Interactions between tRNA identity nucleotides and their recognition sites in glutaminyl-tRNA synthetase determine the cognate amino acid affinity of the enzyme.

Sequence-specific interactions between aminoacyl-tRNA synthetases and their cognate tRNAs both ensure accurate RNA recognition and prevent the binding of noncognate substrates. Here we show for Escherichia coli glutaminyl-tRNA synthetase (GlnRS; EC 6.1.1.18) that the accuracy of tRNA recognition also determines the efficiency of cognate amino acid recognition. Steady-state kinetics revealed that interactions between tRNA identity nucleotides and their recognition sites in the enzyme modulate the amino acid affinity of GlnRS. Perturbation of any of the protein-RNA interactions through mutation of either component led to considerable changes in glutamine affinity with the most marked effects seen at the discriminator base, the 10:25 base pair, and the anticodon. Reexamination of the identity set of tRNA(Gln) in the light of these results indicates that its constituents can be differentiated based upon biochemical function and their contribution to the apparent Gibbs' free energy of tRNA binding. Interactions with the acceptor stem act as strong determinants of tRNA specificity, with the discriminator base positioning the 3' end. The 10:25 base pair and U35 are apparently the major binding sites to GlnRS, with G36 contributing both to binding and recognition. Furthermore, we show that E. coli tryptophanyl-tRNA synthetase also displays tRNA-dependent changes in tryptophan affinity when charging a noncognate tRNA. The ability of tRNA to optimize amino acid recognition reveals a novel mechanism for maintaining translational fidelity and also provides a strong basis for the coevolution of tRNAs and their cognate synthetases.

Detection and localization of individual antibody-antigen recognition events by atomic force microscopy.

A methodology has been developed for the study of molecular recognition at the level of single events and for the localization of sites on biosurfaces, in combining force microscopy with molecular recognition by specific ligands. For this goal, a sensor was designed by covalently linking an antibody (anti-human serum albumin, polyclonal) via a flexible spacer to the tip of a force microscope. This sensor permitted detection of single antibody-antigen recognition events by force signals of unique shape with an unbinding force of 244 +/- 22 pN. Analysis revealed that observed unbinding forces originate from the dissociation of individual Fab fragments from a human serum albumin molecule. The two Fab fragments of the antibody were found to bind independently and with equal probability. The flexible linkage provided the antibody with a 6-nm dynamical reach for binding, rendering binding probability high, 0.5 for encounter times of 60 ms. This permitted fast and reliable detection of antigenic sites during lateral scans with a positional accuracy of 1.5 nm. It is indicated that this methodology has promise for characterizing rate constants and kinetics of molecular recognition complexes and for molecular mapping of biosurfaces such as membranes.

State of the art in continuous speech recognition.

In the past decade, tremendous advances in the state of the art of automatic speech recognition by machine have taken place. A reduction in the word error rate by more than a factor of 5 and an increase in recognition speeds by several orders of magnitude (brought about by a combination of faster recognition search algorithms and more powerful computers), have combined to make high-accuracy, speaker-independent, continuous speech recognition for large vocabularies possible in real time, on off-the-shelf workstations, without the aid of special hardware. These advances promise to make speech recognition technology readily available to the general public. This paper focuses on the speech recognition advances made through better speech modeling techniques, chiefly through more accurate mathematical modeling of speech sounds.