Biology subjects

Bittremieux, W.

Publications and source records attributed to Bittremieux, W..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Accounting for digestion enzyme bias in Casanovo

A key parameter of any proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids "K" or "R." This bias is desirable when the Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identify of the digestion enzyme, alongside each observed spectrum. We then train Casanovo with data generated using several different restriction enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without the enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models.

bioinformatics↗

bioRxiv2024

Communicating mass spectrometry quality information in mzQC with Python, R, and Java

Mass spectrometry is a powerful technique for analyzing molecules in complex biological samples. However, inter- and intra-laboratory variability and bias can affect the data due to various factors, including sample handling and preparation, instrument calibration and performance, and data acquisition and processing. To address this issue, the Quality Control (QC) working group of the Human Proteome Organizations Proteomics Standards Initiative has established the standard mzQC file format for reporting and exchanging information relating to data quality. mzQC is based on the JavaScript Object Notation (JSON) format and provides a lightweight yet versatile file format that can be easily implemented in software. Here, we present open-source software libraries to process mzQC data in three programming languages: Python, using pymzqc; R, using rmzqc; and Java, using jmzqc. The libraries follow a common data model and provide shared functionality to operate on mzQC files, including the (de)serialization and validation of mzQC files. We demonstrate use of the software libraries for extracting, analyzing, and visualizing QC metrics from different sources and show how these libraries can be integrated with each other, with existing software tools, and in automated workflows for the QC of mass spectrometry data. All software libraries are available as open source under the MS-Quality-Hub organization on GitHub (https://github.com/MS-Quality-Hub).

bioinformatics↗

bioRxiv2024

Machine learning strategies to tackle data challenges in mass spectrometry-based proteomics

In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges in the effective development and evaluation of these tools. Here, we highlight the necessity for high-quality, comprehensive datasets to train ML models and advocate for the standardization of data to support robust model development. We emphasize the instrumental role of key datasets like ProteomeTools and MassIVE-KB in advancing ML applications in proteomics and discuss the implications of dataset size on model performance, highlighting that larger datasets typically yield more accurate models. To address data scarcity, we explore algorithmic strategies such as self-supervised pretraining and multi-task learning. Ultimately, we hope that this discussion can serve as a call to action for the proteomics community to collaborate on data standardization and collection efforts, which are crucial for the sustainable advancement and refinement of ML methodologies in the field.

bioinformatics↗

bioRxiv2024

Molecular structure discovery for untargeted metabolomics using biotransformation rules and global molecular networking

Although untargeted mass spectrometry-based metabolomics is crucial for understanding lifes molecular underpinnings, its effectiveness is hampered by low annotation rates of the generated tandem mass spectra. To address this issue, we introduce a novel data-driven approach, Biotransformation-based Annotation Method (BAM), that leverages molecular structural similarities inherent in biochemical reactions. BAM operates by applying biotransformation rules to known anchor molecules, which exhibit high spectral similarity to unknown spectra, thereby hypothesizing and ranking potential structures for the corresponding suspect molecule. BAMs effectiveness is demonstrated by its success in annotating suspect spectra in a global molecular network comprising hundreds of millions of spectra. BAM was able to assign correct molecular structures to 24.2 % of examined anchor-suspect cases, thereby demonstrating remarkable advancement in metabolite annotation.

bioinformatics↗

bioRxiv2023

A cross-attention transformer encoder for paired sequence data

Transformer-based sequence encoding architectures are often limited to a single-sequence input while some tasks require a multi-sequence input. For example, the peptide-MHCII binding prediction task where the input consists of two protein sequences. Current workarounds to solve this input-type mismatch lack resemblance with the biological mechanisms behind the task. As a solution, we propose a novel cross-attention transformer encoder that creates a cross-attended embedding of both input sequences. We compare its classification performance on the peptide-MHCII binding prediction task to a baseline logistic regression model and a default transformer encoder. Finally, we make visualizations of the attention layers to show how the different models learn different patterns.

bioinformatics↗

bioRxiv2023

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

In the field of drug discovery, there is a substantial challenge in seeking out chemical structures that possess desirable pharmacological, toxicological, and pharmacokinetic properties. Complications arise when drugs interfere with the functioning of cardiac ion channels, leading to serious cardiovascular consequences. The discontinuation and removal of numerous approved drugs from the market or at late development stages in the pipeline due to such inhibitory effects further highlight the urgency of addressing this issue. Consequently, the early prediction of potential blockers targeting cardiac ion channels during the drug discovery process is of paramount importance. This study introduces a deep learning framework that computationally determines the cardiotoxicity associated with the voltagegated potassium channel (hERG), the voltage-gated calcium channel (Cav1.2), and the voltage-gated sodium channel (Nav1.5) for drug candidates. The predictive capabilities of three feature representations--molecular fingerprints, descriptors, and graph-based numerical representations-- are rigorously benchmarked. Additionally, a novel training and evaluation dataset framework is presented, enabling predictive model training of drug off-target cardiotoxicity using a comprehensive and large curated dataset covering these three cardiac ion channels. To facilitate these predictions, a robust and comprehensive small molecule cardiotoxicity prediction tool named CToxPred has been developed. It is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred.

pharmacology and toxicology↗

bioRxiv2023

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in TimsTOF

Immunopeptidomics plays a crucial role in identifying targets for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. Low abundant peptides often occur in the field of immunopeptidomics, which is why the highly sensitive timsTOF instruments are increasingly gaining popularity. To improve PSM rescoring for immunopeptides measured using timsTOF instruments, we trained a deep learning-based fragment ion intensity prediction model. 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project were analyzed on a timsTOF-Pro to generate a ground-truth dataset, containing 93,227 MS/MS spectra of 74,847 unique peptides, that was used to fine-tune an existing Prosit model. By applying our fragment ion intensity prediction model, we demonstrate up to 3-fold improvement in the identification of immunopeptides. Furthermore, our approach increased detection of immunopeptides even from low input samples.

bioinformatics↗

bioRxiv2023

The pitfalls of negative data bias for the T-cell epitope specificity challenge

Summary / AbstractEven high-performing machine learning models can have problems when deployed in a real-world setting if the data used to train and test the model contains biases. TCR-epitope binding prediction for novel epitopes is a very important but yet unsolved problem in immunology. In this article, we describe how the technique used to create negative data for the TCR-epitope interaction prediction task can lead to a strong bias and makes that the performance drops to random when tested in a more realistic scenario.

bioinformatics↗

bioRxiv2023

Sequence-to-sequence translation from mass spectra to peptides with a transformer model

A fundamental challenge for any mass spectrometry-based proteomics experiment is the identification of the peptide that generated each acquired tandem mass spectrum. Although approaches that leverage known peptide sequence databases are widely used and effective for well-characterized model organisms, such methods cannot detect unexpected peptides and can be impractical or impossible to apply in some settings. Thus, the ability to assign peptide sequences to the acquired tandem mass spectra without prior information--de novo peptide sequencing--is valuable for gaining biological insights for tasks including antibody sequencing, immunopeptidomics, and metaproteomics. Although many methods have been developed to address this de novo sequencing problem, it remains an outstanding challenge, in part due to the difficulty of modeling the irregular data structure of tandem mass spectra. Here, we describe Casanovo, a machine learning model that uses a transformer neural network architecture to translate the sequence of peaks in a tandem mass spectrum into the sequence of amino acids that comprise the generating peptide. We train a Casanovo model from 30 million labeled spectra and demonstrate that the model outperforms several state-of-the-art methods on a cross-species benchmark dataset. We also develop a version of Casanovo that is fine-tuned for non-enzymatic peptides. Finally, we demonstrate that Casanovos superior performance improves the analysis of immunopeptidomics and metaproteomics experiments and allows us to delve deeper into the dark proteome.

bioinformatics↗

bioRxiv2022

Unified and standardized mass spectrometry data processing in Python using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications, to spectrum plotting from online data repositories, and assisting data analysis tasks for dozens of other projects. Here we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

bioinformatics↗

bioRxiv2022

Semi-supervised machine learning for sensitive open modification spectral library searching

A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular post-processing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semi-supervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semi-supervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.

bioinformatics↗

bioRxiv2022

A Universal Language for Finding Mass Spectrometry Data Patterns

Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.

bioinformatics↗

bioRxiv2022

Comparison of Cosine, Modified Cosine, and Neutral Loss Based Spectral Alignment For Discovery of Structurally Related Molecules

Spectrum alignment of tandem mass spectrometry (MS/MS) data using the modified cosine similarity and subsequent visualization as molecular networks have been demonstrated to be a useful strategy to discover analogs of molecules from untargeted MS/MS-based metabolomics experiments. Recently, a neutral loss matching approach has been introduced as an alternative to MS/MS-based molecular networking, with an implied performance advantage in finding analogs that cannot be discovered using existing MS/MS spectrum alignment strategies. To comprehensively evaluate the scoring properties of neutral loss matching, the cosine similarity, and the modified cosine similarity, similarity measures of 955,228 peptide MS/MS spectrum pairs and 10 million small molecule MS/MS spectrum pairs were compared. This comparative analysis revealed that the modified cosine similarity outperformed neutral loss matching and the cosine similarity in all cases. The data further indicated that the performance of MS/MS spectrum alignment depends on the location and type of the modification, as well as the chemical compound class of fragmented molecules.

bioinformatics↗

bioRxiv2022

Open Access Repository-Scale Propagated Nearest Neighbor Suspect Spectral Library for Untargeted Metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of public MS/MS spectra. Annotations were propagated based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimers brain phenotype. The nearest neighbor suspect spectral library is openly available through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

bioinformatics↗

bioRxiv2022

Interpretable deep learning to uncover the molecular binding patterns determining TCR-epitope interactions

The recognition of an epitope by a T-cell receptor (TCR) is crucial for eliminating pathogens and establishing immunological memory. Prediction of the binding of any TCR-epitope pair is still a challenging task, especially for novel epitopes, because the underlying patterns are largely unknown to domain experts and machine learning models. To achieve a deeper understanding of TCR-epitope interactions, we have used interpretable deep learning techniques to gain insights into the performance of TCR-epitope binding machine learning models. We demonstrate how interpretable AI techniques can be linked to the three-dimensional structure of molecules to offer novel insights into the factors that determine TCR affinity on a molecular level. Additionally, our results show the importance of using interpretability techniques to verify the predictions of machine learning models for challenging molecular biology problems where small hard-to-detect problems can accumulate to inaccurate results.

bioinformatics↗

bioRxiv2022

Open modification searching of SARS-CoV-2-human protein interaction data reveals novel viral modification sites

The outbreak of the SARS-CoV-2 coronavirus, the causative agent of the COVID-19 disease, has led to an ongoing global pandemic since 2019. Mass spectrometry can be used to understand the molecular mechanisms of viral infection by SARS-CoV-2, for example, by determining virus-host protein-protein interactions (PPIs) through which SARS-CoV-2 hijacks its human hosts during infection, and to study the role of post-translational modifications (PTMs). We have reanalyzed public affinity purification mass spectrometry data using open modification searching to investigate the presence of PTMs in the context of the SARS-CoV-2 virus-host PPI network. Based on an over two-fold increase in identified spectra, our detected protein interactions show a high overlap with independent mass spectrometry-based SARS-CoV-2 studies and virus-host interactions for alternative viruses, as well as previously unknown protein interactions. Additionally, we identified several novel modification sites on SARS-CoV-2 proteins that we investigated in relation to their interactions with host proteins. A detailed analysis of relevant modifications, including phosphorylation, ubiquitination, and S-nitrosylation, provides important hypotheses about the functional role of these modifications during viral infection by SARS-CoV-2.

bioinformatics↗

bioRxiv2022

De novo mass spectrometry peptide sequencing with a transformer model

Tandem mass spectrometry is the only high-throughput method for analyzing the protein content of complex biological samples and is thus the primary technology driving the growth of the field of proteomics. A key outstanding challenge in this field involves identifying the sequence of amino acids--the peptide--responsible for generating each observed spectrum, without making use of prior knowledge in the form of a peptide sequence database. Although various machine learning methods have been developed to address this de novo sequencing problem, challenges that arise when modeling tandem mass spectra have led to complex models that combine multiple neural networks and post-processing steps. We propose a simple yet powerful method for de novo peptide sequencing, Casanovo, that uses a transformer framework to map directly from a sequence of observed peaks (a mass spectrum) to a sequence of amino acids (a peptide). Our experiments show that Casanovo achieves state-of-the-art performance on a benchmark dataset using a standard cross-species evaluation framework which involves testing with spectra with never-before-seen peptide labels. Casanovo not only achieves superior performance but does so at a fraction of the model complexity and inference time required by other methods.

bioinformatics↗

bioRxiv2022

A comprehensive evaluation of consensus spectrum generation methods in proteomics

Spectrum clustering is a powerful strategy to minimize redundant mass spectral data by grouping highly similar mass spectra corresponding to repeatedly measured analytes. Based on spectrum similarity, near-identical spectra are grouped in clusters, after which each cluster can be represented by its so-called consensus spectrum for downstream processing. Although several algorithms for spectrum clustering have been adequately benchmarked and tested, the influence of the consensus spectrum generation step is rarely evaluated. Here, we present an implementation and benchmark of common consensus spectrum algorithms, including spectrum averaging, spectrum binning, the most similar spectrum, and the best-identified spectrum. We have analyzed diverse public datasets using two different clustering algorithms (spectra-cluster and MaRaCluster) to evaluate how the consensus spectrum generation procedure influences downstream peptide identification. The BEST and BIN methods were found the most reliable methods for consensus spectrum generation, including for datasets with post-translational modifications (PTM) such as phosphorylation. All source code and data of the present study are freely available on GitHub at https://github.com/statisticalbiotechnology/representative-spectra-benchmark.

bioinformatics↗