Fast fragmentation during surface-induced dissociation: An examination of peptide size and structure

Abstract

We present the results of direct dynamics simulations of surface-induced dissociation for protonated versions of A n K, KA n (n = 1, 3, and 5), AcA 7 K, and AcKA 7 for collisions with a fluorinated self-assembled monolayer surface. We focus on elucidating fast fragmentation events, which takes place in coincidence with the collision event. Such events generate a large number of products, and hence, are not easily understood through chemical intuition. Our simulations show distinct differences between the A n K/AcA 7 K and KA n /AcKA 7 series of peptides, with the former being more reactive, and the latter more selective. Backbone rearrangements and sidechain fragmentation are also seen.

Exploring the Effects of Methylation on the CID of Lysine: A Combined Experimental and Computational Approach

“…18,24,25,34,35 Although the potential energy parameters for Ar CID given in the literature 36 could have been used for the PTM systems, we instead choose to make use of the microcanonical sampling scheme to impart a statistical distribution of internal energy. 37 Good agreement with experiment was achieved, which suggests that sudden or fast fragmentation events 19 are not that significant for these systems. A range of internal energies was considered for each system with lysine, Me 1 -lysine, and Me 2 -lysine having values of 250, 300, and 350 kcal/mol, and Me 3 -lysine having values of 300, 350, 400 kcal/mol.…”

“…Indeed, MS2 spectroscopy is a well-used tool for studying biological systems using both experimental [9][10][11][12] and theoretical, direct dynamics based studies. [13][14][15][16][17][18] That said, trimethylation and acetylation of lysine result in a mass difference of just ∼0.036 Da and requires high-resolution instruments to distinguish. Direct dynamics studies of methylation and acetylation can provide insight into the dynamics taking place during CID.…”

“…Recently, several groups have started to use graph theory to analyze these simulations. 14,[19][20][21][22] The application of graph theory to chemistry is well documented, 23 and its application within direct dynamics simulations typically begins with the definition of the adjacency matrix, which is closely related to what one of the authors termed the con-nectivity matrix in a previous work. 24 There are two primary means of obtaining the adjacency matrix from simulation data, namely the use of distance parameters to determine bonding [20][21][22] or the direct use of the bond order obtained from the quantum mechanical wavefunction used within the dynamics.…”

“…24 There are two primary means of obtaining the adjacency matrix from simulation data, namely the use of distance parameters to determine bonding [20][21][22] or the direct use of the bond order obtained from the quantum mechanical wavefunction used within the dynamics. 14,19 With the adjacency matrix in hand, a graph analysis allows for a determination of the number and types of fragments obtained, which we employ here to analyze the diversity of structures within each mass peak. This classification further simplifies the identification of the relevant mechanisms within the simulations.…”

Exploring the Effects of Methylation on the CID of Protonated Lysine: A Combined Experimental and Computational Approach

“…Recently, several groups have started to use graph theory to analyze these simulations. 14,[19][20][21][22] The application of graph theory to chemistry is well documented, 23 and its application within direct dynamics simulations typically begins with the definition of the adjacency matrix, which is closely related to what one of the authors termed the connectivity matrix in previous work. 24 There are two primary means of obtaining the adjacency matrix from simulation data, namely the use of distance parameters to determine bonding [20][21][22] or the direct use of the bond order obtained from the quantum mechanical wavefunction used within the dynamics.…”