Premium
The elastic net algorithm and protein structure prediction
Author(s) -
Ball Keith D.,
Erman Burak,
Dill Ken A.
Publication year - 2001
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.1158
Subject(s) - travelling salesman problem , simple (philosophy) , computer science , algorithm , protein structure prediction , protein folding , mathematical optimization , monte carlo method , folding (dsp implementation) , global optimization , elastic net regularization , optimization problem , protein structure , mathematics , artificial intelligence , chemistry , philosophy , biochemistry , statistics , epistemology , electrical engineering , engineering , feature selection
Predicting protein structures from their amino acid sequences is a problem of global optimization. Global optima (native structures) are often sought using stochastic sampling methods such as Monte Carlo or molecular dynamics, but these methods are slow. In contrast, there are fast deterministic methods that find near‐optimal solutions of well‐known global optimization problems such as the traveling salesman problem (TSP). But fast TSP strategies have yet to be applied to protein folding, because of fundamental differences in the two types of problems. Here, we show how protein folding can be framed in terms of the TSP, to which we apply a variation of the Durbin–Willshaw elastic net optimization strategy.1 We illustrate using a simple model of proteins with database‐derived statistical potentials and predicted secondary structure restraints. This optimization strategy can be applied to many different models and potential functions, and can readily incorporate experimental restraint information. It is also fast; with the simple model used here, the method finds structures that are within 5–6 Å all‐C α ‐atom RMSD of the known native structures for 40‐mers in about 8 s on a PC; 100‐mers take about 20 s. The computer time τ scales as τ∼ n , where n is the number of amino acids. This method may prove to be useful for structure refinement and prediction. © 2002 Wiley Periodicals, Inc. J Comput Chem 23: 77–83, 2002