In Silico Structural Modeling and Analysis of Elongation Factor-1

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Article Cite This: ACS Omega 2019, 4, 7308−7316

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In Silico Structural Modeling and Analysis of Elongation Factor‑1 Alpha and Elongation Factor-like Protein Kotaro Sakamoto,† Megumi Kayanuma,*,‡ Yuji Inagaki,‡,§ Tetsuo Hashimoto,§ and Yasuteru Shigeta*,‡,∥ †

Leading Graduate School Doctoral Program in Human Biology, ‡Center for Computational Sciences, §Graduate School of Life and Environmental Sciences, and ∥Graduate School of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8577, Japan

ACS Omega 2019.4:7308-7316. Downloaded from pubs.acs.org by 178.159.100.248 on 04/23/19. For personal use only.

S Supporting Information *

ABSTRACT: Translation elongation factor-1alpha (EF-1α) or its paralog elongation factor-like proteins (EFL) interact with an aminoacyl-transfer RNA (aa-tRNA) to play its essential role in elongation of peptide-chain during protein synthesis. Species usually have either an EF-1α or EFL protein; however, some species have both EF-1α and EFL (dual-EFcontaining species). In the dual-EF-containing species, EF-1α appeared to be highly divergent in the sequence. Homology modeling and surface analysis of EF-1α and EFL were performed to examine the hypothesis that the divergent EF1α in the dual-EF-containing eukaryotes does not strongly interact with aa-tRNA compared to the canonical EF-1α and EFL. The subsequent molecular dynamics simulations were carried out to confirm the validity of modeled structures and to analyze their stability. It was found that the molecular surfaces of the divergent EF-1α proteins were negatively charged partly, and thus they might not interact with negatively charged aatRNA as strongly as the canonical ones. The molecular docking simulations between EF-1α/EFL and aa-tRNA also support the hypothesis.

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genes13−20 classified eukaryotes into three types, namely “EF1α-containing”, “EFL-containing”, and “dual-EF-containing” the species using only EF-1α belong to the first type, whereas those using only EFL belong to the second type. So far, dual-EFcontaining species, in which both EF-1α and EFL genes were found, are highly restricted among eukaryotes. The distribution of EF-1α/EFL can be explained by “differential loss” hypothesis, which assumes that the two elongation factors were present in the ancestral eukaryote and losses of one of the two proteins occurred on separate branches of the eukaryotic tree.16−19 According to this hypothesis, dual-EF-containing species corresponds to the ancestral state, implying that these types of eukaryotes provide insights into the early molecular evolution of EF-1α/EFL.15,19,21,22 Although EF-1α and EFL are functionally equivalent to one another as a GTPase in translation, the two factors most likely recycle guanosine-5′-diphosphate (GDP) to GTP in distinct approaches. In EF-1α-containing species, EF-1α (henceforth designated as “solo-EF-1α”) interacts with its guanine nucleotide exchange factor (GEF), EF-1β, to recycle GDP to GTP.23 Nevertheless, EF-1β is unlikely to be the GEF for EFL, as EFL-containing species lack EF-1β with a few

INTRODUCTION Translation elongation factor-1alpha (EF-1α or EF1A), the eukaryotic (and archaebacterial) counterpart to elongation factor Tu (EF-Tu) in bacteria, interacts with aminoacyl-transfer RNAs (aa-tRNAs) in a guanosine triphosphate (GTP)-bindingdependent manner, and delivers them to the acceptor site of the 80S ribosome.1,2 Due to high conservation at the amino acid sequence coupled with ubiquitous distribution in eukaryotes, EF-1α has been considered as one of the major phylogenetic markers,3−5 and models for assessing functional divergence in protein evolution.6,7 Besides the essential role in translation described above, EF-1α is known to be involved in diverse cellular processes also known as moonlighting functions, such as cytoskeletal remodeling, protein folding and degradation, proteolysis, cell signaling modulation, control of cell growth, nuclear export processes, apoptosis, and cell cycle.8−10 For instance, two EF-1α isoforms in human, which share 98% similarity at the amino acid sequence, were found to have distinct arbitrary functions, one involved in apoptosis and the other in cancer development.11 Recent massive accumulation of sequencing data from phylogenetically diverse eukaryotes has raised a question on the ubiquity of EF-1α in eukaryotes. Some eukaryotes appeared to lack EF-1α but a distinct paralog, i.e., elongation factor-like protein (EFL), has been identified.12 The surveys of EF-1α/EFL © 2019 American Chemical Society

Received: December 18, 2018 Accepted: February 11, 2019 Published: April 23, 2019 7308

DOI: 10.1021/acsomega.8b03547 ACS Omega 2019, 4, 7308−7316

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Figure 1. Root-mean-square deviations (RMSD) and gyrations of the MD simulations of proteins (a, b) with GDP (c, d) and with GTP (e, f) for EF-1α of Subulatomonas sp. strain PCMinv5 (red line) and Pythium ultimum DAOM BR144 (purple line) and EFL of Fabomonas tropica strain NYK3C (green line) and Thecamonas trahens ATCC50062 (blue line).

exceptions.15,22 Consistent with the observation described above, a study incorporating a homology model of EFL proposed that EFL can recharge GTP with no GEF.22 In the dual-EF-containing species known to date, not both EF-1α and EFL unlikely participate in the elongation step in translation, as EF-1α appeared to be highly divergent at the amino acid sequence level and transcriptionally suppressed compared to the co-occurring EFL genes. The results from pioneering studies imply that dual-EF-containing species use EFL for translation, whereas the divergent EF-1α (henceforth designated as “div-EF-1α”) is in charge of arbitrary functions involved in nontranslational cellular processes.19 In another word, div-EF-1α in dual-EF-containing species are expected to not necessarily bind to and deliver aa-tRNAs to the ribosomes,

which are the principal functions of solo-EF-1α and EFL. In addition, no case of co-occurrence of div-EF-1α and EF-1β has been found in eukaryotes,22 implying that div-EF-1α lacks EF1β-binding capacity for GDP/GTP recycling. Nevertheless, there is no study that characterized the putative functions of divEF-1α. We here report the results from comparative studies between solo-EF-1α/EFL and div-EF-1α by combining in silico structural modeling, molecular dynamics (MD) simulation, molecular surface analyses and molecular docking simulation. The structural models of EF-1α and EFL were then employed to molecular surface analyses and molecular docking simulations to evaluate whether div-EF-1α binds to aa-tRNAs or EF-1β.19 7309


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RESULTS AND DISCUSSION Homology Modeling. Three-dimensional structures of EF1α and EFL proteins were constructed by the SWISS-MODEL web server24 using the three templates (PDB ID: 1G7C,23 4C0S,25 and 3WXM26). Tables S1−S3 show the sequence identities with the templates and the scores estimating the quality of the homology models. A qualitative model energy analysis (QMEAN) Z-score provides an estimate of the absolute quality of a model and indicates whether the quality of the generated model is comparable to the experimental structures, whereas QMEAN4 is a linear combination of the four statistical potential scores.27,28 A global model quality estimation (GMQE) score reflects the expected accuracy of the alignment and is expressed as a number between 0 and 1.24 For both the QMEAN Z-score and GMQE score, the higher numbers indicate the higher reliability of the generated models. The sequence identities between the template and the div-EF-1α considered in this study ranged from 40 to 70%, which were lower than those calculated from the solo-EF-1α (70−80%). By comparing the QMEAN4 scores, it was shown that the qualities of the generated models of the div-EF-1α were found to be lower than those of the solo-EF-1α. Models e.g., with yeast EF-1α as the template, the QMEAN4 Z-scores ranged from −3.82 to −0.32 in the former, whereas from −0.92 to −0.13 in the latter (Table S1). The sequence identities of EFL of both the dual-EFcontaining and EFL-containing species was lower than 45% and the estimated model quality was also low accordingly (QMEAN4 was lower than −3.52). The sequence identities between the solo- and div-EF-1α sequences and the Aeropyrum sequence varied (Table S3), whereas those between the EFL sequences did not vary significantly. Using Aeropyrum pernix EF-1α as the template structure would be the most plausible for the aa-tRNA binding form because the root-mean-square deviations (RMSD) of Aeropyrum pernix EF-1α (PDB ID: 3WXM) and the crystal structure (PDB ID: 1TTT)29 of EF-Tu bound to Phe-tRNA was only 2.26 Å. There is no GTP-bound eukaryotic EF-1α structure found for the template search and no X-ray crystal structures of eukaryotic EF-1α and aa-tRNA complex has been solved to this date. Assuming that eukaryotic EF-1α proteins have structures similar to the archaeal or bacterial orthologues when they interact with aa-tRNAs, the homology models using the Aeropyrum EF-1α as the template were employed to the following molecular surface analyses and molecular docking simulations to evaluate whether EF-1α/EFL bind to aa-tRNAs. MD Simulations. For more detailed analysis of the structures of EF-1α and EFL, we performed the MD simulations for some selected models. Because many amino acid sequences in an N-terminal domain are missing in many species, we limitedly selected two EF-1α proteins of Pythium ultimum DAOM BR144 (dual-EF-containing species) and Subulatomonas sp. strain PCMinv5 (EF-1α-containing species) and two EFL proteins of Thecamonas trahens ATCC50062 (dual-EFcontaining species) and Fabomonas tropica strain NYK3C (EFLcontaining species) for the MD simulations. To generate initial structures, we employed the MODELLER program30 using the three template structures. The generated models contained all sequences including C-terminal regions, which were not in the models obtained by SWISS-MODEL. We confirmed the reliability of the predicted homology models by comparing the MD simulations for EF-1α of the EF1α-containing species (Subulatomonas EF-1α) and the dual-EF-

containing species (Pythium div-EF-1α structure). Figure 1a,c,e, show the root-mean-square deviations (RMSD) against each of the initial structure which was obtained by homology modeling. Figure 1b,d,f show the radius of gyration (a measure of compactness of the protein), which were almost stable over the simulation time. Comparing the RMSD of the solo-EF-1α and div-EF-1α structures, the latter was slightly more drifted than the former like those of the other two EFLs. This negligible difference might have come from the possible difference in the actual structures in vivo; the solo-EF-1α would have the same structure as the given template; however, the other three would not. Figure S6 shows the changes in the structures and molecular surface after the MD simulations. The structures and their molecular surfaces remained almost the same. There was some slight diversity in the dynamics; however, from these short MD simulations, the predicted homology models were stable, thus reliable. The four proteins shifted in RMSD for about 0.4 nm and became stable within less than 10 ns (Figure 1). The RMSD of the solo-EF-1α structure grew differently from those of other three structures. The EFL of Fabomonas also had slightly different dynamics in gyration. The dynamical differences between predicted models and the instability in their time evolution were both negligible. The atomic distances for GTP-bound or GDP-bound models had slightly larger fluctuations; however, they were still negligible. We might need a longer simulation time to observe a dynamical change in their domain intervals (Figure 2).

Figure 2. Surface electrostatic distribution of the template and aa-tRNA and EF-1α/EFL models, generated using the SWISS-MODEL using Aeropyrum EF-1α (PDB ID: 3WXM) as the template, obtained using the eF-surf web server.31

To extract the essential dynamics and the dominant motions in the proteins, principal component analysis (PCA) was performed using GROMACS. Root-mean-square fluctuations (RMSF) by the first eigenvector are shown in Figure 3. The spectra had the peak at the residue number 231, the boundary region between domains I and II/III. The boundary region between II and III in the EFL models built on the template Aeropyrum EF-1α with GTP (Figure 3c,d: red) were more fluctuating than the standard EF-1α (Figure 3a) and other proteins which were fluctuating uniformly along the residues. RMSFs were also reflecting the dynamics that the GDP forms of the EFL and the EF-1α in the dual-EF-containing were more flexible among the domains I and II/III and the GTP forms of 7310


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Figure 3. Root-mean-square fluctuations of MD simulations of Subulatomonas sp. strain PC Minv5 EF-1α (a), Pythium ultimum DAOM BR144 EF-1α (b), Thecamonas trahens ATCC50062 EFL (c), Fabomonas tropica strain NYK3C EFL (d), with APO: blue, GDP: green, and GTP: red.

EFLs were more flexible between domains II and III. The domain I of EFL proteins where some sequences were added compared to EF-1α22 had less thermodynamic fluctuations than those of EF-1α proteins. This might imply the thermodynamic stability of EFL without GTP or GDP. Figure 4 shows the free energy landscapes (FEL) constructed via PCA of the MD trajectories. The most stable structures of the proteins were extracted from the lowest wells of the FEL and used for the following analysis. Judging from the structural and dynamical properties analyzed here, the MD simulations supported the homology models so that the surface analysis was stable and reliable. Surface Analyses. The molecular surfaces of the modeled EF-1α and EFL proteins were analyzed using the eF-surf web server31 and compared to each other (see Figures 2 and S2−S4). The surface potentials are represented from anionic (red) to cationic (blue) and the hydrophobic area is shown in yellow. Comparing the EF-1α proteins of the single EF-1α-containing and dual-EF-containing species, the latter (Figures S2−S4K− O,P−T) had more negative charges than the former (Figures S2−S4A−J), which would be unfavorable for interactions with negatively charged aa-tRNAs during translation elongation, the canonical function of EF-1α. On the other hand, the EFL proteins of both the dual-EF-containing (Figures S2−S4U− X,Y−Z) and the single EFL-containing species (Figures S2− S4A′−E′) seemed to have smaller surface areas with negative charges than the div-EF-1α proteins (Figures S2−S4K−O,P− T). In particular, the aa-tRNA binding sites on EF-1α in the dual-EF-containing species were negatively charged, whereas the EFL in the same species were less negatively charged, and thus seemed to preserve the binding sites. The aa-tRNA binding sites of EF-1α were previously inferred from the alignment of the yeast EF-1α and EF-Tu 10.7 The 31 sequences of EF-1α and EFL were aligned by BLAST32 with the

yeast EF-1α sequence and then the aa-tRNA binding sites were marked as shown in Table S4. The eF-Surf calculates the electrostatic potentials on the Connolly polygon vertices, so the neighboring polygon vertices to the Cα atoms corresponding to the aligned binding site residues were searched. The electrostatic potentials of the binding sites or the sum of those at the vertices are shown in Table S5. “Neighborhood” values were considered; the electrostatic potentials at the vertices within 5 nm spheres were summed up. The calculation also revealed that some of the divergent EF-1α proteins of Pythium intermedium MAFF306022, Goniomonas sp. ATCC 50180, and Achnanthes kuwaitensis NIES1349 still preserved the positively charged binding sites. Differential loss hypothesis assumes that the divEF-1α proteins have lost the canonical function including aatRNA binding. Nevertheless, the three div-EF-1α proteins might be capable of interacting with aa-tRNAs. Alternatively, they have already stopped interacting with aa-tRNAs; however, the surface electrostatic potentials have not been completely adapted to the loss of the particular function and are still on the way to lose the ability. A tiny swap in amino acid residues or the electron configurations can make a huge difference in the electrostatic potentials, consequently altering the function and the fate of the proteins. All results of surface analysis support that in the dual-EFcontaining species div-EF-1α would not be involved in the translation elongation and EFL might play the role instead as predicted from the sequence diversity and the low expression level of div-EF-1α.19 In addition, comparing EF-1α and EFL, it seemed that the EF-1β binding site33 was less hydrophobic in EFL (Figures S2−S4U−E′) than in EF-1α of the single EF-1αcontaining species (Figures S2−S4A−J). The result suggests that EFL might not bind to EF-1β or other proteins at this region, which is consistent with the previous study indicating that EFL might be able to recharge GTP without EF-1β.22 7311


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Figure 4. PCA-based free energy landscape of (A) Subulatomonas EF-1α (B) with GDP (C) with GTP, (D) Pythium EF-1α (E) with GDP (F) with GTP, (G) Thecamonas EFL (H) with GDP (I) with GTP, (J) Fabomonas EFL (K) with GDP (L) with GTP. The free energy ΔG is given in kcal mol−1 and presented by colors from blue (0 kcal mol−1) to red (15 kcal mol−1).

Finally, we would like to mention that the both solo- and div-EF1α proteins and also EFL proteins seemed to conserve the

domains II and III, which were considered to be important for its molecular switch or the moonlighting functions. 7312


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Figure 5. Interaction prediction (E-scores) by docking simulations between EF-1α/EFL (a) and Phe-tRNA/EF-1β (b). We took the average of all simulations. Error bars are the standard deviations.

sequences were obtained from the literature,19 UniProt and GenBank databases.34 Structural homology models of EF-1α and EFL were generated using the SWISS-MODEL24 with three different X-ray crystal structures as the structural templates. Multiple templates were used to avoid the template structure dependence, as well as to consider the conformational change of EF-1α between the active and inactive forms.35 (i) Saccharomyces cerevisiae (baker’s yeast) eEF1A (EF-1α) in complex with the eEF1Bα subunit of eEF1B (EF-1β) and guanosine-5′monophosphate (PDB ID: 1G7C23), (ii) post-translationally modified Oryctolagus cuniculus (rabbit) isoform 2 of eEF1α (eEF1α2) in complex with guanosine-5′-diphosphate and a Mg2+ ion crystallized as a dimer (PDB ID: 4C0S, chain A was used as the template),25 and (iii) a crenarchaeon Aeropyrum pernix EF-1α in complex with archaeal Pelota, guanosine-5′triphosphate, and a Mg2+ ion (PDB ID: 3WXM, chain A was used as the template)26 were chosen to consider the EF-1β binding form, the GDP-bound inactive form, and the GTPbound active form, respectively. The average form of EF-1α contains 462 amino acid residues, composed of three domains, that is, domain I (residues from 10 to 240), domain II (residues from 241 to 336), and domain III (residues from 337 to 435).36 Regarding the structural differences of the selected templates, the GDP-bound form, which is represented by rabbit EF-1α, is an open form where the distances between domain I and domain II/domain III are distant, whereas the GTP-bound form, which is represented by Aeropyrum EF-1α, domain I rotates by 90° relative to domains II and III and has narrow and closed domain intervals. The cleft created between domains I, II, and III in the GTP-bound form is the binding site for aa-tRNAs and EF-1β.33 Domain I contains the GTP hydrolysis site.37 The comparison of the templates is shown in Figure S1. Molecular Dynamics Simulations. Molecular dynamics (MD) simulations of the selected homology models were carried out to investigate the thermodynamic stability of the modeled structures and to examine the interactions between GDP or GTP and the EF-1α or EFL proteins affecting the structures. MD simulations were performed for EF-1α proteins of Pythium ultimum DAOM BR144 (dual-EF-containing species), Subulatomonas sp. strain PCMinv5 (EF-1α-containing species) and EFL proteins of Thecamonas trahens ATCC50062 (dual-EF-containing species) and Fabomonas tropica strain NYK3C (EFL-containing species). The four representatives were chosen for their good quality of data containing the whole sequences. The initial structures were obtained using homology modeling by the MODELLER program30 using yeast EF-1α, rabbit EF-1α, and Aeropyrum EF-1α. We aligned the target sequences with each of the three template sequences by

Docking Simulations. The docking simulations were performed by employing the MEGADOCK 4.0.2 to consider the interactions between EF-1α/EFL and Phe-tRNA. Figure 5a, Tables S6 and S7 reflect the predicted results of EF-1α/EFL− Phe-tRNA interaction. Table S6 shows the E values representing the PPI scores of the predicted docking of EF-1α/EFL and PhetRNA. EF-1α were generally high, whereas those for the EFL were low in the affinity with Phe-tRNA (p-value = 1.2 × 10−4, pvalue = 1.2 × 10−5) (Figure 5a). Also, the div-EF-1α were low in the affinity (p-value = 0.02) (Figure 5a). This result was consistent with the hypothesis that the div-EF-1α might not interact with aa-tRNA. The docking simulations of EF-1α/EFL and EF-1β (PDB ID: 5O8W, chain B was used for the docking target) revealed that EFL was somewhat less interacting with EF-1β; however, there were no significant changes (p-value = 0.1) in the E values (Figure 1b). EFL could still weakly interact with EF-1β. Table S6 shows the results of the EF-1α/EFL-tRNA interaction before and after the MD simulations. For Subulatomonas EF-1α, the model built with the yeast EF-1α increased the E values after the MD simulation, whereas for the models with the rabbit EF-1α and Aeropyrum EF-1α, the GDPbound and the GTP-bound forms reduced the E values. The other models were consistent with the fact that the GTP-bound forms would be more likely to interact with aa-tRNA and the GDP-bound and the normal forms do not. The decrease of the E value of the homology model of Subulatomonas EF-1α with the EF-1α/GTP template of Aeropyrum or the increase of the E values of the other models with the template of Aeropyrum before and after the MD simulation would not alter the results shown in Figure 5.

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CONCLUSIONS Homology modeling and surface analysis of EF-1α and EFL were performed to examine the hypothesis that divergent EF-1α in the dual-EF-containing eukaryotes do not strongly interact with aa-tRNA compared to the canonical EF-1α. The subsequent MD simulations were carried out to confirm adequacy of the predicted structures and investigate the difference in their dynamical behavior. The molecular surfaces of the divergent ones were negatively charged, and thus they might not interact with negatively charged aa-tRNA. The molecular docking simulations also support this hypothesis.

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COMPUTATIONAL DETAILS

Homology Modeling. There are no crystal structures available for any div-EF-1α of the target species or EFL, so their three-dimensional structures were generated using standard protein homology modeling methods. A total of 31 protein 7313


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Clustal Omega. 38 The MODELLER program was employed to complete the full sequences including the Cterminal regions which were missing in the models generated using the SWISS-MODEL. The GROMACS 5.0.4 program39 was adopted for the MD simulations. Long-range electrostatic interactions were treated with the fast smooth particle-mesh Ewald method,40 and the cutoffs for van der Waals and Coulomb interactions were set to 10 Å. The time-step was 2 fs with P-LINCS algorithms41 for constraining the bond lengths. Proteins were parameterized using the AMBER99SB-ILDN42 force field. The atomic charges for GTP and GDP were determined with the restrained electrostatic potential method using Gaussian 0943 at the B3LYP/6-31G* level of theory and the force field parameters were taken from the general Amber force field44 using the Antechamber program,45 and the generated topology files were converted using the ACPYPE program.46 The models were placed on dodecahedron boxes of the TIP3P47 water model with distances of at least 10 Å from the box edge and neutralized by adding Cl− or Na+ counter ions. First, energy minimization was carried out to relax the structure. Then, we performed short MD simulations for equilibration for 100 ps in the NVT ensemble, followed by another 100 ps at 1.0 bar in the NPT ensemble with applying a position restraining force on the heavy atoms of the protein and ligands, and production run for 100 ns at 300 K. An averaged structure located at the center of the largest cluster was chosen to analyze the molecular surface using the eF-surf web server.31 Quantitative characterization of the conformational dynamics of each system was performed using principal component analysis (PCA). We applied g_covar and g_anaeig utilities of the GROMACS package to attain the covariance matrix of the RMSD of the protein backbone (Cα atoms) and its diagonalisation, yielding a set of eigenvectors and corresponding eigenvalues. Some of the former provide principal axes of the large-amplitude concerted motions characterizing the essential subspace of each protein’s internal dynamics, whereas the latter represents the amplitude of the motion along the eigenvector. The projection of the trajectories on each principal axis shows the width of the essential space explored by the system as a function of time. A comparison of the conformational space sampled by different trajectories generated for the same system can be made to gain insight into the amount of essential space explored by the system during the MD simulation. Surface Analyses. It is useful to investigate the molecular surface to understand the function of proteins, because specific intermolecular interactions such as electrostatic and hydrophobic interactions are important in molecular recognition.48−50 Molecular surfaces of almost all protein structures registered in PDB are available in eF-site database,51 which calculates the polygon-meshed molecular surfaces by the MSP program developed by Connolly,52 then the electrostatic potentials were calculated by solving the Poisson−Boltzmann equations using the SCB program.53 The molecular surfaces and electrostatic potentials for the template proteins were obtained from the eF-site database,51 whereas those of the models were calculated using the eF-surf web server,31 which calculates the molecular surface for the up-loaded structures in the same way as the eF-site database. Docking Simulations. Molecular docking simulations were carried out to examine interactions54 between EF-1α and phenylalanyl-tRNA (Phe-tRNA). MEGADOCK ver. 4.0.255 was employed to perform the docking simulations. We adopted

the default parameter values for all calculations. For the docking score functions, the real Pairwise Shape Complementarity score,55 electrostatics score based on FTDock potentials56 and CHARMM19 atomic charge,57,58 RDE desolvation free energy59 were referred. On the basis of the docking scores of 2000 predicted models, the evaluation value E60 or the protein− protein interaction (PPI) score was then calculated to judge whether each EF-1α/EFL (protein) and Phe-tRNA (ligand) can interact or not. The evaluation value E or the standardized variable was obtained by subtracting the mean from the topscored decoy’s docking score for a protein−ligand pair and then dividing by the standard deviation.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsomega.8b03547. Crystal structures of the template and the molecular surfaces (Figure S1); homology models of EF-1α and EFL proteins with the yeast, rabbit, and Aeropyrum template, respectively (Tables S1−S3); molecular surface seen from the tRNA binding side of EF-1α and EFL models built with the yeast, rabbit, and Aeropyrum template, respectively (Figures S2−S4); molecular surface seen from the backside of EF-1α and EFL models built with the Aeropyrum template (Figure S5); values of net charges and electrostatic potentials (Table S5); structures and molecular surfaces of the averaged structure of 100 ns MD simulation (Figure S6); results of EF-1α/EFL-tRNA and EF-1α/EFL-EF-1β interaction prediction (E-score) (Table S6); results of EF-1α/EFL-tRNA interaction prediction (E-score) before and after MD (Table S7) (PDF)

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AUTHOR INFORMATION

Corresponding Authors

*E-mail: [email protected] (M.K.). *E-mail: [email protected] (Y.S.). ORCID

Kotaro Sakamoto: 0000-0003-0007-3894 Yasuteru Shigeta: 0000-0002-3219-6007 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS All computations were performed at the Center for Computational Sciences (CCS), University of Tsukuba. We especially thank Takanori Hayashi for the useful discussion on MEGADOCK.

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REFERENCES

(1) Riis, B.; Rattan, S. I.; Clark, B. F.; Merrick, W. C. Eukaryotic protein elongation factors. Trends Biochem. Sci. 1990, 15, 420−424. (2) Andersen, G.; Nissen, P.; Nyborg, J.; Andersen, G. R.; Nissen, P.; Nyborg, J. Elongation factors in protein biosynthesis. Trends Biochem Sci 28: 434-441. Trends Biochem. Sci. 2003, 28, 434−441. (3) Roger, A. J.; Sandblom, O.; Doolittle, W. F.; Philippe, H. An evaluation of elongation factor 1 alpha as a phylogenetic marker for eukaryotes. Mol. Biol. Evol. 1999, 16, 218−233. (4) Iwabe, N.; Kuma, K.; Hasegawa, M.; Osawa, S.; Miyata, T. Evolutionary relationship of archaebacteria, eubacteria, and eukaryotes

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In Silico Structural Modeling and Analysis of Elongation Factor-1

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