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Drought stress is one of the main environmental problems encountered by crop growers. Reduction in arable land area and reduced water availability make it paramount to identify and develop strategies to allow crops to be more resilient in water-limiting environments. The plant hormone abscisic acid (ABA) plays an important role in the plants' response to drought stress through its control of stomatal aperture and water transpiration, and transgenic modulation of ABA levels therefore represents an attractive avenue to improve the drought tolerance of crops. Several steps in the ABA-signaling pathway are controlled by ubiquitination involving really interesting new genes (RING) domain-containing proteins. We characterized the maize (Zea mays) RING protein family and identified two novel RING-H2 genes called ZmXerico1 and ZmXerico2 Expression of ZmXerico genes is induced by drought stress, and we show that overexpression of ZmXerico1 and ZmXerico2 in Arabidopsis and maize confers ABA hypersensitivity and improved water use efficiency, which can lead to enhanced maize yield performance in a controlled drought-stress environment. Overexpression of ZmXerico1 and ZmXerico2 in maize results in increased ABA levels and decreased levels of ABA degradation products diphaseic acid and phaseic acid. We show that ZmXerico1 is localized in the endoplasmic reticulum, where ABA 8'-hydroxylases have been shown to be localized, and that it functions as an E3 ubiquitin ligase. We demonstrate that ZmXerico1 plays a role in the control of ABA homeostasis through regulation of ABA 8'-hydroxylase protein stability, representing a novel control point in the regulation of the ABA pathway.

The rapid growth of protein sequence databases has necessitated the development of methods to computationally derive annotation for uncharacterized entries. Most such methods focus on "global" annotation, such as molecular function or biological process. Methods to supply high-accuracy "local" annotation to functional sites based on structural information at the level of individual amino acids are relatively rare. In this chapter we will describe a method we have developed for annotation of functional residues within experimentally-uncharacterized proteins that relies on position-specific site annotation rules (PIR Site Rules) derived from structural and experimental information. These PIR Site Rules are manually defined to allow for conditional propagation of annotation. Each rule specifies a tripartite set of conditions whereby candidates for annotation must pass a whole-protein classification test (that is, have end-to-end match to a whole-protein-based HMM), match a site-specific profile HMM and, finally, match functionally and structurally characterized residues of a template. Positive matches trigger the appropriate annotation for active site residues, binding site residues, modified residues, or other functionally important amino acids. The strict criteria used in this process have rendered high-confidence annotation suitable for UniProtKB/Swiss-Prot features.

Most computational methods for transmembrane protein topology prediction rely on compositional bias of amino acids to locate those hydrophobic domains in transmembrane proteins. Because signal peptides also contain hydrophobic segments, these computational prediction methods often misidentify signal peptides as transmembrane proteins. Here, we present a new approach that combines the SVM-Fisher discrimination method and TMMOD - a hidden Markov model based predictor for transmembrane proteins. While TMMOD alone has already outperformed most existing methods in both identification and topology prediction, this new approach further improves the ability of TMMOD to discriminate between transmembrane proteins and signal peptide containing proteins, reducing mis-prediction of signal peptides by more than 30% in our test data.