Abstract: Comparative modelling is utilized to predict the 3-dimensional conformation of a given protein (target) based on its sequence alignment to experimentally determined protein structure (template). The use of such technique is already rewarding and increasingly widespread in biological research and drug development. The accuracy of the predictions as commonly accepted depends on the score of sequence identity of the target protein to the template. To assess the relationship between sequence identity and model quality, we carried out an analysis of a set of 4753 sequence and structure alignments. Throughout this research, the model accuracy was measured by root mean square deviations of Calpha atoms of the target-template structures. Surprisingly, the results show that sequence identity of the target protein to the template is not a good descriptor to predict the accuracy of the 3-D structure model. However, in a large number of cases, comparative modelling with lower sequence identity of target to template proteins led to more accurate 3-D structure model. As a consequence of this study, we suggest new tips for improving the quality of omparative models, particularly for models whose target-template sequence identity is below 50%.
Abstract: A stochastic algorithm for constructing multiple loops in homology modeling of proteins is presented. The algorithm discards variable values in iterations based on a cost function and on statistical analysis of results. Values that remain are used for constructing an ensemble of best solutions. In test cases, the stochastic algorithm retains all the best solutions, compared to an exhaustive scan of the full set of conformations. Individual loops are constructed by adding dipeptide units. Dipeptide conformations are extracted from a database of proteins and their conformations include bond lengths and all angles. Single loops are constructed from both N- and C- terminals to the center, and loop closure is evaluated by a combination of penalties for the peptide closure and Miyazawa-Jernigan (MJ) [1]. residue-residue interactions with the rest of the protein. Large ensembles of each loop are clustered and re-evaluated with a refined [2]. energy term. The reduced, clustered set of each loop is then employed to construct simultaneously all the loops. The algorithm was applied to construct simultaneously six loops in c-Src kinase family proteins, incorporating a total of 37-40 residues. The best RMSD for reconstructing the loops is 1.45 A for Lck (structure 1QPE in the Protein Data Bank) and 2.54 A for human c-Src (structure 1FMK). The multiple loop conformations with lowest energy have higher RMSD values, of 2.06 A and 3.09 A, respectively. The average RMSD values for the first 1000 conformations are 3.00 A and 3.46 A, respectively. Models for the "open" structures of c-Src and of Jak-2 were constructed on the basis of 1QPE. The Jak-2 model is found to be more flexible in the loops region than its c-Src counterpart.
Abstract: A stochastic search algorithm is applied in order to probe the conformations of cyclic peptides. The search is conducted in two stages. In the first stage, random conformations are generated and evaluated by a penalty function for ring closure ability, following a stepwise construction of each amino acid into the peptide by a random choice of one of its allowed conformations. The allowed conformational ranges of backbone dihedral angles for each amino acid have been extracted from a Data Bank of diverse proteins. Values of dihedral angles that do not contribute favorably to the scoring of ring closure are retained or discarded by a statistical test. Values are discarded up to a point from which all remaining combinations of angles are constructed, scored, sorted, and clustered. In the second stage, side chains have been added and fast optimization was applied to the set of diverse conformations in a "united atoms" approach, with the "Kollman forcefield" of Sybyl 6.8. This iterative stochastic elimination algorithm finds the global minimum and most of the best results, when compared to a full exhaustive search in appropriately sized problems. In larger problems, we compare the results to experimental structures. The root mean square deviation (RMSD) of our best results compared to crystal structures of cyclic peptides with sizes from 4 to 15 amino acids are mostly below 1.0 A up to 8 mers and under 2.0 A for larger cyclic peptides.
Abstract: The problem of global optimization is pivotal in a variety of scientific fields. Here, we present a robust stochastic search method that is able to find the global minimum for a given cost function, as well as, in most cases, any number of best solutions for very large combinatorial "explosive" systems. The algorithm iteratively eliminates variable values that contribute consistently to the highest end of a cost function's spectrum of values for the full system. Values that have not been eliminated are retained for a full, exhaustive search, allowing the creation of an ordered population of best solutions, which includes the global minimum. We demonstrate the ability of the algorithm to explore the conformational space of side chains in eight proteins, with 54 to 263 residues, to reproduce a population of their low energy conformations. The 1,000 lowest energy solutions are identical in the stochastic (with two different seed numbers) and full, exhaustive searches for six of eight proteins. The others retain the lowest 141 and 213 (of 1,000) conformations, depending on the seed number, and the maximal difference between stochastic and exhaustive is only about 0.15 Kcal/mol. The energy gap between the lowest and highest of the 1,000 low-energy conformers in eight proteins is between 0.55 and 3.64 Kcal/mol. This algorithm offers real opportunities for solving problems of high complexity in structural biology and in other fields of science and technology.
Abstract: A novel algorithm was applied to the sequences of bacteriorhodopsin (BRh), of rhodopsin (Rh), and of the two human anaphylatoxin receptors, C5a-receptor (hC5aR) and C3a-receptor (hC3aR), that predicts their transmembrane domains (TMD) according to energy criteria alone, on the basis of their sequences and a template structure for each. Two consecutive criteria were applied for the predictions: the first is hydrophobicity of a sequence of residues, which determines the candidate stretches of residues that form one of the transmembrane helices. The second criterion is an energy function composed of inter residue contact energies, of hydrophobic contributions due to membrane exposure and of the interactions of a few residues with the phospholipid head groups. The sequence of candidate residues for each helix is longer than that of the template, and is finally determined by threading each of the candidate stretches on each of the template helices and evaluating the energy for all possible configurations. Contact energies between residues were taken from a database (Miyazawa S and Jernigan RL (1996) J Mol Biol 256 623-44). The algorithm predicts well the TMD structure of BRh based on its own template, and the TMD structure of Rh conforms well with the model of Baldwin et al (Baldwin JM Schertler GFX and Unger VM (1997) J Biol Chem 272 144-64). Results for the construction of the TMD of hC5aR and hC3aR were compared, employing the template structure of Rh. Most of the results for these receptors are in accord with alignments and with mutation experiments on hC5aR and hC3aR. The predictions may serve as a basis for future mutagenesis experiments of these receptors.
Abstract: The results of crystallographic analysis of a complex between an aspartic proteinase, endothiapepsin, and an inhibitor have been extended through the assignment of protons in the active site, to study various steps in the reaction with a substrate. Mechanistic implications are suggested as a consequence of semiempirical quantum mechanical calculations, indicating that most of the activation energy is required to bring the substrate from an initial binding mode to close distance to a water molecule.
