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Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be updated regularly to ensure its efficacy. Computational tools and analyses have become increasingly important in guiding the process of vaccine selection. By constructing time-series training samples with splittings and embeddings, we develop a computational method for predicting suitable strains as the recommendation of the influenza vaccines using recurrent neural networks (RNNs). The Encoder-decoder architecture of RNN model enables us to perform sequence-to-sequence prediction. We employ this model to predict the prevalent sequence of the H3N2 viruses sampled from 2006 to 2017. The identity between our predicted sequence and recommended vaccines is greater than 98% and the P epitope < 0. 2 indicates their antigenic similarity. The multi-step vaccine prediction further demonstrates the robustness of our method which achieves comparable results in contrast to single step prediction. The results show significant matches of the recommended vaccine strains to the circulating strains. We believe it would facilitate the process of vaccine selection and surveillance of seasonal influenza epidemics. [ABSTRACT FROM AUTHOR]

Avian influenza viruses from migratory birds have managed to cross host species barriers and infected various hosts like human and swine. Epidemics and pandemics might occur when influenza viruses are adapted to humans, causing deaths and enormous economic loss. Receptor-binding specificity of the virus is one of the key factors for the transmission of influenza viruses across species. The determination of host tropism and understanding of molecular properties would help identify the mechanism why zoonotic influenza viruses can cross species barrier and infect humans. In this study, we have constructed computational models for host tropism prediction on human-adapted subtypes of influenza HA proteins using random forest. The feature vectors of the prediction models were generated based on seven physicochemical properties of amino acids from influenza sequences of three major hosts. Feature aggregation and associative rules were further applied to select top 20 features and extract host-associated physicochemical signatures on the combined model of nonspecific subtypes. The prediction model achieved high performance (Accuracy = 0. 9 4 8 , Precision = 0. 9 5 4 , MCC = 0. 9 2 2). Support and confidence rates were calculated for the host class-association rules. The results indicated that secondary structure and normalized Van der Waals volume were identified as more important physicochemical signatures in determining the host tropism. [ABSTRACT FROM AUTHOR]

Dengue fever is a febrile illness caused by Dengue Virus, which belongs to the Flaviviridae family. Among its proteome, the nonstructural protein 5 (NS5) is the biggest and most conserved. It has a primer-independent RNA-dependent RNA polymerase (RdRp) domain at its C-Terminus. Zou et al. studied the biological relevance of the two conserved cavities (named A and B) within the NS5 proteins of dengue virus (DENV) and West Nile Virus (WNV) using mutagenesis and revertant analysis and found four mutations located at cavity B having effects on viral replication. They recommended Cavity B, but not Cavity A as a potential target for drugs against flavivirus RdRp. In this study, we virtually screened the MayBridge drug fragments dataset for potential small molecule binders of cavity B using both AutoDock Vina, the standard docking tool, and QuickVina 2, our previously developed tool. We selected 16 fragments that appeared in the top 100 docking results of each of the representative structures of NS5. Visual inspection suggests that they have reasonable binding poses. The 16 predicted fragments are plausible drug candidates and should be considered for further validation, optimization, and linking to come up with a suitable inhibitor of dengue virus. [ABSTRACT FROM AUTHOR]

A major goal of personalized anti-cancer therapy is to increase the drug effects while reducing the side effects as much as possible. A novel therapeutic strategy called synthetic lethality (SL) provides a great opportunity to achieve this goal. SL arises if mutations of both genes lead to cell death while mutation of either single gene does not. Hence, the SL partner of a gene mutated only in cancer cells could be a promising drug target, and the identification of SL pairs of genes is of great significance in pharmaceutical industry. In this paper, we propose a hybridized method to predict SL pairs of genes. We combine a data-driven model with knowledge of signalling pathways to simulate the influence of single gene knock-down and double genes knock-down to cell death. A pair of genes is considered as an SL candidate when double knock-down increases the probability of cell death significantly, but single knock-down does not. The single gene knock-down is confirmed according to the human essential genes database. Our validation against literatures shows that the predicted SL candidates agree well with wet-lab experiments. A few novel reliable SL candidates are also predicted by our model. [ABSTRACT FROM AUTHOR]

Protein-ligand docking is a computational method to identify the binding mode of a ligand and a target protein, and predict the corresponding binding affinity using a scoring function. This method has great value in drug design. After decades of development, scoring functions nowadays typically can identify the true binding mode, but the prediction of binding affinity still remains a major problem. Here we present CScore, a data-driven scoring function using a modified Cerebellar Model Articulation Controller (CMAC) learning architecture, for accurate binding affinity prediction. The performance of CScore in terms of correlation between predicted and experimental binding affinities is benchmarked under different validation approaches. CScore achieves a prediction with R = 0.7668 and RMSE = 1.4540 when tested on an independent dataset. To the best of our knowledge, this result outperforms other scoring functions tested on the same dataset. The performance of CScore varies on different clusters under the leave-cluster-out validation approach, but still achieves competitive result. Lastly, the target-specified CScore achieves an even better result with R = 0.8237 and RMSE = 1.0872, trained on a much smaller but more relevant dataset for each target. The large dataset of protein-ligand complexes structural information and advances of machine learning techniques enable the data-driven approach in binding affinity prediction. CScore is capable of accurate binding affinity prediction. It is also shown that CScore will perform better if sufficient and relevant data is presented. As there is growth of publicly available structural data, further improvement of this scoring scheme can be expected. [ABSTRACT FROM AUTHOR]

The paper aims to propose a new approach towards human posture reconstruction and animation from monocular video sequences that contain any kind of human postures and movements. This is a way towards low cost motion capture and at the same time it avoids many limitations of those classical methods. A parameterized human skeleton model based on anatomy is adopted where the angular constraints are encoded in the joints. Criterion Function is defined to represent the residuals between feature points in the monocular image and the corresponding points resulted from projecting the human model to the projection plane. By transforming each segment of the human model to achieve the minimum value of the Criterion Function, the proper human posture that resembles the one represented by the monocular image can be generated. Different kinds of adjustments are utilized to adjust the body parts into the proper locations and orientations in 3D space without camera calibration. In order to find the optimal solution effectively in a high-dimensional parameter space by considering all the parameters simultaneously, the method of Genetic Algorithms is proposed. A procedure is developed to recover the whole body posture, and then a human animation system is developed to animate a series of human movements from monocular image sequences, during which information between consecutive frames is considered to improve the accuracy. Our technique makes it feasible to reconstruct any possible human postures, and experimental results from many monocular images and video sequences are encouraging. [ABSTRACT FROM AUTHOR]