Folding simulation of protein models on the structure-based cubo-octahedral lattice with the Contact Interactions algorithm

Thermodynamics of hydrophobic-polar model proteins on the face-centered cubic lattice

The HP model, a coarse-grained protein representation with only hydrophobic (H) and polar (P) amino acids, has already been extensively studied on the simple cubic (SC) lattice. However, this geometry severely restricts possible bond angles, and a simple improvement is to instead use the face-centered cubic (fcc) lattice. In this paper, the density of states and ground state energies are calculated for several benchmark HP sequences on the fcc lattice using the replica-exchange Wang-Landau algorithm and a powerful set of Monte Carlo trial moves. Results from the fcc lattice proteins are directly compared with those obtained from a previous lattice protein folding study with a similar methodology on the SC lattice. A thermodynamic analysis shows comparable folding behavior between the two lattice geometries, but with a greater rate of hydrophobic-core formation persisting into lower temperatures on the fcc lattice.

Distance-dependent hydrophobic-hydrophobic contacts in protein folding simulations

Successful prediction of protein folding from an amino acid sequence is a challenge in computational biology. In order to reveal the geometric constraints that drive protein folding, highlight those constraints kept or missed by distinct lattices and for establishing which class of intra- and inter-secondary structure element interactions is the most relevant for the correct folding of proteins, we have calculated inter-alpha carbon distances in a set of 42 crystal structures consisting of mainly helix, sheet or mixed conformations. The inter-alpha carbon distances were also calculated in several lattice "hydrophobic-polar" models built from the same protein set. We found that helix structures are more prone to form "hydrophobic-hydrophobic" contacts than beta-sheet structures. At a distance lower than or equal to 3.8 Å (very short-range interactions), "hydrophobic-hydrophobic" contacts are almost absent in the native structures, while they are frequent in all the analyzed lattice models. At distances in-between 3.8 and 9.5 Å (short-/medium-range interactions), the best performing lattice for reproducing mainly helix structures is the body-centered-cubic lattice. If protein structures contain sheet portions, lattice performances get worse, with few exceptions observed for double-tetrahedral and body-centered-cubic lattices. Finally, we can observe that ab initio protein folding algorithms, i.e. those based on the employment of lattices and Monte Carlo simulated annealings, can be improved simply and effectively by preventing the generation of "hydrophobic-hydrophobic" contacts shorter than 3.8 Å, by monitoring the "hydrophobic-hydrophobic/polar-polar" contact ratio in short-/medium distance ranges and by using preferentially a body-centered-cubic lattice.

Constraint Logic Programming approach to protein structure prediction

Background

The 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.

Results

Constraint 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.

Conclusions

The 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.