Secondary structure, a missing component of sequence-based minimotif definitions.

Minimotifs are short contiguous segments of proteins that have a known biological function. The hundreds of thousands of minimotifs discovered thus far are an important part of the theoretical understanding of the specificity of protein-protein interactions, posttranslational modifications, and signal transduction that occur in cells. However, a longstanding problem is that the different abstractions of the sequence definitions do not accurately capture the specificity, despite decades of effort by many labs. We present evidence that structure is an essential component of minimotif specificity, yet is not used in minimotif definitions. Our analysis of several known minimotifs as case studies, analysis of occurrences of minimotifs in structured and disordered regions of proteins, and review of the literature support a new model for minimotif definitions that includes sequence, structure, and function.

Predictive Power Estimation Algorithm (PPEA)--a new algorithm to reduce overfitting for genomic biomarker discovery.

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses.

The C-terminal domain of measles virus nucleoprotein belongs to the class of intrinsically disordered proteins that fold upon binding to their physiological partner.

The nucleoprotein of measles virus consists of an N-terminal domain, N(CORE) (aa 1-400), resistant to proteolysis, and a C-terminal domain, N(TAIL) (aa 401-525), hypersensitive to proteolysis and not visible by electron microscopy. Using two complementary computational approaches, we predict that N(TAIL) belongs to the class of natively unfolded proteins. Using different biochemical and biophysical approaches, we show that N(TAIL) is indeed unstructured in solution. In particular, the spectroscopic and hydrodynamic properties of N(TAIL) indicate that this protein domain belongs to the premolten globule subfamily within the class of intrinsically disordered proteins. The isolated N(TAIL) domain was shown to be able to bind to its physiological partner, the phosphoprotein (P), and to undergo an induced folding upon binding to the C-terminal moiety of P [J. Biol. Chem. 278 (2003) 18638]. Using a computational analysis, we have identified within N(TAIL) a putative alpha-helical molecular recognition element (alpha-MoRE, aa 488-499), which could be involved in binding to P via induced folding. We report the bacterial expression and purification of a truncated form of N(TAIL) (N(TAIL2), aa 401-488) devoid of the alpha-MoRE. We show that N(TAIL2) has lost the ability to bind to P, thus supporting the hypothesis that the alpha-MoRE may play a role in binding to P. We have further analyzed the alpha-helical propensities of N(TAIL2) and N(TAIL) using circular dichroism in the presence of 2,2,2-trifluoroethanol. We show that N(TAIL2) has a lower alpha-helical potential compared to N(TAIL), thus suggesting that the alpha-MoRE may be indeed involved in the induced folding of N(TAIL).