Tertiary alphabet for the observable protein structural universe.
Here, we systematically decompose the known protein structural universe into its basic elements, which we dub tertiary structural motifs (TERMs). A TERM is a compact backbone fragment that captures the secondary, tertiary, and quaternary environments around a given residue, comprising one or more disjoint segments (three on average). We seek the set of universal TERMs that capture all structure in the Protein Data Bank (PDB), finding remarkable degeneracy. Only ∼600 TERMs are sufficient to describe 50% of the PDB at sub-Angstrom resolution. However, more rare geometries also exist, and the overall structural coverage grows logarithmically with the number of TERMs. We go on to show that universal TERMs provide an effective mapping between sequence and structure. We demonstrate that TERM-based statistics alone are sufficient to recapitulate close-to-native sequences given either NMR or X-ray backbones. Furthermore, sequence variability predicted from TERM data agrees closely with evolutionary variation. Finally, locations of TERMs in protein chains can be predicted from sequence alone based on sequence signatures emergent from TERM instances in the PDB. For multisegment motifs, this method identifies spatially adjacent fragments that are not contiguous in sequence-a major bottleneck in structure prediction. Although all TERMs recur in diverse proteins, some appear specialized for certain functions, such as interface formation, metal coordination, or even water binding. Structural biology has benefited greatly from previously observed degeneracies in structure. The decomposition of the known structural universe into a finite set of compact TERMs offers exciting opportunities toward better understanding, design, and prediction of protein structure.
Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755.
Craig O
CO
Mackenzie
Department of Computer Science, Dartmouth College, Hanover, NH 03755.
Jianfu
J
Zhou
Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH 03755; gevorg.grigoryan@dartmouth.edu.|Department of Computer Science, Dartmouth College, Hanover, NH 03755.|Department of Biological Sciences, Dartmouth College, Hanover, NH 03755.
Gevorg
G
Grigoryan
D000465:Algorithms; D000595:Amino Acid Sequence; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D009682:Magnetic Resonance Spectroscopy; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*
D011506:Proteins
protein structural universe; sequence–structure relationships; structural degeneracy; structural modularity; tertiary motif