Intra- and intermolecular contact surfaces are routinely calculated for a large array of applications in bioinformatics but are typically approximated from differential solvent accessible surface area calculations and not calculated directly. These approximations do not properly take the effects of neighboring atoms into account and tend to deviate considerably from the true contact surface. We implemented an extension of the original Shrake-Rupley algorithm to accurately estimate interatomic contact surface areas of molecular structures and complexes. Our extended algorithm is able to calculate the contact area of an atom to all nearby atoms by directly calculating overlapping surface patches, taking into account the possible shielding effects of neighboring atoms. Here, we present a versatile software tool and web server for the calculation of contact surface areas, as well as buried surface areas and solvent accessible surface areas (SASA) for different types of biomolecules, such as proteins, nucleic acids and small organic molecules. Detailed results are provided in tab-separated values format for analysis and Protein Databank files for visualization. Direct contact surface area calculation resulted in improved accuracy in a benchmark with a non-redundant set of 245 protein–DNA complexes. SASA-based approximations underestimated protein–DNA contact surfaces on average by 40%. This software tool may be useful for surface-based intra- and intermolecular interaction analyses and scoring function development. A web server, stand-alone binaries for Linux, MacOS and Windows and C++ source code are freely available from http://schuellerlab.org/dr_sasa/. Supplementary data are available at Bioinformatics online.

Pinostilbene is a monomethyl ether analog of the well-known nutraceutical resveratrol. Both compounds have health-promoting properties, but the latter undergoes rapid metabolization and has low bioavailability. O-methylation improves the stability and bioavailability of resveratrol. In plants, these reactions are performed by O-methyltransferases (OMTs). Few efficient OMTs that monomethylate resveratrol to yield pinostilbene have been described so far. Here, we report the engineering of a resveratrol OMT from Vitis vinifera (VvROMT), which has the highest catalytic efficiency in di-methylating resveratrol to yield pterostilbene. In the absence of a crystal structure, we constructed a three-dimensional protein model of VvROMT and identified four critical binding site residues by applying different in silico approaches. We performed point mutations in these positions generating W20A, F24A, F311A, and F318A variants, which greatly reduced resveratrol’s enzymatic conversion. Then, we rationally designed eight variants through comparison of the binding site residues with other stilbene OMTs. We successfully modified the native substrate selectivity of VvROMT. Variant L117F/F311W showed the highest conversion to pinostilbene, and variant L117F presented an overall increase in enzymatic activity. Our results suggest that VvROMT has potential for the tailor-made production of stilbenes.

Summary: Specific recognition of DNA by proteins is a crucial step of many biological processes. PDIviz is a plugin for the PyMOL molecular visualization system that analyzes protein–DNA binding interfaces by comparing the solvent accessible surface area of the complex against the free protein and free DNA. The plugin provides three distinct three-dimensional visualization modes to highlight interactions with DNA bases and backbone, major and minor groove, and with atoms of different pharmacophoric type (hydrogen bond donors/acceptors, hydrophobic and thymine methyl). Each mode comes in three styles to focus the visual analysis on the protein or DNA side of the interface, or on the nucleotide sequence. PDIviz allows for the generation of publication quality images, all calculated data can be written to disk, and a command line interface is provided for automating tasks. The plugin may be helpful for the detailed identification of regions involved in DNA base and shape readout, and can be particularly useful in rapidly pinpointing the overall mode of interaction. Availability and implementation: Freely available at http://melolab.org/pdiviz/ as a PyMOL plugin. Tested with incentive, educational, and open source versions of PyMOL on Windows, Mac and Linux systems. Contact: aschueller@bio.puc.cl Supplementary Information: Supplementary data are available at Bioinformatics online.

Identifying drug–target interactions is a crucial step in discovering novel drugs and for drug repositioning. Network-based methods have shown great potential thanks to the straightforward integration of information from different sources and the possibility of extracting novel information from the graph topology. However, despite recent advances, there is still an urgent need for efficient and robust prediction methods. Here, we present SimSpread, a novel method that combines network-based inference with chemical similarity. This method employs a tripartite drug–drug–target network constructed from protein–ligand interaction annotations and drug–drug chemical similarity on which a resource-spreading algorithm predicts potential biological targets for both known or failed drugs and novel compounds. We describe small molecules as vectors of similarity indices to other compounds, thereby providing a flexible means to explore diverse molecular representations. We show that our proposed method achieves high prediction performance through multiple cross-validation and time-split validation procedures over a series of datasets. In addition, we demonstrate that our method performed a balanced exploration of both chemical ligand space (scaffold hopping) and biological target space (target hopping). Our results suggest robust and balanced performance, and our method may be useful for predicting drug targets, virtual screening, and drug repositioning.

The di.me userware is a decentralised personal information sharing system with a difference: extracted information and observed personal activities are exploited to automatically recognise personal situations, provide privacy-related warnings, and recommend and/or automate user actions. To enable reasoning, personal information from multiple devices and online sources is integrated and transformed to a machine-interpretable format. Aside from distributed personal information monitoring, an intuitive user interface also enables the i) manual customisation of advanced context-driven services and ii) their semi-automatic adaptation across interactive notifications. In this paper we outline how average users interact with the current user interface, and our plans to improve it.

Enzymatic decarboxylation of phenolic acids enables the production of biobased phenolic styrenes under mild reaction conditions. However, the free para‐phenolic group can lead to undesirable side products during polymerization, giving protection of the free phenolic OH group critical importance for the application in adhesives. Here we present a one‐pot two‐step cascade reaction in which phenolic acid decarboxylase from Bacillus subtilis (BsPAD) catalyzes the decarboxylation of coumaric acid, caffeic acid, ferulic acid, and sinapic acid, followed by O‐methylation of the intermediate phenolic styrenes by resveratrol O‐methyltransferase from Vitis vinifera (VvROMT). The reaction sequence avoids the isolation and purification of the reactive intermediate hydroxystyrenes. Characterization of variants with amino acid substitution in the active‐site cavity led to the identification of variants without activity toward the four phenolic acids. This avoids undesired O‐methylation of the starting material, which is not desirable since the decarboxylase does not convert the formed p‐methoxyphenolic acids. Furthermore, variation of amino acids in the active site led to the identification of variants with improved activity for all four phenolic styrenes. The results constitute an important step toward the synthesis of biomass‐derived methoxystyrene derivatives whose photopolymerization yields polymers with outstanding adhesion properties.