Understanding the thermostability and activity of Bacillus subtilis lipase mutants: insights from molecular dynamics simulations.

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Understanding the Thermostability and Activity of Bacillus subtilis Lipase mutants: Insights from Molecular Dynamics Simulations Bipin Singh, Gopalakrishnan Bulusu, and Abhijit Mitra J. Phys. Chem. B, Just Accepted Manuscript • Publication Date (Web): 15 Dec 2014 Downloaded from http://pubs.acs.org on December 16, 2014

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The Journal of Physical Chemistry

Understanding the Thermostability and Activity of Bacillus subtilis Lipase mutants: Insights from Molecular Dynamics Simulations Bipin Singh†, Gopalakrishnan Bulusu†‡*, Abhijit Mitra†* †

Center for Computational Natural Sciences and Bioinformatics (CCNSB), International Institute of Information Technology Hyderabad (IIIT-H), Gachibowli, Hyderabad, 500032, India ‡ TCS Innovation Labs-Hyderabad (Life Sciences Division), Tata Consultancy Services Limited, Madhapur, Hyderabad, 500081, India

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Abstract Improving thermostability of industrial enzymes is an important protein engineering challenge. Point mutations, induced to increase thermostability, affect the structure and dynamics of the target protein in several ways and thus can also affect its activity. There appears to be no general rules for improving thermostabilty of enzymes, without adversely affecting their enzymatic activity. We report MD simulations, of wild type Bacillus subtilis lipase (WT) and its six progressively thermostable mutants (2M, 3M, 4M, 6M, 9M and 12M), performed at different temperatures, to address this issue. Less thermostable mutants (LTMs), 2M to 6M, show WT-like dynamics at all simulation temperatures. However, the two more thermostable mutants (MTMs), show required flexibility at appropriate temperature ranges and maintain conformational stability at high temperature. They show deep and rugged free-energy landscape, confining them within a near-native conformational space by conserving non-covalent interactions, and thus protecting them from possible aggregation. In contrast, the LTMs having marginally higher thermostabilities than WT show greater probabilities of accessing non-native conformations, which, due to aggregation, have reduced possibilities of reverting to their respective native states under refolding conditions. Our analysis indicates the possibility of non-additive effects of point mutations on the conformational stability of LTMs.

Keywords:

Protein Conformational Stability, Protein Unfolding, Protein Flexibility and Rigidity, Non-covalent Interactions, Principal Component Analysis, Free Energy Landscape.

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Introduction Irreversible changes in the three dimensional structure and loss of activity, due to thermal denaturation are common causes of deactivation of enzymatic proteins. Therefore, thermostability is an important consideration for proteins, particularly for those which are involved in bioprocesses at elevated temperatures.1 To date, many experimental and theoretical approaches have been used to investigate the molecular mechanism of protein thermostability. However, in the absence of a universally acceptable and comprehensive theoretical framework for the unambiguous molecular level interpretation of experimentally determinable parameters, our understanding in this area is rather limited. There are various experimental observables that are generally used to assess the thermostability of proteins, such as (i) half-life of thermal inactivation (T1/2), (ii) temperature at which protein loses 50% activity upon incubation for a fixed period of time (T50), (iii) temperature corresponding to denaturation midpoint (Tm), (iv) free energy of unfolding (∆Gu) and (v) optimum temperature of activity (Topt). The T1/2, T50 and Topt generally relate to kinetic stability, whereas Tm and ∆Gu give an idea of thermodynamic stability of proteins.2 Thermodynamic and kinetic stabilities often show positive correlation with each other. Thus, increasing the protein’s resistance to unfolding (higher Tm), usually, also increases its resistance to inactivation (higher T1/2). This is not always true, since the magnitudes of these parameters are governed by a combination of distinct molecular processes.3 The unfolded state generally leads to irreversible denaturation and inactivation (through aggregation). An increase in the stability of the native state, under unfavorable conditions, implies slower accumulation of the unfolded state, and hence to a slower rate of denaturation and inactivation.

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However, exceptions do exist possibly because of the intrinsically low aggregation tendency of the protein sequence and increased surface charge of protein. In these cases, a temperature higher than the corresponding Tm may be required for protein aggregation (or inactivation). However, a protein with higher Tm does not necessarily possess a lower tendency for aggregation and an aggregation resistant protein is not necessarily always thermostable.4 Furthermore, accurate determination of Tm and ∆Gu for a protein undergoing irreversible denaturation due to aggregation, pose a great challenge due to non-applicability of equilibrium thermodynamics.5 Issues such as these complicate matters for experimental as well as theoretical investigations into the molecular basis of thermostability.6 The theoretical studies mainly fall into two categories. One of them consist of studies that utilize sequence and structural data for a group of proteins for a systematic analysis of various features in order to formulate general rules governing thermostability.7 The other category of studies involves the comparison of the structure and dynamics of a single mesophilic enzyme with its thermostable mutants and/or thermophilic homologues.8–21 In these studies, consideration of the difference in free energy of stabilization between mesophilic and thermophilic proteins constitutes one approach of investigation. However, even small differences in free energy of stabilization have often been suggested to account for significant increase in thermostability of proteins22; in such cases, additional factors may be responsible for enhancement in thermostability.

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Another complementary approach is to relate thermostability of proteins to their conformational flexibility at different temperatures. Generally it may be expected that the flexibility of thermophilic proteins would be less in comparison to their mesophilic counterparts, but most experiments do not reveal any clear correlations; and thermostable proteins have been found to show reduced, enhanced or similar flexibility compared to their mesophilic counterparts. 10,11,15,17,23–27 It has also been suggested that the differences in conformational flexibility have functional consequences and thus, can be used to explain observed differences in temperature-activity profiles of thermophilic and mesophilic proteins.15,17,21,28 A plausible hypothesis along these lines suggests that thermophilic proteins tend to achieve an optimal balance of rigidity and flexibility to gain stability and activity simultaneously.29,30 This can be explained by distribution of flexibility and rigidity in different regions of thermophilic proteins.13,31 While flexibility of the active site region is expected to maintain, or even enhance, catalytic activity; rigidity in other regions may contribute towards enhanced thermostability.28 Further, in order to invoke any of these approaches towards a molecular level understanding, one needs to consider different molecular factors such as the number of hydrogen bonds, salt bridges, hydrophobic interactions, better core packing and release of conformational strain.32,33 The actual factors contributing towards increased thermostability may vary from case to case and multiple mechanisms of thermostability can operate simultaneously. Given this scenario, a strategy for investigating the molecular basis of thermostabilty presented itself in the form of a series of publications reporting the experimentally evaluated thermostabilities and crystal structures of several thermostable mutants of a

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mesophilic Bacillus subtilis lipase (Lip A), obtained through directed evolution.34–36 Lipases are water soluble enzymes that catalyze the esterification, hydrolysis and transesterification reactions of long chain triglycerides, often with high regio and/or enantioselectivity. This makes lipases an important group of biocatalysts in organic chemistry with enormous biotechnological applications in detergents, food, pulp and paper industry.37,38 Thermostable lipases, especially from bacteria are of great industrial importance because of their capability to catalyze diverse reactions at elevated temperatures.37 Lip A is a monomeric protein with a mass of 19.4 kDa and exhibits the characteristic folding pattern known as α/β hydrolase fold. LipA has broad substrate specificity but preferentially hydrolyzes C-8 fatty acid esters with temperature optima of activity at 37˚C. As shown in figure 1, the active site of Lip A comprises of amino acids S77, D133 and H156, whereas the peptidic NH atoms of I12 and M78 constitute the oxyanion hole and helps in stabilizing the negatively charged reaction intermediates. However, due to the absence of a lid domain, which is usually present in other lipases, Lip A does not show interfacial activation at the active site.39 Lip A also does not show glycosylation. Thus, it is different from the eukaryotic lipases, which are often glycosylated.40 In the studies mentioned above34–36, the authors have reported detailed experimental investigations on the thermostabilities and catalytic activities of the wild type Lip A (WT) and six of its progressively thermostable mutants containing up to 12 cumulatively introduced point mutations. These studies were accompanied by detailed analyses of their experimentally determined X-ray crystal structures. These 12 mutations were located in different regions of the structure (Table 1 and Figure 1). The crystal structures

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of B. subtilis lipase mutants hardly differ as shown by their pairwise RMSDs with respect to WT (Table 2). Experimentally, it was observed that the thermostabililty of the mutant lipases increased with the number of mutations introduced, without negatively affecting their catalytic activity at moderate temperatures. In particular, the most thermostable mutant with 12 mutations (12M) has been found to show remarkable enhancement in Tm, ∆Gu (Tm ~350 K and ∆Gu ~15.1 kcal mol-1, which is respectively ~22 K and ~3.7 kcal mol-1 higher than WT) and Topt compared to WT.36 This elegantly designed and painstakingly executed body of work provides several meaningful insights regarding the possible role of the different point mutations, in determining the activity and thermostability of these mutant lipases. However, primarily because of the complicated issues involved in the unambiguous molecular level interpretation of experimental measures of thermostability and because of experimental limitations, some of which are mentioned by the authors themselves41, formulation of a comprehensive molecular basis of thermostability of these active mutant enzymes still remains elusive. Table 1. Description of the lipase mutants studied in this work. PDB Id (Mutant name)

No. of Mutations

Mutation: location

Implications from crystal structure

kcat/Km x 102 (mM-1 min-1) at 298 K

1T4M (2M)

2

A132D: Loop N166Y: Helix

Improved solvent interaction, Stacking interaction

-

1T2N (3M)

2+1

L114P: Loop

Anchoring of C-terminal end to rest of the protein

2.2*

3D2A (4M)

2+1+1

I157M: 310 Helix

Increased van der Waals contact

3.9

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F17S: 310 Helix Favorable contacts with N89Y: Csolvent, terminal helix Stabilization of C-terminal of helix

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3D2B (6M)

2+1+1+2

3D2C (9M)

2+1+1+2+3

A15S: Loop A20E: Nterminal helix G111D: Loop

Hydrogen bonds and salt bridge formation, Increased rigidity

3.7

3QMM (12M) 2+1+1+2+3+ 3

M134E:Loop M137P:Loop S163P:Nterminal helix

Hydrogen bonds, Increased rigidity, Stabilization of N-terminal of helix

8.1

3.4

*WT possesses same kcat/Km

Figure 1. Structure of Lip A showing location of (a) all the 12 mutations (stick representation) and secondary structure labels, (b) catalytic residues (sticks representation). The Lip A secondary structure is composed of 6 parallel β-strands (labelled β1- β6) surrounded by 5 α-helices (labelled α1- α5). The 310 helices are shown in blue color. Table 2. Pairwise RMSDs of the crystal structure of mutant lipases with respect to WT. Mutant

2M

3M

4M

6M

9M

12M

RMSD (in Å) 0.31 0.37 0.26 0.34 0.32

0.37

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In this study, we have investigated the variation in conformational dynamics of WT and its 6 thermostable mutants over a range of temperatures. In conjunction with the published experimental results, we also attempted to correlate our observations with the temperature dependent variations, in the free energy landscapes (FELs) of the structures studied, in terms of the local and global physicochemical impact of the mutations involved. We have used the crystal structures of the WT and its 6 progressively thermostable mutants 2M, 3M, 4M, 6M, 9M and 12M as the starting points for 100 ns explicit solvent molecular dynamics simulations at temperatures 328, 339, 350 and 450 K. For WT and its 12M mutant, we also carried out simulations at 300 K. Simulations at the ‘out of range’ high temperature of 450 K were carried out to identify significant trends in conformational changes within the simulation time and to investigate the mechanism of differential stability at elevated temperature.12,16,18

Materials and Methods Details of the systems studied The experimentally available information related to thermostability and activity of all the systems studied in this work is given in Table 3. The 2M, 3M, 4M and 6M mutants are collectively referred as LTMs (less thermostable mutants) and 9M and 12M mutants as MTMs (more thermostable mutants) in the manuscript. Table 3. Available experimental data related to thermostability and activity for all the systems. System

T1/2 (min)

T50 (°C)

Tm (°C)

Topt (°C)

9

∆Gu (kcal mol-1)


Km (mM)

kcat x 102 (min-

kcat/Km x 102 (mM1 min-1)


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

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1

WT

34

2.5 (55°C) , 2.8 (55°C)35 and 3.141

56 and 56.341

35

35

11.39 +/0.7235 and 10.3941

0.97 and 0.9835

) 5.234 and 2.235

---58.3

----61.235

----4535

----13.20 +/0.1735

1.2234 1.9634 and 0.9335

6.834 8.134 and 2.035

5.634 4.134 and 2.235

60.0

63.435

----

1.2535

4.935

3.935

64.2

67.435

5035

13.36 +/0.2835 15.38 +/0.4535

0.8135

2.835

3.435

68.0

71.235

5535

0.7935

2.935

3.735

93.0

78.236

6536

13.75 +/0.436 and 14.34 +/0.2035 15.1 +/0.236

0.5136

4.1436

8.136

55.3 35

and 52.8

35

41

2M 3M

4M 6M 9M

12M

228 (55°C)34 677(55°C)34 and 530 (55°C)35 and 4.4 (60°C)35 22.5 (60°C)35 1307 (60°C)35 and 6.3 (66°C)35 301 (66°C)35 and 4.4 (75°C)42 and 15.2 (85°C)42 430.5 (75°C)36

35

35

35

35

36

34

5.434 and 2.235

Molecular dynamics (MD) simulations All MD simulations were carried out using GROMACS-4.5.343 in conjunction with Amber99sb-ildn44 force field. The starting structures of WT and its 6 mutants, 2M, 3M, 4M, 6M, 9M and 12M were taken from Protein data bank (PDB)45 entries 1ISP, 1T4M, 1T2N, 3D2A, 3D2B, 3D2C and 3QMM respectively. After adding the hydrogens, the protein structures were solvated with TIP3P46 water molecules in a cubic solvent box with a minimal distance between protein surface and box of 0.9 nm. All the crystallographic/structural waters were retained. The systems were minimized using steepest descent algorithm, until it converged with a force tolerance of 100 kJ mol-1nm-1. After minimization each system was equilibrated to the desired temperature through step wise heating protocol in NVT ensemble followed by 1 ns equilibration in NPT

10


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ensemble with position restraints on the protein molecule. Finally production simulation was performed for each system at five different temperatures i.e., at 300 (for WT and 12M), 328, 339, 350 and 450 K for 100 ns under periodic boundary conditions without any restraints on the protein. The temperatures for the simulations were chosen to include the corresponding melting temperatures of WT (~328 K) and 12M (~350 K), and a higher temperature of 450 K was selected to look for any significant structural changes within limited simulation time. To show that the 450 K results are independent of simulation time and temperature, we have also performed a 10 ns simulation at 500 K for all the systems (Figure S1). The temperature and pressure (1 bar) were controlled by using

velocity-rescale

thermostat47

and

Parrinello-Rahman

barostat48

with

a

temperature and pressure coupling time constant of 1.0 ps. Non-bonded interactions were calculated using the particle-mesh Ewald49 method with a cut-off of 0.9 nm. The LINCS algorithm50 was used to constrain all bonds involving hydrogen atoms during simulation.

Analysis of MD simulation trajectories Secondary structure analysis was performed using the DSSP51 program. Other analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration, solvent accessible surface area (SASA), hydrogen bonds and salt bridges were performed using tools within GROMACS simulation package. RMSD calculation was done using the starting structure of each simulation as a reference. For hydrogen bond calculations, a donor-acceptor cutoff distance of 0.35 nm and acceptor-donor-hydrogen bond angle cutoff of 30º were considered. The visual

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analysis of structures and preparation of figures was carried out using Pymol52, VMD53, Xmgrace54 and MATLAB.55

PCA and FEL Analysis Principal components analysis (PCA) was performed on C-alpha atoms of all the systems, to characterize the essential motions governing the conformational transitions during the course of simulation. PCA was done based on the method described by Amadei et al.56 and de Groot et al.57, in which a covariance matrix of C-alpha atomic fluctuation (C) was constructed and diagonalized to obtain eigenvectors and corresponding eigenvalues, where eigenvectors denote the direction of motion along the principal components and the corresponding eigenvalues represent the meansquare fluctuations associated with the principal components.

Where < > indicates the average over time and xi, xj are the Cartesian coordinates of atoms i and j. The free energy landscapes58 have been obtained by calculating the joint probability distribution from the essential plane constructed from the top two eigenvectors. The first few eigenvectors define the most dominant collective motions59, which provide useful description of structural transitions. Conformations sampled during the simulation were projected on this two dimensional plane, and the number of points occupied by each cell was counted. The grid cell containing the maximum number of points is then assigned as the reference cell, with a free energy value of zero. Free energies for all the other cells were assigned with respect to this reference cell. For the ith cell, this can be mathematically expressed as:

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Where, P(Ni) is the estimate of probability density function obtained from a histogram of molecular dynamics data and P(Nref) is the maximum of the probability density function. KB is the Boltzmann constant, and T is the temperature corresponding to each simulation.

DCCM (dynamic cross correlation map) analysis Dynamic cross correlation maps60 were used to detect time correlated motions of all Calpha atom pairs. The matrices of dynamic cross correlations were calculated by using the equation:

Where ∆ri and ∆rj are the displacement from the mean position of the ith and jth atom determined from all the configurations in the trajectory segment being analyzed.

Results and discussion Dynamics of wild type and its mutants exhibit different patterns at different temperatures The C-alpha RMSDs for the WT, LTMs and MTMs at different simulation temperatures 328, 339, 350 and 450 K respectively, are shown in Figures 2 and 3.

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Figure 2. C-alpha RMSD at different simulation temperatures with respect to their starting structure.

Figure 3. Mean C-alpha RMSDs (in nm) at different simulation temperatures. Error bars show the standard deviation for each temperature. 14


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All RMSD plots were very similar and were indicative of stability of all the structures at moderate temperatures viz. at 328 and 339 K. Interestingly, at 350 K, while all other lipases show similar trends, the 12M mutant shows a rise in RMSD. This increase in RMSD is due to enhanced motions including the catalytically relevant loop regions (residues 113-122 and 131-136) of the protein (Figure S2), and could thus be linked to the higher temperature of optimal activity of 12M mutant36 compared to LTMs and 9M. At 450 K, the WT and LTMs show large RMSD (Figures 2 and 3), as also high values of radius of gyration (Rg) (Figures 4 and S4), indicating their unfolding and denaturation. In contrast, the MTMs remain compact at 450 K, indicating the impact of point mutations on the overall conformational stability. Attempts at correlating the relative magnitudes of RMSDs and Rg with the respective experimentally observed thermostabilities, however, held some surprises. It is well known that, the Tm of proteins generally represents their relative thermodynamic stability. A high Tm shows the resistance of a protein to unfold. However, aggregation of a protein can also occur below their Tm and vice-versa. In such cases the prediction of aggregation tendency of a protein based on their Tm can fail. Therefore, a higher Tm does not necessarily mean less aggregation tendency of a protein. Moreover, if the aggregation starts before reaching the unfolding intermediate state, it further promotes unfolding and aggregation, because the prior aggregation shifts the reversible equilibrium of unfolding reaction to favor the unfolded form of a protein.61 Given these facts, we hypothesize that the Tm (melting temperature) in case of LTMs does not truly represent the stability of their native states; rather it shows the presence of complex transition pathways from native to partially unfolded states and the possibility of intermediate states with lower tendency (compared to WT) for irreversible

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denaturation through aggregation. The experimental thermal inactivation data (Table 3), also shows that the 9M mutant undergoes less thermal inactivation at 85°C (358 K) compared to that at 75°C (348 K). This suggests that the 9M mutant has a greater tendency of aggregation at 75°C compared to 85°C, though the latter temperature is much higher than its Tm.

Unexpected behavior of 2M mutant at 450 K On the basis of experimentally measured T1/2 values at 55°C (328 K), the 2M mutant, containing the A132D and N166Y mutations (Tables 1 and 3), was not only considered as more thermostable than WT, it was also selected for further directed evolution.34 It is thus contrary to expectations, that the 2M mutant shows a higher average RMSD and Rg compared to those of the WT (Figures 3 and 4). It has, however been shown subsequently, that though both WT and its single mutant A132D undergo thermal denaturation followed by irreversible aggregation, the A132D mutant, despite being conformationally less stable than the WT, shows higher recovery of activity upon cooling due to its slower aggregation kinetics.41 Thus, we may reconcile the order of thermostability, inferred from experimental T1/2 values on the one hand, and from analysis of simulation trajectories on the other, by invoking a complex interplay of reversible unfolding and irreversible aggregation under experimental conditions. The question that emerges from the above discussion is whether it is appropriate to correlate the experimental parameters of thermostability with conformational stability alone. Apart from such ambiguities in identifying the physicochemical processes affecting thermostability, there are also issues related to accurate measurement of T1/2

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values themselves. This is highlighted by the significant difference in the reported values of T1/2 for the 3M mutant in earlier34 and in later publications35, respectively. We have also computationally assessed the proposed adverse effect of A132D mutation on conformational stability by running 100 ns simulations at 450 K on D132A (reverse) mutants of the 2M and 12M structures. As expected, the reversal of the A132D mutations, from both 2M and 12M, resulted in a significant decrease in average RMSD (Table 4) over their corresponding original values (Figure 3). These data suggest the possible non-additive effects of point mutations on the conformational stability of mutant lipases. The thermal energy which drives internal motion in proteins utilizes the noncovalent interactions network (including the hydrogen bonds) for their manifestation. Point mutations are likely to disrupt existing networks to establish alternative networks. A second mutation may in principle restore the original network or may give rise to yet another network. Thus, the mechanism of stabilization of independent individual mutations may be quite different and the effect of multiple mutations therefore need not be additive. Table 4. RMSD and Rg values (in nm, from 100 ns simulation at 450 K) of in-silico mutant structures without A132D mutation (reversal of A132D mutation). Standard deviations are shown inside the braces. System

Mean RMSD

Mean Rg

2M without A132D mutation

0.28 (0.07)

1.48 (0.02)

12M without A132D mutation

0.20 (0.05)

1.46 (0.01)

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Figure 4. C-alpha radius of gyration (Rg; in nm) at different simulation temperatures. Error bars show the standard deviation for each temperature. Increased active site flexibility and conservation of structural integrity–twin characteristics of elevated temperature dynamics of MTMs The RMSF profile at 328, 339 and 350 K (Figure 5) suggests that both LTMs and MTMs possess enhanced flexibility in catalytically relevant regions of structure. However, at 350 K, we have observed enhanced flexibility particularly for 12M mutant in the catalytically relevant loop regions (residues 113 to 122 and 131-136) from two independent 100 ns simulations (Figure S2). Enhanced flexibility in catalytically relevant regions of LTMs and MTMs can also be observed by the average interatomic distances between these atoms (i.e. peptidic nitrogens of I12 and M78, hydroxyl oxygen of S77, carboxylate oxygen of D133 and imidazole nitrogens of H156) at 328, 339 and 350 K (Figure 6). Moreover, comparison of their respective free energy landscapes (FELs) (Figure 7) and interatomic distances between catalytically relevant residues (Figure 6) at

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300K, do not suggest any significant differences in the flexibility, either in the overall conformation or in the catalytically relevant regions, between WT and 12M.

Figure 5. C-alpha RMSF at different simulation temperatures. Arrows show the regions of enhanced fluctuations at different simulation temperatures. Secondary structure bar is shown in the center.

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Figure 6. Mean interatomic distances (in nm) between catalytically important residues for all the systems at 328, 339, 350, 450 and 300 K (3 independent simulations of 100 ns only for WT (WT_1, WT_2, WT_3) and 12M (12M_1, 12M_2, 12M_3)). Error bars show the standard deviation. The above observations suggest that, the LTMs and MTMs show enhanced flexibility in the catalytically relevant regions at 328, 339 and 350 K. Furthermore, we also found that 12M populated the active state conformation of catalytic serine residue62 at all the simulation temperatures compared to LTMs and WT (see the supplementary Text S1 for a detailed discussion). However, these observations do not show correlation with the previous studies on WT and 12M regarding the rigidity of active site residues.63,64 Also, these observations do not obey the principle, which states that thermostable proteins show

rigidity

at

ambient

temperatures

compared

to

their

mesophilic

counterparts.11,17,19,21 These observations are supported by PCA and FEL analysis. Figures 8 and 9 show the FELs (free energy landscapes), constructed using the two dimensional projection of trajectories at 328 and 339 K, for all the systems. At 328 K,

22


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LTMs and MTMs sampled broader regions on FEL compared to WT. At 339 K, the FELs of 6M and MTMs show the presence of distinct basins separated by relatively high free energy barrier compared to those of WT and other LTMs (2M, 3M, and 4M). As is evident from figures 10 and S5, at 350 K, the 12M mutant shows greater coverage of conformational space along the first two PC’s. In contrast, the LTMs and the WT are found to sample overlapping regions of conformational space, wherein the conformations are largely clustered in restricted regions along PC1 and PC2. The MTMs also show enhanced flexibility in the catalytically relevant loop regions (residues 131-136) of their structure as suggested by the two dimensional projection of the loop regions of trajectory at 328, 339 and 350 K (Figure S3). These observations confirm that, the LTMs and MTMs show flexibility in the catalytically relevant loop regions apart from showing enhanced conformational flexibility. Thus, the role of flexibility in catalytically relevant regions of 12M for its enhanced activity can be emphasized. At 450 K the MTMs show least RMSF (Figure 5) in all the regions, including the N and C-termini of the structure, indicating the enhanced anchoring of terminal regions to the rest of protein. The 9M mutant shows higher flexibility than 12M and lower flexibility than LTMs and WT. As there were no extra stabilizing mutations present at the terminal regions of the 9M structure, the observed rigidity can be attributed to global and long range effects of mutations on structural flexibility or rigidity of proteins.65,66

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Figure 7. Free energy landscapes (FELs) for WT and 12M from 3 independent simulations (100 ns each) at 300 K. The color scale of FEL runs from blue (corresponding to free energy minima) to red (corresponding to free energy maxima) The FELs of WT and 12M mutant show the presence of similar multiple basins suggesting similarity in overall conformational flexibility. 24


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Figure 8. Free energy landscapes (FELs) for all the systems at 328 K. WT shows one major deep basin and two less populated basins. The mutant lipases (2M to 12M) show the presence of more than one deep basin. 25



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Figure 9. Free energy landscapes (FELs) for all the systems at 339 K. The basin splitting (number of distantly located basins, which are separated by relatively high barrier) is more significant in 6M, 9M and 12M mutants, due to their enhanced flexibility. In WT, 2M, 3M and 4M, basins are closely located i.e. separated by relatively low barrier.

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Figure 10. Free energy landscapes (FELs) for all the systems at 350 K. The FEL of 12M mutant shows three distinct well separated basins. Whereas, the FELs of WT and other mutant lipases show 2-3 closely located basins.

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It seems that thermostabilizing mutations incorporated in 12M mutant affected the global structure by rigidifying the loop and regions adjacent to the C-terminus of the protein, where the mutations M134E, M137P and S163P are present. Despite the lower RMSF, the average interatomic distances (Figure 6) between the atoms of catalytically important residues show large fluctuation in MTMs at 450 K. But, MTMs keep the interatomic distances of oxyanion hole forming residues (I12, S77 and M78) under constraint even at 450 K. The possible explanation for this behavior might be linked to the specific activity profile of the 12M mutant, which shows highest activity both at moderate and high temperatures.36 At 450 K, the 12M shows reduced flexibility in the catalytically relevant loop regions compared to that at 350 K. The stapling of the N and C-terminal ends (by point mutations) in 12M prevents the fluctuation of terminal regions at optimal working temperatures. On the other hand, it shows enhanced flexibility in the N and C-terminal regions to accommodate thermal stress during unfolding conditions. These observations suggest that the MTMs retain the required flexibility while maintaining the correct positioning of the catalytically relevant atoms at 450 K, which is essential for maintaining the catalytically relevant conformation in order to preserve the activity at elevated temperatures.

Structural features responsible for the stability of MTMs: A combination of various molecular factors To characterize the overall dynamics of secondary structural elements, in all the systems during their simulation at 450 K, we have quantified the persistence of each residue towards maintaining its original secondary structural element (Figure 11), using

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the hydrogen bond criteria as defined in the DSSP protocol. Some specific observations are listed below: •

Decrease in persistence of α3 helix (residues 78-89), 310 helix (residues 105-109) and α5 helix (residues 163-174) in all the non-MTM lipases.

•

Decrease in persistence of α1 helix (residues 20-29) in WT, 2M, 3M and 4M mutants.

•

Relative decrease in persistence of β6 strand (residues 147-151) in WT and 2M.

•

Formation of non-native β-strand (shown by red tailed arrows at N and C-terminal ends in figure 11) in 4M mutant.

A notable general feature that emerges out of this analysis is that the point mutations in the LTMs, namely, in 2M (A132D and N166Y), 3M (A132D, N166Y and L114P), 4M (A132D, N166Y, L114P and I157M) and 6M (A132D, N166Y, L114P, I157M, F17S and N89Y), either do not seem to stabilize the secondary structures where they are respectively located (Table 1) or they show local stabilization (for e.g. in 4M, the I157M mutation results in decrease in RMSD of the 310 helix; Figure S6e). In contrast, a similar analysis with the MTMs seems to suggest that the additional mutations have a stabilizing influence on their corresponding secondary structure elements (Table 1), apart from the overall stabilization of the conformation. While this generalization is in keeping with the classification of the mutants in terms of LTMs and MTMs, how the mutations influence secondary structure stabilities is not very clear. In fact, these observations do not also help us in discriminating between cause and effect. That is, it does not tell us whether the overall stability is because of the stabilizing effect of the mutations on the secondary structures, or whether the overall stability due to

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mutations, manifests itself in the form of stability of the secondary structures.

Figure 11. The residue-wise probability distribution plot, showing the persistence of each residue to remain in its original secondary structural element at 450 K. The black arrows (with and without tail) show the location of significant structural changes during the simulation. Arrows with red color tail in 4M show the location of non-native beta strand formation.

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In order to understand the molecular basis of the conformational changes in LTMs, as discussed above, we have analyzed the standard parameters of structural stability viz. SASA-H (hydrophobic SASA), protein hydration, hydrogen bonds (between protein atoms and between protein atoms mediated by water molecules), all-atom contacts (excluding hydrogen), hydrophobic contacts and salt bridges. As expected all these structural properties show good correlation in accordance with the experimental thermostabilities of MTMs, suggesting the appropriateness of the force field parameters and therefore of the reliability of the simulation results. However, here too, the exact nature of cause and effect relationship between these individual point mutations and the corresponding properties seems difficult to delineate. In contrast, we have not observed any significant difference between the LTMs and WT in terms of these molecular interactions. Therefore, the role of point mutations (A132D, N166Y, L114P, I157M, F17S and N89Y) in the conformational stability of LTMs is not clear despite the fact that, on the basis of thermal inactivation, free energy of unfolding (with an exception, where a less thermostable 6M mutant possesses higher free energy of unfolding than MTMs; Table 3) and catalytic activity studies, these mutants were shown to be more thermostable than WT. Some important observations from the analysis are given below.

(I) MTMs show least SASA-H, higher occupancies of hydrogen bonds, all-atom contacts, hydrophobic contacts and salt bridges at 450 K It is known that the thermostability and activity of proteins is governed by reduction of SASA-H9,20, preservation of hydrogen bonds67, all-atom contacts68, hydrophobic

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contacts69 and salt bridges.67,70,71 We have observed good correlation between these structural properties and the respective thermostabilities of MTMs, but the LTMs do not show the expected corresponding correlations . The LTMs and WT show increase in the value of SASA-H for the core region of structure (Figure 12), which consists of six parallel beta strands (β1- β6), indicating unfavorable solvent contact resulting from the initiation of unfolding process. In contrast, the 12M mutant shows the least values for SASA-H for the core region of structure followed by that of 9M mutant. It indicates that even at higher temperatures the 12M mutant manages to preserve its hydrophobic packing, which may provide the essential driving force towards evading aggregation at high temperatures, as observed experimentally.36

In addition, the number of solvent molecules in the identified cavities72 was higher for LTMs and WT compared to MTMs (Table S1). This is suggestive of the penetration of water molecules, in the core of the structure, resulting in global structural disruption in WT and LTMs. This could be one of the possible reasons for the experimentally observed aggregation of WT at high temperature.36 The MTMs also possess greater number of hydrogen bonds, all-atom contacts and hydrophobic contacts compared to LTMs and WT at 450 K (Tables S2, S3, S4 and S5).

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Figure 12. Distribution of SASA-H of the core secondary structural region (the six parallel beta strands β1- β6, consist of residues 6-9, 36-38, 71-76, 96-102, 124-130, 147-151 respectively) for all the systems at 450 K. The salt bridges R147-E171, K70-D118, R107-D144, R142-D144, R107-D111 (formed due to G111D mutation in MTMs) and D91-K95 (Figure 13) also show least fluctuation (smaller average distance) and higher occupancy (Table S6) in MTMs. But the LTMs do not show the expected stabilization of all the salt bridges compared to WT, as is clear from the following observations: • Less occupancy for salt bridge R107-D144 in 2M and 6M than in WT. • Less occupancy for salt bridge R147-E171 in LTMs than in WT. • Less occupancy for salt bridge K70-D118 in 2M and 3M than in WT. 33



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• Less occupancy for salt bridge K95-D91 in 2M, 3M and 4M than in WT. Detailed description about these salt bridges and their possible mode of stabilization can be found in supplementary texts (Text S2).

Figure 13. 12M mutant structure showing the location of stable salt bridges (in stick representation).

These observations suggest that the integrity of hydrogen bonds, all-atom contacts, hydrophobic contacts and salt bridges (Tables S2, S3, S4, S5 and S6) are intrinsically related to the conformational stability of MTMs under dynamic conditions, since loss of these interactions facilitates denaturation at high temperatures.

(II) 12M forms stable water mediated hydrogen bonds at 450 K It is generally believed that the hydrophobic core of the protein remains dry during the folded state and penetration of water molecules destabilizes the hydrophobic core of the

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protein structure. However, few experimental and atomistic simulation studies73–76 have reported the role of water as a lubricant for the packing of the hydrophobic core of the protein. These studies point out that the folded state is not completely dry, but that a few water molecules form hydrogen bonds with the protein backbone. Since, buried water molecules have much longer mean residence time than that for bulk water of the first hydration shell; they constitute an integral part of the protein structure.77 It is also shown that internal water molecules can play an important role in influencing protein thermostability.78

In 12M, the backbone carbonyls of residues V99, L124, G172 and side chain oxygen atoms of N98 and T126, were observed to be involved in the formation of water mediated hydrogen bonds (Figure 14) and remain stable with higher occupancy compared to 9M, LTMs and WT. The residues N98, V99, L124 and T126 were located on strands β4 (N98 and V99) and β5 (L124 and T126), which are part of the hydrophobic core of the protein. Residue G172 is located near the C-terminal of helix α5. Therefore, it is possible that these water mediated hydrogen bonds not only provide a strong anchoring between the C-terminal region and the core of the structure but also enhance the compactness of the core region. The greater occupancies of these hydrogen bonds in 12M can be attributed to the overall enhanced structural rigidity due to additional point mutations M134E, M137P, and S163P. The mutations M134E and M137P stabilize the loop, which may further stabilize the downstream strand β5, where the residues L124 and T126 were located. On the other hand, the mutation S163P stabilizes the α5 helix, which might reduce the mobility of residue G172.

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Figure 14. Stereo view of the snapshot from the simulation of 12M mutant, displaying the location of stable solvent mediated hydrogen bonds at 450 K. Amino acid residues and solvent molecules are shown in stick and ball-stick representation respectively.

The stabilization caused by these additional mutations in 12M in the vicinity of their respective location, through conformational rigidity (M137P, S163P) and favorable contacts with solvents (M134E) on the protein surface, could pave the way for the stabilization of these water mediated hydrogen bonds. Thus, we propose that, apart from the other molecular factors, these water mediated hydrogen bonds play significant role in maintaining the secondary structural elements at high temperature and help in preventing the C-terminus unfolding at 450 K.

Native to non-native state transition in LTMs and WT versus. native state stabilization in MTMs: PCA and FEL analysis at 450 K LTMs and WT show large concerted motions in different parts of the structure as shown by the porcupine plots (Figure 15) representing movements along PC1 and PC2. In contrast, the MTMs show restricted motions primarily at the N-terminal region (loop and α1 helix) and in the surface loops of the protein. The detailed account of the motions

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observed along the PC1 and PC2 can be found in supplementary information (Figure S11 and Text S3). The FEL79,80 analysis (Figure 16) brings out the differential pattern of conformational fluctuations, as depicted by the number of basins (major basins are labeled 1, 2, 3 and 4) and the area spanned by different basins. The MTMs show least conformational fluctuation, moving in native (folded states) or near to native basins (Figure 16), suggesting the existence of large free energy barriers (Table S14) between native and non-native states (partially unfolded states). In contrast, the LTMs show transitions from native (labeled 1 and 2) to non-native FEL basins (labeled 3 and 4), suggesting their increased tendency to sample conformationally distant non-native states at high temperatures. Notably, the WT shows higher fluctuation than LTMs and MTMs and follows a constricted transition pathway (a narrow sampling path) from native (labeled 1) to non-native FEL basins (labeled 3 and 4). However, the LTMs populate several intermediate states on FEL, during native to nonnative state transition. Therefore, it might become easy for them to gain access to native state via any of these intermediate states during refolding conditions, and may thus show a lower aggregation tendencies compared to WT at high temperatures. On comparing the average RMSD values and on analyzing the variation in hydrogen bonds, between major FEL basins (Text S4 and Table S14), it was evident that the MTMs resist the breaking of hydrogen bonds and hence remain close to the native conformations at high temperature. In contrast, the LTMs and WT frequently sampled non-native conformations, accompanied by the breaking of larger number of hydrogen bonds. The constricted transition pathway from native to non-native FEL basins by WT (Figure 16), suggests a lower probability of its accessing native conformations from the non-native

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aggregation prone conformations.36 The non-native conformations in LTMs could also lead to aggregation, thus reducing the probability of refolding to their native conformation during refolding conditions. In contrast, the presence of deep basins corresponding to native like ensembles in MTMs, suggest the low probability of leaking into non-native conformations at higher temperatures, which may possibly rationalize their observed aggregation resistance at high temperatures.

Mutation induced changes in residence time of hydrogen bonds and salt bridges and their role in dynamic cross correlation at 450 K We have analyzed the DCCM (dynamic cross correlation map) for all the systems (Figure 17) at 450 K and have attempted to relate our observations with the effect of mutation induced stabilization (or destabilization) of hydrogen bonds and salt bridges in the protein. The correlation effects observed, in particular the variation in their characteristics across mutants, seem to hold some interesting clues towards understanding the behavior of different mutants in terms of the corresponding mutations. The 12M mutant shows strong positive correlation (labeled a to g in 12M) between Cterminus and core β-strands (β1, β3, β4, β5 and β6), due to stabilization of several hydrogen bonds (Tables 5 and S15) present in the vicinity of mutated residues M134E, M137P and S163P. The appearance of slight negative correlation at the N-terminal regions (labeled j to l in 12M) may serve as an effective mechanism for accommodating the thermal motions in the presence of strong positive correlation at C-terminal end. The

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net result of these effects could be the stabilization of C-terminus and core regions in 12M mutant. It is interesting to note in this context, that the 9M mutant shows no significant negative correlation, and the same is found to be noticeably reduced even in the regions corresponding to those where the 6M mutant showed negative correlations. The overall reduction in negative correlation, observed in the 9M mutant, may be primarily due to stabilization of salt bridges K70-D118, R142-D144 and several hydrogen bonds (Tables S6 and S15). However, it is possible that the incorporation of point mutations A15S and A20E might be instrumental in rescuing the 9M mutant from negative correlations, observed in 6M mutant, through the stabilization of hydrogen bonds (Table S15). In the context of the 12M trajectory, however, it seems that the point mutations A15S and A20E, which have rescued the 9M mutant from negative correlation at the N-terminal end, become less effective in minimizing negative correlation in the 12M mutant. An interesting hypothesis that seems to emerge from these observations is that the Nterminal region could serve as a hot spot for future directed evolution studies.

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Figure 15. Porcupine plots, showing the direction and magnitude of dominant motions along the top two principal components (PC1 and PC2) for all the systems at 450 K. The N and C-terminal ends are shown as green and cyan spheres respectively.

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Figure 16. Free energy landscape (FEL) of all the systems along the top two eigenvectors (PC1 and PC2) at 450 K. For all the systems, the FEL is divided into four states (labeled as 1 to 4). All the structures from these states were used to characterize the state wise analysis of hydrogen bond breaking during the simulation.

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The 6M mutant shows reduction in negative correlation at C-terminal end compared to 4M mutant due to stabilization of several hydrogen bonds over the 4M mutant (Table S15). In contrast, it shows negative correlation in β1-α1 (where F17S mutation is located) and β2-α1 regions of protein. The increased negative correlation between α3loop (residues 85-93) and loop (residues 110-120) regions in 6M, compared to 4M, could be due to weakening of the salt bridge R104-D144 and few hydrogen bonds (Tables S6 and S15). The 4M mutant shows increase in negative correlation compared to 2M and 3M mutants due to weakening of several hydrogen bonds compared to 2M and 3M. However, despite the local stabilizing effect of I157M mutation in 4M (decrease in RMSD of the 310 helix; Figure S6e), it does not seem to have significant role in minimizing the negative correlation. The decrease in negative correlation in the 3M mutant compared to 4M is possibly due to greater occupancies of salt bridges R147-E171, R142-D144 and stabilization of several hydrogen bonds (Tables S6 and S15). Whereas, the 3M mutant shows reduction in negative correlation compared to 2M, due to stabilization of all the salt bridges (except K95-D91) and several hydrogen bonds (Tables S6 and S15), it also shows increase in positive correlation between the α4 helix and loop (residues 102-115) region, possibly due to incorporation of L114P mutation. Though, the WT shows greater occupancies for all the salt bridges except for R142D144, compared to 2M mutant, it shows maximum negative correlation in C-terminus and its adjacent regions. Therefore, the large negative correlated motion in WT seems to be responsible for the C-terminus unfolding at 450 K, inducing instability in the whole protein structure.

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Figure 17. Residue wise dynamic cross correlation maps for all the systems at 450 K. The extent of correlation for all residue pairs (C-alpha atomic displacement) during the simulation is shown. The upper triangle of correlation map shows only cross-correlations larger than 0.5 (absolute values) and all the cross-correlations in the lower triangle. Therefore, the upper triangle displays only the most correlated motions. The color scale runs from red (-1) through white (0) to blue (+1). The negative values (marked with boxes) are indicative of anticorrelated motions (displacements along opposite direction), whereas positive values depict correlated motions (displacements along same 43



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direction). Regions of strong positive and negative correlation are labeled with small letters, ranging from “a” to “l” in 12M. Table 5. Annotation of stabilizing factors in 12M and their possible role in positive and negative correlation on DCCM at 450 K (Figure 17). Regions on DCCM of 12M

Hydrogen bonds

a, i and h

Increased occupancy of following hydrogen bonds: N4:ND2-D72:OD2 60.317 N98:ND2-D72:OD2 63.899 S77:N-L102:O 56.335 Increased occupancy of following hydrogen bonds: Y125:N-Q178:O 91.196 Y139:N-V136:O 63.291 L140:N-V136:O 55.557 S141:N-P137:O 67.311 S141:OG-G104:O 85.7 Q178:N-Y125:O 86.154 Decreased occupancy of following hydrogen bond: V6:N-K35:O 60.155; Small decrease in occupancy of several hydrogen bonds at N-terminal end.

b-g

j-l

Salt bridge and solvent mediated hydrogen bond Increased occupancy of solvent mediated hydrogen bonds

Increased occupancy of following salt bridges: R142-D144 R147-E171

The above observations suggest that, both LTMs and MTMs show reduction in negative correlation compared to WT. They also show enhanced positive correlation in different regions of the protein, which can be attributed to either local (in LTMs) or global (in MTMs) effects of incorporated mutations resulting out of the stabilization of different sets of hydrogen bonds. Our comparative DCCM analysis and discussions on how the variations in cross correlation characteristics may be explained, in terms of a complex interplay of different noncovalent interactions arising out of successive mutations, provides the rationale for

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the non-additive effect of point mutations on conformational stabilities of the corresponding mutants. Note that successive individual point mutations, leading to progressive increase in thermostability of the corresponding mutant lipases, in most cases, significantly affect the cross correlation characteristics in different ways. Given also that protein thermostability is related to its dynamics, this implies that the mechanism for stabilization may, in principle, be different for different mutations. This explains why the effects of mutations on thermostability need not be additive.

Balance of thermodynamic and kinetic factors in the thermostability of MTMs For an enzyme to be both kinetically and thermodynamically stable, all the parameters of thermostability viz. T1/2, T50, Tm and Topt must be high in magnitude and they often show correlation with each other, as in the case of MTMs. However, since parameters of thermodynamic stability, i.e. Tm and ∆Gu depend upon various other factors, such as irreversible unfolding or existence of complex unfolding pathway6, this may not always be the case. An enzyme may only be kinetically stable, but may not be thermodynamically stable, as in the case of the lipase mutant developed by Reetz and coworkers81 and the 2M mutant studied in this work. Similarly, an enzyme may only be thermodynamically stable but may not be kinetically stable, as in the case of 6M mutant, which possesses higher ∆Gu but lower Tm than MTMs (Table 3). Our results allow us to hypothesize that higher value of ∆Gu in 6M might be due to the greater stability of unfolded and/or aggregated state compared to its native state. Thus, it is still possible that a protein with a higher ∆Gu value can undergo thermal denaturation more readily than a protein with lower ∆Gu.

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We therefore are of the opinion that in order to understand the thermostability of the MTMs, we need to take into consideration the balance of thermodynamic and kinetic factors, and their corresponding experimental quantifiers, in conjunction with an understanding of the issues which prevent straight forward correlation between them.

Conclusions Modeling the effect of point mutations on the conformational stability of proteins is a challenging task, particularly when multiple mutations are located close to each other, and hence give rise to non-additive effects on the conformational stability of the system. The ability of the reported MD simulations to reproduce the experimentally observed higher conformational stability of MTM’s not only validates the force field used and the simulations themselves, but also provides a conformational basis to explain the stabilizing effects of incorporated mutations. Our results emphasize that, the MTMs maintain their thermostability and activity, both at room and high temperature by activating few essential modes at the catalytically relevant loop regions, keeping the motion of structural regions under constraint to avoid the loss of three dimensional structures. The FEL analysis shows that WT follows a constricted pathway from native to non-native state through less populated intermediate states. It suggests that WT follows a highly cooperative native to non-native state transition in such a way that there is least possibility to gain access to the native state even in the presence of refolding conditions due to aggregation. This supports the experimental observation regarding the irreversible folding to unfolding transition of WT at high temperatures. In the absence of experimental data on aggregation behavior of LTMs (except for 2M), we propose that

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the LTMs may show reversible denaturation to different extent due to presence of native like intermediate states with lesser aggregation propensities compared to WT and can thus explain the variation in stabilities as reported by different experimental parameters. In contrast MTMs show a deep and rugged FEL and fluctuate near to native state at 450 K. Through the analysis of different states on FEL for WT and all the mutants, we show that MTMs mostly stay in conformations retaining stable salt bridges and hydrogen bonds, thus provide them higher probability to stay in functional states even at higher temperatures. Based on the discussed results, we propose that MTMs possess required flexibility in catalytically important regions of structure, both at moderate and high temperatures. Thus, MTMs show balance of thermodynamic and kinetic factors to remain stable and active at high temperatures. Progressive individual mutations in the LTMs studied; do not seem to reveal any clear or consistent trend in the mechanism of conformational stabilization. In fact, our studies such as shown by the MD trajectory analysis of D132A reverse point mutants of 2M and 12M, suggest that mutations incorporated in LTMs may have non-additive effects on conformational stabilities of mutants of later generations. Our findings provide new insights and extend the knowledge provided by experiments to explain the thermostability of mutant lipases.

Author information *

Corresponding authors E-mails: [email protected]; [email protected]

Notes The authors declare no competing financial interest.

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Acknowledgement A.M. and B.S. thank DBT project BT/PR-14715/PBD/16/903/2010 for financial support.

Supporting Information Available The supporting texts, figures and tables discussed in the manuscript are available as supplementary information. This information is available free of charge via the Internet at http://pubs.acs.org/.

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Thermostability of in vitro evolved Bacillus subtilis lipase A: a network and dynamics perspective.

Structural Rigidity and Protein Thermostability in Variants of Lipase A from Bacillus subtilis.

Mutatomics analysis of the systematic thermostability profile of Bacillus subtilis lipase A.

Porphyrin and corrinoid mutants of Bacillus subtilis.

Insights into molecular interactions between CaM and its inhibitors from molecular dynamics simulations and experimental data.

Recombination-deficient mutants of Bacillus subtilis.

Deoxyribonucleoside-requiring mutants of Bacillus subtilis.

Molecular dynamics of the Bacillus subtilis expansin EXLX1: interaction with substrates and structural basis of the lack of activity of mutants.

Understanding Miltefosine-Membrane Interactions Using Molecular Dynamics Simulations.

Kasugamycin-resistant mutants of Bacillus subtilis.

Insights into the role of Asp79(2.50) in β2 adrenergic receptor activation from molecular dynamics simulations.

The Integrin Receptor in Biologically Relevant Bilayers: Insights from Molecular Dynamics Simulations.

Bacillus subtilis mutants dependent on streptomycin.

P-113 peptide: New experimental evidences on its biological activity and conformational insights from molecular dynamics simulations.

CH4 Hydrate Formation between Silica and Graphite Surfaces: Insights from Microsecond Molecular Dynamics Simulations.

Promote potential applications of nanoparticles as respiratory drug carrier: insights from molecular dynamics simulations.

Autolysins and shape change in rodA mutants of Bacillus subtilis.

Isolation, characterization, and mapping of Bacillus subtilis 168 germination mutants.

Molecular insights into DNA binding and anchoring by the Bacillus subtilis sporulation kinetochore-like RacA protein.

Initiation and termination mutants of Bacillus subtilis bacteriophage SPO1.

Magnesium and anion requirements of rodB mutants of Bacillus subtilis.

Tris-thiourea tripodal-based molecules as chloride transmembrane transporters: insights from molecular dynamics simulations.

Voltage-gated ion channel modulation by lipids: insights from molecular dynamics simulations.

Self-assembly of an aspartate-rich sequence from the adenovirus fiber shaft: insights from molecular dynamics simulations and experiments.