Unraveling Energy and Dynamics Determinants to Interpret Protein

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Unraveling energy and dynamics determinants to interpret protein functional plasticity: the limonene-1,2-epoxide-hydrolases case study Silvia Rinaldi, Alessandro Gori, Celeste Annovazzi, Erica Elisa Ferrandi, Daniela Monti, and Giorgio Colombo J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00504 • Publication Date (Web): 15 Mar 2017 Downloaded from http://pubs.acs.org on March 18, 2017

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Journal of Chemical Information and Modeling

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Unraveling energy and dynamics determinants to

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interpret protein functional plasticity: the limonene-

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1,2-epoxide-hydrolase case study

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Silvia Rinaldi, Alessandro Gori, Celeste Annovazzi, Erica E. Ferrandi, Daniela Monti, and

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Giorgio Colombo*.

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Istituto di Chimica del Riconoscimento Molecolare, C.N.R., Via Mario Bianco 9, 20131 Milano,

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Italy

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*Corresponding author: Giorgio Colombo, Istituto di Chimica del Riconoscimento

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Molecolare, CNR; via Mario Bianco 9, 20131 Milano, Italy. E-mail: [email protected].

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Tel: +39-02-28500031, Fax: +39-02-28901239.

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ABSTRACT

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The balance between structural stability and functional plasticity in proteins that share common

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three-dimensional folds is the key factor that drives protein evolvability. The ability to

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distinguish the parts of homologous proteins that underlie common structural organization

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patterns from the parts acting as regulatory modules that can sustain modifications in response to

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evolutionary pressure may provide fundamental insights for understanding sequence-structure-

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dynamics relationships. In applicative terms, this would help develop rational protein design

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methods. Herein, we apply recently developed computational methods, validated by

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experimental tests, to address these questions in a set of homologous enzymes representative of

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the limonene-1,2-epoxide-hydrolase family (LEH) characterized by different stabilities, namely

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Rhodococcus erythropolis-LEH (Re-LEH), Tomks-LEH, CH55-LEH and the two thermostable

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Re-LEH variants Re-LEH-F1b and Re-LEH-P. Our results show that these enzymes, despite

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significant sequence variations, exploit a few highly conserved stabilization determinants to

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guarantee structural stability linked to biological functionality. Multiple sequence analysis shows

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that these key elements are also shared by a larger set of LEHs structural homologues, despite

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very low sequence identity and functional diversity. In this framework, stabilizing elements that

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we hypothesize to have an accessory role are characterized by a lower degree of sequence

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identity and higher mutability. We suggests that our approach can be successfully used to

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pinpoint the distinctive energy fingerprint a class of proteins as well as to locate those

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modulators whose modification could be exploited to tune protein stability and dynamic

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properties.

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Introduction

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The properties of proteins are ultimately determined by the linear sequences of amino acids

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that evolution selected to encode for thermodynamically stable functional three-dimensional

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arrangements.1 Indeed, folding sequences represent a small subset of the large space of all

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possible candidates that can populate a structure performing a certain function.2 The

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relationships between sequence and structure have been extensively investigated by analyzing


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the detailed physico-chemical determinants of the folding and stability of several model

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proteins.3–5

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Theoretical and experimental analyses have indicated that stability in relation to a certain

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function (catalysis, ligand binding, transport etc…) is the key factor in selecting a certain

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structure. In other words, a fold is evolutionary fit as long as it may be stable enough to allow the

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protein to perform the desired biochemical function.6,7 Such protein stability should in principle

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confer tolerance to (a certain number of) mutations and as a consequence may be instrumental to

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protein evolvability and adaptation to different conditions. Importantly, the accumulation of

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mutations while maintaining structural stability may result in entirely new functions.8–10

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Understanding which parts of a protein contribute the most to stability still represents an

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important open issue for both practical and fundamental issues. From the practical point of view,

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the ability to introduce mutations able to modulate the stability of a protein while maintaining a

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certain 3D organization can impact on the development of new catalysts that carry out non-

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natural reactions or are particularly (un)stable in specific conditions. From the fundamental point

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of view, the determination of the necessary requirements of stabilization can help improve our

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understanding of how sequence differences may modulate functionally oriented properties, while

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preserving a certain 3D organization. This latter aspect is strictly connected to the regulation of

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conformational plasticity, whereby the fine-tuning of a certain function often depends on the

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coexistence and selection of different dynamic states.11

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In this paper, we set out to address various aspects of this problem by analyzing and comparing the atomistic simulations of 3 recently crystallized, structurally homologous representatives of


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the limonene-1,2-epoxide-hydrolase family (LEH), namely Rhodococcus erythropolis-LEH (Re-

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LEH), Tomks-LEH, CH55-LEH. These enzymes belong to the epoxide hydrolases (EH)

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superfamily, catalyzing the hydrolysis of an oxirane ring by water addition. Most of EHs share

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high sequence and structure similarity, exhibiting a classical α/β-hydrolase fold.12,13 Recently

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new EH members have been discovered showing atypical reactivity and structural properties.

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This family was named according to the natural substrate (limone-1,2-epoxide) of the first

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isolated member, the Rhodococcus erythropolis-LEH.14 Re-LEH can have several attractive

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applications in industrial synthesis, showing sequential and enantio-convergent conversion of

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different isomers.15 In order to obtain enzymes with improved stability, a metagenomics

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approach was recently exploited to identify new LEHs. CH55-LEH and Tomsk-LEH were

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isolated in hot springs from China and Russia respectively, both collected at moderate high

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temperatures (46° and 55°). Indeed both Tomsk-LEH and CH55-LEH show higher temperature

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optima than Re-LEH,16 while from a structural point of view, they share the Re-LEH typical 3D

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fold. The three enzymes form a stable homodimeric organization, where each monomer features

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a highly curved six-stranded mixed β-sheet, with four α-helices packed onto it to create a deep

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cavity. Despite the structural similarity, the three LEHs display low sequence homology: Tomsk-

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LEH and CH55-LEH show 25% and 31% sequence identity with Re-LEH respectively, whereas

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they share 48% identity between them.

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Therefore, the three LEHs represent an interesting case study, where the balance between a

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conserved 3D fold and sequence differences tunes protein properties. Understanding this

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relationship could contribute to gain insight in the functional significance of protein structural

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stability as well as to designing improved variants for industrial applications. To further test the

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reliability of our approach, two highly thermostable variants of Re-LEH were included in the


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study. Re-LEH-P and Re-LEH-F1b are two mutant enzymes obtained by the FRESCO approach

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in the Janssen lab,17 where the authors could increase the apparent melting temperature by 20 °C

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and 35 °C respectively by means of a limited number of point mutations and the introduction of

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four additional disulfide bonds that stabilize the dimer in the case of Re-LEH-F1b.

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Hence our aim is in particular to identify the minimal, shared key determinants of LEH

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structural stabilization by the detection of common hotspot residues that may underlie the

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observed differences in this family of enzymes. We then attempt to exploit this information to

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define a broader link between sequence differences, the onset of different dynamic/plasticity

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properties and thermostability.

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To reach this goal, we have applied methods for the analysis of protein dynamics and

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energetics recently developed by us.18–20 The work presented here serves also as a test of the

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ability of such computational methods to provide information that can be transferred to the

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experimental realm. Briefly, sets of common residues hypothesized to be fundamental for

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controlling the plasticity and stability are identified. First, different rigidity/flexibility regimes

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are correlated to differences in enzymes’ thermophilicity. Next, comparative analysis of the

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distribution of internal energies pinpoints a limited number of interactions as the key

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determinants (hotspots) of structural stabilization, which are strongly conserved in the different

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members of the enzyme family. On the basis of this evidence, we suggest that these hotspots act

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as scaffolds upon which the stable 3D arrangement of the enzyme is built, while the rest of the

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sequence is modified to different extents to modulate activity and adaptability. The results of

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computational analysis are compared to those obtained through MM-GBSA and computational

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alanine scanning, and experimentally probed by point mutations, enzyme expression and activity

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measurements. Finally, the general validity of the observed differential roles of specific


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sequence-structure combinations is investigated through the analysis of a larger set of homologs.

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Importantly, structural hotspots are found to be highly conserved in a subset of proteins sharing

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the 3D architectures common to LEHs, despite low sequence similarities.

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Materials and Methods

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The experimental procedure for the preparation and characterization of mutants are reported in

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the Supporting Information.

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Theoretical Calculations

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Re-LEH, Tomsk-LEH, CH55-LEH, Re-LEH-P and Re-LEH-F1b (PDB-code 1nu3, 5aif, 5aih,

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4R9K and 4R9L respectively) crystallographic structures were refined by Schrodinger Maestro

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suite (Release 2013-1-9.4, Schrödinger, LLC, New York, NY, 2013). MD simulations were

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performed using AMBER 12.0 package with AMBER ff99SB.21 For computational details on

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model preparation see SI section.

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Distance fluctuations (DF) matrix and Local Flexibility (LF). DF analysis describes the

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dynamic coordination between any two residues.20 It is defined as the time-dependent mean

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square fluctuation of the distance rij between Cα atoms of residues i and j:

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DFij = 〈(rij-〈rij〉)2〉

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Where brackets indicate the time-average over the trajectory.

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Local flexibility is used to assess the intrinsic plasticity properties of a protein undergoing

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structural fluctuations. LF is obtained by calculating the DF of neighboring residues j comprised

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in the interval (i - 2, i + 2) along the sequence.


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Energy Decomposition Method (EDM). Energy Decomposition Method is based on the

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calculation of non-bonded interaction energy matrix Enb (namely van der Waals and electrostatic)

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between residues pairs. Stabilizing hotspots are identified through the eigenvalue decomposition

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of the Enb matrix of pair interactions. Such decomposition is carried out on the representative

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structure of the most populated conformational cluster obtained from cluster analysis of the MD

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trajectories described above. This approach has previously shown to reproduce the behavior of

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the same decomposition carried out by calculating non-bonded interactions on all the structures

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from the whole simulation and then evaluating the average matrix.18,19,22 The highest

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components of the principal eigenvector (associated with the lowest eigenvalue) identify crucial

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amino acids necessary to stabilize a certain protein conformation. See references18,19 for further

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details.

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Results and Discussion.

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Internal dynamics and coordination analysis. We started our comparative analysis of

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hydrolases by dissecting those protein regions whose internal dynamics could define common

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features as well as pinpoint distinctive elements. To this purpose, we analyzed long-range

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communication networks through the calculation of the distance fluctuations (DF) matrix

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between residue pairs: any two residues are defined to be quasi-rigidly coordinated if their DF is

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low.20 DF matrices show a common general pattern among the five LEHs (see figure 1). In

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particular each of the five systems exhibits a characteristic coordination bubble (red square).

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Mapping this bubble onto the structures reveals that this pattern is due to the internal

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coordination among β3, β4 and β5 strands. This region corresponds to a rigid core that defines

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the internal coordination within the proteins. The comparison of the matrices highlights some

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interesting differences. The insertion of the stabilizing mutations in Re-LEH clearly alters the


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global internal dynamic of the enzyme. The Re-LEH-P variant indeed shows higher rigidity and

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an extended coordination pattern with respect to the Re-LEH wild-type; this is particularly

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remarkable in the terminal regions of each monomer. This trend is further strengthened by the

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introduction of the disulfide bridges as distinctively pinpointed by the DF profile of Re-LEH-F1b

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mutant, where the yellow lines (low coordination) corresponding to the N- and C- terminal tails

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that are visible in the Re-LEH DF matrix, almost disappear

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Figure 1. DF matrices of Re-LEH, Re-LEH-P, Re-LEH-F1b, Tomsk-LEH and CH55-LEH. The

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red square shows the zoom on the coordination bubble. Β3, β4 and β5 structural domains are

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mapped into the protein according to the respective color code. The yellow and dark blue areas


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in the matrices are associated to flexible and rigid regions. The color bar on the right reports the

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intensity (Å2) of the fluctuations.

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The comparison of the dynamic behaviors of the three wild-type LEHs demonstrates for

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CH55-LEH a highly coordinated internal dynamics, while Re-LEH has a larger number of

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flexible subdomains. Tomsk-LEH appears to be an intermediated case.

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Overall, these rigidity/coordination trends parallel experimental thermostability data16: the

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increased flexibility in the wild-type Re-LEH may point to a system more prone to unfolding,

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whereas the higher mechanical coordination of the two mutants and CH55-LEH favors their

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stability. From the DF analysis Re-LEH-F1b turns to be the most stable LEH, which also

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reverberates its remarkable experimental thermostability.

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Next, we set out to locate the structural elements responsible for the different flexibilities. The

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aim here is to identify which substructures are exploited to tune the dynamic properties specific

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for each enzyme, despite the highly conserved and shared structural patterns.10

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To this end we calculated and compared the local flexibility (LF) parameter. This analysis

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provides information on the average deformation that is locally experienced by stretches of

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residues. Figure S3 shows that the five hydrolases share very similar profiles; the main

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difference arises from the higher flexibility of Re-LEH N-terminal tails (missing in both CH55-

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LEH and Tomsk-LEH). The N-terminal tails mobility is reduced in the two Re-LEH mutants

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where two residues per monomer have been mutated (S15P and A19K) in both the variants and

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one disulfide bond inserted (I5C-E84C) in the Re-LEH-F1b case. Therefore, the loop close to the

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N-terminus turns out to be an important modulator, whose function is related with an increased


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flexibility, confirming the results of Janssen’ group, whereby Re-LEH thermostability was

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significantly enhanced by means of multiple mutations that kinetically stabilize this region.23

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Energy decomposition reveals common stabilizing hotspots. Next, we set out to identify

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LEHs stabilization determinants using the Energy Decomposition Method (EDM).18 This

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analysis is aimed at verifying whether common sets of residues can be identified as shared

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stabilizing hotspots among the five enzymes, even in the face of low homology. EDM is

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designed to detect energetic residue-couplings that are relevant mainly for the enthalpic

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contribution to the stabilization of a certain 3D structure, providing a simplified view of residue-

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residue pair interactions matrix. Although this method has previously been validated against

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several experimental data,19,24 we reported in the SI the comparison with two other widely used

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methods, namely MM-GBSA decomposition and Alanine Scanning analysis. The convergence of

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the results supports the validity of our approach and conclusions.

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Since we are interested in determining whether a conserved 3D structure can reflect a well-

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defined energy signature, we set out to investigate the relationship between the distribution of

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stabilizing determinants and the shared 3D fold of the hydrolases. Therefore, we used the POSA

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server25 in order to perform multiple protein structure alignment and find out the common

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regions among the five LEHs (defined as blocks, see SI). The latter were then used to simplify

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and filter EDM energy matrices. Namely, only the residues belonging to the common regions

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(blocks) are considered in the analysis. Furthermore an averaged EDM value accounts for the

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contribution of each block to protein stability. POSA alignment returns six blocks (per monomer)

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shared by the five hydrolases, where each block delimits the energy pattern of a common

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structural region. Therefore, the diagonal of the matrix describes the contribution of each

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structural domain to the whole stabilization energy, whereas off-diagonal elements codify for the


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energetic coupling interaction among the different blocks. As a caveat, it must be considered

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again that this approach reports mainly on the enthalpic contribution of different substructures to

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the stabilization of the proteins. To correctly evaluate the different contributions to the full free

208

energy, we should be able to properly consider the most relevant ensemble of conformations in

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the unfolded states of the different proteins: this task is however out of reach for our current

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capabilities. Moreover, it is worth noting that the proteins studied here are dimers, which adds a

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further layer of complexity to the description of non-native states. Therefore, using a simplified

212

method (like EDM) based on information available for the native state appears to be a viable

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solution.

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Corroborating DF outcome, this analysis points out that all the LEHs share a common folding

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core defined by the β4, β5, β6-α4 domains (yellow, green and magenta respectively in figure 2),

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which we label core A (black square). This core engages in a stabilizing interaction with β3

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(orange block), broadening into a second layer of stabilization, core B (white square). This

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folding unit, responsible for the stabilization within each monomer, accounts as well for the main

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energetic coupling between the two monomers in the dimer.


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Figure 2. Enb matrices for Re-LEH, Re-LEH-P, Re-LEH-F1b, Tomsk-LEH and CH55-LEH.

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Shared structural domains identified by colored bars in the matrices are mapped into the protein

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according to the respective color code (bottom left). The relevance of the stabilization

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contribution within the system decreases from blue to yellow.

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Moreover, the matrices point to potential different factors in the fold-stabilization mechanism

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exploited by Tomsk-LEH, CH55-LEH and Re-LEH. The first two LEHs show a more extended

227

stabilizing core, where each subset significantly contributes to the total folding nucleus. On the

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contrary Re-LEH shows a more polarized pattern within each monomer profile. These

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differences help explain the higher thermostability shown by Tomsk-LEH and CH55-LEH16:

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extended and homogenous folding nuclei, where several structural domains contribute to the


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folding energy, could more efficiently stabilize a protein, whereas a localized core, as in Re-

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LEH, may be related to a lower global stability. In this framework the extended cooperative

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interaction among multiple stabilization elements could result in systems that are more prone to

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adapt to changes by withstanding mutations whose impact on the 3D organization is minimized.

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The same trend can be recognized within the Re-LEHs variants subset. Indeed the comparison of

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the three Re-LEH matrices shows the tendency, going from the less stable Re-LEH towards the

237

more stable Re-LEH-F1b, to extend the stabilization nuclei within each monomer. At the same

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time an effect of the mutations on the homodimer symmetry can be observed. The Re-LEH-P

239

energy matrix indicates that the mutations induce a significant stabilization of monomer A. This

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pattern is further reinforced in Re-LEH-F1b. This observation finds a structural explanation by

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comparing the most representative conformations of the three Re-LEH variants obtained by

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cluster analysis of MD trajectories. While the mutations do not alter the distance between the two

243

centroids described by the interface residues of each monomer, they do impact on their

244

respective orientation. In fact the two mutants undergo a closing motion around the plane of the

245

dimer interface, which results in a rotation between the two principal axes of each monomer

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(63.9°, 62.6° and 60.7° for Re-LEH, Re-LEH-P and Re-LEH-F1b respectively) and eventually

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yields a more compact arrangement of the mutants.

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Next, we addressed the question of which residues contribute the most to the global

249

stabilization energy. Hence the non-bonded energy profile associated to the eigenvector with the

250

lowest eigenvalue was selected and analyzed (see Methods and references18,19).

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The first eigenvector profiles shown in figure 3 report on the contribution of each residue to

252

the stabilization energy, in other words, the relative intensities of the eigenvector components

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describe the distribution of stabilizing interactions in the 3D structures of Re-LEH, Tomsk-LEH


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and CH55-LEH. It must be noted that since the energy fingerprint in the three Re-LEHs variants

255

is not affected by the introduction of the mutations (see S5) only Re-LEH profile is reported and

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discussed for clarity; moreover to compare the main folding determinants, the energy profiles

257

have been realigned following sequence alignment (Re-LEH numeration). Interestingly the

258

residues that contribute the most to the energetic stabilization (red circles, peak 1 and 3) belong

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to the same protein sites in each of the three native hydrolases and identify a first group of

260

hotspots. The inspection of these sites reveals that these peaks identify two charged residues (an

261

arginine and a glutamate) for each monomer that are conserved in Re-LEH, Tomsk-LEH and

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CH55-LEH (GLU98-ARG131, GLU77-ARG110, and GLU79-ARG111 in Re-LEH, Tomsk-

263

LEH and CH55-LEH respectively). These residues are close but do not belong to the active sites

264

pocket and stabilize the dimer interfaces by forming a salt bridge interaction with the oppositely

265

charged residue on the opposite monomer; i.e., Tomsk-LEH monomer-A GLU77 and ARG110

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bridge monomer-B ARG110 and GLU77 respectively (see figure 3).

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Figure 3. Left. Components (in absolute value) of the eigenvectors associated with the lowest

269

eigenvalue of the Enb matrices. Because of dimeric symmetry, the graph reports only first

270

monomer profiles, see SI for the whole dimer profile Right. Common stabilizing hotspots

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identified by means of EDM (Tomsk-LEH numeration).

272

The orange circle (peak 2) in figure 3 pinpoints a second stabilizing region shared by the three

273

LEHs. This site corresponds to a hydrophobic segment from β5, lying at the dimeric interface

274

(orange in figure 3). It is worth noting that both the stabilizing groups of hotspots underlie inter-

275

monomer interactions, suggesting that the dimer interface is the key structural element

276

determining the energetic stabilization of the active state of the enzyme, in agreement with

277

recently published experimental data.23

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Interestingly, while the three systems exploit the common hydrophobic hotspot core to

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stabilize the dimer, Re-LEH lacks two important key interactions (table 1). Indeed both Tomsk-

280

LEH and CH55-LEH use a negative charged amino acid (GLU99 and ASP101 in Tomsk-LEH

281

and CH55-LEH respectively) to create an important intra-monomer salt bridge that binds the

282

hotspot arginine (see figure 3). This contributes to connect β5 and β6. Furthermore Tomsk-LEH

283

ARG93 stably links β5 and α4 domains through an electrostatic interaction with GLU117.

284

Similarly, CH55-LEH ARG95 stabilizes the dimeric interface binding an aspartate on the

285

opposite monomer β1.

286

In contrast, in Re-LEH the respective amino acid positions are both replaced by two uncharged

287

residues. This reverberates in decreased contributions to the global energy stabilization of

288

SER115 and GLN121 with respect to the charged residues in Tomsk-LEH and CH55-LEH (see

289

table 1). In the framework of the EDM method, this difference can aptly be considered to fine-


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tune the different stability properties in the three LEHs, with Re-LEH being the least thermally

291

stable.

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Table 1. Hotspot residues corresponding to the second group of mutants. Orange selection

293

indicates hydrophobic amino acids.

Tomsk-LEH ARG93

Residue Residue Residue Rh-LEH CH55-LEH contribution contribution contribution 0.14 SER115 0.08 ARG95 0.14

VAL94

0.14

ILE116

0.13

VAL96

0.14

MET95

0.12

LEU117

0.12

MET97

0.13

GLY96

0.09

GLY118

0.11

GLY98

0.10

THR97

0.13

VAL119

0.11

ALA99

0.10

PHE98

0.12

PHE120

0.07

PHE100

0.10

GLU99

0.14

GLN121

0.04

ASP101

0.11

294 295

Overall, the data show that a limited number of key stabilization determinants, located in

296

common regions can provide a framework upon which a common structural organization can be

297

supported. Once the determinants of a stable fold are in place to guarantee structural stability, the

298

remainder of the sequence can be modulated in response to different evolutionary pressures.

299

Modification of structural determinants. The effect on protein stability of the energy

300

determinants that EDM analyses pointed out was probed by point mutations. Two sets of mutants

301

were designed and experimentally expressed. The properties of the resulting enzymes were

302

tested by means of Circular Dichroism (CD) analysis and evaluating specific activities toward

303

cyclohexene oxide.16 This first group of mutants was expected to destabilize protein stability by

304

perturbing the fundamental GLU77-ARG110 salt bridge interaction by means of side-chain

305

trimming (E77A and R110A) and salt bridge charge inversion (R110E_E77R). The latter


16

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306

allowed to investigate the local effect of the charge inversion whilst the inter-monomer salt

307

bridge was retained. From now on, Tomsk-LEH numeration will be used for clarity.

308

A second set of mutants entailed two new LEHs where the stabilizing contribution of ARG93

309

was altered by mutations of GLU117: deletion of the interaction was probed in the E117A

310

mutant, while the effect of shortening and reducing the conformational mobility of the negative

311

sidechain was tested in E117D. We focused on perturbing ARG93-GLU117 salt bridge since this

312

electrostatics-based stabilizing interaction is missing in Re-LEH, as discussed above. Thus, a

313

possible correlation between the presence of this distinctive determinant and the different

314

thermostabilities could be investigated.

315

Finally, MET95 contribution was also probed by point mutation: MET95 is located at the

316

interface between the two monomers and conserved both in Tomsk-LEH and CH55-LEH, but

317

missing in Re-LEH, suggesting a possible role as distinctive modulator. Therefore the expression

318

of M95C mutant aimed to test the perturbative effect on the interface stability obtainable by

319

altering the local hydrophobicity and hydrogen bond network. It is worth to underline herein that

320

the disulfide bridge formation between the two CYS facing each other on the opposite monomers

321

was out of scope of our investigation and its presence was excluded by SDS-PAGE analysis (see

322

SI).

323

In this scenario, Tomsk-LEH was chosen as reference system where to express and test all

324

mutations. Indeed Re-LEH did not offer a representative testing case due to the particular

325

mobility of its N-terminal tails and the lack of the energy modulators shown by the other LEHs.

326

Among designed mutants, E77A and E77R_R110E were successfully expressed in E. coli.

327

Mutation R110A resulted in negligible expression levels, which may suggest a dramatic impact


17


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Page 18 of 30

328

on the stability of the resulting protein (see figure S9). Wild-type (wt), E77A and E77R_R110E

329

were characterized by means of comparative CD analysis in both far and near UV regions, to

330

assess possible distinctive features within their secondary and tertiary structure, respectively.

331

Likewise, structural thermal stability was also evaluated by running melting temperature (Tm)

332

curves and monitoring the unfolding of secondary (222 nm) and tertiary (296 nm) structure.

333

CD analysis revealed that E77A retained a similar secondary and tertiary structure to the wt

334

(figure S10), as CD spectra in both far and near UV regions are substantially overlapped. On the

335

contrary, E77R_R110E mutant CD spectra suggested that a structural perturbation occurred

336

(figure S11). In this case, while the charge inversion preserves the global electrostatics between

337

the two interfaces, at the intramonomer level, switching to an opposite charge perturbs local

338

networks by introducing repulsive interactions. In particular wt ARG110 is stably engaged in an

339

intramonomer interaction with the hotspot residue GLU99. Thus E77R_R110E mutation implies

340

both the breaking of this stabilizing interaction between two hotspots and the introduction of a

341

local excess of negative charge.

342

As hypothesized, mutation E77A and E77R_R110E significantly altered protein stability;

343

E77A and E77R_R110E showed a Tm of 52.8°C and 55.3°C (near UV), respectively,

344

significantly lower compared to the wt Tm of 70°C (table 2). Interestingly, while Tm of the wt

345

was clearly detectable at 222 nm (69.9°C), there was no clear transition in the denaturation curve

346

of mutants, possibly because of their more flexible or less compact structure.

347 348

Furthermore, mutants’ specific activity toward cyclohexene oxide was significantly lower compared to the wt (table 2). Hence corroborating the computational analyses, hotspot


18

Page 19 of 30

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349

determinants turned out to be fundamental for the enzymatic stability. The perturbation of the

350

latter property was detrimental to biological functionality.

351

Table 2. Tomsk-LEH wt and mutants Tm determined at 222 nm and 296 nm and specific activity

352

toward cyclohexene oxide. The specific activities were determined at 20°C.1.

Enzyme

Tm@222 nm (°C)

Tm@296nm (°C)

Specific activity (mU/mg)

Tomsk-LEH wt

69.9

70.0

320

E77A

n.d.

52.8

164

E77R_R110E

n.d.

55.3

140

E117A

52.9

54.7

188

E117D

51.8

55.2

274

M95C

60.6

58.6

310

353 354

All the other designed mutants, E117A, E117D and M95C were successfully expressed in E.

355

coli (figure S9). CD characterization showed that these mutations did not significantly perturb

356

the secondary structure of the enzyme (figure S13, S14, S15), while a different impact could be

357

noticed on the tertiary structures. Indeed, E117A and E117D showed increased absorption

358

intensities compared to the wt in near UV spectra, consistent with structural perturbation. A

359

similar trend was observed for M95C mutation, though less pronounced. This may reflect an

360

increased tendency to aggregation, due to the destabilization of the dimeric form. This

361

observation agrees with the measured Tm values for these mutants: both E117A and E117D

362

showed lower Tm than wt, whereas M95C showed an intermediated behavior.

1

N.d.= not detected


19


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Page 20 of 30

363

Finally the specific activity toward cyclohexene oxide was tested (see table 2). Interestingly,

364

the effects of the mutated residues were almost negligible for M95C and E117D, which could be

365

explained by the fact that these mutations only partially alter the chemical interactions with the

366

surrounding region (basically by shortening the side chain in both cases). On the contrary E117A

367

showed a lower activity than the other mutants of its group, yet higher compared to the first

368

group of mutants. In general these data point toward a possible correlation between the

369

thermostability (i.e., Tm) and the enzymatic efficiency. Even if the considered subset is too small

370

to be quantitatively statistically relevant, the good computed correlation factor (0.7) suggests a

371

qualitative trend linking mutant induced modulation of thermostability and enzymatic activity.

372

This result could to some extent be expected: in general, destabilized mutants tend to be less

373

efficient as catalysts compared to wild type molecules. The inactivation may in fact be linked to

374

the disruption of the active site pre-organization due to structural instabilities. Alternatively, one

375

may hypothesize a coupling between folding and reaction coordinates that may lead to reactant

376

state destabilization and transition state stabilization in the direction of folding and along the

377

reaction coordinate, respectively, as previously proposed by Aquist and coworkers.26In this

378

framework, perturbing the coupling by destabilizing mutations may also have a negative effect

379

on reactivity. To verify such a mechanistic model, however, different approaches than the one

380

presented here should be used.

381

The effects of mutations in the enzymes studied here can also be viewed in terms of the concept

382

of fold polarity and innovability introduced by Tawfik and coworkers10,27: in their model, the key

383

to innovability (i.e. the ability to support mutations that introduce new functions) is fold polarity,

384

whereby the active site is composed of flexible, loosely packed loops that coexist with a well-

385

separated, highly ordered scaffold. The latter provides the necessary stability to withstand


20

Page 21 of 30

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386

mutations while the active-site loops maintain their conformational plasticity, which may

387

promote the acquisition/optimization of functions.

388

In the LEH structures, much like in the case of dihydrofolate reductase discussed by Tawifk, the

389

active site residues are highly coordinated with the rest of the protein in general and with the

390

most relevant stabilizing hotspots in particular (see S18). In this frame of thought, the presence

391

of a diffuse stabilization nucleus, while on the one hand favors adaptation to higher temperatures,

392

on the other hand opposes the accumulation of mutations resulting in new functions, e.g. in

393

enzymes being functional at lower or higher temperatures than the ones for which they were

394

naturally evolved. Janssen and coworkers were able to overcome this hurdle by the use of

395

carefully designed disulfide bridges.17,23

396

One potentially interesting aspect of the results reported in table 2 is that the more

397

thermostable enzyme shows higher activity than the less stable designed mutants at 20 degrees.

398

This observation is in line with previous observations28–31 which indicated that enhanced stability

399

does not necessarily have to correlate with reduced activity.

400

It is conceivable that the methods used in laboratory evolution, based on various rounds of

401

“targeted” selection and mutation of proteins with a defined activity,32 may in some cases favor

402

molecules that conserve a certain reactivity in the background while stability is being improved.

403

In contrast, natural evolutionary pressure requires activity to be present only at the temperature at

404

which a certain organism needs to survive, discarding all other candidates that are not stable

405

enough despite the fact that they may possibly maintain a certain reactivity.

406

In conclusion, we have shown that the perturbation of predicted hotspots, shared by three

407

proteins with a low degree of homology and identified through a computational unbiased energy-

408

based analysis that does not require previous knowledge on residue-conservation, may impact on


21


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Page 22 of 30

409

the correct folding, stability and biological reactivity, providing rational information on potential

410

ways to perturb stability-activity relationships. Our approach could expectedly represent a valid

411

complement to other methods, such as the ones based on disulfide-bridge stabilization.

412 413

Generalization to a large set of homologues with disparate functions. Finally, we extended

414

our considerations by analyzing a number of structural homologues of the enzymes studied

415

herein. Dali search33 of the Protein Data Bank (PDB) using Re-LEH monomer A as probe

416

returned several structural homologues. Figure 4 shows top ten hits according to Dali score.

417 418

Figure 4. Structural alignment obtained using Re-LEH monomer A as probe. Red and orange

419

squares pinpoint hotspot residues corresponding to the first and second group of mutants

420

respectively.

421

This group consists of different unidentified proteins expressed in several bacteria strains and

422

two digoxigenin binder proteins (4j8t and 5bvb). The structural alignment unveils that GLU77

423

and ARG110 (red squares) are conserved in 9 and 6 structures respectively out of 10. This

424

suggests that different proteins with different functions could exploit a common stabilization

425

mechanism. This is further corroborated if we consider that these proteins show very low

426

sequence identity (from 14 up to 33%). Hotspot residues corresponding to the more hydrophobic

427

stabilization determinants of the interface (orange square) show lower sequence identity


22

Page 23 of 30

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428

conservation among the different structures. This could potentially reflect their accessory role in

429

the protein stabilization. However, it is interesting to note that the high hydrophobic character is

430

conserved in all cases, and 8 homologues out of ten share at least a charged residue on one edge

431

of the hydrophobic segments (5 of them have two charged residues at both ends). These data

432

validate the hypothesis that evolution generates functional diversity by local sequence variation.

433

Even so it preserves a common efficient stabilization strategy, driven by a limited number of

434

shared structural determinants.

435

Conclusions

436

By means of novel computational methods and experimental procedures we have identify the

437

conserved protein regions that mold common properties in a subset of homologous enzymes, the

438

limonene-1,2-epoxide hydrolases. We demonstrated that a limited number of stabilizing

439

determinants, located in conserved segments, define a common and distinguishing energy

440

signature in related proteins, despite low percentage of sequence similarity. The shared

441

determinants are found to have differential roles. The most conserved hotspots are essential for

442

protein stability, whereas a second group of hotspots may play an accessory role, contributing to

443

protein functional plasticity and showing a lower degree of sequence identity. In particular we

444

found that both these hotspot residues lie at the dimeric interfaces of LEHs that turn to be the

445

essential structural regulatory core of this subset of enzymes

446

Despite this common feature, we disentangle the protein regions that possibly act as

447

modulators, driving the mechanism of functional diversification in the three LEHs. Finally, we

448

extend our conclusions to a set of LEHs structural homologues. We report that the observed

449

hotspots are largely conserved in proteins sharing LEHs 3D fold, regardless of low sequence


23


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Page 24 of 30

450

identity and functional promiscuity. Ultimately, our data points at an evolutionary strategy where

451

each 3D fold is characterized by a specific and distinctive energy fingerprint, ruled by few

452

conserved structural determinants. Concurrently functional diversity is ensured by local

453

modulators that can be modified to rationally tune protein properties.

454 455

ASSOCIATED CONTENT

456

Supporting Information. This material is available free of charge via the Internet at

457

http://pubs.acs.org

458 459

AUTHOR INFORMATION

460

*Corresponding author: Giorgio Colombo, Istituto di Chimica del Riconoscimento

461

Molecolare, CNR; via Mario Bianco 9, 20131 Milano, Italy. E-mail: [email protected].

462

Tel: +39-02-28500031, Fax: +39-02-28901239.

463

Author Contributions

464

The manuscript was written through contributions of all authors. All authors have given approval

465

to the final version of the manuscript

466

Funding Sources

467

The authors acknowledge funding from SusChemLombardia: prodotti e processi sostenibili per

468

l’industria lombarda project, Accordo Quadro Regione Lombardia-CNR, Proj. Nr. 18096

469 470

ABBREVIATIONS


24

Page 25 of 30

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471

LEH, limonene-1,2-epoxide-hydrolase; Re-LEH, Rhodococcus erythropolis limonene-1,2-

472

epoxide-hydrolase; Tomks-LEH, Tomks limonene-1,2-epoxide-hydrolase; CH55-LEH, CH55

473

limonene-1,2-epoxide-hydrolase; EH, epoxide hydrolase; PDB, Protein Data Bank (PDB); MD,

474

Molecular Dynamics; DF, Distance fluctuations; LF, Local Flexibility; EDM, Energy

475

Decomposition Method; Enb, non-bonded interaction energy matrix; CD, Circular Dichroism;

476

SDS-PAGE, Sodium Dodecyl Sulphate- PolyAcrylamide Gel Electrophoresis; wt, wild-type;

477

Tm, melting temperature.

478 479

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Page 30 of 30

For Table of Contents Use Only

569

Unraveling energy and dynamics determinants to interpret protein functional plasticity: the limonene-1,2-epoxide-hydrolases case study

Silvia Rinaldi, Alessandro Gori, Celeste Annovazzi, Erica E. Ferrandi, Daniela Monti, and Giorgio Colombo*.

570 571 572


30

Unraveling Energy and Dynamics Determinants to Interpret Protein

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