Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
1998-04-03
2001-05-08
Brusca, John S. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C702S020000, C436S089000, 36, 36
Reexamination Certificate
active
06230102
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to rational drug design, and more particularly, to rational drug design based upon the prediction of a charge distribution on a ligand which minimizes the electrostatic contribution to binding between the ligand and its target molecule in a solvent. The present process also relates to methods and tools for making such predictions and enhanced-binding ligands, and to the diagnostic and therapeutic uses of the ligands so produced.
BACKGROUND OF THE INVENTION
Methods for computational rational drug design include two general approaches: those that screen whole molecules and those that probe local sites and construct molecules through the joining of molecular fragments or grafting of chemical moieties onto a parent structure. DOCK is an example of a whole-molecule algorithm which uses a procedure to find the complementary shape to a given target site (I. D. Kuntz, et al., J. Mol. Biol. 161:269 (1982) (Kuntz); R. L. DesJarlais, et al., J. Med. Chem. 31:722 (1988) (DesJarlais)). Large compound databases can be computationally “screened” by first eliminating molecules whose shape is incompatible with the target site (by computing an overlap with the complementary shape) and then by attempting to rank those that remain with an approximate energy function. This procedure has been successful at identifying a number of ligands that bind to target sites. Unfortunately, X-ray crystal studies have shown that the ligands often bind differently in the site than predicted. One possible reason for this discrepancy between prediction and reality is that although the shape-complementarity algorithm is effective at removing extremely incompatible trial ligands, the approximate energy function is too inexact to define higher-level details of binding.
The MCSS (Multiple Copy Simultaneous Search) algorithm is one of the most popular fragment based approaches to ligand design (P. J. Goodford, J. Med. Chem. 28:849 (1985); A. Miranker and M. Karplus, Proteins: Struct., Funct., Genet. 11:29 (1991); and A. Caflisch, et al., J. Med. Chem. 36:2142 (1993)). The essential idea is to search the region of a binding site and determine locations having especially favorable interaction energy with probes that represent a library of functional groups (carbonyl, amide, amine, carboxylate, hydroxyl, etc.). After the probes are successfully placed in the binding site, various subsets are linked to form coherent molecules. Two approaches to this problem have been developed. One attempts to fit small molecules from a database to join functional groups (HOOK) (M. B. Eisen, et al., Proteins: Struct., Funct., Genet. 19:199 (1995) and the other uses a simulated annealing protocol to grow linker atoms and bonds between fragments to produce ligand candidates with good covalent geometry and non-bonded interactions (DLD, dynamic ligand design) (A. Miranker and M. Karplus, Proteins: Struct., Funct., Genet. 11:29-34 (1991) and 23:472 (1995)).
The current methods for rational drug design are useful for suggesting novel and provocative geometries that appear to roughly compensate hydrogen-bonding groups in the site. Unfortunately, the current methods use approximations which may be inaccurate and which result in difficulties in accurately ranking candidates. Thus, although a number of computational algorithms exist both for the analysis of binding sites and bound complexes and for the rational design of ligands and other drug candidates, structure-based design remains an imprecise and non-deterministic endeavor.
SUMMARY OF THE INVENTION
The limitations of the prior art are overcome by providing for (i) a rigorous treatment of solvation, dielectric, and long-range electrostatic effects operating in both the unbound and the bound state of the target molecule and the ligand candidate, and (ii) a detailed quantitative method for ranking suggested ligands. The present process is based upon the discovery that the crude treatment of solvent, long-range electrostatics, and dielectric effects, as well as the lack of appropriate treatment for the unbound state of the target molecule and the ligand candidate, have limited the rational design and identification of novel ligand candidates for binding to a preselected target molecule. The present computer-implementation overcomes these limitations by providing a process which considers the exchange nature of ligand/target molecule binding, in which interactions with solvent are traded for interactions between a ligand and its complementary target molecule. In contrast to the prior art methods, the process disclosed herein takes into account solvent, long-range electrostatics, and dielectric effects in the binding between a ligand and its target receptor in a solvent.
Accordingly, in one aspect, a process for identifying properties of a ligand for binding to a target molecule (e.g., receptor, enzyme) in a solvent given a representation of a shape of the target molecule in three dimensions is provided. The process involves selecting a shape of the ligand defined in three dimensions, which shape is complementary to (matches) a shape of a selected portion of the target molecule; and determining a representation of a charge distribution on the ligand which minimizes the electrostatic contribution to binding between the ligand and the target molecule in the solvent. In some embodiments, the representation of the charge distribution is a set of multipoles. In other embodiments, the process further involves the step of identifying a molecule having point charges that match the representation of the charge distribution.
These methods are particularly useful for designing enhanced-binding ligands for binding to a target molecule which has a known ligand. As used herein, an enhanced-binding ligand refers to a ligand which has a structure that is based upon that of a known ligand for the target molecule but which is modified in accordance with the methods disclosed herein to have a charge distribution which minimizes the electrostatic contribution to binding between the ligand and the target molecule in a solvent. Thus, the present computer-implemented process provides a method of rational drug design that identifies such improved ligands for binding to a target molecule having a known or predictable three-dimensional structure. The method involves selecting a shape of the ligand defined in three dimensions which matches a shape of a selected portion of the target molecule and determining a representation of a charge distribution on the ligand which minimizes electrostatic contribution to binding between the ligand and the target molecule in the solvent.
The target molecules for which ligands are identified using the claimed process are molecules for which a representation of the three dimensional shape of the molecule is known or can be predicted. Such target molecules include biopolymers and non-biopolymers. Exemplary biopolymers include proteins, nucleic acids, lipids, carbohydrates, and mixtures of the foregoing (e.g., glycoproteins, lipoproteins and so forth). Exemplary non-biopolymers include polyamides, polycarbonates, polyalkylenes, polyalkylene glycols, polyalkylene oxides, polyalkylene terphthalates, polyvinyl alcohols, polyvinyl ethers, polyvinyl esters, poly-vinyl halides, polyvinylpyrrolidone, polyglycolides, polysiloxanes, polyurethanes, alkyl cellulose, polymers of acrylic and methacrylic esters, polyethylene, polypropylene, poly(ethylene glycol), poly(ethylene oxide), poly(ethylene terphthalate), poly(vinyl alcohols), polyvinyl acetate, polyvinyl chloride, polystyrene, polyvinylpyrrolidone, polymers of lactic acid and glycolic acid, polyanhydrides, poly(ortho)esters, polyurethanes, poly(butic acid), poly(valeric acid), poly(lactide-cocaprolactone) and copolymers thereof.
As used herein, the terms “protein” or “polypeptide” are used interchangeably to embrace a variety of biopolymers that are formed of amino acids, e.g., receptors, hormones, and enzymes. It should be understood that as described herein, references to a “protein”, a “polype
Dempster Sara E.
Lee Lee-Peng
Tidor Bruce
Brusca John S.
Massachusetts Institute of Technology
Siu Stephen
Wolf Greenfield & Sacks P.C.
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