3D Protein structure predicted from evolutionary sequence variation


Dr. Chris Sander, Computational Biology Center, Sloane Kettering Cancer Institute, New York, USA

Dr. Debora S. Marks, Systems Biology, Harvard Medical School, Boston, USA


Friday 1st July

Venue: Medawar Lankester Lecture Theatre
Time: 15:00


The trajectory of an evolving protein through sequence space is constrained by the need to maintain structure and function. Residues in spatial proximity tend to co-evolve, yet attempts at inverting the evolutionary record to derive proximity constraints have so far been inadequate. Here we use constraints inferred from evolution to predict de novo  3D protein structures , without use of homology modeling or fragments from known structures. Our evolutionary constraints tackle the major obstacle in state-of-the-art de novo prediction: the ability to sample 3D conformational space. 

The predicted constraints are calculated with a method borrowed form statistical physics using maximum entropy which solves the inverse problem of inferring spatial proximity from patterns of co-evolution. We report prediction for 12 proteins ranging from 50-220 residues in size at a Cα-RMSD of 2.8-5.1 Å. The predicted structures have excellent topological agreement with experimentally determined structures, with structural elements well placed in 3D space, suggesting they can be refined further. In this era of massive genomic sequencing across many species, the evolutionary record captured in sequence alignments provides an increasingly powerful source of predictive information, in particular for protein families that have resisted experimental structure determination.


Chris's webpage: http://www.mskcc.org/mskcc/html/11655.cfm 

Debora's webpage: http://web.me.com/deboramarks/Biology/home.html


sanderposter




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