| 摘要 |
The invention describes methods for proteomic analysis, with multiple applications that involve the mapping of samples in an N-dimensional shape space. The applications include the classification of samples on the basis of the three-dimensional shapes of substances they contain, which leads to novel diagnostic methods that are linked to new kinds of preventive and therapeutic vaccines. A panel of N reagents, for example proteins, called X(j), with j=1 to N, is used. With N>>1, these reagents are used to define an N-dimensional shape space with approximately orthogonal axes. The binding strength of each of the X(j) reagents to each other, as measured, for example, by an ELISA assay, is a NxN matrix K. The matrix K is used to define another set of N reagents called Y(j), with j=1 to N, each of which is a linear combination of the X(j) reagents and each of which is tailored to be complementary to the corresponding X(j) reagent. Each X(j) reagent together with the corresponding Y(j) reagent is used to define a shape space axis that is approximately orthogonal to each of the N-1 shape space axes defined by the other N-1 reagent pairs X(k) and Y(k), where k<>j. Samples can be mapped with respect to each of the N axes of the shape space by measuring the binding of molecules in the samples to each of the X(j) and Y(j) reagents. This mapping enables the definition and measurement of similarity between samples and sets of samples, including, but not limited to, biological samples. The mapping enables classification of samples with respect to categories. The samples may be simple or diverse at the level of molecular shapes, for example proteins or mixtures of proteins, or antibodies contained in serum samples. Applications include quality control for a broad range of substances. The invention also includes the optional use of P reagents, with P>N, to enhance the orthogonality of the N-dimensional shape space.
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