Neural networks and the prediction of protein structure

As a result of large sequencing projects, data banks of protein sequences and structures are growing rapidly. The number of sequences is however one order 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 databases and that knowledge-based methods-can help in approaching a solution of the protein-folding problem. To this aim our group has implemented neural network based predictors capable of performing with some success in different tasks, including predictions of the secondary structure of globular and membrane proteins, of the topology of alpha helical and beta barrel membrane proteins, of stable alpha helical segments suited for protein design. Our predictors can also evaluate the probability of finding a cysteine in a disulphide bridge and/or the connectivity of disulfide bonds. Moreover we have developed methods for predicting contact maps in proteins and protein surfaces suited to form heterocomplexes, tools which can contribute to the goal of predicting the 3D structure starting from the sequence (the so called "ab initio" prediction) and significantly complement results from functional genomics and proteomics. All our predictors take advantage of evolution information derived from the structural alignments of homologous proteins and derived from the sequence and structure databases.