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Effects of Acetylation and Phosphorylation on Subunit Interactions in Three Large Eukaryotic Complexes*

  • Nikolina Šoštarić
    Affiliations
    KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium
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  • Francis J. O'Reilly
    Affiliations
    European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany

    Technical University of Berlin, Berlin, Germany
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  • Piero Giansanti
    Affiliations
    Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Science4Life, Utrecht University, Utrecht, The Netherlands

    Netherlands Proteomics Centre, Utrecht, The Netherlands

    Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
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  • Albert J.R. Heck
    Affiliations
    Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Science4Life, Utrecht University, Utrecht, The Netherlands

    Netherlands Proteomics Centre, Utrecht, The Netherlands
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  • Anne-Claude Gavin
    Affiliations
    European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, Germany
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  • Vera van Noort
    Correspondence
    To whom correspondence should be addressed:KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium. Tel.:+32 1637 9216; Fax:+32 1632 1966;
    Affiliations
    KU Leuven, Centre of Microbial and Plant Genetics, Kasteelpark Arenberg 20, Leuven, B-3001, Belgium

    Leiden University, Institute of Biology Leiden, Leiden, The Netherlands
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  • Author Footnotes
    * This work is supported by Onderzoeksraad, KU Leuven (KU Leuven Research Fund) (to V.N.). N.S. is a doctoral fellow (1112318N) of Fonds Wetenschappelijk Onderzoek (the Research Foundation - Flanders) (FWO). P.G. and A.J.R.H. acknowledge support from the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organization for Scientific Research) (NWO) funding the large-scale proteomics facility [email protected] (Project 184.032.201) embedded in the Netherlands Proteomics Centre. ACG acknowledges support from the CellNetworks (Excellence Initiative of the University of Heidelberg). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders (FWO) and the Flemish Government - department EWI.
    This article contains supplemental material.
Open AccessPublished:September 04, 2018DOI:https://doi.org/10.1074/mcp.RA118.000892
      Protein post-translational modifications (PTMs) have an indispensable role in living cells as they expand chemical diversity of the proteome, providing a fine regulatory layer that can govern protein-protein interactions in changing environmental conditions. Here we investigated the effects of acetylation and phosphorylation on the stability of subunit interactions in purified Saccharomyces cerevisiae complexes, namely exosome, RNA polymerase II and proteasome. We propose a computational framework that consists of conformational sampling of the complexes by molecular dynamics simulations, followed by Gibbs energy calculation by MM/GBSA. After benchmarking against published tools such as FoldX and Mechismo, we could apply the framework for the first time on large protein assemblies with the aim of predicting the effects of PTMs located on interfaces of subunits on binding stability. We discovered that acetylation predominantly contributes to subunits' interactions in a locally stabilizing manner, while phosphorylation shows the opposite effect. Even though the local binding contributions of PTMs may be predictable to an extent, the long range effects and overall impact on subunits' binding were only captured because of our dynamical approach. Employing the developed, widely applicable workflow on other large systems will shed more light on the roles of PTMs in protein complex formation.

      Graphical Abstract

      A protein's functional engagement with other molecules in the cell is finely regulated by an array of post-translational modifications (PTMs)
      The abbreviations used are:
      PTM
      post-translational modification
      CP
      20S yeast proteasome core particle
      CTD
      C-terminal domain of Rpb1 subunit of RNA polymerase II
      MD
      molecular dynamics
      MM/GBSA
      molecular mechanics energies combined with Generalized Born and surface area continuum solvation
      MM/PBSA
      molecular mechanics energies combined with Poisson-Boltzmann and surface area continuum solvation
      RMSD
      root mean square deviation
      RP
      19S yeast proteasome regulatory particle
      TAP-MS
      tandem affinity purification followed by mass spectrometry
      Y2H
      yeast 2-hybrid.
      1The abbreviations used are:PTM
      post-translational modification
      CP
      20S yeast proteasome core particle
      CTD
      C-terminal domain of Rpb1 subunit of RNA polymerase II
      MD
      molecular dynamics
      MM/GBSA
      molecular mechanics energies combined with Generalized Born and surface area continuum solvation
      MM/PBSA
      molecular mechanics energies combined with Poisson-Boltzmann and surface area continuum solvation
      RMSD
      root mean square deviation
      RP
      19S yeast proteasome regulatory particle
      TAP-MS
      tandem affinity purification followed by mass spectrometry
      Y2H
      yeast 2-hybrid.
      (
      • Seet B.T.
      • Dikic I.
      • Zhou M.-M.
      • Pawson T.
      Reading protein modifications with interaction domains.
      ,
      • Benayoun B.A.
      • Veitia R.A.
      A post-translational modification code for transcription factors: sorting through a sea of signals.
      ) that locally change the chemical properties of a protein, and alter its activity, localization and stability (
      • Kim S.C.
      • Sprung R.
      • Chen Y.
      • Xu Y.
      • Ball H.
      • Pei J.
      • Cheng T.
      • Kho Y.
      • Xiao H.
      • Xiao L.
      • Grishin N.V.
      • White M.
      • Yang X.-J.
      • Zhao Y.
      Substrate and functional diversity of lysine acetylation revealed by a proteomics survey.
      ,
      • Cohen P.
      The role of protein phosphorylation in human health and disease.
      ,
      • Audagnotto M.
      • Dal Peraro M.
      Protein post-translational modifications: In silico prediction tools and molecular modeling.
      ). PTMs affect a significant part of eukaryotic and prokaryotic proteins, and different types were studied to different extents, with phosphorylation receiving most of the attention (
      • Cohen P.
      The role of protein phosphorylation in human health and disease.
      ) and becoming the first PTM studied at a proteome-wide level (
      • Beausoleil S.A.
      • Jedrychowski M.
      • Schwartz D.
      • Elias J.E.
      • Villen J.
      • Li J.
      • Cohn M.A.
      • Cantley L.C.
      • Gygi S.P.
      Large-scale characterization of HeLa cell nuclear phosphoproteins.
      ,
      • Olsen J.V.
      • Blagoev B.
      • Gnad F.
      • Macek B.
      • Kumar C.
      • Mortensen P.
      • Mann M.
      Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.
      ). In contrast, it has been appreciated only more recently that lysine acetylation is also a wide-spread modification (
      • Wang Q.
      • Zhang Y.
      • Yang C.
      • Xiong H.
      • Lin Y.
      • Yao J.
      • Li H.
      • Xie L.
      • Zhao W.
      • Yao Y.
      • Ning Z.-B.
      • Zeng R.
      • Xiong Y.
      • Guan K.-L.
      • Zhao S.
      • Zhao G.-P.
      Acetylation of Metabolic enzymes coordinates carbon source utilization and metabolic flux.
      ,
      • Choudhary C.
      • Kumar C.
      • Gnad F.
      • Nielsen M.L.
      • Rehman M.
      • Walther T.C.
      • Olsen J.V.
      • Mann M.
      Lysine acetylation targets protein complexes and co-regulates major cellular functions.
      ). Except its well-described roles in histones (
      • Bannister A.J.
      • Kouzarides T.
      Regulation of chromatin by histone modifications.
      ), it was also found to regulate the activities of other enzymes, such as tubulin (
      • Hammond J.W.
      • Cai D.
      • Verhey K.J.
      Tubulin modifications and their cellular functions.
      ).
      Because of technological developments (
      • Olsen J.V.
      • Blagoev B.
      • Gnad F.
      • Macek B.
      • Kumar C.
      • Mortensen P.
      • Mann M.
      Global, in vivo, and site-specific phosphorylation dynamics in signaling networks.
      ,
      • Zhou H.
      • Watts J.D.
      • Aebersold R.
      A systematic approach to the analysis of protein phosphorylation.
      ), PTMs have been identified on a proteome-wide scale for several organisms, with the resulting data of more than 600,000 individual experimentally found modification sites stored in the freely available on-line database dbPTM (
      • Huang K.-Y.
      • Su M.-G.
      • Kao H.-J.
      • Hsieh Y.-C.
      • Jhong J.-H.
      • Cheng K.-H.
      • Huang H.-D.
      • Lee T.-Y.
      dbPTM 2016: 10-year anniversary of a resource for post-translational modification of proteins.
      ). Availability of PTMs data enabled investigation of their functional roles on a large scale. For instance, the PTMfunc (
      • Beltrao P.
      • Albanèse V.
      • Kenner L.R.
      • Swaney D.L.
      • Burlingame A.
      • Villén J.
      • Lim W.A.
      • Fraser J.S.
      • Frydman J.
      • Krogan N.J.
      Systematic functional prioritization of protein posttranslational modifications.
      ) resource was made, in which the predictions of PTM's functional relevance for 200,000 sites from 11 eukaryotic species is based on the conservation analysis. Moreover, the Mechismo tool (
      • Betts M.J.
      • Lu Q.
      • Jiang Y.
      • Drusko A.
      • Wichmann O.
      • Utz M.
      • Valtierra-Gutiérrez I.A.
      • Schlesner M.
      • Jaeger N.
      • Jones D.T.
      • Pfister S.
      • Lichter P.
      • Eils R.
      • Siebert R.
      • Bork P.
      • Apic G.
      • Gavin A.-C.
      • Russell R.B.
      Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions.
      ) was developed, which estimates the effect of an interface located mutation or modification on interaction stability, based on the observed amino acids interaction patterns, but without the explicit 3D modeling of neither the query, nor the mutated/modified complex. In a recent expansion of the Mechismo work, the accent was placed on detecting phosphorylation sites that act as protein interaction switches (
      • Betts M.J.
      • Wichmann O.
      • Utz M.
      • Andre T.
      • Petsalaki E.
      • Minguez P.
      • Parca L.
      • Roth F.P.
      • Gavin A.-C.
      • Bork P.
      • Russell R.B.
      Systematic identification of phosphorylation-mediated protein interaction switches.
      ) by additionally considering the similarity of the protein with its template, as well as conservation of the phosphorylation site. However, in all these tools the dynamic information is missing, so a mechanistic understanding of PTMs effect is still lacking.
      Various efforts have also been undertaken to understand the functional roles of PTMs in specific (sets of) proteins, using not only experimental, but also computational approaches. For instance, Nishi et al. (
      • Nishi H.
      • Hashimoto K.
      • Panchenko A.R.
      Phosphorylation in protein-protein binding: effect on stability and function.
      ) examined the effect on energetics of binding caused by interface phosphorylations in human protein complexes. The calculations were performed using the empirical force field FoldX(
      • Schymkowitz J.
      • Borg J.
      • Stricher F.
      • Nys R.
      • Rousseau F.
      • Serrano L.
      The FoldX web server: an online force field.
      ), in which side chains of the residues surrounding the phosphorylation site are optimized before binding energy calculation is performed. The obtained distribution was very much shifted toward destabilization, and for approximately one third of the sites destabilization effect was larger than 2 kcal/mol, which was taken as a threshold for a site to have a strong effect on interaction. Several studies also applied molecular dynamics (MD) simulations to assess the effect of PTMs on proteins, e.g. Narayanan et al. (
      • Narayanan A.
      • LeClaire L.L.
      • Barber D.L.
      • Jacobson M.P.
      Phosphorylation of the Arp2 subunit relieves auto-inhibitory interactions for Arp2/3 complex activation.
      ) investigated actin-related protein 2/3 complex, and Kumar et al. (
      • Kumar P.
      • Chimenti M.S.
      • Pemble H.
      • Schönichen A.
      • Thompson O.
      • Jacobson M.P.
      • Wittmann T.
      Multisite phosphorylation disrupts arginine-glutamate salt bridge networks required for binding of cytoplasmic linker-associated protein 2 (CLASP2) to end-binding protein 1 (EB1).
      ) cytoplasmic linker-associated protein 2 binding to end-binding protein 1. Their conclusions were of qualitative nature, for instance they observed the reorientation of proteins, formation and breaking of salt bridges and hydrogen bonds in the respective complexes. Additionally, an MD-based method employing nine physicochemical parameters extracted from the trajectories was recently proposed to predict the impact of phosphorylation on protein-protein interactions (
      • Chiappori F.
      • Mattiazzi L.
      • Milanesi L.
      • Merelli I.
      A novel molecular dynamics approach to evaluate the effect of phosphorylation on multimeric protein interface: the αB-Crystallin case study.
      ).
      Until now, PTMs have been identified in whole cell lysates and mapped to protein structures, but it was unclear if they were actually present in native protein complexes. In this study, we aimed to elucidate the roles of phosphorylation and acetylation sites in three large yeast complexes that are essential for life - exosome, RNA polymerase II and 26S proteasome - by combining experimental and computational approaches (Fig. 1). Exosome catalyzes 3′-5′ ribonucleic acid (RNA) degradation in eukaryotes, which is involved in regulating the amount of transcripts, as well as their maturation and quality control (
      • Makino D.L.
      • Baumgärtner M.
      • Conti E.
      Crystal structure of an RNA-bound 11-subunit eukaryotic exosome complex.
      ). The core of the exosome consists of a hexameric ring (subunits Rrp41, Rrp42, Rrp43, Rrp45, Rrp46 and Mtr3) and a trimeric cap (Rrp4, Rrp40 and Csl4) (
      • Makino D.L.
      • Baumgärtner M.
      • Conti E.
      Crystal structure of an RNA-bound 11-subunit eukaryotic exosome complex.
      ,
      • Wasmuth E.V.
      • Januszyk K.
      • Lima C.D.
      Structure of an Rrp6-RNA exosome complex bound to poly(A) RNA.
      ). In the cytoplasm, this nonamer recruits the catalytically active Rrp44 subunit (
      • Wasmuth E.V.
      • Januszyk K.
      • Lima C.D.
      Structure of an Rrp6-RNA exosome complex bound to poly(A) RNA.
      ), whereas nuclear exosome additionally has Rrp6 subunit and its obligate partner C1D (
      • Zinder J.C.
      • Lima C.D.
      Targeting RNA for processing or destruction by the eukaryotic RNA exosome and its cofactors.
      ). The second complex that we investigated, RNA polymerase II, is responsible for synthesis of all messenger RNA molecules, as well as several noncoding ones in eukaryotic cells (
      • Hsin J.-P.
      • Manley J.L.
      The RNA polymerase II CTD coordinates transcription and RNA processing.
      ,
      • Cramer P.
      • Bushnell D.A.
      • Kornberg R.D.
      Structural Basis of Transcription: RNA Polymerase II at 2.8 Ångstrom Resolution.
      ). Although 10 of its 12 subunits are conserved across species and identical or homologous to those in RNA polymerases I and III (
      • Cramer P.
      • Bushnell D.A.
      • Kornberg R.D.
      Structural Basis of Transcription: RNA Polymerase II at 2.8 Ångstrom Resolution.
      ,
      • Vannini A.
      • Cramer P.
      Conservation between the RNA Polymerase I, II, and III transcription initiation machineries.
      ), the remaining subunits Rpb4 and Rpb7 are specific for RNA polymerase II and are not important for the elongation process (
      • Hahn S.
      Structure and mechanism of the RNA polymerase II transcription machinery.
      ). Till date, the investigation of PTMs' function in RNA polymerase II was mainly focused on phosphorylation of the C-terminal domain (CTD) of its largest subunit, Rpb1, an important regulatory element not found in other RNA polymerases (
      • Hahn S.
      Structure and mechanism of the RNA polymerase II transcription machinery.
      ). CTD is composed of the consensus sequence Tyr-Ser-Pro-Thr-Ser-Pro-Ser repeats (
      • Fuchs S.M.
      • Laribee R.N.
      • Strahl B.D.
      Protein modifications in transcription elongation.
      ), known to change their phosphorylation status during the transcription cycle, and therefore dictate CTD's shape and binding of specific factors (
      • Hahn S.
      Structure and mechanism of the RNA polymerase II transcription machinery.
      ). Other PTM types—OGlcNAcylation, ubiquitylation, methylation, proline isomerization—have also been reported for CTD (
      • Hsin J.-P.
      • Manley J.L.
      The RNA polymerase II CTD coordinates transcription and RNA processing.
      ,
      • Fuchs S.M.
      • Laribee R.N.
      • Strahl B.D.
      Protein modifications in transcription elongation.
      ), as well as acetylation of Lys from the non-consensus repeats found in some organisms (
      • Schröder S.
      • Herker E.
      • Itzen F.
      • He D.
      • Thomas S.
      • Gilchrist D.A.
      • Kaehlcke K.
      • Cho S.
      • Pollard K.S.
      • Capra J.A.
      • Schnölzer M.
      • Cole P.A.
      • Geyer M.
      • Bruneau B.G.
      • Adelman K.
      • Ott M.
      Acetylation of RNA polymerase II regulates growth-factor-induced gene transcription in mammalian cells.
      ). Finally, the proteasome is the major protein degradation machinery present in all three domains of life. Its substrates differ from other proteins in the cell by an attached chain of small proteins, ubiquitins. In eukaryotes, the 26S proteasome contains the proteolytically active 20S core particle (CP), composed of α and β subunits, and the 19S regulatory particle (RP), which together count 33 different protein subunits (
      • Livneh I.
      • Cohen-Kaplan V.
      • Cohen-Rosenzweig C.
      • Avni N.
      • Ciechanover A.
      The life cycle of the 26S proteasome: from birth, through regulation and function, and onto its death.
      ). Acetylation of CP and phosphorylation of both CP and RP subunits were found to affect proteasome activity (
      • Livneh I.
      • Cohen-Kaplan V.
      • Cohen-Rosenzweig C.
      • Avni N.
      • Ciechanover A.
      The life cycle of the 26S proteasome: from birth, through regulation and function, and onto its death.
      ), while phosphorylation of the Rpt6 ATPase subunit of RP was found to have a role in proteasome assembly (
      • Livneh I.
      • Cohen-Kaplan V.
      • Cohen-Rosenzweig C.
      • Avni N.
      • Ciechanover A.
      The life cycle of the 26S proteasome: from birth, through regulation and function, and onto its death.
      ,
      • Satoh K.
      • Sasajima H.
      • Nyoumura K.I.
      • Yokosawa H.
      • Sawada H.
      Assembly of the 26S proteasome is regulated by phosphorylation of the p45/Rpt6 ATPase subunit.
      ). Recently, more than 345 PTMs of 11 different types were detected on the 26S proteasome (
      • Hirano H.
      • Kimura Y.
      • Kimura A.
      Biological significance of co- and post-translational modifications of the yeast 26S proteasome.
      ), however because most of the obtained PTM data is quite novel and originates from large proteomics studies, their roles are still predominantly unknown (
      • Im E.
      • Chung K.C.
      Precise assembly and regulation of 26S proteasome and correlation between proteasome dysfunction and neurodegenerative diseases.
      ).
      Figure thumbnail gr1
      Fig. 1Overview of the workflow. The strategy applied for prediction of the effect of post-translational modifications on binding of subunits in yeast exosome, RNA polymerase II and proteasome 19S regulatory particle.
      In this work, we first employ tandem affinity purification (TAP) followed by high resolution mass spectrometry (MS) in order to obtain the high fidelity information about PTM sites in the three natively purified complexes. Our data set contains a total of 129 acetylation and 41 phosphorylation sites detected within the complexes, almost all of which are novel. Secondly, we employ the available high-resolution 3D data to map the detected PTMs on the protein structures. Our focus is then placed on PTMs that are located at the subunits' interfaces, as such locations are generally more conserved, and therefore more likely functionally important and involved in the regulation of binding affinities (
      • Beltrao P.
      • Albanèse V.
      • Kenner L.R.
      • Swaney D.L.
      • Burlingame A.
      • Villén J.
      • Lim W.A.
      • Fraser J.S.
      • Frydman J.
      • Krogan N.J.
      Systematic functional prioritization of protein posttranslational modifications.
      ,
      • Nishi H.
      • Hashimoto K.
      • Panchenko A.R.
      Phosphorylation in protein-protein binding: effect on stability and function.
      ). Thirdly, in order to elucidate the effects of interface located novel PTMs on binding of subunits, we employ a computational approach consisting of meticulous conformational sampling via molecular dynamics simulations, followed by calculation of the Gibbs energy of binding by MM/GBSA method. We first test the robustness of this approach on yeast Skp1:Met30 system, benchmark it against Mechismo and FoldX on a set of mammalian protein complexes, apply it on the three large complexes, and experimentally validate our results. Finally, we compare the results for yeast complexes with predictions of the Mechismo tool and look into conservation of the PTM sites. Our predictions suggest the locally stabilizing role of the interface located acetylated lysines, and a locally destabilizing one for the phosphorylated residues. Moreover, our approach based on protein dynamics allowed us to capture global effects of PTMs on binding, with even binding of the chains that are not in a direct vicinity of PTMs being affected by their presence.

      DISCUSSION

      In this work, we have extended the knowledge on post-translational modifications in yeast Saccharomyces cerevisiae by identifying novel phosphorylation and acetylation sites, as well as predicted their roles in interactions within three large and essential complexes - exosome, RNA polymerase II and proteasome. The latter was never done in a dynamic fashion and with simultaneously taking into account multiple PTMs in protein complexes of these sizes (up to 7000 amino acid residues in explicit solvent). Finally, we have experimentally tested our computational predictions on the Rrp45:Rrp41 exosome interactions, affected in a stabilizing manner by Rrp45_Lys250 acetylation.
      Employing TAP-MS allowed us to identify the modifications that are indeed present in complexes in their native states. The number of detected acetylation sites is approximately five times larger than the number of phosphorylation sites within each complex. While we successfully captured all subunits of exosome and RNA polymerase II, this was not the case for 26S proteasome, probably because of its size. Employing more baits at different proteasome subunits would likely result in a more complete data set for this complex. Given the high coverage, our PTMs data set is likely comprehensive.
      While it was argued that introduction of point mutations causes only local conformational changes that do not extend on the tertiary structure (
      • Dourado D.F.A.R.
      • Flores S.C.
      A multiscale approach to predicting affinity changes in protein-protein interfaces.
      ), numerous examples show that PTMs, such as phosphorylation and acetylation, can cause major conformational changes with great impact on protein function. For instance, comparison of crystal structures of nonphosphorylated and phosphorylated rabbit muscle glycogen phosphorylase nicely demonstrates how introduction of modification can cause long range effects through the conformational changes. More specifically, phosphorylation of Ser14 in this protein induces adoption of α-helical structure of N terminus, and this change gets transmitted as far as 30 Å further to the active site, resulting in T-R transition (
      • Barford D.
      • Hu S.H.
      • Johnson L.N.
      Structural mechanism for glycogen phosphorylase control by phosphorylation and AMP.
      ). Furthermore, a well-known example of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase enzyme demonstrates how phosphorylation can act as a trigger between two functions of a bifunctional enzyme, again through the conformational changes it induces (
      • Rider M.H.
      • Bertrand L.
      • Vertommen D.
      • Michels P.A.
      • Rousseau G.G.
      • Hue L.
      6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase: head-to-head with a bifunctional enzyme that controls glycolysis.
      ). Finally, phosphorylation of intrinsically disordered protein domains, e.g. the AF1 domain of glucocorticoid receptor, can cause the formation of secondary and tertiary structure, providing a mechanism for functional activation of such domains (
      • Garza A.M.S.
      • Khan S.H.
      • Kumar R.
      Site-specific phosphorylation induces functionally active conformation in the intrinsically disordered N-terminal activation function (AF1) domain of the glucocorticoid receptor.
      ). Although it received less research attention than phosphorylation, lysine acetylations also induce changes that play role in many different regulatory events. For example, acetylation-induced allosteric conformational changes can provide a regulatory switch between two functions of a protein, as was shown for heat shock protein 70 (Hsp70), where acetylation status of Lys77 determines the co-chaperone that will preferably bind to the protein, and consequently whether Hsp70 will take place in protein degradation or refolding (
      • Seo J.H.
      • Park J.-H.
      • Lee E.J.
      • Vo T.T.L.
      • Choi H.
      • Kim J.Y.
      • Jang J.K.
      • Wee H.-J.
      • Lee H.S.
      • Jang S.H.
      • Park Z.Y.
      • Jeong J.
      • Lee K.-J.
      • Seok S.-H.
      • Park J.Y.
      • Lee B.J.
      • Lee M.-N.
      • Oh G.T.
      • Kim K.-W.
      ARD1-mediated Hsp70 acetylation balances stress-induced protein refolding and degradation.
      ). Lysine acetylation can also induce conformational changes in a crosstalk with other post-translational modification, such as methylation in p53 protein (
      • Tong Q.
      • Mazur S.J.
      • Rincon-Arano H.
      • Rothbart S.B.
      • Kuznetsov D.M.
      • Cui G.
      • Liu W.H.
      • Gete Y.
      • Klein B.J.
      • Jenkins L.
      • Mer G.
      • Kutateladze A.G.
      • Strahl B.D.
      • Groudine M.
      • Appella E.
      • Kutateladze T.G.
      An acetyl-methyl switch drives a conformational change in p53.
      ). The notion that PTMs can cause such large changes in protein structure underlines the need for a dynamic approach in prediction of their effects on the protein and its interactions.
      A notable difference also exists between mutations and modifications data sets used in publications. While the benchmark data set of Betts et al. 2017 (
      • Betts M.J.
      • Wichmann O.
      • Utz M.
      • Andre T.
      • Petsalaki E.
      • Minguez P.
      • Parca L.
      • Roth F.P.
      • Gavin A.-C.
      • Bork P.
      • Russell R.B.
      Systematic identification of phosphorylation-mediated protein interaction switches.
      ) had a bias toward stabilizing phosphorylation sites (238 out of 335 sites were experimentally found to enable interactions), mutations data sets dominantly consist of destabilizing sites. Examples include the benchmarking data set of Betts et al. 2015 (
      • Betts M.J.
      • Lu Q.
      • Jiang Y.
      • Drusko A.
      • Wichmann O.
      • Utz M.
      • Valtierra-Gutiérrez I.A.
      • Schlesner M.
      • Jaeger N.
      • Jones D.T.
      • Pfister S.
      • Lichter P.
      • Eils R.
      • Siebert R.
      • Bork P.
      • Apic G.
      • Gavin A.-C.
      • Russell R.B.
      Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions.
      ) based on UniProt data for human site-directed mutations and disease variants, in which 79% of mutations and variants have disabling, 3.2% enabling effect, and the remaining are neutral. Furthermore, in their prediction of affinity changes for interface-located protein mutations, Dourado et al. (
      • Dourado D.F.A.R.
      • Flores S.C.
      A multiscale approach to predicting affinity changes in protein-protein interfaces.
      ) employed a subset of SKEMPI (
      • Moal I.H.
      • Fernández-Recio J.
      SKEMPI: a Structural Kinetic and Energetic database of Mutant Protein Interactions and its use in empirical models.
      ), the data set of heterodimeric protein complexes mutants with experimentally determined ΔΔG, in which 987 out of 1,254 mutations are destabilizing. The difference in biases is likely the result of fundamental difference between post-translational modifications and mutations—while the first have evolved to appear at specific locations, often to modulate protein function and interactions, the appearance of the latter works against what the nature has optimized. Taken together with the fact that PTMs in our data set were detected in native complexes, a more frequent occurrence of stabilizing than destabilizing effects on binding is not surprising.
      In the present work, we have computationally explored the influence that interface located PTMs, majority of which we have newly discovered, have on the subunits' interaction, where we were mainly interested in direction of the change (stabilization versus destabilization). Although the TAP-MS approach allowed the PTMs detection on purified native complexes, from these data it is not possible to distinguish which combination of PTMs was present in which natively purified complex molecule. Ideally, we would simulate structures of complexes with different combinations of PTMs that co-occur in the yeast cell. However, as such data is unavailable, and because proteins are oftentimes modified on multiple sites (
      • van Noort V.
      • Seebacher J.
      • Bader S.
      • Mohammed S.
      • Vonkova I.
      • Betts M.J.
      • Kühner S.
      • Kumar R.
      • Maier T.
      • O'Flaherty M.
      • Rybin V.
      • Schmeisky A.
      • Yus E.
      • Stülke J.
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      • Russell R.B.
      • Heck A.J.
      • Bork P.
      • Gavin A.-C.
      Cross-talk between phosphorylation and lysine acetylation in a genome-reduced bacterium.
      ), in the present study we decided to add all intersubunits located PTMs on the structure simultaneously. The next step to tackle the question of PTM combinations co-existing within native complexes would be the top-down proteomics.
      On a local level, we predict interface phosphorylation sites to be destabilizing, presumably because the negative charge could not be locally compensated because of absence of positively charged groups. On opposite, acetylation of a site dominantly caused stabilization of the binding, usually because of diminishing lysine's positive charge that is not involved in a salt bridge. Thanks to dynamics, we could also capture broader effects that these modifications have on subunits' binding in multimeric complexes, which do not necessarily coincide with the local effect of PTM sites for a subunit in which they are located. For instance, while presence of 20 interface PTMs in exosome was predicted to cause a more stable binding of 9 out of 10 subunits, 18 PTMs in RNA polymerase II appeared to destabilize binding of 8 out of 10 subunits, even though both systems predominantly contained acetylations that are locally stabilizing the binding. As the PTMs are detected in natively purified complexes, we would expect the overall effects to be mainly stabilizing. This result might therefore suggest that those 18 PTMs are likely not simultaneously present in the RNA polymerase II, but were instead present in different RNA polymerase II sub-populations. Similarly, if the predicted effects on the subunits' binding are compared between fully and singly modified exosome, the complex stabilization is achieved by the presence of multiple PTMs, rather than a single one. The magnitude of ΔΔGbind compared with the ΔGbind of nonmodified subunit ranged from 3% for Rpb1 in the RNA polymerase II, to as high as 55% in the case of Rrp41 in the exosome. Therefore, although we can assume the individual effect of a PTM site based on the chemistry of a modification, the overall consequences on subunits' binding is captured thanks to the freedom of residues to reposition in space.
      Compared with Mechismo and FoldX, which produce results in a matter of minutes, our MD-based approach is computationally more intensive, mainly because of MD simulations component. For instance, the 500 ps equilibration phase took 38 h of computational time on a single compute node (CPU, 20 cores, Flemish Supercomputer Center infrastructure) for exosome in explicit water, 56 h for RNA polymerase II, and 180 h for proteasome regulatory particle. Moreover, the 19.5 ns production phase of MD required 58 h for exosome and 102 h for RNA polymerase II using 2 nodes, and 71 h for proteasome using 8 compute nodes. Notably, these simulated complexes are very large, containing 2775, 3570, and 6996 amino acids, respectively. Protein complexes of smaller sizes demand less time, e.g. Dynll1 homodimer from the benchmarking data set (system with a total of 170 amino acids) was equilibrated in 3.5 h on 1 node, while the production phase required 6 h of time on 2 nodes. Certain modifications of the workflow could be made with the aim of reducing the time and/or computational resources needed. For example, time step in equilibration phase could be set to 2 fs instead of 1 fs, which would halve the time required for this step. Moreover, the production phase could be of shorter total duration, especially for systems that stabilize quickly; in a recent study, MD simulations of only 1 ns duration were used for conformational sampling before MM/PBSA (
      • Li M.
      • Petukh M.
      • Alexov E.
      • Panchenko A.R.
      Predicting the impact of missense mutations on protein–protein binding affinity.
      ). Furthermore, more computational resources could be employed for the time saving purposes, especially when dealing with larger systems. If our MD-based approach were to be used for predictions on a larger set of protein complexes (e.g. on a proteome wide level), the above changes would be a good way to reduce the overall analysis time, making such an analysis feasible.
      An important advantage of our computational approach is obtaining a dynamic view of a protein after a PTM is introduced. Although it is known that PTMs can cause conformational changes as exemplified above, alternative approaches such as Mechismo and FoldX do not take dynamics into account. Instead, Mechismo uses static picture, if the amino acids interaction pattern stays unchanged when a PTM is introduced, whereas FoldX optimizes surrounding side chains, but does not allow for any major perturbations of the structure. Our result show that, on opposite, interactions patterns can be changed not only at short, but also at long distances from the PTM site (Fig. 6), which is nicely demonstrated by ΔΔGbind values different from zero for subunits in the singly modified exosome in our study. Further limitations of Mechismo method originate from its dependence on the local rules and the need for training sets, as shown in Results for the three yeast complexes where it failed to make predictions for majority of PTMs. Mechismo also cannot assess what would be the overall effect if multiple PTMs are present in the structure simultaneously. This is rather different from our approach, which requires knowledge of a structure and PTMs' positions only and has no difficulty in making predictions of multiple PTMs' effect to binding in a complex. On the other side, although it supports predictions with multiple PTMs present in complex, FoldX has only a very narrow set of modifications that can be introduced with its “PositionScan” functionality—phosphorylated Ser, Thr and Tyr, and hydroxyproline. On contrary, PyTMs plugin that we use for preparation of the structures for MD enables introduction of 11 different post-translational modifications, including Ser/Thr/Tyr phosphorylation and Lys acetylation. Moreover, Amber force field parameters by Khoury et al. (
      • Khoury G.A.
      • Thompson J.P.
      • Smadbeck J.
      • Kieslich C.A.
      • Floudas C.A.
      Forcefield_PTM: Ab initio charge and AMBER forcefield parameters for frequently occurring post-translational modifications.
      ) are developed for 32 common post-translation modifications. Finally, although these tools are limited on predictions of the effect of interface located modifications, our approach is applicable independent of PTM site's position. In conclusion, the approach we show here is easily applicable to both known and PTMs that are yet to be discovered, independent of their position within the complex.
      The functional roles of the post-translational modifications in the examined complexes remain to be clarified in the cellular conditions. For instance, if the combination of 18 interface PTMs actually is present in a single RNA polymerase II molecule in the cell, the destabilization of subunits' binding might serve to prime this complex for a specific function it has to perform at a certain moment. Finally, it would be very interesting to get a dynamic insight into the PTMs effect on proteome level, especially if it would be accompanied by the knowledge of PTMs regulation. With the pipeline that we developed in this work, more analyses can be done on large complexes in the future to shed more light on the role of post-translational modifications.

      DATA AVAILABILITY

      The MS proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository (
      • Vizcaíno J.A.
      • Côté R.G.
      • Csordas A.
      • Dianes J.A.
      • Fabregat A.
      • Foster J.M.
      • Griss J.
      • Alpi E.
      • Birim M.
      • Contell J.
      • O'Kelly G.
      • Schoenegger A.
      • Ovelleiro D.
      • Pérez-Riverol Y.
      • Reisinger F.
      • Ríos D.
      • Wang R.
      • Hermjakob H.
      The Proteomics Identifications (PRIDE) database and associated tools: status in 2013.
      ) with the data set identifier PXD008324.

      Acknowledgments

      We would like to thank the anonymous reviewers for useful comments to improve the manuscript.

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