Proximity Dependent Biotinylation: Key Enzymes and Adaptation to Proteomics Approaches

The study of protein subcellular distribution, their assembly into complexes and the set of proteins with which they interact with is essential to our understanding of fundamental biological processes. Complementary to traditional assays, proximity-dependent biotinylation (PDB) approaches coupled with mass spectrometry (such as BioID or APEX) have emerged as powerful techniques to study proximal protein interactions and the subcellular proteome in the context of living cells and organisms. Since their introduction in 2012, PDB approaches have been used in an increasing number of studies and the enzymes themselves have been subjected to intensive optimization. How these enzymes have been optimized and considerations for their use in proteomics experiments are important questions. Here, we review the structural diversity and mechanisms of the two main classes of PDB enzymes: the biotin protein ligases (BioID) and the peroxidases (APEX). We describe the engineering of these enzymes for PDB and review emerging applications, including the development of PDB for coincidence detection (split-PDB). Lastly, we briefly review enzyme selection and experimental design guidelines and reflect on the labeling chemistries and their implication for data interpretation.

Sensitive Determination of Proteolytic Proteoforms in Limited Microscale Proteome Samples

Protein N termini unambiguously identify truncated, alternatively translated or modified proteoforms with distinct functions and reveal perturbations in disease. Selective enrichment of N-terminal peptides is necessary to achieve proteome-wide coverage for unbiased identification of site-specific regulatory proteolytic processing and protease substrates. However, many proteolytic processes are strictly confined in time and space and therefore can only be analyzed in minute samples that provide insufficient starting material for current enrichment protocols. Here we present High-efficiency Undecanal-based N Termini EnRichment (HUNTER), a robust, sensitive and scalable method for the analysis of previously inaccessible microscale samples. HUNTER achieved identification of >1000 N termini from as little as 2 μg raw HeLa cell lysate. Broad applicability is demonstrated by the first N-terminome analysis of sorted human primary immune cells and enriched mitochondrial fractions from pediatric cancer patients, as well as protease substrate identification from individual Arabidopsis thaliana wild type and Vacuolar Processing Enzyme-deficient mutant seedlings. We further implemented the workflow on a liquid handling system and demonstrate the feasibility of clinical degradomics by automated processing of liquid biopsies from pediatric cancer patients.

Arabidopsis CTP:phosphocholine cytidylyltransferase 1 is phosphorylated and inhibited by sucrose nonfermenting 1-related protein kinase 1 (SnRK1)

De novo phosphatidylcholine (PC) biosynthesis via the Kennedy pathway involves highly endergonic biochemical reactions that must be fine-tuned with energy homeostasis. Previous studies have shown that CTP:phosphocholine cytidylyltransferase (CCT) is an important regulatory enzyme in this pathway and that its activity can be controlled at both transcriptional and posttranslational levels. Here we identified an important additional mechanism regulating plant CCT1 activity. Comparative analysis revealed that Arabidopsis CCT1 (AtCCT1) contains catalytic and membrane-binding domains that are homologous to those of rat CCT1. In contrast, the C-terminal phosphorylation domain important for stringent regulation of rat CCT1 was apparently missing in AtCCT1. Instead, we found that AtCCT1 contains a putative consensus site (Ser-187) for modification by sucrose nonfermenting 1-related protein kinase 1 (SnRK1 or KIN10/SnRK1.1), involved in energy homeostasis. Phos-tag SDS-PAGE coupled with MS analysis disclosed that SnRK1 indeed phosphorylates AtCCT1 at Ser-187, and we found that AtCCT1 phosphorylation substantially reduces its activity by as much as 70%. An S187A variant exhibited decreased activity, indicating the importance of Ser-187 in catalysis, and this variant was less susceptible to SnRK1-mediated inhibition. Protein truncation and liposome binding studies indicated that SnRK1-mediated AtCCT1 phosphorylation directly affects the catalytic domain rather than interfering with phosphatidate-mediated AtCCT1 activation. Overexpression of the AtCCT1 catalytic domain in Nicotiana benthamiana leaves increased PC content, and SnRK1 co-expression reduced this effect. Taken together, our results suggest that SnRK1 mediates the phosphorylation and concomitant inhibition of AtCCT1, revealing an additional mode of regulation for this key enzyme in plant PC biosynthesis.

Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning

Deep learning models for prediction of three key LC-MS/MS properties from peptide sequences were developed. The LC-MS/MS properties or behaviors are indexed retention times (iRT), MS1 or survey scan charge state distributions, and sequence ion intensities of HCD spectra. A common core deep supervised learning architecture, bidirectional long-short term memory (LSTM) recurrent neural networks was used to construct the three prediction models. Two featurization schemes were proposed and demonstrated to allow for efficient encoding of modifications. The iRT and charge state distribution models were trained with on order of 105 data points each. An HCD sequence ion prediction model was trained with 2 × 106 experimental spectra. The iRT prediction model and HCD sequence ion prediction model provide improved accuracies over the start-of-the-art models available in literature. The MS1 charge state distribution prediction model offers excellent performance. The prediction models can be used to enhance peptide identification and quantification in data-dependent acquisition and data-independent acquisition (DIA) experiments as well as to assist MRM (multiple reaction monitoring) and PRM (parallel reaction monitoring) experiment design.