Constraint Logic Programming approach to protein structure prediction

By Alessandro Dal Palù, Agostino Dovier, Federico Fogolari. 2004

90 citations recorded by the source corpus.

BackgroundThe protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems.ResultsConstraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics.ConclusionsThe results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Subfields: Computer Networks and Communications, Materials Chemistry, Molecular Biology

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Citations in the corpus

Fast and flexible design of novel proteins using graph neural networks (2019, methodology): “To overcome previous difficulties with phrasing protein design as a CSP ( 10 , 11 ), we elucidate the rules governing constraints using deep learning.”
Constraint Logic Programming (2010, methodology): “In particular, CLP has been used to tackle the protein structure prediction problem, which is one of the most challenging problems in biological sciences, and which can be seen as an optimization problem [48, 55].”