Protein structure prediction
Current methods developed for predicting protein structure are reviewed. The most widely used algorithms of Chou and Fasman  and Garnier et al  for predicting secondary structure are compared to the most recent ones including sequence similarity methods [13–15, 17], neural network [18, 19], pattern recognition [20–23] or joint prediction methods . The best of these methods correctly predict 63–65% of the residues in the database with cross-validation for 3 conformations, helix, Î² strand and coil with a standard deviation of 6–8% per protein. However, when a homologous protein is already in the database, the accuracy of prediction by the similarity peptide method of Levin and Garnier reaches about 90% . Some conclusions can be drawn on the mechanism of protein folding. As all the prediction methods only use the local sequence for prediction (± 8 residues maximum) one can infer that 65% of the conformation of a residue is dictated on average by the local sequence, the rest is brought by the folding. The best predicted proteins or peptide segments are those for which the folding has less effect on the conformation. Presently, prediction of tertiary structure is only of practical use when the structure of a homologous protein is already known. Amino acid alignment to define residues of equivalent spatial position is critical for modelling the protein. We showed for serine proteases that secondary structure prediction can help to define a better alignment . Non-homologous segments of the polypeptide chain, such as loops, libraries of known loops and/or energy minimization with various force fields, are used without yet giving satisfactory solutions. An example of modelling by homology, aided by secondary structure prediction on 2 regulatory proteins, Fnr and FixK  is presented.