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• W2023641155 abstract "Virtually all cellular processes are carried out by dynamic molecular assemblies or multiprotein complexes, the compositions of which are largely undefined. They cannot be predicted solely from bioinformatics analyses nor are there well defined techniques currently available to unequivocally identify protein complexes (PCs). To address this issue, we attempted to directly determine the identity of PCs from native microbial biomass using Pyrococcus furiosus, a hyperthermophilic archaeon that grows optimally at 100 °C, as the model organism. Novel PCs were identified by large scale fractionation of the native proteome using non-denaturing, sequential column chromatography under anaerobic, reducing conditions. A total of 967 distinct P. furiosus proteins were identified by mass spectrometry (nano LC-ESI-MS/MS), representing ∼80% of the cytoplasmic proteins. Based on the co-fractionation of proteins that are encoded by adjacent genes on the chromosome, 106 potential heteromeric PCs containing 243 proteins were identified, only 20 of which were known or expected. In addition to those of unknown function, novel and uncharacterized PCs were identified that are proposed to be involved in the metabolism of amino acids (10Berggard T. Linse S. James P. Methods for the detection and analysis of protein-protein interactions.Proteomics. 2007; 7: 2833-2842Crossref PubMed Scopus (336) Google Scholar), carbohydrates (four), lipids (two), vitamins and metals (three), and DNA and RNA (nine). A further 30 potential PCs were classified as tentative, and the remaining potential PCs (13Fields S. Song O. A novel genetic system to detect protein-protein interactions.Nature. 1989; 340: 245-246Crossref PubMed Google Scholar) were classified as weakly interacting. Some major advantages of native biomass fractionation for PC identification are that it provides a road map for the (partial) purification of native forms of novel and uncharacterized PCs, and the results can be utilized for the recombinant production of low abundance PCs to provide enough material for detailed structural and biochemical analyses. Virtually all cellular processes are carried out by dynamic molecular assemblies or multiprotein complexes, the compositions of which are largely undefined. They cannot be predicted solely from bioinformatics analyses nor are there well defined techniques currently available to unequivocally identify protein complexes (PCs). To address this issue, we attempted to directly determine the identity of PCs from native microbial biomass using Pyrococcus furiosus, a hyperthermophilic archaeon that grows optimally at 100 °C, as the model organism. Novel PCs were identified by large scale fractionation of the native proteome using non-denaturing, sequential column chromatography under anaerobic, reducing conditions. A total of 967 distinct P. furiosus proteins were identified by mass spectrometry (nano LC-ESI-MS/MS), representing ∼80% of the cytoplasmic proteins. Based on the co-fractionation of proteins that are encoded by adjacent genes on the chromosome, 106 potential heteromeric PCs containing 243 proteins were identified, only 20 of which were known or expected. In addition to those of unknown function, novel and uncharacterized PCs were identified that are proposed to be involved in the metabolism of amino acids (10Berggard T. Linse S. James P. Methods for the detection and analysis of protein-protein interactions.Proteomics. 2007; 7: 2833-2842Crossref PubMed Scopus (336) Google Scholar), carbohydrates (four), lipids (two), vitamins and metals (three), and DNA and RNA (nine). A further 30 potential PCs were classified as tentative, and the remaining potential PCs (13Fields S. Song O. A novel genetic system to detect protein-protein interactions.Nature. 1989; 340: 245-246Crossref PubMed Google Scholar) were classified as weakly interacting. Some major advantages of native biomass fractionation for PC identification are that it provides a road map for the (partial) purification of native forms of novel and uncharacterized PCs, and the results can be utilized for the recombinant production of low abundance PCs to provide enough material for detailed structural and biochemical analyses. The repository of sequenced genomes is now above 850 (Genomes Online Database). Consequently there is a tremendous need to determine the function of gene products and identify groups of proteins that work together as complexes in distinct cellular processes. Genome-wide functional analyses suggest that there are 200–300 core biological functions that are essential to life (1Martin A.C. Drubin D.G. Impact of genome-wide functional analyses on cell biology research.Curr. Opin. Cell Biol. 2003; 15: 6-13Crossref PubMed Scopus (0) Google Scholar). More often than not the functional units are assemblies composed of multiple proteins (2Alberts B. The cell as a collection of protein machines: preparing the next generation of molecular biologists.Cell. 1998; 92: 291-294Abstract Full Text Full Text PDF PubMed Scopus (906) Google Scholar). Many biological processes involve multiprotein complexes that function as large and efficient machines, such as ribosomes (3Bamford D.H. Gilbert R.J. Grimes J.M. Stuart D.I. Macromolecular assemblies: greater than their parts.Curr. Opin. Struct. Biol. 2001; 11: 107-113Crossref PubMed Scopus (0) Google Scholar, 4Wilson D.N. Nierhaus K.H. Ribosomal proteins in the spotlight.Crit. Rev. Biochem. Mol. Biol. 2005; 40: 243-267Crossref PubMed Scopus (125) Google Scholar), flagella (5Chiu W. Baker M.L. Almo S.C. Structural biology of cellular machines.Trends Cell Biol. 2006; 16: 144-150Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar), and cellulosomes (6Gilbert H.J. Cellulosomes: microbial nanomachines that display plasticity in quaternary structure.Mol. Microbiol. 2007; 63: 1568-1576Crossref PubMed Scopus (100) Google Scholar). In addition, the supramolecular organization of enzymes of partial or entire metabolic pathways as “metabolons” appears to provide certain advantages, such as substrate channeling (7Srere P.A. Complexes of sequential metabolic enzymes.Annu. Rev. Biochem. 1987; 56: 89-124Crossref PubMed Google Scholar, 8Ovadi J. Srere P.A. Macromolecular compartmentation and channeling.Int. Rev. Cytol. 2000; 192: 255-280Crossref PubMed Google Scholar, 9Ovadi J. Saks V. On the origin of intracellular compartmentation and organized metabolic systems.Mol. Cell. Biochem. 2004; 256–257: 5-12Crossref PubMed Google Scholar). The identification of protein-protein interactions and functional, stable associations is extremely important in understanding the biology of a cell. However, predicting the nature of such complexes within a single genome, let alone for hundreds of genomes, remains a major challenge. Although there are currently several methods available to study protein-protein interactions on a genome-wide scale (10Berggard T. Linse S. James P. Methods for the detection and analysis of protein-protein interactions.Proteomics. 2007; 7: 2833-2842Crossref PubMed Scopus (336) Google Scholar, 11Piehler J. New methodologies for measuring protein interactions in vivo and in vitro.Curr. Opin. Struct. Biol. 2005; 15: 4-14Crossref PubMed Scopus (155) Google Scholar, 12Azarkan M. Huet J. Baeyens-Volant D. Looze Y. Vandenbussche G. Affinity chromatography: a useful tool in proteomics studies.J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2007; 849: 81-90Crossref PubMed Scopus (57) Google Scholar), each has severe limitations. The in vivo two-hybrid system (13Fields S. Song O. A novel genetic system to detect protein-protein interactions.Nature. 1989; 340: 245-246Crossref PubMed Google Scholar, 14Uetz P. Hughes R.E. Systematic and large-scale two-hybrid screens.Curr. Opin. Microbiol. 2000; 3: 303-308Crossref PubMed Scopus (99) Google Scholar, 15Koegl M. Uetz P. Improving yeast two-hybrid screening systems.Brief. Funct. Genomics Proteomics. 2007; 6: 302-312Crossref PubMed Scopus (0) Google Scholar) requires tagged proteins, is limited to binary interactions, and is thought to generate a large percentage of false positives (16Deane C.M. Salwinski L. Xenarios I. Eisenberg D. Protein interactions: two methods for assessment of the reliability of high throughput observations.Mol. Cell. Proteomics. 2002; 1: 349-356Abstract Full Text Full Text PDF PubMed Scopus (522) Google Scholar). The epitope tag affinity purification and tandem affinity purification methods have also been used extensively (17Bauer A. Kuster B. Affinity purification-mass spectrometry. Powerful tools for the characterization of protein complexes.Eur. J. Biochem. 2003; 270: 570-578Crossref PubMed Scopus (167) Google Scholar, 18Ho Y. Gruhler A. Heilbut A. Bader G.D. Moore L. Adams S.L. Millar A. Taylor P. Bennett K. Boutilier K. Yang L. Wolting C. Donaldson I. Schandorff S. Shewnarane J. Vo M. Taggart J. Goudreault M. Muskat B. Alfarano C. Dewar D. Lin Z. Michalickova K. Willems A.R. Sassi H. Nielsen P.A. Rasmussen K.J. Andersen J.R. Johansen L.E. Hansen L.H. Jespersen H. Podtelejnikov A. Nielsen E. Crawford J. Poulsen V. Sorensen B.D. Matthiesen J. Hendrickson R.C. Gleeson F. Pawson T. Moran M.F. Durocher D. Mann M. Hogue C.W. Figeys D. Tyers M. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.Nature. 2002; 415: 180-183Crossref PubMed Scopus (2863) Google Scholar, 19Rigaut G. Shevchenko A. Rutz B. Wilm M. Mann M. Seraphin B. A generic protein purification method for protein complex characterization and proteome exploration.Nat. Biotechnol. 1999; 17: 1030-1032Crossref PubMed Scopus (2202) Google Scholar, 20Puig O. Caspary F. Rigaut G. Rutz B. Bouveret E. Bragado-Nilsson E. Wilm M. Seraphin B. The tandem affinity purification (TAP) method: a general procedure of protein complex purification.Methods. 2001; 24: 218-229Crossref PubMed Scopus (1355) Google Scholar, 21Gavin A.C. Bosche M. Krause R. Grandi P. Marzioch M. Bauer A. Schultz J. Rick J.M. Michon A.M. Cruciat C.M. Remor M. Hofert C. Schelder M. Brajenovic M. Ruffner H. Merino A. Klein K. Hudak M. Dickson D. Rudi T. Gnau V. Bauch A. Bastuck S. Huhse B. Leutwein C. Heurtier M.A. Copley R.R. Edelmann A. Querfurth E. Rybin V. Drewes G. Raida M. Bouwmeester T. Bork P. Seraphin B. Kuster B. Neubauer G. Superti-Furga G. Functional organization of the yeast proteome by systematic analysis of protein complexes.Nature. 2002; 415: 141-147Crossref PubMed Scopus (3729) Google Scholar), but the tags can disrupt native protein-protein interactions, and the methods tend to be biased toward proteins that interact with high affinity and/or proteins of high abundance (10Berggard T. Linse S. James P. Methods for the detection and analysis of protein-protein interactions.Proteomics. 2007; 7: 2833-2842Crossref PubMed Scopus (336) Google Scholar). The major limitation with all of these approaches is that they require genetic manipulation of the target organism, an ability limited to only a few well studied systems. Non-genetic techniques to identify protein-protein interactions include co-immunoaffinity precipitation to capture endogenous protein complexes, but this is not a genome-wide approach as it requires highly specific antibodies made against purified proteins (22Masters S.C. Co-immunoprecipitation from transfected cells.Methods Mol. Biol. 2004; 261: 337-350PubMed Google Scholar). Two-dimensional blue native/SDS-PAGE and clear native-PAGE are also two widely used techniques that do not require genetic manipulation and allow for the analysis of protein complexes on a proteome-wide scale in a single experiment (23Farhoud M.H. Wessels H.J. Steenbakkers P.J. Mattijssen S. Wevers R.A. van Engelen B.G. Jetten M.S. Smeitink J.A. van den Heuvel L.P. Keltjens J.T. Protein complexes in the archaeon Methanothermobacter thermautotrophicus analyzed by blue native/SDS-PAGE and mass spectrometry.Mol. Cell. Proteomics. 2005; 4: 1653-1663Abstract Full Text Full Text PDF PubMed Scopus (66) Google Scholar, 24Krause F. Detection and analysis of protein-protein interactions in organellar and prokaryotic proteomes by native gel electrophoresis: (membrane) protein complexes and supercomplexes.Electrophoresis. 2006; 27: 2759-2781Crossref PubMed Scopus (0) Google Scholar, 25Lasserre J.P. Beyne E. Pyndiah S. Lapaillerie D. Claverol S. Bonneu M. A complexomic study of Escherichia coli using two-dimensional blue native/SDS polyacrylamide gel electrophoresis.Electrophoresis. 2006; 27: 3306-3321Crossref PubMed Scopus (84) Google Scholar, 26Pyndiah S. Lasserre J.P. Menard A. Claverol S. Prouzet-Mauleon V. Megraud F. Zerbib F. Bonneu M. Two-dimensional blue native/SDS gel electrophoresis of multiprotein complexes from Helicobacter pylori.Mol. Cell. Proteomics. 2007; 6: 193-206Abstract Full Text Full Text PDF PubMed Scopus (40) Google Scholar). However, they are limited in their dynamic range and typically identify only high abundance proteins (27Gygi S.P. Corthals G.L. Zhang Y. Rochon Y. Aebersold R. Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology.Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 9390-9395Crossref PubMed Google Scholar). The goal of this research is to develop a global method to identify novel protein complexes (PCs) 1The abbreviations used are: PC, protein complex; ACS, acyl-CoA synthetase; CV, conventional; Fd, ferredoxin; FNOR, ferredoxin-NADPH oxidoreductase; HT, high throughput; POR, pyruvate ferredoxin oxidoreductase; SH1, soluble hydrogenase 1; VOR, 2-ketoisovalerate ferredoxin oxidoreductase; KOR, 2-keto acid oxidoreductase; CRISPR, clustered regularly interspaced short palindromic repeats; Cas, CRISPR-associated; LTQ, linear trap quadropole. independent of a genetic system and applicable to any organism with available native biomass. The approach involves multistep, non-denaturing column chromatography where the co-fractionation of proteins is used to identify potential complexes. As a model system we use the hyperthermophilic archaeon Pyrococcus furiosus, an anaerobe that grows optimally at 100 °C (28Fiala G. Stetter K.O. Pyrococcus furiosus sp. nov., represents a novel genus of marine heterotrophic Archaebacteria growing optimally at 100°C.Arch. Microbiol. 1986; 145: 56-61Crossref Google Scholar). Its genome sequence (29Robb F.T. Maeder D.L. Brown J.R. DiRuggiero J. Stump M.D. Yeh R.K. Weiss R.B. Dunn D.M. Genomic sequence of hyperthermophile, Pyrococcus furiosus: implications for physiology and enzymology.Methods Enzymol. 2001; 330: 134-157Crossref PubMed Scopus (185) Google Scholar, 30Poole II, F.L. Gerwe B.A. Hopkins R.C. Schut G.J. Weinberg M.V. Jenney Jr., F.E. Adams M.W. Defining genes in the genome of the hyperthermophilic archaeon Pyrococcus furiosus: implications for all microbial genomes.J. Bacteriol. 2005; 187: 7325-7332Crossref PubMed Scopus (28) Google Scholar) contains 2125 ORFs. A universal feature of prokaryotic genomes is the organization of genes into operons, which form basic transcriptional units (31Bergman N.H. Passalacqua K.D. Hanna P.C. Qin Z.S. Operon prediction for sequenced bacterial genomes without experimental information.Appl. Environ. Microbiol. 2007; 73: 846-854Crossref PubMed Scopus (25) Google Scholar) and are important in functional genomics. Using a neural network, we predicted that 1460 ORFs in the P. furiosus genome are contained within 470 operons (32Tran T.T. Dam P. Su Z.C. Poole F.L. Adams M.W.W. Zhou G.T. Xu Y. Operon prediction in Pyrococcus furiosus.Nucleic Acids Res. 2007; 35: 11-20Crossref PubMed Scopus (26) Google Scholar), 349 of which were validated using DNA microarray data (33Schut G.J. Brehm S.D. Datta S. Adams M.W. Whole-genome DNA microarray analysis of a hyperthermophile and an archaeon: Pyrococcus furiosus grown on carbohydrates or peptides.J. Bacteriol. 2003; 185: 3935-3947Crossref PubMed Scopus (109) Google Scholar, 34Weinberg M.V. Schut G.J. Brehm S. Datta S. Adams M.W. Cold shock of a hyperthermophilic archaeon: Pyrococcus furiosus exhibits multiple responses to a suboptimal growth temperature with a key role for membrane-bound glycoproteins.J. Bacteriol. 2005; 187: 336-348Crossref PubMed Scopus (61) Google Scholar). Operons typically encode functionally related proteins, which can include enzymes of the same pathway as well as heteromeric PCs. Herein heteromeric PCs encoded by two or more adjacent genes are referred to as Type 1 PCs, whereas heteromeric PCs encoded by two or more unlinked genes are referred to as Type 2 PCs. This pilot study focused on identifying stable, Type 1 heteromeric PCs in P. furiosus based on the co-fractionation of proteins during sequential column chromatography steps. In addition, a high throughput (HT) system was devised to allow protein identification by nano-LC-ESI-MS/MS. Our long term objective is to develop HT protocols for novel PC identification on a genome-wide basis using limited amounts of biomass. P. furiosus (DSM 3638) was grown under anaerobic, reducing conditions at 90 °C in a 600-liter fermenter on maltose and peptides and harvested in the late log phase (35Verhagen M.F. Menon A.L. Schut G.J. Adams M.W. Pyrococcus furiosus: large-scale cultivation and enzyme purification.Methods Enzymol. 2001; 330: 25-30Crossref PubMed Scopus (23) Google Scholar). The procedures for preparing anaerobic cell-free extract and for running the first chromatography step have been described previously (35Verhagen M.F. Menon A.L. Schut G.J. Adams M.W. Pyrococcus furiosus: large-scale cultivation and enzyme purification.Methods Enzymol. 2001; 330: 25-30Crossref PubMed Scopus (23) Google Scholar). Briefly 300 g of frozen cells were gently lysed by osmotic shock anaerobically under a continuous flow of argon in 900 ml of 50 mm Tris-HCl (pH 8.0) containing 2 mm sodium dithionite as a reductant (Buffer A) and 0.5 μg/ml DNase I to reduce viscosity. The cell lysate was centrifuged at 100,000 × g for 2 h at 18 °C, and the clear supernatant, representing the cytoplasmic fraction, was immediately loaded onto the first column. This and all subsequent columns were run under anaerobic, reducing conditions where all buffers were degassed and maintained under a positive pressure of argon, and all liquid transfers were made using needles and syringes. A column tree representing the complete fractionation procedure is shown in Fig. 1. All columns were run using an ÄKTA™ basic automated LC system (GE Healthcare). The P. furiosus cytoplasmic fraction was diluted 4-fold in Buffer A to reduce the ionic strength and was loaded onto a 10 × 20-cm (1.5-liter) column of DEAE-Sepharose Fast Flow (GE Healthcare) that had been equilibrated previously with the same buffer until the effluent was anaerobic and reducing. Unbound protein was washed off the column with Buffer A. Bound proteins were eluted using a linear gradient (15 liters) of 0–500 mm NaCl in Buffer A, and 125-ml anaerobic fractions were collected (120 fractions). Any remaining tightly bound proteins were eluted from the column using 1-liter step gradients of 1, 1.5, and 2 m NaCl, respectively, in Buffer A (high salt washes). A subset of 37 gradient fractions from the first column separation were selected using a combination of protein profiles and enzyme activities to create six fraction pools, c1-1 through c1-6. The control proteins and enzyme activities were as follows: c1-1, ferredoxin-NADPH oxidoreductase (FNOR) (36Ma K. Adams M.W.W. Sulfide dehydrogenase from the hyperthermophilic archaeon Pyrococcus furiosus—a new multifunctional enzyme involved in the reduction of elemental sulfur.J. Bacteriol. 1994; 176: 6509-6517Crossref PubMed Google Scholar) and rubrerythrin (37Weinberg M.V. Jenney F.E. Cui X.Y. Adams M.W.W. Rubrerythrin from the hyperthermophilic archaeon Pyrococcus furiosus is a rubredoxin-dependent, iron-containing peroxidase.J. Bacteriol. 2004; 186: 7888-7895Crossref PubMed Scopus (0) Google Scholar); c1-2, 2-ketoisovalerate ferredoxin oxidoreductase (VOR) and pyruvate ferredoxin oxidoreductase (POR) (38Schut G.J. Menon A.L. Adams M.W.W. 2-Ketoacid oxidoreductases from Pyrococcus furiosusThermococcus litoralis.Methods Enzymol. 2001; 331: 144-158Crossref PubMed Scopus (0) Google Scholar); c1-3, aldehyde:ferredoxin oxidoreductase (39Mukund S. Adams M.W.W. The novel tungsten-iron-sulfur protein of the hyperthermophilic archaebacterium, Pyrococcus furiosus, is an aldehyde ferredoxin oxidoreductase. Evidence for its participation in a unique glycolytic pathway.J. Biol. Chem. 1991; 266: 14208-14216Abstract Full Text PDF PubMed Google Scholar) and NADPH-rubredoxin oxidoreductase (40Ma K. Adams M.W.W. A hyperactive NAD(P)H:rubredoxin oxidoreductase from the hyperthermophilic archaeon Pyrococcus furiosus.J. Bacteriol. 1999; 181: 5530-5533Crossref PubMed Google Scholar); c1-4, soluble hydrogenase 1 (SH1) (41Bryant F.O. Adams M.W. Characterization of hydrogenase from the hyperthermophilic archaebacterium, Pyrococcus furiosus.J. Biol. Chem. 1989; 264: 5070-5079Abstract Full Text PDF PubMed Google Scholar); c1-5, rubredoxin (42Blake P.R. Park J.B. Bryant F.O. Aono S. Magnuson J.K. Eccleston E. Howard J.B. Summers M.F. Adams M.W.W. Determinants of protein hyperthermostability—purification and amino-acid-sequence of rubredoxin from the hyperthermophilic archaebacterium Pyrococcus furiosus and secondary structure of the zinc adduct by NMR.Biochemistry. 1991; 30: 10885-10895Crossref PubMed Google Scholar); and c1-6, ferredoxin (Fd) (43Aono S. Bryant F.O. Adams M.W. A novel and remarkably thermostable ferredoxin from the hyperthermophilic archaebacterium Pyrococcus furiosus.J. Bacteriol. 1989; 171: 3433-3439Crossref PubMed Google Scholar). These pools, c1-1 through c1-6, were fractionated through six subsequent c2 columns. The remaining 83 gradient fractions were combined to generate nine additional pools, in this case based solely on SDS-PAGE and protein profiles. These pools, c1-7 through c1-15, were fractionated through nine subsequent c2 columns to give a total of 15 columns at the c2 level (Fig. 1). SDS-PAGE and native PAGE were carried out using precast 4–20% gradient gels (Criterion gel system, Bio-Rad). Protein concentrations were estimated by the method of Bradford (44Bradford M.M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding.Anal. Biochem. 1976; 72: 248-254Crossref PubMed Scopus (201184) Google Scholar) using bovine serum albumin as the standard. Six c3 and four c4 level columns were run using fraction pools that were created for the further purification of the control proteins by the published procedures (Fig. 1). Detailed information about the individual c2, c3, and c4 column chromatography steps is summarized in supplemental Table 1. The steps for denaturation, reduction, alkylation, and digestion were automated for most samples using 96-well plates and a MassPrep robotic liquid handler (Waters, Milford, MA). To each well containing 50 μl of a fraction sample was added 50 μl of pure trifluoroethanol. The plate was heated to 60 °C for 30 min to complete the denaturation process. Disulfide bonds were reduced by adding 10 μl of dithiothreitol (200 mm in 50 mm ammonium bicarbonate) and alkylated by adding 14 μl of iodoacetamide (200 mm). After incubation for 30 min in the dark at 25 °C, the protein was digested for 18 h at 40 °C using 1.5 μg of trypsin (Trypsin Gold mass spectrometry, Promega, Madison, WI). Digestion was halted by lowering the pH to 4 by the addition of 5% formic acid. Two methods were used for separation. A longer gradient was used for more complex samples, whereas a shorter gradient was used in a rapid HT LC-MS/MS analysis of less complex samples. A 96-well plate-based HT method was devised to rapidly separate peptides by reverse phase LC or MS/MS analysis using a two-pump setup. Using an Agilent 1100 microwell autosampler, 8 μl of the digested sample was injected and transported at 50 μl/min (using a quaternary pump) through the dead volume of a microwell autosampler to a C18 trap cartridge on a six-port switching valve. After 3 min, the flow was switched to the nanocapillary pump at a flow rate of 500 nl/min. An analytical column/nanoelectrospray tip (75-μm inner diameter, 6 cm in length) fabricated with a P-2000 (Sutter Instruments) laser puller and packed with C18 resin (5 μm; Agilent Zorbax SB) under high pressure was used to separate the peptides prior to their analysis with an LTQ mass spectrometer in data-dependent MS/MS mode. The reverse phase gradient separation was performed using water and acetonitrile (0.1% formic acid) as the mobile phases. For the HT LC-MS analysis, the gradient consisted of 20–35% acetonitrile over 20 min to 90% over 8 min prior to equilibration in 5% acetonitrile (0.1% formic acid) for 15 min before reinjection. For more complex c1 protein samples, a longer, conventional (CV) gradient elution was used consisting of 5–8% acetonitrile over 5 min to 35% over 113 min, to 55% over 12 min, and then to 98% maintained for 15 min (wash) before re-equilibration at 5% for 15 min. MS/MS analysis was performed on the LTQ linear ion trap mass spectrometer (Thermo Fisher Scientific Inc.) using 2 kV at the tip. One MS spectrum was followed by four MS/MS scans on the most abundant ions after the application of the dynamic exclusion list. The LTQ software utility extractmsn.exe (Thermo Fisher Scientific Inc., version 3, September 27, ©1997–2007) was used to peak pick and extract peak lists from the tandem mass spectra into peak list (.dta) files. Mascot Daemon was used to run this program externally and to submit the searches to Mascot (Matrix Science Ltd.) for protein identification. The Mascot searches were conducted at a 95% confidence level using tryptic peptides derived from the public National Center for Biotechnology Information (NCBI) annotation of the P. furiosus genome (NC_003413, December 2, 2007) (29Robb F.T. Maeder D.L. Brown J.R. DiRuggiero J. Stump M.D. Yeh R.K. Weiss R.B. Dunn D.M. Genomic sequence of hyperthermophile, Pyrococcus furiosus: implications for physiology and enzymology.Methods Enzymol. 2001; 330: 134-157Crossref PubMed Scopus (185) Google Scholar, 30Poole II, F.L. Gerwe B.A. Hopkins R.C. Schut G.J. Weinberg M.V. Jenney Jr., F.E. Adams M.W. Defining genes in the genome of the hyperthermophilic archaeon Pyrococcus furiosus: implications for all microbial genomes.J. Bacteriol. 2005; 187: 7325-7332Crossref PubMed Scopus (28) Google Scholar) containing a total of 2125 protein sequences. The false positive rate was estimated by searching 20 data sets of P. furiosus LC-MS/MS data against a decoy database containing reverse sequences from the P. furiosus genome. A Mascot ion score threshold of 35 for the 20 searches yielded an acceptable and low false positive rate of 0.398 ± 0.57%. Thus, the Mascot ion score threshold was set to 35 for all searches, including single peptide assignments, so that the false positive rate was consistently less than 1%. Furthermore there was little issue with the peptides matching multiple members of a protein family because of the use of a single small proteome. Mascot was searched with a fragment ion mass tolerance of 0.80 Da and a parent ion tolerance of 2.0 Da. Iodoacetamide derivative of cysteine and oxidation of methionine were specified as fixed and variable modifications, respectively. The enzyme was selected as trypsin with a maximum of one missed cleavage allowed for the search. The fragmentation pattern was optimized for the instrument used by selecting the Mascot search parameter as “ESI-trap.” The MS/MS data for all unique peptides identified for each protein are presented in supplemental Table 2. All MS/MS data were stored in a custom Structural Query Language (SQL) Server 2005 database that preserves the hierarchical (parent-child) nature of the multilevel fractionation process. All samples analyzed were assigned two-dimensional barcodes. A user interface developed in Microsoft Access 2003 shows the hierarchical fractionation steps and automatically generates a Microsoft Excel PivotTable for each column that displays the proteins identified by MS/MS along the rows, chromatography fractions along the columns, and the number of peptides detected where they intersect. Identification of potential Type 1 PCs was accomplished by generating text-based PivotTables (described above) for each of the 26 column chromatography steps. The text files were processed by a custom script written in the R statistical language. For each column, this script created a number of tentative PCs consisting of proteins that (i) co-eluted in one or more column fractions and (ii) were encoded by adjacent genes on the same DNA strand (Type 1 PCs). These tentative PCs from each column that contained any proteins in common were merged to generate a final list of potential PCs and assigned unique PC identification numbers. The quality of the predicted PCs (i.e. the association value) was evaluated using a custom algorithm applied to the column data contained in the PivotTable described above (restricted to the potential subunits identified in the prediction step). This algorithm may be used for evaluating both Type 1 and Type 2 PCs. For each predicted PC, the algorithm computes a per fraction subscore based on the proportion of potential subunits within the predicted PC that appear in that fraction and sums these subscores to produce an overall raw score. The assigned subscores range from −0.1 (when half of the potential subunits are observed in a fraction) to 1.0 (when all of the potential subunits are observed in a fraction) with a zero score assigned when none of the subunits are observed in a given fraction. Specifically the function used to assign the subscores is the piecewise linear function with one linear piece between the points (0, 0) and (0.5, −0.1) and another linear piece betwe" @default.
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• W2023641155 title "Novel Multiprotein Complexes Identified in the Hyperthermophilic Archaeon Pyrococcus furiosus by Non-denaturing Fractionation of the Native Proteome" @default.
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