Section 18.3.1.2. Risk of restraints: bias, lack of cross validation

As an alternative to implementation as restraints, statistical information from known structures provides an independent check of structural integrity. However, the more information which is used for restraints, the less is available for such cross validation. For example, the use of a force field in protein refinement that strictly enforces a physical distribution of the protein backbone φ–ψ angles of the Ramachandran plot would transfer error-induced strain into other degrees of freedom while eliminating a Ramachandran analysis as a tool for judging protein quality (Hooft et al., 1997). There is an additional risk associated with the intentional introduction of bias into protein refinement: Restraining a model to conform to known structures is in error if the structure is unique in some way and therefore should not conform to these structures, or if there is erroneous bias in the set of structures from which the restraints were derived. Furthermore, the bias may be exaggerated, especially if the biased feature is so probable that deviations are considered suspect. This can be seen, for example, in the increasing frequency of cis-prolines as protein-structure resolution increases (Stewart et al., 1990): when there is uncertainty in lower-resolution structures, the more probable trans-prolines are preferentially chosen in model building, exaggerating their frequency.