Apparatus and method for automated protein design

Abstract

The present invention relates to apparatus and methods for quantitative protein design and optimization.

1 . A method executed by a computer under the control of a program, said computer including a memory for storing said program, said method comprising the steps of: 
 (A) receiving a protein backbone structure with variable residue positions;    (B) establishing a group of potential rotamers for each of said variable residue positions, wherein at least one variable residue position has rotamers from at least two different amino acid side chains; and    (C) analyzing the interaction of each of said rotamers with all or part of the remainder of said protein backbone structure to generate a set of optimized protein sequences, wherein said analyzing step includes a Dead-End Elimination (DEE) computation.    
   
   
       2 . A method executed by a computer under the control of a program, said computer including a memory for storing said program, said method comprising the steps of: 
 (A) receiving a protein backbone structure with variable residue positions;    (B) classifying each variable residue position as either a core, surface or boundary residue;    (C) establishing a group of potential rotamers for each of said variable residue positions, wherein at least one variable residue position has rotamers from at least two different amino acid side chains; and    (D) analyzing the interaction of each of said rotamers with all or part of the remainder of said protein to generate a set of optimized protein sequences.    
   
   
       3 . A method according to  claim 2  wherein said analyzing step comprises a DEE computation.  
   
   
       4 . A method according to  claim 1  or  2  wherein said set of optimized protein sequences comprises the globally optimal protein sequence.  
   
   
       5 . A method according to  claim 1  or  3  wherein said DEE computation is selected from the group consisting of original DEE and Goldstein DEE.  
   
   
       6 . A method according to  claim 1  or  2  wherein said analyzing step includes the use of at least one scoring function.  
   
   
       7 . A method according to  claim 6  wherein said scoring function is selected from the group consisting of a Van der Waals potential scoring function, a hydrogen bond potential scoring function, an atomic solvation scoring function, an electrostatic scoring function and a secondary structure propensity scoring function.  
   
   
       8 . A method according to  claim 6  wherein said analyzing step includes the use of at least two scoring functions.  
   
   
       9 . A method according to  claim 6  wherein said analyzing step includes the use of at least three scoring functions.  
   
   
       10 . A method according to  claim 6  wherein said analyzing step includes the use of at least four scoring functions.  
   
   
       11 . A method according to  claim 6  wherein said atomic solvation scoring function includes a scaling factor that compensates for over-counting.  
   
   
       12 . A method according to  claim 1  or  2  further comprising testing at least one member of said set to produce experimental results.  
   
   
       13 . A method according to  claim 4  further comprising 
 (D) generating a rank ordered list of additional optimal sequences from said globally optimal protein sequence.    
   
   
       14 . A method according to  claim 13  wherein said generating includes the use of a Monte Carlo search.  
   
   
       15 . A method according to  claim 2  wherein said analyzing step step comprises a Monte Carlo computation.  
   
   
       16 . A method according to  claim 13  further comprising: 
 (E) testing some or all of said protein sequences from said ordered list to produce potential energy test results.    
   
   
       17 . A method according to  claim 16  further comprising: 
 (F) analyzing the correspondence between said potential energy test results and theoretical potential energy data.    
   
   
       18 . A method according to  claim 1  or  2  further comprising altering at least one supersecondary structure parameter value of said protein backbone structure prior to establishing said potential rotamer group.  
   
   
       19 . An optimized protein sequence generated by the method of  claim 1  or  2 .  
   
   
       20 . A nucleic acid sequence encoding a protein sequence according to  claim 19 .  
   
   
       21 . An expression vector comprising the nucleic acid of  claim 20 .  
   
   
       22 . A host cell comprising the nucleic acid of  claim 20 .  
   
   
       23 . A protein having a sequence that is at least about 5% different from a known protein sequence and is at least 20% more stable than the known protein sequence.  
   
   
       24 . A computer readable memory to direct a computer to function in a specified manner, comprising: 
 a side chain module to correlate a group of potential rotamers for residue positions of a protein backbone model;    a ranking module to analyze the interaction of each of said rotamers with all or part of the remainder of said protein to generate a set of optimized protein sequences.    
   
   
       25 . A computer readable memory according to  claim 24  wherein said ranking module includes a van der Waals scoring function component.  
   
   
       26 . A computer readable memory according to  claim 24  wherein said ranking module includes an atomic solvation scoring function component.  
   
   
       27 . A computer readable memory according to  claim 24  wherein said ranking module includes a hydrogen bond scoring function component.  
   
   
       28 . A computer readable memory according to  claim 24  wherein said ranking module includes a secondary structure scoring function component.  
   
   
       29 . A computer readable memory according to  claim 24  further comprising 
 an assessment module to assess the correspondence between potential energy test results and theoretical potential energy data.