Limitations of De Novo Prediction Methods
A major limitation of de novo protein prediction methods is the extraordinary amount of computer time required to successfully solve for the native confirmation of a protein. Distributed methods, such as Rosetta@home, have attempted to ameliorate this by recruiting individuals who then volunteer idle home computer time in order to process data. Even these methods face challenges, however. For example, a distributed method was utilized by a team of researchers at the University of Washington and the Howard Hughes Medical Institute to predict the tertiary structure of the protein T0283 from its amino acid sequence. In a blind test comparing the accuracy of this distributed technique with the experimentally confirmed structure deposited within the Protein Databank (PDB), the predictor produced excellent agreement with the deposited structure. However, the time and number of computers required for this feat was enormous – almost two years and approximately 70,000 home computers, respectively.
One method proposed to overcome such limitations involves the use of Markov models (see Markov chain Monte Carlo). One possibility is that such models could be constructed in order to assist with free energy computation and protein structure prediction, perhaps by refining computational simulations.
Read more about this topic: De Novo Protein Structure Prediction
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