Optimizing strategy for the discovery of compositionally-biased or low-complexity regions in proteins.

Proteins can contain tracts dominated by a subset of amino acids and that have a functional significance. These are often termed 'low-complexity regions' (LCRs) or 'compositionally-biased regions' (CBRs). However, a wide spectrum of compositional bias is possible, and program parameters used to annotate these regions are often arbitrarily chosen. Also, investigators are sometimes interested in longer regions, or sometimes very short ones. Here, two programs for annotating LCRs/CBRs, namely SEG and fLPS, are investigated in detail across the whole expanse of their parameter spaces. In doing so, boundary behaviours are resolved that are used to derive an optimized systematic strategy for annotating LCRs/CBRs. Sets of parameters that progressively annotate or 'cover' more of protein sequence space and are optimized for a given target length have been derived. This progressive annotation can be applied to discern the biological relevance of CBRs, e.g., in parsing domains for experimental constructs and in generating hypotheses. It is also useful for picking out candidate regions of interest of a given target length and bias signature, and for assessing the parameter dependence of annotations. This latter application is demonstrated for a set of human intrinsically-disordered proteins associated with cancer.