Molecular design using the minireceptor concept

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials

A new approach, MOBILE, is presented that models protein binding-sites including bound ligand molecules as restraints. Initially generated, homology models of the target protein are refined iteratively by including information about bioactive ligands as spatial restraints and optimising the mutual interactions between the ligands and the binding-sites. Thus optimised models can be used for structure-based drug design and virtual screening. In a first step, ligands are docked into an averaged ensemble of crude homology models of the target protein. In the next step, improved homology models are generated, considering explicitly the previously placed ligands by defining restraints between protein and ligand atoms. These restraints are expressed in terms of knowledge-based distance-dependent pair potentials, which were compiled from crystallographically determined protein-ligand complexes. Subsequently, the most favourable models are selected by ranking the interactions between the ligands and the generated pockets using these potentials. Final models are obtained by selecting the best-ranked side-chain conformers from various models, followed by an energy optimisation of the entire complex using a common force-field. Application of the knowledge-based pair potentials proved efficient to restrain the homology modelling process and to score and optimise the modelled protein-ligand complexes. For a test set of 46 protein-ligand complexes, taken from the Protein Data Bank (PDB), the success rate of producing near-native binding-site geometries (rmsd<2.0A) with MODELLER is 70% when the ligand restrains the homology modelling process in its native orientation. Scoring these complexes with the knowledge-based potentials, in 66% of the cases a pose with rmsd <2.0A is found on rank 1. Finally, MOBILE has been applied to two case studies modelling factor Xa based on trypsin and aldose reductase based on aldehyde reductase.

Molecular modeling as a powerful technique for understanding small-large molecules interactions

In the present review we summarize recent work, aimed at a better understanding of the interactions in macromolecule ligand complexes, performed by means of computational tools such as pseudoreceptor generation, molecular docking, conformational search and energy minimization. While the first approach has been applied when the three-dimensional structural properties of the biological target were unknown, the remaining protocols exploited the knowledge of the overall structure of the involved macromolecules and their active sites. Molecular modeling techniques were used in the cases reported to study and propose macromolecular binding sites and to predict their interactions with bioactive conformers of the ligands.

The oxetane ring in taxol

Numerous structure-activity studies combining synthesis and bioassay have been performed for the anti-cancer drug Taxol. The four-membered D-ring, an oxetane, is one of four structural features regarded to be essential for biological activity. This proposition is examined by application of a Taxol-epothilone minireceptor, K(i) estimation for microtubule binding and docking of Taxol analogues into a model of the Taxol-tubulin complex. In this way, we evaluate the two characteristics considered responsible for oxetane function: (1) rigidification of the tetracyclic Taxol core to provide an appropriate framework for presenting the C-2, C-4, C-13 side chains to the microtubule protein and (2) service as a hydrogen-bond acceptor. An energy decomposition analysis for a series of Taxol analogues demonstrates that the oxetane ring clearly operates by both mechanisms. However, a broader analysis of four-membered ring containing compounds, C- and D-seco derivatives, and structures with no oxetane equivalent underscores that the four-membered ring is not necessary for Taxol analogue bioactivity. Other functional groups and ligand-protein binding characteristics are fully capable of delivering Taxol biobehavior as effectively as the oxetane D-ring. This insight may contribute to the design and development of novel anticancer drugs.

A unified and quantitative receptor model for the microtubule binding of paclitaxel and epothilone

[formula: see text] Paclitaxel and epothilone represent the two major classes of antimicrotubule agents that promote tubulin polymerization and, presumably, mitotic arrest during cell division. A common minireceptor binding site model at beta-tubulin has been constructed for these structurally divergent compounds. Utilizing 20 amino acids identified in photoaffinity labeling experiments, the 3-D model correlates measured and predicted Ki's with r = 0.99 and rms(delta Gcalc-delta Gexp) = 0.2 kcal/mol. In addition, the model predicts the affinity of compounds not used in the training set and explains much of the SAR for the paclitaxel and epothilone families.

A computational model of the nicotinic acetylcholine binding site

We have derived a model of the nicotinic acetylcholine binding site. This was accomplished by using three known agonists (acetylcholine, nicotine and epibatidine) as templates around which polypeptide side chains, found to be part of the receptor cavity from published molecular biology studies, are allowed to flow freely in molecular dynamics simulations and mold themselves around these templates. The resulting supramolecular complex should thus be a complement, both in terms of steric effects as well as electronic effects, to the agonists and it should be a good estimation of the true receptor cavity structure. The shapes of those minireceptor cavities equilibrated rapidly on the simulation time scale and their structural congruence is very high, implying that a satisfactory model of the nicotinic acetylcholine binding site has been achieved. The computational methodology was internally tested against two rigid and specific antagonists (dihydro-beta-erytroidine and erysoidine), that are expected to give rise to a somewhat differently shaped binding site compared to that derived from the agonists. Using these antagonists as templates there were structural reorganizations of the initial receptor cavities leading to distinctly different cavities compared to agonists. This indicates that adequate times and temperatures were used in our computational protocols to achieve equilibrium structures for the agonists. Overall, both minireceptor geometries for agonists and antagonists are similar with the exception of one amino acid (ARG209).

Theozymes and compuzymes: theoretical models for biological catalysis

A theozyme is a theoretical enzyme constructed by computing the optimal geometry for transition-state stabilization by functional groups. It is created in order to permit quantitative assessment of catalytic function. Theozymes have been used to elucidate the role of transition-state stabilization in the mechanisms underlying enzyme- and antibody-catalyzed hydroxyepoxide cyclizations, eliminations and decarboxylations, peptide and ester hydrolyses, and pericyclic and radical reactions. The enzymes studied include orotodine monophosphate decarboxylase, HIV protease and ribonucleotide reductase.