Biomedical subjects

M L Beckers

Publications and source records attributed to M L Beckers.

4 recordsLinked to original sources

PubMed1998

Predicting nucleic acid torsion angle values using artificial neural networks.

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles, chi, zeta and alpha from torsion angles delta, epsilon, beta and gamma for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for delta, epsilon, beta and gamma are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.

Base Sequence↗

PubMed1997

Application of a genetic algorithm in the conformational analysis of methylene-acetal-linked thymine dimers in DNA: comparison with distance geometry calculations.

The three-dimensional spatial structure of a methylene-acetal-linked thymine dimer present in a 10 basepair (bp) sense-antisense DNA duplex was studied with a genetic algorithm designed to interpret NOE distance restraints. Trial solutions were represented by torsion angles. This means that bond angles for the dimer trial structures are kept fixed during the genetic algorithm optimization. Bond angle values were extracted from a 10 bp sense-antisense duplex model that was subjected to energy minimization by means of a modified AMBER force field. A set of 63 proton-proton distance restraints defining the methylene-acetal-linked thymine dimer was available. The genetic algorithm minimizes the difference between distances in the trial structures and distance restraints. A large conformational search space could be covered in the genetic algorithm optimization by allowing a wide range of torsion angles. The genetic algorithm optimization in all cases led to one family of structures. This family of the methylene-acetal-linked thymine dimer in the duplex differs from the family that was suggested from distance geometry calculations. It is demonstrated that the bond angle geometry around the methylene-acetal linkage plays an important role in the optimization.

Algorithms↗

PubMed1996

Parallel processing of chemical information in a local area network--II. A parallel cross-validation procedure for artificial neural networks.

This paper describes a parallel cross-validation (PCV) procedure, for testing the predictive ability of multi-layer feed-forward (MLF) neural networks models, trained by the generalized delta learning rule. The PCV program has been parallelized to operate in a local area computer network. Development and execution of the parallel application was aided by the HYDRA programming environment, which is extensively described in Part I of this paper. A brief theoretical introduction on MLF networks is given and the problems, associated with the validation of predictive abilities, will be discussed. Furthermore, this paper comprises a general outline of the PCV program. Finally, the parallel PCV application is used to validate the predictive ability of an MLF network modeling a chemical non-linear function approximation problem which is described extensively in the literature.

Artificial Intelligence↗

PubMed1996

Parallel processing of chemical information in a local area network--III. Using genetic algorithms for conformational analysis of biomacromolecules.

Multi-dimensional nuclear magnetic resonance experiments are an excellent means of revealing the three-dimensional structure of biomacromolecules in solution. However, the search space in the conformational analysis of biomacromolecules, using multi-dimensional NMR data, is huge and complex. This calls for global optimization techniques with good sampling properties. This paper describes a genetic algorithm that optimizes the fit between (simulated) experimental two-dimensional Nuclear Overhauser Effect spectra and the corresponding calculated spectra for trial structures. This is a very computational intensive procedure. Speed-up of performance is achieved by parallelizing the algorithm, i.e. creating small subpopulations of trial structures, each of which can be processed on different processors. Good sampling behavior is obtained by initializing each subpopulation with its own random seed and the introduction of a migration operator. The latter replaces the best performing individual from one subpopulation with the worst performing individual from another subpopulation after a predetermined number of generations. A parallel genetic algorithm for the conformational analysis of nucleic acids is developed using the software package HYDRA. It is demonstrated that, for the data sets used in the study, a considerable reduction in computation time is obtained for the parallel genetic algorithm as compared to a sequential implementation, while the same optimal solutions are found.

Algorithms↗