Biology subjects

Eng, J. K.

Publications and source records attributed to Eng, J. K..

6 recordsLinked to original sources

bioRxiv2026

Short Linear Motifs as a General Organizing Principle of the Nuclear-Receptor Proximal Interactome

The AR-interactome comprises [~]1,000 androgen receptor-interacting proteins (AR-IPs), yet how a single receptor engages so many partners across compartments remains mechanistically unclear. We integrate proximity-labeling quantitative mass spectrometry across cytosolic, microsomal, and nuclear compartments of LNCaP prostate cancer cells and resolve 4,751 AR-proximal interacting proteins (AR-PIPs), more than four times the size of the AR-interactome. Anchoring on the LXXLL coactivator recognition motif, LXXLL motifs are systematically depleted in AR-PIPs after length control, consistent with low-affinity, transient engagement at the AR AF-2 charge clamp. LXXLL-bearing AR-PIPs include AR itself and canonical AR coactivators altered by amplification, deletion, or motif-spanning mutations in metastatic and castration-resistant prostate cancers. AR-V7, which lacks AF-2, retains LXXLL-depleted Mode 1 partners and loses the LXXLL-enriched Mode 2 cloud, thereby validating a two-mode engagement framework for nuclear receptor-proximal interactomes.

bioinformatics↗

bioRxiv2026

SYNTAX Reads the Motif and Domain Grammar of the Androgen Receptor Proximal Interactome

A receptors interactome is usually reported as a protein list, yet the sequence rules that organize it have never been read out. We introduce SYNTAX, a scan of 356 short linear motif classes and 573 protein domains that corrects the length, burden, and degeneracy artifacts of motif counting, and apply it to 4,751 androgen receptor (AR)-proximal interacting proteins (AR-PIPs) across three subcellular compartments and an androgen time course. ARs signature coactivator motif, LXXLL, is the most depleted class, while the neighborhood speaks a disorder-resident signaling vocabulary of SUMOylation sites, SPOP and SIAH degrons, and nuclear-import signals. The paradox resolves once incidental motifs are removed. ARs coactivator core forms a small, 9-fold-enriched inner shell within a signaling periphery. The grammar reproduces in an estrogen receptor-beta interactome (r = 0.89) and generalizes to the 5-HT2A serotonin receptor and EGFR. SYNTAX interfaces with the Predictive Proximal Proteome (PPP) Database, a queryable AR-PIP resource.

bioinformatics↗

bioRxiv2024

Comet fragment-ion indexing for enhanced peptide sequencing

Fragment ion indexing has significantly improved the efficiency of proteomics database search tools. This work implements fragment ion indexing in Comet, a widely-used, open-source search engine. We demonstrate that this enhancement maintains scoring and identification accuracy while substantially increasing peptide spectral matching speeds across multiple applications, including open modification searches, immunopeptidomics, and real-time searches. Comet-FI reduced search speeds by up to 94%, enabling the rapid analysis of complex data types like immunopeptidomes. Fragment-ion indexing enables Comet to keep pace with modern instrumentation and expanding applications in proteomics, reinforcing its utility in diverse proteomics workflows and its integration with a wide range of proteomics tools and platforms.

bioinformatics↗

bioRxiv2022

Coisolation of peptide pairs for peptide identification and MS/MS-based quantification

SILAC-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a data dependent acquisition strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. In an initial feasibility experiment, this peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions, which are difficult to deconvolute in MS1-based quantification. This study demonstrates the initial feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

systems biology↗

bioRxiv2019

Full-featured, real-time database searching platform enables fast and accurate multiplexed quantitative proteomics.

Multiplexed quantitative analyses of complex proteomes enable deep biological insight. While a multitude of workflows have been developed for multiplexed analyses, the most quantitatively accurate method (SPS-MS3) suffers from long acquisition duty cycles. We built a new, real-time database search (RTS) platform, Orbiter, to combat the SPS-MS3 methods longer duty cycles. RTS with Orbiter enables the elimination of SPS-MS3 scans if no peptide matches to a given spectrum. With Orbiters online proteomic analytical pipeline, which includes RTS and false discovery rate analysis, it was possible to process a single spectrum database search in less than 10 milliseconds. The result is a fast, functional means to identify peptide spectral matches using Comet, filter these matches, and more efficiently quantify proteins of interest. Importantly, the use of Comet for peptide spectral matching allowed for a fully featured search, including analysis of post-translational modifications, with well-known and extensively validated scoring. These data could then be used to trigger subsequent scans in an adaptive and flexible manner. In this work we tested the utility of this adaptive data acquisition platform to improve the efficiency and accuracy of multiplexed quantitative experiments. We found that RTS enabled a 2-fold increase in mass spectrometric data acquisition efficiency. Orbiters RTS was able to quantify more than 8000 proteins across 10 proteomes in half the time of an SPS-MS3 analysis (18 hours for RTS, 36 hours for SPS-MS3).

cell biology↗

bioRxiv2019

Proteomics Standards Initiative Extended FASTA Format (PEFF)

Mass spectrometry-based proteomics enables the high-throughput identification and quantification of proteins, including sequence variants and post-translational modifications (PTMs), in biological samples. However, most workflows require that such variations be included in the search space used to analyze the data, and doing so remains challenging with most analysis tools. In order to facilitate the search for known sequence variants and PTMs, the Proteomics Standards Initiative (PSI) has designed and implemented the PSI Extended FASTA Format (PEFF). PEFF is based on the very popular FASTA format but adds a uniform mechanism for encoding substantially more metadata about the sequence collection as well as individual entries, including support for encoding known sequence variants, PTMs, and proteoforms. The format is very nearly backwards compatible, and as such, existing FASTA parsers will require little or no changes to be able to read PEFF files as FASTA files, although without supporting any of the extra capabilities of PEFF. PEFF is defined by a full specification document, controlled vocabulary terms, a set of example files, software libraries, and a file validator. Popular software and resources are starting to support PEFF, including the sequence search engine Comet and the knowledge bases neXtProt and UniProtKB. Widespread implementation of PEFF is expected to further enable proteogenomics and top-down proteomics applications by providing a standardized mechanism for encoding protein sequences and their known variations. All the related documentation, including the detailed file format specification and example files, are available at http://www.psidev.info/peff.

bioinformatics↗