Computational Omics and Systems Biology Group

The CEP5 peptide promotes abiotic stress tolerance, as revealed by quantitative proteomics, and attenuates the AUX/IAA equilibrium in Arabidopsis

S. Smith, S. Zhu, L. Joos, I. Roberts, N. Nikonorova, L. D. Vu, E. Stes, H. Cho, A. Larrieu, W. Xuan, B. Goodall, B. Van De Cotte, J. M. Waite, A. Rigal, S. R. R. Harborough, G. Persiau, S. Vanneste, G. K. Kirschner, E. Vandermarliere, L. Martens, Y. Stahl, D. Audenaert, J. Friml, G. Felix, R. Simon, M. Bennett, A. Bishopp, G. De Jaeger, K. Ljung, S. Kepinski, S. Robert, J. Nemhauser, I. Hwang, K. Gevaert, T. Beeckman, and I. De Smet
Molecular & Cellular Proteomics, 2020

Peptides derived from non-functional precursors play important roles in various developmental processes, but also in (a)biotic stress signaling. Our (phospho)proteome-wide analyses of C-terminally encoded peptide 5 (CEP5)-mediated changes revealed an impact on abiotic stress-related processes. Drought has a dramatic impact on plant growth, development and reproduction, and the plant hormone auxin plays a role in drought responses. Our genetic, physiological, biochemical and pharmacological results demonstrated that CEP5-mediated signaling is relevant for osmotic and drought stress tolerance in Arabidopsis, and that CEP5 specifically counteracts auxin effects. Specifically, we found that CEP5 signaling stabilizes AUX/IAA transcriptional repressors, suggesting the existence of a novel peptide-dependent control mechanism that tunes auxin signaling. These observations align with the recently described role of AUX/IAAs in stress tolerance and provide a novel role for CEP5 in osmotic and drought stress tolerance.

The Age of Data-Driven Proteomics: How Machine Learning Enables Novel Workflows

R. Bouwmeester, R. Gabriels, T. Van Den Bossche, L. Martens, and S. Degroeve
PROTEOMICS, 2020

A lot of energy in the field of proteomics is dedicated to the application of challenging experimental workflows, which include metaproteomics, proteogenomics, data independent acquisition (DIA), non-specific proteolysis, immunopeptidomics, and open modification searches. These workflows are all challenging because of ambiguity in the identification stage; they either expand the search space and thus increase the ambiguity of identifications, or, in the case of DIA, they generate data that is inherently more ambiguous. In this context, machine learning-based predictive models are now generating considerable excitement in the field of proteomics because these predictive models hold great potential to drastically reduce the ambiguity in the identification process of the above-mentioned workflows. Indeed, the field has already produced classical machine learning and deep learning models to predict almost every aspect of a liquid chromatography-mass spectrometry (LC-MS) experiment. Yet despite all the excitement, thorough integration of predictive models in these challenging LC-MS workflows is still limited, and further improvements to the modeling and validation procedures can still be made. In this viewpoint we therefore point out highly promising recent machine learning developments in proteomics, alongside some of the remaining challenges.

Removing the hidden data dependency of DIA with predicted spectral libraries

B. Van Puyvelde, S. Willems, R. Gabriels, S. Daled, L. De Clerck, S. Vande Casteele, A. Staes, F. Impens, D. Deforce, L. Martens, S. Degroeve, and M. Dhaenens
PROTEOMICS, 2020

Data‐independent acquisition (DIA) generates comprehensive yet complex mass spectrometric data, which imposes the use of data‐dependent acquisition (DDA) libraries for deep peptide‐centric detection. Here, it is shown that DIA can be redeemed from this dependency by combining predicted fragment intensities and retention times with narrow window DIA. This eliminates variation in library building and omits stochastic sampling, finally making the DIA workflow fully deterministic. Especially for clinical proteomics, this has the potential to facilitate inter‐laboratory comparison.

Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions

A. S. Ferreira Diamantino Coelho e Silva, R. Bouwmeester, L. Martens, and S. Degroeve
BIOINFORMATICS, 2020

Anatomy and evolution of database search engines : a central component of mass spectrometry based proteomic workflows

K. Verheggen, H. Raeder, F. S. Berven, L. Martens, H. Barsnes, and M. Vaudel
MASS SPECTROMETRY REVIEWS, 2020

A comparison of collision cross section values obtained via travelling wave ion mobility-mass spectrometry and ultra high performance liquid chromatography-ion mobility-mass spectrometry : application to the characterisation of metabolites in rat urine

L. C. Nye, J. P. Williams, N. C. Munjoma, M. P. Letertre, M. Coen, R. Bouwmeester, L. Martens, J. R. Swann, J. K. Nicholson, R. S. Plumb, M. McCullagh, L. A. Gethings, S. Lai, J. I. Langridge, J. P. Vissers, and I. D. Wilson
JOURNAL OF CHROMATOGRAPHY A, 2019

Updated MS²PIP web server delivers fast and accurate MS² peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques

R. Gabriels, L. Martens, and S. Degroeve
NUCLEIC ACIDS RESEARCH, 2019

(MSPIP)-P-2 is a data-driven tool that accurately predicts peak intensities for a given peptide's fragmentation mass spectrum. Since the release of the (MSPIP)-P-2 web server in 2015, we have brought significant updates to both the tool and the web server. In addition to the original models for CID and HCD fragmentation, we have added specialized models for the TripleTOF 5600+ mass spectrometer, for TMT-labeled peptides, for iTRAQ-labeled peptides, and for iTRAQ-labeled phosphopeptides. Because the fragmentation pattern is heavily altered in each of these cases, these additional models greatly improve the prediction accuracy for their corresponding data types. We have also substantially reduced the computational resources required to run (MSPIP)-P-2, and have completely rebuilt the web server, which now allows predictions of up to 100 000 peptide sequences in a single request. The MS(2)PIPweb server is freely available at https://iomics.ugent.be/ms2pip/.

Challenges and promise at the interface of metaproteomics and genomics : an overview of recent progress in metaproteogenomic data analysis

H. Schiebenhoefer, T. Van Den Bossche, S. Fuchs, B. Y. Renard, T. Muth, and L. Martens
EXPERT REVIEW OF PROTEOMICS, 2019

Introduction: The study of microbial communities based on the combined analysis of genomic and proteomic data - called metaproteogenomics - has gained increased research attention in recent years. This relatively young field aims to elucidate the functional and taxonomic interplay of proteins in microbiomes and its implications on human health and the environment. Areas covered: This article reviews bioinformatics methods and software tools dedicated to the analysis of data from metaproteomics and metaproteogenomics experiments. In particular, it focuses on the creation of tailored protein sequence databases, on the optimal use of database search algorithms including methods of error rate estimation, and finally on taxonomic and functional annotation of peptide and protein identifications. Expert opinion: Recently, various promising strategies and software tools have been proposed for handling typical data analysis issues in metaproteomics. However, severe challenges remain that are highlighted and discussed in this article; these include: (i) robust false-positive assessment of peptide and protein identifications, (ii) complex protein inference against a background of highly redundant data, (iii) taxonomic and functional post-processing of identification data, and finally, (iv) the assessment and provision of metrics and tools for quantitative analysis.

Comprehensive and empirical evaluation of machine learning algorithms for small molecule LC retention time prediction

R. Bouwmeester, L. Martens, and S. Degroeve
ANALYTICAL CHEMISTRY, 2019

Liquid chromatography is a core component of almost all mass spectrometric analyses of (bio)molecules. Because of the high-throughput nature of mass spectrometric analyses, the interpretation of these chromatographic data increasingly relies on informatics solutions that attempt to predict an analyte's retention time. The key components of such predictive algorithms are the features these are supplies with, and the actual machine learning algorithm used to fit the model parameters. Therefore, we have evaluated the performance of seven machine learning algorithms on 36 distinct metabolomics data sets, using two distinct feature sets. Interestingly, the results show that no single learning algorithm performs optimally for all data sets, with different types of algorithms achieving top performance for different types of analytes or different protocols. Our results thus show that an evaluation of machine learning algorithms for retention time prediction is needed to find a suitable algorithm for specific analytes or protocols. Importantly, however, our results also show that blending different types of models together decreases the error on outliers, indicating that the combination of several approaches holds substantial promise for the development of more generic, high-performing algorithms.

An update on the moFF algorithm for label-free quantitative proteomics

A. Argentini, A. Staes, B. A. Grüning, S. Mehta, C. Easterly, T. Griffin, P. Jagtap, F. Impens, and L. Martens
JOURNAL OF PROTEOME RESEARCH, 2019

moFF is a modular and operating-system-independent tool for quantitative analysis of label-free mass-spectrometry-based proteomics data. The moFF workflow, comprising matching-between-runs and apex quantification, can be applied to any upstream search engine's output, along with the corresponding Thermo or mzML raw file. We here present moFF 2.0, with improvements in speed through multithreading, the use of a new raw file access library, and a novel filtering approach in the matching-between-runs module. This filter allows moFF to correctly identify features that are present in one run but not in another, as demonstrated using spiked-in iRT peptides. Moreover, moFF 2.0 also provides a new peptide summary export that can be used in downstream statistical analysis. moFF is open source and freely available and can be downloaded from https://github.com/compomics/moFF