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Combination therapy aims to synergistically enhance efficacy or reduce toxic side effects and has widely been used in clinical practice. However, with the rapid increase in the types of drug combinations, identifying the synergistic relationships between drugs remains a highly challenging task. This paper proposes a novel deep learning model MMFSyn based on multimodal drug data combined with cell line features. Firstly, to ensure the full expression of drug molecular features, multiple modalities of drugs, including Morgan fingerprints, atom sequences, molecular diagrams, and atomic point cloud data, are extracted using SMILES. Secondly, for different modal data, a Bi-LSTM, gMLP, multi-head attention mechanism, and multi-scale GCNs are comprehensively applied to extract the drug feature. Then, it selects appropriate omics features from gene expression and mutation omics data of cancer cell lines to construct cancer cell line features. Finally, these features are combined to predict the synergistic anti-cancer drug combination effect. The experimental results verify that MMFSyn has significant advantages in performance compared to other popular methods, with a root mean square error of 13.33 and a Pearson correlation coefficient of 0.81, which indicates that MMFSyn can better capture the complex relationship between multimodal drug combinations and omics data, thereby improving the synergistic drug combination prediction.

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BACKGROUND: Proteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein-protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge. RESULTS: In this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution. CONCLUSIONS: To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.

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The efficiency of pharmacotherapy is significantly influenced by the crystal habit and polymorphic form of the drugs. Especially due to the anisotropy of different facets in crystalline material, crystal habit impacts the physicochemical properties and behaviors of a drug, which has been rarely reported. This paper describes a facile method for online monitoring of crystal plane orientation of favipiravir (T-705) by Raman spectroscopy. Firstly, we investigated the synergy of multiple physicochemical fields (solvation, agitated flow fields, etc.), and then prepared favipiravir crystals with different orientations in a controllable manner. Secondly, to establish the connection between crystal planes and Raman spectra, the favipiravir crystals were theoretically analyzed at the molecular and structural levels using density functional theory (DFT) and three dimensional (3D) visualization tools. Finally, we based on standard samples and applied it to 12 actual samples to evaluate the crystal habit of favipiravir. The results are similar to the classical X-ray diffraction (XRD) method. Additionally, the XRD method is difficult to be monitored online, while the Raman method is non-contact, fast, and requires no sample preparation, showing a great application prospect in the pharmaceutical process.

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MOTIVATION: Protein-protein interaction (PPI) networks and transcriptional regulatory networks are critical in regulating cells and their signaling. A thorough understanding of PPIs can provide more insights into cellular physiology at normal and disease states. Although numerous methods have been proposed to predict PPIs, it is still challenging for interaction prediction between unknown proteins. In this study, a novel neural network named AFTGAN was constructed to predict multi-type PPIs. Regarding feature input, ESM-1b embedding containing much biological information for proteins was added as a protein sequence feature besides amino acid co-occurrence similarity and one-hot coding. An ensemble network was also constructed based on a transformer encoder containing an AFT module (performing the weight operation on vital protein sequence feature information) and graph attention network (extracting the relational features of protein pairs) for the part of the network framework. RESULTS: The experimental results showed that the Micro-F1 of the AFTGAN based on three partitioning schemes (BFS, DFS and the random mode) on the SHS27K and SHS148K datasets was 0.685, 0.711 and 0.867, as well as 0.745, 0.819 and 0.920, respectively, all higher than that of other popular methods. In addition, the experimental comparisons confirmed the performance superiority of the proposed model for predicting PPIs of unknown proteins on the STRING dataset. AVAILABILITY AND IMPLEMENTATION: The source code is publicly available at https://github.com/1075793472/AFTGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Drug repositioning, an important method of drug development, is utilized to discover investigational drugs beyond the originally approved indications, expand the application scope of drugs, and reduce the cost of drug development. With the emergence of increasingly drug-disease-related biological networks, the challenge still remains to effectively fuse biological entity data and accurately achieve drug-disease repositioning. This paper proposes a new drug repositioning method named EMPHCN based on enhanced message passing and hypergraph convolutional networks (HGCN). It firstly constructs the homogeneous multi-view information with multiple drug similarity features and then extracts the intra-domain embedding of drugs through the combination of HGCN and channel attention mechanism. Secondly, inter-domain information of known drug-disease associations is extracted by graph convolutional networks combining node and edge embedding (NEEGCN), and a heterogeneous network composed of drugs, proteins and diseases is built as an important auxiliary to enhance the inter-domain message passing of drugs and diseases. Besides, the intra-domain embedding of diseases is also extracted through HGCN. Ultimately, intra-domain and inter-domain embeddings of drugs and diseases are integrated as the final embedding for calculating the drug-disease correlation matrix. Through 10-fold cross-validation on some benchmark datasets, we find that the AUPR of EMPHCN reaches 0.593 (T1) and 0.526 (T2), respectively, and the AUC achieves 0.887 (T1) and 0.961 (T2) respectively, which shows that EMPHCN has an advantage over other state-of-the-art prediction methods. Concerning the new disease association prediction, the AUC of EMPHCN through the five-fold cross-validation reaches 0.806 (T1) and 0.845 (T2), which are 4.3% (T1) and 4.0% (T2) higher than the second best existing methods, respectively. In the case study, EMPHCN also achieves satisfactory results in real drug repositioning for breast carcinoma and Parkinson's disease.

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Virus infestation can seriously harm the host plant's growth and development. Turnip yellows virus (TuYV) infestation of host plants can cause symptoms, such as yellowing and curling of leaves and root chlorosis. However, the regulatory mechanisms by which TuYV affects host growth and development are unclear. Hence, it is essential to mine small RNA (sRNA) and explore the regulation of sRNAs on plant hosts for disease control. In this study, we analyzed high-throughput data before and after TuYV infestation in Arabidopsis using combined genetics, statistics, and machine learning to identify 108 specifically expressed and critical functional sRNAs after TuYV infection. First, comparing the expression levels of sRNAs before and after infestation, 508 specific sRNAs were significantly up-regulated in Arabidopsis after infestation. In addition, the results show that AI models, including SVM, RF, XGBoost, and CNN using two-dimensional convolution, have robust classification features at the sequence level, with a prediction accuracy of about 96.8%. A comparison of specific sRNAs with genome sequences revealed that 247 matched precisely with the TuYV genome sequence but not with the Arabidopsis genome, suggesting that TuYV viruses may be their source. The 247 sRNAs predicted target genes and enrichment analysis, which identified 206 Arabidopsis genes involved in nine biological processes and three KEGG pathways associated with plant growth and viral stress tolerance, corresponding to 108 sRNAs. These findings provide a reference for studying sRNA-mediated interactions in pathogen infection and are essential for establishing a vital resource of regulation network for the virus infecting plants and deepening the understanding of TuYV virus infection patterns. However, further validation of these sRNAs is needed to gain a new understanding.

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Exponential amplification reaction (EXPAR) has attracted much attention due to its simple primers and high amplification efficiency, but its applications are hindered by severe non-specificity amplification. Convenient exogenous chemical modification methods modified the entire template while inhibiting both non-specific and specific amplification. In this paper, we proposed a new self-passivating template with the phosphorothioate strategy to effectively improve the detection limit and applicability of EXPAR. We phosphorothioated several bases where the sequence was prone to form transient intermolecular 3'-end hybridization, thereby inhibiting the non-specific interactions and preventing the extension of templates by DNA polymerase. The melting temperature (Tm) curve and density functional theory (DFT) proved that the stability of hydrogen bonds between phosphorothioated bases did decrease. Benefitting from this strategy, the detection limit had been improved by 3 orders of magnitude. Moreover, due to the antioxidation property of phosphorothioate, this strategy showed good stability in serum, reflecting its excellent prospects in clinical sampling and detection.

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New drugs and illicit synthesized mixtures detection at crime scenes is a great challenge for detection method, which requires anti-interference and ultrasensitive methods to detect methamphetamine (METH) in seized street samples and biological fluids. Herein, we constructed a surface-enhanced Raman sensing method based on aligner mediated cleavage (AMC) of nucleic acid for quantitative detection of METH for the first time. This method we proposed relied on AMC to achieve programmable sequence-specific cleavage of METH aptamer linked by gold nanoparticles (METH aptamer-Au NPs), the cleavage product-Au NPs conjugates (cleavage aptamer-Au NPs) would hybridize with complementary DNA (cDNA)-Au NPs, resulting in the aggregation of the Au NPs and concomitant plasmonic coupling effect. Besides, due to the base number of METH aptamer-Au NPs was decreased, the interparticle distance of the Au NPs was shortened, which increased the electric field enhancement factor. Thus, under the irradiation of the laser, rhodamine 6G (R6G) adsorbed on Au NPs generated a strong Raman signal. The detection limit reached 7 pM, the linear range was from 10 pM to 10 nM, and this detection method also showed good anti-interference ability and reproducibility in serum.

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Canagliflozin (CG) was a highly effective, selective and reversible inhibitor of sodium-dependent glucose co-transporter 2 developed for the treatment of type 2 diabetes mellitus. The crystal structure of CG monohydrate (CG-H2O) was reported for the first time while CG hemihydrate (CG-Hemi) had been reported in our previous research. Solubility and dissolution rate results showed that the solubility of CG-Hemi was 1.4 times higher than that of CG-H2O in water and hydrochloric acid solution, and the dissolution rates of CG-Hemi were more than 3 folds than CG-H2O in both solutions. Hirshfeld surface analysis showed that CG-H2O had stronger intermolecular forces than CG-Hemi, and water molecules in CG-H2O participated three hydrogen bonds, forming hydrogen bond networks. These crystal structure features might make it more difficult for solvent molecules to dissolve CG-H2O than CG-Hemi. All these analyses might explain why the dissolution performance of CG-Hemi was better than CG-H2O. This work provided an approach to predict the dissolution performance of the drug based on its crystal structure.

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Surface-enhanced Raman scattering (SERS) substrates capable of working under laser excitation in a broad wavelength range are highly desirable in diverse application fields. Here, we demonstrate that the bioinspired Ag brochosomes, hollow microscale particles with submicroscale pits, have broadband and omnidirectional SERS performance. The SERS performance of the Ag brochosomes under near-infrared laser excitation makes them promising for applications in biosensing fields, such as the sensitive detection of Staphylococcus aureus bacteria and bovine hemoglobin protein. Additionally, the SERS intensity was insensitive to the incident angle of the laser beam, resulting from the spherical structure of the Ag brochosomes. The omnidirectional SERS performance makes the Ag brochosomes have application potential for in-the-field analysis using a hand-held Raman spectrometer for which it is difficult to accurately control the laser beam normal to the SERS substrates. Overall, the broadband and omnidirectional brochosome SERS substrates will find applications in diverse fields, particularly in biomedicine and in-the-field analysis.

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Surface-enhanced Raman scattering (SERS) has been recognized as a promising sensing technique in biomedical/biosensing applications and analytical chemistry. Silver (Ag) nanostructures have the strongest SERS enhancement, but suffer from severe enhancement degradation induced by oxidation. Here, we introduce electrochemical reduction of silver oxide to produce Ag SERS substrates on request to partially circumvent the SERS enhancement degradation problem of Ag SERS substrates. Silver oxide nanostructures were first prepared in pure silver citrate aqueous solutions with controllable morphologies depending on the electrodeposition parameters. The transition process from silver oxide to Ag was investigated by density functional theory calculations. Based on the understanding of the transition mechanism, heating treatment, applying reducing agent, and electrochemical reduction were adopted to transform silver oxide to Ag. Notably, no organic agents were introduced neither in the electrodeposition of silver oxide nor electrochemical transformation of silver oxide to Ag. The electrochemical reduction strategy could produce Ag SERS substrates with a 'clean' surface with outstanding SERS performance in a simple as well as cost and time effective manner. Ag SERS substrates can be used in biomedical/biosensing fields. The approach through electrochemical reduction of silver oxide to generate Ag SERS substrate may push forward practical application process of Ag SERS substrates.

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Entecavir was used for the treatment of chronic hepatitis B through inhibiting hepatitis B virus. The anhydrous form of entecavir (ENT-A) often appeared as impurity polymorph in the manufacturing process of entecavir monohydrate (ENT-H) such as granulation, drying and compression. Since different crystal forms might affect drug bioavailability and therapeutic effect, it was vital to control the ENT-A content of the drug product. The work aimed to develop useful methods to assess ENT-A weight percentage in ENT-H. Powder X-ray diffractometry (PXRD) and Raman spectrometric methods were applied. Binary mixtures with different ratios of pure ENT-H and pure ENT-A were scanned using PXRD and Raman to obtain spectra. Then peak heights and peak areas versus weight percentage were used to construct calibration curves. The best linear regression analysis data for PXRD and Raman method were found to be R2 = 0.9923 and R2 = 0.9953, in the weight ratio range of 2.1-20.2% w/w% of ENT-A in binary mixtures. Limit of detection (LOD) of ENT-A was 0.38% and limit of quantitation (LOQ) was 1.15% for PXRD method. LOD and LOQ for Raman method were 0.48% and 1.16%. The results showed that PXRD and Raman methods: both were precise and accurate, and could be used for measurement of ENT-A content in the selected weight percentage range. Partial least squares (PLS) algorithm with four data pre-processing methods: including multiplicative scatter correlation (MSC), standard normal variate (SNV), first and second derivatives were applied and evaluated using prediction errors. The best performance of PLS was R2 = 0.9958 with RMSEC (0.44%) and RMSEP (0.65%). Multivariate analysis for Raman spectra showed similar good results with univariate analysis, and would be an advantageous method when there were overlapped peaks in the spectra. In summary, the proposed PXRD and Raman method could be developed for the quality control of ENT-H. And Raman was a more promising method in industrial practice due to its slightly better precision, accuracy and time-saving advantage.

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A green and simple approach has been developed to synthesize un-coated Ag nanoparticles (AgNPs) in situ on the surface of thiol-group-functionalized silica dioxide microspheres (TSMs) in the aqueous solution. As soon as the Ag+ ions attach onto the surface of TSMs, nucleation and growth of AgNPs can spontaneously complete within one minute without other reducing agents or capping agents. The main reason is that the self-assembled silane-layer formed by mercaptosilane molecules could reduce the Ag0 formation energy, transport electrons efficiently, improve the nucleation density, and protect AgNPs against oxidation. Thus, the supported AgNPs show excellent chemical/photochemical stability in air and solution. Meanwhile, the size of as-prepared AgNPs could be controlled by tuning the concentration of Ag+ ions. This process provides a general route to generate bare AgNPs on the surface of silica dioxide in situ, which might be extended to other materials and is promising in developing novel methodologies for making supported noble metal NPs with desired structure and properties.