OrganogenesisDB: A Comprehensive Database Exploring the Cell-Type Identities and Gene Expression Dynamics during Organogenesis.

Organogenesis, the phase of embryonic development that starts at the end of gastrulation and continues until birth is the critical process for understanding cellular differentiation and maturation during organ development. The rapid development of single-cell transcriptomics technology has led to many novel discoveries in understanding organogenesis while also accumulating a large quantity of data. To fill this gap, OrganogenesisDB (http://organogenesisdb.com/), which is a comprehensive database dedicated to exploring cell-type identification and gene expression dynamics during organogenesis, is developed. OrganogenesisDB contains single-cell RNA sequencing data for more than 1.4 million cells from 49 published datasets spanning various developmental stages. Additionally, 3324 cell markers are manually curated for 1120 cell types across 9 human organs and 4 mouse organs. OrganogenesisDB leverages various analysis tools to assist users in annotating and understanding cell types at different developmental stages and helps in mining and presenting genes that exhibit specific patterns and play key regulatory roles during cell maturation and differentiation. This work provides a critical resource and useful tool for deciphering cell lineage determination and uncovering the mechanisms underlying organogenesis.

Myeloid cell-expressed MNDA enhances M2 polarization to facilitate the metastasis of hepatocellular carcinoma.

Stable infiltration of myeloid cells, especially tumor-associated M2 macrophages, acts as one of the essential features of the tumor immune microenvironment by promoting the malignant progression of hepatocellular carcinoma (HCC). However, the factors affecting the infiltration of M2 macrophages are not fully understood. In this study, we found the molecular subtypes of HCC with the worst prognosis are characterized by immune disorders dominated by myeloid cell infiltration. Myeloid cell nuclear differentiation antigen (MNDA) was significantly elevated in the most aggressive subtype and exhibited a positively correlation with M2 infiltration and HCC metastasis. Moreover, MNDA functioned as an independent prognostic predictor and has a good synergistic effect with some existing prognostic clinical indicators. We further confirmed that MNDA was primarily expressed in tumor M2 macrophages and contributed to the enhancement of its polarization by upregulating the expression of the M2 polarization enhancers. Furthermore, MNDA could drive the secretion of M2 macrophage-derived pro-metastasis proteins to accelerate HCC cells metastasis both in vivo and in vitro. In summary, MNDA exerts a protumor role by promoting M2 macrophages polarization and HCC metastasis, and can serve as a potential biomarker and therapeutic target for HCC.

NRDE2 deficiency impairs homologous recombination repair and sensitizes hepatocellular carcinoma to PARP inhibitors.

To identify novel susceptibility genes for hepatocellular carcinoma (HCC), we performed a rare-variant association study in Chinese populations consisting of 2,750 cases and 4,153 controls. We identified four HCC-associated genes, including NRDE2, RANBP17, RTEL1, and STEAP3. Using NRDE2 (index rs199890497 [p.N377I], p = 1.19 × 10-9) as an exemplary candidate, we demonstrated that it promotes homologous recombination (HR) repair and suppresses HCC. Mechanistically, NRDE2 binds to the subunits of casein kinase 2 (CK2) and facilitates the assembly and activity of the CK2 holoenzyme. This NRDE2-mediated enhancement of CK2 activity increases the phosphorylation of MDC1 and then facilitates the HR repair. These functions are eliminated almost completely by the NRDE2-p.N377I variant, which sensitizes the HCC cells to poly(ADP-ribose) polymerase (PARP) inhibitors, especially when combined with chemotherapy. Collectively, our findings highlight the relevance of the rare variants to genetic susceptibility to HCC, which would be helpful for the precise treatment of this malignancy.

Proteomics Identifies LUC7L3 as a Prognostic Biomarker for Hepatocellular Carcinoma.

Alternative splicing has been shown to participate in tumor progression, including hepatocellular carcinoma. The poor prognosis of patients with HCC calls for molecular classification and biomarker identification to facilitate precision medicine. We performed ssGSEA analysis to quantify the pathway activity of RNA splicing in three HCC cohorts. Kaplan-Meier and Cox methods were used for survival analysis. GO and GSEA were performed to analyze pathway enrichment. We confirmed that RNA splicing is significantly correlated with prognosis, and identified an alternative splicing-associated protein LUC7L3 as a potential HCC prognostic biomarker. Further bioinformatics analysis revealed that high LUC7L3 expression indicated a more progressive HCC subtype and worse clinical features. Cell proliferation-related pathways were enriched in HCC patients with high LUC7L3 expression. Consistently, we proved that LUC7L3 knockdown could significantly inhibit cell proliferation and suppress the activation of associated signaling pathways in vitro. In this research, the relevance between RNA splicing and HCC patient prognosis was outlined. Our newly identified biomarker LUC7L3 could provide stratification for patient survival and recurrence risk, facilitating early medical intervention before recurrence or disease progression.

Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.

De novo peptide sequencing is a promising approach for novel peptide discovery, highlighting the performance improvements for the state-of-the-art models. The quality of mass spectra often varies due to unexpected missing of certain ions, presenting a significant challenge in de novo peptide sequencing. Here, we use a novel concept of complementary spectra to enhance ion information of the experimental spectrum and demonstrate it through conceptual and practical analyses. Afterward, we design suitable encoders to encode the experimental spectrum and the corresponding complementary spectrum and propose a de novo sequencing model $\pi$-HelixNovo based on the Transformer architecture. We first demonstrated that $\pi$-HelixNovo outperforms other state-of-the-art models using a series of comparative experiments. Then, we utilized $\pi$-HelixNovo to de novo gut metaproteome peptides for the first time. The results show $\pi$-HelixNovo increases the identification coverage and accuracy of gut metaproteome and enhances the taxonomic resolution of gut metaproteome. We finally trained a powerful $\pi$-HelixNovo utilizing a larger training dataset, and as expected, $\pi$-HelixNovo achieves unprecedented performance, even for peptide-spectrum matches with never-before-seen peptide sequences. We also use the powerful $\pi$-HelixNovo to identify antibody peptides and multi-enzyme cleavage peptides, and $\pi$-HelixNovo is highly robust in these applications. Our results demonstrate the effectivity of the complementary spectrum and take a significant step forward in de novo peptide sequencing.

From PROTAC to TPD: Advances and Opportunities in Targeted Protein Degradation.

PROTAC is a rapidly developing engineering technology for targeted protein degradation using the ubiquitin-proteasome system, which has promising applications for inflammatory diseases, neurodegenerative diseases, and malignant tumors. This paper gives a brief overview of the development and design principles of PROTAC, with a special focus on PROTAC-based explorations in recent years aimed at achieving controlled protein degradation and improving the bioavailability of PROTAC, as well as TPD technologies that use other pathways such as autophagy and lysosomes to achieve targeted protein degradation.

Proteomic Characterization Identifies Clinically Relevant Subgroups of Gastrointestinal Stromal Tumors.

BACKGROUND & AIMS: Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the gastrointestinal tract, and it has high metastatic and recurrence rates. We aimed to characterize the proteomic features of GIST to understand biological processes and treatment vulnerabilities. METHODS: Quantitative proteomics and phosphoproteomics analyses were performed on 193 patients with GIST to reveal the biological characteristics of GIST. Data-driven hypotheses were tested by performing functional experiments using both GIST cell lines and xenograft mouse models. RESULTS: Proteomic analysis revealed differences in the molecular features of GISTs from different locations or with different histological grades. MAPK7 was identified and functionally proved to be associated with tumor cell proliferation in GIST. Integrative analysis revealed that increased SQSTM1 expression inhibited the patient response to imatinib mesylate. Proteomics subtyping identified 4 clusters of tumors with different clinical and molecular attributes. Functional experiments confirmed the role of SRSF3 in promoting tumor cell proliferation and leading to poor prognosis. CONCLUSIONS: Our study provides a valuable data resource and highlights potential therapeutic approaches for GIST.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment.

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

Repopulating Kupffer cells originate directly from hematopoietic stem cells.

BACKGROUND: Kupffer cells (KCs) originate from yolk-sac progenitors before birth. Throughout adulthood, they self-maintain independently from the input of circulating monocytes (MOs) at a steady state and are replenished within 2 weeks after having been depleted, but the origin of repopulating KCs in adults remains unclear. The current paradigm dictates that repopulating KCs originate from preexisting KCs or monocytes, but there remains a lack of fate-mapping evidence. METHODS: We first traced the fate of preexisting KCs and that of monocytic cells with tissue-resident macrophage-specific and monocytic cell-specific fate-mapping mouse models, respectively. Secondly, we performed genetic lineage tracing to determine the type of progenitor cells involved in response to KC-depletion in mice. Finally, we traced the fate of hematopoietic stem cells (HSCs) in an HSC-specific fate-mapping mouse model, in the context of chronic liver inflammation induced by repeated carbon tetrachloride treatment. RESULTS: By using fate-mapping mouse models, we found no evidence that repopulating KCs originate from preexisting KCs or MOs and found that in response to KC-depletion, HSCs proliferated in the bone marrow, mobilized into the blood, adoptively transferred into the liver and differentiated into KCs. Then, in the chronic liver inflammation context, we confirmed that repopulating KCs originated directly from HSCs. CONCLUSION: Taken together, these findings provided in vivo fate-mapping evidence that repopulating KCs originate directly from HSCs, which presents a completely novel understanding of the cellular origin of repopulating KCs and shedding light on the divergent roles of KCs in liver homeostasis and diseases.

DeepRTAlign: toward accurate retention time alignment for large cohort mass spectrometry data analysis.

Retention time (RT) alignment is a crucial step in liquid chromatography-mass spectrometry (LC-MS)-based proteomic and metabolomic experiments, especially for large cohort studies. The most popular alignment tools are based on warping function method and direct matching method. However, existing tools can hardly handle monotonic and non-monotonic RT shifts simultaneously. Here, we develop a deep learning-based RT alignment tool, DeepRTAlign, for large cohort LC-MS data analysis. DeepRTAlign has been demonstrated to have improved performances by benchmarking it against current state-of-the-art approaches on multiple real-world and simulated proteomic and metabolomic datasets. The results also show that DeepRTAlign can improve identification sensitivity without compromising quantitative accuracy. Furthermore, using the MS features aligned by DeepRTAlign, we trained and validated a robust classifier to predict the early recurrence of hepatocellular carcinoma. DeepRTAlign provides an advanced solution to RT alignment in large cohort LC-MS studies, which is currently a major bottleneck in proteomics and metabolomics research.

RNF149 Promotes HCC Progression through Its E3 Ubiquitin Ligase Activity.

Hepatocellular carcinoma (HCC) accounts for over 80% of cases among liver cancer, with high incidence and poor prognosis. Thus, it is of valuable clinical significance for discovery of potential biomarkers and drug targets for HCC. In this study, based on the proteomic profiling data of paired early-stage HCC samples, we found that RNF149 was strikingly upregulated in tumor tissues and correlated with poor prognosis in HCC patients, which was further validated by IHC staining experiments of an independent HCC cohort. Consistently, overexpression of RNF149 significantly promoted cell proliferation, migration, and invasion of HCC cells. We further proved that RNF149 stimulated HCC progression via its E3 ubiquitin ligase activity, and identified DNAJC25 as its new substrate. In addition, bioinformatics analysis showed that high expression of RNF149 was correlated with immunosuppressive tumor microenvironment (TME), indicating its potential role in immune regulation of HCC. These results suggest that RNF149 could exert protumor functions in HCC in dependence of its E3 ubiquitin ligase activity, and might be a potential prognostic marker and therapeutic target for HCC treatment.

RUPE-phospho: Rapid Ultrasound-Assisted Peptide-Identification-Enhanced Phosphoproteomics Workflow for Microscale Samples.

Global phosphoproteome profiling can provide insights into cellular signaling and disease pathogenesis. To achieve comprehensive phosphoproteomic analyses with minute quantities of material, we developed a rapid and sensitive phosphoproteomics sample preparation strategy based on ultrasound. We found that ultrasonication-assisted digestion can significantly improve peptide identification by 20% due to the generation of longer peptides that can be detected by mass spectrometry. By integrating this rapid ultrasound-assisted peptide-identification-enhanced proteomic method (RUPE) with streamlined phosphopeptide enrichment steps, we established RUPE-phospho, a fast and efficient strategy to characterize protein phosphorylation in mass-limited samples. This approach dramatically reduces the sample loss and processing time: 24 samples can be processed in 3 h; 5325 phosphosites, 4549 phosphopeptides, and 1888 phosphoproteins were quantified from 5 µg of human embryonic kidney (HEK) 293T cell lysate. In addition, 9219 phosphosites were quantified from 1-2 mg of OCT-embedded mouse brain with 120 min streamlined RUPE-phospho workflow. RUPE-phospho facilitates phosphoproteome profiling for microscale samples and will provide a powerful tool for proteomics-driven precision medicine research.

Quartet protein reference materials and datasets for multi-platform assessment of label-free proteomics.

BACKGROUND: Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories. RESULTS: Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics. CONCLUSION: Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.

Digital image analysis allows objective stratification of patients with silent PIT1-lineage pituitary neuroendocrine tumors.

Studies describing the clinical presentation and prognosis of patients with silent PIT1 (pituitary specific transcription factor)-lineage pituitary neuroendocrine tumors (PitNETs) are rare. We identified patients with positive PIT1 tumor staining but without evidence of hormone hypersecretion at a tertiary center. Clusters were obtained according to cell morphology and immunostaining from each patient's digitally segmented whole slide image. We compared the clinical presentations, radiological features, and prognoses of the different clusters. We identified 146 patients (68 male, 42.9 ± 14.1 years old) with silent PIT1-lineage PitNETs. Morphology clustering suggested that tumors with large nuclei and apparent eccentricity were associated with a higher proportion of aggressiveness and a higher hazard of recurrence [hazard ratio (HR): 2.64, (95% CI, 1.06-6.55), p = 0.037]. Immunohistochemical clustering suggested that tumors with thyroid stimulating hormone (TSH) staining or all negative PIT1-lineage hormones were associated with a higher proportion of aggressiveness and a higher risk of recurrence [HR: 12.4, (95% CI, 1.60-93.5), p = 0.015]. We obtained three-tier risk profiles by combining morphological and immunohistochemical clustering. Patients with the high-risk profile presented the highest recurrence rate compared with those in the medium-risk and low-risk profiles [HR: 3.54, (95% CI, 1.40-8.93), p = 0.002]. In conclusion, digital image analysis based on cell morphology and immunohistochemical staining allows objective stratification of patients with silent PIT1-lineage tumors. Typical morphological characteristics of high-risk tumors are large tumor nuclei and high eccentricity, and typical immunostaining characteristics are TSH staining or negative staining for all PIT1-lineage hormones.

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients.

Background: Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection. Methods: Patients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots. Results: 118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves. Conclusion: The nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.

Aberrant LYZ expression in tumor cells serves as the potential biomarker and target for HCC and promotes tumor progression via csGRP78.

Hepatocellular carcinoma (HCC) takes the predominant malignancy of hepatocytes with bleak outcomes owing to high heterogeneity among patients. Personalized treatments based on molecular profiles will better improve patients' prognosis. Lysozyme (LYZ), a secretory protein with antibacterial function generally expressed in monocytes/macrophages, has been observed for the prognostic implications in different types of tumors. However, studies about the explicit applicative scenarios and mechanisms for tumor progression are still quite limited, especially for HCC. Here, based on the proteomic molecular classification data of early-stage HCC, we revealed that the LYZ level was elevated significantly in the most malignant HCC subtype and could serve as an independent prognostic predictor for HCC patients. Molecular profiles of LYZ-high HCCs were typical of those for the most malignant HCC subtype, with impaired metabolism, along with promoted proliferation and metastasis characteristics. Further studies demonstrated that LYZ tended to be aberrantly expressed in poorly differentiated HCC cells, which was regulated by STAT3 activation. LYZ promoted HCC proliferation and migration in both autocrine and paracrine manners independent of the muramidase activity through the activation of downstream protumoral signaling pathways via cell surface GRP78. Subcutaneous and orthotopic xenograft tumor models indicated that targeting LYZ inhibited HCC growth markedly in NOD/SCID mice. These results propose LYZ as a prognostic biomarker and therapeutic target for the subclass of HCC with an aggressive phenotype.

Macrophage-Derived Cathepsin S Remodels the Extracellular Matrix to Promote Liver Fibrogenesis.

BACKGROUND & AIMS: Liver fibrosis is an intrinsic wound-healing response to chronic injury and the major cause of liver-related morbidity and mortality worldwide. However, no effective diagnostic or therapeutic strategies are available, owing to its poorly characterized molecular etiology. We aimed to elucidate the mechanisms underlying liver fibrogenesis. METHODS: We performed a quantitative proteomic analysis of clinical fibrotic liver samples to identify dysregulated proteins. Further analyses were performed on the sera of 164 patients with liver fibrosis. Two fibrosis mouse models and several biochemical experiments were used to elucidate liver fibrogenesis. RESULTS: We identified cathepsin S (CTSS) up-regulation as a central node for extracellular matrix remodeling in the human fibrotic liver by proteomic screening. Increased serum CTSS levels efficiently predicted liver fibrosis, even at an early stage. Secreted CTSS cleaved collagen 18A1 at its C-terminus, releasing endostatin peptide, which directly bound to and activated hepatic stellate cells via integrin α5ß1 signaling, whereas genetic ablation of Ctss remarkably suppressed liver fibrogenesis via endostatin reduction in vivo. Further studies identified macrophages as the main source of hepatic CTSS, and splenectomy effectively attenuated macrophage infiltration and CTSS expression in the fibrotic liver. Pharmacologic inhibition of CTSS ameliorated liver fibrosis progression in the mouse models. CONCLUSIONS: CTSS functions as a novel profibrotic factor by remodeling extracellular matrix proteins and may represent a promising target for the diagnosis and treatment of liver fibrosis.

Proteomic characteristics reveal the signatures and the risks of T1 colorectal cancer metastasis to lymph nodes.

The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free liquid chromatography tandem mass spectrometry (LC-MS/MS) and established classifiers for predicting LNM in T1 CRC. An effective 55-proteins prediction model was built by machine learning and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47), achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2, respectively. We further built a simplified classifier with nine proteins, and achieved an AUC of 0.824. The simplified classifier was performed excellently in two external validation cohorts. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of five proteins was used to build an IHC predict model with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC.

Most patients with early-stage colorectal cancer can be treated with a minimally invasive procedure. Surgeons use a flexible tool to remove precancerous or cancerous cells, cutting the risk of death from colorectal cancer in half. But a small number of early-stage colorectal cancer patients are at risk of their cancer spreading to the lymph nodes. These patients need more extensive surgery. Clinicians use risk stratification tools to decide which patients need more extensive surgery. Unfortunately, the existing risk stratification tools are not very accurate. The current approach, which analyzes colon tissue for cancerous changes, classifies 70% to 80% of early-stage colorectal cancer patients as high risk for cancer spread. But only about 8% to 16% of patients in the high risk group have lymph node metastasis. As a result, many patients undergo unnecessary, invasive surgery. Zhuang, Zhuang, Chen, Qin, et al. developed a more accurate way to predict which patients are at risk of lymph node metastasis using proteins. In the experiments, the team analyzed the proteins in tumor samples from 143 patients with early colorectal cancer who did not have lymph node metastases and 78 patients with metastases. Zhuang et al. then used machine learning to build a prediction tool that used 55 proteins to identify patients at risk of metastases. The new approach was more accurate than existing tools and simplified versions with only nine or five proteins also performed better than existing tools. This work provides preliminary evidence that protein-based models using as few as five proteins can more accurately identify which patients are at risk of metastasis. These models may reduce the number of patients who undergo unnecessary invasive surgery. The experiments also identified potential targets for therapies to prevent or treat lymph metastases. For example, they showed that low levels of the RHOT2 protein predict metastasis.

Nucleophilic covalent ligand discovery for the cysteine redoxome.

With an eye toward expanding chemistries used for covalent ligand discovery, we elaborated an umpolung strategy that exploits the 'polarity reversal' of sulfur when cysteine is oxidized to sulfenic acid, a widespread post-translational modification, for selective bioconjugation with C-nucleophiles. Here we present a global map of a human sulfenome that is susceptible to covalent modification by members of a nucleophilic fragment library. More than 500 liganded sulfenic acids were identified on proteins across diverse functional classes, and, of these, more than 80% were not targeted by electrophilic fragment analogs. We further show that members of our nucleophilic fragment library can impair functional protein-protein interactions involved in nuclear oncoprotein transport and DNA damage repair. Our findings reveal a vast expanse of ligandable sulfenic acids in the human proteome and highlight the utility of nucleophilic small molecules in the fragment-based covalent ligand discovery pipeline, presaging further opportunities using non-traditional chemistries for targeting proteins.