Heuristic Algorithms for the Maximum Colorful Subtree Problem

In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this computational task can be formulated as the Maximum Colorful Subtree problem, which is NP-hard. Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem - and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured. Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem. Unfortunately, as is often the case in bioinformatics, the structure of the (chemically) true solution is not known to us; therefore we can only evaluate against the optimal solution of an instance. Evaluating the quality of a heuristic based on scores can be misleading: Even a slightly suboptimal solution can be structurally very different from the optimal solution, but it is the structure of a solution and not its score that is relevant for the downstream analysis. To this end, we propose a different evaluation setup: Given a set of candidate instances of which exactly one is known to be correct, the heuristic in question solves each instance to the best of its ability, producing a score for each instance, which is then used to rank the instances. We then evaluate whether the correct instance is ranked highly by the heuristic. We find that one particular heuristic consistently ranks the correct instance in a top position. We also find that the scores of the best heuristic solutions are very close to the optimal score; in contrast, the structure of the solutions can deviate significantly from the optimal structures. Integrating the heuristic allowed us to speed up computations in practice by a factor of 100-fold

MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers

Mass spectrometry is a key tool in the study of small molecules, playing an important role in metabolomics, drug discovery, and environmental chemistry. Tandem mass spectra capture fragmentation patterns that provide key structural information about a molecule and help with its identification. Practitioners often rely on spectral library searches to match unknown spectra with known compounds. However, such search-based methods are limited by availability of reference experimental data. In this work we show that graph transformers can be used to accurately predict tandem mass spectra. Our model, MassFormer, outperforms competing deep learning approaches for spectrum prediction, and includes an interpretable attention mechanism to help explain predictions. We demonstrate that our model can be used to improve reference library coverage on a synthetic molecule identification task. Through quantitative analysis and visual inspection, we verify that our model recovers prior knowledge about the effect of collision energy on the generated spectrum. We evaluate our model on different types of mass spectra from two independent MS datasets and show that its performance generalizes. Code available at github.com/Roestlab/massformer.Comment: 14 pages (10 without bibliography), 5 figures, 3 table

First report on brorphine : the next opioid on the deadly new psychoactive substance horizon?

New psychoactive substances (NPS) continue to appear on the drug market. Until recently, new synthetic opioids, which are amongst the most dangerous NPS, primarily encompassed analogues of the potent analgesic fentanyl. Lately, also other new synthetic opioids have increasingly started to surface. This is the first report on the identification and full chemical characterization of brorphine, a novel potent synthetic opioid with a piperidine benzimidazolone structure. Brorphine was identified in a powder and in the serum of a patient seeking medical help for detoxification. Liquid chromatography–high resolution mass spectrometry (LC–HRMS) identified an exact mass of m/z 400.1020 and 402.1005 for the compound, corresponding to both bromine isotopes. Further chemical characterization was performed by gas chromatography–mass spectrometry (GC–MS), LC–diode array detection (DAD) and Fourier-transform infrared (FT-IR) spectroscopy analyses. Finally, the structure was confirmed by performing 1H- and 13C-NMR spectroscopy. In vitro biological activity of brorphine was determined by a cell-based µ-opioid receptor (MOR) activation assay, resulting in an EC50 of 30.9 nM (13.5 ng/mL) and an Emax of 209% relative to hydromorphone, confirming the high potency and efficacy of this compound. In a serum sample of the patient, brorphine and a hydroxy-metabolite were found using the LC–HRMS screening method. The presence of opioid activity in the serum was also confirmed via the activity-based opioid screening assay. The occurrence of brorphine is yet another example of how the illicit drug market is continuously evolving in an attempt to escape international legislation. Its high potency poses a serious and imminent health threat for any user

Chorismate mutase and isochorismatase, two potential effectors of the migratory nematode Hirschmanniella oryzae, increase host susceptibility by manipulating secondary metabolite content of rice

Hirschmanniella oryzae is one of the most devastating nematodes on rice, leading to substantial yield losses. Effector proteins aid the nematode during the infection process by subduing plant defence responses. In this research we characterized two potential H. oryzae effector proteins, chorismate mutase (HoCM) and isochorismatase (HoICM), and investigated their enzymatic activity and their role in plant immunity. Both HoCM and HoICM proved to be enzymatically active in complementation tests in mutant Escherichia coli strains. Infection success by the migratory nematode H. oryzae was significantly higher in transgenic rice lines constitutively expressing HoCM or HoICM. Expression of HoCM, but not HoICM, increased rice susceptibility against the sedentary nematode Meloidogyne graminicola also. Transcriptome and metabolome analyses indicated reductions in secondary metabolites in the transgenic rice plants expressing the potential nematode effectors. The results presented here demonstrate that both HoCM and HoICM suppress the host immune system and that this may be accomplished by lowering secondary metabolite levels in the plant

MIST-CF: Chemical formula inference from tandem mass spectra

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parameterized fragmentation tree construction and scoring. In this work we extend our previous spectrum Transformer methodology into an energy based modeling framework, MIST-CF, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge dataset, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or post-processing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formula with data driven learning

The Wasserstein Distance as a Dissimilarity Measure for Mass Spectra with Application to Spectral Deconvolution

We propose a new approach for the comparison of mass spectra using a metric known in the computer science under the name of Earth Mover\u27s Distance and in mathematics as the Wasserstein distance. We argue that this approach allows for natural and robust solutions to various problems in the analysis of mass spectra. In particular, we show an application to the problem of deconvolution, in which we infer proportions of several overlapping isotopic envelopes of similar compounds. Combined with the previously proposed generator of isotopic envelopes, IsoSpec, our approach works for a wide range of masses and charges in the presence of several types of measurement inaccuracies. To reduce the computational complexity of the solution, we derive an effective implementation of the Interior Point Method as the optimization procedure. The software for mass spectral comparison and deconvolution based on Wasserstein distance is available at https://github.com/mciach/wassersteinms