Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes

Henrik Bohr, Peter Røgen, Karl J. Jalkanen

Research output: Contribution to journalConference articleResearchpeer-review


Electronic structures of small peptides were calculated 'ab initio' with the help of Density Functional Theory (DFT) and molecular dynamics that rendered a set of conformational states of the peptides. For the structures of these states it was possible to derive atomic polar tensors that allowed us to construct vibrational spectra for each of the conformational states with low energy. From the spectra, neural networks could be trained to distinguish between the various states and thus be able to generate a larger set of relevant structures and their relation to secondary structures of the peptides. The calculations were done both with solvent atoms (up to ten water molecules) and without, and hence the neural networks could be used to monitor the influence of the solvent on hydrogen bond formation. The calculations at this stage only involved very short peptide fragments of a few alanine amino acids but already at this stage they could be compared with reasonable agreements to experiments. The neural networks are shown to be good in distinguishing the different conformers of the small alanine peptides. especially when in the gas phase. Also the task of predicting protein fold-classes, defined from line-geometry, seems promising.
Original languageEnglish
JournalComputers & Chemistry
Issue number1
Pages (from-to)65-77
Publication statusPublished - Dec 2001
EventSymposium on Artificial Intelligenc in Bioinformatics - Birmingham, United Kingdom
Duration: 1 Apr 20001 Apr 2000


ConferenceSymposium on Artificial Intelligenc in Bioinformatics
CountryUnited Kingdom


  • density functional theory
  • molecular dynamics
  • protein fold-classes

Fingerprint Dive into the research topics of 'Applications of neural network prediction of conformational states for small peptides from spectra and of fold classes'. Together they form a unique fingerprint.

Cite this