MIND4OLIGOS: Determining the Monoisotopic Mass of Oligonucleotides Observed in High-Resolution Mass Spectrometry.

Oligonucleotide therapeutics have emerged as an important class of drugs offering targeted therapeutic strategies that complement traditional modalities, such as monoclonal antibodies and small molecules. Their unique ability to precisely modulate gene expression makes them vital for addressing previously undruggable targets. A critical aspect of developing these therapies is characterizing their molecular composition accurately. This includes determining the monoisotopic mass of oligonucleotides, which is essential for identifying impurities, degradants, and modifications that can affect the drug efficacy and safety. Mass spectrometry (MS) plays a pivotal role in this process, yet the accurate interpretation of complex mass spectra remains challenging, especially for large molecules, where the monoisotopic peak is often undetectable. To address this issue, we have adapted the MIND algorithm, originally developed for top-down proteomics, for use with oligonucleotide data. This adaptation allows for the prediction of monoisotopic mass from the more readily detectable, most-abundant peak mass, enhancing the ability to annotate complex spectra of oligonucleotides. Our comprehensive validation of this modified algorithm on both in silico and real-world oligonucleotide data sets has demonstrated its effectiveness and reliability. To facilitate wider adoption of this advanced analytical technique, we have encapsulated the enhanced MIND algorithm in a user-friendly Shiny application. This online platform simplifies the process of annotating complex oligonucleotide spectra, making advanced mass spectrometry analysis accessible to researchers and drug developers. The application is available at https://valkenborg-lab.shinyapps.io/mind4oligos/.

A compositional data model to predict the isotope distribution for average peptides using a compositional spline model.

We propose an updated approach for approximating the isotope distribution of average peptides given their monoisotopic mass. Our methodology involves in-silico cleavage of the entire UNIPROT database of human-reviewed proteins using Trypsin, generating a theoretical peptide dataset. The isotope distribution is computed using BRAIN. We apply a compositional data modelling strategy that utilizes an additive log-ratio transformation for the isotope probabilities followed by a penalized spline regression. Furthermore, due to the impact of the number of sulphur atoms on the course of the isotope distribution, we develop separate models for peptides containing zero up to five sulphur atoms. Additionally, we propose three methods to estimate the number of sulphur atoms based on an observed isotope distribution. The performance of the spline models and the sulphur prediction approaches is evaluated using a mean squared error and a modified Pearson's χ2 goodness-of-fit measure on an experimental UPS2 data set. Our analysis reveals that the variability in spectral accuracy, that is, the variability between MS1 scans, contributes more to the errors than the approximation of the theoretical isotope distribution by our proposed average peptide model. Moreover, we find that the accuracy of predicting the number of sulphur atoms based on the observed isotope distribution is limited by measurement accuracy.

NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer.

BACKGROUND: Lung cancer can be detected by measuring the patient's plasma metabolomic profile using nuclear magnetic resonance (NMR) spectroscopy. This NMR-based plasma metabolomic profile is patient-specific and represents a snapshot of the patient's metabolite concentrations. The onset of non-small cell lung cancer (NSCLC) causes a change in the metabolite profile. However, the level of metabolic changes after complete NSCLC removal is currently unknown. PATIENTS AND METHODS: Fasted pre- and postoperative plasma samples of 74 patients diagnosed with resectable stage I-IIIA NSCLC were analyzed using 1H-NMR spectroscopy. NMR spectra (s = 222) representing two preoperative and one postoperative plasma metabolite profile at three months after surgical resection were obtained for all patients. In total, 228 predictors, i.e., 228 variables representing plasma metabolite concentrations, were extracted from each NMR spectrum. Two types of supervised multivariate discriminant analyses were used to train classifiers presenting a strong differentiation between the pre- and postoperative plasma metabolite profiles. The validation of these trained classification models was obtained by using an independent dataset. RESULTS: A trained multivariate discriminant classification model shows a strong differentiation between the pre- and postoperative NSCLC profiles with a specificity of 96% (95% CI [86-100]) and a sensitivity of 92% (95% CI [81-98]). Validation of this model results in an excellent predictive accuracy of 90% (95% CI [77-97]) and an AUC value of 0.97 (95% CI [0.93-1]). The validation of a second trained model using an additional preoperative control sample dataset confirms the separation of the pre- and postoperative profiles with a predictive accuracy of 93% (95% CI [82-99]) and an AUC value of 0.97 (95% CI [0.93-1]). Metabolite analysis reveals significantly increased lactate, cysteine, asparagine and decreased acetate levels in the postoperative plasma metabolite profile. CONCLUSIONS: The results of this paper demonstrate that surgical removal of NSCLC generates a detectable metabolic shift in blood plasma. The observed metabolic shift indicates that the NSCLC metabolite profile is determined by the tumor's presence rather than donor-specific features. Furthermore, the ability to detect the metabolic difference before and after surgical tumor resection strongly supports the prospect that NMR-generated metabolite profiles via blood samples advance towards early detection of NSCLC recurrence.

The isotope distribution: A rose with thorns.

The isotope distribution, which reflects the number and probabilities of occurrence of different isotopologues of a molecule, can be theoretically calculated. With the current generation of (ultra)-high-resolution mass spectrometers, the isotope distribution of molecules can be measured with high sensitivity, resolution, and mass accuracy. However, the observed isotope distribution can differ substantially from the expected isotope distribution. Although differences between the observed and expected isotope distribution can complicate the analysis and interpretation of mass spectral data, they can be helpful in a number of specific applications. These applications include, yet are not limited to, the identification of peptides in proteomics, elucidation of the elemental composition of small organic molecules and metabolites, as well as wading through peaks in mass spectra of complex bioorganic mixtures such as petroleum and humus. In this review, we give a nonexhaustive overview of factors that have an impact on the observed isotope distribution, such as elemental isotope deviations, ion sampling, ion interactions, electronic noise and dephasing, centroiding, and apodization. These factors occur at different stages of obtaining the isotope distribution: during the collection of the sample, during the ionization and intake of a molecule in a mass spectrometer, during the mass separation and detection of ionized molecules, and during signal processing.

Machine learning approach for the prediction of the number of sulphur atoms in peptides using the theoretical aggregated isotope distribution.

RATIONALE: The observed isotope distribution is an important attribute for the identification of peptides and proteins in mass spectrometry-based proteomics. Sulphur atoms have a very distinctive elemental isotope definition, and therefore, the presence of sulphur atoms has a substantial effect on the isotope distribution of biomolecules. Hence, knowledge of the number of sulphur atoms can improve the identification of peptides and proteins. METHODS: In this paper, we conducted a theoretical investigation on the isotope properties of sulphur-containing peptides. We proposed a gradient boosting approach to predict the number of sulphur atoms based on the aggregated isotope distribution. We compared prediction accuracy and assessed the predictive power of the features using the mass and isotope abundance information from the first three, five and eight aggregated isotope peaks. RESULTS: Mass features alone are not sufficient to accurately predict the number of sulphur atoms. However, we reach near-perfect prediction when we include isotope abundance features. The abundance ratios of the eighth and the seventh, the fifth and the fourth, and the third and the second aggregated isotope peaks are the most important abundance features. The mass difference between the eighth, the fifth or the third aggregated isotope peaks and the monoisotopic peak are the most predictive mass features. CONCLUSIONS: Based on the validation analysis it can be concluded that the prediction of the number of sulphur atoms based on the isotope profile fails, because the isotope ratios are not measured accurately. These results indicate that it is valuable for future instrument developments to focus more on improving spectral accuracy to measure peak intensities of higher-order isotope peaks more accurately.

R Shiny App for the Automated Deconvolution of NMR Spectra to Quantify the Solid-State Forms of Pharmaceutical Mixtures.

Bioavailability and chemical stability are important characteristics of drug products that are strongly affected by the solid-state structure of the active pharmaceutical ingredient (API). In pharmaceutical development and quality control activities, solid-state NMR (ssNMR) has proved to be an excellent tool for the detection and accurate quantification of undesired solid-state forms. To obtain correct quantitative outcomes, the resulting spectrum of an analytical sample should be deconvoluted into the individual spectra of the pure components. However, the ssNMR deconvolution is particularly challenging due to the following: the relatively large line widths that may lead to severe peak overlap, multiple spinning sidebands as a result of applying Magic Angle Spinning (MAS), and highly irregular peak shapes commonly observed in mixture spectra. To address these challenges, we created a tailored and automated deconvolution approach of ssNMR mixture spectra that involves a linear combination modelling (LCM) of previously acquired reference spectra of pure solid-state components. For optimal model performance, the template and mixture spectra should be acquired under the same conditions and experimental settings. In addition to the parameters controlling the contributions of the components in the mixture, the proposed model includes terms for spectral processing such as phase correction and horizontal shifting that are all jointly estimated via a non-linear, constrained optimisation algorithm. Finally, our novel procedure has been implemented in a fully functional and user-friendly R Shiny webtool (hence no local R installation required) that offers interactive data visualisations, manual adjustments to the automated deconvolution results, and the traceability and reproducibility of analyses.

Envemind: Accurate Monoisotopic Mass Determination Based On Isotopic Envelope.

Nowadays, monoisotopic mass is used as an important feature in top-down proteomics. Knowing the exact monoisotopic mass is helpful for precise and quick protein identification in large protein databases. However, only in spectra of small molecules the monoisotopic peak is visible. For bigger molecules like proteins, it is hidden in noise or undetected at all, and therefore its position has to be predicted. By improving the prediction of the peak, we contribute to a more accurate identification of molecules, which is crucial in fields such as chemistry and medicine. In this work, we present the envemind algorithm, which is a two-step procedure to predict monoisotopic masses of proteins. The prediction is based on an isotopic envelope. Therefore, envemind is dedicated to spectra where we are able to resolve the one dalton separated isotopic variants. Furthermore, only single-molecule spectra are allowed, that is, spectra that do not require prior deconvolution. The algorithm deals with the problem of off-by-one dalton errors, which are common in monoisotopic mass prediction. A novel aspect of this work is a mathematical exploration of the space of molecules, where we equate chemical formulas and their theoretical spectrum. Since the space of molecules consists of all possible chemical formulas, this approach is not limited to known substances only. This makes optimization processes faster and enables to approximate theoretical spectrum for a given experimental one. The algorithm is available as a Python package envemind on our GitHub page https://github.com/PiotrRadzinski/envemind.

Four layer multi-omics reveals molecular responses to aneuploidy in Leishmania.

Aneuploidy causes system-wide disruptions in the stochiometric balances of transcripts, proteins, and metabolites, often resulting in detrimental effects for the organism. The protozoan parasite Leishmania has an unusually high tolerance for aneuploidy, but the molecular and functional consequences for the pathogen remain poorly understood. Here, we addressed this question in vitro and present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. Our analyses unambiguously establish that aneuploidy in Leishmania proportionally impacts the average transcript- and protein abundance levels of affected chromosomes, ultimately correlating with the degree of metabolic differences between closely related aneuploid strains. This proportionality was present in both proliferative and non-proliferative in vitro promastigotes. However, as in other Eukaryotes, we observed attenuation of dosage effects for protein complex subunits and in addition, non-cytoplasmic proteins. Differentially expressed transcripts and proteins between aneuploid Leishmania strains also originated from non-aneuploid chromosomes. At protein level, these were enriched for proteins involved in protein metabolism, such as chaperones and chaperonins, peptidases, and heat-shock proteins. In conclusion, our results further support the view that aneuploidy in Leishmania can be adaptive. Additionally, we believe that the high karyotype diversity in vitro and absence of classical transcriptional regulation make Leishmania an attractive model to study processes of protein homeostasis in the context of aneuploidy and beyond.