Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Chemical analysis
Reexamination Certificate
1998-04-10
2001-02-13
Brusca, John S. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Chemical analysis
C706S045000, C706S047000
Reexamination Certificate
active
06188965
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to an apparatus and method for quantitative protein design and optimization.
BACKGROUND OF THE INVENTION
De novo protein design has received considerable attention recently, and significant advances have been made toward the goal of producing stable, well-folded proteins with novel sequences. Efforts to design proteins rely on knowledge of the physical properties that determine protein structure, such as the patterns of hydrophobic and hydrophilic residues in the sequence, salt bridges and hydrogen bonds, and secondary structural preferences of amino acids. Various approaches to apply these principles have been attempted. For example, the construction of &agr;-helical and &bgr;-sheet proteins with native-like sequences was attempted by individually selecting the residue required at every position in the target fold (Hecht, et al.,
Science
249:884-891 (1990); Quinn, et al.,
Proc. Nati. Acad. Sci USA
91:8747-8751 (1994)). Alternatively, a minimalist approach was used to design helical proteins, where the simplest possible sequence believed to be consistent with the folded structure was generated (Regan, et al.,
Science
241:976-978 (1988); DeGrado, et al.,
Science
243:622-628 (1989); Handel, et al.,
Science
261:879-885 (1993)), with varying degrees of success. An experimental method that relies on the hydrophobic and polar (HP) pattern of a sequence was developed where a library of sequences with the correct pattern for a four helix bundle was generated by random mutagenesis (Kamtekar, et al.,
Science
262:1680-1685 (1993)). Among non de novo approaches, domains of naturally occurring proteins have been modified or coupled together to achieve a desired tertiary organization (Pessi, et al.,
Nature
362:367-369 (1993); Pomerantz, et al.,
Science
267:93-96 (1995)).
Though the correct secondary structure and overall tertiary organization seem to have been attained by several of the above techniques, many designed proteins appear to lack the structural specificity of native proteins. The complementary geometric arrangement of amino acids in the folded protein is the root of this specificity and is encoded in the sequence.
Several groups have applied and experimentally tested systematic, quantitative methods to protein design with the goal of developing general design algorithms (Hellinga, et al.,
J. Mol. Biol
. 222: 763-785 (1991); Hurley, et al.,
J. Mol. Biol
. 224:1143-1154 (1992); Desjarlaisl, et al.,
Protein Science
4:2006-2018 (1995); Harbury, et al.,
Proc. Natl. Acad. Sci. USA
92:8408-8412 (1995); Klemba, et al.,
Nat. Struc. Biol
. 2:368-373 (1995); Nautiyal, et al.,
Biochemistry
34:11645-11651 (1995); Betzo, et al.,
Biochemistry
35:6955-6962 (1996); Dahiyat, et al.,
Protein Science
5:895-903 (1996); Jones,
Protein Science
3:567-574 (1994); Konoi, et al.,
Proteins: Structure, Function and Genetics
19:244-255 (1994)). These algorithms consider the spatial positioning and steric complementarity of side chains by explicitly modeling the atoms of sequences under consideration. To date, such techniques have typically focused on designing the cores of proteins and have scored sequences with van der Waals and sometimes hydrophobic solvation potentials.
In addition, the qualitative nature of many design approaches has hampered the development of improved, second generation, proteins because there are no objective methods for learning from past design successes and failures.
Thus, it is an object of the invention to provide computational protein design and optimization via an objective, quantitative design technique implemented in connection with a general purpose computer.
SUMMARY OF THE INVENTION
In accordance with the objects outlined above, the present invention provides methods executed by a computer under the control of a program, the computer including a memory for storing the program. The method comprising the steps of receiving a protein backbone structure with variable residue positions, establishing a group of potential rotamers for each of the variable residue positions, wherein at least one variable residue position has rotamers from at least two different amino acid side chains, and analyzing the interaction of each of the rotamers with all or part of the remainder of the protein backbone structure to generate a set of optimized protein sequences. The methods further comprise classifying each variable residue position as either a core, surface or boundary residue. The analyzing step may include a Dead-End Elimination (DEE) computation. Generally, the analyzing step includes the use of at least one scoring function selected from the group consisting of a Van der Waals potential scoring function, a hydrogen bond potential scoring function, an atomic solvation scoring function, a secondary structure propensity scoring function and an electrostatic scoring function. The methods may further comprise generating a rank ordered list of additional optimal sequences from the globally optimal protein sequence. Some or all of the protein sequences from the ordered list may be tested to produce potential energy test results.
In an additional aspect, the invention provides nucleic acid sequences encoding a protein sequence generated by the present methods, and expression vectors and host cells containing the nucleic acids.
In a further aspect, the invention provides 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, and 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. The memory may further comprise an assessment module to assess the correspondence between potential energy test results and theoretical potential energy data.
REFERENCES:
Harbury et al., “High-Resolution Protein Design with Backbone Freedom,”Science,282:1462-1467 (1998).
Kortemme et al., “Design of a 20-Amino Acid, Three-Stranded &bgr;-Sheet Protein,”Science,281:253-256 (1988).
Koehl et al., “De Novo Protein Design. I. In Search of Stability and Specificity,”J. Mol. Biol.,293:1161-1181 (1999).
Gordon et al. “Energy functions for protein design,”Curr. Opinion in Struct. Biol.,9:509-513 (1999).
Cornell et al., “A Second Generation Force Field for the Simulation of Proteins, Nucleic Acids, and Organic Molecules,”J. Am. Chem. Soc.,117:5179-5197 (1995).
Villegas et al., “Stabilization of proteins by rational design of &agr;-helix stability using helix/coil transition theory,”Folding&Design,1(1):29-34 (1995).
Hurley et al., “Design and Structural Analysis of Alternative Hydrophobic Core Packing Arrangements in Bacteriophage T4 Lysozyme,”J. Mol. Biol.,224:1143-1159(1992).
Tuffery et al., “A New Approach to the Rapid Determination of Protein Side Chain Conformations,”J. of Biomolecular Struct.&Dynamics,8(6):1267-1289 (1991).
Brooks et al., “CHARMM: A Program for Macromolecular Energy, Minimization, and Dynamics Calculations,”J. of Computational Chemistry,4(2):187-217 (1983).
Rappé et al., “Charge Equilibration for Molecular Dynamics Simulations,”J. Phys. Chem.,95:3358-3363 (1991).
Mayo et al., “DREIDING: A Generic Force Field for Molecular Simulations,”J. Phys. Chem.,94:8897-8909 (1990).
Harbury et al., “Repacking protein cores with backbone freedom: Structure prediction for coiled coils,”Proc. Natl. Acad. Sci. USA,92:8408-8412 (1995).
Kono et al., “Energy Minimization Method Using Automata Network for Sequence and Side-Chain Conformation Prediction from Given Backbone Geometry,”Proteins: Structure, Function, and Genetics,19:244-255 (1994).
Lee et al., “Accurate prediction of the stability and activity effects of site-directed mutagenesis on a protein core,”Nature,352:448-451 (1991).
van Gunsteren et al., “Prediction of the Activity and Stability Effects of Site-directed Mutagenesis on a Protein Core,”J. Mol. Biol.,227:389-395 (1992).
Wesson et al., “Atomic solvation paramet
Dahiyat Bassil I.
Gordon D. Benjamin
Mayo Stephen L.
Street Arthur
Brusca John S.
California Institute of Technology
Flehr Hohbach Test Albritton & Herbert LLP
Kim Young
Silva, Esq. Robin M.
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