title: Exploiting evolutionary relationships for predicting protein structures.
abstract: In the last few years there have been many developments in computational biology, particularly with regard to novel, imaginative exploitation of genomic data. Disappointingly, there has been a lack of progress in the methodology for prediction of protein structures. In the last several years, however, promising new methods have finally begun to emerge. These methods are increasing the power and scope of the methodology, but, most importantly, they are generating new areas of investigation that we believe will accelerate progress in the field. In this review we describe recent developments and highlight the implications of their success as well as areas where efforts should be focused.
authors.affiliation: Department of Biochemical Sciences A. Rossi Fanelli, University La Sapienza, P. le Aldo Moro 5, 00185 Rome, Italy. anna.tramontano@uniroma1.it
authors.forename: Anna
authors.initials: A
authors.lastname: Tramontano
authors.forename: Veronica
authors.initials: V
authors.lastname: Morea
mesh_terms: D000595:Amino Acid Sequence; D030562:Databases, Protein; D019143:Evolution, Molecular; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*; D017386:Sequence Homology, Amino Acid
chemical_list: D011506:Proteins