Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target.

Drugs that target histone deacetylase (HDAC) entered the pharmacopoeia in the 2000s. However, some enigmatic phenotypes suggest off-target engagement. Here, we developed a quantitative chemical proteomics assay using immobilized HDAC inhibitors and mass spectrometry that we deployed to establish the target landscape of 53 drugs. The assay covers 9 of the 11 human zinc-dependent HDACs, questions the reported selectivity of some widely-used molecules (notably for HDAC6) and delineates how the composition of HDAC complexes influences drug potency. Unexpectedly, metallo-ß-lactamase domain-containing protein 2 (MBLAC2) featured as a frequent off-target of hydroxamate drugs. This poorly characterized palmitoyl-CoA hydrolase is inhibited by 24 HDAC inhibitors at low nanomolar potency. MBLAC2 enzymatic inhibition and knockdown led to the accumulation of extracellular vesicles. Given the importance of extracellular vesicle biology in neurological diseases and cancer, this HDAC-independent drug effect may qualify MBLAC2 as a target for drug discovery.

Leveraging Protein Dynamics to Identify Functional Phosphorylation Sites using Deep Learning Models.

Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different "omics" levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamics-centric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDL-PAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.

Comprehensive Map of the Artemisia annua Proteome and Quantification of Differential Protein Expression in Chemotypes Producing High versus Low Content of Artemisinin.

Artemisia annua is well known for biosynthesizing the antimalarial drug artemisinin. Here, a global proteomic profiling of A. annua is conducted with identification of a total of 13 403 proteins based on the genome sequence annotation database. Furthermore, a spectral library is generated to perform quantitative proteomic analysis using data independent acquisition mass spectrometry. Specifically, proteins between two chemotypes that produce high (HAP) and low (LAP) artemisinin content, respectively, are comprehensively quantified and compared. 182 proteins are identified with abundance significantly different between these two chemotypes means after the statistic use the p-value and fold change it is found 182 proteins can reach the demand conditions which represent the expression are significantly different between the high artemisnin content plants (HAPs) and the low artemisnin content plants (LAPs). Data are available via ProteomeXchange with identifier PXD015547. Overall, this current study globally identifies the proteome of A. annua and quantitatively compares the targeted sub-proteomes between the two cultivars of HAP and LAP, providing systematic information on metabolic pathways of A. annua.

Data-independent acquisition-based quantitative proteomic analysis reveals differences in host immune response of peripheral blood mononuclear cells to sepsis.

This study was aimed to uncover proteins that are differentially expressed in sepsis. Data-independent acquisition (DIA) was used for analysis to identify differentially expressed proteins in peripheral blood mononuclear cells (PBMCs) of patients. A total of 24 non-septic intensive care unit (ICU) patients, 11 septic shock patients and 27 patients diagnosed with sepsis were recruited for the mass spectrometry (MS) discovery. PBMCs were isolated from routine blood samples and digested into peptides. A DIA workflow was developed using a quadrupole-Orbitrap liquid chromatography LC-MS system, and mass spectra peaks were extracted by Skyline software. Orthogonal partial least-squares discriminant analysis (OPLS-DA) and partial least-squares discriminant analysis (PLS-DA) were applied to distinguish the patient groups at the level of fragment ion and peptide. Differentially expressed proteins in the patient groups were verified by enzyme-linked immunosorbent assay (ELISA). Receiver-operating characteristic (ROC) curves were used to evaluate the protein expression. A total of 1062 fragment ions and 122 proteins were identified in the MS-DIA analysis conducted by Skyline software. Using gene ontology clustering analysis, we discovered that 51 of the 122 identified proteins were associated with biological processes, including carbon metabolism, biosynthesis of antibiotics, platelet activation, bacterial invasion of epithelial cells and complement, and coagulation cascades. Among them, five proteins (high-mobility group box1 [HMGB1], matrix metalloproteinase 8 [MMP8], neutrophil gelatinase-associated lipocalin [NGAL], lactotransferrin [LTF] and grancalcin [GCA]) were identified by ELISA as closely related to the development of sepsis. The ROC curves displayed good sensitivity and specificity.

Exosomal proteome analysis of human plasma to monitor sepsis progression.

Exosomes are cell-derived vesicles containing RNA, lipid, and protein, which act in body immune response, intercellular signaling and some other important biological processes. Exosomes have been extensively studied in the past several years on their disease related mechanisms and potential roles to monitor disease progression as biomarkers. Compared with analyzing exosome RNA, comprehensive proteome profiling of exosomes in clinical samples (e.g. blood) are highly demanded but limited mainly due to lack of a reproducible method for efficient exosome extraction. In this study, we evaluated and optimized an exosome preparation approach using one-step ultracentrifugation through an Optiprep™ cushion. Exosomes prepared via this method and analyzed by mass spectrometry using Q-Exactive plus, has led to reproducible identification and quantification of 200 + proteins from human plasma samples of as little as 300 µL. Therefore, such a straightforward exosome extract method has enable us to deeply profile exosome proteomes from human blood at a scale of clinical studies. As a proof of principal, we practiced this approach in analyzing the exosome proteomic profiles of blood samples collected from a sepsis patient during six time points after diagnosis. Among the 238 proteins identified and quantified across the 6 samples, protein SPTLC3 involved in the sphingolipid metabolism, shows a negative correlation (p = 0.02, correlation coefficient = -0.984) with disease progression indicated by body temperature (BD) and C-reactive protein (CRP). Therefore, SPTLC3 could be an interesting target for future study on molecular mechanism of sepsis development, as well as potential classifier to monitor clinical progression of sepsis.

Optimized chemical proteomics assay for kinase inhibitor profiling.

Solid supported probes have proven to be an efficient tool for chemical proteomics. The kinobeads technology features kinase inhibitors covalently attached to Sepharose for affinity enrichment of kinomes from cell or tissue lysates. This technology, combined with quantitative mass spectrometry, is of particular interest for the profiling of kinase inhibitors. It often leads to the identification of new targets for medicinal chemistry campaigns where it allows a two-in-one binding and selectivity assay. The assay can also uncover resistance mechanisms and molecular sources of toxicity. Here we report on the optimization of the kinobead assay resulting in the combination of five chemical probes and four cell lines to cover half the human kinome in a single assay (∼ 260 kinases). We show the utility and large-scale applicability of the new version of kinobeads by reprofiling the small molecule kinase inhibitors Alvocidib, Crizotinib, Dasatinib, Fasudil, Hydroxyfasudil, Nilotinib, Ibrutinib, Imatinib, and Sunitinib.

New affinity probe targeting VEGF receptors for kinase inhibitor selectivity profiling by chemical proteomics.

Solid tumors are dependent for growth on nutrients and the supply of oxygen, which they often acquire via neoangiogenesis. Vascular endothelial growth factors and the corresponding receptors (VEGFRs) play central roles in this process, and consequently, the blockade of this pathway is one therapeutic strategy for cancer treatment. A number of small molecules inhibiting VEGFR inhibitors have been developed for clinical use, and a comprehensive view of target selectivity is important to assess the therapeutic as well as risk potential of a drug molecule. Recent advances in mass spectrometry-based chemical proteomics allow analyses of drug-target interactions under close-to-physiological conditions, and in this study, we report on the design, synthesis, and application of a small molecule affinity probe as a tool for the selectivity profiling of VEGFR and other kinase inhibitors. The probe is capable of binding >132 protein kinases, including angiokinases such as VEGFRs, PDGFRs, and c-KIT from lysates of cancer cell lines or human placenta tissue. Combining the new probe with Kinobeads in competitive binding assays, we were able to identify nanomolar off-targets of the VEGFR/PDGFR inhibitors pazopanib and axitinib. Because of its broad binding spectrum, the developed chemical tool can be generically used for the discovery of kinase inhibitor targets, which may contribute to a more comprehensive understanding of the mechanisms of action of such drugs.

ChemMapper: a versatile web server for exploring pharmacology and chemical structure association based on molecular 3D similarity method.

SUMMARY: ChemMapper is an online platform to predict polypharmacology effect and mode of action for small molecules based on 3D similarity computation. ChemMapper collects >350 000 chemical structures with bioactivities and associated target annotations (as well as >3 000 000 non-annotated compounds for virtual screening). Taking the user-provided chemical structure as the query, the top most similar compounds in terms of 3D similarity are returned with associated pharmacology annotations. ChemMapper is designed to provide versatile services in a variety of chemogenomics, drug repurposing, polypharmacology, novel bioactive compounds identification and scaffold hopping studies. AVAILABILITY: http://lilab.ecust.edu.cn/chemmapper/. CONTACT: xfliu@ecust.edu.cn or hlli@ecust.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

HNRNPA2B1 as a potential therapeutic target for thymic epithelial tumor recurrence: An integrative network analysis.

Thymic epithelial tumors (TETs) are rare malignant tumors, and the molecular mechanisms of both primary and recurrent TETs are poorly understood. Here we established comprehensive proteomic signatures of 15 tumors (5 recurrent and 10 non-recurrent) and 15 pair wised tumor adjacent normal tissues. We then proposed an integrative network approach for studying the proteomics data by constructing protein-protein interaction networks based on differentially expressed proteins and a machine learning-based score, followed by network modular analysis, functional enrichment annotation and shortest path inference analysis. Network modular analysis revealed that primary and recurrent TETs shared certain common molecular mechanisms, including a spliceosome module consisting of RNA splicing and RNA processing, but the recurrent TET was specifically related to the ribosome pathway. Applying the shortest path inference to the collected seed gene module identified that the ribonucleoprotein hnRNPA2B1 probably serves as a potential target for recurrent TET therapy. The drug repositioning combined molecular dynamics simulations suggested that the compound ergotamine could potentially act as a repurposing drug to treat recurrent TETs by targeting hnRNPA2B1. Our study demonstrates the value of integrative network analysis to understand proteotype robustness and its relationships with genotype, and provides hits for further research on cancer therapeutics.

Proteomic Portrait of Human Lymphoma Reveals Protein Molecular Fingerprint of Disease Specific Subtypes and Progression.

An altered proteome in lymph nodes often suggests abnormal signaling pathways that may be associated with diverse lymphatic disorders. Current clinical biomarkers for histological classification of lymphomas have encountered many discrepancies, particularly for borderline cases. Therefore, we launched a comprehensive proteomic study aimed to establish a proteomic landscape of patients with various lymphatic disorders and identify proteomic variations associated with different disease subgroups. In this study, 109 fresh-frozen lymph node tissues from patients with various lymphatic disorders (with a focus on Non-Hodgkin's Lymphoma) were analyzed by data-independent acquisition mass spectrometry. A quantitative proteomic landscape was comprehensively characterized, leading to the identification of featured protein profiles for each subgroup. Potential correlations between clinical outcomes and expression profiles of signature proteins were also probed. Two representative signature proteins, phospholipid-binding proteins Annexin A6 (ANXA6) and Phospholipase C Gamma 2 (PLCG2), were successfully validated via immunohistochemistry. We also evaluated the capability of acquired proteomic signatures to segregate multiple lymphatic abnormalities and identified several core signature proteins, such as Sialic Acid Binding Ig Like Lectin 1 (SIGLEC1) and GTPase of immunity-associated protein 5 (GIMAP5). In summary, the established lympho-specific data resource provides a comprehensive map of protein expression in lymph nodes during multiple disease states, thus extending the existing human tissue proteome atlas. Our findings will be of great value in exploring protein expression and regulation underlying lymphatic malignancies, while also providing novel protein candidates to classify various lymphomas for more precise medical practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-022-00075-w.

Comprehensive analysis of O-glycosylation of amyloid precursor protein (APP) using targeted and multi-fragmentation MS strategy.

BACKGROUND: The aberrant proteolytic processing of amyloid precursor protein (APP) into amyloid ß peptide (Aß) in brain is a critical step in the pathogenesis of Alzheimer's disease (AD). As an O-glycosylated protein, O-glycosylation of APP is considered to be related to Aß generation. Therefore, comprehensive analysis of APP O-glycosylation is important for understanding its functions. METHODS: We developed a Targeted MS approach with Multi-Fragmentation techniques (TMMF strategy), and successfully characterized O-glycosylation profiling of APP695 expressed in HEK-293 T cells. We calculated relative abundance of glycopeptides with various O-glycosites and O-glycans, and further investigated the alteration of APP O-glycosylation upon TNF-α treatment. RESULTS: A total of 14 O-glycosites were identified on three glycopeptides of APP, and at least four O-glycans including GalNAc (Tn antigen), core 1, and mono-/di-sialylated core 1 glycans were determinant at the residues of Thr576 and Thr577. We found a dense cluster of truncated O-glycans on the region nearby beginning of E2 domain and high abundance of sialylated O-glycans on the region close to ß-cleavage site. Moreover, we also observed that TNF-α could upregulate the expression of APP and the truncated O-glycans on APP in HEK-293 T cell. CONCLUSION: Our study established an intact O-glycopeptide MS analysis strategy for APP O-glycopeptide identification with enhanced fragmentation efficiency and detection sensitivity. These results provide a comprehensive O-glycosylation map of APP expressed in HEK-293 T cell. GENERAL SIGNIFICANCE: The accurate O-glycosites and O-glycan structures on APP may lead to a better understanding of the roles O-glycosylation plays in the processing and functions of APP.

Data independent acquisition-mass spectrometry (DIA-MS)-based comprehensive profiling of bone metastatic cancers revealed molecular fingerprints to assist clinical classifications for bone metastasis of unknown primary (BMUP).

BACKGROUND: Bone metastasis is the third most common metastatic cancers worldwide. It is a group of highly heterogeneous diseases with various potential cancer primaries. Among them, one third was diagnosed as bone metastasis of unknown primary (BMUP) due to lack of indication for the primary tumor even after comprehensive examinations. Thus, the prognosis of BMUP is often very poor since the treatment was largely empirical and untargeted. To assist identification of the primary tumor, a series of molecular markers including traditional tissue-specific histochemistry as well as gene and mRNA markers were developed with moderate to good sensitivity and specificity. METHODS: In this paper, we carried out a comprehensive expression profiling for fresh-frozen tissue samples of bone metastasis from lung, prostate and liver cancers using high resolution, data-independent-acquisition mass spectrometry (DIA-MS). The proteome variation was analyzed and protein classifiers were prioritized. RESULTS: Over 6,000 proteins were quantified from 18 samples, which, to the best of our knowledge, was never achieved before. Further statistical analysis and bioinformatics data mining revealed 4 significant proteins (RFIP1, CK15, ESYT2, and MAL2) with excellent discriminating capabilities with AUCs higher than 0.8. CONCLUSIONS: The comprehensive proteome map of bone metastases will complement available genomic and transcriptomic data. Newly discovered protein classifiers will expand current diagnostic arsenal for tissue of origin studies in BMUP. Furthermore, the proteome map generated in this study by DIA-MS allows future data re-mining as our knowledge advances to assist investigation of bone metastasis and progression of tumors as well as the development of diagnostic tools and prognosis management for BMUPs.

Deciphering tissue-based proteome signatures revealed novel subtyping and prognostic markers for thymic epithelial tumors.

Thymic epithelial tumors (TETs) belong to a group of tumors that rarely occur, but have unresolved mechanisms and heterogeneous clinical behaviors. Current care of TET patients demands biomarkers of high sensitivity and specificity for accurate histological classification and prognosis management. In this study, 134 fresh-frozen tissue samples (84 tumor, 40 tumor adjacent, and 10 normal thymus) were recruited to generate a quantitative and systematic view of proteomic landscape of TETs. Among them, 90 samples were analyzed by data-independent acquisition mass spectrometry (DIA-MS) leading to discovery of novel classifying molecules among different TET subtypes. The correlation between clinical outcome and the identified molecules was probed, and the prioritized proteins of interest were further validated on the remaining samples (n = 44) via parallel reaction monitoring (PRM) as well as immunohistochemical and confocal imaging analysis. In particular, two proteins, the cellular mRNA deadenylase CCR4 (carbon catabolite repressor 4)-NOT (negative on TATA) complex subunit 2/9 (CNOT2/9) and the serine hydroxymethyltransferase that catalyzes the reversible interconversions of serine and glycine (SHMT1), were found at dramatic low levels in the thymic epithelia of more malignant subtype, thymic squamous cell carcinoma (TSCC). Interestingly, the mRNA levels of these two genes were shown to be closely correlated with prognosis of the TET patients. These results extended the existing human tissue proteome atlas and allowed us to identify several new protein classifiers for TET subtyping. Newly identified subtyping and prognosis markers, CNOT2/9 and SHMT1, will expand current diagnostic arsenal in terms of higher specificity and prognostic insights for TET diagnosis and management.

O-linked N-acetylgalactosamine modification is present on the tumor suppressor p53.

BACKGROUND: Mucin-type O-glycosylation (referred to as O-GalNAc glycosylation) is the most abundant O-glycosylation on membrane and secretory proteins. Recently several evidences suggest that nuclear or cytoplasmic proteins might also have O-GalNAc glycosylation. However, what nucleocytoplasmic proteins are O-GalNAc glycosylated and what the biological function of this modification in cells are still poorly understood. Previously, we reported the tumor suppressor p53 could be O-GalNAc glycosylated in vitro. To investigate the existence and function of O-GalNAc glycosylation on nucleocytoplasmic proteins in cell, p53 as a representative nucleocytoplasmic protein was studied. METHODS: Using lectin blotting with GalNAc specific lectins, enzymatic treatments with O-GlcNAcase, core 1 ß1, 3-galactosyltransferase and O-glycosidase, and metabolic labeling with un-O-acetylated GalNAz in UDP-Gal/UDP-GalNAc 4-epimerase (GALE) knockout cells, we validated the O-GalNAc glycosylation on p53. Using mass spectrometry analysis and site-directed mutagenesis, we identified the glycosylated sites and studied the functions of O-GalNAc glycosylation on p53. RESULTS: The p53 was O-GalNAc glycosylated in cells. Ser121 residue was one of the glycosylated sites on p53. The O-GalNAc glycosylation at Ser121 was associated with the stability and activity of p53. CONCLUSIONS: These results revealed that the O-GalNAc glycosylation was a novel modification on p53. GENERAL SIGNIFICANCE: Our study provided a pilot evidence that the O-GalNAc glycosylation existed on nucleocytoplasmic protein.

Efficient iron/copper cocatalyzed S-arylations of thiols with aryl halides.

A practical and promising protocol was developed for S-arylations of various thiols with different substituted aryl halides. The reactions were readily facilitated to afford desired thioethers under mild conditions in good to excellent yields. The versatility, air-stability, operational simplicity of this method, in addition to the higher yields and shorter reaction time it provides, highlight the potential of using this method in large scale library synthesis involving carbon-heteroatom formation.

Discovering Protein Biomarkers from Clinical Peripheral Blood Mononuclear Cells Using Data-Independent Acquisition Mass Spectrometry.

Global proteomics analyses are traditionally performed in data-dependent acquisition (DDA) mode, which results in inadequate reproducibility across large sample cohorts due to the under-sampling inherent to shotgun proteomics. Recently, data-independent acquisition (DIA) strategies were introduced to allow reproducible detection and quantification of thousands of proteins with consistent sensitivity across samples. Here, we present an approach to analyze changes to the protein network in human peripheral blood mononuclear cells (PBMCs) from clinical blood samples, using DIA as a unique platform for biomarker discovery. We describe how to generate spectral PBMC proteome libraries by applying peptide fractionation followed by DDA analysis, and then how to apply DIA methods to PBMC samples from individual patients using a high-resolution Orbitrap Fusion mass spectrometer.

In-Depth Characterization of Mass Spectrometry-Based Proteomic Profiles Revealed Novel Signature Proteins Associated with Liver Metastatic Colorectal Cancers.

Liver metastasis is the most common form of metastatic colorectal cancers during the course of the disease. The global change in protein abundance in liver metastatic colorectal cancers and its role in metastasis establishment have not been comprehensively analyzed. In the present study, fresh-frozen tissue samples including normal colon/localized/liver metastatic CRCs from each recruited patient were analyzed by quantitative proteomics using a multiplexed TMT labeling strategy. Around 5000 protein groups were quantified from all samples. The proteomic profile of localized/metastatic CRCs varied greatly from that of normal colon tissues; differential proteins were mainly from extracellular regions and participate in immune activities, which is crucial for the chronic inflammation signaling pathways in the tumor microenvironment. Further statistical analysis revealed 47 proteins exhibiting statistical significance between localized and metastatic CRCs, of which FILI1P1 and PLG were identified for the first time in proteomic data, which were highly associated with liver metastasis in CRCs.

A new chemical probe for quantitative proteomic profiling of fibroblast growth factor receptor and its inhibitors.

Recent advances in mass spectrometry-based chemical proteomics allow unbiased analysis of drug-target interactions under close to physiological conditions. In this study, we designed and synthesized two small molecule probes targeting fibroblast growth factor receptors (FGFRs) and applied them to evaluate the selectivity profiles of the FGFR inhibitors Dovitinib and Orantinib. Probe F2 was capable of enriching all members of the FGF receptor family as well as other kinases involved in cancer such as KDR, FLT4 and RET from lysates of cancer cells or human placenta tissue. In combination with the established Kinobeads™ approach, probe F2 facilitated the identification of the target spectrum of the two inhibitors confirming many of the previously identified (off-) targets such as AURKA, FLT4-VEGFR3, IKBKE and PDGFRß. The newly synthesized probe enlarges the arsenal of chemical proteomic tools for the expression profiling of kinases and selectivity profiling of their inhibitors. It will also be useful in applications aiming at a better understanding of a drug's cellular mechanisms of action as well as highlighting potential beneficial or adverse side effects. BIOLOGICAL SIGNIFICANCE: The synthesis of a new chemical affinity probe targeting FGF-receptors and many other kinases improved the general scope of drug selectivity profiling by chemical proteomics. The application of the developed chemical tool identified most of the known targets for the advanced clinical kinase inhibitors Dovitinib and Orantinib thus exemplify the practical utility of the developed probe and the results obtained shed further light on how these drugs exert their anti-cancer activity in cells. More generally speaking, the significance of the work is that the molecular tools presented here extend the application scope of kinobeads based kinase profiling to FGFR/VEGFR/PDGFR families, which thus may be generically employed for selectivity profiling of kinase inhibitors using chemical proteomics. The overall aim of such studies is to improve our understanding of how target as well as off-target profiles can be used to assess or predict the therapeutic efficacy of a drug.