iVaccine-Deep: Prediction of COVID-19 mRNA vaccine degradation using deep learning.

Messenger RNA (mRNA) has emerged as a critical global technology that requires global joint efforts from different entities to develop a COVID-19 vaccine. However, the chemical properties of RNA pose a challenge in utilizing mRNA as a vaccine candidate. For instance, the molecules are prone to degradation, which has a negative impact on the distribution of mRNA among patients. In addition, little is known of the degradation properties of individual RNA bases in a molecule. Therefore, this study aims to investigate whether a hybrid deep learning can predict RNA degradation from RNA sequences. Two deep hybrid neural network models were proposed, namely GCN_GRU and GCN_CNN. The first model is based on graph convolutional neural networks (GCNs) and gated recurrent unit (GRU). The second model is based on GCN and convolutional neural networks (CNNs). Both models were computed over the structural graph of the mRNA molecule. The experimental results showed that GCN_GRU hybrid model outperform GCN_CNN model by a large margin during the test time. Validation of proposed hybrid models is performed by well-known evaluation measures. Among different deep neural networks, GCN_GRU based model achieved best scores on both public and private MCRMSE test scores with 0.22614 and 0.34152, respectively. Finally, GCN_GRU pre-trained model has achieved the highest AuC score of 0.938. Such proven outperformance of GCNs indicates that modeling RNA molecules using graphs is critical in understanding molecule degradation mechanisms, which helps in minimizing the aforementioned issues. To show the importance of the proposed GCN_GRU hybrid model, in silico experiments has been contacted. The in-silico results showed that our model pays local attention when predicting a given position's reactivity and exhibits interesting behavior on neighboring bases in the sequence.

Molecular modeling of cationic porphyrin-anthraquinone hybrids as DNA topoisomerase IIß inhibitors.

Human DNA Topoisomerase II has been regarded as a promising target in anticancer drug discovery. In the present study, we designed six porphyrin-anthraquinone hybrids bearing pyrazole or pyridine group as meso substituents and evaluated their potentials as DNA Topoisomerase IIß inhibitor. First, we investigated the binding orientation of porphyrin hybrids into DNA topoisomerase IIß employing AutoDock 4.2 and then performed 20-ns molecular dynamics simulations to see the dynamic stability of each porphyrin-Topo IIß complex using Amber 14. We found that the binding of porphyrin hybrids occured through intercalation and groove binding mode in addition interaction with the amino acid residues constituting the active cavity of Topo IIß. Each porphyrin-Topo IIß complex was stabilized during 20-ns dynamics simulations. The MM-PBSA free energy calculation shows that the binding affinities of porphyrin hybrids were modified with the number of meso substituent. Interestingly, the affinity of all porphyrin hybrids to Topo IIß was stronger than that of native ligand (EVP), indicating the potential of the designed porphyrin to be considered in experimental research.