtained from ITC show that the enthalpy and entropy of binding between PAA and protease 1ST3 are both negative, while the final driving force for interaction might be a competition of the two processes (Figure 4A).46 Interaction starts by disolvation/solvation process when the polyelectrolyte binds to the protease (Figure 4B). The release of water results in an increase in the entropy of the system, i.e. ΔS > 0 and so -TΔS < 0. But the desolvation free energy may still be unfavorable (>0) determined by ΔH.47 In addition, assuming that internal degrees of freedom of PAA chain are essentially “frozen” on binding state, the corresponding unfavorable entropic contribution can be explained by the loss in vibrational degrees of freedom of the PAA structure (Figure 4B).48 A favorable contribution to binding entropy which derives from desolvation cannot overcome the unfavorable contribution from “freezing” polyelectrolyte degrees of freedom on binding. It can be concluded that the protease-PAA interaction is dominated by electrostaticand/or hydrogen bonds while the primary driving force is most likely the electrostatic interaction between negative carboxylic groups (RCOO-) of PAA and cationic residues (i.e. RNH3+) on the protease surface (Figure 4B). This result is in good agreement with what has been achieved by MD simulations. Similar interaction mode is expected for the longer chain γ-PGA polyelectrolyte, while additional hydrophobic contributions might be involved due to the hydrophobic backbone in the γ-PGA monomers.3 Analysis of effect of ionic strength shows that the boosted protease activity in the presence of PAA is not affected by increasing ionic strength while in the case of γ-PGA the proteolytic activity is decreased by gradually increased ionic strength (Figure S18 in SI). We assume that the difference arises from different interaction of PAA and γ-PGA with protease in the presence of counter ions. MD simulations indicated that the protease has two favorable polyelectrolyte binding regions, specifically surface amino acid residues in the close proximity of Ca2+ binding sites (i.e. Ca-1, Ca-2). MD simulations with more number of polymer chains (PAA or γ-PGA) did not show simultaneous binding of two polymer chains to protease (see Figure S19 in SI). Although the boosting of protease activity induced by charged polymers has been previously reported,1, 3 the detailed molecular mechanisms underlying the boosting of protease catalytic activity remain unclear. Kurinomaru et al. reported the formation of EPCs through an electrostatic binding of the anionic PAA polyelectrolyte to the cationic 24 ACS Paragon Plus Environment

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α-chymotrypsin surface without changing its secondary structure.1 As a mechanism for boosting of enzyme activity, favorable electrostatic interactions were postulated between complementary charged artificial substrates and polyelectrolytes surrounding the protease.1 However, a significant finding of our study is that we not only observe the boosting for polyelectrolyte and substrate (Suc-AAPF-pNA) with similar charges but also for complex substrates (skim-milk and CO-3). These observations raise the questions about how the binding of polymer lead to increased enzyme activity. Kurinomaru et al. indicated that the boosting of α-chymotrypsin activity by polyelectrolytes is related to increased affinity of protease for the artificial substrate.1 In addition, modulation of the charge distribution on an enzyme surface has been reported to affect the diffusional association between the protease and artificial substrates.1 Predominant role of charged patches on the protein surface in the formation of complexes with polyelectrolytes is also described.49 It is also reported that the insertion or removal of charges on a thermolysin-like protease surface by single amino acid substitutions altered the catalysis of the protease due to the formation of large electrostatic networks on the surface affecting the active site dynamics.50 Therefore, it is plausible that reorganization of the polarity leads to an increase in the affinity of the protease for the Suc-AAPF-pNA substrate and thereby facilitates substrate uptake into the substrate binding site. Calculated electrostatic potential distribution on protease surface upon binding of PAA (Figure S20 in SI) clearly show that neutralization of the positive charged residues especially close to the Ca2+ binding sites changes the surface charge and slightly the polarity of binding cleft of the protease. In addition, to determine the effect of charge of polyelectrolytes (PAA, γ-PGA), the protease activity in the presence and absence of polyelectrolytes in varied pH was quantified (Figure S2 in SI). The latter experiment revealed that the boosting of protease 1ST3 activity only appears when PAA and γ-PGA are highly negative charged to allow a favorable interaction with the protease 1ST3 surface causing an activity boost. Therefore, as common boosting mechanism, we assume that the binding of PAA and γ-PGA is crucial to enable better degradation of the artificial substrate SucAAPF-pNA due to surface charge modulation of the protease. As shown in Figure 2, the boosting behavior of polyelectrolytes on the protease on a cotton surface immobilized with a complex protein-mixture showed the maximal boosting (up to 3-times improvement) compared to soluble substrates (Suc-AAPF25 ACS Paragon Plus Environment

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pNA, skim-milk). The boosting mechanism on a cotton surface seems to follow similar mechanism of the formation of protease-polyelectrolyte complexes in solution, which in turn might increase the affinity of protease toward the complex protein mixture immobilized on the cotton surface. Solubility experiments shown in Figure 2E-F clearly demonstrate that PAA and γ-PGA alone are slightly able to solubilize the proteinmixture on the cotton surface. Since PAA and γ-PGA are negatively charged and the cotton surface is fully covered by the CO-3 fabric stain, a stable interaction between the polyelectrolytes and the cellulose surface are probably very rare.51 In order to validate our proposed mechanism in complex system, and to prove the hotspot for interactions, we have (semi)rationally engineered protease variants. Sitesaturation mutagenesis on computationally predicted residues (S76, I77, A188, V238, N242, K245) around the two Ca2+ binding sites of the protease revealed variants with enhanced boosting (up to ~1.5-times additional boosting) on the CO-3 substrate for both PAA and γ-PGA. This observed boosting confirms the identified positions for interaction of protease and PAA and γ-PGA. We assume more favorable interactions between protease variants and polyelectrolytes. Comparison of boosting of PAA and γ-PGA shows less boosting effect of γ-PGA (up to 2-times) for the complex substrates (skim-milk and CO-3). This clearly indicates that the interaction regions on the protease surface might alter the boosting efficiency of the polyelectrolyte especially for more complex substrates (Figure 2C-F). We also observed that the length and probably the conformational state of PAA and γ-PGA affect the boosting on the protease 1ST3 (Figure S8 in SI). Recently, we have reported an unanticipated mechanism of electrostatic attractive interactions of negatively charged PAA with like-charged surfactants via calcium ions bridging.52 Incorporation of the protease 1ST3 into polyelectrolyte-surfactant nanoreactors results in a synergistically enhanced solubility performance due to interaction of the polyelectrolyte with the protease. It is anticipated that the combination of enzyme boosting and nonreactors concept launch a new direction of researches on intermolecular interactions of biological molecules possessing Ca2+ binding sites, new synthetic delivery systems and designing surface modifiers. In summary, we identified and quantified boosting of a protease performance caused by the interaction with polyelectrolytes. The increased boosting by tailoring of enzymes proposes the synergistic redesign of the interaction interfaces between the enzymes 26 ACS Paragon Plus Environment

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(through protein engineering) and polyelectrolyte/polymer engineering (e.g. length, charge) a promising approach to improve enzyme activity.

5. Conclusion The workflow of biophysical characterization (FCS, ITC and CD), computational studies and protease reengineering discovered that electrostatic interaction between PAA and γ-PGA and residues in the proximity of two calcium binding sites of protease 1ST3 form a PECs that boost protease performance. Reengineering of enzymepolyelectrolytes interface proved to be a robust approach to improve protease activity and can likely be transferred to other proteases with Ca2+ binding sites. The obtained knowledge and approach in protease-polyelectrolytes can be very probably generalized towards increasing the activity of other enzymes such as lipases, amylases, cellulases and carbonic anhydrases that show a polymer boosting effect and have Ca2+ binding sites. The generated molecular understanding of the proteasepolyelectrolyte complex and its interface represents from our point of view a convergence of two research fields (protein engineering and polymer chemistry) to improve enzymes performance in multi-component systems such as laundry, food, pharmaceutical or cosmetics.

Supporting information (SI) Supporting Data and Figures, Detailed explanation of Material and Method such as Protease purification via ion exchange chromatography (IEC), Labelling of protease and size exclusion chromatography (SEC), Fluorescence correlation spectroscopy (FCS), Isothermal Titration Calorimetry (ITC), Site-saturation Mutagenesis Library Generation, Site-directed Mutagenesis Protease Variant Generation, CD spectroscopy (CD), Molecular dynamics (MD) simulations. The Supporting Information is available free of charge on the ACS Publications website at DOI:

Authors information

Corresponding Author [email protected] 27 ACS Paragon Plus Environment

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Notes: The authors declare no competing financial interest.

Abbreviations SSM: Site-saturation Mutagenesis, SDM: Site-directed mutagenesis, EPCs: enzymepolyelectrolyte complexes, MD simulations: Molecular dynamics simulations, FCS: Fluorescence correlation spectroscopy, ITC: Isothermal titration calorimetry, CD: Circular dichroism, PAA: Poly(acrylic acid); γ-PGA: Poly(L-γ-glutamic acid), Suc-AAPFpNA: succinyl-L-Ala-L-Ala-L-Pro-L-Phe-p-nitroanilide,

Acknowledgment This research was partially funded by Henkel AG & Co. KGaA, Düsseldorf, Germany, as part of the Henkel Innovation Campus for Advanced Sustainable Technologies (HICAST) project. Simulations were performed with computing resources granted by JARA-HPC from RWTH Aachen University under projects RWTH0116 and JARA0169.


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References 1. Kurinomaru, T.; Tomita, S.; Hagihara, Y.; Shiraki, K., Enzyme Hyperactivation System Based on a Complementary Charged Pair of Polyelectrolytes and Substrates. Langmuir 2014, 30, 3826-3831. 2. Yoon, S. H.; Robyt, J. F., Activation and Stabilization of 10 Starch-Degrading Enzymes by Triton X-100, Polyethylene Glycols, and Polyvinyl Alcohols. Enzyme Microb. Technol. 2005, 37, 556-562. 3. Lee, E. H.; Tsujimoto, T.; Uyama, H.; Sung, M. H.; Kim, K.; Kuramitsu, S., Enhancement of Enzyme Activity and Stability by Poly(Gamma-Glutamic Acid). Polym. J. 2010, 42, 818-822. 4. Reye, J. T.; Maxwell, K.; Rao, S.; Lu, J.; Banerjee, S., Cationic Polyacrylamides Enhance Rates of Starch and Cellulose Saccharification. Biotechnol. Lett 2009, 31, 1613-1616. 5. Romero, O.; Rivero, C. W.; Guisan, J. M.; Palomo, J. M., Novel EnzymePolymer Conjugates for Biotechnological Applications. PeerJ 2013, 1, e27. 6. Stepankova, V.; Bidmanova, S.; Koudelakova, T.; Prokop, Z.; Chaloupkova, R.; Damborsky, J., Strategies for Stabilization of Enzymes in Organic Solvents. ACS Catal. 2013, 3, 2823-2836. 7. Suthiwangcharoen, N.; Nagarajan, R., Enhancing Enzyme Stability by Construction of Polymer-Enzyme Conjugate Micelles for Decontamination of Organophosphate Agents. Biomacromolecules 2014, 15, 1142-1152. 8. Wong, D. E.; Dai, M.; Talbert, J. N.; Nugen, S. R.; Goddard, J. M., Biocatalytic Polymer Nanofibers for Stabilization and Delivery of Enzymes. J. Mol. Catal. B: Enzym. 2014, 110, 16-22. 9. Gaertner, H. F.; Puigserver, A. J., Increased Activity and Stability of Poly(Ethylene Glycol)-Modified Trypsin. Enzyme Microb. Technol. 1992, 14, 150-155. 10. Kazan, D.; Erarslan, A., Stabilization of Escherichia Coli Penicillin G Acylase by Polyethylene Glycols against Thermal Inactivation. Appl. Biochem. Biotechnol. 1997, 62, 1-13. 11. Tomita, S.; Nagasaki, Y.; Shiraki, K., Different Mechanisms of Action of Poly(Ethylene Glycol) and Arginine on Thermal Inactivation of Lysozyme and Ribonuclease A. Biotechnol. Bioeng. 2012, 109, 2543-2552. 12. Rao, M. B.; Tanksale, A. M.; Ghatge, M. S.; Deshpande, V. V., Molecular and Biotechnological Aspects of Microbial Proteases. Microbiol. Mol. Biol. Rev. 1998, 62, 597-635. 13. Li, S.; Yang, X.; Yang, S.; Zhu, M.; Wang, X., Technology Prospecting on Enzymes: Application, Marketing and Engineering. Comput. Struct. Biotechnol. J. 2012, 2, e201209017. 14. Trincone, A., Marine Enzymes for Biocatalysis: Sources, Biocatalytic Characteristics and Bioprocesses of Marine Enzymes. WoodheadPublishing: Cambridge,UK, 2013. 15. Vojcic, L.; Jakob, F.; Martinez, R.; Hellmuth, H.; O’Connell, T.; Mühl, H.; Lorenz, M. G.; Schwaneberg, U., Engineering Proteases for Industrial Applications. In Applied Biocatalysis: From Fundamental Science to Industrial Applications, Wiley-VCH: Weinheim, Germany, 2016, p. 101-120. 16. Vojcic, L.; Pitzler, C.; Koerfer, G.; Jakob, F.; Martinez, R.; Maurer, K.-H.; Schwaneberg, U., Advances in Protease Engineering for Laundry Detergents. N. Biotechnol. 2015, 32, 629-634.


ACS Catalysis 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

17. Martinez, R.; Jakob, F.; Tu, R.; Siegert, P.; Maurer, K. H.; Schwaneberg, U., Increasing Activity and Thermal Resistance of Bacillus Gibsonii Alkaline Protease (BgAP) by Directed Evolution. Biotechnol. Bioeng. 2013, 110, 711-720. 18. Jakob, F.; Martinez, R.; Mandawe, J.; Hellmuth, H.; Siegert, P.; Maurer, K.-H.; Schwaneberg, U., Surface Charge Engineering of a Bacillus Gibsonii Subtilisin Protease. Appl. Microbiol. Biotechnol. 2013, 97, 6793-6802. 19. Cheng, F.; Zhu, L.; Schwaneberg, U., Directed Evolution 2.0: Improving and Deciphering Enzyme Properties. Chem. Commun. 2015, 51, 9760-9772. 20. Kurinomaru, T.; Kameda, T.; Shiraki, K., Effects of Multivalency and Hydrophobicity of Polyamines on Enzyme Hyperactivation of Α-Chymotrypsin. J. Mol. Catal. B: Enzym. 2015, 115, 135-139. 21. Li, C.; Li, W.; Holler, T. P.; Gu, Z.; Li, Z., Polyethylene Glycols Enhance the Thermostability of Β-Cyclodextrin Glycosyltransferase from Bacillus Circulans. Food Chem. 2014, 164, 17-22. 22. Kayitmazer, A. B.; Seeman, D.; Minsky, B. B.; Dubin, P. L.; Xu, Y., Protein– Polyelectrolyte Interactions. Soft Matter 2013, 9, 2553-2583. 23. Wu, D.; Schanze, K. S., Protein Induced Aggregation of Conjugated Polyelectrolytes Probed with Fluorescence Correlation Spectroscopy: Application to Protein Identification. ACS Appl. Mater. Interfaces 2014, 6, 7643-7651. 24. Goddette, D.; Paech, C.; Yang, S.; Mielenz, J.; Bystroff, C.; Wilke, M.; Fletterick, R., The Crystal Structure of the Bacillus Lentus Alkaline Protease, Subtilisin BL, at 1.4 Å Resolution. J. Mol. Biol. 1992, 228, 580-595. 25. Kim, U.; Schulz, B. M.; Carty, W. M., Adsorption of Poly(Acrylic Acid) on Commercial Ball Clay. Colloidal Ceramic Processing of Nano-, Micro-, and MacroParticulate Systems 2004, 152, 129-138. 26. Kawamura, F.; Doi, R. H., Construction of a Bacillus Subtilis Double Mutant Deficient in Extracellular Alkaline and Neutral Proteases. J. Bacteriol. 1984, 160, 4424. 27. DelMar, E. G.; Largman, C.; Brodrick, J. W.; Geokas, M. C., A Sensitive New Substrate for Chymotrypsin. Anal. Biochem. 1979, 99, 316-20. 28. Van Der Spoel, D.; Lindahl, E.; Hess, B.; Groenhof, G.; Mark, A. E.; Berendsen, H. J., Gromacs: Fast, Flexible, and Free. J. Comput. Chem. 2005, 26, 1701-1718. 29. Hornak, V.; Abel, R.; Okur, A.; Strockbine, B.; Roitberg, A.; Simmerling, C., Comparison of Multiple Amber Force Fields and Development of Improved Protein Backbone Parameters. Proteins 2006, 65, 712-725. 30. Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L., Comparison of Simple Potential Functions for Simulating Liquid Water. J. Chem. Phys. 1983, 79, 926-935. 31. Parrinello, M.; Rahman, A., Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method. J. Appl. Phys. 1981, 52, 7182-7190. 32. Nosé, S.; Klein, M., Constant Pressure Molecular Dynamics for Molecular Systems. Mol. Phys. 1983, 50, 1055-1076. 33. Essmann, U.; Perera, L.; Berkowitz, M. L.; Darden, T.; Lee, H.; Pedersen, L. G., A Smooth Particle Mesh Ewald Method. J. Chem. Phys. 1995, 103, 8577-8593. 34. Hess, B., P-Lincs: A Parallel Linear Constraint Solver for Molecular Simulation. J. Chem. Theory Comput. 2008, 4, 116-122. 35. Hess, B.; Bekker, H.; Berendsen, H. J.; Fraaije, J. G., Lincs: A Linear Constraint Solver for Molecular Simulations. J. Comput. Chem. 1997, 18, 1463-1472. 36. Krieger, E.; Koraimann, G.; Vriend, G., Increasing the Precision of Comparative Models with Yasara NOVA-a Self‐Parameterizing Force Field. Proteins 2002, 47, 393402. 30 ACS Paragon Plus Environment

Page 30 of 33

Page 31 of 33 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ACS Catalysis

37. Kumari, R.; Kumar, R.; Consortium, O. S. D. D.; Lynn, A., G-MMPBSA- a Gromacs Tool for High-Throughput Mm-Pbsa Calculations. J. Chem. Inf. Model. 2014, 54, 1951-1962. 38. Baker, N. A.; Sept, D.; Joseph, S.; Holst, M. J.; McCammon, J. A., Electrostatics of Nanosystems: Application to Microtubules and the Ribosome. Proc. Natl. Acad. Sci. U.S.A. 2001, 98, 10037-10041. 39. Wang, X.; Zhao, X.; Huang, D.; Pan, X.; Qi, Y.; Yang, Y.; Zhao, H.; Cheng, G., Proteomic Analysis and Cross Species Comparison of Casein Fractions from the Milk of Dairy Animals. Sci. Rep. 2017, 7, 1-9. 40. Hristov, P.; Mitkov, I.; Sirakova, D.; Mehandgiiski, I.; Radoslavov, G., Measurement of Casein Micelle Size in Raw Dairy Cattle Milk by Dynamic Light Scattering. In Milk Proteins-from Structure to Biological Properties and Health Aspects, InTech: Rijeka, 2016. 41. Müller-Buschbaum, P.; Gebhardt, R.; Roth, S.; Metwalli, E.; Doster, W., Effect of Calcium Concentration on the Structure of Casein Micelles in Thin Films. Biophys. J. 2007, 93, 960-968. 42. Zhang, Y.-L.; Zhang, Z.-Y., Low-Affinity Binding Determined by Titration Calorimetry Using a High-Affinity Coupling Ligand: A Thermodynamic Study of Ligand Binding to Protein Tyrosine Phosphatase 1b. Anal. Biochem. 1998, 261, 139-148. 43. Edwards, P. M., Origin 7.0: Scientific Graphing and Data Analysis Software. J. Chem. Inf. Model. 2002, 42, 1270-1271. 44. Kidd, R. D.; Yennawar, H. P.; Sears, P.; Wong, C.-H.; Farber, G. K., A Weak Calcium Binding Site in Subtilisin BPN Has a Dramatic Effect on Protein Stability. J. Am. Chem. Soc. 1996, 118, 1645-1650. 45. Voordouw, G.; Milo, C.; Roche, R. S., Role of Bound Calcium Ions in Thermostable, Proteolytic Enzymes. Separation of Intrinsic and Calcium Ion Contributions to the Kinetic Thermal Stability. Biochemistry 1976, 15, 3716-3724. 46. Wang, S.; Chen, K.; Xu, Y.; Yu, X.; Wang, W.; Li, L.; Guo, X., Protein Immobilization and Separation Using Anionic/Cationic Spherical Polyelectrolyte Brushes Based on Charge Anisotropy. Soft Matter 2013, 9, 11276-11287. 47. Syme, N. R.; Dennis, C.; Bronowska, A.; Paesen, G. C.; Homans, S. W., Comparison of Entropic Contributions to Binding in a “Hydrophilic” Versus “Hydrophobic” Ligand−Protein Interaction. J. Am. Chem. Soc. 2010, 132, 8682-8689. 48. Koschek, K.; Durmaz, V.; Krylova, O.; Wieczorek, M.; Gupta, S.; Richter, M.; Bujotzek, A.; Fischer, C.; Haag, R.; Freund, C., Peptide-Polymer Ligands for a Tandem WW-Domain, an Adaptive Multivalent Protein-Protein Interaction: Lessons on the Thermodynamic Fitness of Flexible Ligands. Beilstein J. Org. Chem. 2015, 11, 837– 847. 49. Endo, A.; Kurinomaru, T.; Shiraki, K., Hyperactivation of α-Chymotrypsin by the Hofmeister Effect. J. Mol. Catal. B: Enzym. 2017, 133, S432-S438. 50. de Kreij, A.; van den Burg, B.; Venema, G.; Vriend, G.; Eijsink, V. G.; Nielsen, J. E., The Effects of Modifying the Surface Charge on the Catalytic Activity of a Thermolysin-Like Protease. J. Biol. Chem. 2002, 277, 15432-15438. 51. Biermann, O. Molecular Dynamics Simulation Study of Polyelectrolyte Adsorption on Cellulose Surfaces. Universität Dortmund, 2002. 52. Thiele, M. J.; Davari, M. D.; Hofmann, I.; König, M.; Lopez, C. G.; Vojcic, L.; Richtering, W.; Schwaneberg, U.; Tsarkova, L. A., Enzyme‐Compatible Dynamic Nanoreactors from Electrostatically Bridged Like‐Charged Surfactants and Polyelectrolytes. Angew. Chem. Int. Ed. 2018, 57, 9402-9407.


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Ca2+

Boosting


EnzymeâPolyelectrolyte Complexes Boost the Catalytic Performance

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