Most mitochondrial genes of Trypanosoma brucei do not contain the necessary information to make translatable mRNAs. These transcripts must undergo RNA editing, a posttranscriptional process by which uridine residues are added and deleted from mitochondrial mRNAs. RNA editing is believed to be catalyzed by a ribonucleoprotein complex containing endonucleolytic, terminal uridylyl transferase (TUTase), 3′ uridine-specific exonucleolytic (U-exo), and ligase activities. None of the catalytic enzymes for RNA editing have been identified. Here we describe the identification of two candidate RNA ligases (48 and 52 kDa) that are core catalytic components of the T. brucei ribonucleoprotein editing complex. Both enzymes share homology to the covalent nucleotidyl transferase superfamily and contain five key signature motifs, including the active site KXXG. In this report, we present data on the proposed 48 kDa RNA editing ligase. We have prepared polyclonal antibodies against recombinant 48 kDa ligase that specifically recognize the trypanosome enzyme. When expressed in trypanosomes as an epitope-tagged fusion protein, the recombinant ligase localizes to the mitochondrion, associates with RNA editing complexes, and adenylates with ATP. These findings provide strong support for the enzymatic cascade model for kinetoplastid RNA editing.

Several recent publications illustrated advantages of using sequence profiles in recognizing distant homologies between proteins. At the same time, the practical usefulness of distant homology recognition depends not only on the sensitivity of the algorithm, but also on the quality of the alignment between a prediction target and the template from the database of known proteins. Here, we study this question for several supersensitive protein algorithms that were previously compared in their recognition sensitivity (Rychlewski et al., 2000). A database of protein pairs with similar structures, but low sequence similarity is used to rate the alignments obtained with several different methods, which included sequence–sequence, sequence–profile, and profile–profile alignment methods. We show that incorporation of evolutionary information encoded in sequence profiles into alignment calculation methods significantly increases the alignment accuracy, bringing them closer to the alignments obtained from structure comparison.

In general, alignment quality is correlated with recognition and alignment score significance. For every alignment method, alignments with statistically significant scores correlate with both correct structural templates and good quality alignments. At the same time, average alignment lengths differ in various methods, making the comparison between them difficult. For instance, the alignments obtained by FFAS, the profile–profile alignment algorithm developed in our group are always longer that the alignments obtained with the PSI-BLAST algorithms. To address this problem, we develop methods to truncate or extend alignments to cover a specified percentage of protein lengths. In most cases, the elongation of the alignment by profile–profile methods is reasonable, adding fragments of similar structure. The examples of erroneous alignment are examined and it is shown that they can be identified based on the model quality.

Distant homologies between proteins are often discovered only after three-dimensional structures of both proteins are solved. The sequence divergence for such proteins can be so large that simple comparison of their sequences fails to identify any similarity. New generation of sensitive alignment tools use averaged sequences of entire homologous families (profiles) to detect such homologies. Several algorithms, including the newest generation of BLAST algorithms and BASIC, an algorithm used in our group to assign fold predictions for proteins from several genomes, are compared to each other on the large set of structurally similar proteins with little sequence similarity. Proteins in the benchmark are classified according to the level of their similarity, which allows us to demonstrate that most of the improvement of the new algorithms is achieved for proteins with strong functional similarities, with almost no progress in recognizing distant fold similarities.

It is also shown that details of profile calculation strongly influence its sensitivity in recognizing distant homologies. The most important choice is how to include information from diverging members of the family, avoiding generating false predictions, while accounting for entire sequence divergence within a family. PSI-BLAST takes a conservative approach, deriving a profile from core members of the family, providing a solid improvement without almost any false predictions. BASIC strives for better sensitivity by increasing the weight of divergent family members and paying the price in lower reliability. A new FFAS algorithm introduced here uses a new procedure for profile generation that takes into account all the relations within the family and matches BASIC sensitivity with PSI-BLAST like reliability.