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Coronaviruses have threatened humans repeatedly, especially COVID-19 caused by SARS-CoV-2, which has posed a substantial threat to global public health. SARS-CoV-2 continuously evolves through random mutation, resulting in a significant decrease in the efficacy of existing vaccines and neutralizing antibody drugs. It is critical to assess immune escape caused by viral mutations and develop broad-spectrum vaccines and neutralizing antibodies targeting conserved epitopes. Thus, we constructed CovEpiAb, a comprehensive database and analysis resource of human coronavirus (HCoVs) immune epitopes and antibodies. CovEpiAb contains information on over 60 000 experimentally validated epitopes and over 12 000 antibodies for HCoVs and SARS-CoV-2 variants. The database is unique in (1) classifying and annotating cross-reactive epitopes from different viruses and variants; (2) providing molecular and experimental interaction profiles of antibodies, including structure-based binding sites and around 70 000 data on binding affinity and neutralizing activity; (3) providing virological characteristics of current and past circulating SARS-CoV-2 variants and in vitro activity of various therapeutics; and (4) offering site-level annotations of key functional features, including antibody binding, immunological epitopes, SARS-CoV-2 mutations and conservation across HCoVs. In addition, we developed an integrated pipeline for epitope prediction named COVEP, which is available from the webpage of CovEpiAb. CovEpiAb is freely accessible at https://pgx.zju.edu.cn/covepiab/. [ABSTRACT FROM AUTHOR]

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy. [ABSTRACT FROM AUTHOR]

The FDA Modernization Act 2.0 has brought nonclinical drug evaluation into a new era. In vitro models are widely used and play an important role in modern drug development and evaluation, including early candidate drug screening and preclinical drug efficacy and toxicity assessment. Driven by regulatory steering and facilitated by well-defined physiology, novel in vitro skin models are emerging rapidly, becoming the most advanced area in alternative testing research. The revolutionary technologies bring us many in vitro skin models, either laboratory-developed or commercially available, which were all built to emulate the structure of the natural skin to recapitulate the skin's physiological function and particular skin pathology. During the model development, how to achieve balance among complexity, accessibility, capability, and cost-effectiveness remains the core challenge for researchers. This review attempts to introduce the existing in vitro skin models, align them on different dimensions, such as structural complexity, functional maturity, and screening throughput, and provide an update on their current application in various scenarios within the scope of chemical testing and drug development, including testing in genotoxicity, phototoxicity, skin sensitization, corrosion/irritation. Overall, the review will summarize a general strategy for in vitro skin model to enhance future model invention, application, and translation in drug development and evaluation. [ABSTRACT FROM AUTHOR]

Predicting the biological properties of molecules is crucial in computer-aided drug development, yet it's often impeded by data scarcity and imbalance in many practical applications. Existing approaches are based on self-supervised learning or 3D data and using an increasing number of parameters to improve performance. These approaches may not take full advantage of established chemical knowledge and could inadvertently introduce noise into the respective model. In this study, we introduce a more elegant transformer-based framework with focused attention for molecular representation (TransFoxMol) to improve the understanding of artificial intelligence (AI) of molecular structure property relationships. TransFoxMol incorporates a multi-scale 2D molecular environment into a graph neural network + Transformer module and uses prior chemical maps to obtain a more focused attention landscape compared to that obtained using existing approaches. Experimental results show that TransFoxMol achieves state-of-the-art performance on MoleculeNet benchmarks and surpasses the performance of baselines that use self-supervised learning or geometry-enhanced strategies on small-scale datasets. Subsequent analyses indicate that TransFoxMol's predictions are highly interpretable and the clever use of chemical knowledge enables AI to perceive molecules in a simple but rational way, enhancing performance. [ABSTRACT FROM AUTHOR]

Aims Trastuzumab, the first humanized monoclonal antibody that targets human epidermal growth factor receptor 2 (ERBB2/HER2), is currently used as a first-line treatment for HER2 (+) tumours. However, trastuzumab increases the risk of cardiac complications without affecting myocardial structure, suggesting a distinct mechanism of cardiotoxicity. Methods and results We used medium from trastuzumab-treated human umbilical vein endothelial cells (HUVECs) to treat CCC-HEH-2 cells, the human embryonic cardiac tissue-derived cell lines, and human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to assess the crosstalk between vascular endothelial cells (VECs) and cardiomyocytes. Protein mass spectrometry analysis was used to identify the key factors from VECs that regulate the function of cardiomyocytes. We applied RNA-sequencing to clarify the mechanism, by which PTX3 causes cardiac dysfunction. We used an anti-human/rat HER2 (neu) monoclonal antibody to generate a rat model that was used to evaluate the effects of trastuzumab on cardiac structure and function and the rescue effects of lapatinib on trastuzumab-induced cardiac side effects. Medium from trastuzumab-treated HUVECs apparently impaired the contractility of CCC-HEH-2 cells and iPSC-CMs. PTX3 from VECs caused defective cardiomyocyte contractility and cardiac dysfunction in mice, phenocopying trastuzumab treatment. PTX3 affected calcium homoeostasis in cardiomyocytes, which led to defective contractile properties. EGFR/STAT3 signalling in VECs contributed to the increased expression and release of PTX3. Notably, lapatinib, a dual inhibitor of EGFR/HER2, could rescue the cardiac complications caused by trastuzumab by blocking the release of PTX3. Conclusion We identified a distinct mode of cardiotoxicity, wherein the activation of EGFR/STAT3 signalling by trastuzumab in VECs promotes PTX3 excretion, which contributes to the impaired contractility of cardiomyocytes by inhibiting cellular calcium signalling. We confirmed that lapatinib could be a feasible preventive agent against trastuzumab-induced cardiac complications and provided the rationale for the combined application of lapatinib and trastuzumab in cancer therapy. [ABSTRACT FROM AUTHOR]

Atomic charges play a very important role in drug-target recognition. However, computation of atomic charges with high-level quantum mechanics (QM) calculations is very time-consuming. A number of machine learning (ML)-based atomic charge prediction methods have been proposed to speed up the calculation of high-accuracy atomic charges in recent years. However, most of them used a set of predefined molecular properties, such as molecular fingerprints, for model construction, which is knowledge-dependent and may lead to biased predictions due to the representation preference of different molecular properties used for training. To solve the problem, we present a new architecture based on graph convolutional network (GCN) and develop a high-accuracy atomic charge prediction model named DeepAtomicCharge. The new GCN architecture is designed with only the atomic properties and the connection information between the atoms in molecules and can dynamically learn and convert molecules into appropriate atomic features without any prior knowledge of the molecules. Using the designed GCN architecture, substantial improvement is achieved for the prediction accuracy of atomic charges. The average root-mean-square error (RMSE) of DeepAtomicCharge is 0.0121 e, which is obviously more accurate than that (0.0180 e) reported by the previous benchmark study on the same two external test sets. Moreover, the new GCN architecture needs much lower storage space compared with other methods, and the predicted DDEC atomic charges can be efficiently used in large-scale structure-based drug design, thus opening a new avenue for high-performance atomic charge prediction and application. [ABSTRACT FROM AUTHOR]

ABSTRACT MAT1, an assembly factor and targeting subunit of both cyclin-dependent kinase-activating kinase (CAK) and general transcription factor IIH (TFIIH) kinase, regulates cell cycle and transcription. Previous studies show that expression of intact MAT1 protein is associated with expansion of human hematopoietic stem cells (HSC), whereas intrinsically programmed or retinoic acid (RA)-induced MAT1 fragmentation accompanies granulocytic differentiation of HSC or leukemic myeloblasts. Here we determined that, in humanized mouse microenvironment, MAT1 overexpression resisted intrinsic MAT1 fragmentation to sustain hematopoietic CD34+ cell expansion while preventing granulopoiesis. Conversely, we mimicked MAT1 fragmentation in vitro and in a mouse model by overexpressing a fragmented 81-aa MAT1 polypeptide (pM9) that retains the domain for assembling CAK but cannot affix CAK to TFIIH-core. Our results showed that pM9 formed ΔCAK by competing with MAT1 for CAK assembly to mimic MAT1 fragmentation-depletion of CAK. This resulting ΔCAK acted as a dominant negative to inhibit the growth and metastasis of different leukemic myeloblasts, with or without RA resistance, by concurrently suppressing CAK and TFIIH kinase activities to inhibit cell cycle and gene transcription. These findings suggest that the intrinsically programmed MAT1 expression and fragmentation regulate granulopoiesis by inversely coordinating CAK and TFIIH activities, whereas pM9 shares a mechanistic resemblance with MAT1 fragmentation in suppressing myeloid leukemogenesis. S tem C ells 2013;31:1942-1953 [ABSTRACT FROM AUTHOR]

Introduction TPN729 MA is a newly developed phosphodiesterase type 5 inhibitor ( PDE5i) for the treatment of erectile dysfunction, which offers potential for greater selectivity and longer duration of action than PDE5i in current clinical use. Aim We investigated the in vitro inhibitory potency and selectivity of TPN729 MA on PDE isozymes, and its efficacy in animal models. Methods The inhibition of 11 human recombinant PDEs by TPN729 MA, sildenafil, and tadalafil were determined using radioimmunoassay. The effect of TPN729 MA and sildenafil on intracavernous pressure ( ICP), blood pressure ( BP), and ICP/ BP ratio were determined in a rat model of erection induced by electric stimulation and in a dog model of erection induced by sodium nitroprusside injection. Main Outcome Measures The main outcome measures were IC50 of TPN729 MA, sildenafil, and tadalafil for PDE1- PDE11; maximum ICP; BP and ICP/ BP ratio. Results The IC50 of TPN729 MA, sildenafil, and tadalafil for PDE5 was 2.28, 5.22, and 2.35 nM, respectively. TPN729 MA showed 248, 366, 20, and 2671-fold selectivity against PDE1, PDE4, PDE6, and PDE11, respectively. TPN729 MA showed excellent selectivity against PDE2, 3, 7, 8, 9, and 10 (>10,000-fold). In the rat model of erection, TPN729 MA (5.0 and 2.5 mg/kg), but not sildenafil, significantly increased the maximum ICP compared with vehicle. Significantly increased ICP/ BP was observed in the TPN729 MA (5.0 mg/kg) group at all time points, in the TPN729 MA (2.5 mg/kg) group at 75, 90, 105, and 120 minutes time points, and in sildenafil group at 75 and 90 minutes time points compared with vehicle. In the dog model of erection, TPN729 MA and sildenafil significantly increased ICP and ICP/ BP but showed no significant effect on BP compared with vehicle. Conclusions TPN729 MA is a potent PDE5i with a balanced selectivity profile. TPN729 MA shows excellent in vitro and in vivo potency, and a longer effect on erectile function than sildenafil in animal model. Wang Z, Zhu D, Yang X, Li J, Jiang X, Tian G, Terrett NK, Jin J, Wu H, He Q, Yang B, and Shen J. The selectivity and potency of the new PDE5 Inhibitor TPN729MA. J Sex Med 2013;10:2790-2797. [ABSTRACT FROM AUTHOR]