Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Specificity quantification of biomolecular recognition and its implication for drug discovery

Highly efficient and specific biomolecular recognition requires both affinity and specificity. Previous quantitative descriptions of biomolecular recognition were mostly driven by improving the affinity prediction, but lack of quantification of specificity. We developed a novel method SPA (SPecificity and Affinity) based on our funneled energy landscape theory. The strategy is to simultaneously optimize the quantified specificity of the “native” protein-ligand complex discriminating against “non-native” binding modes and the affinity prediction. The benchmark testing of SPA shows the best performance against 16 other popular scoring functions in industry and academia on both prediction of binding affinity and “native” binding pose. For the target COX-2 of nonsteroidal anti-inflammatory drugs, SPA successfully discriminates the drugs from the diversity set, and the selective drugs from non-selective drugs. The remarkable performance demonstrates that SPA has significant potential applications in identifying lead compounds for drug discovery

Dynamics of Wind Setdown at Suez and the Eastern Nile Delta

BACKGROUND: Wind setdown is the drop in water level caused by wind stress acting on the surface of a body of water for an extended period of time. As the wind blows, water recedes from the upwind shore and exposes terrain that was formerly underwater. Previous researchers have suggested wind setdown as a possible hydrodynamic explanation for Moses crossing the Red Sea, as described in Exodus 14. METHODOLOGY/PRINCIPAL FINDINGS: This study analyzes the hydrodynamic mechanism proposed by earlier studies, focusing on the time needed to reach a steady-state solution. In addition, the authors investigate a site in the eastern Nile delta, where the ancient Pelusiac branch of the Nile once flowed into a coastal lagoon then known as the Lake of Tanis. We conduct a satellite and modeling survey to analyze this location, using geological evidence of the ancient bathymetry and a historical description of a strong wind event in 1882. A suite of model experiments are performed to demonstrate a new hydrodynamic mechanism that can cause an angular body of water to divide under wind stress, and to test the behavior of our study location and reconstructed topography. CONCLUSIONS/SIGNIFICANCE: Under a uniform 28 m/s easterly wind forcing in the reconstructed model basin, the ocean model produces an area of exposed mud flats where the river mouth opens into the lake. This land bridge is 3-4 km long and 5 km wide, and it remains open for 4 hours. Model results indicate that navigation in shallow-water harbors can be significantly curtailed by wind setdown when strong winds blow offshore

AutoClickChem: Click Chemistry in Silico

Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu

Rates of Viral Evolution Are Linked to Host Geography in Bat Rabies

Rates of evolution span orders of magnitude among RNA viruses with important implications for viral transmission and emergence. Although the tempo of viral evolution is often ascribed to viral features such as mutation rates and transmission mode, these factors alone cannot explain variation among closely related viruses, where host biology might operate more strongly on viral evolution. Here, we analyzed sequence data from hundreds of rabies viruses collected from bats throughout the Americas to describe dramatic variation in the speed of rabies virus evolution when circulating in ecologically distinct reservoir species. Integration of ecological and genetic data through a comparative Bayesian analysis revealed that viral evolutionary rates were labile following historical jumps between bat species and nearly four times faster in tropical and subtropical bats compared to temperate species. The association between geography and viral evolution could not be explained by host metabolism, phylogeny or variable selection pressures, and instead appeared to be a consequence of reduced seasonality in bat activity and virus transmission associated with climate. Our results demonstrate a key role for host ecology in shaping the tempo of evolution in multi-host viruses and highlight the power of comparative phylogenetic methods to identify the host and environmental features that influence transmission dynamics

IVSPlat 1.0: an integrated virtual screening platform with a molecular graphical interface

<p>Abstract</p> <p>Background</p> <p>The virtual screening (VS) of lead compounds using molecular docking and pharmacophore detection is now an important tool in drug discovery. VS tasks typically require a combination of several software tools and a molecular graphics system. Thus, the integration of all the requisite tools in a single operating environment could reduce the complexity of running VS experiments. However, only a few freely available integrated software platforms have been developed.</p> <p>Results</p> <p>A free open-source platform, IVSPlat 1.0, was developed in this study for the management and automation of VS tasks. We integrated several VS-related programs into a molecular graphics system to provide a comprehensive platform for the solution of VS tasks based on molecular docking, pharmacophore detection, and a combination of both methods. This tool can be used to visualize intermediate and final results of the VS execution, while also providing a clustering tool for the analysis of VS results. A case study was conducted to demonstrate the applicability of this platform.</p> <p>Conclusions</p> <p>IVSPlat 1.0 provides a plug-in-based solution for the management, automation, and visualization of VS tasks. IVSPlat 1.0 is an open framework that allows the integration of extra software to extend its functionality and modified versions can be freely distributed. The open source code and documentation are available at <url>http://kyc.nenu.edu.cn/IVSPlat/.</url></p

BeadArray Expression Analysis Using Bioconductor

Illumina whole-genome expression BeadArrays are a popular choice in gene profiling studies. Aside from the vendor-provided software tools for analyzing BeadArray expression data (GenomeStudio/BeadStudio), there exists a comprehensive set of open-source analysis tools in the Bioconductor project, many of which have been tailored to exploit the unique properties of this platform. In this article, we explore a number of these software packages and demonstrate how to perform a complete analysis of BeadArray data in various formats. The key steps of importing data, performing quality assessments, preprocessing, and annotation in the common setting of assessing differential expression in designed experiments will be covered

Trapping virtual pores by crystal retro-engineering

Stable guest-free porous molecular crystals are uncommon. By contrast, organic molecular crystals with guest-occupied cavities are frequently observed, but these cavities tend to be unstable and collapse on removal of the guests—this feature has been referred to as ‘virtual porosity’. Here, we show how we have trapped the virtual porosity in an unstable low-density organic molecular crystal by introducing a second molecule that matches the size and shape of the unstable voids. We call this strategy ‘retro-engineering’ because it parallels organic retrosynthetic analysis, and it allows the metastable two-dimensional hexagonal pore structure in an organic solvate to be trapped in a binary cocrystal. Unlike the crystal with virtual porosity, the cocrystal material remains single crystalline and porous after removal of guests by heating

Immune Activation, Cd4+ T Cell Counts, and Viremia Exhibit Oscillatory Patterns over Time in Patients with Highly Resistant HIV Infection

The rates of immunologic and clinical progression are lower in patients with drug-resistant HIV compared to wild-type HIV. This difference is not fully explained by viral load. It has been argued that reductions in T cell activation and/or viral fitness might result in preserved target cells and an altered relationship between the level of viremia and the rate of CD4+ T cell loss. We tested this hypothesis over time in a cohort of patients with highly resistant HIV. Fifty-four antiretroviral-treated patients with multi-drug resistant HIV and detectable plasma HIV RNA were followed longitudinally. CD4+ T cell counts and HIV RNA levels were measured every 4 weeks and T cell activation (CD38/HLA-DR) was measured every 16 weeks. We found that the levels of CD4+ T cell activation over time were a strong independent predictor of CD4+ T cell counts while CD8+ T cell activation was more strongly associated with viremia. Using spectral analysis, we found strong evidence for oscillatory (or cyclic) behavior in CD4+ T cell counts, HIV RNA levels, and T cell activation. Each of the cell populations exhibited an oscillatory behavior with similar frequencies. Collectively, these data suggest that there may be a mechanistic link between T cell activation, CD4+ T cell counts, and viremia and lends support for the hypothesis of altered predator-prey dynamics as a possible explanation of the stability of CD4+ T cell counts in the presence of sustained multi-drug resistant viremia

Designing Focused Chemical Libraries Enriched in Protein-Protein Interaction Inhibitors using Machine-Learning Methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com