Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study

<p>Alongside the validation, the concept of applicability domain (AD) is probably one of the most important<br>aspects which determine the quality as well as reliability of the established quantitative structure–activity relationship (QSAR) models. To date, a variety of approaches for AD estimation have been devised which can be applied to particular type of QSAR models and their practical utilization is extensively elaborated in the literature. The present study introduces a novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the Euclidean distance (ED) metric in the<br>structure-representation vector space. The performance of the method was evaluated and explained in a case study by using a pre-built and validated CP ANN model for prediction of the transport activity of the transmembrane protein bilitranslocase for a diverse set of compounds. The method was tested on two more datasets in order to confirm its performance for evaluation of the applicability domain in CP ANN models. The chemical compounds determined as potential outliers, i.e., outside of the CP ANN model AD, were confirmed in a comparative AD assessment by using the leverage approach. Moreover, the method offers a graphical depiction of the AD for fast and simple determination of the extreme points.</p

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Additional file 2. File containing CPANNatNIC source files

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Additional file 17. File containing results obtained for additional tests on eight datasets

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Additional file 10. File with read-across results for acute toxicity validation set

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Additional file 16. File with read-across results for bio-concentration factor external set

2D ¹H-¹⁵N HSQC spectrum.

<p>It is acquired on Varian VNMRS 800 NMR spectrometer at 298 K on natural abundance of ¹⁵N isotope. The sequence-specific assignments of main conformation of GSVQCAGLISLPIAIEFTKKKK peptide are presented. The resonance signals coming from side chain NH₂ group for Gln4 are also shown.</p

Radial distribution function (RDF) plots.

<p>RDF of hydrophobic (black), hydrophilic (red) parts of SDS micelle and water molecules (green) and the heavy atoms in side chains for all residues with the exception of Gly220 and Gly226.</p

3D Ramachandran histograms for the backbone torsion angles φ and ψ (A) andtwo dimensional plots of the secondary structure analysis (B).

<p>In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038967#pone-0038967-g005" target="_blank">Fig 5A</a> the analysis was performed for the residues that were predicted to from the transmembrane helix: 221–238 for the TM 3 helix and 220–238 for the TM 3A helix. Each exported conformation of the peptide in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0038967#pone-0038967-g005" target="_blank">Fig 5B</a>, generated by the 20 ns MD simulation was analyzed for is the secondary structure. Purple colour depicts the alpha helix structure, green indicates the turn structure and blue depicts the 3–10 helical structure. Selected residue numbers on the y-axis corresponds to the residues numbers of the BTL sequence.</p

Schematic representation of the computational and experimental investigations of the BTL transmembrane region.

<p>Schematic representation of the computational and experimental investigations of the BTL transmembrane region.</p

The four predicted transmembrane regions of BTL.

<p>The gray circles denote the antibodies tested.</p