Research Associate, Università degli Studi di Trieste
Valerio Ferrario is currently a Research Associate at University of Trieste, in the laboratory of computational and applied biocatalysis headed by Professor Lucia Gardossi. His work deals with computational methods for in-silico enzyme evolution.
He got his degree in pharmaceutical biotechnology at the University of Milan, Italy in 2005, under the supervision of Professor Francesco Molinari, working on application of microbial cell in organic solvents for biocatalytic transformations.
He worked from 2007 at the University of Trieste in the group of Professor Lucia Gardossi on the development of computational methods for describing the conformational behaviour of lipases in different media. That work was part of his PhD thesis that was defended on April 2010.
Further professional experiences:
Research fellow sponsored by Industriale Chimica s.r.l. Saronno, Italy, from 2005 to 2007, working on of biocatalytic selective reduction and hydroxylation of steroids.
Research fellow granted by ERASMUS placement, working at the University of Groningen, The Netherlands, from June to September 2008 under the supervision of Professor Siewert-Jan Marrink on coarse grained molecular dynamic simulation of enzymes.
Authors: Valerio Ferrario, Cynthia Ebert, Lucia Gardossi | Dipartimento di Scienze Chimiche e Farmaceutiche, Università degli Studi di Trieste, Piazzale Europa 1, 34127 Trieste, Italy
Basically, the same engineering concepts used in automotive fields can be applied into the field of rational enzyme engineering. The development of rational approaches for performing in-silico screening of virtual mutants and will be presented. This approach would like to be complementary and integrate theoretical approaches by designing a framework embracing different computational methods, such as active site mapping, MM methods and QSAR optimization strategies. A computational infrastructure is used for integrating all the software employed for the different steps of the mutant design, modeling and scoring, within an optimization environment able to learn, generation after generation, the correlation between mutations and their efficiency, thus accelerating the evolution of the system. The framework relies on the software “modeFRONTIER” that organizes a flexible and versatile work-flow. These features make the approach highly tunable and conversely distant from the concept of “black box”.
The software makes use of a genetic algorithm for the optimization implemented in a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals; enzyme mutants in this specific case) for an optimization problem evolves toward better solutions. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple individuals are selected from the current population (based on their fitness), and modified (recombined and mutated) to form a new population. The new population is then used in the next iteration of the algorithm. Commonly, the algorithm terminates when either a maximum number of generations has been produced, or a satisfactory fitness level has been reached for the population.