Enhancing Molecular Shape Comparison by Weighted Gaussian Functions

Shape comparing technologies based on Gaussian functions have been widely used in virtual screening of drug discovery. For efficiency, most of them adopt the First Order Gaussian Approximation (FOGA), in which the shape density of a molecule is represented as a simple sum of all individual atomic shape densities. In the current work, the effectiveness and error in shape similarity calculated by such an approximation are carefully analyzed. A new approach, which is called the Weighted Gaussian Algorithm (WEGA), is proposed to improve the accuracy of the first order approximation. The new approach significantly improves the accuracy of molecular volumes and reduces the error of shape similarity calculations by 37% using the hard-sphere model as the reference. The new algorithm also keeps the simplicity and efficiency of the FOGA. A program based on the new method has been implemented for molecular overlay and shape-based virtual screening. With improved accuracy for shape similarity scores, the new algorithm also improves virtual screening results, particularly when a shape-feature combo scoring function is used

Energy decomposition analyses and sequence alignment analyses.

<p>(a−g) binding energy decomposition results for each modeled system. (a) PGG & H1N1/PR8, (b) PGG & H1N1/WSN, (c) PGG & H3N2/HK, (d) TGG & H1N1/PR8, (e) EGCG & H1N1/PR8, (f) EA & H1N1/PR8, (g) Bar chart combining the results from (a) ∼ (f). The residues included are those within 8 Å distance from the ligand binding site. The unit of the each residue’s contribution to total binding energy is kcal/mol. (h) Multiple sequence alignment of HA from A/Brevig Mission/1/1918(H1N1), A/Puerto Rico/8/1934(H1N1), A/California/07/2009(H1N1), A/HK/8/1968(H3N2), A/Vietnam/1203/2004(H5N1), A/HZ/1/2013(H7N9), and A/Netherlands/219/03(H7N7). The sequences were aligned using ClustalW algorithm by MegAlign (LaserGene v7.1, DNASTAR Inc.) (i) Phylogenetic tree of hemagglutinin sequences used for sequence alignment.</p

pGG analogs directly bind and aggregate HA molecules.

<p>(a) SPR results of the associations and dissociations of pGG analogs and HA molecules. K_a and K_d were calculated by fitting the binding model (Langmuir) to sensorgram curves using ProteOn Manager Software. (c-f) Native PAGE and silver stains reveal the effects of binding of PGG on the mobility of HA after pre-incubation with or without PGG. Four subtypes of purified proteins of HA (A/Puerto Rico/8/1934 (H1N1), A/California/04/2009 (H1N1), A/Netherlands/219/03 (H7N7), and A/Anhui/1/2005 (H5N1)) were tested and shown as panels (b), (c), (d), and (e), respectively.</p

AFM and TEM experimental results confirm that pGG analogs agglutinate influenza virus particles without significantly disrupting the flu viral membrane structures.

<p>(a−e): AFM experimental results. Purified influenza virus A/WSN/33was pre-incubated with either DMSO, PGG, or TritonX-100 for 60 minutes on ice. The AFM image size is 3 μm from 80 scans. The data was statistically analyzed using the Student’s t test. *<i>p</i><0.01. (f-h): Influenza virus A/WSN/33 (10⁶ PFU) was incubated with either PGG, TritonX-100, or DMSO for 60 minutes on ice. Boxed areas with the full line are shown at a higher magnification.</p

Proposed process of PGG aggregating the flu-virus particles.

<p>(a) PGG binds with two different HA trimers through H-bonds and π stacking interactions at the RBDs. (b) A pair of HA trimers are aggregated by a PGG molecule. The paired HA trimers are further aggregated to form HA oligomers. (c) The HA polymers are formed from the PGG-induced HA oligomers. (d) The flu-virus particles are aggregated by PGG.</p

pGG analogs inhibit viral infectivity and block viral entry.

<p>(A) The viral infectivity was titrated directly via a plaque assay on MDCK cells. Values represent the mean of three independent experiments, and error bars show the standard deviation of the mean. (B) Influenza virus A/WSN/33(10⁵PFU) was incubated with the compounds (10 μM) or DMSO (0.1%, v/v) on ice for one hour and then inoculated to MDCK cells at 37°C for one hour. Cells were fixed and immunofluorescence stained at two hours post-infection and analyzed by fluorescence microscopy. Cells were co-stained with anti-nucleoprotein antibody and Hoechst 33342. (C) The number of positive cells (green) and the total number of cells were counted, and the ratio of positive cells to total cells was calculated. 500 cells were included for each group. The data was statistically analyzed using the Student’s t test. *<i>p</i><0.01.</p

SA competitively inhibits PGG-induced HA aggregation.

<p>(a) The effects of SA on the electrophoretic mobility shift of HA and HA incubated with PGG. The relative density of HA monomer is statistically analyzed using the Student’s t test. *p<0.01 (b) The quantitative calculation of the decrease of HA monomers and oligomers with or without SA.</p

Molecular Dynamics-Based Virtual Screening: Accelerating the Drug Discovery Process by High-Performance Computing

High-performance computing (HPC) has become a state strategic technology in a number of countries. One hypothesis is that HPC can accelerate biopharmaceutical innovation. Our experimental data demonstrate that HPC can significantly accelerate biopharmaceutical innovation by employing molecular dynamics-based virtual screening (MDVS). Without using HPC, MDVS for a 10K compound library with tens of nanoseconds of MD simulations requires years of computer time. In contrast, a state of the art HPC can be 600 times faster than an eight-core PC server is in screening a typical drug target (which contains about 40K atoms). Also, careful design of the GPU/CPU architecture can reduce the HPC costs. However, the communication cost of parallel computing is a bottleneck that acts as the main limit of further virtual screening improvements for drug innovations

Molecular Dynamics-Based Virtual Screening: Accelerating the Drug Discovery Process by High-Performance Computing

High-performance computing (HPC) has become a state strategic technology in a number of countries. One hypothesis is that HPC can accelerate biopharmaceutical innovation. Our experimental data demonstrate that HPC can significantly accelerate biopharmaceutical innovation by employing molecular dynamics-based virtual screening (MDVS). Without using HPC, MDVS for a 10K compound library with tens of nanoseconds of MD simulations requires years of computer time. In contrast, a state of the art HPC can be 600 times faster than an eight-core PC server is in screening a typical drug target (which contains about 40K atoms). Also, careful design of the GPU/CPU architecture can reduce the HPC costs. However, the communication cost of parallel computing is a bottleneck that acts as the main limit of further virtual screening improvements for drug innovations

Origin of Photocarrier Losses in Iron Pyrite (FeS₂) Nanocubes

Iron pyrite has received significant attention due to its high optical absorption. However, the loss of open circuit voltage (<i>V</i>_oc) prevents its further application in photovoltaics. Herein, we have studied the photophysics of pyrite by ultrafast laser spectroscopy to understand fundamental limitation of low <i>V</i>_oc by quantifying photocarrier losses in high quality, stoichiometric, and phase pure {100} faceted pyrite nanocubes. We found that fast carrier localization of photoexcited carriers to indirect band edge and shallow trap states is responsible for major carrier loss. Slow relaxation component reflects high density of defects within the band gap which is consistent with the observed Mott-variable range hopping (VRH) conduction from transport measurements. Magnetic measurements strikingly show the magnetic ordering associated with phase inhomogeneity, such as FeS_2−δ (0 ≤ δ ≤ 1). This implies that improvement of iron pyrite solar cell performance lies in mitigating the intrinsic defects (such as sulfur vacancies) by blocking the fast carrier localization process. Photocarrier generation and relaxation model is presented by comprehensive analysis. Our results provide insight into possible defects that induce midgap states and facilitate rapid carrier relaxation before collection