Deep learning for protein structure prediction and design-progress and applications

Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.

A Rational Design of a Multi-Epitope Vaccine Against SARS-CoV-2 Which Accounts for the Glycan Shield of the Spike Glycoprotein

The ongoing global health crisis caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the virus which leads to Coronavirus Disease 2019 (COVID-19) has impacted not only the health of people everywhere, but the economy in nations across the world. While vaccine candidates and therapeutics are currently undergoing clinical trials, there is yet to be a proven effective treatment or cure for COVID-19. In this study, we have presented a synergistic computational platform, including molecular dynamics simulations and immunoinformatics techniques, to rationally design a multi-epitope vaccine candidate for COVID-19. This platform combines epitopes across Linear B Lymphocytes (LBL), Cytotoxic T Lymphocytes (CTL) and Helper T Lymphocytes (HTL) derived from both mutant and wild-type spike glycoproteins from SARS-CoV-2 with diverse protein conformations. In addition, this vaccine construct also takes the considerable glycan shield of the spike glycoprotein into account, which protects it from immune response. We have identified a vaccine candidate (a 35.9 kDa protein), named COVCCF, which is composed of 5 LBL, 6 HTL, and 6 CTL epitopes from the spike glycoprotein of SARS-CoV-2. Using multi-dose immune simulations, COVCCF induces elevated levels of immunoglobulin activity (IgM, IgG1, IgG2), and induces strong responses from B lymphocytes, CD4 T-helper lymphocytes, and CD8 T-cytotoxic lymphocytes. COVCCF induces cytokines important to innate immunity, including IFN-γ, IL4, and IL10. Additionally, COVCCF has ideal pharmacokinetic properties and low immune-related toxicities. In summary, this study provides a powerful, computational vaccine design platform for rapid development of vaccine candidates (including COVCCF) for effective prevention of COVID-19.