Molecular Modeling Investigation of the Interaction between Humicola

Subscriber access provided by UNIV OF TEXAS DALLAS

Computational Chemistry

Molecular Modeling Investigation of the Interaction between Humicola insolens Cutinase and SDS Surfactant Suggests a Mechanism for Enzyme Inactivation. Lisbeth Ranvkilde Kjølbye, Anne Kjær Laustsen, Mikkel Vestergaard, Xavier Periole, Leonardo De Maria, Allan Svendsen, Andrea Coletta, and Birgit Schiøtt J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.8b00857 • Publication Date (Web): 07 Mar 2019 Downloaded from http://pubs.acs.org on March 10, 2019

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 55 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

Journal of Chemical Information and Modeling

Molecular Modeling Investigation of the Interaction between Humicola insolens Cutinase and SDS Surfactant Suggests a Mechanism for Enzyme Inactivation. Lisbeth Ravnkilde Kjølbyea, Anne Laustsena, Mikkel Vestergaarda, Xavier Periolea, Leonardo De Mariab,¥, Allan Svendsenb, Andrea Colettaa, Birgit Schiøtta

Department of Chemistry, Aarhus University, Langelandsgade 140, 8000 Aarhus C, Denmark

Novozymes A/S, DK-2880 Bagsværd, Denmark

.



[email protected]



[email protected]

ACS Paragon Plus Environment

Journal of Chemical Information and Modeling 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

Page 2 of 55

Present Addresses ¥ Medicinal Chemistry Department, Respiratory, Inflammation and Autoimmunity IMED Biotech Unit, AstraZeneca, Gothenburg 43183 Mölndal, Sweden

ABSTRACT

One of the largest commercial applications of enzymes and surfactants is as main components in modern detergents. The high concentration of surfactant compounds usually present in detergents can, however, negatively affect the enzymatic activity. To remedy this drawback, it is of great importance to characterize the interaction between the enzyme and the surfactant molecules at an atomistic resolution. The protein enzyme cutinase from the thermophilic and saprophytic fungus called Humicola insolens, (HiC) is a promising candidate for use in detergents thanks to its hydrolase activity targeting mostly biopolyesters (e.g. cutine). HiC is however inhibited by low concentrations of sodium dodecyl sulphate (SDS), an ubiquitous surfactant. In this work, we investigate the interaction between HiC and SDS using molecular dynamics simulations. Simulations of HiC dissolved in different acqueous concentrations of SDS show the interaction between HiC and SDS


Page 3 of 55 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


monomers as well as the formation and dynamics of SDS micelles on the surface of the enzyme. These results suggest a mechanism of cutinase inhibition by SDS, which involves the nucleation of aggregates of SDS molecules on hydrophobic patches on the Cutinase surface. Notably, a primary binding site for monomeric SDS is identified near the active site of HiC constituting a possible nucleation point for micelles and leading to the blockage of the entrance to the enzymatic site. Detailed analysis of the simulations allow us to suggest a set of residues from the SDS binding site on HiC to probe as engineered mutations aimed at reducing SDS binding to HiC thereby decreasing SDS inhibition of HiC.

INTRODUCTION Nowadays, protein showing enzymatic activity (enzymes for short) are used for several pharmaceutical, biological and industrial applications such as detergents.1 Modern detergents are a mixture of components, among which of highest importance are enzymes and surface-active agents (surfactants). The high concentration of surfactants in the mixture can, however, cause denaturation of proteins, inhibiting their enzymatic activity. A major challenge is, therefore, to stabilize the enzyme structure and maintain its activity.1 In order



Page 4 of 55

to achieve this, it is fundamental to gain knowledge about the interaction between the enzyme and surfactants at an atomistic level. The interaction between the enzyme and the surfactant has been investigated using several experimental techniques,2–4 but atomistic details and their dynamic nature are still missing. Molecular dynamics (MD) simulations are a powerful tool in exploring and understanding the enzyme-surfactant interaction along with the dynamics of both enzyme and surfactant. Surfactants are the main active compound in detergents and have the ability to dissolve insoluble greasy stains. Surfactants in aqueous solution exist in two forms: monomeric or micellar. The most referred and used parameter to distinguish between these two forms is the critical micelle concentration (cmc). Above cmc, the pure aqueous surfactant starts selfassembling forming micelles and below cmc the surfactant remain in monomeric form.5 Anionic surfactants are some of the most commonly used surfactants. A general trait of anionic surfactants is the ability to denature proteins. The binding of monomeric anionic surfactants to a protein typically results in small locally conformational changes, while binding of anionic micelles typically leads to cooperative global unfolding of the protein.2,5–8


Page 5 of 55 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


One of the most well-known anionic surfactants is sodium dodecyl sulfate (SDS) which consists of a twelve-carbon alkyl tail and a sulfate head group.1 Several studies of the selfassembly of SDS molecules into micelles using MD are available, all addressing the aggregation number and micellar structure.9–12. The formation of small aggregates of SDS followed by fusion of these aggregates into larger micelles has been recently reported in an MD study.13 The study used Potential of Mean Force (PMF) calculations to observe the crossing of a free energy barrier during the fusion of two aggregates. The two small aggregates, upon meeting, form first an elongated shape and later a more stable and spherical micelle. As mentioned above, a known and commonly exploited trait of SDS is its ability to denature proteins. Upon binding of SDS micelles to the protein global unfolding typically occurs. The process depends on the specific secondary structure of the protein, making it difficult to predict how SDS will affect a given protein14. However, two models describing the SDSprotein complex have been presented: the “wrap” and the “aggregation” model. In the wrap model the unfolded protein wraps itself around the SDS molecules, while in the aggregation model the SDS aggregates on hydrophobic patches on the surface of the protein.1,8,15,16



Page 6 of 55

Since as much as 50% of the active compounds in detergents can be anionic surfactants, the design of surfactant-insensitive enzymes is imperative for their industrial application.

Humicola insolens cutinase (HiC) is an extracellular enzyme secreted by the saprophytic fungus Humicola insolens. The natural function of HiC is to catalyze the hydrolysis of ester bonds in cutin, which is a bio-polyester, the main component in cuticle found in higher plants.17–20 HiC exhibits low substrate selectivity, since the loops (also called “lids” in this context) guarding the catalytic triad Ser105, His173 and Asp160 are small and hence not efficient in keeping water-soluble substrates out of the active site21,22 (see Figure 1). For comparison, the lids of lipases, the most commonly used enzymes in the detergent industry, are longer hence allowing only access to fatty substrates. Because of this feature, HiC is categorized as an intermediate between lipases and esterases.18,21,23 Due to its low substrate selectivity and lack of need for interfacial activation, HiC is a promising candidate for application in the detergent industry. The fungus Humicola insolens can grow and function at temperatures up to 58o C, which is a desired feature for enzymes used in detergents. Generally, HiC has shown to maintain activity in higher temperatures and a wider range of pH-values when compared to lipases.24 However, HiC also shows a


Page 7 of 55 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


high degree of sensitivity towards the presence of SDS, meaning even low concentrations of SDS inactivates the enzyme.18 The interaction between HiC and SDS is therefore of great interest for improving the enzymatic properties. Several different methods have been applied to study this interaction. Gathered from small-angle neutron scattering (SANS),25 isothermal titration calorimetric (ITC),25–27 differential scanning calorimetric (DSC)27 and MD simulations studies,18 the interaction between HiC and SDS can roughly be divided into four stages depending on the SDS concentration. These stages are at low, medium, medium-high and high SDS concentration (see Figure 2). The exact borders in form of molar ratio or concentration between the stages are not entirely clear, since there is variation in the measurements from the different experimental data, along with the fact that the borders depend on the temperature as well. The first stage at low SDS concentration, below ~10 SDS:HiC molar ratio, only minor changes in HiC occurs. From kinetic data, it was suggested that monomeric SDS acts as a competitive inhibitor towards HiC and is proposed to induce local conformational changes around the active site.18,26,27 At the second stage, medium SDS concentration, around 1030 molar ratio, conformational changes indicating unfolding of HiC begin to be



Page 8 of 55

detectable.18,25–27 With increasing concentration more SDS molecules bind to HiC resulting in dimerization of HiC-SDS complexes as detected with SANS.25 At the third stage, mediumhigh concentration (30-90 molar ratio), HiC becomes denatured along with the dissociation of the dimers.18,25–27 However, the overall physical dimensions of HiC only change marginally. At the last fourth stage above 90 molar ratio, at high concentration, the denatured HiC is saturated with SDS molecules (Figure 2).27 Generally, from these studies and observations the conclusion is drawn that the interaction between SDS and HiC seems to differ from the general “wrap” model of how SDS interacts with proteins and be more akin to follow the “aggregation” model. This was also previously observed in MD studies, where SDS was shown to interact with hydrophobic areas on the surface of HiC.18 Based on the studies mentioned above, we hypothesize that SDS aggregates on hydrophobic patches on the surface of HiC. We further propose a binding site for monomeric SDS at the entrance to the active site, resulting in SDS blocking the site and inactivating the enzyme. The analysis of the MD simulations is divided into two parts: the interactions between SDS and HiC with focus on monomeric SDS followed by the formations and dynamics of micelles in the absence and presence of HiC.


Page 9 of 55 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


METHODS System Preparation The structure of HiC (residues 3 to 193 out of 194) was extracted from the crystal structure deposited in the PDB with the code 4OYY.18 The PDB entry contains 12 structures of the HiC enzyme, with a RMSD of C-alpha positions below 0.2 Å. Chain A was selected, and prepared using the Schödinger molecular modeling progam. The missing sidechains of the arginine residues 51 and 141 were build using PRIME.28 The PDB structure contained residues 3-193 out of the 194 in the native enzyme, the N- and C-terminal residues of HiC (Gly3 and Arg193) were respectively capped with acetate and amino-methyl groups. PROPKA3.1 was used for investigating the protonation states,29 resulting in the two histidine residues 49 and 173 modeled as Nε and Nδ-tautomer, respectively. In Table 1 an overview of the performed simulations is presented. The HiC model was solvated in a cubic periodic box filled with water (HiC system in Table 1) or using three increasing concentration of SDS in water (H6S, H40S and H200S corresponding to 8, 31 and 200mM SDS respectively, see Table 1). In the H40S and H200S systems, SDS molecules were added using the GROMACS tool “insert-molecules” in random orientation.



Page 10 of 55

In the 6S system, a SDS molecule was manually placed on each of the six faces of a cube centered on HiC and at a distance of ~1.4 nm from the protein surface. The system was solvated with TIP3P water and neutralized with NaCl at 200mM concentration. As a control of SDS aggregation, corresponding simulations in absence of HiC were carried out (system 40S and 200S in Table 1). Each system was simulated in three independent replicas differing by the set of starting velocities. Simulation Protocol Atomistic simulations of all the systems were performed in GROMACS 5.130 and the CHARMM36 force-field.31,32 The sampling was performed integrating the equation of motion with the leap-frog algorithm. All the systems were minimized using 5000 steps of steepest descent. The systems were then equilibrated to 310K and 1atm for 1ns using Berendsen thermostat and barostat33 including cartesian restrains to the protein C-alpha atoms and SDS heavy atoms. A production run of 500 ns in the NPT ensemble was then performed in three independent replicas (with different starting velocities) using the V-Rescale thermostat,34 applied separately for the protein and the solvent (τ = 1.0 ps-1) and the Parrinello-Rahman Barostat (τ =1.0 ps-1).35 Periodic boundary conditions were applied and


Page 11 of 55 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


the electrostatic interaction were treated with PME (Particle Mesh Ewald) method using a cut-off of 1.2 nm.36 Van der Waals interactions were truncated, smoothly switching the force to zero between 1.0 and 1.2 nm. The bonds involving hydrogens atoms were constrained to their reference value using the LINCS algorithm37 permitting an integration time-step of 2.0 fs. System coordinates were saved every 10 ps during the production run. The resulting trajectories were analyzed using various methods described in the following section. VMD38 was used for molecular visualization while the Python package matplotlib39 package was used for all plots. Clustering of SDS Molecules into Micelles For clustering of the SDS molecules into micelles, an algorithm based on geometrical considerations was implemented in a script using the package MDAnalysis40 in Python 2.7.1-3. The geometrical properties employed were derived from Sammalkorpi et al.9 Three distances were measured between all the SDS molecules; the distances between the fifth carbon atom (C5), the twelfth carbon atom (C12) and the center of mass (COM). Three cutoff values R1, R2, and R3 are then employed to categorize the SDS molecules into micelles. If either one of the three distances is within R1, two within R2 or all within R3, the SDS



Page 12 of 55

molecules are categorized as forming a micelle. The cut-off values [R1, R2 and R3] were set to 0.56, 0.625, and 0.75 nm, respectively.This set of values allows the algorithm to capture both small micelles, containing less than 10 SDS molecules, and larger micelles containing more than 10 SDS molecules. A micelle was counted as such only if it contained at least 3 SDS molecules. To avoid PBC artifacts, due to the periodic boundaries, the trajectories were prepared using the gromacs tool “trjconv” with the flag -pbc cluster. SDS/HiC Contact Analysis In this work a contact was counted if as any atom of an SDS molecule were being within 4 Å of any atom of a protein residue. To identify all the contacts, all the distances between the residues and the SDS molecules were first generated using the gromacs tool “pairdist”. These distances were then processed using a Python script to calculate probability of contact for each residue. SDS Binding Site Determination The frequency of pair contacts formation between individual SDS molecule and a given HiC residue would provides a good picture of the “hot spots” (surface of the protein where


Page 13 of 55 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


SDS are often in contact) but the detail of the binding of each SDS molecule to HiC would be missing. To achieve a more precise identification of SDS “binding sites” on the protein we adopted an approach that we call “phrase clustering” after the work of Arnarez et al. on the binding of lipids to membrane proteins41. This approach can be extended more generally to the statistical analysis of any ligand/receptor interactions data. Although the method can be kept as general as possible, here the sets of atoms of the receptor (HiC) and the ligand (SDS) are divided in a series of disjointed sub-sets, referred to as “residue” in the following. This was indeed the sub-division used for the identification of SDS binding sites on the HiC protein. Following this approach, for each MD frame and for each SDS molecule, a list of “phrases” consisting of the receptor residues found within contact distance (4Å) was created. This “phrases” list was used as a base for the calculation, for each pair of residues (𝑖,𝑗) of HiC of the number 𝑛(𝑖,𝑗) of MD frames in which both residues are in contact with the same SDS molecule. We then define a scoring for the “dissimilarity” between receptor residues in terms of their pattern of interaction with SDS from the matrix (𝑖,𝑗) : 𝑑(𝑖,𝑗) = ― log

(𝑛(𝑖,𝑗)𝑁 + 1), where N = total number of frames



Page 14 of 55

This score relates to the “self-information” or the “surprisal” of the event “residue i and j are in contact with the same SDS molecule”. 𝑑(𝑖,𝑗) = 0 if residues i an j are in contact with the same SDS across the entire simulation, while 𝑑(𝑖,𝑗) = log (𝑁) if they are never found in contact with the same SDS molecule. The matrix 𝑑(𝑖,𝑗), was used to cluster the residues of HiC using a complete-linkage hierarchical clustering method. The clustering is used to identify set of residues sharing a threshold mutual “pseudo-distance”; i.e. joint probability of contact with the ligand. These groups form SDS “Binding Sites”. The clustering of residues is still subject to a certain degree of subjectivity due to the choice of a cut-off for the “pseudodistance” criterion. RESULTS A total of six systems were considered (see Table 1): HiC alone as a reference system and in the presence of either 6, 40 or 200 SDS molecules (representing low, medium and high SDS concentration) as well as 2 pure SDS systems (representing near and above critical micelle concentration). The formation of SDS micelles was observed in all systems except for the pure HiC and the H6S systems where the surfactants don’t form aggregates larger than 3 SDS molecules.


Page 15 of 55 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


Interaction between SDS and HiC Protein Stability The structural stability of cutinase during the MD simulations was monitored by C-alpha RMSD and RMSF calculation (Supplementary figure S1-2). The protein in pure aqueous solution is quite stable (max RMSD of circa 2 Å) and the most flexible regions corresponds to the first 10 residues on the N-terminal and the small helix encompassing residues 64-73 that also corresponds to one of the two “lids” (Lid A) covering the enzyme active site (colored in blue in Figure 1). The presence of SDS in the solution slightly affects the internal flexibility of Cutinase. In some of the replica simulations an increase of RMSD (Figure S1) is observed. The calculation of RMSD of the protein not including the first 10 protein residues confirm that the observation can be mostly attributed to a higher flexibility of the N-terminal in presence of SDS (see also RMSF Figure S2). Although the higher N-terminal flexibility could be interpreted as an indication of an early stage of protein unfolding, we should point out that this is observed only in one out of three replicas. We believe that the time-scale of the simulations here presented is too short to fully support this hypothesis. More interestingly, the presence of the surfactant induces a dampening of Lid A (residues 64-73) flexibility as



Page 16 of 55

shown by RMSF (Figure S2) coupled with an increased fluctuation of the other lid covering the binding site (residues 157-172, Lid B). This effect is observed only in 1 out of 3 replicas for both H40S and H200S, and can be related to the interaction of the protein with larger SDS micelles as it will be further discussed in a following paragraph about “Micelle Formation and Dynamics”. Although we cannot fully exclude other effects of SDS that may induce Cutinase to unfold, these are likely to happen on a much longer time-scale (~ms) than presented in this work (ns~μs). Contact Calculations In order to quantify the interaction between SDS and HiC observed in the simulations H6S H40S and H200S, for each protein residue we measured the fraction of time that a surfactant molecule was found within 4 Å of each HiC residue. The reported fractions where obtained over the three replicas of each system combined together and are reported in Table S1-S4 (Supporting Information), Figure 3 and Figure S8. The contact between the surfactant and HiC was further subdivided considering either the hydrophobic tail or the polar head of the


Page 17 of 55 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


SDS molecule. The tail moiety was defined as the 12-carbon chain while the head moiety was defined as the sulfate head group (see Figure 4 panel E). From inspection of Figure 3, it can be observed how the surface area of HiC in contact with the surfactant increases with increasing amount of SDS molecules present. The area of interaction is relatively small in the H6S system, and progressively increases with the SDS concentration in the H40S and H200S system. A consistent site of interaction observed in the three systems is the entrance to the active site of the enzyme. The individual contact percentages for the tail and head moieties of SDS in system H6S were calculated and are shown in Figure 4 panel A-C. The interaction between the surfactant and HiC occurs mainly through the hydrophobic tail of SDS. Analysis of the interaction profile of the SDS tail permits us to identify four areas of interaction with HiC. The areas are the lower part of the two lids A and B, guarding the active site, (residues 64-73 and 157-172, respectively), the loop positioned in between the two lids (residues 26-40) and the residues 103-107 located close to the active Ser-105 of the catalytic triad (see Figure 4 panel D). All these sites are in close vicinity of the entrance to the active site containing the catalytic triad (Ser105, Asp160, and His173). These



Page 18 of 55

observations are in accordance with the observations from the contact calculations considering the entire SDS molecule. Similar calculations for the tail and head moieties of SDS for the systems H40S and H200S show the same trend, where the interaction between SDS and HiC occur mainly through the SDS tail (data available in Supporting Information). Taken together these data suggest that the main site of interaction of SDS on the protein surface is centered on the area surrounding the catalytic triad “lids” independent of the SDS concentration. Binding Site for Monomeric SDS Binding sites of monomeric SDS molecules on the surface of HiC were identified via a connectivity-clustering of protein residues (see “phrase-clustering” descripted in the method section). The analysis, performed on H6S systems, resulted in the hierarchical clustering reported in Figure 5. The hierarchy is represented as a “tree-graph” (or dendrogram) in which each “leaf” represent a residue. The residues are subsequently joined in “branches”, “trunks” and so on. These groupings are what we define as “binding-site”: groups of residues having a given probability of simultaneously being observed in contact with a SDS molecule.


Page 19 of 55 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


The primary binding site (BS1) for SDS observable in Figure 5, includes the residues Ser28, Thr29, Glu30, Ile36, Thr37, Ile169, and Leu174 (see also Figure 6). These residues can be seen as two individual sites in the dendrogram in Figure 5, however, upon visual inspection, it is clear that the residues compose the top and bottom part of BS1. All the residues in BS1 are within close range (average distance 11.2±0.3 Å) of the catalytic triad: Ser105, Asp160 His173 (see Figure 6). A third binding site identified with the dendrogram includes residues Asn69, Phe70, Thr135, Val162, Leu167 and Ile168, referred to as BS2. Also these residues are in close proximity of the catalytic site of HiC (see Figure 6) but they all have a lower probability of contact with SDS monomers. In the following, we focused the analysis on the identification of the primary binding orientation of SDS to BS1. A SDS molecule was considered as being in contact with BS1 if the average of the surfactant minimum distances with each of the residues in BS1 was lower than 6 Å. A cutoff larger than the one previously used to define contact (4 Å) was used. The reason of a larger cut-off resides in the different nature of the two measure. In the case of BS identification we were looking at interaction of single residue with single SDS molecules, and then grouping them in pairs and growing groups. In this second case we want to select



Page 20 of 55

frames in which a defined group of multiple residues are in average close to an SDS molecule. The fact that here we are looking at an average across a larger group of residues warrant for a less strict cut-off. The resulting structures of the SDS molecules falling below this average distance were then clustered using the gromos method implemented in Gromacs42 using a cut-off of 0.25 nm. The two largest clusters contained ~ 52 % and 18 % of the total number of conformations previously selected. In Figure 7 the centroid structures of the two clusters, referred to as C1 and C2, are shown. Generally, the interaction between the protein residues at BS1 and SDS only consists of hydrophobic interactions. Inspection of electrostatic interactions show only a transient interaction between the anionic “head” of SDS molecules and Arg193 of HiC for 5.6% of total simulation time. Another observation is the insertion of the SDS tail into the cavity of the active site. Two different insertion points seem to be present in C1 and C2. In C1, the SDS tail inserts itself from the side, in between the two lids A and B and above the loop positioned between the two lids. In C2, the SDS tail inserts itself from the top in between the two lids. SDS interact with the same protein residues regardless of the insertion point, and in both C1 and C2 blocks for any substrate to enter the active site. Time-series analysis


Page 21 of 55 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


show that C1 and C2 are two distinct conformational basins (Supplementary Information Figure S9), since it was never observed a direct transition between the two conformations. Micelles Formation and Dynamics Micelle formation and dynamics in presence or absence of HiC were analyzed using a clustering method suggested by Sammalkorpi et al.9 (see methods for further details). The growing and fusion of micelles was observed monitoring the time evolution of the size of aggregates identified by the clustering as a function of time (see Figure 8 and Figure 9). In this paper (unless stated otherwise), we refer to the number of SDS molecules contained in a given micelle as “micelle size”. Formation of micelles with a minimum size of 20 SDS and 10 SDS molecule is observed in the systems containing 40 or 200 SDS molecules respectively, both in absence and presence of HiC. As a general trend from all the systems, the formation of micelles can be divided into two phases. In the first phase, SDS molecules quickly aggregate (“growing” phase, highlighted with gray-shaded areas in Figure 8). The growing phase time is shorter (~10-15 ns) at higher SDS concentration (shaded areas Figure 8 panel C, D) while increased significantly (~100 ns) at lower concentration (shaded areas Figure 8 panel A, B). In the



Page 22 of 55

second phase (“fusion” phase), two micelles get in contact and fuse into a larger micelle, which then remains stable throughout the rest of the simulation. This event can be observed as a sudden increase of micelle size (marked by arrows in Figure 8). The final maximum sizes of micelles are not significantly different in the presence or absence of HiC (compare panel A with panel B and panel C with panel D in Figure 8). The presence of HiC does not seem to affect the SDS aggregation rate. A more detailed picture of the SDS aggregation dynamic is reported in Figure 9 where the time evolution of micelle sizes is reported also for micelle of size below 10 and below 20, together with selected snapshots of fusion events. For the system at medium SDS concentration, in absence of HiC (40S), fusion is observed in only one replica out of three. The fusion occurs just after 200 ns, with a micelle of a size below 10 merging with another micelle of size between 10 and 20 into a micelle with a size above 20 (see Figure 9 panel A). In the presence of HiC at medium SDS concentration (H40S) similar fusion events were observed in two out of three replicas. Both events occurred shortly after 100 ns of simulation (Figure 9 panel B).


Page 23 of 55 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


Fusion events are observed in all the replica simulations of the systems with high SDS concentration in absence of HiC (200S), while fusion events are present in two out of three replicas of the system with high SDS concentration and in the presence of HiC (H200S). For system 200S, replica 1 fusion of two micelles with sizes around 20 occurs around 100 ns resulting in a micelle with a size around 40 (Figure 9, panel C). In replica 1 of the system H200S, fusion can be observed around 230 ns, again with two micelles of size ~20 and ~30 fusing into a micelle with a final size ~50 (Figure 9 panel D). In the third replica of the system H200S, no fusion event is observed. However, two micelles can be seen interacting with the lids guarding the active site. The micelles in the other two replicas are positioned at the same location before the fusion events occur. For all the systems in presence of HiC at medium and high SDS concentration, micelle fusion occurs on the surface of the cutinase, and the resulting micelle position itself above or on the side of the active site, highlighted with a black circle in Figure 3, thereby blocking the entrance and hence inactivating the enzyme. The position the two fusing micelles and the final fused micelle is right on top of the, in contact with the two “lids” and corresponds to the spot highlighted in Figure 2 and the binding sites shown in Figure 6.



Page 24 of 55

The mechanism for micelle formation previously described by Kawada et al.,13 resembles what we observe here. In summary, the formation of micelles, appears to be a two-stages mechanism, where the SDS molecules firstly aggregates and grows followed by a sudden fusion of two aggregates resulting in a large micelle. For the systems with 200 SDS molecules, the size of the micelles reaches approximately 50 both with and without HiC present, while the systems containing 40 SDS molecules the size of the micelle reaches is just below 40 in the presence of HiC and below 30 in the absence of HiC. Although this may be an indication of the ability of the enzyme to increase the size or stability of larger micelles the data is not conclusive, nor experimental data are available suggesting such feature. DISCUSSION The MD simulations presented here permit us to propose a possible description of the mechanism of interaction between HiC and SDS that build on previous simulation studies limited to SDS micelle dynamics. The preferred HiC/SDS interaction site are found on top of the lids covering the enzyme catalytic triad. This site, constitute also a site of interaction with small and large SDS micelle. The SDS inhibition of HiC activity may be explained as the obstruction of the catalytic site access. Another interesting observations were the “driven


Page 25 of 55 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


micelle fusion” events (Figure 9) on the protein surface. HiC surface around its catalytic site has the tendency to attract monomeric SDS molecules. Surfactant molecules once interacting with the protein surface may have a higher probability to find other SDS molecules thanks to the dimensionality reduction of the search from 3D to 2D space. The time-scale sampling of our simulation is too short to allow us to give indication of whether HiC may act as a stabilizer of large micelles or increase their rate of formation. However the simulations suggest a more subtle mechanism in which the formation of larger micelles is driven by fusion of smaller micelles localized on the top of the protein binding site. The preferred localization of the fusion events can be linked to the existence of 2 binding sites for monomeric SDS molecules as found using the residue hierarchical clustering and referred to as BS1 and BS2 (Figure 5 and Figure 6). These two binding sites can act as nucleation point for micelle growth and (due to their closeness) subsequent fusion. In light of this picture a possible strategy for the improvement of HiC activity in presence of SDS may be the modification of residues on the observed SDS “binding sites”. Interestingly, mutation of some of these residues has shown to potentially improve the activity of HiC.43 Further confirmation of the proposed mechanism will need more



Page 26 of 55

experimental work coupled with other simulation techniques such as enhanced-sampling or coarse-grained modelling. FUNDING Funding for the research carried out in this work comes from the Danish Council for Independent Research in Technology and Production (DFF-FTP 0602-01788b to Birgit Schiøtt). Andrea Coletta thanks the Aarhus University Research Foundation (AUFF-Nova) for funding his Post-Doc.

ACKNOWLEDGEMENTS

The numerical results presented in this work were obtained at the Centre for Scientific Computing, Aarhus (Grendel Supercomputer, CSCAA.dk) and the DeIC-SDU eScience Center (Abacus-2.0 Supercomputer).

SUPPORTING INFORMATION

The following files are available free of charge.

Additional Figures S1-9 (PDF)


Page 27 of 55 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


Tables of average percentage of contacts between HiC residues and SDS molecules (MS Excel)



Page 28 of 55

TABLES

Table 1 Summary of Molecular Dynamics simulation reported in the article

System

Ratio

Box size (nm3)

[SDS]

#Na:Cl

name

(HiC:SDS)

HiC

1:0

6.9 × 6.9 × 6.9

0

41:43

H6S

1:6

10.6 × 10.6 × 10.6

8

152:148 38884

H40S

1:40

12.9 × 12.9 × 12.93

31

305:267 70647

H200S

1:200

13.6 × 13.6 × 13.6

133

508:310 79498

40S

0:40

12.9 × 12.9 × 12.9

31

307:267 71498

200S

0:200

11.8 × 11.8 × 11.8

200

500:300 50409

(mM)

#Water molecules 10247

Table 2 Summary of micelle fusion event observed in the various replica MD simulation

Fusion

Events

Observed Syste

Rep.

Rep.

Rep.

m

1

2

3

40S

Yes

No

No

H40S

Yes

Yes

No

200S

Yes

Yes

Yes


Page 29 of 55 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

H200S Yes


Yes

No



Page 30 of 55

FIGURES

Figure 1. Molecular representation of HiC with the protein shown as green ribbon. The two lids (residues 64 to 73 and residues 157 to 172) guarding the catalytic triad (Ser-105, Asp160 and His-173), shown in blue and red, respectively. The catalytic triad is shown in licorice.


Page 31 of 55 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


Figure 2. Schematic representation of the different stages of interaction between SDS and HiC, with the lines indicating the boarders found through different experimental techniques, such as DSC, ITC and SANS. HiC is a stable monomer at low molar ratio. With increasing molar ratio HiC and SDS create dimers or even oligomers, which eventually dissociate with increasing molar ratio, along with HiC start unfolding. At high molar ratio HiC is denatured and saturated with SDS. The Image is adapted from Nielsen AD et al.27



Page 32 of 55

Figure 3. The solvent exposed surface of HiC coloured according to the average percentage of contact calculated over the three replicas of each system: H6S, H40S and H200S. The contact percentages were calculated considering whole SDS molecules over trajectories skipped every 10 ps. The consistent site of interaction is the entrance to the active site (highlighted with a black circle).


Page 33 of 55 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


Figure 4. Average percentage of contact between SDS and HiC calculated over the three replicas of the system H6S. A) Average percentages of contact of the entire SDS molecule; regions of HiC with high percentage of contact are highlighted with shaded areas (residues 26 to 40 in orange, residues 103 to 107 in purple, residues 64 to 73 in blue and residues 157 to 172 in red) B) Average percentage of contact of the 12-carbon tail of SDS (see panel E). C) Average percentage of contact of the sulphate-head of SDS (see panel E). D) Molecular representation of HiC with the protein shown as green ribbon. The four areas of



Page 34 of 55

contact identified from the panels in A-C are colored using the same color scheme. The catalytic triad of HiC (Ser-105, Asp-160 and His-173), is represented as orange liquorice. E) Chemical structure of and SDS molecule, the subdivision into “tail” and “head” used for the percentage of contact calculation (see panel A-C) is also reported.

Figure 5. Hierarchical clustering of HiC residues based on pair interaction with SDS. Theimage reproduce the “Proximity” matrix of residue pairs derived from probability of


Page 35 of 55 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


simulations contact with SDS. The rows and columns are reordered based on the complete linkage clustering of residues. The hierarchical tree used for reordering is reported on the right axis. The identified SDS “binding sites” with the highest probability of contacts (BS1 and BS2 in the text) are highlighted with black lines next to the dendrograms and with black squared box in the matrix. The dendrogram branches of the two halves forming BS1 are colored in blue and orange, the branches corresponding to BS2 are colored in red.

Figure 6. Molecular representations of HiC shown as green ribbons. The enzyme catalytic triad (Ser-105, Asp-160 and His-173) are shown as liquorice. The two sub-groups of



Page 36 of 55

residues composing BS1, the primary binding site for monomeric SDS, are reported as spheres in blue (Ser28, Thr29, Leu66, Ile169, Leu174) and in orange (Glu30, Pro31, Ile36, Thr37, Tyr104). The residues of BS2 (Asn69, Phe70, Thr135, Val162 Leu167 and Ile168) are reported as red spheres. The residues with SDS contact percentage higher than 50% in at least one replica are labelled.


Page 37 of 55 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


Figure 7. Molecular structure of the most representing binding modes of monomeric SDS to HiC. The structure are centroid of the two largest clusters (C1 and C2) of SDS bound structures. HiC is represented as green ribbons, protein residues within 6 Å of the SDS



Page 38 of 55

molecule represented as liquorice, SDS molecule is represented as red spheres. The zoom in images show the residues contacting SDS through hydrophobic interactions with the tail of SDS.

Figure 8. Time evolution of the size of the largest micelle system 40S (A) H40S (B) 200S (C) and H200S (D). For each system the replicas 1-3 are reported in red, blue and green respectively. The plots show running averages over a 100 ns window Jumps in micelle size are highlighted with small arrows and are the result of a fusion of two micelles (see also Figure 6). The shaded gray areas indicate the micelle “growing“ phases (see text).


Page 39 of 55 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


Figure 9. Time evolution of the largest micelle observed at 500 ns in 40S (A) H40S (B) 200S (C) and H200S (D) systems. Instantaneous values (recorded every 100 ps) are reported as dots. In red is the size of the largest micelles containing less than 10 SDS molecules, in blue



Page 40 of 55

the size of the largest micelle containing between 10 and 20 and green the largest with size above 20. Running averages over 100 ns are shown as solid lines. On the right fusion events of two micelles are shown with the SDS molecules represented as orange licorice and HiC as green ribbons. The fusion events are represented in the graphs with the gray see through area covering the entire process from beginning to end and the black vertical line indicating the intimidate of the fusion.


Page 41 of 55 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


Table of Content Graphic



Page 42 of 55

REFERENCES

(1)

Otzen, D. Protein-Surfactant Interactions: A Tale of Many States. Biochim. Biophys. Acta Proteins Proteomics 2011, 1814, 562–591.

(2)

Turro, N. J.; Lei, X.-G.; Ananthapadmanabhan, K. P.; Aronson, M. Spectroscopic Probe Analysis of Protein-Surfactant Interactions: The BSA/SDS System. Langmuir 1995.

(3)

Valstar, A.; Almgren, M.; Brown, W.; Vasilescu, M. The Interaction of Bovine Serum Albumin with Surfactants Studied by Light Scattering. Langmuir 2000, 16, 922–927.

(4)

Valstar, A.; Brown, W.; Almgren, M. The Lysozyme−Sodium Dodecyl Sulfate System Studied by Dynamic and Static Light Scattering. Langmuir 1999, 15, 2366–2374.

(5)

Jones, M. N. Surfactant Interactions with Biomembranes and Proteins. Chemical Society Reviews. 1992, pp 127–136.

(6)

Tian, J.; Sethi, A.; Anunciado, D.; Vu, D. M.; Gnanakaran, S. Characterization of a Disordered Protein during Micellation: Interactions of α-Synuclein with Sodium Dodecyl Sulfate. J. Phys. Chem. B 2012, 116, 4417–4424.

(7)

Bhuyan, A. K. On the Mechanism of SDS-Induced Protein Denaturation. Biopolymers 2010, 93, 186–199.

(8)

Otzen, D. E.; Oliveberg, M. Burst-Phase Expansion of Native Protein Prior to Global Unfolding in SDS. J. Mol. Biol. 2002, 315, 1231–1240.

(9)

Sammalkorpi, M.; Karttunen, M.; Haataja, M. Structural Properties of Ionic Detergent Aggregates: A Large-Scale Molecular Dynamics Study of Sodium Dodecyl Sulfate. J. Phys. Chem. B 2007, 111, 11722–11733.


Page 43 of 55 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

(10)


Marrink, S. J.; Tieleman, D. P.; Mark, A. E. Molecular Dynamics Simulation of the Kinetics of Spontaneous Micelle Formation. J. Phys. Chem. B 2000, 104, 12165–12173.

(11)

Kawada, S.; Komori, M.; Fujimoto, K.; Yoshii, N.; Okazaki, S. Molecular Dynamics Study of the Formation Mechanisms of Ionic SDS and Nonionic C12E8micelles and N-Dodecane Droplets. Chem. Phys. Lett. 2016, 646, 36–40.

(12)

Sammalkorpi, M.; Karttunen, M.; Haataja, M. Ionic Surfactant Aggregates in Saline Solutions: Sodium Dodecyl Sulfate (SDS) in the Presence of Excess Sodium Chloride (NaCl) or Calcium Chloride (CaCl2). J. Phys. Chem. B 2009, 113, 5863–5870.

(13)

Kawada, S.; Fujimoto, K.; Yoshii, N.; Okazaki, S. Molecular Dynamics Study of the Potential of Mean Force of SDS Aggregates. J. Chem. Phys. 2017, 147, 084903.

(14)

Nielsen, M. M.; Andersen, K. K.; Westh, P.; Otzen, D. E. Unfolding of β-Sheet Proteins in SDS. Biophys. J. 2007.

(15)

Mattice, W. L.; Riser, J. M.; Clark, D. S. Conformational Properties of the Complexes Formed by Proteins and Sodium Dodecyl Sulfate. Biochemistry 1976, 15, 4264–4272.

(16)

Andersen, K. K.; Oliveira, C. L.; Larsen, K. L.; Poulsen, F. M.; Callisen, T. H.; Westh, P.; Pedersen, J. S.; Otzen, D. The Role of Decorated SDS Micelles in Sub-CMC Protein Denaturation and Association. J. Mol. Biol. 2009, 391, 207–226.

(17)

Dutta, K.; Sen, S.; Veeranki, V. D. Production, Characterization and Applications of Microbial Cutinases. Process Biochemistry. 2009, pp 127–134.

(18)

Kold, D.; Dauter, Z.; Laustsen, A. K.; Brzozowski, A. M.; Turkenburg, J. P.; Nielsen, A. D.; Koldsø, H.; Petersen, E.; Schiøtt, B.; De Maria, L.; Wilson KS; Svendsen A; Wimmer R.



Page 44 of 55

Thermodynamic and Structural Investigation of the Specific SDS Binding of Humicola Insolens Cutinase. Protein Sci. 2014, 23, 1023–1035. (19)

Carvalho, C. M. L.; Aires-Barros, M. R.; Cabral, J. M. S. Cutinase: From Molecular Level to Bioprocess Development. Biotechnology and Bioengineering. 1999, pp 17–34.

(20)

Schäfer, W. The Role of Cutinase in Fungal Pathogenicity. Trends Microbiol. 1993, 1, 69–71.

(21)

Martinez, C.; Nicolas, A.; van Tilbeurgh, H.; Egloff, M. P.; Cambillau, C.; Cudrey, C.; Verger, R. Cutinase, a Lipolytic Enzyme with a Preformed Oxyanion Hole. Biochemistry 1994, 33, 83–89.

(22)

Chen, S.; Tong, X.; Woodard, R. W.; Du, G.; Wu, J.; Chen, J. Identification and Characterization of Bacterial Cutinase. J. Biol. Chem. 2008, 283, 25854–25862.

(23)

Chahinian, H.; Nini, L.; Boitard, E.; Dubès, J. P.; Comeau, L. C.; Sarda, L. Distinction between Esterases and Lipases: A Kinetic Study with Vinyl Esters and TAG. Lipids 2002, 37, 653–662.

(24)

Baker, P. J.; Poultney, C.; Liu, Z.; Gross, R.; Montclare, J. K. Identification and Comparison of Cutinases for Synthetic Polyester Degradation. Appl. Microbiol. Biotechnol. 2012, 93, 229– 240.

(25)

Nielsen, A. D.; Arleth, L.; Westh, P. Interactions of Humicola Insolens Cutinase with an Anionic Surfactant Studied by Small-Angle Neutron Scattering and Isothermal Titration Calorimetry. Langmuir 2005, 21, 4299–4307.

(26)

Nielsen, A. D.; Arleth, L.; Westh, P. Analysis of Protein-Surfactant Interactions - A Titration Calorimetric and Fluorescence Spectroscopic Investigation of Interactions between Humicola Insolens Cutinase and an Anionic Surfactant. Biochim. Biophys. Acta - Proteins Proteomics


Page 45 of 55 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


2005, 1752, 124–132. (27)

Nielsen, A. D.; Borch, K.; Westh, P. Thermal Stability of Humicola Insolens Cutinase in Aqueous SDS. J. Phys. Chem. B 2007, 111, 2941–2947.

(28)

Prime, Version 2.1, 2009. Schrödinger LLC: New York, NY 2010.

(29)

Li, H.; Robertson, A. D.; Jensen, J. H. Very Fast Empirical Prediction and Rationalization of Protein PK a Values. Proteins Struct. Funct. Genet. 2005, 61, 704–721.

(30)

Abraham, M. J.; Murtola, T.; Schulz, R.; Páll, S.; Smith, J. C.; Hess, B.; Lindah, E. Gromacs: High Performance Molecular Simulations through Multi-Level Parallelism from Laptops to Supercomputers. SoftwareX 2015, 1–2, 19–25.

(31)

Best, R. B.; Zhu, X.; Shim, J.; Lopes, P. E. M.; Mittal, J.; Feig, M.; MacKerell, A. D. Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone φ, ψ and Side-Chain Χ1and Χ2Dihedral Angles. J. Chem. Theory Comput. 2012, 8, 3257–3273.

(32)

Huang, J.; Mackerell, A. D. CHARMM36 All-Atom Additive Protein Force Field: Validation Based on Comparison to NMR Data. J. Comput. Chem. 2013, 34, 2135–2145.

(33)

Berendsen, H. J. C.; Postma, J. P. M.; Van Gunsteren, W. F.; Dinola, A.; Haak, J. R. Molecular Dynamics with Coupling to an External Bath. J. Chem. Phys. 1984, 81, 3684–3690.

(34)

Bussi, G.; Donadio, D.; Parrinello, M. Canonical Sampling through Velocity Rescaling. J. Chem. Phys. 2007, 126.

(35)

Parrinello, M.; Rahman, A. Polymorphic Transitions in Single Crystals: A New Molecular Dynamics Method. J. Appl. Phys. 1981, 52, 7182–7190.



(36)

Page 46 of 55

Darden, T.; York, D.; Pedersen, L. Particle Mesh Ewald: An N·log(N) Method for Ewald Sums in Large Systems. J. Chem. Phys. 1993, 98, 10089–10092.

(37)

Hess, B.; Bekker, H.; Berendsen, H. J. C.; Fraaije, J. G. E. M. LINCS: A Linear Constraint Solver for Molecular Simulations. J. Comput. Chem. 1997, 18, 1463–1472.

(38)

Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual Molecular Dynamics. J. Mol. Graph. 1996, 14, 33–38.

(39)

Hunter, J. D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 2007, 9, 99–104.

(40)

Gowers, R.; Linke, M.; Barnoud, J.; Reddy, T.; Melo, M.; Seyler, S.; Domański, J.; Dotson, D.; Buchoux, S.; Kenney, I.; O. Beckstein. MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations. In Proceedings of the 15th Python in Science Conference; 2016; pp 98–105.

(41)

Arnarez, C.; Mazat, J. P.; Elezgaray, J.; Marrink, S. J.; Periole, X. Evidence for Cardiolipin Binding Sites on the Membrane-Exposed Surface of the Cytochrome Bc1. J. Am. Chem. Soc. 2013, 135, 3112–3120.

(42)

Daura, X.; Gademann, K.; Jaun, B.; Seebach, D.; van Gunsteren, W. F.; Mark, A. E. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999.

(43)

Shirke, A. N.; Butterfoss, G. L.; Saikia, R.; Basu, A.; de Maria, L.; Svendsen, A.; Gross, R. A. Engineered Humicola Insolens Cutinase for Efficient Cellulose Acetate Deacetylation. Biotechnol. J. 2017, 12.


Page 47 of 55 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


Molecular representation of HiC with the protein shown as green ribbon. The two lids (residues 64 to 73 and residues 157 to 172) guarding the catalytic triad (Ser-105, Asp-160 and His-173), shown in blue and red, respectively. The catalytic triad is shown in licorice.



Schematic representation of the different stages of interaction between SDS and HiC, with the lines indicating the boarders found through different experimental techniques, such as DSC, ITC and SANS. HiC is a stable monomer at low molar ratio. With increasing molar ratio HiC and SDS create dimers or even oligomers, which eventually dissociate with increasing molar ratio, along with HiC start unfolding. At high molar ratio HiC is denatured and saturated with SDS. The Image is adapted from Nielsen AD et al.27


Page 48 of 55

Page 49 of 55 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


The solvent exposed surface of HiC coloured according to the average percentage of contact calculated over the three replicas of each system: H6S, H40S and H200S. The contact percentages were calculated considering whole SDS molecules over trajectories skipped every 10 ps. The consistent site of interaction is the entrance to the active site (highlighted with a black circle).



Average percentage of contact between SDS and HiC calculated over the three replicas of the system H6S. A) Average percentages of contact of the entire SDS molecule; regions of HiC with high percentage of contact are highlighted with shaded areas (residues 26 to 40 in orange, residues 103 to 107 in purple, residues 64 to 73 in blue and residues 157 to 172 in red) B) Average percentage of contact of the 12-carbon tail of SDS (see panel E). C) Average percentage of contact of the sulphate-head of SDS (see panel E). D) Molecular representation of HiC with the protein shown as green ribbon. The four areas of contact identified from the panels in A-C are colored using the same color scheme. The catalytic triad of HiC (Ser-105, Asp160 and His-173), is represented as orange liquorice. E) Chemical structure of and SDS molecule, the subdivision into “tail” and “head” used for the percentage of contact calculation (see panel A-C) is also reported. 253x173mm (300 x 300 DPI)


Page 50 of 55

Page 51 of 55 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


Hierarchical clustering of HiC residues based on pair interaction with SDS. Theimage reproduce the “Proximity” matrix of residue pairs derived from probability of simulations contact with SDS. The rows and columns are reordered based on the complete linkage clustering of residues. The hierarchical tree used for reordering is reported on the right axis. The identified SDS “binding sites” with the highest probability of contacts (BS1 and BS2 in the text) are highlighted with black lines next to the dendrograms and with black squared box in the matrix. The dendrogram branches of the two halves forming BS1 are colored in blue and orange, the branches corresponding to BS2 are colored in red.



Molecular representations of HiC shown as green ribbons. The enzyme catalytic triad (Ser-105, Asp-160 and His-173) are shown as liquorice. The two sub-groups of residues composing BS1, the primary binding site for monomeric SDS, are reported as spheres in blue (Ser28, Thr29, Leu66, Ile169, Leu174) and in orange (Glu30, Pro31, Ile36, Thr37, Tyr104). The residues of BS2 (Asn69, Phe70, Thr135, Val162 Leu167 and Ile168) are reported as red spheres. The residues with SDS contact percentage higher than 50% in at least one replica are labelled.


Page 52 of 55

Page 53 of 55 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


Molecular structure of the most representing binding modes of monomeric SDS to HiC. The structure are centroid of the two largest clusters (C1 and C2) of SDS bound structures. HiC is represented as green ribbons, protein residues within 6 Å of the SDS molecule represented as liquorice, SDS molecule is represented as red spheres. The zoom in images show the residues contacting SDS through hydrophobic interactions with the tail of SDS.



Time evolution of the size of the largest micelle system 40S (A) H40S (B) 200S (C) and H200S (D). For each system the replicas 1-3 are reported in red, blue and green respectively. The plots show running averages over a 100 ns window Jumps in micelle size are highlighted with small arrows and are the result of a fusion of two micelles (see also Figure 6). The shaded gray areas indicate the micelle “growing“ phases (see text). 169x109mm (300 x 300 DPI)


Page 54 of 55

Page 55 of 55 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


Time evolution of the largest micelle observed at 500 ns in 40S (A) H40S (B) 200S (C) and H200S (D) systems. Instantaneous values (recorded every 100 ps) are reported as dots. In red is the size of the largest micelles containing less than 10 SDS molecules, in blue the size of the largest micelle containing between 10 and 20 and green the largest with size above 20. Running averages over 100 ns are shown as solid lines. On the right fusion events of two micelles are shown with the SDS molecules represented as orange licorice and HiC as green ribbons. The fusion events are represented in the graphs with the gray see through area covering the entire process from beginning to end and the black vertical line indicating the intimidate of the fusion. 148x176mm (300 x 300 DPI)


Molecular Modeling Investigation of the Interaction between Humicola

Recommend Documents