  The evolutionary trajectory of a protein through sequence space is
constrained by function and three-dimensional (3D) structure. Residues in
spatial proximity tend to co-evolve, yet attempts to invert the evolutionary
record to identify these constraints and use them to computationally fold
proteins have so far been unsuccessful. Here, we show that co-variation of
residue pairs, observed in a large protein family, provides sufficient
information to determine 3D protein structure. Using a data-constrained maximum
entropy model of the multiple sequence alignment, we identify pairs of
statistically coupled residue positions which are expected to be close in the
protein fold, termed contacts inferred from evolutionary information (EICs). To
assess the amount of information about the protein fold contained in these
coupled pairs, we evaluate the accuracy of predicted 3D structures for proteins
of 50-260 residues, from 15 diverse protein families, including a G-protein
coupled receptor. These structure predictions are de novo, i.e., they do not
use homology modeling or sequence-similar fragments from known structures. The
resulting low C{\alpha}-RMSD error range of 2.7-5.1{\AA}, over at least 75% of
the protein, indicates the potential for predicting essentially correct 3D
structures for the thousands of protein families that have no known structure,
provided they include a sufficiently large number of divergent sample
sequences. With the current enormous growth in sequence information based on
new sequencing technology, this opens the door to a comprehensive survey of
protein 3D structures, including many not currently accessible to the
experimental methods of structural genomics. This advance has potential
applications in many biological contexts, such as synthetic biology,
identification of functional sites in proteins and interpretation of the
functional impact of genetic variants.
