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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.

A simple approach for the sensitive detection of distant relationships among protein families and for sequence--structure alignment via comparison of hidden Markov models based on their quasi-consensus sequences is presented. Using a previously published benchmark dataset, the approach is demonstrated to give better homology detection and yield alignments with improved accuracy in comparison to an existing state-of-the-art dynamic programming profile--profile comparison method. This method also runs significantly faster and is therefore suitable for a server covering the rapidly increasing structure database. A server based on this method is available at http://liao.cis.udel.edu/website/servers/modmod Contact:roland.dunbrack@fccc.edu; lliao@mail.eecis.udel.edu

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.

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.

In this work, we present a hidden Markov model for predicting the topology of transmembrane proteins. Our model differs from TMHMM (Sonnhammer et al) both in the architecture of the loop submodels on both sides of the membrane and in the model training procedure. Using maximum likelihood parameter estimation with significant regularization, the model was trained and cross-validated on two sets of sequences with known topology. On the first set of 83 sequences, the prediction accuracy of our model for membrane domain locations and topology are both 89% while TMHMM reported 83% for domain locations and 77% for topology. On the second dataset of 160 sequences, our prediction accuracies are 89% for locations and 84% for topology: both surpassing significantly those of TMHMM (83% and 77%).

Motivation: Knowledge of the transmembrane helical topology can help identify binding sites and infer functions for membrane proteins. However, because membrane proteins are hard to solubilize and purify, only a very small amount of membrane proteins have structure and topology experimentally determined. This has motivated various computational methods for predicting the topology of membrane proteins. Results: We present an improved hidden Markov model, TMMOD, for the identification and topology prediction of transmembrane proteins. Our model uses TMHMM as a prototype, but differs from TMHMM by the architecture of the submodels for loops on both sides of the membrane and also by the model training procedure. In cross-validation experiments using a set of 83 transmembrane proteins with known topology, TMMOD outperformed TMHMM and other existing methods, with an accuracy of 89% for both topology and locations. In another experiment using a separate set of 160 transmembrane proteins, TMMOD had 84% for topology and 89% for locations. When utilized for identifying transmembrane proteins from non-transmembrane proteins, particularly signal peptides, TMMOD has consistently fewer false positives than TMHMM does. Application of TMMOD to a collection of complete genomes shows that the number of predicted membrane proteins accounts for ∼20--30% of all genes in those genomes, and that the topology where both the N- and C-termini are in the cytoplasm is dominant in these organisms except for Caenorhabditis elegans. Availability: http://liao.cis.udel.edu/website/servers/TMMOD/ Contact: lliao@cis.udel.edu

Summary: A public server for evaluating the accuracy of protein sequence alignment methods is presented. CASA is an implementation of the alignment accuracy benchmark presented by Sauder et al. (Proteins, 40, 6–22, 2000). The benchmark currently contains 39321 pairwise protein structure alignments produced with the CE program from SCOP domain definitions. The server produces graphical and tabular comparisons of the accuracy of a user’s input sequence alignments with other commonly used programs, such as BLAST, PSI-BLAST, Clustal W, and SAM-T99. Availability: The server is located at http://capb.dbi.udel.edu/casa. Contact: RL_ Dunbrack@fccc.edu * To whom correspondence should be addressed.