Báo cáo khoa học: Computer-assisted mass spectrometric analysis of naturally occurring and artificially introduced cross-links in proteins and protein complexes potx

Besides matching the in silico pre-dicted reaction products with the corresponding mass spectrometric data using mass band filtering, a most important, unique feature of virtualmslab is i

naturally occurring and artificially introduced cross-links

in proteins and protein complexes

Leo J de Koning1, Piotr T Kasper1, Jaap Willem Back1, Merel A Nessen1, Frank Vanrobaeys2, Jozef Van Beeumen2, Ermanno Gherardi3, Chris G de Koster1and Luitzen de Jong1

1 Biomolecular Mass Spectrometry group, Swammerdam Institute for Life Sciences, University of Amsterdam, the Netherlands

2 Laboratory of Protein Biochemistry and Protein Engineering, University of Gent, Belgium

3 MRC Centre, Cambridge, UK

Mass spectrometry has become a major tool in the

structural analysis of proteins and protein complexes

and in large scale analysis of the function of genes

(proteomics) [1]

For mass spectrometric analysis, the proteins and

proteomes under study are usually first subjected to

proteolytic digestion or chemical cleavage A large

number of informatics tools has been developed that

helps in extracting relevant information from the com-plex mass spectrometric data [2] Most of these pro-grams match the in silico predicted digest with the corresponding mass spectrometric data for protein identification and for mapping protein modifications Novel strategies and methodologies in proteomics urge for dedicated programs that further integrate mass spectrometric analyses with biochemical experiments

Keywords

cross-linking; data analysis; protein

structure; mass spectrometry; NK1

Correspondence

L de Jong, Swammerdam Institute for Life

Sciences, Mass spectometry group,

University of Amsterdam, Nieuwe

Achtergracht 166, Amsterdam, 1018 WV,

the Netherlands

Fax: +31 20 525 6971

Tel: +31 20 525 5691

E-mail: l.dejong@science.uva.nl

(Received 23 September 2005, revised 28

October 2005, accepted 7 November 2005)

doi:10.1111/j.1742-4658.2005.05053.x

A versatile software tool, virtualmslab, is presented that can perform advanced complex virtual proteomic experiments with mass spectrometric analyses to assist in the characterization of proteins The virtual experimen-tal results allow rapid, flexible and convenient exploration of sample prepar-ation strategies and are used to generate MS reference databases that can be matched with the real MS data obtained from the equivalent real experi-ments Matches between virtual and acquired data reveal the identity and nature of reaction products that may lead to characterization of post-trans-lational modification patterns, disulfide bond structures, and cross-linking

in proteins or protein complexes The most important unique feature of this program is the ability to perform multistage experiments in any user-defined order, thus allowing the researcher to vary experimental approaches that can be conducted in the laboratory Several features of virtualmslab are demonstrated by mapping both disulfide bonds and artificially introduced protein cross-links It is shown that chemical cleavage at aspartate residues

in the protease resistant RNase A, followed by tryptic digestion can be opti-mized so that the rigid protein breaks up into MALDI-MS detectable frag-ments, leaving the disulfide bonds intact We also show the mapping of a number of chemically introduced cross-links in the NK1 domain of hepato-cyte growth factor⁄ scatter factor The virtualmslab program was used to explore the limitation and potential of mass spectrometry for cross-link studies of more complex biological assemblies, showing the value of high performance instruments such as a Fourier transform mass spectrometer The program is freely available upon request

Abbreviations

BS 3 , bis(sulfosuccinimidyl)suberate; HGF/SF, hepatocyte growth factor ⁄ scatter factor; NEM, N-ethylmaleimide; RNase A, ribonuclease A; SAXS, small angle X-ray scattering; TFA, trifluoroacetic acid.

To support our protein studies we have developed a

tool, virtualmslab, which allows us to perform a

variety of advanced virtual proteomics experiments

with MS analyses Besides matching the in silico

pre-dicted reaction products with the corresponding mass

spectrometric data using mass band filtering, a most

important, unique feature of virtualmslab is its

experiment editor, allowing to calculate the results of:

(a) any virtual, complete or partial, protein

modifica-tion reacmodifica-tion including cleavages, either simultaneously

or in any desired order; and (b) in and out filtering of

defined reaction products

Our aim is to establish general, rapid and relatively

simple procedures for the analysis of naturally

occur-ring cross-links, e.g disulfide bonds, and artificially

introduced cross-links in proteins Cross-links impose

distance constraints on amino acid residues that can be

used to model the 3-D structure of proteins and

pro-tein complexes [3–7] Mass spectrometric analysis of

peptides derived from digested cross-linked proteins is

exceptionally suited for the rapid, sensitive and precise

mapping of the cross-links [3,6,8] To support mass

spectrometric analysis of cross-links in proteins,

soft-ware tools have been developed [9–12] for the

identifi-cation of digest fragments where peptides are linked

together However, available software suffers from

lim-itations, often preventing general application in

cross-link analysis [6]

Here we demonstrate several features of the

virtual-mslabprogram for protein cross-link analysis

As a model system for the analysis of the disulfide

bond structure of a protein we used ribonuclease A

(RNase A), for which the 3-D structure is known in

detail [13], and the disulfide bond structure has been

established already in 1960 [14] We use the

virtual-mslab program to explore a successful experimental

strategy to assess the disulfide bond structure from a

single MALDI-TOF mass spectrum

In a separate study the Met receptor tyrosine

kin-ase and its ligand, hepatocyte growth factor⁄ scatter

factor (HGF⁄ SF) were used to explore and test a

cross-linking strategy with virtualmslab Signal

transduction via the Met receptor is involved in cell

growth and migration during embryogenesis as well

as in cancer [15] but both the assembly of the

HGF⁄ SF complex and the basis for receptor

activa-tion remain poorly understood Insight into the

spa-tial arrangement of the HGF⁄ SF–Met complex can

be obtained by chemical cross-linking To examine

the viability of a mass spectrometric analysis of

chemically induced cross-links for this complex, the

experimental strategy has been tested by carrying out

virtual cross-linking with successive MS analysis

Following the virtual experiments, mapping of cross-links in the NK1 domain of HGF⁄ SF treated with

an amine specific cross-linking agent has been accomplished, based on the virtualmslab assisted analysis of the MS data from the corresponding un-fractionated tryptic digest

Results and discussion

General setup ofVIRTUALMSLAB virtualmslab is used to perform complex virtual proteomic experiments and integrate these with mass spectrometric analyses The virtual experimental results allow rapid and convenient exploration of proteomics strategies and are used to generate MS reference data-bases that can be matched with the real MS data obtained from the equivalent real experiments Mat-ches between virtual and real data reveal the identity and nature of reaction products that may lead to the characterization of post-translational modification pat-terns, disulfide bond structures, chemical modifications and cross-linking in protein mixtures, complexes, and assemblies

Proteins

A single protein, a list of proteins from a mixture or from a protein complex or a complete proteome under study, can be entered or imported (in fasta format) into the program as amino acid sequences Amino acid residues, N- and C-terminal end groups, and modifica-tions can be custom defined, including specific isotopic and⁄ or virtual labelling to keep track of specific amino acid residues in the analyses The entered proteins or a custom selection can be concurrently added to the experiments

The virtual experiment The protein or protein mixture can be subjected to an experiment including several subsequent and⁄ or paral-lel steps The individual steps include:

l any customizable chemical and proteolytic cleavage, with optional specific isotope or virtual element substi-tution;

l modifications of amino acid residues, partial sequences and⁄ or end groups, with optional specific isotope or virtual element substitution;

l in- and⁄ or out-filtering of reaction products contain-ing any combination of specific amino acid residues, partial sequences and end groups;

l mass band-filtering;

l partial unintentional modifications (such as

oxida-tion, deamidaoxida-tion, etc.) of specific amino acid residues,

partial sequences and end groups

Multipass experiment editor

A unique feature of virtualmslab is the ability to

perform the above listed calculations in succession and

in any desired order Cleavage, modification and

filter-ing may be carried out in different steps, and the

resulting virtual experimental peptide mixture may

sug-gest alternatives for performing the real experiment in

a certain sequence in the laboratory

For instance, if amines are modified prior to

enzy-matic cleavage, the result is different from a

modifi-cation to amines that has been introduced after

proteolysis; in the first instance cleaved peptides carry

free amino termini, in the second instance these amines

are considered to be modified Upon running the

pro-grammed experiment, the resulting peptide mixture

database is displayed with various sorting criteria for

inspection

Mass spectrometric data

To match virtual with real data, mass spectra are

imported into the program as monoisotopic

mass⁄ intensity lists in ascii format virtualmslab is

capable of providing reference lists for use in internal

calibrations Masses (m⁄ z-values) in any generated

digest or MS⁄ MS prediction, including those of

multi-ply charged ions, can be double clicked for inclusion

in a reference list that can subsequently be exported in

asciiformat Lists like these serve as input for internal

calibration in many MS software packages LC⁄ MS

data can be time-segmented and the segments are

indi-vidually processed, normalized to the base peak in the

segment and imported as a series Each individually

imported mass spectrum can be activated or

deactiva-ted for adding to a combined spectrum number⁄

mass⁄ intensity list, which is used for matching

Data matching

Data matching can be achieved in matching quests In

each quest, matching criteria can be defined for

search-ing unmodified and post-translationally modified

pep-tides, peptides with an internal disulfide or chemically

induced cross-link (intrapeptide cross-link products),

and peptide pairs bonded together with a disulfide

bridge or a chemically induced cross-link (interpeptide

cross-link products) Each mass in the combined mass

list is matched within a custom defined mass window

with the masses of the peptides from the virtual experi-ment, selected according to the quest criteria A match can be performed for a number of quests simulta-neously For instance, a digest of a disulfide-containing protein (described in more detail below) can be analysed for the presence of unmodified peptides (quest 1), pep-tides containing an internal disulfide linkage (quests 2),

or peptide pairs connected by a disulfide bond (quest 3) The resulting output sheet, illustrated in Figure 1 shows the experimental mass list with the match quests assign-ments For convenient analyses of the assignments the result table can be sorted on each heading

Platform virtualmslab runs on a Microsoft Visual Basic plat-form and is freely available from the author LJdK, ldk@science.uva.nl

Mapping of disulfide bonds with aid

of VIRTUALMSLAB Disulfide bonds in proteins can be mapped by mass spectrometric identification of the corresponding digest peptides [16] For this, efficient cleavage between cys-teine containing sections of the protein, leaving the disulfide bridges intact, is essential However, disulfide bonded proteins often have a rigid structure rendering the native protein resistant to cleavage by proteases In that case, chemical cleavage may be considered, such

as the use of cyanogen bromide to cleave at methion-ine residues, or pH 2 at elevated temperature to cleave peptides bonds at the C- or N-terminal side of aspar-tate residues RNase A was used as a model protein to show the development of a procedure with the aid of virtualmslab for mapping disulfide bonds in a rigid protease resistant protein [14].Virtual experiments with the virtualmslab program showed that MALDI-MS detectable fragments, with masses ranging from 800

to4000 atomic mass units, could be generated by ini-tial specific acid cleavage in front of and behind aspar-tate residues [17,18] to break-up the rigid protein, followed by tryptic cleavage which takes place behind lysine and arginine residues

Experimentally, RNase A was cleaved by treatment

at pH 2, followed by trypsin digestion and mass analy-sis of the resulting peptide mixture Based on a single MALDI-FTICR mass spectrum, 42 fragments were assigned by virtualmslab within a mass window of

4 p.p.m., corresponding to a sequence coverage of

> 90% Figure 1 shows part of the output sheet for the assignment over three quests The first quest matches all unmodified peptide masses (specified by

the question mark) to the experimental masses The

second quest matches the combined masses of all pairs

of peptides, each containing at least one cysteine minus

the mass of two hydrogen atoms, assigning the

disul-fide linked peptides The third quest matches the mass

of all peptides containing cysteine minus the mass of

two hydrogen atoms, assigning the peptides with an

internal disulfide link

From the assignments, a peptide map was

construc-ted as shown in Fig 2 Due to partial cleavage at

both D and R⁄ K, many overlapping peptides were

observed About 80% of all peaks in the

MALDI-FTICR mass spectrum with intensity above 5% of the

base peak could be assigned, assuming cleavage at D

or K⁄ R This demonstrates the high specificity of

chemical cleavage at aspartate residues We were

aware of the possible occurrence of deamidations of

asparagines and subsequent partial cleavage at the

resulting aspartate residues However, virtualmslab

analysis allowing partial modification of N to D

fol-lowed by partial cleavage on the newly formed D

resi-dues, showed no matches for the resulting peptides

This indicates the absence of severe deamidations under our experimental conditions Of the 42 assigned fragments, a total of 23 were unambiguously attrib-uted to peptides with a correct disulfide bridge, consid-ering four disulfide linkages in RNase A Of the 23 disulfide-containing fragments, three were assigned to the C26–C84 linkage, 12 to C40–C95, four to C58– C110, and four to C65–C72 Several disulfide-linked peptides were also present as free SH-containing pep-tides, indicating partial in-source reduction of disul-fides [19] It should be noted that this phenomenon enables assignment of pairs of in-source cleavage prod-ucts to corresponding disulfide linked peptides, the sum of the masses of the cleavage products, due to incorporation of two H atoms, being 2 atomic mass units more than the mass of the parent compounds This information can be used to confirm the results of the virtualmslab analysis

Despite the overwhelming evidence for the correct disulfide linkages, three minor peaks were assigned by virtualmslab to peptides with conflicting disulfide linkages; two of these correspond to a peptide with

Fig 1 Part of the output sheet of the VIRTUALMSLAB analysis of the MALDI-FTICR-MS data of the RNase A digest peptide mixture The first column lists the mass spectrum with spectrum number The second column lists the numbers of the matches corresponding to the quest numbers on the VIRTUALMSLAB console shown in the inset Column 3 lists the theoretical masses of the assignments with the match error in p.p.m Columns 4 and 5 list the peptide assignments with the precursor proteins (in this experiments this is only RNase A), the peptide posi-tion in the protein and the residue sequence.

an internal C40–C58 linkage, the third corresponds to

a peptide with an internal C84–C95 1inkage These species can conceivably be naturally occurring disul-fide-bridge variants, or can be the result of disulfide interchange reactions during the experiment Disulfide interchanges can in principle be catalysed by free thiols at neutral or high pH If this were the case, a thiol scavenger should be able to prevent disulfide interchange To investigate this possibility we added N-ethylmaleimide (NEM) to the acid-cleaved RN-ase A preparation before the start of the digestion at

pH 8.0 by trypsin It should be noted that at this pH NEM not only reacts with SH groups, but to a lesser extent also with amines The presence of NEM during trypsin digestion therefore results in complex peptide mixtures, due to partial modification at the amino terminus and at lysine residues, and because modifica-tion at lysine residues prevents cleavage by trypsin Accordingly, analysis using virtualmslab including modifications with NEM in the match quests results

in the assignment of no fewer than 84 peptides Of these, 31 represent free SH-containing peptides, as the result of in-source decay, and 53 are correct disulfide-linked species No unambiguous evidence was found for peptides with internal C40–C58, C84–C95 or any other conflicting linkages under these conditions, indi-cating that their minor presence in the absence of NEM must have been the result of disulfide inter-change reactions A possible explanation is the phe-nomenon of b-elimination [20], occurring under the alkaline conditions during trypsin digestion, creating the necessary catalyst for the interchange reaction Even trace amounts of free sulfhydryl groups can trigger a cascade of reshuffling of disulfide-linked peptides, which may explain the minor formation of the detected peptides with an internal C40–58 or C84–95 disulfide bond Ambiguities caused by these interchange reactions can be resolved by adding NEM before and during digestion

In conclusion, it appears that well-controlled acidic cleavage followed by tryptic digestion effectively breaks

up the rigid RNase A molecule into MALDI-MS detectable fragments, leaving the vulnerable disulfide bonds intact The virtualmslab analysis of the data from a single MALDI-mass spectrum acquired with a high performance FTICR mass spectrometer unambig-uously reveals the origin of all disulfide bonds

Identification of cross-links in the NK1 domain

of HGF/SF HGF⁄ SF and its receptor Met stimulate cell growth, cell differentiation and migration during embryogenesis In

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Fig 2 Peptide map constructed from the VIRTUALMSLAB

assign-ments of the MALDI-FTICR-MS data of the RNase A digest peptide

mixture The first column shows the sequence with the four well

established disulfide links The second column shows the peptides

resulting from the in-source MALDI reduction of S–S-linked

pep-tides Column 3 shows the linked peptides, clearly confirming all

four established disulfide links Column 4 shows the peptides

asso-ciated with conflicting internal disulfide bridges.

cancer they promote invasive growth in surrounding

tissues and metastasis of the tumour Both proteins

are produced as inactive singular proenzymes, which

upon cleavage form an active disulfide-linked a⁄ b

heterodimer Several individual domains of both Met

and HGF⁄ SF have been elucidated, but the 3-D

struc-tures of the full-length proteins are not yet resolved The

NK1 domain of the a-chain of HGF⁄ SF is found to be

the main interaction site with Met, while the b-chain

might make additional interactions To obtain a model

of the interaction of Met and HGF⁄ SF, the complex has

been subjected to solution phase small angle X-ray

scattering (SAXS) (Gherardi, E., Sandin, S., Petoukhov,

M V., Finch, J., O¨fverstedt, L.-G., Nunez, R., Blundell,

T L., Vande Wonde, G F., Skoglund, U & Svergun,

D I., unpubished data) Experimentally determined

constraints on the distances of amino acid residues

should be helpful to either discard or confirm the

solu-tions obtained by SAXS Identification of the sites of

artificially induced cross-links can provide such distance

constraints and, with these constraints, a detailed model

of the interaction between the two proteins can be

designed, based on SAXS data and the known 3-D

structures of single protein domains Such a model will

be of great value both to understand how HGF⁄ SF

interaction with Met leads to receptor dimerization

and signal transduction and to design Met inhibitors as

anticancer drugs [15]

Mass spectrometric analysis of digests of cross-linked

proteins is known to be a powerful way to identify sites

of cross-linking [3,6,8] However, the identification of

cross-linked sites in biological assemblies as complicated

as the HGF⁄ SF-Met complex are unprecedented We

use the amine-specific homobifunctional cross-linker

bis(sulfosuccinimidyl)suberate (BS3) Besides reaction

with amines, the activated ester is also susceptible to

hydrolysis, which may lead to single labelling, i.e

modi-fication of amines without actual cross-linking Clearly,

the above analyses of the naturally occurring

disulfide-linkages in RNase A must be taken a step further for

this complex which is build from four peptide chains

over two disulfide-linked ab heterodimers The complex

has more than 1600 amino acid residues adding up to a

mass of over 180 kDa, and it has 98 lysine residues

which can be heterogeneously cross-linked or singly

labelled by the cross-linking reagent

To anticipate limitations of a mass spectrometric

analysis of this complicated system, analysis has first

been completed with the virtualmslab program The

above HGF⁄ SF-Met complex was subjected to

reduc-tion and alkylareduc-tion of cysteine residues by

iodaceta-mide, followed by digestion with trypsin, allowing

a maximum of three miscleavages Mass filtering

between 200 and 4500 Da resulted in a digest mixture

of 534 peptides From this, a database was generated

of all possible realistic peptide pairs linked together with BS3 via their lysine residue, excluding lysines cleaved by trypsin From this database of 16 554 BS3 -linked peptide pairs, the mass list was extracted and taken as our virtual mass spectrum Figure 3 shows the mass distribution of cross-linked peptide pairs, illustrating that most of the cross-linked peptide pairs have masses > 3000 Da Each entry of this mass spec-trum was then matched against the complete theoret-ical set of peptides, including unmodified peptides, peptides that are modified by a partially hydrolysed cross-linker, intrapeptide cross-linking products and interpeptide cross-linking products The match presents all peptide candidates for assignment of each mass in the spectrum as a function of the match mass window

It shows how many alternative peptide candidate assignments can be anticipated if the experimental mass spectrum is searched for cross-linked peptides at

a specific instrumental mass accuracy In Fig 4 the results are summarized as the average number of pep-tide candidates for all 16 554 masses in the virtual spectrum, segmented in four mass ranges, vs the mass window As expected, the number of candidates comes down to almost 1 for all mass ranges if the mass win-dow is zoomed in to 0 p.p.m Still, for about 15% of the mass entries an alternative candidate, beside the

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Mass (Dalton) Fig 3 Calculated mass distribution of the BS 3 cross-linked peptide pairs in the tryptic digest of the HGF ⁄ SF Met protein complex allowing cross-links between all lysine residues.

authentic cross-linked peptide pair is given Most of

these alternatives are due to shifted tryptic cleavage

places for the peptides with RK, RR, KK and KR

sequence elements which will yield peptides with

identi-cal elemental composition Nevertheless, these

alter-native assignments will pinpoint the same cross-link

When the mass window is zoomed out, the number of

candidate peptides gradually increases to 2.5 for the

low mass segment of m 1000–2000 Da This number

appears to level off if the window becomes broader

than ±60 p.p.m The gradual increase indicates that

for this mass range the density of the candidate

pep-tide masses is relatively low The levelling-off points

out that the distribution of the alternative candidate

peptide masses around the mass of the authentic

cross-linked peptide pair is about ±60 p.p.m wide This

limited width is a consequence of the known

discon-tinuous mass distribution of peptides [10] For

compar-ison, the gap between m⁄ z 2000 and m ⁄ z 2001 is

500 p.p.m For the highest mass segment of m 4000–

5000 Da, which covers most of the cross-linked

pep-tides (see Fig 3) the number of peptide candidates

rap-idly increases with increasing detection mass window,

while this number only begins to level off to about 10

outside ±90 p.p.m This indicates that, for the higher

mass range, the density of candidate peptides masses is

much higher and the mass distribution width has

increased to over ±90 p.p.m For comparison, the gap

between m⁄ z 5000 and m ⁄ z 5001 is 200 p.p.m

The above virtual analysis reveals that instrumental

mass accuracy is crucial For mass accuracies better

that 2 p.p.m., such as can be obtained with a high performance Fourier transform mass spectrometer, most of the identifications can be based on accurate mass with additional tandem mass spectrometric valid-ation For mass accuracies better that 20 p.p.m the identification is filtered to three or four possible candi-dates (see Fig 4) This moderate number of alternative candidates should still allow unambiguous identifica-tion based on addiidentifica-tional tandem mass spectrometric validation It thus appears that a cross-linking approach to obtain structural information about an assembly as complicated as the HGF⁄ SF-Met complex

is feasible, especially with adequate fractionation of the peptide mixture, e.g by reversed phase HPLC

To experimentally test this finding, we have carried out the mass spectrometric analysis of a cross-linked peptide mixture with at least the same or higher com-plexity as a reversed phase HPCL fraction of a peptide mixture derived from cross-linked HGF⁄ SF-Met We chose the NK1 domain of HGF⁄ SF as the test protein for these experiments The size of NK1, with 183 resi-dues adding up to almost 22 kDa, is roughly one-tenth

of that of the entire HGF⁄ SF complex and therefore

of similar complexity as an average reversed phase HPLC fraction from the complex, assuming sorting of the peptides in at least 10 fractions Moreover, a 3-D structure of the NK1 domain is available, so that cross-link identification can be validated BS3was used

to covalently cross-link amines within the NK1 unit Cross-linked and control preparations were sub-jected to SDS⁄ PAGE Subsequently, protein bands corresponding to the monomeric NK1 were treated with trypsin and the resulting peptide mixtures were mass analysed The processed MS data were loaded into the virtualmslab program and matched with the corresponding virtual experimental results A total of

13 peaks in the MALDI-TOF mass spectrum of the cross-linked NK1 digest could be related to cross-link-ing products Some of these peaks were matched with one or two alternative assignments within a mass win-dow of ±30 p.p.m corresponding to the mass accu-racy of our MALDI-TOF instrument As anticipated, the relatively limited average number of possible pep-tide assignments found for the cross-linked NK1 is smaller than the average number of three candidate assignments found by the virtualmslab program for the entire HGF⁄ SF-Met complex in a mass window of

±30 p.p.m (Fig 4)

Based on the peptide assignments, a list of candi-date cross-links is given in Table 1 Four of these candidate cross-links have been confirmed by tandem mass spectrometric analyses of the corresponding cross-linked peptides using either ESI-QTOF or

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Mass accuracy (ppm)

1000-2000 Dalton 2000-3000 Dalton 3000-4000 Dalton 4000-5000 Dalton

Fig 4 Calculated average number of peptide candidates within a

mass window at different mass ranges in a tryptic digest mixture

of BS 3 cross-linked HGF ⁄ SF Met protein complex (for details see

text).

MALDI-TOFTOF (Fig 5) These validated

cross-links were fit into an available crystal structure of the

protein (PDB: 1BHT) [21] It was found that the

measured distances between amino groups are com-patible with the calculated distance of 11.4 A˚ which can be spanned by the BS3 cross-linker (Fig 6) Another candidate cross-linked peptide pair connect-ing K44 and K91 was assigned by virtualmslab Tandem MS data allowed neither confirmation nor rejection of the assignment, still leaving open the pos-sibility that it corresponds to an unknown species However, also this candidate cross-link fits nicely into the 3-D structure of NK1 (Fig 6)

The candidate links in Table 1 suggest cross-linking between the N-terminal part of the protein [Y28 (N-terminus) and K34] with the region including K132, K137 and K170, which are close together However, the first seven residues of the protein N-ter-minal region, specified as amino acids 28–34, are not resolved in the crystal structure and links to their amine groups cannot be drawn This can be explained

by assuming flexibility of the seven N-terminal resi-dues that might localize preferentially into this region Alternatively, we may assume that K132, K137 and K170 have a relatively high reactivity towards the cross-linking agent, enabling them to trap the flexible amino terminus

The results imply that a single MALDI-TOF mass spectrum with moderate mass accuracy of an unfract-ionated proteolytic digest of a cross-linked protein can disclose significant information on the protein struc-ture This opens new avenues in the computer assisted analysis of more complex biological assemblies, by combining advanced peptide separation techniques

Table 1 Candidate cross-links found in NK1 using BS 3 as a

cross-linking agent The cross-link candidates are nominated by the

VIRTUALMSLAB program by assigning peaks in the MALDI-TOF mass

spectrum of the tryptic digest of cross-linked NK1 to the

corres-ponding cross-linked peptides Residue Y28 is the N-terminal

resi-due in the construct used.

Residue 1 Residue 2

Assigned peaks (m ⁄ z)

Experimental mass discrepancy (p.p.m)

a Identification of the corresponding assigned cross-linked peptide

has been confirmed by tandem MS b Assigned cross-linked peptide

shows no alternative noncross-linked peptide assignments. c

As-signed cross-linked peptide shows one alternative noncross-linked

peptide assignments d Assigned cross-linked peptide shows two

alternative noncross-linked peptide assignments e MS ⁄ MS data is

shown in Fig 5.

A

B

Fig 5 MALDI-TOF ⁄ TOF MS ⁄ MS analysis

of a NK1 cross-linked peptide with m ⁄ z 2503.3 NK1 K137 is linked to NK1 K170 (see Table 1) (A) Structures of the cross-linked peptide Observed fragment ions are indicated (B) MALDI-TOF ⁄ TOF MS ⁄ MS data: fragment ion annotations correspond

to the annotations in A.

with mass analysis, and by taking advance of the high

mass accuracy of FTICR-MS

In conclusion, it appears that advanced mass

spectro-metric studies on proteins can significantly be

promo-ted by software tools, like the virtualmslab program,

that can merge and tune mass spectrometric analysis

with biochemical experiments In contrast to other

available software such as asap [10], ms2assign [9] and

searchxlinksthe unique multistage experiment editor

in our program is a convenient tool to predict and

optimize possible outcomes beforehand, which saves

time in finding successful experimental strategies asap

and searchxlinks [11] have the order of events hard

coded into the program and do not allow for multipass

experiments ms2assign has the unique feature to

han-dle MS⁄ MS data, which all other programs, including

virtualmslab cannot virtualmslab also allows for

a large number of candidate proteins to be input in

one single analysis The recently described program

cplm[12] is flawed, in the sense that it only candidates

the match with the least mass deviation for a given

observed mass, thus bypassing critical assessment and verification

The potential of our software program has been shown for the cross-link studies presented in this paper However, the applications can be extended with other studies, including studies comprising entire cellu-lar proteomes

Experimental procedures

Materials N-ethylmaleimide, HCl, and the gradient grade solvents: acetonitrile, ethanol and water were from Merck (Darms-tadt, Germany) The cross-linking agent BS3 was from Pierce (Rockford, MA, USA) Ribonuclease A and lyso-zyme were from Sigma-Aldrich Chemie GmbH (Steinheim, Germany) Trypsin (sequencing grade) was from Roche Diagnostics GmbH (Mannheim, Germany)

The NK1 fragment of HGF⁄ SF was expressed in the yeast Pichia pastoris and purified from culture supernatants [22]

Cross-linking Protein cross-linking was carried out with BS3 by incuba-ting the protein at a concentration of 0.5 mgÆmL)1(23 lm)

in a 50 mm Na-phosphate buffer, 150 mm NaCl, pH 7.4, with 1 mm cross-linker, for 30 min at room temperature Cross-link spacer distances were approximated as described

by Green et al [23]

Preparation of peptides For cleavage at asparte [18], proteins (0.1 mgÆmL)1) were dissolved in 0.013 m HCl (pH 2) and incubated in a closed plastic Eppendorf vial, in an oven at 108
C for 2 h Diges-tion by trypsin, both in the presence and absence of 10 mm NEM, was carried out in 100 mm NH4HCO3at 37
C for

4 h using a protease : substrate ratio of 1 : 50 (w⁄ w) In gel digestion by trypsin of Coomassie stained protein bands was carried according to published procedures [24] Peptide mixtures were desalted and concentrated by ZipTip lC18

pipette tips (Millipore Corporation, Billerica, MA, USA), washed with 0.1% (v⁄ v) trifluoroacetic acid (TFA) or 1% (v⁄ v) formic acid solution and eluted with a solution con-taining 50% (v⁄ v) acetonitrile and 0.1% (v ⁄ v) TFA or 1% (v⁄ v) formic acid

Mass spectrometry MALDI-MS analyses were performed with a TofSpec 2EC mass spectrometer (Micromass, Wythenshawe, UK) in the reflectron mode Peptides were mixed in a 1 : 1 ratio

Fig 6 Space filled model of the NK1-domain of HGF ⁄ SF (1BHT).

Four confirmed (solid lines) and one candidate cross-link (dashed

line) are shown in this model Measured distances between the

linked amino acids are indicated The different angles between the

two views A and B are indicated by the arrows The model was

visualized using PYMOL (http://www.pymol.org).

(v⁄ v) with a 10 mgÆmL)1 matrix

(a-cyano-4-hydroxycin-namic acid) solution in a 50 : 50 (v⁄ v) ethanol ⁄ acetonitrile

mixture For analyses, 0.5 lL of the mixture was spotted

on a MALDI steel target plate and allowed to dry MALDI

ultra high resolution accurate mass analysis was performed

with a 7T ApexQ FTICR-MS instrument (Bruker

Dalton-ics, Bremen, Germany) For the analyses, an aliquot of

0.5 lL peptide mixture was mixed with a 10 mgÆmL)1

dihydroxybenzoic acid solution containing 0.1% (v⁄ v) TFA

in a 30 : 70 (v⁄ v) acetonitrile ⁄ water mixture, spotted onto a

Bruker Daltonics AnchorChipTM, and allowed to dry

MALDI MS⁄ MS analyses were performed with TOF ⁄ TOF

4700 Proteomics Analyser (Applied Biosystems,

Framing-ham, CA, USA) The sample (0.5 lL) was cocrystallized

with an equal volume of matrix solution (7 mgÆmL)1

a-cy-ano-4-hydroxycinnamic acid dissolved in 50% v⁄ v

acetonit-rile⁄ 0.1% TFA in water) and applied to the target Prior to

analysis, the instrument was externally mass calibrated with

a standard peptide mixture, as outlined by the

manufac-turer Electrospray ionization MS and MS⁄ MS analyses

were performed with a QTOF mass spectrometer

(Micro-mass) Peptide mixtures were directly infused from gold

pla-ted nanospray tips (New Objective, Woburn, MA, USA)

into the ESI-QTOF Selected ions were collided with Argon

in the hexapole collision cell, at a pressure of 4· 10)5mbar

measured on the quadrupole Penning gauge Recorded

spectra were internally mass calibrated on signals from

trypsin autodigestion fragments and unambiguously

identi-fied digest fragments from the proteins studied Mass

spec-tra were deconvoluted to lists of monoisotopic masses,

which were analysed using the virtualmslab program

Suggested nomenclature [9,25] for fragment ions from

cross-linked peptides has been used

Acknowledgements

This work was supported by grants of the Netherlands

Organization for Scientific Research (NWO), Chemical

Sciences division (CW) and Regieorgaan Genomics

The ApexQ FTICR-mass spectrometer was largely

funded by NWO-CW and the TofSpec 2EC and

QTOF by NWO, Medical Sciences division

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