A novel motif identified in dependence receptors.

Programmed cell death signaling is a critical feature of development, cellular turnover, oncogenesis, and neurodegeneration, among other processes. Such signaling may be transduced via specific receptors, either following ligand binding-to death receptors-or following the withdrawal of trophic ligands-from dependence receptors. Although dependence receptors display functional similarities, no common structural domains have been identified. Therefore, we employed the Multiple Expectation Maximization for Motif Elicitation and the Motif Alignment and Search Tool software programs to identify a novel transmembrane motif, dubbed dependence-associated receptor transmembrane (DART) motif, that is common to all described dependence receptors. Of 3,465 human transmembrane proteins, 25 (0.7%) display the DART motif. The predicted secondary structure features an alpha helical structure, with an unusually high percentage of valine residues. At least four of the proteins undergo regulated intramembrane proteolysis. To date, we have not identified a function for this putative domain. We speculate that the DART motif may be involved in protein processing, interaction with other proteins or lipids, or homomultimerization.

The elastic net algorithm and protein structure prediction.

Predicting protein structures from their amino acid sequences is a problem of global optimization. Global optima (native structures) are often sought using stochastic sampling methods such as Monte Carlo or molecular dynamics, but these methods are slow. In contrast, there are fast deterministic methods that find near-optimal solutions of well-known global optimization problems such as the traveling salesman problem (TSP). But fast TSP strategies have yet to be applied to protein folding, because of fundamental differences in the two types of problems. Here, we show how protein folding can be framed in terms of the TSP, to which we apply a variation of the Durbin-Willshaw elastic net optimization strategy. We illustrate using a simple model of proteins with database-derived statistical potentials and predicted secondary structure restraints. This optimization strategy can be applied to many different models and potential functions, and can readily incorporate experimental restraint information. It is also fast; with the simple model used here, the method finds structures that are within 5-6 A all-Calpha-atom RMSD of the known native structures for 40-mers in about 8 s on a PC; 100-mers take about 20 s. The computer time tau scales as tau approximately n, where n is the number of amino acids. This method may prove to be useful for structure refinement and prediction.