Chemistry: natural resins or derivatives; peptides or proteins; – Proteins – i.e. – more than 100 amino acid residues
Reexamination Certificate
2002-05-14
2004-12-14
Carlson, Karen Cochrane (Department: 1653)
Chemistry: natural resins or derivatives; peptides or proteins;
Proteins, i.e., more than 100 amino acid residues
Reexamination Certificate
active
06831154
ABSTRACT:
FIELD OF THE INVENTION
The present invention generally relates to protein—protein interactions, particularly to protein complexes formed by protein—protein interactions and methods of use thereof.
BACKGROUND OF THE INVENTION
The prolific output from numerous genomic sequencing efforts, including the Human Genome Project, is creating an ever-expanding foundation for large-scale study of protein function. Indeed, this emerging field of proteomics can appropriately be viewed as a bridge that connects DNA sequence information to the physiology and pathology of intact organisms. As such, proteomics—the large-scale study of protein function—will likely be starting point for the development of many future pharmaceuticals. The efficiency of drug development will therefore depend on the diversity and robustness of the methods used to elucidate protein function, i.e., the proteomic tools that are available.
Several approaches are generally known in the art for studying protein function. One method is to analyze the DNA sequence of a particular gene and the amino acid sequence coded by the gene in the context of sequences of genes with known functions. Generally, similar functions can be predicted based on sequence homologies. This “homology method” has been widely used, and powerful computer programs have been designed to facilitate homology analysis. See, e.g., Altschul et al.,
Nucleic Acids Res.,
25.3389-3402 (1997). However, this method is useful only when the function of a homologous protein is known.
Another useful approach is to interfere with the expression of a particular gene in a cell or organism and examine the consequent phenotypic effects. For example, Fire et al.,
Nature,
391:806-811 (1998) disclose an “RNA interference” assay in which double-stranded RNA transcripts corresponding to a particular target gene are injected into cells or organisms to determine the phenotype associated with the disrupted expression of that gene. Alternatively, transgenic technologies can be utilized to delete or “knock out” a particular gene in an organism and the effect of the gene knockout is determined. See e.g., Winzeler et al.,
Science,
285:901-906 (1999); Zambrowicz et al.,
Nature,
392:608-611 (1998). The phenotypic effects resulting from the disruption of expression of a particular gene can shed some light on the functions of the gene. However, the techniques involved are complex and the time required for a phenotype to appear can be long, especially in mammals. In addition, in many cases disruption of a particular gene may not cause any detectable phenotypic effect.
Gene functions can also be uncovered by genetic linkage analysis. For example, genes responsible for certain diseases may be identified by positional cloning. Alternatively, gene function may be inferred by comparing genetic variations among individuals in a population and correlating particular phenotypes with the genetic variations. Such linkage analyses are powerful tools, particularly when genetic variations exist in a traceable population from which samples are readily obtainable. However, readily identifiable genetic diseases are rare and samples from a large population with genetic variations are not easily accessible. In addition, it is also possible that a gene identified in a linkage analysis does not contribute to the associated disease or symptom but rather is simply linked to unknown genetic variations that cause the phenotypic defects.
With the advance of bioinformatics and publication of the full genome sequence of many organisms, computational methods have also been developed to assign protein functions by comparative genome analysis. For example, Pellegrini et al.,
Proc. Natl. Acad. Sci. USA
96:4285-4288 (1999) discloses a method that constructs a “phylogenetic profile” that summarizes the presence or absence of a particular protein across a number of organisms as determined by analyzing the genome sequences of the organisms. A protein's function is predicted to be linked to another protein's function if the two proteins share the same phylogenetic profile. Another method, the Rosetta Stone method, is based on the theory that separate proteins in one organism are often expressed as separate domains of a fusion protein in another organism. Because the separate domains in the fusion protein are predictably associated with the same function, it can be reasonably predicted that the separate proteins are associated with same functions. Therefore, by discovering separate proteins corresponding to a fusion protein, i.e., the “Rosetta Stone sequence,” functional linkage between proteins can be established. See Marcotte et al.,
Science,
285:751-753 (1999); Enright et al.,
Nature,
402:86-90 (1999). Another computational method is the “gene neighbor method.” See Dandekar et al.,
Trends Biochem. Sci.,
23:324-328 (1998); Overbeek et al.,
Proc. Natl. Acad. Sci. USA
96:2896-2901 (1999). This method is based on the likelihood that if two genes are found to be neighbors in several different genomes, the proteins encoded by the genes share a common function.
While the methods described above are useful in analyzing protein functions, they are constrained by various practical limitations such as unavailability of suitable samples, inefficient assay procedures, and limited reliability. The computational methods are useful in linking proteins by function. However, they are only applicable to certain proteins, and the linkage maps established therewith are sketchy. That is, the maps lack specific information that describes how proteins function in relation to each other within the functional network. Indeed, none of the methods places the identified protein functions in the context of protein—protein interactions.
In contrast with the traditional view of protein function, which focuses on the action of a single protein molecule, a modern expanded view of protein function defines a protein as an element in an interaction network. See Eisenberg et al.,
Nature,
405:823-826 (2000). That is, a full understanding of the functions of a protein will require knowledge of not only the characteristics of the protein itself, but also its interactions or connections with other proteins in the same interacting network. In essence, protein—protein interactions form the basis of almost all biological processes, and each biological process is composed of a network of interacting proteins. For example, cellular structures such as cytoskeletons, nuclear pores, centrosomes, and kinetochores are formed by complex interactions among a multitude of proteins. Many enzymatic reactions are associated with large protein complexes formed by interactions among enzymes, protein substrates, and protein modulators. In addition, protein—protein interactions are also part of the mechanisms for signal transduction and other basic cellular functions such as DNA replication, transcription, and translation. For example, the complex transcription initiation process generally requires protein—protein interactions among numerous transcription factors, RNA polymerase, and other proteins. See e.g., Tjian and Maniatis,
Cell,
77:5-8 (1994).
Because most proteins function through their interactions with other proteins, if a test protein interacts with a known protein, one can reasonably predict that the test protein is associated with the functions of the known protein, e.g., in the same cellular structure or same cellular process as the known protein. Thus, interaction partners can provide an immediate and reliable understanding towards the functions of the interacting proteins. By identifying interacting proteins, a better understanding of disease pathways and the cellular processes that result in diseases may be achieved, and important regulators and potential drug targets in disease pathways can be identified.
There has been much interest in protein—protein interactions in the field of proteomics. A number of biochemical approaches have been used to identify interacting proteins. These approaches generally employ the affinities between i
Baker Jonathan A.
Carlson Karen Cochrane
Desai Anand
Myriad Genetics Inc.
Myriad IP Dept.
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