Jnet is a neural network protein secondary structure prediction algorithm that works by applying multiple sequence alignments, alongside PSIBLAST and HMM profiles. Consensus techniques are applied that predict the final secondary structure more accurately. It was written as part of a continuing study to improve protein secondary structure prediction.
Jnet can also predict 2 state solvent exposure at 25, 5 and 0% relativeexposure. Positions where the different prediction methods do not agree are marked as no jury positions. A separate network is applied for these positions, which improves the cross-validated accuracy. A reliability index indicates which residues are predicted with a high confidence.
- Cuff JA, Barton GJ (1999). Application of enhanced multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins, 40:502-511.