title: Protein structure prediction from sequence variation.
abstract: Genomic sequences contain rich evolutionary information about functional constraints on macromolecules such as proteins. This information can be efficiently mined to detect evolutionary couplings between residues in proteins and address the long-standing challenge to compute protein three-dimensional structures from amino acid sequences. Substantial progress has recently been made on this problem owing to the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved understanding of covariation may help identify functional residues involved in ligand binding, protein-complex formation and conformational changes. We expect computation of covariation patterns to complement experimental structural biology in elucidating the full spectrum of protein structures, their functional interactions and evolutionary dynamics.
authors.affiliation: Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA. ecreview@hms.harvard.edu
authors.forename: Debora S
authors.initials: DS
authors.lastname: Marks
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Hopf
authors.forename: Chris
authors.initials: C
authors.lastname: Sander
mesh_terms: D000595:Amino Acid Sequence; D003198:Computer Simulation; D014644:Genetic Variation / Q000235:genetics*; D008956:Models, Chemical; D008957:Models, Genetic; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins