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BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O12 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O12. XILMASS: A CROSS-LINKED PEPTIDE IDENTIFICATION ALGORITHM Şule Yılmaz 1,2,3* , Masa Cernic 4 , Friedel Drepper 5 , Bettina Warscheid 5 , Lennart Martens 1,2,3 & Elien Vandermarliere 1,2,3 . Medical Biotechnology Center, VIB, Ghent, Belgium 1 ; Department of Biochemistry, Ghent University, Ghent, Belgium 2 ; Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium 3 ; Department of Biochemistry, Molecular and Structural Biology, Jožef Stefan Institute, Ljubljana, Slovenia 4 ; Functional Proteomics and Biochemistry, Department of Biochemistry and Functional Proteomics, Institute for Biology II and BIOSS Centre for Biological Signaling Studies, University of Freiburg, Freiburg, Germany 5 . *sule.yilmaz@ugent.be Chemical cross-linking coupled with mass spectrometry (XL-MS) facilitates the determination of protein structure and the understanding of protein interactions. The current computational approaches rely on different strategies with a limited number of open-source and easy-to-use search algorithms. We therefore built a novel cross-linked peptide identification algorithm, called Xilmass which has a novel database construction and a new scoring function adapted from traditional database search algorithms. We compared the performance of Xilmass against one of the most popular and publicly available algorithms: pLink, and a recently published algorithm Kojak. We found that Xilmass identified 140 spectra whereas Kojak and pLink identified 119 and 35, respectively. We mapped the cross-linking sites on the structure which resulted in the identification of 20 possible cross-linking sites. These findings show that Xilmass allows the identification of cross-linking sites. INTRODUCTION The structure of a protein is crucial for its functionality. Protein structure is commonly determined by X-ray crystallography or nuclear magnetic resonance (NMR). X- ray crystallography is only feasible for crystallizable proteins and NMR has a protein size limitation. Due to these restrictions, protein complexes are much more difficult to approach with these classical methods. However, chemical cross-linking of the complex coupled with mass spectrometry (XL-MS) allows to study of these protein complexes. The identification of the measured fragmentation spectra is a challenging task. One approach to identify cross-linked peptides is to linearize crosslinked peptide-pairs in order to generate a database to perform traditional search engines (Maiolica et al., 2007). However, a traditional search engine is not directly applicable to identify cross-linked peptides. Another approach is to rely on the usage of labeled cross-linkers, but this has a decreased performance when unlabeled cross-linkers are used. We therefore built an algorithm, Xilmass, which is designed for the identification of XL- MS fragmentation spectra without linearization of peptides and the requirement of labeled cross-linkers. We also introduced a new way of representation of a cross-linked peptide database and directly implemented a new scoring function. METHODS The data sets were derived from human calmodulin (CaM) and the actin binding domain of plectin (plectin-ABD) which were cross-linked by DSS. The data sets were analyzed on a Velos Orbitrap Elite. Cross-linked peptides were identified by Xilmass, pLink (Yang et al., 2012) and Kojak (Hoopmann et al., 2015). The identifications of both Xilmass and Kojak were validated by Percolator (Käll et al., 2007) at q-value=0.05. pLink returned a validated list at FDR=0.05. The findings on cross-linking sites were validated with the aid of the available structures (Plectin PDB-entry: 4Q57 and calmodulin PDB-entry: 2F3Y). The cross-linking sites were predicted by X-Walk (Kahraman et al., 2011) and PyMOL was used for the visualization. RESULTS & DISCUSSION We compared the number of identified spectra and crosslinking sites from Xilmass, pLink and Kojak. Xilmass identified 140 spectra whereas Kojak and pLink identified 119 and 35 spectra, respectively (at FDR=0.05). Xilmass identified 53 cross-linking sites from the 140 spectra with 37 obtained from at least 2 peptide-to-spectrum matches (PSMs). Kojak identified more cross-linking sites (60), however, only 26 cross-linking sites have at least 2 PSMs. The identified cross-linking sites by Xilmass were manually verified on the structure (Figure1). We defined 20 cross-linking sites as possible (Cα-Cα distances within 30Å (orange)) and not-predicted (Cα-Cα distances exceeding 30Å (blue)). These findings show that Xilmass allows the identification of cross-linking sites. FIGURE 1. The identified cross-linking sites were mapped on the plectin protein structure to manually verify them (PDB-entry:4Q57) REFERENCES Hoopmann ,M R et al. Journal of Proteome Research, 14, 2190–2198 (2015) Kahraman,A. et al. Bioinformatics, 27, 2163–2164 (2011) Käll,L. et al. Nature Methods, 4, 923–925 (2007) Maiolica,A. et al. Molecular & cellular proteomics:MCP, 6, 2200–2211 (2007) Yang,B. et al. Nature Methods, 9, 904–906 (2012) 32
BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015 Abstract ID: O13 Oral presentation 10th Benelux Bioinformatics Conference bbc 2015 O13. AUTOMATED ANATOMICAL INTERPRETATION OF DIFFERENCES BETWEEN IMAGING MASS SPECTROMETRY EXPERIMENTS Nico Verbeeck 1* , Jeffrey Spraggins ,2 , Yousef El Aalamat 3,4 , Junhai Yang 2 , Richard M. Caprioli 2 , Bart De Moor 3,4 ,Etienne Waelkens 5,6 & Raf Van de Plas 1,2 . Delft Center for Systems and Control (DCSC), Delft University of Technology 1 ; Mass Spectrometry Research Center (MSRC),Vanderbilt University 2 ; STADIUS Center for Dynamical Systems, Signal Processing, and Data Analytics, Dept. of Electrical Engineering (ESAT), KU Leuven 3 ; iMinds Medical IT, KU Leuven 4 ; Dept. of Cellular and Molecular Medicine, KU Leuven 5 ; Sybioma, KU Leuven 6 . * n.verbeeck@tudelft.nl Imaging mass spectrometry (IMS) is a powerful molecular imaging technology that generates large amounts of data, making manual analysis often practically infeasible. In this work we aid the differential analysis of multiple IMS datasets by linking these data to an anatomical atlas. Using matrix factorization based multivariate analysis techniques, we are able to identify differential biomolecular signals between individual tissue samples in an obesity case study on mouse brain. The resulting differential signals are then automatically interpreted in terms of anatomical structures using a convex optimization approach and the Allen Mouse Brain Atlas. The automated anatomical interpretation facilitates much deeper exploration by the biomedical expert for these types of very rich data sets. INTRODUCTION Imaging Mass Spectrometry (IMS) is a relatively new molecular imaging technology that enables a user to monitor the spatial distributions of hundreds of biomolecules in a tissue slice simultaneously. This unique property makes IMS an immensely valuable technology in biomedical research. However, it also leads to very large amounts of data in a single analysis (e.g. >1 TB), making manual analysis of these data increasingly impractical. In order to aid the exploration of these data, we have recently developed a framework that integrates IMS data with an anatomical atlas. The framework uses the anatomical data in the atlas to automatically interpret the IMS data in terms of anatomical structures, and guides the user towards relevant findings within a single tissue section. In this work, we extend this framework towards the automated interpretation of biomolecular differences between multiple IMS datasets. METHODS We demonstrate our method on IMS data of multiple mouse brain sections, and use the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to the MALDI-based IMS measurements. We spatially map the data of each individual IMS dataset to the anatomical atlas using both rigid and non-rigid registration techniques. This establishes a common reference space and allows for direct comparison of spatial locations between the different IMS datasets. Group Independent Component Analysis (GICA) is then used to automatically extract the differentially expressed biomolecular patterns, after which convex optimization is used to automatically interpret the differential components in terms of known anatomical structures (Verbeeck et al, 2014), directly listing the anatomical areas in which changes occur. RESULTS & DISCUSSION We demonstrate our approach in an obesity case study on mouse brain. All tissue sections are cryosectioned at 10 μm and thaw-mounted onto ITO coated glass slides after which they are sublimated with CMBT matrix. MALDI IMS images are collected using the Bruker 15T solariX FTICR MS with a spatial resolution of 50 μm, collecting approximately 35,000 pixels per experiment. The IMS data of the different experiments are registered to the anatomical reference space provided by the Allen Mouse Brain Atlas, establishing an inter-experiment study-wide reference space. Analysis of the IMS measurements using GICA reveals multiple biomolecular patterns that differentiate between the various dietary conditions examined by the study. The retrieved differentially expressed biomolecular patterns are then translated to combinations of anatomical structures using our convex optimization approach, similar to what a human investigator intends to do. This automated interpretation of inter-experiment differences can serve as a great accelerator in the exploration of IMS data, as it avoids the time-and resource-intensive step of having a histological expert manually interpret the differential patterns. FIGURE 1. Automated anatomical interpretation of a biomolecular pattern that is differentially expressed in coronal mouse brain sections between a high fat and a low fat diet in our obesity case study. REFERENCES Verbeeck, N. et al. Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases. Anal. Chem. 86, 8974–8982 (2014). 33
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