Background
Computer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of protein–ligand complexes. Therefore, many scoring functions based on machine learning or deep learning have been developed to improve prediction accuracies in recent years. In this work, we proposed a novel featurization method, generating a new scoring function model based on 3D convolutional neural network.
Results
This work showed the results from testing four architectures and three featurization methods, and outlined the development of a novel deep 3D convolutional neural network scoring function model. This model simplified feature engineering, and in combination with Grad-CAM made the intermediate layers of the neural network more interpretable. This model was evaluated and compared with other scoring functions on multiple independent datasets. The Pearson correlation coefficients between the predicted binding affinities by our model and the experimental data achieved 0.7928, 0.7946, 0.6758, and 0.6474 on CASF-2016 dataset, CASF-2013 dataset, CSAR_HiQ_NRC_set, and Astex_diverse_set, respectively. Overall, our model performed accurately and stably enough in the scoring power to predict the binding affinity of a protein–ligand complex.
Conclusions
These results indicate our model is an excellent scoring function, and performs well in scoring power for accurately and stably predicting the protein–ligand affinity. Our model will contribute towards improving the success rate of virtual screening, thus will accelerate the development of potential drugs or novel biologically active lead compounds.
Background
MicroRNAs have been suggested as potential regulators in the development of multiple myeloma (MM) through affecting the expression of their target genes. This study aimed to investigate the effects of miR-19a-3p in MM, and its underlying mechanisms in regulating cell proliferation and invasion.
Methods
Bone marrow samples from 25 MM patients and 12 healthy donors were collected and miR-19a-3p and Wnt1 mRNA expression was assessed. The effects of miR-19a-3p on cell proliferation, migration, and invasion in U226 and RPMI-8226 MM cells were evaluated by miR-19a-3p overexpression. Luciferase assays were performed to explore the potential target genes. Knock down or overexpression of Wnt1 was used to explore the effects of miR-19a-3p on cell growth, migration, and invasion.
Results
The expression of miR-19a-3p was downregulated in MM and cell lines, while Wnt1 mRNA levels were increased. Overexpression of miR-19a-3p inhibited cell proliferation, migration, and invasion in U226 and RPMI-8226 cells. Additionally, western blot assays revealed that miR-19a-3p could suppress Wnt1, β-catenin, cyclin D1, and c-Myc expression. Knockdown of Wnt1 also inhibited cell growth, migration, and invasion. Moreover, luciferase reporter assay revealed direct binding between Wnt1 and miR-19a-3p. Wnt1 overexpression partially reversed the suppressive effects of miR-19a-3p on cell proliferation, migration, and invasion in U266 cells.
Conclusions
The expression of miR-19a-3p was downregulated in MM patients and MM cell lines. Overexpression of miR-19a-3p inhibited proliferation, migration, and invasion by targeting Wnt1 via the Wnt/β-catenin signaling pathway.
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