MIND4OLIGOS: Determining the Monoisotopic Mass of Oligonucleotides Observed in High-Resolution Mass Spectrometry.

Oligonucleotide therapeutics have emerged as an important class of drugs offering targeted therapeutic strategies that complement traditional modalities, such as monoclonal antibodies and small molecules. Their unique ability to precisely modulate gene expression makes them vital for addressing previously undruggable targets. A critical aspect of developing these therapies is characterizing their molecular composition accurately. This includes determining the monoisotopic mass of oligonucleotides, which is essential for identifying impurities, degradants, and modifications that can affect the drug efficacy and safety. Mass spectrometry (MS) plays a pivotal role in this process, yet the accurate interpretation of complex mass spectra remains challenging, especially for large molecules, where the monoisotopic peak is often undetectable. To address this issue, we have adapted the MIND algorithm, originally developed for top-down proteomics, for use with oligonucleotide data. This adaptation allows for the prediction of monoisotopic mass from the more readily detectable, most-abundant peak mass, enhancing the ability to annotate complex spectra of oligonucleotides. Our comprehensive validation of this modified algorithm on both in silico and real-world oligonucleotide data sets has demonstrated its effectiveness and reliability. To facilitate wider adoption of this advanced analytical technique, we have encapsulated the enhanced MIND algorithm in a user-friendly Shiny application. This online platform simplifies the process of annotating complex oligonucleotide spectra, making advanced mass spectrometry analysis accessible to researchers and drug developers. The application is available at https://valkenborg-lab.shinyapps.io/mind4oligos/.

MeRgeION: a Multifunctional R Pipeline for Small Molecule LC-MS/MS Data Processing, Searching, and Organizing.

Small molecule structure elucidation using tandem mass spectrometry (MS/MS) plays a crucial role in life science, bioanalytical, and pharmaceutical research. There is a pressing need for increased throughput of compound identification and transformation of historical data into information-rich spectral databases. Meanwhile, molecular networking, a recent bioinformatic framework, provides global displays and system-level understanding of complex LC-MS/MS data sets. Herein we present meRgeION, a multifunctional, modular, and flexible R-based toolbox to streamline spectral database building, automated structural elucidation, and molecular networking. The toolbox offers diverse tuning parameters and the possibility to combine various algorithms in the same pipeline. As an open-source R package, meRgeION is ideally suited for building spectral databases and molecular networks from privacy-sensitive and preliminary data. Using meRgeION, we have created an integrated spectral database covering diverse pharmaceutical compounds that was successfully applied to annotate drug-related metabolites from a published nontargeted metabolomics data set as well as reveal the chemical space behind this complex data set through molecular networking. Moreover, the meRgeION-based processing workflow has demonstrated the usefulness of a spectral library search and molecular networking for pharmaceutical forced degradation studies. meRgeION is freely available at: https://github.com/daniellyz/meRgeION2.

Synthesis of Phosphorodiamidate Oligonucleotide Dimers.

Azido nucleosides couple with phosphoramidites via an initial iminophosphorane, which eliminates acrylonitrile to generate the coupled dimer P(V) product. The vulnerable phosphite triester intermediate is bypassed entirely, making the methodology very suitable to solution-phase synthesis. This new coupling protocol requires no protection of the 5'-OH function and provides a new method of installing internucleosidic phosphorodiamidate bonds with near quantitative yields.

Current and future deep learning algorithms for tandem mass spectrometry (MS/MS)-based small molecule structure elucidation.

RATIONALE: Structure elucidation of small molecules has been one of the cornerstone applications of mass spectrometry for decades. Despite the increasing availability of software tools, structure elucidation from tandem mass spectrometry (MS/MS) data remains a challenging task, leaving many spectra unidentified. However, as an increasing number of reference MS/MS spectra are being curated at a repository scale and shared on public servers, there is an exciting opportunity to develop powerful new deep learning (DL) models for automated structure elucidation. ARCHITECTURES: Recent early-stage DL frameworks mostly follow a "two-step approach" that translates MS/MS spectra to database structures after first predicting molecular descriptors. The related architectures could suffer from: (1) computational complexity because of the separate training of descriptor-specific classifiers, (2) the high dimensional nature of mass spectral data and information loss due to data preprocessing, (3) low substructure coverage and class imbalance problem of predefined molecular fingerprints. Inspired by successful DL frameworks employed in drug discovery fields, we have conceptualized and designed hypothetical DL architectures to tackle the above issues. For (1), we recommend multitask learning to achieve better performance with fewer classifiers by grouping structurally related descriptors. For (2) and (3), we introduce feature engineering to extract condensed and higher-order information from spectra and structure data. For instance, encoding spectra with subtrees and pre-calculated spectral patterns add peak interactions to the model input. Encoding structures with graph convolutional networks incorporates connectivity within a molecule. The joint embedding of spectra and structures can enable simultaneous spectral library and molecular database search. CONCLUSIONS: In principle, given enough training data, adapted DL architectures, optimal hyperparameters and computing power, DL frameworks can predict small molecule structures, completely or at least partially, from MS/MS spectra. However, their performance and general applicability should be fairly evaluated against classical machine learning frameworks.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation.

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

Mass spectrometric characterization of intact desferal-conjugated monoclonal antibodies for immuno-PET imaging.

RATIONALE: Immuno-PET imaging may prove to be a diagnostic and progression/intervention biomarker for Alzheimer's disease (AD) with improved sensitivity and specificity. Immuno-PET imaging is based on the coupling of an antibody with a chelator that captures a radioisotope thus serving as an in-vivo PET ligand. A robust and quality controlled process for linking the chelator to the-antibody is fundamental for the success of this approach. METHODS: The structural integrities of two monoclonal antibodies (trastuzumab and JRF/AßN/25) and the quantity of desferal-based chelator attached following modification of the antibodies were assessed by online desalting and intact mass analysis. Enzymatic steps for the deglycosylation and removal of C-terminal lysine was performed sequentially and in a single tube to improve intact mass data. RESULTS: Intact mass analysis demonstrated that inclusion of enzymatic processing was critical to correctly derive the quantity of chelator linked to the monoclonal antibodies. For trastuzumab, enzymatic cleaving of the glycans was sufficient, whilst additional removal of the C-terminal lysine was necessary for JRF/AßN/25 to ensure reproducible assessment of the relatively low amount of attached chelator. CONCLUSIONS: An efficient intact mass analysis-based process was developed to reproducibly determine the integrity of monoclonal antibodies and the quantity of attached chelator. This technique could serve as an essential quality control approach for the development and production of immuno-PET tracers.

TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry.

Small molecule identification is a crucial task in analytical chemistry and life sciences. One of the most commonly used technologies to elucidate small molecule structures is mass spectrometry. Spectral library search of product ion spectra (MS/MS) is a popular strategy to identify or find structural analogues. This approach relies on the assumption that spectral similarity and structural similarity are correlated. However, popular spectral similarity measures, usually calculated based on identical fragment matches between the MS/MS spectra, do not always accurately reflect the structural similarity. In this study, we propose TransExION, a Transformer based Explainable similarity metric for IONS. TransExION detects related fragments between MS/MS spectra through their mass difference and uses these to estimate spectral similarity. These related fragments can be nearly identical, but can also share a substructure. TransExION also provides a post-hoc explanation of its estimation, which can be used to support scientists in evaluating the spectral library search results and thus in structure elucidation of unknown molecules. Our model has a Transformer based architecture and it is trained on the data derived from GNPS MS/MS libraries. The experimental results show that it improves existing spectral similarity measures in searching and interpreting structural analogues as well as in molecular networking. SCIENTIFIC CONTRIBUTION: We propose a transformer-based spectral similarity metrics that improves the comparison of small molecule tandem mass spectra. We provide a post hoc explanation that can serve as a good starting point for unknown spectra annotation based on database spectra.

SynONIM: A Comprehensive Database of Synthetic Oligonucleotide Modifications and Impurities to Aid in Their Characterization by Mass Spectrometry.

Given the resurgence of oligonucleotides in the biotherapeutic space, there is a profound focus on their characterization by mass spectrometry. These therapeutic moieties commonly employ synthetic modifications to aid in increasing efficacy and stability; however, these modifications can also increase the complexity of mass spectrometry data analysis. Additionally, various stress conditions can affect both the observed level and type of impurities stemming from the variety of utilized modifications. Within the oligonucleotide analytical development community, a clear desire exists for a unified database of synthetic oligonucleotide modifications and impurities where information regarding structure, mass, and shorthand nomenclature can be contained. To address this, the authors have prepared an online database and webtool of synthetic oligonucleotide impurities and modifications, SynONIM, to centrally locate information key to the mass spectrometry community. SynONIM can be queried by elemental composition lost or gained, mass shift, shorthand notation, nucleotide location, and species origin.

Lipophilic Salts and Lipid-Based Formulations for Bridging the Food Effect Gap of Venetoclax.

Lipid based formulations (LBF) have shown to overcome food dependent bioavailability for some poorly water-soluble drugs. However, the utility of LBFs can be limited by low dose loading due to a low drug solubility in LBF vehicles. This study investigated the solubility and drug loading increases in LBFs using lipophilic counterions to form lipophilic salts of venetoclax. Venetoclax docusate was formed from venetoclax free base and verified by 1H NMR. Formation of stable venetoclax-fatty acid associations with either oleic acid or decanoic acid were attempted, however, the molecular associations were less consistent based on 1H NMR. Venetoclax docusate displayed a up to 6.2-fold higher solubility in self-emulsifying drug delivery systems (SEDDS) when compared to the venetoclax free base solubility resulting in a higher dose loading. A subsequent bioavailability study in landrace pigs demonstrated a 2.5-fold higher bioavailability for the lipophilic salt containing long chain SEDDS compared to the commercially available solid dispersion Venclyxto® in the fasted state. The bioavailability of all lipophilic salt SEDDS in the fasted state was similar to Venclyxto® in the fed state. This study confirmed that lipophilic drug salts increase the dose loading in LBFs and showed that lipophilic salt-SEDDS combinations may be able to overcome bioavailability limitations of drugs with low inherent dose loading in lipid vehicles. Furthermore, the present study demonstrated the utility of a LBF approach, in combination with lipophilic salts, to overcome food dependent variable oral bioavailability of drugs.

Characterization of In Vitro G-Quadruplex Formation of Imetelstat Telomerase Inhibitor.

Imetelstat (GRN163L) is a potent and specific telomerase inhibitor currently in clinical development for the treatment of hematological malignancies such as myelofibrosis and myelodysplastic syndrome. It is a 13-mer N3'-P5' thio-phosphoramidate oligonucleotide covalently functionalized at the 5'-end with a palmitoyl lipid moiety through an aminoglycerol linker. As a competitive inhibitor of human telomerase, imetelstat directly binds to the telomerase RNA component sequence (hTR) in the catalytic site of the enzyme and acts as a direct competitor of human telomere binding. Administration of imetelstat causes progressive shortening of the telomeres, thereby inhibiting malignant cells' proliferation. We report here the ability of imetelstat to form stable, parallel, intermolecular G-quadruplex structures in vitro. The impact of the ionic environment on the formation and stability of imetelstat higher-order structure was investigated through circular dichroism spectroscopy, thermal denaturation analysis, and size-exclusion chromatography. We demonstrated that different structural elements, such as the 5'-palmitoyl linker and the thio-phosphoramidate backbone, critically contribute to G-quadruplex stability. Experiments further showed that G-quadruplex formation does not hamper binding to the hTR oligonucleotide sequence in vitro.

A Compositional Model to Predict the Aggregated Isotope Distribution for Average DNA and RNA Oligonucleotides.

Structural modifications of DNA and RNA molecules play a pivotal role in epigenetic and posttranscriptional regulation. To characterise these modifications, more and more MS and MS/MS- based tools for the analysis of nucleic acids are being developed. To identify an oligonucleotide in a mass spectrum, it is useful to compare the obtained isotope pattern of the molecule of interest to the one that is theoretically expected based on its elemental composition. However, this is not straightforward when the identity of the molecule under investigation is unknown. Here, we present a modelling approach for the prediction of the aggregated isotope distribution of an average DNA or RNA molecule when a particular (monoisotopic) mass is available. For this purpose, a theoretical database of all possible DNA/RNA oligonucleotides up to a mass of 25 kDa is created, and the aggregated isotope distribution for the entire database of oligonucleotides is generated using the BRAIN algorithm. Since this isotope information is compositional in nature, the modelling method is based on the additive log-ratio analysis of Aitchison. As a result, a univariate weighted polynomial regression model of order 10 is fitted to predict the first 20 isotope peaks for DNA and RNA molecules. The performance of the prediction model is assessed by using a mean squared error approach and a modified Pearson's χ2 goodness-of-fit measure on experimental data. Our analysis has indicated that the variability in spectral accuracy contributed more to the errors than the approximation of the theoretical isotope distribution by our proposed average DNA/RNA model. The prediction model is implemented as an online tool. An R function can be downloaded to incorporate the method in custom analysis workflows to process mass spectral data.

MESSAR: Automated recommendation of metabolite substructures from tandem mass spectra.

Despite the increasing importance of non-targeted metabolomics to answer various life science questions, extracting biochemically relevant information from metabolomics spectral data is still an incompletely solved problem. Most computational tools to identify tandem mass spectra focus on a limited set of molecules of interest. However, such tools are typically constrained by the availability of reference spectra or molecular databases, limiting their applicability of generating structural hypotheses for unknown metabolites. In contrast, recent advances in the field illustrate the possibility to expose the underlying biochemistry without relying on metabolite identification, in particular via substructure prediction. We describe an automated method for substructure recommendation motivated by association rule mining. Our framework captures potential relationships between spectral features and substructures learned from public spectral libraries. These associations are used to recommend substructures for any unknown mass spectrum. Our method does not require any predefined metabolite candidates, and therefore it can be used for the hypothesis generation or partial identification of unknown unknowns. The method is called MESSAR (MEtabolite SubStructure Auto-Recommender) and is implemented in a free online web service available at messar.biodatamining.be.

Strategies for Identity Testing of Therapeutic Oligonucleotide Drug Substances and Drug Products.

A risk-based approach for routine identity testing of therapeutic oligonucleotide drug substances and drug products is described. Risk analysis of solid-phase oligonucleotide synthesis indicates that intact mass measurement is a powerful technique for confirming synthesis of the intended oligonucleotide. Further risk assessment suggests that the addition of a second, sequence-sensitive identity test, which relies on a comparison of some property of the sample to a reference standard of proven identity, results in a sufficient test of identity for most oligonucleotide drug substances and products. Alternative strategies for drug product identity testing are presented. The analysis creates a common way to communicate risk and should result in a harmonized approach to identity testing that avoids the unnecessary analytical burden associated with routine de novo sequencing, without compromising quality or patient safety.

Proteomic profiling of cellular targets of lipopolysaccharide-induced signalling in Nicotiana tabacum BY-2 cells.

Plants constantly monitor for pathogen challenge and utilize a diverse array of adaptive defense mechanisms, including differential protein regulation, during pathogen attack. A proteomic analysis of Nicotiana tabacum BY-2 cells was performed in order to investigate the dynamic changes following perception of bacterial lipopolysaccharides. A multiplexed proteome analysis, employing two-dimensional difference-in-gel-electrophoresis with CyDye DIGE fluors, as well as Ruthenium II tris (bathophenanthroline disulfonate) fluorescence staining and Pro-Q Diamond phosphoprotein-specific gel staining, monitored over 1500 proteins and resulted in the identification of 88 differentially regulated proteins and phosphoproteins responsive to LPS(B.cep.)-elicitation. Functional clustering of the proteins both at the level of their abundance and phosphorylation status, revealed 9 proteins involved in transport, ion homeostasis and signal transduction. A large number of responsive proteins were found to be involved in metabolism- and energy-related processes (36), representing various metabolic pathways. Another abundant category corresponded to proteins classified as molecular chaperones and involved in protein destination/targeting (12). Other categories of proteins found to be LPS(B.cep.)-responsive and differentially regulated include cell structure- and cytoskeletal rearrangement proteins (8) and proteins involved in transcription and translation as well as degradation (11). The results indicate that LPS(B.cep.) induces metabolic reprogramming and changes in cellular activities supporting protein synthesis, -folding, vesicle trafficking and secretion; accompanied by changes to the cytoskeleton and proteosome function. Many of the identified proteins are known to be interconnected at various levels through a complex web of activation/deactivation, complex formation, protein-protein interactions, and chaperoning reactions. The presented data offers novel insights and further evidence for the biochemical action of LPS(B.cep.) as a resistance elicitor, a pathogen-associated molecular pattern molecule and triggering agent of defense responses associated with innate immunity.

Heterogeneity assessment of antibody-derived therapeutics at the intact and middle-up level by low-flow sheathless capillary electrophoresis-mass spectrometry.

Antibody-based pharmaceuticals often encompass a complex structural heterogeneity requiring enhanced analytical methods for reliable characterization of variants and degradation products. We have explored the capabilities of low-flow sheathless capillary electrophoresis-mass spectrometry (CE-MS) for the high-resolution and sensitive profiling of antibody therapeutics. Near-zero electroosmotic flow was achieved by employing a novel neutral capillary coating that also prevents protein adsorption. CE-MS analysis of intact model proteins using an acidic background electrolyte demonstrated satisfactory performance, with overall migration-time RSDs below 2.2% from three different capillaries tested. For system evaluation, three nanobody preparations, including mono- and bivalent forms, and three monoclonal antibodies (mAbs) were analyzed. Intact nanobodies were resolved from their degradation products, which could be assigned to deamidated, cleaved, and truncated forms at the C-terminal tag. Excellent resolution of isomeric deamidated products was obtained. The mAbs were analyzed intact and after digestion by the endoproteinase IdeS (middle-up approach). CE-MS of intact mAbs provided resolution of clipped species (e.g. light chain and light chain-heavy chain fragments) from the native protein. Moreover, glycoforms containing sialic acids were resolved from their non-sialylated counterparts. For IdeS-digested, F (ab)2 and Fc/2 portions where efficiently resolved for the three mAbs. Whereas the migration time of the Fc/2 fragments was fairly similar, the migration time of the F (ab)2 part was strongly varied among the mAbs. For all mAbs, separation of Fc/2 charge variants - including sialylated glycoforms and other post-translational modifications, such as loss of C-terminal lysine or asparagine deamidation - was achieved. This allowed a detailed and reliable assessment of the Fc/2 heterogeneity (18-33 proteoforms) of the three analyzed mAbs.

Capillary electrophoresis-tandem mass spectrometry as a highly selective tool for the compositional and site-specific assessment of multiple peptide-deamidation.

Site-specific mapping of multiple deamidations in peptides is a challenging analytical task. In this work, capillary electrophoresis-tandem mass spectrometry (CE-MS/MS) is presented as a high-resolution tool for the detailed characterization of these subtle modifications in peptides. The 4.5-kDa peptide drug TRI-1144, which contains five closely-positioned potential deamidation sites, was selected as model compound. TRI-1144 was exposed to acidic conditions and/or elevated temperatures for 1-14 h. Stressed samples were analyzed using a background electrolyte (BGE) of 150 mM ammonium formate (pH 6.0) in combination with a capillary coated with a bilayer of Polybrene-dextran sulfate. Separation of deamidated and deacetylated TRI-1144 species, including several positional isomers, was greatly enhanced by adding up to 40 vol% of acetonitrile-isopropanol (87.5:12.5, v/v) to the BGE, allowing reliable determination of the number of deamidations/deacetylations per degradation product. Collision-induced dissociation MS/MS was conducted on the separated peptide components in order to reveal the exact position of deamidation on the peptide chain. Obtained fragment ions showed overlapping isotopic distributions in their MS/MS spectra resulting from the comigration of different isomeric deamidated species. Comparison of theoretical and measured isotope distributions for specific y ions of peptide fragments yielded the identity and relative abundance of isomeric deamidated products. The developed CE-MS/MS methodology was used for the highly selective evaluation of TRI-1144 stability under different stress conditions, providing detailed qualitative and semi-quantitative degradation maps of the peptide drug.

Synthesis and Evaluation of a Zr-89-Labeled Monoclonal Antibody for Immuno-PET Imaging of Amyloid-ß Deposition in the Brain.

PURPOSE: The aim of this study was to evaluate the in vitro and in vivo characteristics of [(89)Zr]JRF/AßN/25, a radiolabeled monoclonal antibody directed against amyloid-ß (Aß). PROCEDURES: JRF/AßN/25 was labeled with (89)Zr following modification with desferal. The affinity of the tracer for Aß1-40 was determined in a saturation binding assay. In vitro stability was evaluated, and in vivo plasma stability and biodistribution of [(89)Zr]Df-Bz-JRF/AßN/25 were determined in wild-type mice. To evaluate whether the antibody can cross the blood-brain barrier, brain uptake in wild-type mice was additionally assessed by ex vivo autoradiography. RESULTS: [(89)Zr]Df-Bz-JRF/AßN/25 was obtained in an average radiochemical yield of 50 % and a radiochemical purity of >97 %. A saturation binding assay demonstrated specific binding of [(89)Zr]Df-Bz-JRF/AßN/25 to Aß1-40 with nanomolar affinity. The tracer was stable in buffer and proved to be stable in vivo with >92 % intact monoclonal antibody (mAb) remaining in the plasma at 48 h post injection. A biodistribution study showed a slow blood clearance with no significant accumulation of activity in any of the organs. Furthermore, [(89)Zr]Df-Bz-JRF/AßN/25 demonstrated modest brain penetration, which slowly decreased in time. This cerebral uptake was confirmed by ex vivo autoradiography. CONCLUSIONS: [(89)Zr]Df-Bz-JRF/AßN/25 binds with high affinity to Aß1-40. The tracer displays an acceptable in vivo stability and is able to cross the blood-brain barrier. [(89)Zr]Df-Bz-JRF/AßN/25 might therefore be a potential candidate for in vivo imaging of Aß deposition in the brain.

A generic approach for "shotgun" analysis of the soluble proteome of plant cell suspension cultures.

Cell suspension cultures from different plant species act as important model systems for studying cellular processes in plant biology and are often used as "green factories" for the production of valuable secondary metabolites and recombinant proteins. While mass spectrometry based proteome analysis techniques are ideally suited to study plant cell metabolism and other fundamental cellular processes from a birds eye perspective, they remain underused in plant studies. We describe a comprehensive sample preparation and multidimensional 'shotgun' proteomics strategy that can be generically applied to plant cell suspension cultures. This strategy was optimized and tested on an Arabidopsis thaliana ecotype Landsberg erecta culture. Furthermore, the implementation of strong cation exchange chromatography as a peptide fractionation step is elaborately tested. Its utility in mass spectrometry based proteome analysis is discussed. Using the presented analytical platform, over 13,000 unique peptides and 2640 proteins could be identified from a single plant cell suspension sample. Finally, the experimental setup is validated using Nicotiana tabacum cv. "Bright Yellow-2" (BY-2) plant cell suspension cultures, thereby demonstrating that the presented analytical platform can also be valuable tool in proteome analysis of non-genomic model systems.

Next generation functional proteomics in non-model plants: A survey on techniques and applications for the analysis of protein complexes and post-translational modifications.

The congruent development of computational technology, bioinformatics and analytical instrumentation makes proteomics ready for the next leap. Present-day state of the art proteomics grew from a descriptive method towards a full stake holder in systems biology. High throughput and genome wide studies are now made at the functional level. These include quantitative aspects, functional aspects with respect to protein interactions as well as post translational modifications and advanced computational methods that aid in predicting protein function and mapping these functionalities across the species border. In this review an overview is given of the current status of these aspects in plant studies with special attention to non-genomic model plants.

Unraveling tobacco BY-2 protein complexes with BN PAGE/LC-MS/MS and clustering methods.

To understand physiological processes, insight into protein complexes is very important. Through a combination of blue native gel electrophoresis and LC-MS/MS, we were able to isolate protein complexes and identify their potential subunits from Nicotiana tabacum cv. Bright Yellow-2. For this purpose, a bioanalytical approach was used that works without a priori knowledge of the interacting proteins. Different clustering methods (e.g., k-means and hierarchical clustering) and a biclustering approach were evaluated according to their ability to group proteins by their migration profile and to correlate the proteins to a specific complex. The biclustering approach was identified as a very powerful tool for the exploration of protein complexes of whole cell lysates since it allows for the promiscuous nature of proteins. Furthermore, it searches for associations between proteins that co-occur frequently throughout the BN gel, which increases the confidence of the putative associations between co-migrating proteins. The statistical significance and biological relevance of the profile clusters were verified using functional gene ontology annotation. The proof of concept for identifying protein complexes by our BN PAGE/LC-MS/MS approach is provided through the analysis of known protein complexes. Both well characterized long-lived protein complexes as well as potential temporary sequential multi-enzyme complexes were characterized.