Experimentally Derived and Computationally Optimized Backbone Conformational Statistics for Blocked Amino Acids

Experimentally
derived, amino acid specific backbone dihedral angle
distributions are invaluable for modeling data-driven conformational
equilibria of proteins and for enabling quantitative assessments of
the accuracies of molecular mechanics force fields. The protein
coil library that is extracted from analysis of high-resolution
structures of proteins has served as a useful proxy for quantifying
intrinsic and context-dependent conformational distributions of amino
acids. However, data that go into coil libraries will have hidden
biases, and ad hoc procedures must be used to remove these biases.
Here, we combine high-resolution biased information from protein structural
databases with unbiased low-resolution information from spectroscopic
measurements of blocked amino acids to obtain experimentally derived
and computationally optimized coil-library landscapes for each of
the 20 naturally occurring amino acids. Quantitative descriptions
of conformational distributions require parsing of data into conformational
basins with defined envelopes, centers, and statistical weights. We
develop and deploy a numerical method to extract conformational basins.
The weights of conformational basins are optimized to reproduce quantitative
inferences drawn from spectroscopic experiments for blocked amino
acids. The optimized distributions serve as touchstones for assessments
of intrinsic conformational preferences and for quantitative comparisons
of molecular mechanics force fields.