Prediction of Collision-Induced-Dissociation Spectra of Peptides with Post-translational or Process-Induced Modifications

Mass spectrometry, combined with collision-induced dissociation (CID), has become the method of choice for analyzing protein post-translational and process-induced modifications. However, confident and automated identification of modifications and modification sites is often challenged by the diversity of modifications and their labile nature under typical CID conditions. An accurate prediction of the CID spectra of modified peptides will improve the reliability of automated determination of modifications and modification sites. In this article, the kinetic model for the prediction of peptide CID spectra is extended to the prediction of the CID spectra of modified peptides. The mathematical model for predicting CID spectra of peptides with enzymatic and chemical modifications such as (1) phosphorylation of serine, threonine, and tyrosine, (2) S-carboxymethylation and carbamidomethylation of cysteine, (3) different stages of oxidation of methionine, tryptophan, and cysteine, (4) glycation of lysine, (5) O-mannosylation of serine, (6) hydroxylation of lysine, and (7) N-monomethylation and N-dimethylation of lysine is described. The mathematical model, once established with CID spectra of peptides with known modifications and modification sites, is able to predict CID spectra with excellent accuracy in ion intensities, facilitating more reliable identification of modification and modification sites

Fully Unattended Online Protein Digestion and LC–MS Peptide Mapping

LC–MS based peptide mapping, i.e., proteolytic digestion followed by LC–MS/MS analysis, is the method of choice for protein primary structural characterization. Manual proteolytic digestion is usually a labor-intensive procedure. In this work, a novel method was developed for fully automated online protein digestion and LC–MS peptide mapping. The method generates LC–MS data from undigested protein samples without user intervention by utilizing the same HPLC system that performs the chromatographic separation with some additional modules. Each sample is rapidly digested immediately prior to its LC–MS analysis, minimizing artifacts that can grow over longer digestion times or digest storage times as in manual or automated offline digestion methods. In this report, we implemented the method on an Agilent 1290 Infinity II LC system equipped with a Multisampler. The system performs a complete digestion workflow including denaturation, disulfide reduction, cysteine alkylation, buffer exchange, and tryptic digestion. We demonstrated that the system is capable of digesting monoclonal antibodies and other proteins with excellent efficiency and is robust and reproducible and produces fewer artifacts than manually prepared digests. In addition, it consumes only a few micrograms of material as most of the digested sample protein is subjected to LC–MS analysis

Improved Protein Hydrogen/Deuterium Exchange Mass Spectrometry Platform with Fully Automated Data Processing

Protein hydrogen/deuterium exchange (HDX) followed by protease digestion and mass spectrometric (MS) analysis is accepted as a standard method for studying protein conformation and conformational dynamics. In this article, an improved HDX MS platform with fully automated data processing is described. The platform significantly reduces systematic and random errors in the measurement by introducing two types of corrections in HDX data analysis. First, a mixture of short peptides with fast HDX rates is introduced as internal standards to adjust the variations in the extent of back exchange from run to run. Second, a designed unique peptide (PPPI) with slow intrinsic HDX rate is employed as another internal standard to reflect the possible differences in protein intrinsic HDX rates when protein conformations at different solution conditions are compared. HDX data processing is achieved with a comprehensive HDX model to simulate the deuterium labeling and back exchange process. The HDX model is implemented into the in-house developed software MassAnalyzer and enables fully unattended analysis of the entire protein HDX MS data set starting from ion detection and peptide identification to final processed HDX output, typically within 1 day. The final output of the automated data processing is a set (or the average) of the most possible protection factors for each backbone amide hydrogen. The utility of the HDX MS platform is demonstrated by exploring the conformational transition of a monoclonal antibody by increasing concentrations of guanidine

Improved Protein Hydrogen/Deuterium Exchange Mass Spectrometry Platform with Fully Automated Data Processing

Protein hydrogen/deuterium exchange (HDX) followed by protease digestion and mass spectrometric (MS) analysis is accepted as a standard method for studying protein conformation and conformational dynamics. In this article, an improved HDX MS platform with fully automated data processing is described. The platform significantly reduces systematic and random errors in the measurement by introducing two types of corrections in HDX data analysis. First, a mixture of short peptides with fast HDX rates is introduced as internal standards to adjust the variations in the extent of back exchange from run to run. Second, a designed unique peptide (PPPI) with slow intrinsic HDX rate is employed as another internal standard to reflect the possible differences in protein intrinsic HDX rates when protein conformations at different solution conditions are compared. HDX data processing is achieved with a comprehensive HDX model to simulate the deuterium labeling and back exchange process. The HDX model is implemented into the in-house developed software MassAnalyzer and enables fully unattended analysis of the entire protein HDX MS data set starting from ion detection and peptide identification to final processed HDX output, typically within 1 day. The final output of the automated data processing is a set (or the average) of the most possible protection factors for each backbone amide hydrogen. The utility of the HDX MS platform is demonstrated by exploring the conformational transition of a monoclonal antibody by increasing concentrations of guanidine

G/U and Certain Wobble Position Mismatches as Possible Main Causes of Amino Acid Misincorporations

A mass spectrometry-based method was developed to measure amino acid substitutions directly in proteins down to a level of 0.001%. When applied to recombinant proteins expressed in <i>Escherichia coli</i>, monoclonal antibodies expressed in mammalian cells, and human serum albumin purified from three human subjects, the method revealed a large number of amino acid misincorporations at levels of 0.001–0.1%. The detected misincorporations were not random but involved a single-base difference between the codons of the corresponding amino acids. The most frequent base differences included a change from G to A, corresponding to a G­(mRNA)/U­(tRNA) base pair mismatch during translation. We concluded that under balanced nutrients, G­(mRNA)/U­(tRNA) mismatches at any of the three codon positions and certain additional wobble position mismatches (C/U and/or U/U) are the main causes of amino acid misincorporations. The hypothesis was tested experimentally by monitoring the levels of misincorporation at several amino acid sites encoded by different codons, when a protein with the same amino acid sequence was expressed in <i>E. coli</i> using 13 different DNA sequences. The observed levels of misincorporation were different for different codons and agreed with the predicted levels. Other less frequent misincorporations may occur due to G­(DNA)/U­(mRNA) mismatch during transcription, mRNA editing, U­(mRNA)/G­(tRNA) mismatch during translation, and tRNA mischarging

DataSheet_1_Predicting colorectal cancer risk: a novel approach using anemia and blood test markers.docx

Background and objectivesColorectal cancer remains an important public health problem in the context of the COVID-19 (Corona virus disease 2019) pandemic. The decline in detection rates and delayed diagnosis of the disease necessitate the exploration of novel approaches to identify individuals with a heightened risk of developing colorectal cancer. The study aids clinicians in the rational allocation and utilization of healthcare resources, thereby benefiting patients, physicians, and the healthcare system.MethodsThe present study retrospectively analyzed the clinical data of colorectal cancer cases diagnosed at the Affiliated Hospital of Guilin Medical University from September 2022 to September 2023, along with a control group. The study employed univariate and multivariate logistic regression as well as LASSO (Least absolute shrinkage and selection operator) regression to screen for predictors of colorectal cancer risk. The optimal predictors were selected based on the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. These predictors were then utilized in constructing a Nomogram Model for predicting colorectal cancer risk. The accuracy of the risk prediction Nomogram Model was assessed through calibration curves, ROC curves, and decision curve analysis (DCA) curves.ResultsClinical data of 719 patients (302 in the case group and 417 in the control group) were included in this study. Based on univariate logistic regression analysis, there is a correlation between Body Mass Index (BMI), red blood cell count (RBC), anemia, Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelet count (PLT), Red Cell Distribution Width-Standard Deviation (RDW-SD), and the incidence of colorectal cancer. Based on the findings of multivariate logistic regression analysis, the variables of BMI and RBC exhibit a decrease, while anemia and PLT demonstrate an increase, all of which are identified as risk factors for the occurrence of colorectal cancer. LASSO regression selected BMI, RBC, anemia, and PLT as prediction factors. LASSO regression and multivariate logistic regression analysis yielded the same results. A nomogram was constructed based on the 4 prediction factors identified by LASSO regression analysis to predict the risk of colorectal cancer. The AUC of the nomogram was 0.751 (95% CI, OR: 0.708-0.793). The calibration curves in the validation and training sets showed good performance, indicating that the constructed nomogram model has good predictive ability. Additionally, the DCA demonstrated that the nomogram model has diagnostic accuracy.ConclusionThe Nomogram Model offers precise prognostications regarding the likelihood of Colorectal Cancer in patients, thereby helping healthcare professionals in their decision-making processes and promoting the rational categorization of patients as well as the allocation of medical resources.</p

Conformational Difference in Human IgG2 Disulfide Isoforms Revealed by Hydrogen/Deuterium Exchange Mass Spectrometry

Both recombinant and natural human IgG2 antibodies have several different disulfide bond isoforms, which possess different global structures, thermal stabilities, and biological activities. A detailed mapping of the structural difference among IgG2 disulfide isoforms, however, has not been established. In this work, we employed hydrogen/deuterium exchange mass spectrometry to study the conformation of three major IgG2 disulfide isoforms known as IgG2-B, IgG2-A1, and IgG2-A2 in two recombinant human IgG2 monoclonal antibodies. By comparing the protection factors between amino acid residues in isoforms B and A1 (the classical form), we successfully identified several local regions in which the IgG2-B isoform showed more solvent protection than the IgG2-A1 isoform. On the basis of three-dimensional structural models of IgG2, these identified regions were located on the Fab domains, close to the hinge, centered on the side where the two Fab arms faced each other in spatial proximity. We speculated that in the more solvent-protected B isoform, the two Fab arms were brought into contact by the nonclassical disulfide bonds, resulting in a more compact global structure. Loss of Fab domain flexibility in IgG2-B could limit its ability to access cell-surface epitopes, leading to reduced antigen binding potency. The A2 isoform was previously found to have disulfide linkages similar to those of the classical A1 isoform, but with different biophysical behaviors. Our data indicated that, compared to IgG2-A1, IgG2-A2 had less solvent protection in some heavy-chain Fab regions close the hinge, suggesting that the A2 isoform had more flexible Fab domains

Discovery of Undefined Protein Cross-Linking Chemistry: A Comprehensive Methodology Utilizing ¹⁸O‑Labeling and Mass Spectrometry

Characterization of protein cross-linking, particularly without prior knowledge of the chemical nature and site of cross-linking, poses a significant challenge, because of their intrinsic structural complexity and the lack of a comprehensive analytical approach. Toward this end, we have developed a generally applicable workflowXChem-Finderthat involves four stages: (1) detection of cross-linked peptides via ¹⁸O-labeling at C-termini; (2) determination of the putative partial sequences of each cross-linked peptide pair using a fragment ion mass database search against known protein sequences coupled with a de novo sequence tag search; (3) extension to full sequences based on protease specificity, the unique combination of mass, and other constraints; and (4) deduction of cross-linking chemistry and site. The mass difference between the sum of two putative full-length peptides and the cross-linked peptide provides the formulas (elemental composition analysis) for the functional groups involved in each cross-linking. Combined with sequence restraint from MS/MS data, plausible cross-linking chemistry and site were inferred, and ultimately confirmed, by matching with all data. Applying our approach to a stressed IgG2 antibody, 10 cross-linked peptides were discovered and found to be connected via thioethers originating from disulfides at locations that had not been previously recognized. Furthermore, once the cross-link chemistry was revealed, a targeted cross-link search yielded 4 additional cross-linked peptides that all contain the C-terminus of the light chain

DataSheet1_Simultaneous effect of different chromatographic conditions on the chromatographic retention of pentapeptide derivatives (HGRFG and NPNPT).DOCX

Introduction: Oligopeptides exhibit great prospects for clinical application and its separation is of great importance in new drug development.Methods: To accurately predict the retention of pentapeptides with analogous structures in chromatography, the retention times of 57 pentapeptide derivatives in seven buffers at three temperatures and four mobile phase compositions were measured via reversed-phase high-performance liquid chromatography. The parameters (kHA, kA, and pKa) of the acid–base equilibrium were obtained by fitting the data corresponding to a sigmoidal function. We then studied the dependence of these parameters on the temperature (T), organic modifier composition (φ, methanol volume fraction), and polarity (PmN parameter). Finally, we proposed two six-parameter models with (1) pH and T and (2) pH and φ or PmN as the independent variables. These models were validated for their prediction capacities by linearly fitting the predicted retention factor k-value and the experimental k-value.Results: The results showed that logkHA and logkA exhibited linear relationships with 1/T, φ or PmN for all pentapeptides, especially for the acid pentapeptides. In the model of pH and T, the correlation coefficient (R2) of the acid pentapeptides was 0.8603, suggesting a certain prediction capability of chromatographic retention. Moreover, in the model of pH and φ or PmN, the R2 values of the acid and neutral pentapeptides were greater than 0.93, and the average root mean squared error was approximately 0.3, indicating that the k-values could be effectively predicted.Discussion: In summary, the two six-parameter models were appropriate to characterize the chromatographic retention of amphoteric compounds, especially the acid or neutral pentapeptides, and could predict the chromatographic retention of pentapeptide compounds.</p