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Theory of evolutionary couplings and application to the prediction of protein 3D structure and fitness

Chris Sander and Debora Marks ( Chris Sander, Memorial Sloan Kettering Cancer Center; Debora Marks, Harvard Medical School )

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 on this problem has become possible because of the explosive growth in available sequences and the application of global statistical methods. In addition to three-dimensional structure, the improved analysis of covariation helps 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. Collaboration between the Sander group at MSKCC and the Debora Marks group at Harvard Medical School. Use the Link server to compute EVcouplings and to predict 3D structure for large sequence families. Refs:  Link - Protein 3D Structure from high-throughput sequencing; Link<Link> - 3D structure of transmembrane proteins from evolutionary constraints; Link - Sequence co-evolution gives 3D contacts and structures of protein complexes.



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