ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein–DNA Interaction Hotspots

Protein–DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein–DNA interactions holds great significance for revealing the intricate mechanisms in protein–DNA recognition and for providing essential guidance for protein engineering. Aiming at protein–DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively

Magnetic field mapper based on rotating coils

This thesis presents a magnetic field mapper based on rotating coils. The requirements, the architecture, the conceptual design, and the prototype for straight magnets were shown. The proposed system is made up of a rotating coil transducer and a train-like system for longitudinal motion and positioning inside magnet bore. The mapper allows a localized measurement of magnetic fields and the variation of the harmonic multipole content in the magnet ends. The proof-of-principle demonstration and the experimental characterization of the rotating-coil transducer specifically conceived for mapping validated the main objective of satisfying the magnetic measurement needs of the next generation of compact accelerators

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein–DNA Interaction Hotspots

Protein–DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein–DNA interactions holds great significance for revealing the intricate mechanisms in protein–DNA recognition and for providing essential guidance for protein engineering. Aiming at protein–DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively

ESPDHot: An Effective Machine Learning-Based Approach for Predicting Protein–DNA Interaction Hotspots

Protein–DNA interactions are pivotal to various cellular processes. Precise identification of the hotspot residues for protein–DNA interactions holds great significance for revealing the intricate mechanisms in protein–DNA recognition and for providing essential guidance for protein engineering. Aiming at protein–DNA interaction hotspots, this work introduces an effective prediction method, ESPDHot based on a stacked ensemble machine learning framework. Here, the interface residue whose mutation leads to a binding free energy change (ΔΔG) exceeding 2 kcal/mol is defined as a hotspot. To tackle the imbalanced data set issue, the adaptive synthetic sampling (ADASYN), an oversampling technique, is adopted to synthetically generate new minority samples, thereby rectifying data imbalance. As for molecular characteristics, besides traditional features, we introduce three new characteristic types including residue interface preference proposed by us, residue fluctuation dynamics characteristics, and coevolutionary features. Combining the Boruta method with our previously developed Random Grouping strategy, we obtained an optimal set of features. Finally, a stacking classifier is constructed to output prediction results, which integrates three classical predictors, Support Vector Machine (SVM), XGBoost, and Artificial Neural Network (ANN) as the first layer, and Logistic Regression (LR) algorithm as the second one. Notably, ESPDHot outperforms the current state-of-the-art predictors, achieving superior performance on the independent test data set, with F1, MCC, and AUC reaching 0.571, 0.516, and 0.870, respectively

Additional file 6: Table S2. of Annexin A2 could enhance multidrug resistance by regulating NF-κB signaling pathway in pediatric neuroblastoma

Significantly regulated protein between priamry and resistant cell lines (p < 0.05 and fold of change > 2). (XLSX 98 kb

Additional file 1: Figure S1. of Annexin A2 could enhance multidrug resistance by regulating NF-ÎşB signaling pathway in pediatric neuroblastoma

Morphological observation and IC90 for NB cell line SK-N-BE(1) and SK-N-BE(2) a. Morphological characteristics of NB cell lines SK-N-BE(1) and SK-N-BE(2). b. IC90 (90% of maximal inhibitory concentration) of multiple current chemotherapeutic drug for these two NB cell lines are significantly different. (PDF 3375 kb

SAHA Regulates Histone Acetylation, Butyrylation, and Protein Expression in Neuroblastoma

Emerging evidence suggests that suberoylanilide hydroxamic acid (SAHA), a clinically approved HDAC inhibitor for cutaneous T-cell lymphoma, shows promising clinical benefits in neuroblastoma, the most common extra cranial solid neoplasm with limited choice of therapeutic intervention. However, the molecular mechanism under which the compound exerts its antitumor effect remains elusive. Here we report a quantitative proteomics study that determines changes of protein expression, histone lysine acetylation, and butyrylation in response to SAHA treatment. We detected and quantified 28 histone lysine acetylation and 18 histone lysine butyrylation marks, most of which are dramatically induced by SAHA. Importantly, we identified 11 histone K_bu sites as novel histone marks in human cells. Furthermore, quantitative proteomic analysis identified 5426 proteins, among which 510 proteins were up-regulated and 508 proteins were down-regulated (significant <i>p</i> value <0.05). The subsequent bioinformatics analysis identified distinct SAHA-response gene ontology (GO) categories and signaling pathways, including cellular metabolism and DNA-dependent pathways. Our study therefore reveals new histone epigenetic marks and offers key insights into the molecular mechanism by which SAHA regulates proteomic changes in neuroblastoma cells and identifies biomarker candidates for SAHA

Characterization of Protein Lysine Propionylation in <i>Escherichia coli</i>: Global Profiling, Dynamic Change, and Enzymatic Regulation

Propionylation at protein lysine residue is characterized to be present in both eukaryotic and prokaryotic species. However, the majority of lysine propionylation substrates still remain largely unknown. Using affinity enrichment and mass-spectrometric-based proteomics, we identified 1467 lysine propionylation sites in 603 proteins in <i>E. coli</i>. Quantitative propionylome analysis further revealed that global lysine propionylation level was drastically increased in response to propionate treatment, a carbon source for many microorganisms and also a common food preservative. The results indicated that propionylation may play a regulatory role in propionate metabolism and propionyl-CoA degradation. In contrast with lysine acetylation and succinylation, our results revealed that the lysine propionylation level of substrates showed an obvious decrease in response to high glucose, suggesting a distinct role of propionylation in bacteria carbohydrate metabolism. This study further showed that bacterial lysine deacetylase CobB and acetyltransferase PatZ could also have regulatory activities for lysine propionylation in <i>E. coli</i>. Our quantitative propionylation substrate analysis between <i>cobB</i> wild-type and <i>cobB</i> knockout strain led to the identification of 13 CobB potentially regulated propionylation sites. Together, these findings revealed the broad propionylation substrates in <i>E. coli</i> and suggested new roles of lysine propionylation in bacterial physiology

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