The proteome landscape of the kingdoms of life

Müller-Reif, Johannes B.; Geyer, Philipp E.; Colaço, Ana R.; Treit, Peter V.; Strauss, Maximilian T.; Oroshi, Mario; Doll, Sophia; Winter, Sebastian Virreira; Bader, Jakob; Köhler, Niklas; Theis, Fabian J.; Santos, Alberto; Mann, Matthias

doi:10.1038/s41586-020-2402-x

Cited by 154 publications

(153 citation statements)

References 36 publications

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“…MS/MS spectra were searched against the UniProt Knowledgebase (UniProtKB) Human proteome database (downloaded in 2015) with 90,411 total sequences as previously reported 22 . UniProtKB is a comprehensive 31 and widely used database 32 , which contains both reviewed Swiss-Prot and predicted TrEMBL sequences 33 . Since the additional depth provided by off-line bRPLC enables the sequencing of rare protein isoforms, this complete database will give us full potential for protein identification.…”

Section: Lc-ms/ms and Tmt Data Acquisition On An Orbitrap Lumos Massmentioning

confidence: 99%

Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease

et al. 2020

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Alzheimer’s disease (AD) is characterized by an early, asymptomatic phase (AsymAD) in which individuals exhibit amyloid-beta (Aβ) plaque accumulation in the absence of clinically detectable cognitive decline. Here we report an unbiased multiplex quantitative proteomic and phosphoproteomic analysis using tandem mass tag (TMT) isobaric labeling of human post-mortem cortex (n = 27) across pathology-free controls, AsymAD and symptomatic AD individuals. With off-line high-pH fractionation and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) on an Orbitrap Lumos mass spectrometer, we identified 11,378 protein groups across three TMT 11-plex batches. Immobilized metal affinity chromatography (IMAC) was used to enrich for phosphopeptides from the same TMT-labeled cases and 51,736 phosphopeptides were identified. Of these, 48,992 were quantified by TMT reporter ions representing 33,652 unique phosphosites. Two reference standards in each TMT 11-plex were included to assess intra- and inter-batch variance at the protein and peptide level. This comprehensive human brain proteome and phosphoproteome dataset will serve as a valuable resource for the identification of biochemical, cellular and signaling pathways altered during AD progression.

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Section: Lc-ms/ms and Tmt Data Acquisition On An Orbitrap Lumos Massmentioning

confidence: 99%

Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease

et al. 2020

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“…[24][25][26][27] This paved the possibility for wider application of high data density machine learning techniques to address peptide retention time prediction problems. [27][28][29] Clemmer and co-workers led the way in the development of IMS technology for proteomic applications, 30 peptide IMS data collection, and modeling peptide ion mobility. 8,31 Valentine et al…”

Section: Introductionmentioning

confidence: 99%

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry

Chang

Yeung

Spicer

et al. 2020

Preprint

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The contribution of peptide amino-acid sequence to collision cross-section values (CCS) has been investigated using a dataset of ∼134,000 peptides of four different charge states (1+ to 4+). The migration data was acquired using a two-dimensional LC/trapped ion mobility spectrometry/quadrupole/time-of-flight MS analysis of HeLa cell digests created using 7 different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into 8 groups (four charges for both tryptic/non-tryptic peptides). Position dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide’s ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position dependent ISP behaviour consistent with electrostatic interaction with the peptide macro-dipole, which affects the peptide helicity.

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“…However, different PTM's can only be measured using proteomics techniques. As the quality, reproducibility, scalability and accessibility of mass spectrometry methods keep increasing [58], we expect proteomics to start playing a more and more prominent role in AFP.…”

Section: Protein Representationmentioning

confidence: 99%

Automatic Gene Function Prediction in the 2020’s

Makrodimitris

Ham

Reinders

2020

Genes

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The current rate at which new DNA and protein sequences are being generated is too fast to experimentally discover the functions of those sequences, emphasizing the need for accurate Automatic Function Prediction (AFP) methods. AFP has been an active and growing research field for decades and has made considerable progress in that time. However, it is certainly not solved. In this paper, we describe challenges that the AFP field still has to overcome in the future to increase its applicability. The challenges we consider are how to: (1) include condition-specific functional annotation, (2) predict functions for non-model species, (3) include new informative data sources, (4) deal with the biases of Gene Ontology (GO) annotations, and (5) maximally exploit the GO to obtain performance gains. We also provide recommendations for addressing those challenges, by adapting (1) the way we represent proteins and genes, (2) the way we represent gene functions, and (3) the algorithms that perform the prediction from gene to function. Together, we show that AFP is still a vibrant research area that can benefit from continuing advances in machine learning with which AFP in the 2020s can again take a large step forward reinforcing the power of computational biology.

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The proteome landscape of the kingdoms of life

Cited by 154 publications

References 36 publications

Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease

Global quantitative analysis of the human brain proteome and phosphoproteome in Alzheimer’s disease

Sequence-specific model for predicting peptide collision cross-section values in proteomic ion mobility spectrometry

Automatic Gene Function Prediction in the 2020’s

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