| Abstract
| - Preferred conformations of amino acid side chains have been well established through statistically obtainedrotamer libraries. Typically, these provide bond torsion angles allowing a side chain to be traced atom byatom. In cases where it is desirable to reduce the complexity of a protein representation or prediction, fixingall side-chain atoms may prove unwieldy. Therefore, we introduce a general parametrization to allow positionsof representative atoms (in the present study, these are terminal atoms) to be predicted directly given backboneatom coordinates. Using a large, culled data set of amino acid residues from high-resolution protein crystalstructures, anywhere from 1 to 7 preferred conformations were observed for each terminal atom of thenon-glycine residues. Side-chain length from the backbone Cα is one of the parameters determined for eachconformation, which should itself be useful. Prediction of terminal atoms was then carried out for a second,nonredundant set of protein structures to validate the data set. Using four simple probabilistic approaches,the Monte Carlo style prediction of terminal atom locations given only backbone coordinates produced anaverage root mean-square deviation (RMSD) of ∼3 Å from the experimentally determined terminal atompositions. With prediction using conditional probabilities based on the side-chain χ1 rotamer, this averageRMSD was improved to 1.74 Å. The observed terminal atom conformations therefore provide reasonableand potentially highly accurate representations of side-chain conformation, offering a viable alternative toexisting all-atom rotamers for any case where reduction in protein model complexity, or in the amount ofdata to be handled, is desired. One application of this representation with strong potential is the predictionof charge density in proteins. This would likely be especially valuable on protein surfaces, where side chainsare much less likely to be fixed in single rotamers. Prediction of ensembles of structures provides a methodto determine the probability density of charge and atom location; such a prediction is demonstrated graphically.
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