Biology subjects

Chang, A. T.

Publications and source records attributed to Chang, A. T..

5 recordsLinked to original sources

bioRxiv2026

Carafe2 enables high quality in silico spectral library generation for timsTOF data-independent acquisition proteomics

Data-independent acquisition (DIA) proteomics enables reproducible and systematic peptide detection and quantification, and trapped ion mobility spectrometry (TIMS) on the timsTOF platform further improves DIA by synchronizing ion mobility separation with quadrupole precursor sampling. Analyzing the highly multiplexed spectra generated by DIA typically relies on spectral libraries, and fully leveraging the additional ion mobility dimension requires these libraries to include accurate retention time, fragment ion intensity, and ion mobility annotations. Existing in silico spectral library generation tools either lack ion mobility support entirely or rely on models trained on data-dependent acquisition (DDA) data, that can introduce a mismatch that may not capture unique experiment-specific biases when applied to each respective timsTOF dataset. Carafe is a software tool that uses deep learning models to generate high-quality, experiment-specific in silico libraries by training directly on DIA data. In this study, we extend Carafe to generate libraries for timsTOF DIA data, which involves fine-tuning retention time (RT), fragment ion intensity, and ion mobility prediction models using timsTOF DIA data. Carafe2 operates directly on native timsTOF raw data (Bruker .d directories) without the need for data conversion. We demonstrate the performance of Carafe2 across a wide range of DIA applications, including global proteome, phosphoproteome, and plasma proteome datasets. Comparing Carafe2 fine-tuned RT, fragment ion intensity, and ion mobility prediction models with pretrained DDA models, we find that Carafe2 models outperform pretrained models on a variety of DIA datasets. We then demonstrate the utility of in silico libraries generated by Carafe2 for peptide detection on several different types of timsTOF DIA datasets by comparing with the libraries generated with DDA-trained AlphaPeptDeep models, DIA-NN built-in models, and empirical spectral libraries generated from DDA experiments.

bioinformatics↗

bioRxiv2025

Mistranslating tRNA variants impact the proteome and phosphoproteome of Saccharomyces cerevisiae

Transfer RNAs (tRNAs) ensure accurate decoding of the genetic code. However, mutations in tRNAs can lead to mis-incorporation of an amino acid that differs from the genetic message in a process known as mistranslation. As mistranslating tRNAs modify how the genetic message is decoded, they have potential as therapeutic tools for diseases caused by nonsense and missense mutations. Despite this, they also produce proteome-wide mis-made proteins which can disrupt proteostasis. To better understand the impact of mistranslating tRNA variants, we profile the proteome and phosphoproteome of yeast expressing three different mistranslating tRNAs. While the overall impacts were similar, the extent of growth defects and proteome changes varied with the substitution type. Although the global impacts were modest, mistranslation influenced key cellular processes, including proteostasis, cell cycle and translation. These findings highlight the need to consider cellular consequences when developing mistranslating tRNAs for therapeutic applications.

systems biology↗

bioRxiv2024

Multiplex, multimodal mapping of variant effects in secreted proteins

Despite widespread advances in DNA sequencing, the functional consequences of most genetic variants remain poorly understood. Multiplexed Assays of Variant Effect (MAVEs) can measure the function of variants at scale, and are beginning to address this problem. However, MAVEs cannot readily be applied to the [~]10% of human genes encoding secreted proteins. We developed a flexible, scalable human cell surface display method, Multiplexed Surface Tethering of Extracellular Proteins (MultiSTEP), to measure secreted protein variant effects. We used MultiSTEP to study the consequences of missense variation in coagulation factor IX (FIX), a serine protease where genetic variation can cause hemophilia B. We combined MultiSTEP with a panel of antibodies to detect FIX secretion and post-translational modification, measuring a total of 44,816 effects for 436 synonymous variants and 8,528 of the 8,759 possible missense variants. 49.6% of possible F9 missense variants impacted secretion, post-translational modification, or both. We also identified functional constraints on secretion within the signal peptide and for nearly all variants that caused gain or loss of cysteine. Secretion scores correlated strongly with FIX levels in hemophilia B and revealed that loss of secretion variants are particularly likely to cause severe disease. Integration of the secretion and post-translational modification scores enabled reclassification of 63.1% of F9 variants of uncertain significance in the My Life, Our Future hemophilia genotyping project. Lastly, we showed that MultiSTEP can be applied to a wide variety of secreted proteins. Thus, MultiSTEP is a multiplexed, multimodal, and generalizable method for systematically assessing variant effects in secreted proteins at scale.

genomics↗

bioRxiv2023

Automated Enrichment of Phosphotyrosine Peptides for High-Throughput Proteomics

Phosphotyrosine (pY) enrichment is critical for expanding fundamental and clinical understanding of cellular signaling by mass spectrometry-based proteomics. However, current pY enrichment methods exhibit a high cost per sample and limited reproducibility due to expensive affinity reagents and manual processing. We present rapid-robotic phosphotyrosine proteomics (R2-pY), which uses a magnetic particle processor and pY superbinders or antibodies. R2-pY handles 96 samples in parallel, requires 2 days to go from cell lysate to mass spectrometry injections, and results in global proteomic, phosphoproteomic and tyrosine specific phosphoproteomic samples. We benchmark the method on HeLa cells stimulated with pervanadate and serum and report over 4000 unique pY sites from 1 mg of peptide input, strong reproducibility between replicates, and phosphopeptide enrichment efficiencies above 99%. R2-pY extends our previously reported R2-P2 proteomic and global phosphoproteomic sample preparation framework, opening the door to large-scale studies of pY signaling in concert with global proteome and phosphoproteome profiling.

systems biology↗

bioRxiv2020

2-amino-1,3-benzothiazole-6-carboxamide Preferentially Binds the Tandem Mismatch Motif r(UY:GA)

RNA helices are often punctuated with non-Watson-Crick features that can be the target of chemical compounds, but progress towards identifying small molecules specific for non-canonical elements has been slow. We have used a tandem UU:GA mismatch motif (5-UG-3:5-AU-3) embedded within the helix of an RNA hairpin as a model to identify compounds that bind the motif specifically. The three-dimensional structure of the RNA hairpin and its interaction with a small molecule compound identified through a virtual screen are presented. The G-A of the mismatch forms a sheared pair upon which the U-U base pair stacks. The hydrogen bond configuration of the U-U pair involves the O2 of the U adjacent to the G and the O4 of the U adjacent to the A. The G-A and U-U pairs are flanked by A-U and G-C base pairs, respectively, and the mismatch exhibits greater stability than when the motif is within the context of other flanking base pairs or when the 5-3 orientation of the G-A and U-U is swapped. Residual dipolar coupling constants were used to generate an ensemble of structures against which a virtual screen of 64,480 small molecules was performed to identify candidate compounds that the motif specifically binds. The tandem mismatch was found to be specific for one compound, 2-amino-1,3-benzothiazole-6-carboxamide, which binds with moderate affinity but extends the motif to include the flanking A-U and G-C base pairs. The finding that affinity for the UU:GA mismatch is flanking sequence dependent emphasizes the importance of motif context and potentially increases the number of small non-canonical features within RNA that can be specifically targeted by small molecules.

biochemistry↗