# US 8,568,992 B2 · Antibody categorization based on binding characteristics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,568,992 B2 - Document ID: US8568992B2 - Status: Granted - Kind: B2 - Application number: 13/480,356 - Filed: 2012-05-24 - Published: 2013-04-04 - Granted: 2013-10-29 - Earliest claimed: 2001-12-03 - Assignees: Amgen Fremont Inc. - Applicants: Wynn L. Walker; Michael L. Gallo; Xiao-Chi Jia; Keith Joho; Jaspal Singh Kang - Inventors: Wynn L. Walker; Michael L. Gallo; Xiao-Chi Jia; Keith Joho; Jaspal Singh Kang - Web page: https://patentgrep.com/patent/US8568992B2 ## CPC - G01N 33/543: with an insoluble carrier for immobilising immunochemicals - G01N 33/54306: {Solid-phase reaction mechanisms} - G01N 33/58: involving labelled substances (G01N33/53 takes precedence) - G01N 33/6878: {in epitope analysis} - G06F 17/16: Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} - G16B 40/00: ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding - G16B 40/30: Unsupervised data analysis - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G16B: BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY ## Abstract Methods for categorizing antibodies based on their epitope binding characteristics are described. Methods and systems for determining the epitope recognition properties of different antibodies are provided. Also provided are data analysis processes for clustering antibodies on the basis of their epitope recognition properties and for identifying antibodies having distinct epitope binding characteristics. ## Claims ### Claim 1 An antibody selected using an antibody competition assay method for determining antibodies that bind to an epitope on an antigen, comprising: providing a set of antibodies that bind to an antigen; labelling each antibody of said set to form a labelled reference antibody set such that each labelled reference antibody is distinguishable from every other labelled reference antibody in said labelled reference antibody set; selecting a probe antibody from said set of antibodies that bind to the antigen; contacting said probe antibody with said labelled reference antibody set in the presence of said antigen; detecting said probe antibody in a complex comprising said antigen, one labelled reference antibody bound to said antigen, and said labelled probe antibody bound to said antigen; identifying each said labelled reference antibody bound to said antigen in each said complex; determining whether said probe antibody competes with any reference antibody in said labelled reference antibody set, wherein competition indicates that said probe antibody binds to the same epitope as another antibody in said set of antibodies that bind to an antigen. ### Claim 2 (depends on 1) The method of claim 1, further comprising labelling said probe antibody to form a labelled probe antibody. ### Claim 3 (depends on 2) The method of claim 2, wherein said labelled probe antibody comprises a label selected from an enzymatic label, a colorimetric label, a fluorescent label, or a radioactive label. ### Claim 4 (depends on 1) The method of claim 1, further comprising providing a labelled detection antibody for detecting said probe antibody. ### Claim 5 (depends on 4) The method of claim 4, wherein said labelled detection antibody comprises a label selected from an enzymatic label, a colorimetric label, a fluorescent label, or a radioactive label. ### Claim 6 (depends on 1) The method of claim 1, wherein each antibody of set of antibodies that bind to an antigen is labelled with a uniquely colored bead to form a labelled reference antibody set such that each labelled reference antibody is distinguishable from every other labelled reference antibody in said labelled reference antibody set. ### Claim 7 (depends on 6) The method of claim 6, wherein said each uniquely colored bead has a distinct emission spectrum. ### Claim 8 (depends on 6) The method of claim 6, wherein said probe antibody is a labelled probe antibody. ### Claim 9 (depends on 6) The method of claim 6, further comprising providing a labelled detection antibody for detecting said probe antibody. ### Claim 10 (depends on 1) The method of claim 1, further comprising a method for characterizing antibodies based on binding characteristics, comprising: providing input data representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen; normalizing said input data to generate a normalized intensity matrix; computing at least one dissimilarity matrix comprising generating a threshold matrix from said normalized intensity matrix and computing a dissimilarity matrix from said threshold matrix; and clustering antibodies based on dissimilarity values in cells of said dissimilarity matrix, to determine epitope binding patterns of said set of antibodies that bind to an antigen. ### Claim 11 (depends on 10) The method of claim 10, wherein the Competitive Pattern Recognition (CPR) process is used for characterizing antibodies based on binding characteristics. ### Claim 12 (depends on 10) The method of claim 10 wherein said input data is generated by a high throughput competitive antibody assay. ### Claim 13 (depends on 12) The method of claim 12, wherein said high throughput competitive antibody assay is the Multiplexed Competitive Antibody Binning (MCAB) assay. ### Claim 14 (depends on 10) The method of claim 10, wherein said input data comprises signal intensity values representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen. ### Claim 15 (depends on 14) The method of claim 14, wherein said input data comprising signal intensity values representing the outcomes of at least one antibody competition assay comprises input data stored in matrix form. ### Claim 16 (depends on 15) The method of claim 15, wherein said input data stored in matrix form comprises a two-dimensional matrix. ### Claim 17 (depends on 15) The method of claim 15, wherein said data stored in matrix form comprises a multidimensional matrix. ### Claim 18 (depends on 15) The method of claim 15 wherein said input data stored in matrix form comprises a plurality of matrices. ### Claim 19 (depends on 15) The method of claim 15, wherein said input data stored in matrix form comprises signal intensity values representing the outcomes of an antibody competition assay carried out using a multi-well format, wherein each cell of said matrix represents the outcome of the assay carried out in one well of said multi-well format. ### Claim 20 (depends on 15) The method of claim 15, wherein said normalizing said input data to generate a normalized intensity matrix comprises generating a background-normalized intensity matrix by subtracting a first matrix comprising signal intensity values from a first antibody competition assay in which antigen was not added, from a second matrix comprising signal intensity values from a second antibody competition assay in which antigen was added. ### Claim 21 (depends on 20) The method of claim 20, comprising setting a minimum threshold value for blocking buffer values and adjusting any blocking buffer values below said threshold value to said threshold value prior to said generating said normalized intensity matrix. ### Claim 22 (depends on 21) The method of claim 21, further comprising dividing each value in a column of said background-normalized intensity matrix by the blocking buffer intensity value for said column. ### Claim 23 (depends on 22) The method of claim 22, wherein said normalizing step further comprises normalizing relative to the baseline signal for probe antibodies, comprising dividing each said column of said intensity-normalized matrix by its corresponding diagonal value to generate a final intensity-normalized matrix. ### Claim 24 (depends on 23) The method of claim 23, wherein each said diagonal value is compared with a user-defined threshold value and any said diagonal value below said user-defined threshold value is adjusted to said threshold value prior to said dividing each column by its corresponding diagonal value. ### Claim 25 (depends on 21) The method of claim 21, wherein said normalizing step further comprises generating an intensity-normalized matrix by dividing each value in a row of said background-normalized intensity matrix by the blocking buffer intensity value for said row. ### Claim 26 (depends on 25) The method of claim 25, wherein said normalizing step further comprises normalizing relative to the baseline signal for probe antibodies, comprising dividing each said row of said intensity-normalized matrix by its corresponding diagonal value to generate a final intensity-normalized matrix. ### Claim 27 (depends on 26) The method of claim 26, wherein each said diagonal value is compared with a user-defined threshold value and any said diagonal value below said user-defined threshold value is adjusted to said threshold value prior to said dividing each row by its corresponding diagonal value. ### Claim 28 (depends on 20) The method of claim 20, wherein said generating said threshold matrix comprises setting the normalized valued in each cell of said normalized intensity matrix to a value of one (1) or zero (0), wherein normalized values less than or equal to a threshold value are set to a value of zero (0) and normalized values greater to said threshold value are set to a value of one (1). ### Claim 29 (depends on 28) The method of claim 28, wherein said at least one dissimilarity matrix is computed by providing said threshold matrix of ones and zeroes and determining the number of positions in which each pair of rows differs. ### Claim 30 (depends on 29) The method of claim 29, wherein a plurality of dissimilarity matrices are computing using a plurality of threshold values. ### Claim 31 (depends on 30) The method of claim 30, wherein the average of said plurality of dissimilarity matrices is computed. ### Claim 32 (depends on 31) The method of claim 31, comprising providing said average of said plurality of dissimilarity matrices as input to said clustering step. ### Claim 33 (depends on 10) The method of claim 10, wherein said clustering antibodies based on said dissimilarity values in cells of said dissimilarity matrix comprises hierarchical clustering. ### Claim 34 (depends on 33) The method of claim 33, wherein said hierarchical clustering comprises generating a hierarchy of nested subsets of antibodies within said set of antibodies that bind to an antigen, comprising: determining the pair of antibodies in said set having the lowest dissimilarity value; determining the pair of antibodies having the next lowest dissimilarity value; interatively repeating said determining said pair of antibodies having the next lowest dissimilarity value until one pair of antibodies remains, such that a hierarchy of nested subsets is generated that indicates the similarity of competition patterns within said set of antibodies; and determining clusters based on competition patterns. ### Claim 35 (depends on 34) The method of claim 34, wherein data from said clustering step is captured. ### Claim 36 (depends on 35) The method of claim 35, wherein data is captured by automated means. ### Claim 37 (depends on 34) The method of claim 34, wherein said clustering step generates a display. ### Claim 38 (depends on 37) The method of claim 37, wherein said display is in a format compatible with data input device or computer. ### Claim 39 (depends on 37) The method of claim 37, wherein said display comprises a dissimilarity matrix. ### Claim 40 (depends on 39) The method of claim 39, wherein clusters are determined by visual inspection of the dissimilarity value in each cell of said dissimilarity matrix. ### Claim 41 (depends on 39) The method of claim 39, wherein said dissimilarity matrix comprises cells having a visual indicator of the cluster to which the antibody pair represented by said cell belongs. ### Claim 42 (depends on 41) The method of claim 41, wherein said visual indicator is a color. ### Claim 43 (depends on 41) The method of claim 41, wherein said visual indicator is shading. ### Claim 44 (depends on 41) The method of claim 41, wherein said visual indicator is patterning. ### Claim 45 (depends on 37) The method of claim 37, wherein said display is a dendrogram defined by dissimilarity values computed for said set of antibodies. ### Claim 46 (depends on 45) The method of claim 45, wherein said dendrogram comprises branches representing antibodies in said set of antibodies, wherein the arrangement of branches represents relationships between antibodies within said set of antibodies, further wherein said arrangement represents clusters of antibodies within said set of antibodies. ### Claim 47 (depends on 46) The method of claim 46, further wherein said dendrogram comprises branches wherein the length of any said branch represents the degree of similarity between the binding pattern of antibodies or cluster of antibodies represented by said branch. ### Claim 48 (depends on 10) The method of claim 10, comprising providing input data representing the outcomes of a plurality of antibody competition assays, wherein each assay represents an individual experiment using a set of antibodies that bind to an antigen, further wherein each said experiment comprises at least one antibody that is also tested in at least one other experiment. ### Claim 49 (depends on 48) The method of claim 48, comprising generating an individual normalized intensity matrix for each said individual experiment and further comprising generating a single normalized intensity matrix by computing the average intensity value of each antibody pair represented in each individual normalized intensity matrix. ### Claim 50 (depends on 49) The method of claim 49, further comprising generating a single dissimilarity matrix representing each antibody pair tested in said plurality of antibody competition assays. ### Claim 51 An antibody selected using a method for characterizing antibodies based on binding characteristics, comprising: providing a labelled reference antibody set such that each labelled reference antibody in said set is distinguishable from every other labelled reference antibody in said labelled reference antibody set, and wherein each said reference antibody binds to an antigen; selecting a probe antibody from said set of antibodies that binds to the antigen; contacting said probe antibody with said labelled reference antibody set in the presence of said antigen; detecting said probe antibody in a complex comprising said antigen, one labelled reference antibody bound to said antigen, and said labelled probe antibody bound to said antigen; identifying each said labelled reference antibody bound to said antigen in each said complex; determining whether said probe antibody competes with any reference antibody in said labelled reference antibody set, wherein competition indicates that said probe antibody binds to the same epitope as another antibody in said set of antibodies that bind to an antigen; providing input data representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen, further wherein said input data comprises signal intensity values representing the outcomes of at least one antibody competition assay using a set of antibodies that bind to an antigen; storing said input data in matrix format; subtracting a first matrix comprising signal intensity values from a first antibody competition assay in which antigen was not added, from a second matrix comprising signal intensity values from a second antibody competition assay in which antigen was added, to form a background normalized intensity matrix; calculating the value in each diagonal cell of said background normalized intensity matrix; determining cells in each column of said background normalized intensity matrix having values significantly higher than the value in each diagonal cell of said column; and grouping columns having similar binding patterns as a bin. ### Claim 52 (depends on 51) The method of claim 51, wherein said cells in said background normalized intensity matrix having values significantly higher than said value in each corresponding diagonal cell of said column have values at least two times said value in each diagonal cell. ### Claim 53 (depends on 1, 51) The antibody of claim 1 or 51, wherein said antibody is a monoclonal antibody. ### Claim 54 (depends on 1, 51) The antibody of claim 1 or 51, wherein said antibody is an IgG antibody. ### Claim 55 (depends on 54) The antibody of claim 54, wherein said antibody is an IgG2, IgG3, or IgG4 antibody. ### Claim 56 (depends on 1, 51) The antibody of claim 1 or 51, wherein said antibody is an IgKappa antibody. ### Claim 57 (depends on 1, 51) The antibody of claim 1 or 51, wherein said antibody is a human antibody.