Synthesis and biological evaluation of orally active anti-Trypanosoma agents.

In previous studies, we developed anti-trypanosome tubulin inhibitors with promising in vitro selectivity and activity against Human African Trypanosomiasis (HAT). However, for such agents, oral activity is crucial. This study focused on further optimizing these compounds to enhance their ligand efficiency, aiming to reduce bulkiness and hydrophobicity, which should improve solubility and, consequently, oral bioavailability. Using Trypanosoma brucei brucei cells as the parasite model and human normal kidney cells and mouse macrophage cells as the host model, we evaluated 30 new analogs synthesized through combinatorial chemistry. These analogs have fewer aromatic moieties and lower molecular weights than their predecessors. Several new analogs demonstrated IC50s in the low micromolar range, effectively inhibiting trypanosome cell growth without harming mammalian cells at the same concentration. We conducted a detailed structure-activity relationship (SAR) analysis and a docking study to assess the compounds' binding affinity to trypanosome tubulin homolog. The results revealed a correlation between binding energy and anti-Trypanosoma activity. Importantly, compound 7 displayed significant oral activity, effectively inhibiting trypanosome cell proliferation in mice.

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 a lack of proven effective treatments or cures 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.Communicated by Ramaswamy H. Sarma.

Comprehensive characterization of protein-protein interactions perturbed by disease mutations.

Technological and computational advances in genomics and interactomics have made it possible to identify how disease mutations perturb protein-protein interaction (PPI) networks within human cells. Here, we show that disease-associated germline variants are significantly enriched in sequences encoding PPI interfaces compared to variants identified in healthy participants from the projects 1000 Genomes and ExAC. Somatic missense mutations are also significantly enriched in PPI interfaces compared to noninterfaces in 10,861 tumor exomes. We computationally identified 470 putative oncoPPIs in a pan-cancer analysis and demonstrate that oncoPPIs are highly correlated with patient survival and drug resistance/sensitivity. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay, and also demonstrate the functional consequences of two of these on tumor cell growth. In summary, this human interactome network framework provides a powerful tool for prioritization of alleles with PPI-perturbing mutations to inform pathobiological mechanism- and genotype-based therapeutic discovery.

Repurposing of FDA-Approved Toremifene to Treat COVID-19 by Blocking the Spike Glycoprotein and NSP14 of SARS-CoV-2.

The global pandemic of Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to the death of more than 675,000 worldwide and over 150,000 in the United States alone. However, there are currently no approved effective pharmacotherapies for COVID-19. Here, we combine homology modeling, molecular docking, molecular dynamics simulation, and binding affinity calculations to determine potential targets for toremifene, a selective estrogen receptor modulator which we have previously identified as a SARS-CoV-2 inhibitor. Our results indicate the possibility of inhibition of the spike glycoprotein by toremifene, responsible for aiding in fusion of the viral membrane with the cell membrane, via a perturbation to the fusion core. An interaction between the dimethylamine end of toremifene and residues Q954 and N955 in heptad repeat 1 (HR1) perturbs the structure, causing a shift from what is normally a long, helical region to short helices connected by unstructured regions. Additionally, we found a strong interaction between toremifene and the methyltransferase nonstructural protein (NSP) 14, which could be inhibitory to viral replication via its active site. These results suggest potential structural mechanisms for toremifene by blocking the spike protein and NSP14 of SARS-CoV-2, offering a drug candidate for COVID-19.

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.

In Silico Insights into Protein-protein Interaction Disruptive Mutations in the PCSK9-LDLR complex.

Gain-of-function mutations in PCSK9 (proprotein convertase subtilisin/kexin type 9) lead to reduced uptake of LDL (low density lipoprotein) cholesterol and, therefore, increased plasma LDL levels. However, the mechanism by which these mutants reduce LDL reuptake is not fully understood. Here, we have used molecular dynamics simulations, MM/PBSA (Molecular Mechanics/Poisson-Boltzmann Surface Area) binding affinity calculations, and residue interaction networks, to investigate the protein-protein interaction (PPI) disruptive effects of two of PCSK9's gain-of-function mutations, Ser127Arg and Asp374Tyr on the PCSK9 and LDL receptor complex. In addition to these PPI disruptive mutants, a third, non-interface mutation (Arg496Trp) is included as a positive control. Our results indicate that Ser127Arg and Asp374Tyr confer significantly improved binding affinity, as well as different binding modes, when compared to the wild-type. These PPI disruptive mutations lie between the EGF(A) (epidermal growth factor precursor homology domain A) of the LDL receptor and the catalytic domain of PCSK9 (Asp374Tyr) and between the prodomain of PCSK9 and the ß-propeller of the LDL receptor (Ser127Arg). The interactions involved in these two interfaces result in an LDL receptor that is sterically inhibited from entering its closed conformation. This could potentially implicate the prodomain as a target for small molecule inhibitors.

Role of sediment resuspension in the remobilization of particulate-phase metals from coastal sediments.

The release of particulate-phase trace metals due to sediment resuspension has been investigated by combining erosion chamber experiments that apply a range of shear stresses typically encountered in coastal environments with a shear stress record simulated by a hydrodynamic model. Two sites with contrasting sediment chemistry were investigated. Sediment particles enriched in silver, copper, and lead, 4-50 times greater than the bulk surface-sediment content, were the first particles to be eroded. As the shear-stress level was increased in the chamber, the total mass eroded increased, butthe enrichment of these trace metals fell, approaching the bulk-sediment content. From the temporal distribution of shear stress generated by the hydrodynamic model for a site in Boston Harbor, resuspension fluxes were estimated. The erosion threshold of this site is exceeded during spring tides, releasing the particles enriched in trace metals into the water column. Due to the higher trace metal content and the regularity of resuspension, low-energy resuspension events (up to a shear stress of 0.2 N/m(2)) contribute up to 60% of the resuspension metal flux in an average year. The estimated annual quantity of copper and lead resuspended into the water column is higher than estimates of the total riverine flux for these metals. These results indicate that sediment resuspension is a very important mechanism for releasing metals into the water column and provide new insight into the chemical and physical processes controlling the long-term fate of trace metals in contaminated sediments.

A proposed alternative to phase-space recycling using the adaptive kernel density estimator method.

We have implemented a nonparametric density estimation technique, the adaptive kernel density estimator (AKDE), to generate additional phase space (PS) variables in the vicinity of simulated PS points in Monte Carlo linear accelerator simulation. The method involves the placement of kernels at simulated PS points that have a "window width" that depends on the density of simulated PS points. This method has been tested on known one-dimensional (1-D) and two-dimensional (2-D) probability density functions (PDFs) and has been used to sample (photons only) from PS files generated from accelerator simulations. The original simulated PS vector (x, y, u, v, E) was reduced to a rotationally invariant PS vector (r, theta, alpha, E) that takes advantage of the azimuthal symmetry (phi) above the collimating jaws. The new PS vector (r', theta', alpha', E') is sampled in the vicinity of the sampled PS vector (r, theta, alpha, E). The first step in assessing the accuracy of the method was a correlation analysis among the AKDE generated PS variables compared with correlations among the original PS variables. "In-air" particle fluence distributions between AKDE samples and the original PS distribution showed agreement within 2% (-8.8% to 6.8%) across the entire phase space plane. Central axis energy distributions and angular distributions agreed on average to within 1.5% (range = -1.5% to 6.6%) and 0.1% (range = 0 to 3.0%), respectively. Dose profiles were calculated for field sizes 3 x 3 cm2, 10 x 10 cm2, and 30 x 30 cm2 for AKDE and compared against calculations performed with PS recycling. AKDE calculated depth doses and profiles were within 2% and 2%/1 mm, respectively, of those computed using PS recycling.

Estimation of flux distributions with Monte Carlo functional expansion tallies.

Monte Carlo methods provide a powerful technique for estimating the average radiation flux in a volume (or across a surface) in cases where analytical solutions may not be possible. Unfortunately, Monte Carlo simulations typically provide only integral results and do not offer any further details about the distribution of the flux with respect to space, angle, time or energy. In the functional expansion tally (FET) a Monte Carlo simulation is used to estimate the functional expansion coefficients for flux distributions with respect to an orthogonal set of basis functions. The expansion coefficients are then used in post-processing to reconstruct a series approximation to the true distribution. Discrete event FET estimators are derived and their application in estimating radiation flux or current distributions is demonstrated. Sources of uncertainty in the FET are quantified and estimators for the statistical and truncation errors are derived. Numerical results are presented to support the theoretical development.