Ancient Biomolecules and Evolutionary Inference.

Over the past three decades, studies of ancient biomolecules-particularly ancient DNA, proteins, and lipids-have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, today the field stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field.

A human interactome in three quantitative dimensions organized by stoichiometries and abundances.

The organization of a cell emerges from the interactions in protein networks. The interactome is critically dependent on the strengths of interactions and the cellular abundances of the connected proteins, both of which span orders of magnitude. However, these aspects have not yet been analyzed globally. Here, we have generated a library of HeLa cell lines expressing 1,125 GFP-tagged proteins under near-endogenous control, which we used as input for a next-generation interaction survey. Using quantitative proteomics, we detect specific interactions, estimate interaction stoichiometries, and measure cellular abundances of interacting proteins. These three quantitative dimensions reveal that the protein network is dominated by weak, substoichiometric interactions that play a pivotal role in defining network topology. The minority of stable complexes can be identified by their unique stoichiometry signature. This study provides a rich interaction dataset connecting thousands of proteins and introduces a framework for quantitative network analysis.

Systematic Detection of Amino Acid Substitutions in Proteomes Reveals Mechanistic Basis of Ribosome Errors and Selection for Translation Fidelity.

The translation machinery and the genes it decodes co-evolved to achieve production throughput and accuracy. Nonetheless, translation errors are frequent, and they affect physiology and protein evolution. Mapping translation errors in proteomes and understanding their causes is hindered by lack of a proteome-wide experimental methodology. We present the first methodology for systematic detection and quantification of errors in entire proteomes. Following proteome mass spectrometry, we identify, in E. coli and yeast, peptides whose mass indicates specific amino acid substitutions. Most substitutions result from codon-anticodon mispairing. Errors occur at sites that evolve rapidly and that minimally affect energetic stability, indicating selection for high translation fidelity. Ribosome density data show that errors occur at sites where ribosome velocity is higher, demonstrating a trade-off between speed and accuracy. Treating bacteria with an aminoglycoside antibiotic or deprivation of specific amino acids resulted in particular patterns of errors. These results reveal a mechanistic and evolutionary basis for translation fidelity.

Quantitative, high-resolution proteomics for data-driven systems biology.

Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.

Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics.

Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .

Author Correction: The dental proteome of Homo antecessor.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The dental proteome of Homo antecessor.

The phylogenetic relationships between hominins of the Early Pleistocene epoch in Eurasia, such as Homo antecessor, and hominins that appear later in the fossil record during the Middle Pleistocene epoch, such as Homo sapiens, are highly debated1-5. For the oldest remains, the molecular study of these relationships is hindered by the degradation of ancient DNA. However, recent research has demonstrated that the analysis of ancient proteins can address this challenge6-8. Here we present the dental enamel proteomes of H. antecessor from Atapuerca (Spain)9,10 and Homo erectus from Dmanisi (Georgia)1, two key fossil assemblages that have a central role in models of Pleistocene hominin morphology, dispersal and divergence. We provide evidence that H. antecessor is a close sister lineage to subsequent Middle and Late Pleistocene hominins, including modern humans, Neanderthals and Denisovans. This placement implies that the modern-like face of H. antecessor-that is, similar to that of modern humans-may have a considerably deep ancestry in the genus Homo, and that the cranial morphology of Neanderthals represents a derived form. By recovering AMELY-specific peptide sequences, we also conclude that the H. antecessor molar fragment from Atapuerca that we analysed belonged to a male individual. Finally, these H. antecessor and H. erectus fossils preserve evidence of enamel proteome phosphorylation and proteolytic digestion that occurred in vivo during tooth formation. Our results provide important insights into the evolutionary relationships between H. antecessor and other hominin groups, and pave the way for future studies using enamel proteomes to investigate hominin biology across the existence of the genus Homo.

Enamel proteome shows that Gigantopithecus was an early diverging pongine.

Gigantopithecus blacki was a giant hominid that inhabited densely forested environments of Southeast Asia during the Pleistocene epoch1. Its evolutionary relationships to other great ape species, and the divergence of these species during the Middle and Late Miocene epoch (16-5.3 million years ago), remain unclear2,3. Hypotheses regarding the relationships between Gigantopithecus and extinct and extant hominids are wide ranging but difficult to substantiate because of its highly derived dentognathic morphology, the absence of cranial and post-cranial remains1,3-6, and the lack of independent molecular validation. We retrieved dental enamel proteome sequences from a 1.9-million-year-old G. blacki molar found in Chuifeng Cave, China7,8. The thermal age of these protein sequences is approximately five times greater than that of any previously published mammalian proteome or genome. We demonstrate that Gigantopithecus is a sister clade to orangutans (genus Pongo) with a common ancestor about 12-10 million years ago, implying that the divergence of Gigantopithecus from Pongo forms part of the Miocene radiation of great apes. In addition, we hypothesize that the expression of alpha-2-HS-glycoprotein, which has not been previously observed in enamel proteomes, had a role in the biomineralization of the thick enamel crowns that characterize the large molars in Gigantopithecus9,10. The survival of an Early Pleistocene dental enamel proteome in the subtropics further expands the scope of palaeoproteomic analysis into geographical areas and time periods previously considered incompatible with the preservation of substantial amounts of genetic information.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens.

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

Benchmarking differential expression, imputation and quantification methods for proteomics data.

Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray- and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus.

Role for ribosome-associated quality control in sampling proteins for MHC class I-mediated antigen presentation.

Mammalian cells present a fingerprint of their proteome to the adaptive immune system through the display of endogenous peptides on MHC-I complexes. MHC-I-bound peptides originate from protein degradation by the proteasome, suggesting that stably folded, long-lived proteins could evade monitoring. Here, we investigate the role in antigen presentation of the ribosome-associated quality control (RQC) pathway for the degradation of nascent polypeptides that are encoded by defective messenger RNAs and undergo stalling at the ribosome during translation. We find that degradation of model proteins by RQC results in efficient MHC-I presentation, independent of their intrinsic folding properties. Quantitative profiling of MHC-I peptides in wild-type and RQC-deficient cells by mass spectrometry showed that RQC substantially contributes to the composition of the immunopeptidome. Our results also identify endogenous substrates of the RQC pathway in human cells and provide insight into common principles causing ribosome stalling under physiological conditions.

Accurate and Automated High-Coverage Identification of Chemically Cross-Linked Peptides with MaxLynx.

Cross-linking combined with mass spectrometry (XL-MS) provides a wealth of information about the three-dimensional (3D) structure of proteins and their interactions. We introduce MaxLynx, a novel computational proteomics workflow for XL-MS integrated into the MaxQuant environment. It is applicable to noncleavable and MS-cleavable cross-linkers. For both, we have generalized the Andromeda peptide database search engine to efficiently identify cross-linked peptides. For noncleavable peptides, we implemented a novel dipeptide Andromeda score, which is the basis for a computationally efficient N-squared search engine. Additionally, partial scores summarize the evidence for the two constituents of the dipeptide individually. A posterior error probability (PEP) based on total and partial scores is used to control false discovery rates (FDRs). For MS-cleavable cross-linkers, a score of signature peaks is combined with the conventional Andromeda score on the cleavage products. The MaxQuant 3D peak detection was improved to ensure more accurate determination of the monoisotopic peak of isotope patterns for heavy molecules, which cross-linked peptides typically are. A wide selection of filtering parameters can replace the manual filtering of identifications, which is often necessary when using other pipelines. On benchmark data sets of synthetic peptides, MaxLynx outperforms all other tested software on data for both types of cross-linkers and on a proteome-wide data set of cross-linked Drosophila melanogaster cell lysate. The workflow also supports ion mobility-enhanced MS data. MaxLynx runs on Windows and Linux, contains an interactive viewer for displaying annotated cross-linked spectra, and is freely available at https://www.maxquant.org/.

High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis.

Peptide fragmentation spectra are routinely predicted in the interpretation of mass-spectrometry-based proteomics data. However, the generation of fragment ions has not been understood well enough for scientists to estimate fragment ion intensities accurately. Here, we demonstrate that machine learning can predict peptide fragmentation patterns in mass spectrometers with accuracy within the uncertainty of measurement. Moreover, analysis of our models reveals that peptide fragmentation depends on long-range interactions within a peptide sequence. We illustrate the utility of our models by applying them to the analysis of both data-dependent and data-independent acquisition datasets. In the former case, we observe a q-value-dependent increase in the total number of peptide identifications. In the latter case, we confirm that the use of predicted tandem mass spectrometry spectra is nearly equivalent to the use of spectra from experimental libraries.

MaxQuant Software for Ion Mobility Enhanced Shotgun Proteomics.

Ion mobility can add a dimension to LC-MS based shotgun proteomics which has the potential to boost proteome coverage, quantification accuracy and dynamic range. Required for this is suitable software that extracts the information contained in the four-dimensional (4D) data space spanned by m/z, retention time, ion mobility and signal intensity. Here we describe the ion mobility enhanced MaxQuant software, which utilizes the added data dimension. It offers an end to end computational workflow for the identification and quantification of peptides and proteins in LC-IMS-MS/MS shotgun proteomics data. We apply it to trapped ion mobility spectrometry (TIMS) coupled to a quadrupole time-of-flight (QTOF) analyzer. A highly parallelizable 4D feature detection algorithm extracts peaks which are assembled to isotope patterns. Masses are recalibrated with a non-linear m/z, retention time, ion mobility and signal intensity dependent model, based on peptides from the sample. A new matching between runs (MBR) algorithm that utilizes collisional cross section (CCS) values of MS1 features in the matching process significantly gains specificity from the extra dimension. Prerequisite for using CCS values in MBR is a relative alignment of the ion mobility values between the runs. The missing value problem in protein quantification over many samples is greatly reduced by CCS aware MBR.MS1 level label-free quantification is also implemented which proves to be highly precise and accurate on a benchmark dataset with known ground truth. MaxQuant for LC-IMS-MS/MS is part of the basic MaxQuant release and can be downloaded from http://maxquant.org.

The coming age of complete, accurate, and ubiquitous proteomes.

High-resolution mass spectrometry (MS)-based proteomics has progressed tremendously over the years. For model organisms like yeast, we can now quantify complete proteomes in just a few hours. Developments discussed in this Perspective will soon enable complete proteome analysis of mammalian cells, as well, with profound impact on biology and biomedicine.

MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides.

Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox. MaxQuant.Live builds on the fast application programming interface of quadrupole Orbitrap mass analyzers to control data acquisition in real-time (freely available at www.maxquant.live). Its graphical user interface enables advanced data acquisition strategies, such as in-depth characterization of peptides of interest. Online recalibration in mass, retention time, and intensity dimensions extends this concept to more than 25,000 peptides per run. Our "global targeting" strategy combines the best of targeted and shotgun approaches.

MaxQuant.Live Enables Global Targeting of More Than 25,000 Peptides.

Mass spectrometry (MS)-based proteomics is often performed in a shotgun format, in which as many peptide precursors as possible are selected from full or MS1 scans so that their fragment spectra can be recorded in MS2 scans. Although achieving great proteome depths, shotgun proteomics cannot guarantee that each precursor will be fragmented in each run. In contrast, targeted proteomics aims to reproducibly and sensitively record a restricted number of precursor/fragment combinations in each run, based on prescheduled mass-to-charge and retention time windows. Here we set out to unify these two concepts by a global targeting approach in which an arbitrary number of precursors of interest are detected in real-time, followed by standard fragmentation or advanced peptide-specific analyses. We made use of a fast application programming interface to a quadrupole Orbitrap instrument and real-time recalibration in mass, retention time and intensity dimensions to predict precursor identity. MaxQuant.Live is freely available (www.maxquant.live) and has a graphical user interface to specify many predefined data acquisition strategies. Acquisition speed is as fast as with the vendor software and the power of our approach is demonstrated with the acquisition of breakdown curves for hundreds of precursors of interest. We also uncover precursors that are not even visible in MS1 scans, using elution time prediction based on the auto-adjusted retention time alone. Finally, we successfully recognized and targeted more than 25,000 peptides in single LC-MS runs. Global targeting combines the advantages of two classical approaches in MS-based proteomics, whereas greatly expanding the analytical toolbox.

The PRIDE database and related tools and resources in 2019: improving support for quantification data.

The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world's largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.

Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification.

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography-mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer.

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, whereas mass spectra are acquired in about 100 microseconds with time-of-flight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive because of its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al., PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 600,000 fragmentation spectra in standard 120 min LC runs are achievable, which can be used for near exhaustive precursor selection in complex mixtures or accumulating the signal of weak precursors. In 120 min single runs of HeLa digest, MaxQuant identified more than 6,000 proteins without matching to a library and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored.