We describe the crystal structure of the complete Thermus thermophilus 70S ribosome containing bound messenger RNA and transfer RNAs (tRNAs) at 5.5 angstrom resolution. All of the 16S, 23S, and 5S ribosomal RNA (rRNA) chains, the A-, P-, and E-site tRNAs, and most of the ribosomal proteins can be fitted to the electron density map. The core of the interface between the 30S small subunit and the 50S large subunit, where the tRNA substrates are bound, is dominated by RNA, with proteins located mainly at the periphery, consistent with ribosomal function being based on rRNA. In each of the three tRNA binding sites, the ribosome contacts all of the major elements of tRNA, providing an explanation for the conservation of tRNA structure. The tRNAs are closely juxtaposed with the intersubunit bridges, in a way that suggests coupling of the 20 to 50 angstrom movements associated with tRNA translocation with intersubunit movement.

Background Recent advances in sequencing technologies have greatly increased the identification of mutations in cancer genomes. However, it remains a significant challenge to identify cancer-driving mutations, since most observed missense changes are neutral passenger mutations. Various computational methods have been developed to predict the effects of amino acid substitutions on protein function and classify mutations as deleterious or benign. These include approaches that rely on evolutionary conservation, structural constraints, or physicochemical attributes of amino acid substitutions. Here we review existing methods and further examine eight tools: SIFT, PolyPhen2, Condel, CHASM, mCluster, logRE, SNAP, and MutationAssessor, with respect to their coverage, accuracy, availability and dependence on other tools. Results Single nucleotide polymorphisms with high minor allele frequencies were used as a negative (neutral) set for testing, and recurrent mutations from the COSMIC database as well as novel recurrent somatic mutations identified in very recent cancer studies were used as positive (non-neutral) sets. Conservation-based methods generally had moderately high accuracy in distinguishing neutral from deleterious mutations, whereas the performance of machine learning based predictors with comprehensive feature spaces varied between assessments using different positive sets. MutationAssessor consistently provided the highest accuracies. For certain combinations metapredictors slightly improved the performance of included individual methods, but did not outperform MutationAssessor as stand-alone tool. Conclusions Our independent assessment of existing tools reveals various performance disparities. Cancer-trained methods did not improve upon more general predictors. No method or combination of methods exceeds 81% accuracy, indicating there is still significant room for improvement for driver mutation prediction, and perhaps more sophisticated feature integration is needed to develop a more robust tool.

Coupled translocation of tRNA and mRNA in the ribosome during protein synthesis is one of the most challenging and intriguing problems in the field of translation. We highlight several key questions regarding the mechanism of translocation, and discuss possible mechanistic models in light of the recent crystal structures of the ribosome and its subunits. ß 2002 Published by Elsevier Science B.V. on behalf of the Federation of European Biochemical Societies.

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We have noted consistent structural similarities among unrelated proteases. In comparison with other proteins of similar size, proteases have smaller than average surface areas, smaller radii of gyration, and higher C ␣ densities. These findings imply that proteases are, as a group, more tightly packed than other proteins. There are also notable differences in secondary structure content between these two groups of proteins: proteases have fewer helices and more loops. We speculate that both high packing density and low ␣-helical content coevolved in proteases to avoid autolysis. By using the structural parameters that seem to show some separation between proteases and nonproteases, a neural network has been trained to predict protease function with over 86% accuracy. Moreover, it is possible to identify proteases whose folds were not represented during training. Similar structural analyses may be useful for identifying other classes of proteins and may be of great utility for categorizing the flood of structures soon to flow from structural genomics initiatives.T he genome sequencing projects currently underway have given birth to a new pursuit: determining the threedimensional structures of an organism's proteome. This new endeavor, dubbed ''structural genomics,'' has an initial goal of solving the structures of proteins that have little or no sequence identity to proteins of known structure so as to map out protein fold space most efficiently and to provide modeling scaffolds for proteins of biomedical interest (1-3). Structures solved to meet this goal will include proteins of unknown function as has recently been reported (4, 5). Deducing the functions of proteins from their structures would be beneficial, because it could suggest possible roles for a much larger group of homologous proteins from other organisms.Herein, we investigate whether a broad class of proteins of similar function, but not necessarily similar fold or catalytic mechanism, has distinguishing structural characteristics. We focus on the proteases, a very well studied class of proteins. Before the development of recombinant methods for protein expression, digestive enzymes were the subjects of many early structural and mechanistic studies, because they were easy to obtain in large quantities from natural sources (6). Today, the database of protease structures has grown to include a variety of molecules that play critical roles in many biological processes ranging from viral replication to the development and growth of an organism.As with nearly all biological processes, protease activity must be regulated tightly. Regulation is particularly important for proteases, because all proteins, at some level, are their natural substrates. Different mechanisms have arisen for protease regulation. These include inhibition by specific protease inhibitors as well as synthesis as zymogens with covalently attached, inhibitory prosegments (7). Proteases may also be restricted to certain parts of the cell (e.g., the proteasome) or function only under spe...

T h e ribosome is a complex molecular machine, with moving parts, many of which are structural elements of rRNA. We compared the X-ray crystal structures of three different functional states of the 30 S ribosomal subunit -two from crystal structures of the isolated 30 S subunit from the Ramakrishnan group and one from a complex of the 70 S ribosome. Even though all three structures are in what could be called the 'ground state' of the subunit, many conformational differences are found, distributed over the whole structure. A striking example is the undulating movement of the penultimate stem of 16s rRNA, which forms several intersubunit bridges with the 50 S subunit.

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DINAMO is freely available as a local application or Web-based Java applet at http://tito.ucsc.edu/dinamo