Edgardo A. Ferrán

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A new method based on neural networks to cluster proteins into families is described. The network is trained with the Kohonen unsupervised learning algorithm, using matrix pattern representations of the protein sequences as inputs. The components (x, y) of these 20 x 20 matrix patterns are the normalized frequencies of all pairs xy of amino acids in each(More)
An artificial neural network was used to cluster proteins into families. The network, composed of 7 x 7 neurons, was trained with the Kohonen unsupervised learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of 447 proteins, belonging to 13 different families. As a result of the training, and without any a priori(More)
Dysregulation of Toll-like receptor (TLR) responses to pathogens can lead to pathological inflammation or to immune hyporesponsiveness and susceptibility to infections, and may affect adaptive immune responses. TLRs are therefore attractive therapeutic targets. We assessed the potential of the TLR co-receptor CD14 as a target for therapeutics by(More)
We have recently proposed a method, based on artificial neural networks (ANNs) to cluster protein sequences into families according to their degree of sequence similarity. The network was trained with an unsupervised learning algorithm, using, as inputs, matrix patterns derived from the bipeptide composition of the protein sequences. We describe here some(More)
We have recently described a method based on Artificial Neural Networks to cluster protein sequences into families. The network was trained with Kohonen's unsupervised-learning algorithm using, as inputs, matrix patterns derived from the bipeptide composition of the proteins. We show here the application of that method to classify 1758 protein sequences,(More)
We discovered a constitutively activating mutation (CAM) V308E for the neurotensin NT1 receptor. Molecular dynamics (MD) performed for the CAM NT1-V308E exhibiting a high spontaneous activity, and for the wild-type NT1 without basal activity, show dramatic conformational changes for the CAM. To test if the two MD models could be valuable active and inactive(More)
The DLS-VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low-throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds. First, DLS identifies modulators(More)
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