Genetic definition of a protein-splicing domain: Functional mini-inteins support structure predictions and a model for intein evolution

Inteins are protein-splicing elements, most of which contain conserved sequence blocks that define a family of homing endonucleases. Like group I introns that encode such endonucleases, inteins are mobile genetic elements. Recent crystallography and computer modeling studies suggest that inteins consist of two structural domains that correspond to the endonuclease and the protein-splicing elements. To determine whether the bipartite structure of inteins is mirrored by the functional independence of the protein-splicing domain, the entire endonuclease component was deleted from the Mycobacterium tuberculosis recA intein. Guided by computer modeling studies, and taking advantage of genetic systems designed to monitor intein function, the 440-aa Mtu recA intein was reduced to a functional mini-intein of 137 aa. The accuracy of splicing of several mini-inteins was verified. This work not only substantiates structure predictions for intein function but also supports the hypothesis that, like group I introns, mobile inteins arose by an endonuclease gene invading a sequence encoding a small, functional splicing element.

Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

Evidence in support of a four transmembrane-pore-transmembrane topology model for the Arabidopsis thaliana Na+/K+ translocating AtHKT1 protein, a member of the superfamily of K+ transporters

The Arabidopsis thaliana AtHKT1 protein, a Na+/K+ transporter, is capable of mediating inward Na+ currents in Xenopus laevis oocytes and K+ uptake in Escherichia coli. HKT1 proteins are members of a superfamily of K+ transporters. These proteins have been proposed to contain eight transmembrane segments and four pore-forming regions arranged in a mode similar to that of a K+ channel tetramer. However, computer analysis of the AtHKT1 sequence identified eleven potential transmembrane segments. We have investigated the membrane topology of AtHKT1 with three different techniques. First, a gene fusion alkaline phosphatase study in E. coli clearly defined the topology of the N-terminal and middle region of AtHKT1, but the model for membrane folding of the C-terminal region had to be refined. Second, with a reticulocyte-lysate supplemented with dog-pancreas microsomes, we demonstrated that N-glycosylation occurs at position 429 of AtHKT1. An engineered unglycosylated protein variant, N429Q, mediated Na+ currents in X. laevis oocytes with the same characteristics as the wild-type protein, indicating that N-glycosylation is not essential for the functional expression and membrane targeting of AtHKT1. Five potential glycosylation sites were introduced into the N429Q. Their pattern of glycosylation supported the model based on the E. coli-alkaline phosphatase data. Third, immunocytochemical experiments with FLAG-tagged AtHKT1 in HEK293 cells revealed that the N and C termini of AtHKT1, and the regions containing residues 135–142 and 377–384, face the cytosol, whereas the region of residues 55–62 is exposed to the outside. Taken together, our results show that AtHKT1 contains eight transmembrane-spanning segments.

Voice-processing technologies--their application in telecommunications.

As the telecommunications industry evolves over the next decade to provide the products and services that people will desire, several key technologies will become commonplace. Two of these, automatic speech recognition and text-to-speech synthesis, will provide users with more freedom on when, where, and how they access information. While these technologies are currently in their infancy, their capabilities are rapidly increasing and their deployment in today's telephone network is expanding. The economic impact of just one application, the automation of operator services, is well over $100 million per year. Yet there still are many technical challenges that must be resolved before these technologies can be deployed ubiquitously in products and services throughout the worldwide telephone network. These challenges include: (i) High level of accuracy. The technology must be perceived by the user as highly accurate, robust, and reliable. (ii) Easy to use. Speech is only one of several possible input/output modalities for conveying information between a human and a machine, much like a computer terminal or Touch-Tone pad on a telephone. It is not the final product. Therefore, speech technologies must be hidden from the user. That is, the burden of using the technology must be on the technology itself. (iii) Quick prototyping and development of new products and services. The technology must support the creation of new products and services based on speech in an efficient and timely fashion. In this paper I present a vision of the voice-processing industry with a focus on the areas with the broadest base of user penetration: speech recognition, text-to-speech synthesis, natural language processing, and speaker recognition technologies. The current and future applications of these technologies in the telecommunications industry will be examined in terms of their strengths, limitations, and the degree to which user needs have been or have yet to be met. Although noteworthy gains have been made in areas with potentially small user bases and in the more mature speech-coding technologies, these subjects are outside the scope of this paper.