Technical University of Denmark

About: Technical University of Denmark is a education organization based out in Kongens Lyngby, Hovedstaden, Denmark. It is known for research contribution in the topics: Population & Catalysis. The organization has 24126 authors who have published 66394 publications receiving 2443649 citations. The organization is also known as: Danmarks Tekniske Universitet & DTU.

Reliable prediction of T-cell epitopes using neural networks with novel sequence representations

Abstract: In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

New group contribution method for estimating properties of pure compounds

Abstract: A new group contribution method for the estimation of properties of pure organic compounds is presented. Estimation is performed at two levels: the basic level uses contributions from first-order groups, while the next higher level uses a small set of second-order groups having the first-order groups as building blocks. Thus, the method provides both a first-order approximation (first-order group contributions) and a more accurate second-order prediction (first- and second-order group contributions). This article discusses methods for prediction of normal boiling point, normal melting point, critical pressure, critical temperature, critical volume, standard enthalpy of vaporization at 298 K, standard Gibbs energy, and standard enthalpy of formation at 298 K. The predictions are based exclusively on the molecular structure of the compound, and the method is able to distinguish among isomers. Compared to the currently-used methods, this technique demonstrates significant improvements in accuracy and applicability.