Prediction of protein structures using a Hopfield network

Summary form only given. Under proper conditions, a globular protein adopts a unique 3D structure that is encoded in an amino acid sequence. The theoretical prediction of this structure, and the pathways followed during the folding process, are an important problem in structural molecular biology. Several works have explored the application of genetic algorithms and neural networks to the determination of the protein structure. There are several techniques of computational simulation that can be used to study structure of proteins; methods of Monte Carlo, simulated annealing, genetic algorithms and neural networks. This work discusses the possibilities to use neural networks in the study of macromolecule structures and presents a example of a Hopfield network to predict the structure of a protein and discusses the results and possible future works using neural networks and genetic algorithms to design new proteins and drugs. This paper used a Hopfield network to predict a primary sequence and the tertiary structure of the core of the cytochrome b₅₆₂. The neural network was implemented using the programming language C and the simulations were run on Silicon Graphics