Unraveling Allosteric Mechanisms of Enzymatic Catalysis with an

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Unraveling Allosteric Mechanisms of Enzymatic Catalysis with an Evolutionary Analysis of Residue-residue Contact Dynamical Changes Phuoc J. Vu, Xin-Qiu Yao, Mohamed Faizan Momin, and Donald Hamelberg ACS Catal., Just Accepted Manuscript • DOI: 10.1021/acscatal.7b04263 • Publication Date (Web): 01 Feb 2018 Downloaded from http://pubs.acs.org on February 2, 2018

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Unraveling Allosteric Mechanisms of Enzymatic Catalysis with an Evolutionary Analysis of Residue-residue Contact Dynamical Changes

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Phuoc Jake Vu1, Xin-Qiu Yao1, Mohamed Momin, Donald Hamelberg*

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Department of Chemistry, Georgia State University, Atlanta, Georgia 30303-2515, USA.

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Equal contributions

*Corresponding to: Prof. Donald Hamelberg; Department of Chemistry, Georgia State University, 29 Peachtree Center Ave NE, Atlanta, Georgia 30303-2515, USA. Telephone: (404) 413-5564; E-mail: [email protected].

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1 ACS Paragon Plus Environment

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Abstract

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The evolution of protein conformational dynamics contains important information about protein function and regulation. Here, we describe an approach to dynamical-evolution analysis based on multiple microsecond molecular dynamics simulations and residueresidue contact analysis. We illustrate our approach by comparing three human cyclophilin isoforms, cyclophilin A, D, and E, which belong to a family of enzymes catalyzing peptidyl-prolyl cis-trans isomerization. Our results reveal that despite distinct overall equilibrium conformations between cyclophilins under substrate-free conditions, functional dynamical changes resembling substrate-binding and catalytic processes tend to be conserved. Key residues displaying either concerted or specific dynamical changes among isoforms during the reactions are identified, which delineate two distinct allosteric pathways for cyclophilin function consistent with recent nuclear magnetic resonance experiments. A sequence-based coevolution analysis is also employed for further understanding dynamical consequences. Our results collectively provide a framework where both common and specific functional mechanisms of a protein family can be elucidated.

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Keywords: evolution, allosteric regulation, enzyme dynamics, residue-residue contact, cyclophilin, molecular dynamics


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Introduction

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Protein internal motions or dynamics have been increasingly recognized to play a crucial

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role in protein function and regulation.1-3 Typical protein dynamics span a broad range of

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spatiotemporal scales, from subtle backbone and sidechain fluctuations (~10-9s) and domain

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motions (~10-6s) to large-amplitude conformational changes normally observed in

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biological molecular machines (10-3-102s). Notable examples in which dynamics determine

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function include the dynamical rearrangements in enzymes to facilitate catalytic turnover,4

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the conformational changes in transporters to pump small molecules in and out of cell

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membrane,5 the force-producing structural changes in molecular motors,6 and the prevailing

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roles of dynamics in the allosteric regulation during signal transduction.7-9 Despite

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numerous efforts in inspecting protein dynamics with both experimental and computational

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methods, such as nuclear magnetic resonance (NMR)10 and molecular dynamics (MD)

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simulation,11 how protein molecules harness thermal fluctuations for function remains

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elusive.

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Protein dynamics reflect the underlying energy landscape, which is predominantly

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determined by protein sequence. In this perspective, evolution modified protein dynamics

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and function by altering the energy landscape. During evolution, certain patterns of protein

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dynamics must be conserved to retain the core protein function that arose early. Similar to

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how multiple sequence alignment screens out variable residues to detect structurally or

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functionally critical sites, comparing protein dynamics at aligned residues across a protein


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family identifies dynamically conserved sites that are intimately related to the core function.

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Meanwhile, variable dynamics in synergy with variable sequences across family members

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underlie the subtle functional diversity, and hence, comparative analysis of dynamics also

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helps to foster our understanding of protein functional specificity (e.g., selective substrate

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binding or distinct kinetics). Indeed, this new paradigm of evolution-based approach to

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delineating the complex sequence-structure-dynamics-function relationship has gained

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increasing interests. By comparing the crystallographic structures of several homologous

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proteins under distinct functional states, Babu and colleagues recently derived the universal

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mechanisms governing the activation of heterotrimeric guanine nucleotide-binding proteins

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(G proteins) and G protein-coupled receptors.12-13 With X-ray crystallography and NMR

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spectroscopy, Wright and colleagues compared the dynamics of human and Escherichia

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coli dihydrofolate reductase (DHFR), an enzyme that catalyzes the NADPH-dependent

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reduction of dihydrofolate to tetrahydrofolate, and identified the residue determinants

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underlying the distinct catalytic efficiency and robustness between the human and the

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bacterial enzymes.14 In addition to the comparisons between extant proteins, the structures

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and dynamics of ancestral proteins have been modeled with a computational ancestor

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reconstruction method, which empowers a direct tracking of the evolution of protein

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dynamics.15-17 These recent advances demonstrate how a study of protein dynamics in the

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context of evolution provides unprecedented insights into functional mechanisms. The

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knowledge obtained complements that derived from a large-scale evolutionary analysis

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aimed at inferring sequence and structural traits underlying conserved and divergent

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functions of enzyme superfamilies18-19 and can be further leveraged to develop new


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algorithms for rational protein design and drug discovery. However, a general approach to

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the evolutionary analysis of conformational dynamics for most biological systems is still

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lacking.

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Cyclophilin A (CypA) is a ubiquitous protein where function is strongly coupled with

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dynamics.4, 20 As a peptidyl-prolyl cis-trans isomerase (PPIase), cyclophilin A catalyzes the

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interconversion between the cis (with the peptidyl-prolyl torsion angle ω=0º) and the trans

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(ω=±180º) states of the prolyl peptide bond in Xaa-Pro motifs, where Xaa represents any

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amino acid (Figure 1A & B). Humans have 17 cyclophilin isoforms (Figure S1), among

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which CypA is the best characterized, both experimentally and computationally.

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Cyclophilins are known targets for immunosuppressant, and their function is critical to

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many important cellular processes, including protein folding and signal transduction.21

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Cyclophilin D (CypD) and cyclophilin E (CypE) are the family members closest to CypA,

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with high sequence identity (68-75%) and almost identical structures to CypA (the

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backbone root mean square deviation, or RMSD, is within 0.50-0.65 Å; see Figure 1A).

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The three isoforms of cyclophilin have the same PPIase activity; however, they function in

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different subcellular locations: CypA is generally found in the cytosol, CypD in the

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mitochondria matrix, and CypE in the nucleus.22 We recently examined the dynamical

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properties, represented by the breaking and formation of residue-residue contacts, of CypA

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under various substrate-binding and mutational conditions using a combined approach of

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MD simulations and NMR experiments.23 In particular, we found an interesting ‘dynamic


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cluster’ containing residues showing substantial dynamical changes upon substrate binding

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located ~15 Å away from the active site. This finding reveals a new allosteric mechanism in

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CypA, but its generality across the cyclophilin family is not known.

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In this work, we develop a new approach for general evolutionary analysis of protein

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conformational dynamics based on multiple microsecond-long MD simulations and the

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similar contact analysis method previously described.23 We illustrate our approach by

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comparing the dynamics between human CypA, CypD, and CypE derived from MD

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simulations (totaling 26 µs). For each cyclophilin isoform, three functional states

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collectively representing substrate-binding and catalytic isomerization processes are

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examined. This allows us to evaluate the conservation of dynamics across isoforms with

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respect to distinct enzymatic processes. Dissecting the contact dynamics further enables us

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to identify the key residues determining the common and isoform-specific dynamical

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changes. We also perform a sequence coevolution analysis to identify correlated amino acid

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substitutions between residues in the cyclophilin family and find that dynamically

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conserved contacts do not always represent the most highly coevolving residue pairs,

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suggesting that dynamics is the missing link and sequence and structure alone cannot fully

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describe function. The consensus groups of residues derived from the dynamical

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conservation analysis are consistent with recent NMR experiments.24

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Figure 1. Backbone and active site structures are highly similar across CypA, CypD, and CypE. (A) The substrate-free crystallographic structures of CypA (white; PDB: 3K0M), CypD (red; PDB: 4O8H), and CypE (green; PDB: 3UCH) are superimposed and are represented as cartoon. The modeled substrate (See Methods) is displayed as cartoon (yellow) with ‘Gly-Pro’ motif shown as licorice and colored by atom types. The enlarged view shows the substrate-binding pocket represented as white transparent surface. Side chains of active site residues are displayed as sticks and are color-coded the same as backbone. (B) Schematic cis-trans isomerization of the ‘Gly-Pro’ motif. Ts, transition state.

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Results and Discussion

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Protein backbone and residue side chain dynamics under the substrate-free state are

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distinct among CypA, CypD, and CypE.

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Fluctuation analysis and principal component analysis (PCA) of MD simulation trajectories

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reveal that backbone dynamics are different between CypA, CypD, and CypE. Multiple

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long-time (2.2-2.7 µs) MD simulations were performed under substrate-free conditions for


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each cyclophilin isoform. Snapshots of the latter 2-µs of each simulation trajectory were

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analyzed. Residue-wise averaged root mean square fluctuation (RMSF) of backbone atoms

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derived from the simulations shows that, whilst the atomic fluctuations at most residues

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look similar across isoforms, the fluctuations located at the β4-β5 loop (the loop between

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the fourth and the fifth β-strands), the β5-β6 loop, the β6-β7 loop, and the α2-β8 loop (the

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loop between the second α-helix and the eighth β-strand) are apparently different (Figure

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2A). In general, CypA and CypE are more flexible than CypD. PCA was then performed on

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the Cartesian coordinates of backbone atoms from the simulations to examine backbone

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conformational distributions at equilibrium (Figure 2B). It reveals that CypE has the

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broadest distribution and samples multiple conformational states in the subspace spanned

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by the top two principal components (i.e., PC 1 and PC 2, which collectively capture nearly

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50% of total atomic mean displacements or variance), indicating the overall highest

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flexibility of CypE among the isoforms. CypA has a distribution with modest broadness but

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it still samples at least two conformational states, and it samples the space largely

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overlapped with that populated by CypE. In contrast, CypD has the narrowest distribution,

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which is clearly separated from those of CypA and CypE, and samples only one

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conformational state, indicating that the backbone of CypD is rigid and has distinct

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equilibrium properties from CypA and CypE.

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Inspection of residue-residue contacts reveals distinct side chain dynamics among CypA,

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CypD, and CypE. A pair of residues is in contact whenever their minimal non-hydrogen


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atomic distance is at most 4.5 Å and they are separated by at least three residues in

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sequence (i to i+n, n≥3). The 4.5 Å-threshold employed here represents a well defined

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interaction range for most amino acids and has been applied to contact analysis in previous

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simulation studies.23, 25-26 Other parameters were also used to test the robustness of our

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results (See discussion all over Results and Discussion). For each residue pair, the

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probability of contact formation during the simulation was calculated and compared

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between cyclophilin isoforms. It shows that the residue pairs with substantial difference in

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contact formation probability (i.e., |df=fCypY-fCypX|≥0.1, where fCypX and fCypY are contact

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probabilities calculated for the two compared isoforms and the threshold 0.1 represents an

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error estimate of f23) distribute all over the cyclophilin molecule for all the comparisons

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(Figure 2C-E), indicating that side chain dynamics are distinct among the isoforms. In

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summary, although in both sequence and structure cyclophilin isoforms are very similar,

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they possess different conformational ensembles at equilibrium at least under the substrate-

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free state.

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Figure 2. Backbone and sidechain dynamics under the substrate-free state are different between CypA, CypD, and CypE. (A) Residue-wise averaged root mean square fluctuation (RMSF) of backbone atoms derived from the MD simulations under substratefree conditions. Residue numbers are based on CypA. Secondary structure elements are annotated as black (α helices) and grey (β strands) rectangles at the top and bottom of the plot. (B) PCA performed on the Cartesian coordinates of backbone atoms from the simulations for CypA (grey), CypD (red), and CypE (green). Simulation-generated conformational snapshots are projected in the subspace spanned by the two principal components capturing the largest structural variance (PC1 and PC2; the number in the axis label indicates the percentage of variance captured by the corresponding PC). Probability density distributions of the conformational samples are represented as contour lines. The sampled space of the CypA and CypD simulations are also outlined. (C-E) Contact probability difference under the substrate-free state between isoforms (df=fCypY-fCypX, where f is the probability of contact formation and CypX and CypY are the corresponding cyclophilins under the comparison CypX/CypY) mapped to the crystal structure of CypA (PDB: 1M9F). Blue and red cylinders represent contacts with df≥0.1 and df≤-0.1, respectively, where cylinder radius is proportional to |df|. The yellow star indicates the active site.


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Dynamical changes of residue-residue contacts during substrate binding are conserved

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between CypA and CypE but are not conserved between CypA/E and CypD.

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Although the conformational dynamics under an individual state (i.e., the substrate-free

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state) are very different between the cyclophilin isoforms, dynamical changes from the

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substrate-free to the substrate-bound state display a certain extent of similarity. Multiple

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2.4-µs additional simulations were performed under substrate-bound conditions where the

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peptidyl-prolyl torsion angle (ω) was in the cis-conformation (termed ‘cis-bound state’).

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The probability difference of contact formation during simulations between the substrate-

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free and the cis-bound states was used to characterize the dynamical changes during

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substrate binding. Analysis of the CypA simulations reveals a site ~15 Å away from the

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active site showing substantial dynamical changes (|df=fY-fX|≥0.1, where fX and fY are

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contact probabilities calculated for the two compared states) of residue contacts (Figure

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3A). This observation resembles the dynamic cluster identified in the previous simulation

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study of CypA,23 although the substrate employed in the present work is five-residue longer

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than that used in the previous study.23 This consistency indicates that the revealed

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dynamical changes are an intrinsic dynamical characteristic of CypA independent from the

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identity of the bound substrate. Intriguingly, overall similar patterns of dynamical changes

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are observed between CypA and CypE (Figure 3A & C). A large portion of contacts that

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are either more often formed (with an increase of contact probability) or more often broken

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(decrease of contact probability) upon substrate binding in CypA are shown to have the


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same direction of changes in CypE (Figure 3E). These contacts with the same trends of

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changes from one state to the other between isoforms are defined as ‘dynamically

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conserved contacts.’

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To quantitatively measure the overall similarity of dynamical changes between isoforms,

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we developed a dynamical conservation index (DCI), which is defined by the percentage of

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dynamically conserved contacts. In the calculation of DCI, contacts showing small

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dynamical changes (absolute contact probability difference |df|

Unraveling Allosteric Mechanisms of Enzymatic Catalysis with an

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