Computational combinatorial ligand design: Application to human ?-thrombin

Abstract

A new method is presented for computer-aided ligand design by combinatorial selection of fragments that bind favorably to a macromolecular target of known three-dimensional structure. Firstly, the multiple-copy simultaneous-search procedure (MCSS) is used to exhaustively search for optimal positions and orientations of functional groups on the surface of the macromolecule (enzyme or receptor fragment). The MCSS minima are then sorted according to an approximated binding free energy, whose solvation component is expressed as a sum of separate electrostatic and nonpolar contributions. The electrostatic solvation energy is calculated by the numerical solution of the linearized Poisson-Boltzmann equation, while the nonpolar contribution to the binding free energy is assumed to be proportional to the loss in solvent-accessible surface area. The program developed for computational combinatorial ligand design (CCLD) allows the fast and automatic generation of a multitude of highly diverse compounds, by connecting in a combinatorial fashion the functional groups in their minimized positions. The fragments are linked as two atoms may be either fused, or connected by a covalent bond or a small linker unit. To avoid the combinatorial explosion problem, pruning of the growing ligand is performed according to the average value of the approximated binding free energy of its fragments. The method is illustrated here by constructing candidate ligands for the active site of human alpha-thrombin. The MCSS minima with favorable binding free energy reproduce the interaction patterns of known inhibitors. Starting from these fragments, CCLD generates a set of compounds that are closely related to high-affinity thrombin inhibitors. In addition, putative ligands with novel binding motifs are suggested. Probable implications of the MCSS-CCLD approach for the evolving scenario of drug discovery are discussed.

Locating interaction sites on proteins: The crystal structure of thermolysin soaked in 2% to 100% isopropanol

“…Currently, solvation 32 and entropic effects are not properly accounted for computationally but as more empirical data becomes available this may improve. 1 Work is in progress using other systems and solvents (acetone, phenol, acetonitrile, ethanol, etc) to construct experimental functionality maps and to compare the experimentally determined solvent positions with positions predicted by computational approaches.…”

Fragment Docking to Proteins with the Multi‐copy Simultaneous Search Methodology

“…A straightforward implicit solvent model to implement is based on a distance-dependent dielectric constant. We note that it has been argued that the use of a distance-dependent dielectric constant during MCSS minimizations increases the number of minima that one finds relative to protocols that employ a vacuum potential [18]. However, in previous applications on endothiapepsin, minima obtained with a distance-dependent dielectric constant were very similar to minima obtained with e=1 [5].…”

Ligand design by a combinatorial approach based on modeling and experiment: application to HLA-DR4

“…A similar cutoff scheme is to use the free energy of solvation for nonionic probes and one half the solvation enthalpy for charged groups [92]. Caflisch introduced using the electrostatic free energy of solvation of the molecular probe as computed using the Linearized Poisson-Boltzmann equation as the energy criterion [16]. This may not be an effective filter for nonpolar probes, however, because these have computed electrostatic solvation free energies of zero.…”

“…In previous uses of MCSS for combinatorial ligand design [15,16], the fragments placed by MCSS were connected to form synthetically accessible candidate ligands by other programs such as HOOK [17] and DLD [18]. An alternative is to use the molecular probe preferences to focus a combinatorial library that is synthesized and tested with an appropriate assay.…”