Single crystals of the amino acid l-α-alanine have been X-irradiated at room temperature, and the free radical formation has been studied using X-band and K-band EPR, ENDOR, and EIE (ENDOR-induced EPR) spectroscopy in the temperature interval 220−295 K. Aided by the results from EIE, as well as ENDOR from selected magnetic field positions, nine hyperfine coupling tensors were obtained and assigned to three different radicals. Room-temperature relaxation behaviors characterized by efficient W1 x and W1e and by slow W1 n relaxation rates allowed for determination of the signs of the various hyperfine couplings from the ENDOR spectra obtained at room temperature. The temperature dependence of the W1 x relaxation is qualitatively discussed. The EPR spectra from alanine are dominated by the well-known resonance of the “stable alanine radical”, SAR, formed by a net deamination of the protonated alanine anion. Precise hyperfine coupling tensors due to the α-proton coupling, the methyl group coupling, and a dipolar coupling to a methyl group of a neighboring molecule, as well as the g tensor, are given for this radical. Spectral simulations show that these parameters in a satisfactory manner reproduce all observable features of the resonance from this radical. Radical R2, apparently formed in roughly the same amounts as SAR, exhibits the structure H3N+−•C(CH3)C(O)O-. It is formed from alanine by a net H-abstraction from the Cα position. The hyperfine coupling tensor to the freely rotating methyl group was obtained from both X-band and K-band data. K-band spectra obtained at several temperatures between 220 and 290 K revealed that the amino group is not freely rotating; that is, the three protons of the amino group are locked in their hydrogen bonds also after radical formation. A significant increase in ENDOR line widths upon increasing temperature made the ENDOR lines due to the amino protons practically nonobservable at 295 K. However, the three corresponding hyperfine coupling tensors were easily obtained from K-band ENDOR data at 220 K. The B 0 and B 2 values for β-coupling to N+−H fragments were determined to be −4.3 and 117.6 MHz, respectively. Due to partly unresolved nitrogen hyperfine interaction leading to larger line widths, the individual EPR lines from radical R2 are of far less intensity as compared to those of the SAR. However, simulations strongly indicate that there is an almost equal relative distribution (60%:40%) of the two radicals. Two hyperfine coupling tensors were assigned to two conformations of a third minority radical species (radical R3) which tentatively is suggested to be the species H2N−•C(CH3)C(OH)O. Possible mechanisms for the formation of the radicals are discussed in light of the basic radiation chemistry of the amino acids. The simultaneous presence of two stable radicals of similar relative amounts in alanine may have consequences for the use of alanine as a radiation dosimeter.

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Current technology is producing high throughput biomedical data at an ever-growing rate. A common approach to interpreting such data is through network-based analyses. Since biological networks are notoriously complex and hard to decipher, a growing body of work applies graph embedding techniques to simplify, visualize, and facilitate the analysis of the resulting networks. In this review, we survey traditional and new approaches for graph embedding and compare their application to fundamental problems in network biology with using the networks directly. We consider a broad variety of applications including protein network alignment, community detection, and protein function prediction. We find that in all of these domains both types of approaches are of value and their performance depends on the evaluation measures being used and the goal of the project. In particular, network embedding methods outshine direct methods according to some of those measures and are, thus, an essential tool in bioinformatics research.Frontiers in Genetics | www.frontiersin.org Additionally, the learned embeddings are often applicable for downstream analysis, either by direct interpretation of the embedding space or through the application of machine learning techniques which are designed for vectorial data. Beyond its computational advantages, network embedding is natural to use in biological problems that concern physical entities (such as proteins) that function in 3D space. In such scenarios, Euclidean representations may capture many of the functional properties of those entities. Finally, by working in lower dimensional space, the results are more likely to be robust to the noise inherently present in the networks. Indeed, recent network denoising approaches employed embedding for this purpose .In this review, we describe several current approaches for graph embedding including spectral-based, diffusion-based and deep-learning-based methods. We provide comparisons applying representative embedding approaches to fundamental problems in network biology with using the networks directly in three distinct tasks: protein network alignment, protein module detection, and protein function prediction (Figure 1). We further review network embedding methods and their application to network denoising and pharmacogenomics. We conclude that network embedding methods are an essential component in the bioinformatics tool box.