Andrea Bazzoli

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This paper presents a memetic algorithm with self-adaptive local search, applied to protein structure prediction in an HP, cubic-lattice model. Besides describing in detail how the algorithm works, we report experimental results that justify important implementation choices, such as the introduction of speciation mechanisms and the extensive application of(More)
The epidermal growth factor receptor (EGFR) protein tyrosine kinase (PTK) is an important protein target for anti-tumor drug discovery. To identify potential EGFR inhibitors, we conducted a quantitative structure-activity relationship (QSAR) study on the inhibitory activity of a series of quinazoline derivatives against EGFR tyrosine kinase. Two 2D-QSAR(More)
Protein-protein interactions are among today's most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more "traditional" drug discovery(More)
— Stating protein design as an energy minimization in the space of amino acid sequences and rotamer combinations is usual. In the current paper an evolutionary algorithm built on this paradigm is presented, where each candidate sequence is threaded by SCWRL onto a fixed backbone structure and the energy of the resulting protein is estimated by FOLD-X. The(More)
Here is described an evolutionary algorithm that predicts the native structure of single-chain proteins by minimizing a fitness function on a discretized conformational space. Predictions are made ab initio, i.e., without taking any known protein structure as a starting template for the search. The computational model of protein considers only the heavy(More)
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