![]() ![]() The rigid-geometry approximation uses fixed bond lengths and angles, leaving torsion angles as the only variables needed to define the structure. This paradigm underlies the restraints used to guide protein structure refinement (e.g., Evans, 2007) and is also the basis of the rigid-geometry approximation used to simplify model building in the most successful structure-prediction packages such as Rosetta and I-TASSER ( Rohl et al., 2004 Zhang, 2009). Since the work of Pauling and Corey (1951), protein model building at all levels has been guided by the assumption that the peptide backbone has a certain ideal geometry independent of context ( Figure 1). ![]() Structural details at the 0.1 Å scale guide our understanding of enzyme catalysis, how mutations cause disease, and what makes a good inhibitor and potential drug. Protein structures derived both from crystallographic refinement and predictive modeling both stand to benefit from incorporation of the new paradigm. To facilitate adoption of this new paradigm, we have created a conformation-dependent library of covalent bond lengths and bond angles and shown that it has improved accuracy over existing methods without any additional variables to optimize. The trends have a rational, structural basis that can be explained by avoidance of atomic clashes or optimization of favorable electrostatic interactions. Here, we use a nonredundant set of ultrahigh-resolution protein structures to define these conformation-dependent variations. Both quantum-mechanics calculations and empirical analyses have shown this is an incorrect simplification in that backbone covalent geometry actually varies systematically as a function of the Φ and Ψ backbone dihedral angles. Protein structure determination and predictive modeling have long been guided by the paradigm that the peptide backbone has a single, context-independent ideal geometry. ![]()
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