prof. Rita Casadio, Friday 11 May -- h: 15.00 -- Sala Seminari Ovest
As a result of large sequencing projects, data banks of protein sequences and structures are growing rapidly. The number of sequences is however orders of magnitude larger than the number of structures known at atomic level and this is so in spite of the efforts in accelerating processes aiming at the resolution of protein structure.
Tools have been developed in order to bridge the gap between sequence and protein 3D structure, based on the notion that information is to be retrieved from the data bases and that knowledge-based methods can help in approaching a solution of the protein folding problem. By this several futures can be predicted starting from a protein sequence such as structural and functional motifs and domains, including the topological organisation of a protein inside the membrane phase, and the formation of disulfide bonds in a folded protein structure (1). Our group has been contributing to the field with different computational methods, mainly based on machine learning (neural networks (NNs), hidden markov models (HMMs), support vector machines (SVMs), hidden neural networks (HNNs) and extreme learning machines (ELMs)) and capable of computing the likelihood of a given feature starting from the protein sequence (