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  Systems and methods for antibody engineeringThe Patent Description & Claims data below is from USPTO Patent Application 20080050357. Brief Patent Description - Full Patent Description - Patent Application Claims
CROSS-REFERENCE TO RELATED APPLICATIONS [0001]This application claims benefit, under 35 U.S.C. .sctn. 119(e), of U.S. Provisional Patent Application No. 60/491,815 filed on Aug. 1, 2003 which is incorporated herein, by reference, in its entirety. This application also claims benefit, under 35 U.S.C. .sctn. 119(e), of U.S. Provisional Patent Application No. 60/536,357 filed on Jan. 14, 2004 which is incorporated herein, by reference, in its entirety. This application also claims benefit, under 35 U.S.C. .sctn. 119(e), of U.S. Provisional Patent Application No. 60/536,862 filed on Jan. 15, 2004 which is incorporated herein, by reference, in its entirety. 1. FIELD OF THE INVENTION [0002]The field of this invention relates to computer systems and methods for designing sets of antibody variants and tools for relating the functional properties of such antibodies to their sequences. These relationships can then be used to determine the relationship between an antibody's sequence and commercially relevant properties of that antibody. Such sequence-function relationships may be used to design and synthesize commercially useful antibody compositions. 2. BACKGROUND OF THE INVENTION [0003]Because of the immense size of sequence space, there is no effective way to systematically screen all possible permutations of an antibody for a desired property. To test each possible amino acid at each position in an antibody, rapidly leads to such a large number of molecules to be tested such that no available methods of synthesis or testing are feasible. Furthermore, most molecules generated in such a way would lack any measurable level of the desired property. Total sequence space is very large and the functional solutions in this space are sparsely distributed. [0004]Two primary approaches have to date been used to identify antibody molecules with desired properties: mechanistic and empirical. There are significant limitations to both of these approaches. The mechanistic approach is often hampered by insufficient knowledge of the system to be improved, meaning either that considerable resources must be devoted to characterizing the system (for example by obtaining high quality protein crystal structures and relating these to the properties of interest), or that meaningful predictions cannot be made. In contrast, the empirical approach requires no mechanistic understanding, but relies upon direct measurements of an antibody's properties to select those variants that are improved. This strength is also its weakness; large numbers of variants cannot typically be tested under conditions that are identical to those of the final application. High throughput screens are widely used to provide surrogate measurements of the properties of interest, but these are often inadequate: binding of an antibody to an antigen is often an inadequate predictor of clinical or diagnostic function. [0005]Empirical engineering of antibodies relies upon creating and testing sets of variants, then using this information to design and synthesize subsequent sets of variants that are enriched for components that contribute to the desired activity. A key limitation for any empirical antibody engineering is in developing a good assay for antibody function. The assay must measure antibody properties that are relevant to the final application, but must also be capable of testing a sufficient number of variants to identify what may be only a small fraction that are actually improved. The difficulty of creating such an assay is particularly relevant when optimizing antibodies for complex functions that are difficult to measure in high throughput. Examples include reduction of viral titer or the killing of tumor cells. [0006]Large numbers of variants cannot typically be tested under conditions that are identical to those of the final application. High throughput screens are widely used to provide surrogate measurements of the properties of interest, but these are often inadequate. As examples, binding of an antibody to an antigen in a phage display assay can have little bearing on its ultimate usefulness as a therapeutic protein. [0007]Limitations in current methods for searching through antibody sequences for specific commercially relevant functionalities creates a need in the art for methods that can design and synthesize small numbers of variants for functional testing and that can use the resulting sequence and functional information to design and synthesize small numbers of variants improved for a desired commercially useful activity. Limitations in current methods for choosing surrogate screens appropriate for empirical antibody engineering creates a need in the art for methods that can design and create small numbers of variants that can then be tested for specific commercially relevant functionalities. 3. SUMMARY OF THE INVENTION [0008]The systems and methods described here apply novel computational biology and data mining techniques to important molecular design problems. In particular, novel ways to map antibody sequence space are described. Such maps are used to direct perturbations or modifications of the antibody sequences in order to perturb or modify the activity of the antibodies in a controlled fashion. [0009]Methods are disclosed for biological engineering using the design and synthesis of a set of sequences containing designed substitutions that are statistically representative of a sequence space, and that contain a high fraction of antibodies possessing desired properties. In addition to its functionality, each antibody is also designed to maximize the information that the set of antibodies contains regarding the contribution of substitutions to the desired antibody properties and to the contributions resulting from interactions between substitutions. This in essence is a map of the sequence space that can also be used to design perturbations to modify the functionality of the antibody as desired. [0010]The information used to create the substitutions that define the sequence space can be derived from one or more of (i) multiple sequence alignments, (ii) phylogenetic reconstructions of ancestral sequences, (iii) analysis of families or superfamilies of antibodies related by sequence, structure, function or partial function, (iv) analysis of monomer substitution probabilities within classes of antibody, (v) three dimensional structures (e.g., molecular models, X-ray crystallographic structures, nuclear magnetic resonance models, molecular dynamic simulations), (vi) immunogenic constraints, (vii) prior knowledge about the structure and/or function of the sequences upon which design of the antibody set is to be based, or (viii) any similar information pertaining to a related or homologous antibody. In one embodiment of the invention, this process is automated by use of an expert system that acquires domain knowledge and captures it is a knowledge database. This process can provide a score or rank order of substitutions to be incorporated, and a reasoning based on user specified constraints and domain specific data. [0011]Generally speaking, the first step in the design and manufacture of the statistically representative sequence sets of this invention is the definition of the initial sequence space to be searched. This involves defining one or more reference sequences, identifying positions that are likely to tolerate alteration, and identifying substitutions at these positions that are likely to be acceptable or to produce desired changes in the properties of the antibody. All possible combinatorial strings of polymeric biological molecules define the total defined sequence space to be searched. Each substitution at each position is typically enumerated in silico and the acceptability defined computationally. Desirability or acceptability of each possible substitution is calculated according to one or more criteria. Such calculations can be performed by a computational system using the knowledge database, user specified constraints, and/or domain and antibody specific data. [0012]The present invention also provides a more formal systematic method for selecting substitution positions. The use of a formal system involves quantitative scores and/or filters for assessing the favorability of substitution positions and the substitutions possible at those positions. Formalizing the system for substitution selection allows for the development of an automated system for antibody optimization or humanization. The parameters, filters and scores can be adjusted based on data from the scientific literature and data from experiments designed or interpreted by the automated system. By adjusting the scores and filters, substitutions that are predicted to be favorable can be aligned with those found experimentally to be favorable. Continuous refinement of these scores and filters based on experimental or computational data provides a way for the antibody optimization system to learn and improve. This formalization and learning capability are an aspect of the invention. [0013]The second step in the design and manufacture of the statistically representative sequence sets of this invention is to define a subspace of the total sequence space to be searched in each iteration of the synthesis testing and correlating process. Typically the total allowed space matrix contains 10.sup.5-10.sup.50 antibodies, many orders of magnitude larger than can be synthesized and measured under commercially relevant conditions. Such commercially relevant conditions are presently limited to numbers in the range of 10.sup.1-10.sup.3. The number of antibody variants that can be synthesized and tested under appropriate conditions is defined by the availability of resources. The number of variant positions and the number of substitutions that can be tested at each of those positions is then calculated, such that each substitution will be present in a statistically representative fraction of the set of antibodies to be synthesized. Additionally, when using search methods like Tabu, Ant optimization or similar techniques, the space can be searched on a sequence by sequence basis by using a memory of the space that has been visited previously and the properties encountered. [0014]Typical experimental design methods can introduce more changes in an antibody than the antibody can tolerate to remain functional. Adaptations of these methods, for example by using covering algorithms to reduce the total number of substitutions in each antibody variant, while maximizing the number of different combinations of pairs of substitutions is another aspect of the invention. [0015]The third step in the design and manufacture of the statistically representative sequence sets (or sequence sets relevant for specific optimization techniques) of this invention is to create a set of variant antibodies. This can be performed by synthesizing the antibody sequences defined and designed in the first two steps. The systematic design of such variants is one aspect of the present invention. The antibodies can be synthesized individually, or in a multiplexed set that is subsequently deconvoluted by sequencing or some other appropriate method. Alternatively, the antibodies can be created as a library of variants. Many methods have been described in the art for creating such libraries. See, for example, Stemmer (1994) Proc Natl Acad Sci USA 91: 10747-51.; Stemmer (1994) Nature 370: 389-91.; Crameri et al. (1996) Nat Med 2: 100-2.; Crameri et al. (1998) Nature 391: 288-291; Ness et al. (1999) Nat Biotechnol 17: 893-896; Volkov et al. (1999) Nucleic Acids Res 27: e18.; Volkov et al. (2000) Methods Enzymol 328: 447-56.; Volkov et al. (2000) Methods Enzymol 328: 456-63.; Coco et al. (2001) Nat Biotechnol 19: 354-9.; Gibbs et al. (2001) Gene 271: 13-20.; Ninkovic et al. (2001) Biotechniques 30: 530-4, 536.; Coco et al. (2002) Nat Biotechnol 20: 1246-50.; Ness et al. (2002) Nat Biotechnol 20: 1251-5.; Aguinaldo et al. (2003) Methods Mol Biol 231: 105-10.; Coco (2003) Methods Mol Biol 231: 111-27.; and Sun et al. (2003) Biotechniques 34: 278-80, 282, 284 passin. Alternatively, specifically designed antibodies can be synthesized individually. [0016]After synthesis, the designed set(s) of antibodies are characterized functionally to measure the properties of interest. This requires the development of an assay or surrogate assay faithful to the property or properties of ultimate interest and to test some members of the set of variants for more than one property, including the property of ultimate interest. Data mining techniques are then employed to characterize the functions of the variants and to derive a relationship between antibody sequences and properties. Optionally, the characterization data can be used to provide information in a subsequent iteration of the method, aiding in the design of a subsequent set of statistically representative variants that can be synthesized and tested to obtain a molecule with even more desirable properties. The data from additional iterations of this process can also be used to refine the data mining algorithms and models produced from the first set of data. The knowledge created about the sequence space can in turn be incorporated into the knowledge database for evaluating the substitutions in the light of this data and recalculating the scores or rank order of the substitutions. These processes are aspects of the present invention. [0017]Additionally, combinations of the methods described herein can be made with other techniques such as directed evolution, DNA shuffling, family shuffling and/or systematic scanning approaches. These can be performed in any order and for any number of iterations to produce the products described herein. All such combinations are within the scope of the invention. 4. BRIEF DESCRIPTION OF THE DRAWINGS [0018]FIG. 1 illustrates an overview of the architecture of an Expert System in accordance with an embodiment of the present invention. [0019]FIG. 2 illustrates a flowchart for an antibody engineering method using integrated information sources to choose initial substitutions, and sequence-activity relationships to assess them in accordance with an embodiment of the present invention. Continue reading... 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