USE OF COMPUTATIONALLY DERIVED PROTEIN STRUCTURES OF GENETIC POLYMORPHISMS IN PHARMACOGENOMICS FOR DRUG DESIGN AND CLINICAL APPLICATIONS

1 . A computer-based method for predicting clinical responses in patients based on genetic polymorphisms, comprising:

using a computer to identify proteins that are the products of a gene in the genome of patients, the gene exhibiting genetic polymorphisms,

obtaining from a computer amino acid sequences of the proteins that are the products of a gene in the genome of patients, the gene exhibiting genetic polymorphisms,

using a computer to construct 3-dimensional (3-D) protein structural variant models for the proteins that are the products of a gene in the genome of patients, the gene exhibiting genetic polymorphisms, wherein the 3-D model is obtained by a method selected from the group consisting of experimental methods, searching protein structure databases, homology modeling by comparison to an existing protein family member sequence and structure, and combinations thereof;

using a computer to perform energetic or dynamic refinement of the 3-D protein structural variant models;

using a computer to dock drugs against the protein structural variant models coded by the genome of the patients, the drugs having known activity;

building a computer-based relational database based on refined protein structural variants and observed clinical data associated with particular polymorphisms exhibited in the patients, wherein the database comprises: 3-D molecular coordinates for structural variant-drug complex models; and observed clinical data associated with the genetic polymorphisms,

using a computer to screen or compare a 3-D model of a protein from the patient to the structures contained in the computer-based database by:

identifying structures in the computer-based database that are similar to the 3-D model of the protein from the patient;

using a computer to predict a clinical outcome for the patient using the clinical data associated with the identified structures and variant-drug complex models, including the step of:

using a computer to compare free energy changes between a wild type and the protein structural variant models coded by the genome of the patients; and

outputting the result of the method to a computer interface display.

2 . The method of claim 2 , further comprising: providing the computer-based relational database with a molecular graphics interface that interfaces with the computer-based relational database for 3-D molecular structure visualization; providing the computer-based relational database with functionality that interfaces with the computer-based relational database for protein sequence and structural analysis; and providing the computer-based database with searching tools that interface with the computer-based relational database.

3 . The method of claim 1 , wherein the computer-based relational database further comprises 3-D molecular structural data of structural variant models.

4 . The method of claim 2 , wherein the computer-based relational database further comprises 3-D molecular structural data of structural variant models.

5 . A computer-based method for predicting clinical responses in patients based on genetic polymorphisms, comprising:

using a computer to construct a 3-D protein model based on a gene sequence in the genome of patients, the gene exhibiting polymorphisms, wherein the 3-D model is obtained by a method selected from the group consisting of experimental methods, searching protein structure databases, homology modeling by comparison to an existing protein family member sequence and structure, and combinations thereof;

using a computer to perform energetic or dynamic refinement of the 3-D protein model;

using a computer to dock drugs against protein structural variant models coded by the genome of the patients, the drugs having known activity;

using a computer to screen or compare the 3-D model of the protein from the patient to the 3-D structures contained in a computer-based relational database of 3-D protein structures by:

identifying structures in the computer-based relational database that are similar to the model of the protein derived from the patient;

using a computer to predict a clinical outcome for the patient based on clinical data associated with the identified structures and a drug interaction, including the step of:

using a computer to compare free energy changes between a wild type and the protein structural variant models coded by the genome of the patients; and

outputting the result of the method to a computer interface display.

6 . The method of claim 5 , wherein the step of determining a 3-D protein model is performed by a method selected from the group consisting of experimental methods, searching computer-based protein structure databases, homology modeling by comparison to a protein family member sequence and structure, computational protein structure prediction and combinations thereof.

7 . The method of claim 5 , wherein the step of determining a 3-D protein model is performed by homology modeling.

8 . The method of claim 1 , wherein the proteins that are the products of a gene exhibiting genetic polymorphisms are proteases.

9 . The method of claim 5 , wherein the gene that encodes polymorphisms is a protease gene.

10 . The method of claim 1 further comprising designing a new drug for a patient exhibiting a particular genetic polymorphism.

11 . The method of claim 1 further comprising selecting a drug therapy for a patient exhibiting a particular genetic polymorphism.

12 . The method of claim 1 wherein the step of performing energetic refinement comprises performing molecular mechanics calculations or molecular dynamics simulations.

13 . The method of claim 1 wherein the step of performing dynamic refinement comprises using a simulated annealing protocol.

14 . The method of claim 5 wherein the step of performing energetic refinement comprises performing molecular mechanics calculations or molecular dynamics simulations.