|
|
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 features can be predicted starting from a protein sequence such as structural and functional motifs and domains, including the topological organization of a protein inside the membrane phase, and the formation of disulfide bonds in a folded protein structure. Different computational methods, mainly based on machine learning (neural networks (NNs), hidden markov models (HMMs), support vector machines (SVMs) and capable of computing the likelihood of a given feature starting from the protein sequence can be used to endow protein sequence with structural features. The process of large scale proteome annotation (endowing sequences with functional and structural features) will be debate by focusing also on how to describe protein function with Gene Ontologies and predicting them from protein sequence.
|
|
|
FACULTY
|
|
