The Role of Proton Transfer in Surface-Induced Dissociation - The

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The Role of Proton Transfer in Surface-Induced Dissociation Zackary Gregg,† Waleed Ijaz,‡ Stephen Jannetti,†,§ and George L. Barnes*,† †

Department of Chemistry and Biochemistry, and ‡Department of Biology, Siena College, Loudonville, New York 12211, United States ABSTRACT: Elucidating the mechanism of surface induced dissociation (SID) is a long-standing, important problem within mass spectrometry. In a recent work [Ijaz, W.; Gregg, Z.; Barnes, G. L. J. Phys. Chem Lett. 2013, 4, 3935−3939], our atomistic simulations reveal that complexes frequently form following fragmentation. In this work, we focus on the proton motion and mechanism of the initial fragmentation event. We propose and utilize a new definition for classification of events based on proton motion and illustrate that short-time fragmentation events can be either proton driven or energy transfer driven. Our results show that for proton driven fragmentation there is a strong correlation between proton motion and fragmentation. In addition, the mechanism for fragmentation does not depend strongly on collision energy, and one of the most important criteria controlling the proton transfer pathway is distance. We develop and use a kinetic model that describes the lag time between a relevant proton transfer and fragmentation. Although the mechanism for fragmentation is collision energy independent, the lag time shows a strong collision energy dependence. fluorinated octanethiol organic self-assembled monolayer surface (FSAM). These simulations revealed that it is common for complexes to form following the initial fragmentation event. As a consequence, the excess proton is not isolated and can contribute to secondary reaction events. Experiments have also recently shown that complexes could be important.21,22 Direct dynamics simulations have previously been used to confirm the experimental interpretation that “shattering” fragmentation5−7,9 (fragmentation that takes place on impact) does in fact occur.20,23−28 Shattering events occur very quickly during the impulsive collision between the peptide and the surface, and hence the common belief has been that these trajectories would not follow the statistical reaction pathways that are typically considered within the mobile proton model. However, without a definition of “statistical” and “nonstatistical” that is easily applicable to relatively short-time scale simulations, it has been difficult to make definitive classifications of fragmentation events. In this work, we further examine the gly8-H+ + FSAM system. The FSAM efficiently transfers energy into the peptide, making fragmentation more likely. In addition, shattering fragmentation has previously been observed for FSAM collision systems, meaning that both fast and slow processes can be investigated with the same data set. Here, we will focus on the proton motion and mechanism of the initial fragmentation event. Toward that end, we have defined two classes of fragmentation events that are inspired by the mobile proton model: proton driven and energy transfer driven fragmentation. We present evidence that suggests the mechanism for peptide fragmentation is relatively constant with respect to collision energy; however, the rate at which

1. INTRODUCTION Surface induced dissociation (SID), the fragmentation of molecular ions through the collisional activation with a surface, is a well-studied field.1−7 Fragmentation occurs due to energy transfer between translational motion and internal modes. SID generates a “fingerprint” fragmentation pattern and hence is a useful analytic technique for identification of unknown ions. This technique is frequently used to study collisions between protonated peptides and organic self-assembled monolayers (SAMs),5,8−11 and hence, there is a long-standing interest in gaining an atomic view of both the physical and chemical properties of these systems.2,5,12−14 Although it is clear that energy transfer allows fragmentation to take place, the exact mechanism for the process has not been determined experimentally. That said, the empirical mobile proton model of Wysocki and co-workers yields a qualitatively pleasing description of fragmentation and matches the observed peptide fragments quite well.15−17 This model relies on increased proton mobility due to the gradual “heating” that arises from the collision-induced energy transfer. The motion of the proton to thermodynamically less stable sites along the peptide backbone weakens certain predictable bonds and results in peptide fragmentation. In short, this statistical model of fragmentation is based on the idea that proton hops induce bond cleavage. A theoretical treatment of the fragmentation pathways, based on the mobile proton model, has been described previously in the literature.18,19 These studies present detailed investigations regarding the rate of proton migration from minima to minima using RRKM theory and transition state theory. While these studies are very useful, they cannot capture dynamical processes, such as those discussed in our previous work20 involving molecular dynamics simulations of hyperthermal collisions between protonated octaglycine (gly8-H+) and a © 2014 American Chemical Society

Received: July 15, 2014 Revised: August 26, 2014 Published: August 29, 2014 22149

dx.doi.org/10.1021/jp507069x | J. Phys. Chem. C 2014, 118, 22149−22155

The Journal of Physical Chemistry C

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fragmentation occurs has a strong collision energy dependence. In addition, the lag time between a proton hop and the fragmentation event is discussed and modeled using a simplified kinetic scheme. Lastly, we examine shattering fragmentation events and show preliminary evidence that suggests that some of these short-time events are in fact proton driven.

Table 1. Characterization of the N1 to O1 Proton Transfer Transition Statea

2. COMPUTATIONAL APPROACH In this work, we predominately use the same data set obtained in our prior work.20 Additional trajectories were performed at a collision energy of 30 eV to increase the total number of fragmentation events. We refer to our prior work and to the computational approach in Yang et al.29 as well as Barnes and co-workers30 for details concerning the simulation method. In short, we perform direct dynamics simulations31−33 with the intramolecular potential energy of the peptide obtained using the semiempirical RM1 method34 and that of both the surface and surface−peptide interaction from a well-known force field.29 The surface and the peptide are given an initial 300 K internal energy distribution and are separated by 30 Å. The peptide is randomly orientated and imparted a collision energy between 30 and 110 eV. Hamilton’s equations of motion were integrated using a sixth order sympletic integration scheme35 with a step size of 1 fs to a total time of 16 ps with output written every 50 fs. The vast majority of trajectories conserve energy very well (within 0.1% of the collision energy). Those that did not conserve to within 1% of the collision energy were recalculated with a 0.5 fs time step. All simulations were performed with VENUS36,37 coupled to MOPAC5.016nm.38 In our analysis, we make extensive use of the bond order matrix, which was calculated at every step in the trajectory. At equilibrium, this matrix would easily define the connectivity within the molecule. However, reactivity takes place far from equilibrium. As described previously,20 we developed a set of criteria beyond bond order to track chemical changes during a trajectory. We used stringent definitions that might report a fragmentation time that is larger than other definitions; however, this ensures that the bond cleavages identified are real, rather than simple bond stretches. As a consequence, the fragmentation times, and hence lag times, should be considered upper bounds rather than absolute numbers. Greater detail is provided in our previous manuscript.20 To enable our discussion, we number each atom starting from the n-terminus; that is, N1 is the terminal nitrogen and N2 is the nitrogen involved in the first peptide bond. During the course of the simulations, it became clear that an important transition state involved the transfer of a proton from the terminal nitrogen (N1) to the first carbonyl oxygen (O1). A similar transition state in diglycine was characterized by Paizs et al.;19 however, given the age of that work, the calculation was performed using B3LYP/6-31G(d) level of theory. The transition state in diglycine should be very similar to that found in larger polyglycine peptides and is small enough to allow for a high-level treatment. Here we have characterized this transition state using B3LYP with Dunnings augmented, correlation consistent, double- and triple-ζ basis sets. Following the geometry optimization with the DFT method, CCSD(T) calculations were performed to account for correlation effects. The results, given in Table 1, were compared to several semiempirical methods. RM1 yields good qualitative agreement with the higher-level calculations and is significantly better than the other semiempirical methods considered. Given the large

method

N1 protonation

TS

O1 protonation

B3LYP/6-31G(d)b CCSD(T)/aug-cc-pVDZc CCSD(T)/aug-cc-pVTZd RM1 PM7 PM6-D PM6 AM1

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.49 4.17 3.82 6.04 8.02 9.40 9.30 19.41

1.80 1.97 1.77 5.37 5.46 8.72 8.99 1.86

a

All energies are relative to the N1 geometry and given in kcal/mol. From ref 19. cB3LYP/aug-cc-pVDZ geometry. dB3LYP/aug-ccpVTZ geometry. b

amount of energy deposited into the internal degrees of freedom of the peptide by the collision, the 2−4 kcal/mol differences are acceptable. The FSAM typically transfers ∼18− 26% of the collision energy into internal degrees of freedom, meaning that for 30 eV collisions the 2−4 kcal/mol error corresponds to at most 1.6 to 3.2% of the available internal energy. Previous works have also shown that RM1 accurately reproduces other dissociation pathways.30 All DFT and ab initio calculations were performed using Gaussian0939 while the semiempirical calculations were performed using MOPAC2012.40,41

3. RESULTS AND DISCUSSION We begin our discussion of the results by examining the time dependent nature of fragmentation revealed in the survival plot shown in Figure 1. The times reported in this figure, and in the

Figure 1. Survival plot for intact gly8-H+ as a function of time. Time is measured relative to bouncing off the surface. At time zero, a nonunity survival fraction is observed, which shows the presence of “shattering” trajectories.

remainder of the paper, are relative to the classical turning point of the peptide, that is, when the peptide’s center of mass velocity reverses and it “bounces off” of the surface. This reference point places the emphasis on postcollision dynamics, defines a common time zero for each trajectory, and remediates the issue that the peptide spends a different amount of time traveling to the surface for each collision energy. When looking at this figure, it is clear that the survival fraction depends strongly on collision energy, with larger collision energies favoring fast initial fragmentation events. A comparison to experiment shows that when des-Arg1-bradykinin, another octapeptide, collides with an FSAM, all trajectories fragment above ∼45 eV,10 which suggests that additional dynamics are likely to take place outside the time scale of our simulations, an 22150



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important observation given the complexation dynamics that we described in a prior work.20 Two classifications of trajectories are commonly made in computational works: “statistical” and “shattering.” Generally the distinction between the two has been based on the time scale of the process, namely that the shattering fragmentation takes place within several hundred femtoseconds of bouncing off the surface.23,28,42 Shattering trajectories are thought of as nonstatistical based solely on the impulsive nature and short time scale involved. Here we propose classifying trajectories as proton driven and energy transfer driven based on proton motion within the peptide relative to fragmentation events. We define a proton driven trajectory to be one in which a proton moves to or away from one of four heavy atoms considered strongly important to the bond cleavage of interest (see Figure 2). Two of these heavy atoms, (A−B), are directly involved in

Figure 2. Definition of the atoms A, B, X, and Y relating to the A−B bond cleavage. If a proton moves to or from any of these four heavy atoms, it is considered a “relevant” hop. Trajectories in which such a hop takes place prior to the fragmentation event are considered proton driven.

the bond that is cleaved, while the other two (X and Y) are covalently linked to either A or B. If a proton moves to or from these atoms, it will directly affect the covalent bond, which makes it a “relevant” proton hop and classifies the trajectory as proton driven. If no relevant proton hop occurs related to the A−B bond cleavage, then the trajectory is classified as energy transfer driven. These definitions are in-line with and inspired by the thinking used in the empirical mobile proton model. Note that in this work we only classify trajectories based on the initial fragmentation event and it is certainly possible for a subsequent fragmentation event to be of either class. Looking again at Figure 1, our results show that shattering collisions take place, as evident by the survival fraction being less than one at time zero. When we examine these shattering collisions using the definition of proton driven fragmentation, our preliminary results show that proton driven shattering is possible, contrary to the common belief based on the impulsive nature and short time scale of such events. As an example, snapshots from a proton driven shattering trajectory are shown in Figure 3. This trajectory also results in a ring structure as one of the products. Similar ring structured products were seen in gly8-H+ + diamond collisions in the work of Hase and coworkers,23 and it is likely that some of these rearrangement products were also proton driven shattering collisions. We note that ring formation is only one possibility, but if ring structures frequently form due to shattering, this has important implications for sequence scrambling. Preliminary results show that the percentage of shattering collisions that are also proton driven can be surprisingly large. For example, at 70 and 90 eV, we find that 14% and 9.5%, respectively, of shattering collisions are proton driven. The reverse is also possible: longtime energy transfer driven fragmentation. Shattering was previously thought of as a nonstatistical process that would not be described by RRKM theory. These results show that short-

Figure 3. Illustration of a shattering collision. The surface has been removed for clarity. The first frame shows the peptide prior to the collision event in a folded state. 53 fs prior to the bounce, a proton transfer takes place between N1 and N5. However, at this point, the peptide is otherwise intact. Three fs prior to the bounce, the peptide fragments at the C8−N5 bond. Shortly afterward, a new covalent bond forms between C8 and N1. An additional proton transfer takes place between N1 and O5 (not shown), leading to the species given at 447 fs past the bounce. A complex is formed between (NH2CH2)+ and HOC-gly3, which eventually recombines. We note that this proton driven shattering collision results in a cyclic gly4 structure as well.

time events can follow a mechanism typically considered as proton driven and hence highlights the need for a clear definition of statistical and nonstatistical since the time scale of the dynamics alone is not necessarily a distinguishing characteristic between the two. We also observe that radical species can form during these short-time events and that this class of trajectory is quite complicated, frequently involving multistep mechanisms that take place in quick succession. A future study will carefully examine these short-time trajectories to disentangle their proton and energy transfer driven nature. The proton hop behavior that we observe for both short- and long-time proton driven trajectories provides strong evidence that illustrates how proton motion can cause peptide fragmentation, and is consistent with the ideas put forth in the mobile proton model. However, this correlation does not give the full story. Examining the distribution of hops required prior to fragmentation, shown in Figure 4, reveals the result that the collision energy does not strongly affect the number of hops necessary to induce fragmentation. This is intriguing given that other types of fragmentation events, such as shattering, show a strong collision energy dependence. The initial hop 22151



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Table 2. Most Likely Starting and Ending Heavy Atoms during Relevant Proton Hop start N1 N1 O2 O2 N1 N1

Figure 4. Hop distribution for the lowest and highest collision energies studied in this work. The hops required before the initial fragmentation do not strongly depend on the collision energy. For the time frame considered here, it is likely that the first hop will induce fragmentation.

N1 N1 O2 O2

results in a fragmentation event roughly 50% of the time. There is also a striking odd−even relationship seen in the hop distribution. To further explore the fragmentation pathways, we focus on the trajectories that required three proton hops and found that there are four cases to consider: (1) A → B → A → B, (2) A → B → C → B, (3) A → B → A → C, and (4) A → B → C → D where A, B, C, and D all represent different protonation states of the peptide, with fragmentation occurring from the bold state. Cases 1 and 2 we term as “rehops.” In these cases, a proton moves to and from a certain protonation state (B) before finally fragmenting from the B state. These two cases make up the majority of three hop fragmentation events, with a probability of 41% and 20%, respectively, at a collision energy of 30 eV and 38% and 10% at 110 eV. A pertinent example of the first case involves the protonation states with the excess proton on the terminal nitrogen (state A) and on the first carbonyl oxygen (state B). In these trajectories, the two heavy atom sites are “fighting” for a proton, until eventually state B “wins”, which induces peptide fragmentation. Case three is what we term a “reset” trajectory, in which protons are exchanged, but after two hops the peptide returns to its initial protonation state. The third hop then results in fragmentation from the state C. These trajectories account for roughly a third of the three-hop fragmentation events. The last case is the most complex and involves the largest number of protonation states, but it is a minor pathway at 30 eV, accounting for only 6% of the three hop trajectories. As collision energy increases, the fraction of 3-hop trajectories that follow this case grows, with 16% in this class at 110 eV. We have also examined the heavy atom starting and ending points involved in the relevant proton transfer with the most likely partners shown in Table 2. We find that the terminal nitrogen (N1) and the first carbonyl oxygen (O1) are the most probable. When this transfer takes place, the bond between the carbonyl carbon and the carbon alpha to it breaks. That this pathway is most frequently observed in the simulations suggests that the distance between heavy atom partners is one of the most important factors controlling proton transfer, and explains why simulations to date generally find that (NH2CH2)+ is the most likely product. It is likely that if other initial protonation sites were included in simulations, especially side-chain protonation sites, different products would be present due to the presence of new intramolecular forces and new proton transfer pathways. We note that the starting and ending points that are most frequently predicted are the same at all collision energies, which again supports the notion that the typical mechanism for proton transfer and fragmentation is independent of collision energy.

N1 N1 O2 O2 N1 N1 O2 O2 N1 N1 O2 O2

end 30 eV O1 N2 O1 N2 N7 N8 50 eV O1 N2 O1 N2 70 eV O1 N2 O1 N2 90 eV O2 N2 O1 N2 110 eV O1 N2 O1 N2

fraction 0.41 0.29 0.11 0.03 0.01 0.01 0.45 0.25 0.05 0.02 0.41 0.22 0.06 0.02 0.41 0.22 0.05 0.03 0.39 0.22 0.04 0.03

We now turn our focus to the time dependence of fragmentation. The simulations show that there is usually a short lag time between the proton transfer and the fragmentation event. However, this is not always the case. Using a simple average for the lag time leads to misleading results because there is a significant long-time tail in the probability distribution of lag time vs fragmentation probability as shown in the inset of Figure 5. Trajectories that have a large lag time are rare events, but they are real. We seek a way to model the entire continuum of fragmentation events. Toward that end, we frame the process of moving from an intact peptide to a fragmented peptide in terms of intramolecular vibrational relaxation (IVR) and realize such a process can be

Figure 5. Cumulative distribution function for lag time between fragmentation and the relevant hop at a collision energy of 30 eV (red) and 110 eV (blue) that were obtained from the genetic algorithm fit. The inset shows the raw probability distributions obtained from the simulations for the same collision energies. 90% of trajectories fragment with a lag time of 1.5 ps or less for 110 eV, while it takes 2.5 ps for 90% of trajectories to fragment at 30 eV. 22152



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4. SUMMARY We have presented results from the direct dynamics simulation of the gly8-H+ + FSAM collision system with a focus on the mechanism and proton motion related to the initial fragmentation event. The mobile proton model is frequently used to describe how proton motion drives peptide fragmentation. Proton motion within the peptide changes the bonding nature of certain bonds and makes them much more likely to break as energy flows within the molecule. Taking inspiration from this model, we have proposed a general definition to describe fragmentation events as proton driven or energy transfer driven that is based on proton motion to relevant heavy atom sites. Preliminary analysis using this definition reveals that proton driven shattering is possible and can account for a significant portion of shattering trajectories (14% at 70 eV). These short-time shattering events, both proton driven and energy transfer driven, have complicated mechanisms that result in interesting rearrangement products, such as cyclic peptides. Radical species were also observed in the simulations, and future studies that focus on such events will be of significant interest. The results strongly suggest that the overall mechanism of fragmentation is independent of collision energy. The number of proton hops prior to fragmentation shows little collision energy dependence, and in fact, the first hop is most often all that is required for fragmentation. By tracking proton motion, we also find that distance is one of the most important criteria regarding which proton transfer pathway is most probable. Again, the heavy atom sites that are most often involved in the relevant proton transfer event are largely independent of collision energy, which reinforces the notion that the mechanism is collision energy independent. Although the mechanism of peptide fragmentation is independent of collision energy, the rate of fragmentation shows a strong collision energy dependence. Fragmentation happens more rapidly for larger collision energies. While most trajectories fragment quickly following proton transfer, some trajectories take a significant amount of time to fragment. We present a simplified kinetic model that reproduces the cumulative distribution function for the lag time between the relevant proton transfer and peptide fragmentation. Genetic algorithm fits yield rate constants for both fragmentation and energy flow within the molecule. These simulations provide valuable atomistic details concerning the mechanism of SID in protonated peptide systems. It is striking that the mechanism is largely collision energy independent and that one of the most important factors in the initial proton transfer pathway is the distance between protonation sites. Future studies that involve more chemically complex peptides are underway. The addition of side-chain protonation sites and new intramolecular forces could have a dramatic effect on the rate of fragmentation, the fragmentation products, and the proton pathways involved in the process.

modeled using a kinetic scheme. Performing a precise analysis of the IVR in this system would be a very difficult task given that there are 177 degrees of freedom and that a large amount of excess energy has been deposited into the peptide. Thankfully, greatly simplified kinetic schemes that are inspired by detailed IVR calculations can be devised which match the observed trends in the simulation data quite well. Our kinetic model begins with the assumption that there are ensembles of modes that induce proton motion and others that do not. At the moment that the proton transfers, we assume that the population in the relevant ensemble is unity and that all other ensembles are unpopulated. From this time on, energy can flow out of the starting ensemble and into the others, and fragmentation can occur directly from the initial ensemble or from any of the other ensembles. Within this framework, various rate constants can be set to zero to make a more or less complex kinetic scheme. Genetic algorithm fits of rate constants for several such schemes were performed, and we present, in Table 3, the results for the simplest (Scheme 1) that fit the data Table 3. Rate Constants Obtained through Genetic Algorithm Fitting collision energy (eV)

kf (1/ps)

k1 (1/ps)

k2 (1/ps)

30 50 70 90 110

3.82 5.13 6.81 5.31 2.68

1.53 1.61 2.27 3.03 3.30

0.86 0.99 1.31 1.15 1.16

Scheme 1. Kinetic Scheme Used to Fit the Cumulative Distribution Shown in Figure 5

in Figure 5. The quality of the fits to the raw simulation data is very good (indistinguishable on the scale of Figure 5), and the model yields some nice qualitative features. These fits allow for an easy comparison of the rate of fragmentation following the relevant proton transfer. We find that 90% of trajectories fragment within 1.5 ps at 110 eV, while it takes 2.5 ps to reach the same fragmentation efficiency at 30 eV. The lag time clearly decreases with increasing collision energy, which is consistent with an increase in the internal energy available. The k1 rate constant consistently increases with collision energy and is always the fastest path from an intact to fragmented peptide. This makes qualitative sense given that the modes involved in proton hopping are likely related to bond stretching and bending close to the A−B bond cleavage site. The model also shows that IVR out of the proton transfer ensemble is fast and increases with collision energy until the k1 pathway begins to dominate the dynamics. It should be noted that the quality of the fits are not greatly affected by the kf value, and the decrease in this value at 90 and 110 eV could be an artifact of the faster fragmentation dynamics taking place.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Present Address

§ S.J.: Department of Chemistry, Hunter College of CUNY, 695 Park Avenue, New York, NY 10065.

Notes

The authors declare no competing financial interest. 22153



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ACKNOWLEDGMENTS G.L.B. gratefully acknowledges support from the Siena College New Faculty Start-up Fund and the Summer Scholars program. Computer resources were also provided in part from the National Science Foundation through XSEDE resources Grant No. TG-CHE120104.

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