In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate ...predictive models for fragment ion intensities impairs the realization of the full potential of these approaches. Here, we extended the ProteomeTools synthetic peptide library to 550,000 tryptic peptides and 21 million high-quality tandem mass spectra. We trained a deep neural network, termed Prosit, resulting in chromatographic retention time and fragment ion intensity predictions that exceed the quality of the experimental data. Integrating Prosit into database search pipelines led to more identifications at >10× lower false discovery rates. We show the general applicability of Prosit by predicting spectra for proteases other than trypsin, generating spectral libraries for data-independent acquisition and improving the analysis of metaproteomes. Prosit is integrated into ProteomicsDB, allowing search result re-scoring and custom spectral library generation for any organism on the basis of peptide sequence alone.
The target landscape of clinical kinase drugs Klaeger, Susan; Heinzlmeir, Stephanie; Wilhelm, Mathias ...
Science (American Association for the Advancement of Science),
12/2017, Letnik:
358, Številka:
6367
Journal Article
Recenzirano
Odprti dostop
Kinase inhibitors are important cancer therapeutics. Polypharmacology is commonly observed, requiring thorough target deconvolution to understand drug mechanism of action. Using chemical proteomics, ...we analyzed the target spectrum of 243 clinically evaluated kinase drugs. The data revealed previously unknown targets for established drugs, offered a perspective on the "druggable" kinome, highlighted (non)kinase off-targets, and suggested potential therapeutic applications. Integration of phosphoproteomic data refined drug-affected pathways, identified response markers, and strengthened rationale for combination treatments. We exemplify translational value by discovering SIK2 (salt-inducible kinase 2) inhibitors that modulate cytokine production in primary cells, by identifying drugs against the lung cancer survival marker MELK (maternal embryonic leucine zipper kinase), and by repurposing cabozantinib to treat FLT3-ITD-positive acute myeloid leukemia. This resource, available via the ProteomicsDB database, should facilitate basic, clinical, and drug discovery research and aid clinical decision-making.
Abstract
ProteomicsDB (https://www.ProteomicsDB.org) is a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ...ProteomicsDB was first released in 2014 to enable the interactive exploration of the first draft of the human proteome. To date, it contains quantitative data from 78 projects totalling over 19k LC-MS/MS experiments. A standardized analysis pipeline enables comparisons between multiple datasets to facilitate the exploration of protein expression across hundreds of tissues, body fluids and cell lines. We recently extended the data model to enable the storage and integrated visualization of other quantitative omics data. This includes transcriptomics data from e.g. NCBI GEO, protein-protein interaction information from STRING, functional annotations from KEGG, drug-sensitivity/selectivity data from several public sources and reference mass spectra from the ProteomeTools project. The extended functionality transforms ProteomicsDB into a multi-purpose resource connecting quantification and meta-data for each protein. The rich user interface helps researchers to navigate all data sources in either a protein-centric or multi-protein-centric manner. Several options are available to download data manually, while our application programming interface enables accessing quantitative data systematically.
Abstract
ProteomicsDB (https://www.ProteomicsDB.org) started as a protein-centric in-memory database for the exploration of large collections of quantitative mass spectrometry-based proteomics data. ...The data types and contents grew over time to include RNA-Seq expression data, drug-target interactions and cell line viability data. In this manuscript, we summarize new developments since the previous update that was published in Nucleic Acids Research in 2017. Over the past two years, we have enriched the data content by additional datasets and extended the platform to support protein turnover data. Another important new addition is that ProteomicsDB now supports the storage and visualization of data collected from other organisms, exemplified by Arabidopsis thaliana. Due to the generic design of ProteomicsDB, all analytical features available for the original human resource seamlessly transfer to other organisms. Furthermore, we introduce a new service in ProteomicsDB which allows users to upload their own expression datasets and analyze them alongside with data stored in ProteomicsDB. Initially, users will be able to make use of this feature in the interactive heat map functionality as well as the drug sensitivity prediction, but ultimately will be able to use all analytical features of ProteomicsDB in this way.
We describe ProteomeTools, a project building molecular and digital tools from the human proteome to facilitate biomedical research. Here we report the generation and multimodal liquid ...chromatography-tandem mass spectrometry analysis of >330,000 synthetic tryptic peptides representing essentially all canonical human gene products, and we exemplify the utility of these data in several applications. The resource (available at http://www.proteometools.org) will be extended to >1 million peptides, and all data will be shared with the community via ProteomicsDB and ProteomeXchange.
High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural ...information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.
Crystal structure databases offer ample opportunities to derive small molecule conformation preferences, but the derived knowledge is not systematically applied in drug discovery research. We address ...this gap by a comprehensive and extendable expert system enabling quick assessment of the probability of a given conformation to occur. It is based on a hierarchical system of torsion patterns that cover a large part of druglike chemical space. Each torsion pattern has associated frequency histograms generated from CSD and PDB data and, derived from the histograms, traffic-light rules for frequently observed, rare, and highly unlikely torsion ranges. Structures imported into the corresponding software are annotated according to these rules. We present the concept behind the tree of torsion patterns, the design of an intuitive user interface for the management and usage of the torsion library, and we illustrate how the system helps analyze and understand conformation properties of substructures widely used in medicinal chemistry.
The landscape of structural variation (SV) including complex duplication and translocation patterns is far from resolved. SV detection tools usually exhibit low agreement, are often geared toward ...certain types or size ranges of variation and struggle to correctly classify the type and exact size of SVs.
We present Gustaf (Generic mUlti-SpliT Alignment Finder), a sound generic multi-split SV detection tool that detects and classifies deletions, inversions, dispersed duplications and translocations of ≥ 30 bp. Our approach is based on a generic multi-split alignment strategy that can identify SV breakpoints with base pair resolution. We show that Gustaf correctly identifies SVs, especially in the range from 30 to 100 bp, which we call the next-generation sequencing (NGS) twilight zone of SVs, as well as larger SVs >500 bp. Gustaf performs better than similar tools in our benchmark and is furthermore able to correctly identify size and location of dispersed duplications and translocations, which otherwise might be wrongly classified, for example, as large deletions.
Molecular patterns are widely used for compound filtering in molecular design endeavors. They describe structural properties that are connected with unwanted physical or chemical properties like ...reactivity or toxicity. With filter sets comprising hundreds of structural filters, an analytic approach to compare those patterns is needed. Here we present a novel approach to solve the generic pattern comparison problem. We introduce chemically inspired fingerprints for pattern nodes and edges to derive an easy-to-compare pattern representation. On two annotated pattern graphs we apply a maximum common subgraph algorithm enabling the calculation of pattern inclusion and similarity. The resulting algorithm can be used in many different ways. We can automatically derive pattern hierarchies or search in large pattern collections for more general or more specific patterns. To the best of our knowledge, the presented algorithm is the first of its kind enabling these types of chemical pattern analytics. Our new tool named SMARTScompare is an implementation of the approach for the SMARTS language, which is the quasi-standard for structural filters. We demonstrate the capabilities of SMARTScompare on a large collection of SMARTS patterns from real applications.
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