Our research develops “hybrid” computational methods for the design and screening of virtual mutants. Therefore, experimental efforts can later be focused only on few selected mutants. In silico methods integrates experimental data, molecular modeling and multivariate statistics with the aim of correlating structures with functions of enzymes.

a)      3D-QSAR methods identify hot-spots for mutations able to confer specific catalytic functions

b)      3D-bioinformatic analysis makes use of effective molecular descriptors (BioGPS) able to extract structural, electronic and physical-chemical features of enzymes. Then, they are statistically analyzed to find correlations between structures and functions. (collaboration with Univ. Perugia, http://www.chm.unipg.it/cheminf).

c)      Computational methods can be automated and accelerated by integrating all actions and software inside platforms for multi-object optimization (collaboration with ESTECO S.p.A.).