Presently, phosphorylation of proteins is the most studied and best understood PTM. However, the analysis of phosphoproteins and phosphopeptides is still one of the most challenging tasks in contemporary proteome research. Since not every phosphoprotein is accessible by a certain method and identification of the phosphorylated amino acid residue is required in the majority of cases, various strategies for the detection and localization of phosphorylations have been developed. Identification and localization of protein phosphorylations is mostly done by MS nowadays but phosphoproteins and -peptides are often suppressed in comparison to the unphosphorylated species if measured in complex mixtures. Thus, the isolation of pure phosphopeptide samples is a main task. This review gives an overview over the most frequently used methods in isolation and detection of phosphoproteins and -peptides such as specific enrichment or separation strategies as well as the localization of the phosphorylated residues by various mass spectrometric techniques.

During the last decade, protein analysis and proteomics have been established as new tools for understanding various biological problems. As the identification of proteins after classical separation techniques, such as two-dimensional gel electrophoresis, have become standard methods, new challenges arise in the field of proteomics. The development of "functional proteomics" combines functional characterization, like regulation, localization and modification, with the identification of proteins for deeper insight into cellular functions. Therefore, different mass spectrometric techniques for the analysis of post-translational modifications, such as phosphorylation and glycosylation, have been established as well as isolation and separation methods for the analysis of highly complex samples, e.g. protein complexes or cell organelles. Furthermore, quantification of protein levels within cells is becoming a focus of interest as mass spectrometric methods for relative or even absolute quantification have currently not been available. Protein or genome databases have been an essential part of protein identification up to now. Thus, de novo sequencing offers new possibilities in protein analytical studies of organisms not yet completely sequenced. The intention of this review is to provide a short overview about the current capabilities of protein analysis when addressing various biological problems.

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In various areas of research, proteomics and particularly the quantification of proteins and peptides renders a useful addition to biochemical experiments. The range of possible applications varies from supervision of concentration changes of relevant proteins during biogenesis to differential proteomics approaches, distinguishing, for instance, healthy and diseased states. Furthermore, mass spectrometry-based peptide quantification yields the possibility of using highly sensitive bottom-up approaches for determination of protein regulations as well as multiplexing capability. Thereby, changes in protein abundances may be linked to specific cellular states bearing the opportunity to reveal marker proteins for several diseases.

In vitro to in vivo extrapolation represents a critical challenge in toxicology. In this paper we explore extrapolation strategies for acetaminophen (APAP) based on mechanistic models, comparing classical (CL) homogeneous compartment pharmacodynamic (PD) models and a spatial-temporal (ST), multiscale digital twin model resolving liver microarchitecture at cellular resolution. The models integrate consensus detoxification reactions in each individual hepatocyte. We study the consequences of the two model types on the extrapolation and show in which cases these models perform better than the classical extrapolation strategy that is based either on the maximal drug concentration (Cmax) or the area under the pharmacokinetic curve (AUC) of the drug blood concentration. We find that an CL-model based on a well-mixed blood compartment is sufficient to correctly predict the in vivo toxicity from in vitro data. However, the ST-model that integrates more experimental information requires a change of at least one parameter to obtain the same prediction, indicating that spatial compartmentalization may indeed be an important factor.

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