We have determined the crystal structure of both non-glycosylated (accession code: 3 fpr) and glycosylated Evasin-1 (accession code: 3 fpt), to 1.7 Å and 2.70 Å respectively.
This paper is part of a series of publications covering the four Phébus FP tests using a PWR fuel bundle: FPT-0, FPT-1, FPT-2, and FPT-3, excluding the FPT-4 one, related to the study of the release of low-volatility FP and transuranic elements from a debris bed and a pool of melted fuel.
Thirteen data sets for which state-of-the-art QSAR models were reported in literature were revisited in order to benchmark 2D-FPT biological activity-explanatory propensities.
So, it can be noted that the QuBiLS-MIDAS models yield statistically better predictions than the other methodologies considered, with the exception of the 2D-FPT approach.
The 2D-FPT models were developed by using SQS framework that determines linear and non-linear models (see Table 8), while the model corresponding to COSMOsar3D is based on the PLS technique.
Topological (2D) Fuzzy Pharmacophore Triplets (2D-FPT), using the number of interposed bonds to measure separation between the atoms representing pharmacophore types, were employed to establish and validate Quantitative Structure-Activity Relationships (QSAR).
In the specific case of the 2D-FPT methodology for ACHE and DHFR datasets, the achieved results are based on non-linear models while the proposed outcomes are determined with linear models.
As can be seen in Additional file 1: Table S6A, there are global differences among the considered methods, with the QuBiLS-MIDAS models being those with the best performance followed by the 2D-FPT, O3Q and COSMOsar3D approaches, respectively, with a Kendall's W [56] concordance level of 0.607 (see Additional file 1: Table S6B).
Beyond the benchmarking studies, in which 2D-FPT models validated better than, or as well as the best among reported literature equations, including 3D overlay-dependent computer-intensive techniques, this work is concerned with the in-depth analysis of the 'topological pharmacophores' defined by the sets of triplets entering the models.
Fortunately, while CoMFA restricts training molecule diversity in order to get meaningful overlays, 2D-FPT models can (and should!) be trained on sets of compounds of arbitrary diversity – addition of assumed inactive random compounds to the structurally homogeneous thrombin inhibitor series produced models that successfully extrapolate to molecules of different topology.
