Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Resistance to therapeutic antibodies caused by on-target point mutations is a major obstacle in anticancer therapy, creating an \"unmet clinical need.\" To tackle this problem, researchers are developing new generations of antibody drugs that can overcome the resistance mechanisms of existing agents. We have previously reported a structure-guided and phage-assisted evolution (SGAPAE) approach to evolve cetuximab, a therapeutic antibody, to effectively reverse the resistance driven by EGFRS492R or EGFRG465R mutations, without changing the binding epitope or compromising the antibody efficacy. In this protocol, we provide detailed instructions on how to use the SGAPAE approach to evolve cetuximab, which can also be applied to other therapeutic antibodies for reversing on-target point mutation-mediated resistance. The protocol consists of four steps: structure preparation, computational prediction, phage display library construction, and antibody candidate selection.

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

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Density functional theory computations reveal mechanistic insights into Cu and chiral phosphoric acid (CPA) catalyzed enantioconvergent amination of racemic benzenesulfonohydrazide. The O-O bond homolysis of tert-butyl 4-phenylbutaneperoxoate was found to be the turnover-limiting step with a total free energy barrier of 19.1 kcal/mol. The enantioconvergent amination is realized to obtain the same intermediate through prochiral carbon atom. The order and mode of hydrogen atom transferred by CPA and tert-butyloxy have a significant influence on the enantioselectivity and energy barriers. The olefinic side product generated by β-hydride elimination is 9.9 kcal/mol thermodynamically less favourable. A series of phosphoric acids are predicted as promising co-catalysts with lower barriers for O-O bond homolysis.

Identification of endocrine-disrupting chemicals (EDCs) is crucial in the reduction of human health risks. However, it is hard to do so because of the complex mechanisms of the EDCs. In this study, we propose a novel strategy named EDC-Predictor to integrate pharmacological and toxicological profiles for the prediction of EDCs. Different from conventional methods that only focus on a few nuclear receptors (NRs), EDC-Predictor considers more targets. It uses computational target profiles from network-based and machine learning-based methods to characterize compounds, including both EDCs and non-EDCs. The best model constructed by these target profiles outperformed those models by molecular fingerprints. In a case study to predict NR-related EDCs, EDC-Predictor showed a wider applicability domain and higher accuracy than four previous tools. Another case study further demonstrated that EDC-Predictor could predict EDCs targeting other proteins rather than NRs. Finally, a free web server was developed to make EDC prediction easier (http://lmmd.ecust.edu.cn/edcpred/). In summary, EDC-Predictor would be a powerful tool in EDC prediction and drug safety assessment.

Intrinsically Disordered Proteins (IDPs) and Regions (IDRs) exist widely. Although without well-defined structures, they participate in many important biological processes. In addition, they are also widely related to human diseases and have become potential targets in drug discovery. However, there is a big gap between the experimental annotations related to IDPs/IDRs and their actual number. In recent decades, the computational methods related to IDPs/IDRs have been developed vigorously, including predicting IDPs/IDRs, the binding modes of IDPs/IDRs, the binding sites of IDPs/IDRs, and the molecular functions of IDPs/IDRs according to different tasks. In view of the correlation between these predictors, we have reviewed these prediction methods uniformly for the first time, summarized their computational methods and predictive performance, and discussed some problems and perspectives.

BACKGROUND: Total glucosides of peony (TGP) is extracted from Paeonia lactiflora Pallas, which has been approved for rheumatoid arthritis (RA) treatment. There were approximately 15 monoterpene glycosides identified in TGP. Pervious researches focused on the effects of TGP and the major ingredient paeoniflorin (PF), but the functions of other monoterpene glycosides and their interactions were not clear. Network pharmacology has been one of the new strategies for multi-target drug discovery. In this study, we investigate the functions of all components of TGP and their interactions in RA treatment based on network pharmacology methods.
METHODS: The components of TGP were searched out the Web of Science, PubMed, China National Knowledge Infrastructure databases; then we identified the potential targets based of chemical similarity in the Similarity Ensemble Approach. The molecular related with RA were obtained from DrugBank, GeneCards, DisGeNET and Online Mendelian Inheritance in Man (OMIM) databases. The components-targets-disease network was constructed and analyzed with Cytoscape software; Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted with R for function analysis. The hub components-targets interactions were validated with Autodock Vina.
RESULTS: Twenty potential targets of TGP were predicted for RA treatment. The major components of TGP, PF and albiflorin (AF) had more predicted targets. Hub targets of TGP were LGALS3/9, VEGFA, FGF1, FGF2, IL-6, IL-2, SELP, PRKCA and ERAP1. These targets ameliorated RA mainly through inhibiting leukocyte recruitment and angiogenesis. Enriched pathways including VEGFR pathway, signaling by interleukins, PI3K-Akt signaling pathway, platelet activation, extracellular matrix organization, and so on. The combination of PF, AF and lactiflorin (LF) with the hub targets was further validated using docking program.
CONCLUSIONS: We investigated the comprehensive mechanism of TGP for RA treatment. We analyzed the different targets of the components in TGP and predicted the new effects of TGP on inhibiting leukocyte recruitment and angiogenesis. This study provides a better understanding of TGP on the RA treatment.

BACKGROUND: Channel proteins are proteins that can transport molecules past the plasma membrane through free diffusion movement. Due to the cost of labor and experimental methods, developing a tool to identify channel proteins is necessary for biological research on channel proteins.
METHODS: 17 feature coding methods and four machine learning classifiers to generate 68-dimensional data probability features. Then, the two-step feature selection strategy was used to optimize the features, and the final prediction Model M16-LGBM (light gradient boosting machine) was obtained on the 16-dimensional optimal feature vector.
RESULTS: A new predictor, CAPs-LGBM, was proposed to identify the channel proteins effectively.
CONCLUSIONS: CAPs-LGBM is the first channel protein machine learning predictor was used to construct the final prediction model based on protein primary sequences. The classifier performed well in the training and test sets.

Oxidative stress is known to be involved in and possibly a key driver of the development of numerous chronic diseases, including cancer. It is highly desired to have a capability to reliably estimate the level of intracellular oxidative stress as it can help to identify functional changes and disease phenotypes associated with such a stress, but the problem proves to be very challenging. We present a novel computational model for quantitatively estimating the level of oxidative stress in tissues and cells based on their transcriptomic data. The model consists of (i) three sets of marker genes found to be associated with the production of oxidizing molecules, the activated antioxidation programs and the intracellular stress attributed to oxidation, respectively; (ii) three polynomial functions defined over the expression levels of the three gene sets are developed aimed to capture the total oxidizing power, the activated antioxidation capacity and the oxidative stress level, respectively, with their detailed parameters estimated by solving an optimization problem and (iii) the optimization problem is so formulated to capture the relevant known insights such as the oxidative stress level generally goes up from normal to chronic diseases and then to cancer tissues. Systematic assessments on independent datasets indicate that the trained predictor is highly reliable and numerous insights are made based on its application results to samples in the TCGA, GTEx and GEO databases.