Piotr Prostko

Accurate prediction of isotope distributions is essential for biomolecule identification and quantitation in mass spectrometry. Existing algorithms, such as recursive expansions or generating-function methods, achieve high accuracy but suffer from limited scalability, specialized handling of polyisotopic elements such as sulfur, and poor adaptability to chemical modifications. We present IsoSwap, a convolution-based framework that reformulates isotope distribution calculations as structured linear algebra operations. By representing isotopic profiles as discrete probability vectors indexed by neutron count, we express convolution as outer-product expansions and introduce Hankel and Toeplitz matrices. This formulation directly yields both aggregated isotope probabilities and centroid masses, and naturally extends to deconvolution for modeling leaving groups. IsoSwap computes complete isotopic envelopes in milliseconds, supports GPU acceleration, and generalizes to higher-order convolutions without element-specific adaptations. On simulated phosphorothioate-modified oligonucleotides, IsoSwap reproduces distributions computed directly from the modified elemental formula to floating-point precision, at approximately 5.6 times the speed of direct recomputation. Applied to an experimental mass spectrum of myoglobin, the operators interconvert the apo and heme-bound holo forms in both directions, with centroid-mass accuracy below 1 ppm. This provides a scalable, algebraically transparent method for predicting and transforming isotope distributions.

Accurate quantification in LC–MS experiments becomes challenging when analytes overlap both chromatographically and in mass spectra, as conventional extracted ion chromatogram-based methods can yield biased results by double-counting shared isotope signals. This limitation is particularly relevant for oligonucleotides, where degradation products and synthetic impurities frequently co-elute with the intended full-length product, complicating relative quantification. To address this, we developed DECAF, a straightforward and computationally efficient procedure for deconvoluting overlapping isotope patterns directly from MS1 data. The method models experimental isotope distributions as mixtures of theoretical templates across retention time, generating deconvoluted ion chromatograms whose peak areas accurately reflect the contributions of individual components. We demonstrate the utility of DECAF on two pharmaceutically relevant oligonucleotide mixture datasets, where it reliably estimated mixing proportions and enabled visualisation of component-specific elution profiles. Analysis of a typical sample required one to two minutes, underscoring the method’s practical efficiency. DECAF provides a transparent and accessible alternative to existing commercial software, with promising applications in pharmaceutical analysis and quality control.

Oligonucleotides are currently one of the most rapidly advancing classes of therapeutic modalities. Understanding critical quality attributes, such as the impurity profile, stability, potential metabolites, and sequence conformity, is the key to their ultimate success. To obtain the information presented above, liquid chromatography-mass spectrometry (LC-MS) is often employed. However, data interpretation can be challenging due to multiple charge states, unknown species, chemical noises, and overlapping isotope envelopes (OIEs) of distinct but unresolved species. To address these challenges, we have developed an MS-platform agnostic, multifunctional R package and a web-tool for automated and interactive MS and MS² data analysis, OligoDistiller. From the MS spectrum of a complex mixture of two synthetic strands, our tool OligoDistiller annotated and quantified 45 oligonucleotide-related features including 13 unknown impurities and 6 OIEs, which all together explained 90.8% of the detected peaks. Also, major product ions were assigned from the MS² spectrum of a 47-mer DNA strand, covering 80.3% of the oligonucleotide sequence. We provide not only diverse isotope quality metrics for each annotated feature but also an interactive data review module allowing for direct inspection of the part of raw spectrum linked to a selected feature. This tool OligoDistiller is freely available at https://github.com/daniellyz/OligoDistiller.

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/.

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.

Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson’s χ² goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.