Specific and reversible immobilization of proteins tagged to the affinity polypeptide C-LytA on functionalized graphite electrodes

We have developed a general method for the specific and reversible immobilization of proteins fused to the choline-binding module C-LytA on functionalized graphite electrodes. Graphite electrode surfaces were modified by diazonium chemistry to introduce carboxylic groups that were subsequently used to anchor mixed self-assembled monolayers consisting of N,N-diethylethylenediamine groups, acting as choline analogs, and ethanolamine groups as spacers. The ability of the prepared electrodes to specifically bind C-LytA-tagged recombinant proteins was tested with a C-LytA-β-galactosidase fusion protein. The binding, activity and stability of the immobilized protein was evaluated by electrochemically monitoring the formation of an electroactive product in the enzymatic hydrolysis of the synthetic substrate 4-aminophenyl β-D-galactopyranoside. The hybrid protein was immobilized in an specific and reversible way, while retaining the catalytic activity. Moreover, these functionalized electrodes were shown to be highly stable and reusable. The method developed here can be envisaged as a general, immobilization procedure on the protein biosensor field.

Improving drug discovery using hybrid softcomputing methods

Virtual screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. Most VS methods suppose a unique binding site for the target, but it has been demonstrated that diverse ligands interact with unrelated parts of the target and many VS methods do not take into account this relevant fact. This problem is circumvented by a novel VS methodology named BINDSURF that scans the whole protein surface in order to find new hotspots, where ligands might potentially interact with, and which is implemented in last generation massively parallel GPU hardware, allowing fast processing of large ligand databases. BINDSURF can thus be used in drug discovery, drug design, drug repurposing and therefore helps considerably in clinical research. However, the accuracy of most VS methods and concretely BINDSURF is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of the scoring functions used in BINDSURF we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve BINDSURF VS predictions.