Data processing: measuring – calibrating – or testing – Measurement system in a specific environment – Biological or biochemical
Reexamination Certificate
2000-05-16
2004-04-13
Brusca, John S. (Department: 1631)
Data processing: measuring, calibrating, or testing
Measurement system in a specific environment
Biological or biochemical
C435S006120, C536S023100, C536S024100
Reexamination Certificate
active
06721663
ABSTRACT:
Specific protein interactions are critical events in most biological processes and a clear idea of the way proteins interact, their three dimensional structure and the types of molecules which might block or enhance interaction are critical aspects of the science of drug discovery in the pharmaceutical industry.
Proteins are made up of strings of amino acids and each amino acid in a string is coded for by a triplet of nucleotides present in DNA sequences (Stryer 1997). The linear sequence of DNA code is read and translated by a cell's synthetic machinery to produce a linear sequence of amino acids which then fold to form a complex three-dimensional protein.
The mechanisms which govern protein folding are multi-factorial and the summation of a series of interactions between biophysical phenomena and other protein molecules (Stryer 1997). Virtually all molecules signal by non-covalent attachment to another molecule (“binding”). Despite the conceptual simplicity and tremendous importance of molecular recognition, the forces and energetics that govern it are poorly understood. This is owed to the fact that the two primary binding forces (electrostatics and van der Waals interactions) are weak, and roughly of the same order of magnitude. Moreover, binding at any interface is complicated by the presence of solvent (water), solutes (metal ions and salt molecules), and dynamics within the protein, all of which can inhibit or enhance the binding reaction.
In general it is held that the primary structure of a protein determines its tertiary structure. A large volume of work supports this view and many sources of software are available to the scientists in order to produce models of protein structures (Sansom 1998). In addition, a considerable effort is underway in order to build on this principle and generate a definitive database demonstrating the relationships between primary and tertiary protein structures. This endeavour is likened to the human genome project and is estimated to have a similar cost (Gaasterland 1998).
Despite this assembly of background knowledge it is clear that there are considerable limitations in our abilities to predict protein structures and that these become very apparent when computational methods are applied during drug discovery programs. For many experienced practitioners the use of ‘docking’ programmes (which seek to examine protein-ligand interactions in detail) are ‘disappointing’ (Sansom 1998).
Consider this example. A typical growth factor has a molecular weight of 15,000 to 30,000 daltons, whereas a typical small molecule drug has a molecular weight of 300-700. Moreover, X-ray crystal structures of small molecule-protein complexes (such as biotin-avidin) or enzyme-substrates show that they usually bind in crevices, not to flat areas of the protein. Thus relative to enzymes and receptors, protein-protein targets are non-traditional and the pharmaceutical community has had very limited success in developing drugs that bind to them using currently available approaches to lead discovery. High throughput screening technologies in which large (combinatorial) libraries of synthetic compounds are screened against a target protein(s) have failed to produce a significant number of lead compounds.
It is possible that a large portion of the difficulties experienced in attempting to apply such computer programs to drug discovery result from an over-reliance on the consensus dogma that primary structure predicts tertiary structure.
This consensus view of the determinants of protein structure has been re-evaluated in the light of experiments with colicin E1 (Goldstein 1998). This scientific work demonstrated that ‘modules of secondary structure that make up a given protein are not rigidly constrained in a single set of interactions that lead to a unique three-dimensional structure’ (Goldstein 1998).
The data generated in such studies also presents further issues for large structural projects such as that described by Gaasterland (1998). Proteins are identified and their function ascribed by the homology searches for particular structural elements associated with a given function (e.g. transmembrane domains, enzyme cleavage sites, &bgr;-barrel fold etc.). In effect there exists a circular logic to the way in which protein structures are explored and described and this hampers our understanding of the true biological significance since we are only searching for those things we already know.
‘Given these considerations, structural genomists might consider assigning a high priority to understanding the extent to which protein-protein and other molecular interactions determine native folding patterns before their databases get too full’ (Goldstein 1998).
The binding of large proteinaceous signaling molecules (such as hormones) to cellular receptors regulates a substantial portion of the control of cellular processes and functions. These protein-protein interactions are distinct from the interaction of substrates to enzymes or small molecule ligands to seven-transmembrane receptors. Protein-protein interactions occur over relatively large surface areas, as opposed to the interactions of small molecule ligands with serpentine receptors, or enzymes with their substrates, which usually occur in focused “pockets” or “clefts.”
Many major diseases result from the inactivity or hyperactivity of large protein signaling molecules. For example, diabetes mellitus results from the absence or ineffectiveness of insulin, and dwarfism from the lack of growth hormone. Thus, simple replacement therapy with recombinant forms of insulin or growth hormone heralded the beginnings of the biotechnology industry. However, nearly all drugs that target protein-protein interactions or that mimic large protein signaling molecules are also large proteins. Protein drugs are expensive to manufacture, difficult to formulate, and must be given by injection or topical administration.
It is generally believed that because the binding interfaces between proteins are very large, traditional approaches to drug screening or design have not been successful. In fact, for most protein-protein interactions, only small subsets of the overall intermolecular surfaces are important in defining binding affinity.
‘One strongly suspects that the many crevices, canyons, depressions and gaps, that punctuate any protein surface are places that interact with numerous micro- and macro-molecular ligands inside the cell or in the extra-cellular spaces, the identity of which is not known’ (Goldstein 1998).
Despite these complexities, recent evidence suggests that protein-protein interfaces are tractable targets for drug design when coupled with suitable functional analysis and more robust molecular diversity methods. For example, the interface between hGH and its receptor buries ~1300 Sq. Angstroms of surface area and involves 30 contact side chains across the interface. However, alanine-scanning mutagenesis shows that only eight side-chains at the center of the interface (covering an area of about 350 Sq. Angstroms) are crucial for affinity. Such “hot spots” have been found in numerous other protein-protein complexes by alanine-scanning, and their existence is likely to be a general phenomenon.
The problem therefore is to define the small subset of regions that define the binding or functionality of the protein.
The important commercial reason for this is that a more efficient way of doing this would greatly accelerate the process of drug development.
These complexities are not insoluble problems and newer theoretical methods should not be ignored in the drug design process. Nonetheless, in the near future there are no good algorithms that allow one to predict protein binding affinities quickly, reliably, and with high precision (Sunesis website 17/9/99).
The invention provides a method and a software tool for processing sequence data and a method and a software tool for protein structure analysis, and the data forming the product of each method, as defined in the appended independent claims to which reference should be
Heal Jonathan R.
Roberts Gareth W.
Brusca John S.
Finnegan Henderson Farabow Garrett & Dunner L.L.P.
Proteom Limited
Zhou Shubo “Joc”
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