Assembly of protein tertiary structures from secondary structures using optimized potentials

Abstract

We present a simulated annealingbased method for the prediction of the tertiary structures of proteins given knowledge of the secondary structure associated with each amino acid in the sequence. The backbone is represented in a detailed fashion whereas the sidechains and pairwise interactions are modeled in a simplified way, following the LINUS model of Srinivasan and Rose. A perceptron-based technique is used to optimize the interaction potentials for a training set of three proteins. For these proteins, the procedure is able to reproduce the tertiary structures to below 3 Å in root mean square deviation (rmsd) from the PDB targets. We present the results of tests on twelve other proteins. For half of these, the lowest energy decoy has a rmsd from the native state below 6 Å and, in 9 out of 12 cases, we obtain decoys whose rmsd from the native states are also well below 5 Å. Proteins 2003;52:155-165.

Constraint Logic Programming approach to protein structure prediction

“…A similar conclusion has been reached using a different model by Maritan and coworkers [21]. The RMSD on core residues is, in all but one case, less than 5.0 Å.…”

Substructured molecular dynamics using multibody dynamics algorithms

“…As with the previous test case, the conformational variations of the protein are studied using the Ramachandran plot and the per residue φ and ψ angle plots. Figure (17) shows the Ramachandran plot, where the relative motion between the residues are plotted on the φ − ψ angle scale, which is in agreement with the comparable plot presented in [57]. The smear of the dots on the plot shows the variations of respective pairs of the φ − ψ angles.…”

Dependency between consecutive local conformations helps assemble protein structures from secondary structures using Go potential and greedy algorithm

“…A common simplification in ab initio calculations is to discretize the search of the protein fold by using sets of small fragments or rigid blocks associated with the predicted secondary structures and to assemble these building blocks for locating the ground state 2–9. ROSETTA, the most popular tool for protein assembly from fragments, identifies 3 and 9 residue fragments of known structures with local sequences similar to the target sequence and assembles them using Monte Carlo simulated annealing or genetic algorithm 10.…”