Guiding protein-ligand docking with different experimental NMR-data

Today's scoring functions are one of the main reasons that state-of-the-art protein-ligand dockings fail in about 20 % to 40 % of the targets due to the sometimes severe approximations they make. However these approximations are necessary for performance reasons. One possibility to overcome these problems is the inclusion of additional, preferably experimental information in the docking process. Especially ligand-based NMR experiments that are far less demanding than the solution of the whole complex structure are helpful.Here we present the inclusion of three different types of NMR-data into the ChemPLP scoring function of our docking tool PLANTS. First, STD and intra-ligand trNOE spectra were used to obtain distant constraints between ligand and protein atoms. This approach proved beneficial for the docking of larger peptide ligands i. e. the epitope of MUC-1 glycoprotein to the SM3 antibody.In the second part the usefulness of INPHARMA data is shown by combinig a score, evaluating the agreement between simulated and measured INPHARMA spectra, with the PLANTS ChemPLP scoring function. First results from rescoring after local optimization of the poses and full docking experiments are shown

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

pKa based protonation states and microspecies for protein-ligand docking

In this paper we present our reworked approach to generate ligand protonation states with our structure preparation tool SPORES (Structure PrOtonation and REcognition System). SPORES can be used for the preprocessing of proteins and protein–ligand complexes as e.g. taken from the Protein Data Bank as well as for the setup of 3D ligand databases. It automatically assigns atom and bond types, generates different protonation, tautomeric states as well as different stereoisomers. In the revised version, pKa calculations with the ChemAxon software MARVIN are used either to determine the likeliness of a combinatorial generated protonation state or to determine the titrable atoms used in the combinatorial approach. Additionally, the MARVIN software is used to predict microspecies distributions of ligand molecules. Docking studies were performed with our recently introduced program PLANTS (Protein–Ligand ANT System) on all protomers resulting from the three different selection methods for the well established CCDC/ASTEX clean data set demonstrating the usefulness of especially the latter approach

Ligand protonation states and stereoisomers in virtual screening

Ligand structure preparation is an essential step during the setup of virtual screening (VS) experiments. In most cases only the crystal structure of a protein-ligand complex is known. Thus, the protonation of the ligand and the binding site is largely unknown and often only limited information about hybridization and connectivity in the ligand structure is provided by the pdb. While manual preparation of the ligand structures is the most accurate method, it is by far too time consuming for bigger datasets. In VS experiments the large number of structures, which are often obtained from different sources, leads to additional problems. A consistent treatment of all active and inactive structures is needed to prevent a preferential treatment of some of the structures, which could lead to artificial enrichments. Additionally, changes in the protonation of the ligand (and the protein), when binding to the active site, have to be considered. The large amount of inactive ligand structures are usually taken from organic molecule data banks like ZINC. In this case, the hydrogen atoms and hybridization are usually known but the data banks often provide only one tautomeric form or stereoisomer for each molecule. Because different protomers/stereoisomers can lead to huge differences in affinity the other stereoisomers of selected ligands should be tested too. To ensure an equal treatment of all structures for VS experiment we present an automated procedure called structure protonation and recognition system (SPORES) for the setup of VS datasets. It can be used to protonate structures from pdb files and to generate different protonation states, tautomers and stereoisomers. It is based on 3D coordinates only and does not use information about the binding site for ligand preparation or information about active ligands for the setup of the protein binding site. The influence of ligand protonation and stereoisomers on the docking results with PLANTS and Gold was first tested on the well-defined ASTEX clean dataset. Afterward several VS experiments on different target were conducted with PLANTS, in which the influence of ligand protonation states and stereoisomers on the enrichment was tested

Use of back-calculated protein chemical shift perturbations in fragment docking

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