Abstract: Provided herein are various methods for identifying candidate substrates for the E3 ligase machinery.

Abstract: Training a neural network for signal analysis in NMR spectra may use a plurality of computed NMR raw spectra, each raw spectrum being associated with a different NMR active molecule having a known number of protons (#P). Peaks of the raw spectra may be broadened to generate a broadened spectrum for each raw spectrum. For each broadened spectrum, its integral function may be computed to count the number of protons associated with peaks of the respective broadened spectrum. Signal intervals may be identified as intervals in the broadened spectrum where the integral function increases approximately by multiples of the value associated with a single proton so that the total number of counted protons matches the known number of protons (#P) of the associated molecule. The obtained spectra with associated labels for the identified signal intervals are provided as the training data set to the neural network.

Abstract: Formula (1), X1 represents O or NR6, X2 represents O or NR6, X3 represents O or NR1, R1 represents H or C1 to C3 alkyl, R2 represents H, or linear or branched C1-C8 alkyl optionally including one or more heteroatoms, cyclopropyl, cyclobutyl, cyclohexyl or oxetanyl or amino acid side chain or protected amino acid side chain, or R1 and R2 may form together a saturated, partly saturated or unsaturated 5 or 6 membered ring system, optionally substituted, R3 represents H, cyclopentyl, cyclohexyl, aryl or hydroxyaryl, aryl or hydroxyaryl being optionally substituted by fluorine, or linear or branched C1 to C8 alkyl optionally including a hetero atom, R4 represents phenyl or 5- or 6-membered heterocycles including one or more nitrogen or oxygen atoms optionally substituted with 1 to 4, respectively 5 fluorine atoms, and R6 represents H, or linear or branched alkyl chain having 1 to 3 carbon atoms.

Abstract: Methods for processing and storing genomic information in a secure manner are described. In particular, methods for processing, splitting and storing genomic information or portions thereof are disclosed. An individual's genomic information is digitized and a splitting algorithm applied to fragment and randomise the digitized genomic information into at least two separate datasets. Access to at least one dataset is retained by the individual and the second dataset is stored on a central server as a secure database record. Each dataset in isolation presents uninformative data and it is only when all datasets are combined that the data is capable of being presented into a useable and informative format.

Abstract: A system and method for processing and storing personal information in a secure manner is described. In particular, a system and method for processing, splitting and storing genomic information or portions thereof in a secure electronic format is disclosed. An individual's genomic sequence is digitized and a splitting algorithm applied to fragment and randomise the digitized genomic information into at least two separate datasets. One dataset is retained by the individual and the second dataset is stored on a central server as a secure database record. Each dataset in isolation presents uninformative data and it is only when both datasets are combined, using a reconstruction algorithm to recombine the separate dataset data for an individual that the digitized data is capable of being presented into a useable and informative format.