Computational analysis of the receptor binding specificity of novel influenza A/H7N9 viruses

The investigation into the effect of the length of RGD peptides and temperature on the interaction with the αIIbβ3 integrin: a molecular dynamic study

The tripeptide Arg-Gly-Asp acid (RGD) is a protein sequence in the binding of proteins to cell surfaces, and is involved in various biological processes such as cell adhesion to the extracellular matrix, platelet activation, hemostasis, etc. The C2 domain of the Von Willebrand Factor (VWF), containing the RGD motif, plays an important role in the initial homeostasis process. It binds to the αIIbβ3 integrin and stimulates platelet aggregation. We have investigated, using the molecular Dynamic (MD) simulation method, the effect of the RGD-peptide length, and temperature variation, on the binding to the αIIbβ3 integrin receptor. We examined 10 different structural modes of the αIIbβ3 at three different temperatures; 237 K, 310 K and 318 K. Our findings show that the amino acids that form a binding pocket include Asp₂₂₄, Tyr₂₃₄, Ser₂₂₆, Tyr₁₉₀, Tyr₁₈₉, Trp₂₆₀, Trp₂₆₂, Asp_259, Lys₂₅₃, Arg₂₁₄, Asp₂₁₇, Ser₁₆₁ and Ala₂₁₈ and that the ligand-receptor interaction was increased at higher temperatures. It was also found that the increase in the number of ligands' amino acids and their types (% glycine) plays an important role in the stability, conformation, and ligand-receptor interaction.Communicated by Ramaswamy H. Sarma.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

VirPreNet: a weighted ensemble convolutional neural network for the virulence prediction of influenza A virus using all eight segments

MOTIVATION

Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics.

RESULTS

In this article, we propose a weighted ensemble convolutional neural network (CNN) for the virulence prediction of influenza A viruses named VirPreNet that uses all eight segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the eight-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble CNN is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet's main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence.

AVAILABILITY AND IMPLEMENTATION

Codes and data to generate the VirPreNet are publicly available at https://github.com/Rayin-saber/VirPreNet.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular insights into evolution, mutations and receptor-binding specificity of influenza A and B viruses from outpatients and hospitalized patients in Singapore

BACKGROUND

This study compared the genomes of influenza viruses that caused mild infections among outpatients and severe infections among hospitalized patients in Singapore, and characterized their molecular evolution and receptor-binding specificity.

METHODS

The complete genomes of influenza A/H1N1, A/H3N2 and B viruses that caused mild infections among outpatients and severe infections among inpatients in Singapore during 2012-2015 were sequenced and characterized. Using various bioinformatics approaches, we elucidated their evolutionary, mutational and structural patterns against the background of global and vaccine strains.

RESULTS

The phylogenetic trees of the 8 gene segments revealed that the outpatient and inpatient strains overlapped with representative global and vaccine strains. We observed a cluster of inpatients with A/H3N2 strains that were closely related to vaccine strain A/Texas/50/2012(H3N2). Several protein sites could accurately discriminate between outpatient versus inpatient strains, with site 221 in neuraminidase (NA) achieving the highest accuracy for A/H3N2. Interestingly, amino acid residues of inpatient but not outpatient isolates at those sites generally matched the corresponding residues in vaccine strains, except at site 145 of hemagglutinin (HA). This would be especially relevant for future surveillance of A/H3N2 strains in relation to their antigenicity and virulence. Furthermore, we observed a trend in which the HA proteins of influenza A/H3N2 and A/H1N1 exhibited enhanced ability to bind both avian and human host cell receptors. In contrast, the binding ability to each receptor was relatively stable for the HA of influenza B.

CONCLUSIONS

Overall, our findings extend our understanding of the molecular and structural evolution of influenza virus strains in Singapore within the global context of these dynamic viruses.