The Arginine Anomaly: Arginine Radicals Are Poor Hydrogen Atom Donors in Electron Transfer Induced Dissociations

Arginine amide radicals are generated by femtosecond electron transfer to protonated arginine amide cations in the gas phase. A fraction of the arginine radicals formed (2-amino-5-dihydroguanid-1‘-yl-pentanamide, 1H) is stable on the 6.7 μs time scale and is detected after collisional reionization. The main dissociation of 1H is loss of a guanidine molecule from the side chain followed by consecutive dissociations of the 2-aminopentanamid-5-yl radical intermediate. Intramolecular hydrogen atom transfer from the guanidinium group onto the amide group is not observed. These results are explained by ab initio and density functional theory calculations of dissociation and transition state energies. Loss of guanidine from 1H is calculated to require a transition state energy of 68 kJ mol-1, which is substantially lower than that for hydrogen atom migration from the guanidine group. The loss of guanidine competes with the reverse migration of the arginine α-hydrogen atom onto the guanidyl radical. RRKM calculations of dissociation kinetics predict the loss of guanidine to account for >95% of 1H dissociations. The anomalous behavior of protonated arginine amide upon electron transfer provides an insight into electron capture and transfer dissociations of peptide cations containing arginine residues as charge carriers. The absence of efficient hydrogen atom transfer from charge-reduced arginine onto sterically proximate amide group blocks one of the current mechanisms for electron capture dissociation. Conversely, charge-reduced guanidine groups in arginine residues may function as radical traps and induce side-chain dissociations. In light of the current findings, backbone dissociations in arginine-containing peptides are predicted to involve excited electronic states and proceed by the amide superbase mechanism that involves electron capture in an amide π* orbital, which is stabilized by through-space coulomb interaction with the remote charge carriers

Test materials: (a) cement (b) sand (c) RBA.

In order to overcome the discreteness of input data and training data in deep neural network (DNN), the multivariable response surface function was used to revise input data and training data in this paper. The loss function based on the data on the response surface was derived, DNN based on multivariable response surface function (MRSF-DNN) was established. MRSF-DNN model of recycled brick aggregate concrete compressive strength was established, in which coarse aggregate volume content, fine aggregate volume content and water cement ratio are influencing factors. Furthermore, the predictive analysis and extended analysis of MRSF-DNN model were carried out. The results show that: MRSF-DNN model had high prediction accuracy, the correlation coefficient between the real values and the forecast values was 0.9882, the relative error was between -0.5% and 1%. Furthermore, MRSF-DNN had more stable prediction ability and stronger generalization ability than DNN.</div

MOESM1 of Identification of ESM1 overexpressed in head and neck squamous cell carcinoma

Additional file 1. ESM1 expression of 21 paired samples of HNSCC with clinical and pathological features

Response surface of <i>P</i>_s, <i>P</i>_g and <i>f</i>_cu: (a) Three dimensional response surface, (b) Response surface projection.

Response surface of Ps, Pg and fcu: (a) Three dimensional response surface, (b) Response surface projection.</p

Influence of activation function on loss function.

Influence of activation function on loss function.</p

The RSM-DNN model structure.

In order to overcome the discreteness of input data and training data in deep neural network (DNN), the multivariable response surface function was used to revise input data and training data in this paper. The loss function based on the data on the response surface was derived, DNN based on multivariable response surface function (MRSF-DNN) was established. MRSF-DNN model of recycled brick aggregate concrete compressive strength was established, in which coarse aggregate volume content, fine aggregate volume content and water cement ratio are influencing factors. Furthermore, the predictive analysis and extended analysis of MRSF-DNN model were carried out. The results show that: MRSF-DNN model had high prediction accuracy, the correlation coefficient between the real values and the forecast values was 0.9882, the relative error was between -0.5% and 1%. Furthermore, MRSF-DNN had more stable prediction ability and stronger generalization ability than DNN.</div

Linear response surface of three IVs: (a) The connection among <i>x</i>₁, <i>x</i>₂ and <i>y</i>, (b) The connection among <i>x</i>₁, <i>x</i>₃ and <i>y</i>, (c) The connection among <i>x</i>₂, <i>x</i>₃ and <i>y</i>.

Linear response surface of three IVs: (a) The connection among x1, x2 and y, (b) The connection among x1, x3 and y, (c) The connection among x2, x3 and y.</p

Influence of volume content of CA and FA on RBACCS.

Influence of volume content of CA and FA on RBACCS.</p

Extended analysis results of MRSF-DNN: (a) RBA volume content is in the range of 30%-35%, (b) RBA volume content is in the range of 50%-55%.

Extended analysis results of MRSF-DNN: (a) RBA volume content is in the range of 30%-35%, (b) RBA volume content is in the range of 50%-55%.</p

Summary of datasets with RBA volume contents of 40%-50%.

The datasets in the experiment (84). Water/cement ratio, sand volume fraction, and RBA volume fraction in that datasets are considered. (XLS)</p