Piyumi R. Amarasinghe, Lloyd Allison, Peter J. Stuckey, Maria Garcia de la Banda, Arthur M. Lesk, Arun S. Konagurthu. Bioinformatics, 39, supp.1, pp.i357-i367, doi:10.1093/bioinformatics/btad251, June 2023.

The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles (⟨φ, ψ, χ₁, χ₂, ...⟩) ... provides an alternative to the commonly used rotamer libraries ... Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (φψχal).

Also see [lcb. ... /phisical].