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.

Two-dimensional infrared spectroscopy of peptides by phase-controlled femtosecond vibrational photon echoes

Two-dimensional infrared spectra of peptides are introduced that are the direct analogues of two- and three-pulse multiple quantum NMR. Phase matching and heterodyning are used to isolate the phase and amplitudes of the electric fields of vibrational photon echoes as a function of multiple pulse delays. Structural information is made available on the time scale of a few picoseconds. Line narrowed spectra of acyl-proline-NH2 and cross peaks implying the coupling between its amide-I modes are obtained, as are the phases of the various contributions to the signals. Solvent-sensitive structural differences are seen for the dipeptide. The methods show great promise to measure structure changes in biology on a wide range of time scales.

Fourier transform infrared spectroscopy reveals a rigid α-helical assembly for the tetrameric Streptomyces lividans K+ channel

The structure of the tetrameric K+ channel from Streptomyces lividans in a lipid bilayer environment was studied by polarized attenuated total reflection Fourier transform infrared spectroscopy. The channel displays approximately 43% α-helical and 25% β-sheet content. In addition, H/D exchange experiments show that only 43% of the backbone amide protons are exchangeable with solvent. On average, the α-helices are tilted 33° normal to the membrane surface. The results are discussed in relationship to the lactose permease of Escherichia coli, a membrane transport protein.

Experimental evidence for hydrogen-bonded network proton transfer in bacteriorhodopsin shown by Fourier-transform infrared spectroscopy using azide as catalyst.

Experimental evidence for proton transfer via a hydrogen-bonded network in a membrane protein is presented. Bacteriorhodopsin's proton transfer mechanism on the proton uptake pathway between Asp-96 and the Schiff base in the M-to-N transition was determined. The slowdown of this transfer by removal of the proton donor in the Asp-96-->Asn mutant can be accelerated again by addition of small weak acid anions such as azide. Fourier-transform infrared experiments show in the Asp-96-->Asn mutant a transient protonation of azide bound to the protein in the M-to-N transition and, due to the addition of azide, restoration of the IR continuum band changes as seen in wild-type bR during proton pumping. The continuum band changes indicate fast proton transfer on the uptake pathway in a hydrogen-bonded network for wild-type bR and the Asp-96-->Asn mutant with azide. Since azide is able to catalyze proton transfer steps also in several kinetically defective bR mutants and in other membrane proteins, our finding might point to a general element of proton transfer mechanisms in proteins.