This reduces the binding affinities of all the tested compounds by 8 to 20 folds. The in-silico designed mutants, M421F-ERaLBD and M421IERaLBD, were properly folded and active in solution, as shown by their CD spectra and binding properties. These findings highlight the advantages of sequence analysis and the use of mutations that are rare but yet present as natural variants in some species. This approach highly increases the possibility that the resulting mutants are properly folded and active, something that is not always guaranteed when mutants are generated BMS-354825 Src-bcr-Abl inhibitor through randomization technologies or other strategies. As an added bonus, the isolated ligand binding domain that we used proved to be more stable than the full-length ER protein used in other published or commercial assays. Introduction of the rational mutation M421F further increased protein stability, both in terms of increased resistance to thermal denaturation and of ligand binding activity after prolonged storage. Moreover, this kind of receptors can be used as biorecognition element for label-free detection by means of highly sensible techniques, such as those based on SPR, QCM or MC. Such detection systems could be then applied for the EDC screening in complex matrices such as food, aquaculture, fresh and seawater as well as for screening of chemicals with potential EDC activity. Our results indicate, as a proof of concept, that the combination of structural and sequence analysis with computational simulations, allow the successful rational design of ER mutants with desired binding properties. We think that the workflow illustrated in this manuscript could be successfully applied to the rational design of other ER mutants or to the modification of other ligand binding proteins. During animal development asymmetric signals set up during the early cleavage stages are utilized to initiate different pathways of cell type specific differentiation. Individual cells undergo a complex sequential and combinatorial pattern of differential activation/repression of gene activity that are causally required for the correct assignment of cell identity. The body plan is thus formed by interactions between genes and proteins. A collection of such interactions defines a gene regulatory network. A GRN can be described using mathematical models. The goal of modeling GRNs is to understand the basic properties of these networks. Various mathematical frameworks have been proposed for the description of GRNs. Some models are quantitative, some models include time or spatial compartments.