Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison.

Ubiquitin-specific-processing protease 7 (USP7) is a promising target protein for cancer therapy, and great attention has been given to the identification of USP7 inhibitors. Traditional virtual screening methods have now been successfully applied to discover USP7 inhibitors aiming at reducing costs and speeding up time in several studies. However, due to their unsatisfactory accuracy, it is still a difficult task to develop USP7 inhibitors. In this study, multiple supervised learning classifiers were built to distinguish active USP7 inhibitors from inactive ligands. Physicochemical descriptors, MACCS keys, ECFP4 fingerprints and SMILES were first calculated to represent the compounds in our in-house dataset. Two deep learning (DL) models and nine classical machine learning (ML) models were then constructed based on different combinations of the above molecular representations under three activity cutoff values, and a total of 15 groups of experiments (75 experiments) were implemented. The performance of the models in these experiments was evaluated, compared and discussed using a variety of metrics. The optimal models are ensemble learning models when the dataset is balanced or severely imbalanced, and SMILES-based DL performs the best when the dataset is slightly imbalanced. Meanwhile, multimodal data fusion in some cases can improve the performance of ML and DL models. In addition, SMOTE, unbiased decoy selection and SMILES enumeration can improve the performance of ML and DL models when the dataset is severely imbalanced, and SMOTE works the best. Our study established highly accurate supervised learning classification models, which would accelerate the development of USP7 inhibitors. Some guidance was also provided for drug researchers in selecting supervised models and molecular representations as well as handling imbalanced datasets.

[Differential Expression of Long Non-coding RNA Cancer Susceptibility Candidate 2 and Imprinted Gene H19 in Extrahepatic Cholangiocarcinoma].

Objective To investigate the expression and the potential roles of long non-coding RNA(lncRNA)cancer susceptibility candidate 2(CASC2)and imprinted gene H19 in extrahepatic cholangiocarcinoma(ECC). Methods Four samples from patients with ECC were collected for high-throughput sequencing which was conducted to reveal the transcriptomic profiles of lncRNA CASC2 and H19.Bioinformatics tools were employed to predict the potential roles of the two genes.Another 22 ECC tissue samples and the cholangiocarcinoma cell lines(RBE,QBC939,HuH-28,and HuCCT1)with different degrees of differentiation were selected for validation.The para-carcinoma tissue and normal human intrahepatic biliary epithelial cell(HIBEC)were used as the control groups.The expression levels of lncRNA CASC2 and H19 in carcinoma tissue,para-carcinoma tissue,and cell lines were determined by real-time quantitative polymerase chain reaction(qRT-PCR).The correlation analysis was carried out for the clinical indicators of patients with the expression levels of the target genes. Results The two target genes showed significantly different expression between carcinoma tissue and para-carcinoma tissue(all P<0.05).Specifically,CASC2 had higher expression level in the carcinoma tissue than in the para-carcinoma tissue(t=1.262,P=0.025),whereas the expression of H19 showed an opposite trend(t=1.285,P=0.005).The expression levels of CASC2 in QBC939(t=8.114,P=0.015)and HuH-28(t=9.202,P=0.012)cells were significantly higher than that in the control group.The expression levels of H19 were significantly lower in RBE(t=-10.244,P<0.001),QBC939(t=-10.476,P<0.001),HuH-28(t=-19.798,P<0.001),and HuCCT1(t=-16.193,P=0.004)cells than in the control group.Bioinformatics analysis showed that CASC2 was mainly involved in the metabolic process and H19 in the development of multicellular organisms.Both CASC2 and H19 were related to catalytic activity.The expression level of lncRNA CASC2 was correlated with pathological differentiation(χ 2=6.222,P=0.022)and lymph node metastasis(χ2=5.455,P=0.020),and that of lncRNA H19 with pathological differentiation(χ2=1.174,P=0.029)and tumor size(χ2=-0.507,P=0.037). Conclusions In the case of ECC,lncRNA CASC2 and H19 have transcription disorders.lncRNA CASC2 is generally up-regulated in the carcinoma tissue,while H19 is down-regulated.Both genes have the potential to become new molecular markers for ECC.

Molecular dynamics simulation study of the structural characteristics of water molecules confined in functionalized carbon nanotubes.

Molecular dynamics (MD) simulations were performed to study the structural properties of water molecules confined in functionalized carbon nanotubes (CNTs). Four CNTs, two armchair-type (6, 6), (7, 7) and two zigzag-type (10, 0), (12, 0) CNTs, representing different helicities and different diameters, were chosen and functionalized at their open ends by the hydrophilic -COOH and the hydrophobic -CH3 groups. The structural properties of water molecules inside the functionalized CNTs, including the orientation distributions of dipole moment and O-H bonds, the length of the single-file water chain, and the average number of hydrogen bonds, were analyzed during a process of simulations. MD simulation results in this work showed that the -CH3 functional groups exert little special effects on the structural properties of water molecules. It is mainly due to the relatively small size of the -CH3 group and its hydrophobic nature, which is consistent with hydrophobic CNTs. For CNTs functionalized by -COOH groups, the configurations of -COOH groups, incurvature or excurvature, determine whether water molecules can enter the CNTs. The incurvature or excurvature configurations of -COOH groups are the results of synergy effects of the CNTs' helicity and diameter and control the flow direction of water molecules in CNTs.