Minimum energy configurations of the 2-dimensional HP-model of proteins by self-organizing networks.
We use self-organizing maps (SOM) as an efficient tool to find the minimum energy configurations of the 2-dimensional HP-models of proteins. The usage of the SOM for the protein folding problem is similar to that for the Traveling Salesman Problem. The lattice nodes represent the cities whereas the neurons in the network represent the amino acids moving towards the closest cities, subject to the HH interactions. The valid path that maximizes the HH contacts corresponds to the minimum energy configuration of the protein. We report promising results for the cases when the protein completely fills a lattice and discuss the current problems and possible extensions. In all the test sequences up to 36 amino acids, the algorithm was able to find the global minimum and its degeneracies.
D000465:Algorithms; D055598:Chemical Phenomena; D002627:Chemistry, Physical; D057927:Hydrophobic and Hydrophilic Interactions; D008433:Mathematics; D016571:Neural Networks, Computer; D011487:Protein Conformation; D017510:Protein Folding; D013816:Thermodynamics