Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2000-09-05
2003-02-11
Mizrahi, Diane D. (Department: 2175)
Data processing: database and file management or data structures
Database design
Data structure types
Reexamination Certificate
active
06519611
ABSTRACT:
RELATED APPLICATIONS
This application claims priority under 35 U.S.C. 119 to Singapore Application Number 9904404-2 filed on Sep. 6, 1999.
COPYRIGHT NOTICE
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent file or records, but otherwise reserves all copyrights whatsoever.
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to methods and apparatus for detecting protein and nucleic acid targets of a drug molecule. Aspects of the invention relate to selecting proteins and nucleic acids from a biomolecular cavity database that a drug can bind to; both geometrically and chemically. This general field is known as “Molecular Modeling” (MM) and “Computer Assisted Molecular Design” (CAMD). When used for pharmaceutical discovery, this field is referred to as “Computer-Aided Drug Design” (CADD).
2. Description of the Related Art and Summary
The Need for a Computer Drug Target Prediction Method
A number of strategies have been proposed and used for rational discovery of new drugs. These include combinatorial chemistry, which is described in “Directed combinatorial chemistry”, J. C. Hogan Jr. Nature 384 suppl., 17-19 (1996); high-throughput screening, which is described in “High-throughput screening for drug discovery”, J. R. Broach and J. Thorner. Nature 384 suppl., 14-16 (1996).; QSAR, which is described in “Strategies for indirect computer-aided drug Design”, Loew G H, Villar H O, Alkorta I, Pharm Res 10, 475-86 (1993); and structure-based molecular design, which is described in “Structure based drug design”, Blundell. Nature 384 suppl., 23-26 (1996).
All of these strategies focus on the design of lead compounds (drug precursors) having biological activity against a defined protein or nucleic acid target. Possible interactions of these compounds with other proteins and nucleic acids are not accounted for by these methods. These interactions have implications to secondary therapeutic effects, unwanted side effect and toxicity. Although good biological activity is a necessity, a drug candidate needs to pass additional tests and clinical trials for side effect, toxicity, bioavailability as well as efficacy. These tests and clinical trials are very costly and time consuming.
Computer methods and apparatus for identifying protein and nucleic acid targets can facilitate fast-speed predictions of drug-protein and drug-nucleic acid interactions that might have implications for possible side effects, toxicity and other unwanted effects without costly and time-consuming tests and clinical trials. This is particularly important given that much of the $350 million and 12 years spent on average for commercial drugs have been squandered on many drug candidates that failed to ever reach the market. Surveys of drug discovery costs and times can be found in “Strategic choices facing the pharmaceutical industry: A case for innovation”, J. Drews, Drug. Discov. Today 2, 72-78 (1997); “New drug development in the United States from 1963 to 1990”, J. A. DiMasi, N. R. Bryant, and L. Lasagna, Clin Pharmacol Ther. 50, 471-486 (1991).
The molecular targets of a number of natural product drugs, particularly those from traditional medicinal plants, are unknown. This hinders the effort to design new drugs based on the molecular mechanism of these drugs. Experimental determination of molecular targets of a natural product is often slow and costly. However, a computer method for drug target identification would offer a fast and low cost approach to search possible drug targets.
Feasibility of Computer Drug Target Identification
A computer drug target identification strategy is feasible if: (1) a sufficiently diverse set of protein and nucleic acid 3D structures is available, and (2) a sufficiently fast and accurate drug identification algorithm is available under currently available and affordable computer systems. Prediction of therapeutic effects, side effects and toxicity requires knowledge of protein functions. As explained below, these conditions are being met.
At present, the 3D structure of 11,346 proteins, 557 protein
ucleic acid complexes and 857 nucleic acids have been released in the Protein Databank (PDB), and the number increases at a rate of more than 100 per month, as described in PDB home page http://www.rcsb.org/pdb/. About 17% of the proteins in PDB have unique sequences, as described in “Bridging the protein sequence structure gap by structure predictions”, B. Rost, C. Sander. Annu Rev Biophys Biomol Struct 25, 113-136 (1996). Thus, the number of proteins has reached a meaningful level to cover therapeutic, metabolic, side effect, and toxicity targets. The introduction of high-throughput analysis methods is expected to enable the determination of 10,000 proteins with unique sequence within 5 years, as discussed in “100,000 protein structures for the biologist”. A Sali, Nat Struct Biol. 5, 1029-1032 (1998).
Thus, a sufficiently diverse set of proteins and nucleic acids in PDB is expected in a few years. New advances in functional genomics and proteomics is providing information useful for predicting therapeutic effects, side effects and toxicity. Functional genomics is described in “Functional genomics: It's all how you read it”, P. Hieter and M. Boguski, Science 278, 601-602 (1997). Proteomics is described in “Proteomics. An ambitious drug development platform attempts to link gene sequence to expressed phenotype under various physiological states”. A. Persidis. Nature Biotech. 16, 393-394 (1998).
All drugs appear to bind to cavities of proteins or nucleic acids. Thus, a biomolecular cavity database can be introduced to facilitate computer drug target identification. A method is disclosed for computer automated generation of a biomolecular cavity database from entries of a biomolecule 3D structure database. This database can include all proteins and nucleic acids in PDB and it contains information about geometric and chemical features of cavities along with the 3D structure and chemical properties of the host biomolecules.
High-speed drug target identification can be achieved by a disclosed flexible ligand-biomolecule inverse docking algorithm. This algorithm searches a biomolecular cavity database to find proteins and nucleic acids to which a given drug or ligand can bind or weakly bind to. Testing results show that the average CPU time is 14-20 days for searching a cavity database containing a few thousands of proteins and nucleic acids.
Existing Computer Methods are not Capable of Drug Target Identification
Existing ligand-protein docking algorithms are useful only to cavities of a limited size. Human intervention is generally required to locate binding site or to derive a reduced model of a large cavity. Thus it is impractical to use existing method for automated docking of a drug to an arbitrarily chosen protein or nucleic acid in a database. For comparison, the disclosed vector-vector matching algorithm is capable of dealing with cavities significantly larger than that of existing methods.
Moreover, existing methods for generating protein cavity profiles are not suitable for development of a biomolecular cavity database. These methods normally require human intervention to either locate a binding site or to generate a specific cavity profile, which makes it impractical to generate cavity profiles for thousands of proteins and nucleic acids.
Existing methods are described in “Structure-based strategies for drug design and discovery”, I. D., Kuntz, Science 257, 1078-1082 (1992); “Hammerhead: Fast, fully automated docking of flexible ligands to protein binding sites”, W. Welch, J. Ruppert, and A. N. Jain. Chem. Biol. 3, 449-462 (1996); “Characterization of receptors with a new negative image: Use in molecular docking and lead optimization”, C. M. Oshiro, and I. D. Kuntz. Proteins Struct. Func. Genet. 30, 311-336 (1998).
Pot
Knobbe Martens Olson & Bear LLP
Mizrahi Diane D.
Mofiz Apu M
National University of Singapore
LandOfFree
Method and apparatus for computer automated detection of... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Method and apparatus for computer automated detection of..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Method and apparatus for computer automated detection of... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3180473