Pentatricopeptide repeat (PPR) proteins with an E domain have been identified as specific factors for C to U RNA editing in plant organelles. These PPR proteins bind to a unique sequence motif 5′ of their target editing sites. Recently, involvement of a combinatorial amino acid code in the P (normal length) and S type (short) PPR domains in sequence specific RNA binding was reported. PPR proteins involved in RNA editing, however, contain not only P and S motifs but also their long variants L (long) and L2 (long2) and the S2 (short2) motifs. We now find that inclusion of these motifs improves the prediction of RNA editing target sites. Previously overlooked RNA editing target sites are suggested from the PPR motif structures of known E-class PPR proteins and are experimentally verified. RNA editing target sites are assigned for the novel PPR protein MEF32 (mitochondrial editing factor 32) and are confirmed in the cDNA.

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SUMMARYA method is proposed to systematically transform a constrained optimal control problem (OCP) into an unconstrained OCP, which can be treated in the standard calculus of variations. The considered class of constraints comprises up to m input constraints and m state constraints with well-defined relative degree, where m denotes the number of inputs of the given nonlinear system. Starting from an equivalent normal form representation, the constraints are incorporated into a new system dynamics by means of saturation functions and differentiation along the normal form cascade. This procedure leads to a new unconstrained OCP, where an additional penalty term is introduced to avoid the unboundedness of the saturation function arguments if the original constraints are touched. The penalty parameter has to be successively reduced to converge to the original optimal solution. The approach is independent of the method used to solve the new unconstrained OCP. In particular, the constraints cannot be violated during the numerical solution and a successive reduction of the constraints is possible, e.g. to start from an unconstrained solution. Two examples in the single and multiple input case illustrate the potential of the approach. For these examples, a collocation method is used to solve the boundary value problems stemming from the optimality conditions.