Biology subjects

Paez, J. S.

Publications and source records attributed to Paez, J. S..

5 recordsLinked to original sources

bioRxiv2026

Structure-free, site-resolved contrastive learningextends small-molecule discovery beyond the reachof structure-based modeling

Virtual screening asks which molecules, among an enormous space of drug-like chemistry, are worth synthesizing and testing against a protein target. Most modern methods answer by building and scoring an explicit three-dimensional pose through molecular docking, or the co-folding models that now approach experimental accuracy. Building these poses presumes a well-defined pocket, and the non-orthosteric, cryptic, and intrinsically disordered sites where unexplored ligandability lies offer none. There, these methods fail to generalize. Here we present Ptarmigan-1, a contrastive model that co-embeds the residues of a protein with candidate small molecules in a shared latent space, from sequence and two-dimensional chemistry alone, and without ever constructing a pose. Engagement reduces to the proximity of precomputed embeddings. Freed from the pose, Ptarmigan-1 trains directly on chemoproteomic and bioactivity data of mixed resolution, scores a compound in ten milliseconds rather than the tens of seconds a co-folding model demands, and resolves each prediction to the residues a compound engages. On well-folded, orthosteric targets it performs comparably to a collection of co-folding and docking models, and on covalent, cryptic, and disordered sites it matches or exceeds them. It localizes reversible and covalent inhibitors to the pockets they engage, even for targets withheld from training, and screens the entire human proteome against a library of 3.4 billion compounds in under a day. By decoupling molecular recognition from structure, Ptarmigan-1 recasts virtual screening as a reusable index that continuously improves as data accumulate.

molecular biology↗

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

Single-cell heterogeneity in interferon induction potential is heritable and governed by variation in cell state

Type I and III interferons (IFNs) are among the first lines of defense against viral infection, yet they are generally only produced by a tiny fraction of infected cells. Here, we show that cellular heterogeneity in IFN induction potential upon treatment with immunostimulatory RNA is not due to variability in sensing of stimuli but instead is shaped by heterogeneity in tonic cell signaling state. Using complementary single-cell approaches, we found that baseline variation in the c-Jun N-terminal kinase (JNK) and activator protein (AP)-1 transcription factor families correlated with IFNL1 expression predisposition. We further show that drug-based inhibition of JNK signaling virtually eliminates the innate antiviral response to immunostimulatory RNA. Finally, we show that single cell heterogeneity in IFN induction potential is heritable and stably maintained over numerous generations. Together, our study emphasizes the influence of intrinsic variability in cell state on innate immune regulation and IFN induction heterogeneity.

systems biology↗

bioRxiv2025

Native, Spatiotemporal Profiling of the Global Human Regulome

The regulome, comprising transcription factors, cofactors, chromatin remodelers, and other regulatory proteins, forms the core machinery by which cells interpret signals and execute gene expression programs. Despite its central role in development, disease, and drug response, the regulome remains largely uncharted at scale due to its dynamic, low-abundance, and chromatin-associated nature. Here, we present a method for scalable, regulome profiling for global, compartment-resolved quantification of native regulome proteins. By enriching DNA- and chromatin-associated proteins and profiling them using high-throughput, label-free DIA mass spectrometry, regulome profiling captures chromatin-associated proteins across 36 human cell lines and thousands of perturbations. The resulting Regulome Atlas recovers nearly 60% of known human transcription factors, reveals lineage-specific TF localization, and distinguishes active nuclear engagement from latent, unbound states. We demonstrate that regulome profiles resolve acute immune pathway activation prior to transcriptional changes, identify previously unrecognized drug-induced regulome responses, and enable proteome-scale readouts of compound target engagement and complex remodeling. This work establishes a foundational resource for decoding the regulatory proteome and provides a blueprint for integrating regulome data into next-generation models of cellular behavior.

cell biology↗

bioRxiv2019

Transglutaminase-2 facilitates extracellular vesicle-mediated establishment of the metastatic niche

AbstractThe ability of breast cancer cells to interconvert between epithelial and mesenchymal states contributes to their metastatic potential. As opposed to cell autonomous effects, the impact of epithelial-mesenchymal plasticity (EMP) on primary and metastatic tumor microenvironments remains poorly characterized. Herein we utilize global gene expression analyses to characterize a metastatic model of EMP as compared to their non-metastatic counterparts. Using this approach we demonstrate that upregulation of the extracellular matrix crosslinking enzyme tissue transglutaminase-2 (TGM2) is part of novel gene signature that only emerges in metastatic cells that have undergone induction and reversion of epithelial-mesenchymal transition (EMT). Consistent with our model system patient survival is diminished when primary tumors demonstrate enhanced levels of TGM2 in conjunction with its substrate, fibronectin. Targeted depletion of TGM2 inhibits metastasis, while overexpression of TGM2 is sufficient to enhance this process. In addition to being present within cells, we demonstrate a robust increase in the amount of TGM2 and crosslinked fibronectin present within extracellular vesicle (EV) fractions derived from metastatic breast cancer cells. Confocal microscopy of these EVs suggests that FN becomes fibrillated on their surface via a TGM2 and Tesin1-dependent process. Upon in vivo administration, the ability of tumor-derived EVs to induce metastatic niche formation and enhance subsequent pulmonary tumor growth requires the presence and activity of TGM2. Finally, we develop a novel 3D model of the metastatic niche to demonstrate that education of pulmonary fibroblasts via pretreatment with tumor-derived EVs promotes subsequent growth of breast cancer cells in a TGM2-dependent fashion. Overall, our studies illustrate a novel mechanism through which EMP contributes to metastatic niche development and distant metastasis via tumor-derived EVs containing abberent levels of TGM2 and fibular FN.

cancer biology↗