Abstract:
This method enables computational analysis and manipulation of DNA and protein sequence data such as is found in large public databases. The method allows systematic searches of such data to identify portions of sequences which code for key intermolecular surfaces or regions of specific protein targets. In a first example, two amino acid sequences are input (steps  1, 2 ) to an iterative procedure (steps  4-6 ). A frame size is selected in terms of a number of sequence elements. The procedure then compares pairs of frames, one from each sequence, to identify intramolecular and intermolecular regions on the basis of relationships between amino acids according to a predetermined coding scheme. The probability of existence of each region within the coding scheme is then evaluated and those regions for which the probability is greater than a predetermined threshold are discarded. The procedure outputs the remaining regions. In a second example, protein structure data is input to an iterative procedure which evaluates for each frame in the protein structure a complementary relationship score between the amino acids in the frame and each amino acid within a predetermined distance from the frame. The procedure outputs each frame for which the score equals or exceeds a predetermined threshold score.

Description:
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&#39;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, β-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 made. Preferred or advantageous features of the invention are set out in dependent subclaims. 
     The invention provides a method and a software tool for use in analysing and manipulating sequence data (e.g. both DNA and protein) such as is found in large databases (see Table 1). Advantageously it may enable the conducting of systematic searches to identify the sequences which code for key intermolecular surfaces or “hot spots” on specific protein targets. 
     This technology may advantageously have significant applications in the application of informatics to sequence databases in order to identify lead molecules for important pharmaceutical targets. 
     
       
         
               
             
               
               
               
             
               
             
               
               
               
             
               
             
               
               
               
             
           
               
                 TABLE 1 
               
             
             
               
                   
               
               
                 PROTEIN AND NUCLEOTIDE SEQUENCE DATABASES 
               
               
                 AMENABLE FOR ANALYSIS USING THE INVENTION 
               
               
                 THE CONCEPTUAL BASIS FOR THE INVENTION 
               
             
          
           
               
                   
                 Database 
                 Description 
               
               
                   
                   
               
             
          
           
               
                 Major Nucleic acid databases 
               
             
          
           
               
                   
                 Genbank 
                 The Genbank database is a repository for 
               
               
                   
                 NCBI 
                 nucleotide data. 
               
               
                   
                 National 
                 The NCBI provides facilities to search for 
               
               
                   
                 Center for 
                 sequences in Genbank by text or by 
               
               
                   
                 Biotechnology 
                 sequence similarity and to submit new 
               
               
                   
                 information 
                 sequences. 
               
               
                   
                 EMBL 
                 The EMBL database is a repository for 
               
               
                   
                   
                 nucleotide data. 
               
               
                   
                   
                 The EBI provides facilities to search for 
               
               
                   
                   
                 sequences by text or by sequence similarity 
               
               
                   
                   
                 and to submit new sequences. 
               
               
                   
                 DbEST 
                 The dbEST database is a repository for 
               
               
                   
                   
                 Expressed Sequence Tags (EST) data. 
               
               
                   
                 Unigene 
                 The Unigene database is a repository for 
               
               
                   
                   
                 clustered EST data. 
               
               
                   
                   
                 UniGene is an experimental system for 
               
               
                   
                   
                 automatically partitioning EST sequences 
               
               
                   
                   
                 into a non-redundant set of gene-oriented 
               
               
                   
                   
                 clusters. Each UniGene cluster contains 
               
               
                   
                   
                 sequences that represent a unique gene, as 
               
               
                   
                   
                 well as related information such as the 
               
               
                   
                   
                 tissue types in which the gene has been 
               
               
                   
                   
                 expressed and map location. 
               
               
                   
                   
                 Unigene is split up in sections, catagorized 
               
               
                   
                   
                 by species origin. The current three 
               
               
                   
                   
                 sections are Human (hsuinigene), Mouse 
               
               
                   
                   
                 (mmunigene) and Rat (rnunigene) EST 
               
               
                   
                   
                 clusters. 
               
               
                   
                 STACK 
                 STACK is a public database of sequences 
               
               
                   
                   
                 expressed in the human genome. 
               
               
                   
                   
                 The STACK project aims to make the most 
               
               
                   
                   
                 comprehensive representation of the 
               
               
                   
                   
                 sequence of each of the expressed genes 
               
               
                   
                   
                 in the human genome, by extensive 
               
               
                   
                   
                 processing of gene fragments to make 
               
               
                   
                   
                 provide a carefully joined set of consensus 
               
               
                   
                   
                 sequences for each gene. A new method to 
               
               
                   
                   
                 extensively process gene fragments to 
               
               
                   
                   
                 make accurate alignment, prevent errors 
               
               
                   
                   
                 and provide a carefully joined set of 
               
               
                   
                   
                 consensus sequences for each gene. 
               
             
          
           
               
                 Major Protein Sequence databases 
               
             
          
           
               
                   
                 SWISS-PROT 
                 Curated protein sequence database 
               
               
                   
                   
                 which strives to provide a high level 
               
               
                   
                   
                 of annotations (such as the 
               
               
                   
                   
                 description of the function of a 
               
               
                   
                   
                 protein, its domains structure, post- 
               
               
                   
                   
                 translational modifications, variants, 
               
               
                   
                   
                 etc), a minimal level of redundancy 
               
               
                   
                   
                 and high level of integration with 
               
               
                   
                   
                 other databases. 
               
               
                   
                 TrEMBL 
                 Supplement of SWISS-PROT that 
               
               
                   
                   
                 contains all the translations of 
               
               
                   
                   
                 EMBL nucleotide sequence entries 
               
               
                   
                   
                 not yet integrated in SWISS-PROT. 
               
               
                   
                 OWL 
                 Non-redundant composite of 4 
               
               
                   
                   
                 publicly available primary sources: 
               
               
                   
                   
                 SWISS-PROT, PIR (1-3), GenBank 
               
               
                   
                   
                 (translation) and NRL-3D. SWISS- 
               
               
                   
                   
                 PROT is the highest priority source, 
               
               
                   
                   
                 all others being compared against it 
               
               
                   
                   
                 to eliminate identical and trivially 
               
               
                   
                   
                 different sequences. The strict 
               
               
                   
                   
                 redundancy criteria render OWL 
               
               
                   
                   
                 relatively “small” and hence efficient 
               
               
                   
                   
                 in similarity searches. 
               
               
                   
                 PIR Protein 
                 A comprehensive, annotated, and 
               
               
                   
                 Information 
                 non-redundant set of protein 
               
               
                   
                 Resource 
                 sequence databases in which 
               
               
                   
                   
                 entries are classified into family 
               
               
                   
                   
                 groups and alignments of each 
               
               
                   
                   
                 group are available. 
               
               
                   
                 SPTR 
                 Comprehensive protein sequence 
               
               
                   
                   
                 database that combines the high 
               
               
                   
                   
                 quality of annotation in SWISS- 
               
               
                   
                   
                 PROT with the completeness of the 
               
               
                   
                   
                 weekly updated translation of 
               
               
                   
                   
                 protein coding sequences from the 
               
               
                   
                   
                 EMBL nucleotide database. 
               
               
                   
                 NRL_3D 
                 The NRL_3D database is produced 
               
               
                   
                   
                 by PIR from sequence and 
               
               
                   
                   
                 annotation information extracted 
               
               
                   
                   
                 from the Brookhaven Protein 
               
               
                   
                   
                 Databank (PDB) of crystallographic 
               
               
                   
                   
                 3D structures. 
               
               
                   
                   
               
             
          
         
       
     
     The Origins of Complementary Peptide Theory 
     DNA is composed of two helical strands of nucleotides (see FIG.  10 ). The concepts governing the genetic code and the fact that DNA codes for protein sequences are well known (Stryer 1997). The ‘sense’ strand codes for the protein, and as such, attracts all the attention of molecular biologists and protein chemists alike. The purpose of the other ‘anti-sense’ strand is more elusive. To most, its function is relegated to that of a molecular ‘support’ for the ‘sense’ strand, which is used when DNA is replicated (Stryer 1997) but is of little immediate functional significance for the day to day activities of cellular processes. 
     Some research would suggest a greater role of the antisense strand of DNA above that of the basic conceptual model of replication. In particular, it had been noticed that there appeared to be a potential functional relationship between sense and anti-sense strands in viruses. Mekler (1969) observed that several minus stranded virus complexes contained protein components translated from the mRNA complementary to the RNA of the viral gene. Mekler postulated that the significance of this finding was that because this viral protein interacts strongly with the RNA from which the mRNA was generated, a peptide chain may associate specifically with the coding strand of its own gene. It was later thought that this may provide a rationale for the ability of a protein to regulate the transcription of its own gene. 
     Mekler&#39;s original theory was supported by studies on antigen processing pathways. Specifically, an antibody-synthesizing RNA complex was found to bind to its antigen with high affinity (Fishman and Adler, 1967). Mekler contended that these results demonstrated the ability of a protein antigen to regulate its own synthesis by binding to the mRNA encoding the antibody (Mekler, 1969). As the binding between the active centre of the antibody and the antigenic determinant is well known to be based on associations of polypeptide chains, he purported that two interacting polypeptides may be encoded in complementary strands of DNA (FIG.  11 ). Mekler also analysed the proposed interacting regions of pancreatic ribonuclease A and recorded that reading the complementary RNA of one of the interacting chains in the 5′-3′ direction yielded the sequence of the other interactant. From these observations he suggested that there existed a specific code of interaction between amino acid side chains encoded by complementary codons at the RNA level (Table 2). 
     Collectively, these observations represented the first predictions of a sense-complementary peptide-binding complex. 
     One key feature of Mekler&#39;s theory was that due to the degeneracy of the genetic code one amino acid may be complementary related to as many as four others, allowing for a large variety of possible interacting sequences (Table 2). 
     
       
         
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 2 
               
             
             
               
                   
               
               
                 THE AMINO ACID PAIRINGS RESULTING FROM READING THE ANTICODON FOR 
               
               
                 NATURALLY OCCURING AMINO ACID RESIDUES IN THE 5′-3′ DIRECTION 
               
             
          
           
               
                   
                   
                 Comple- 
                   
                   
                   
                 Comple- 
                 Comple- 
               
               
                 Amino 
                   
                 mentary 
                 Complementary 
                 Amino 
                   
                 mentary 
                 mentary 
               
               
                 Acid 
                 codon 
                 codon 
                 Amino acid 
                 Acid 
                 codon 
                 codon 
                 Amino acid 
               
               
                   
               
               
                 Alanine 
                 GCA 
                 UGC 
                 Cysteine 
                 Serine 
                 UCA 
                 UGA 
                 Stop 
               
               
                   
                 GCG 
                 CGC 
                 Arginine 
                   
                 UCC 
                 GGA 
                 Glycine 
               
               
                   
                 GCC 
                 GGC 
                 Glycine 
                   
                 UCG 
                 CGA 
                 Arginine 
               
               
                   
                 GCU 
                 AGC 
                 Serine 
                   
                 UCU 
                 AGA 
                 Arginine 
               
               
                   
                   
                   
                   
                   
                 AGC 
                 GCU 
                 Alanine 
               
               
                   
                   
                   
                   
                   
                 AGU 
                 ACU 
                 Threonine 
               
               
                 Arginine 
                 CGG 
                 CCG 
                 Proline 
                 Glutamine 
                 CAA 
                 UUG 
                 Leucine 
               
               
                   
                 CGA 
                 UCG 
                 Serine 
                   
                 CAG 
                 CUG 
                 Leucine 
               
               
                   
                 CGC 
                 GCG 
                 Alanine 
               
               
                   
                 CGU 
                 ACG 
                 Threonine 
               
               
                   
                 AGG 
                 CCU 
                 Proline 
               
               
                   
                 AGA 
                 UCU 
                 Serine 
               
               
                 Aspartic 
                 GAC 
                 GUC 
                 Valine 
                 Glycine 
                 GGA 
                 UCC 
                 Serine 
               
               
                 Acid 
                 GAU 
                 AUC 
                 Isoleucine 
                   
                 GGC 
                 GCC 
                 Alanine 
               
               
                   
                   
                   
                   
                   
                 GGU 
                 ACC 
                 Threonine 
               
               
                   
                   
                   
                   
                   
                 GGG 
                 CCC 
                 Proline 
               
               
                 Asparagine 
                 AAC 
                 GUU 
                 Valine 
                 Histidine 
                 CAC 
                 GUG 
                 Valine 
               
               
                   
                 AAU 
                 AUU 
                 Isoleucine 
                   
                 CAU 
                 AUG 
                 Methionine 
               
               
                 Cysteine 
                 UGU 
                 ACA 
                 Threonine 
                 Isoleucine 
                 AUA 
                 UAU 
                 Tyrosine 
               
               
                   
                 UGC 
                 GCA 
                 Alanine 
                   
                 AUC 
                 GAU 
                 Aspartic acid 
               
               
                   
                   
                   
                   
                   
                 AUU 
                 AAU 
                 Asparagine 
               
               
                 Glutamic 
                 GAA 
                 UUC 
                 Phenylalanine 
                 Leucine 
                 CUG 
                 CAG 
                 Glutamine 
               
               
                 Acid 
                 GAG 
                 CUC 
                 Leucine 
                   
                 CUC 
                 GAG 
                 Glutamic acid 
               
               
                   
                   
                   
                   
                   
                 CUU 
                 AAG 
                 Lysine 
               
               
                   
                   
                   
                   
                   
                 UUA 
                 UAA 
                 Stop 
               
               
                   
                   
                   
                   
                   
                 CUA 
                 UAG 
                 Stop 
               
               
                   
                   
                   
                   
                   
                 UUG 
                 CAA 
                 Glutamine 
               
               
                   
                   
                   
                   
                   
                 CUG 
                 CAG 
                 Glutamine 
               
               
                 Lysine 
                 AAA 
                 UUU 
                 Phenylalanine 
                 Threonine 
                 ACA 
                 UGU 
                 Cysteine 
               
               
                   
                 AAG 
                 CUU 
                 Leucine 
                   
                 ACG 
                 CGU 
                 Arginine 
               
               
                   
                   
                   
                   
                   
                 ACC 
                 GGU 
                 Glycine 
               
               
                   
                   
                   
                   
                   
                 ACU 
                 AGU 
                 Serine 
               
               
                 Methionine 
                 AUG 
                 CAU 
                 Histidine 
                 Tryptophan 
                 UGG 
                 CCA 
                 Proline 
               
               
                 Phenylalanine 
                 UUU 
                 AAA 
                 Lysine 
                 Tyrosine 
                 UAC 
                 GUA 
                 Valine 
               
               
                   
                 UUC 
                 GAA 
                 Glutamic Acid 
                   
                 UAU 
                 AUA 
                 Isoleucine 
               
               
                 Proline 
                 CCA 
                 UGG 
                 Tryptophan 
                 Valine 
                 GUA 
                 UAC 
                 Tyrosine 
               
               
                   
                 CCC 
                 GGG 
                 Glycine 
                   
                 GUG 
                 CAC 
                 Histidine 
               
               
                   
                 CCU 
                 AGG 
                 Arginine 
                   
                 GUC 
                 GAC 
                 Aspartic Acid 
               
               
                   
                 CCG 
                 CGG 
                 Arginine 
                   
                 GUU 
                 AAC 
                 Asparagine 
               
               
                   
               
             
          
         
       
     
     Further Theoretical Developments 
     In 1981, Mekler revised his original theory and described a ‘general stereochemical genetic code’ (Mekler and Idlis, 1981) in which it was reported that the complementary pairings detailed in the above table formed three distinct groupings (FIG.  11 ). 
     Mekler noted that, in general, amino acids with non-polar side chains were related by complementary code to amino acids with polar side chains. He did not provide an explanation for this. Further theoretical considerations on the possibility of complementary-sense peptide recognition were independently developed by Biro (1981), Root-Bernstein (1982) and Blalock and Smith (1984). Biro (1981) conducted a computational comparison of DNA sequences: encoding protein ligand-receptor segments and showed that there were many complementary regions between them, giving rise to complementary related polypeptides. 
     Blalock and Smith (1984) observed that the hydropathic character of an amino acid residue is related to the identity of the middle letter of the triplet codon from which it is transcribed. Specifically, a triplet codon with thymine (T) as its middle base codes for a hydrophobic residue whilst adenine (A) codes for a hydrophilic residue. A triplet codon with middle bases cytosine (C) or guanine (G) encode residues which are relatively neutral and with similar hydropathy scores. Hydropathy is an index of the affinity of an amino acid for a polar environment, hydrophilic residues yielding a more negative score, whilst hydrophobic residues exhibit more positive scores. Kyte and Doolittle (1982) conceived the most widely used scale of this type. The observed relationship between the middle base of a triplet codon and residue hydropathy entails that peptides encoded by complementary DNA will exhibit complementary, or inverted, hydropathic profiles. 
     It was proposed that because two peptide sequences encoded in complementary DNA strands display inverted hydropathic profiles, they may form amphipathic secondary structures, and bind to one another (Bost et al., 1985). 
     Complementary peptides have been reported to form binding complexes with their ‘sense’ peptide counterparts (Root-Bernstein and Holsworthy, 1998). Evidence of such an interaction has now been reported for over forty different systems from many different authors (Table 3). 
     The reports listed cite experiments showing specific interactions between complementary peptide pairs. As such they demonstrate a variety of ways in which these peptide ligands may be utilised. 
     The scope of this analysis for explaining the interactions between proteins was further developed by Blalock to propose a Molecular Recognition Theory (MRT) (Bost and Blalock 1985, Blalock 1995, FIG.  13 ). This theory suggests that a ‘molecular recognition’ code of interaction exists between peptides encoded by complementary strands of DNA based on the observation that such peptides will exhibit inverted hydropathic profiles. 
     Blalock suggested that it is the linear pattern of amino acid hydropathy scores in a sequence (rather than the combination of specific residue identities), that defines the secondary structure environment. Furthermore, lie suggested that sequences with inverted hydropathic profiles are complementary in shape by virtue of inverse forces determining their steric relationships. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 3 
               
             
             
               
                   
               
               
                 LITERATURE REGARDING GENERATION OF 
               
               
                 COMPLEMENTARY PEPTIDES WITH BIOLOGICAL EFFECTS 
               
             
          
           
               
                 System tested 
                 Reference Index 
               
               
                   
               
               
                 ACTH 
                 Bost et al. (1985) 
               
               
                 Anaphylatoxin C5a 
                 Baranyi et al. (1996) 
               
               
                 Angiogenin 
                 Gho et al. (1997) 
               
               
                 Angiotensin II 
                 Elton et al. (1988), Soffer et al. (1987) 
               
               
                 Arginine vasopressin 
                 Johnson et al. (1988), Lu et al. (1991) 
               
               
                 □-endorphin 
                 Shahabi et al. (1992) 
               
               
                 Big Endothelin 
                 Fassina et al. (1992b) 
               
               
                 Bradykinin 
                 Fassina et al. (1992c) 
               
               
                 Calcium mimetic peptide 
                 Dillon et al. (1991) 
               
               
                 c-Raf protein 
                 Fassina et al. (1989b) 
               
               
                 Cystatin 
                 Ghiso et al. (1990) 
               
               
                 Dopamine receptor 
                 Nagy et al (1991) 
               
               
                 Enkephalin 
                 Carr et al. (1989) 
               
               
                 Fibrinogen 
                 Pasqualini et al. (1989), 
               
               
                   
                 Gartner et al. (1991b) 
               
               
                 Fibronectin 
                 Brentani et al. (1988) 
               
               
                 □-Endorphin 
                 Carr et al. (1986) 
               
               
                 Gastrin terminal peptide 
                 McGuigan et al. (1992), Jones (1972) 
               
               
                 GH-RH 
                 Grosvenor and Balint (1989) 
               
               
                 Idiotypic antibodies 
                 Bost and Blalock (1989) 
               
               
                 Insulin 
                 Knutson (1988) 
               
               
                 Integrin 
                 Derrick et al. (1997) 
               
               
                 Interferon □ 
                 Johnson et al. (1982) 
               
               
                 Interferon □ 
                 Scalpol et al. (1992) 
               
               
                 Interleukin 2 
                 Weigent et al. (1986), 
               
               
                   
                 Fassina et al. (1995) 
               
               
                 Laminin receptor 
                 Castronov, V et al. (1991) 
               
               
                 LR-RH 
                 Mulchahey et al. (1986) 
               
               
                 Melanocyte stimulating hormone 
                 Al-Obeidi, F. A. et al. (1989) 
               
               
                 mosquito oostatic receptor 
                 Borovsky et al. (1994) 
               
               
                 Myelin protein antibody 
                 Zhou et al. (1993) 
               
               
                 Nicotinic receptor 
                 Radding et al. (1992) 
               
               
                 Neurophysin II 
                 Fassina et al (1989b) 
               
               
                 Ovine prolactin 
                 Bajpai et al. (1991) 
               
               
                 Opiate receptor 
                 Carr et al. (1987) 
               
               
                 Prion protein 
                 Martins et al. (1997) 
               
               
                 Ribonuclease S peptide 
                 Shai et al. (1989) 
               
               
                 Somatostatin 
                 Campbell-Thompson (1993) 
               
               
                 Substance P 
                 Bret-Dibat et al. (1994) 
               
               
                 T15autoreactive antibody 
                 Kang et al. (1988) 
               
               
                 Vasopressin 1 receptor 
                 Kelly et al. (1990) 
               
               
                 Vitronectin 
                 Gartner et al. (1991b) 
               
               
                   
               
             
          
         
       
     
     Deriving a Complementary Peptide in the 3′-5′ Reading Frame 
     As a corollary to his original work, Blalock contended that as well as reading a complementary codon in the usual 5′-3′ direction, reading a complementary codon in the 3′-5′ would also yield amino acid sequences that displayed opposite hydropathic profiles (Bost et al., 1985). This follows from the observation that the middle base of a triplet codon determines the hydropathy index of the residue it codes for, and thus reading a codon in the reverse direction may change the identity, but not the hydropathic nature of the coded amino acid (Table 4). 
     Statistical studies at the DNA level must take into account the degeneracy of the genetic code as it allows for the existence of larger inter- or intramolecular complementary sequences without maintaining complementarity at the DNA level. In this vein, recent work by Baranyi et al. (1995) details a new protein structural motif called the Antisense Homology Box (AHB). Following an analysis of a protein sequence data bank for possible intramolecular complementary pairs, it was noted that there are many more regions of complementary peptide complementarity within the structures than statistically expected. 
     The reported frequency of these motifs is, on average, one per fifty residues. AHB areas have already been shown to be able to act as molecular recognition sites by studies involving function inhibition with peptide complements. Specifically, the endothelin peptide (ET-1) was inhibited by a 14 residue fragment of the endothelin A receptor in a smooth muscle relaxation assay (Baranyi et al., 1996), whilst complementary encoded regions of the C5a receptor antagonize C5a anaphylatoxin (Baranyi et al., 1996). These studies suggest that many interactions in nature may result from contacts between complementary related polypeptides. 
     
       
         
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 4 
               
             
             
               
                   
               
               
                 The relationships between amino acids and the residues encoded in the complementary strand 
               
               
                 reading 3′-5′ 
               
             
          
           
               
                   
                   
                 Comple- 
                   
                   
                   
                 Comple- 
                 Comple- 
               
               
                 Amino 
                   
                 mentary 
                 Complementary 
                 Amino 
                   
                 mentary 
                 mentary 
               
               
                 Acid 
                 Codon 
                 codon 
                 Amino acid 
                 Acid 
                 codon 
                 codon 
                 Amino acid 
               
               
                   
               
               
                 Alanine 
                 GCA 
                 CGU 
                 Arginine 
                 Serine 
                 UCA 
                 AGU 
                 Serine 
               
               
                   
                 GCG 
                 CGC 
                   
                   
                 UCC 
                 AGQ 
                 Arginine 
               
               
                   
                 GCC 
                 CGG 
                   
                   
                 UCG 
                 AGC 
                 Serine 
               
               
                   
                 GCU 
                 CGA 
                   
                   
                 UCU 
                 AGA 
                 Arginine 
               
               
                   
                   
                   
                   
                   
                 AGC 
                 UCG 
                 Serine 
               
               
                   
                   
                   
                   
                   
                 AGU 
                 UCA 
                 Serine 
               
               
                 Arginine 
                 CGG 
                 GCC 
                 Alanine 
                 Glutamine 
                 CAA 
                 GUU 
                 Valine 
               
               
                   
                 CGA 
                 GCU 
                 Alanine 
                   
                 CAG 
                 GUC 
                 Valine 
               
               
                   
                 CGC 
                 GCG 
                 Alanine 
               
               
                   
                 CGU 
                 GCA 
                 Alanine 
               
               
                   
                 AGG 
                 UCC 
                 Serine 
               
               
                   
                 AGA 
                 UCU 
                 Serine 
               
               
                 Aspartic 
                 GAC 
                 GUC 
                 Valine 
                 Glycine 
                 GGA 
                 CCU 
                 Proline 
               
               
                 Acid 
                 GAU 
                 AUC 
                 Isoleucine 
                   
                 GGC 
                 CCG 
                 Proline 
               
               
                   
                   
                   
                   
                   
                 GGU 
                 CCA 
                 Proline 
               
               
                   
                   
                   
                   
                   
                 GGG 
                 CCC 
                 Proline 
               
               
                 Asparagine 
                 AAC 
                 UUG 
                 Leucine 
                 Histidine 
                 CAC 
                 GUG 
                 Valine 
               
               
                   
                 AAU 
                 UUA 
                 Leucine 
                   
                 CAU 
                 GUA 
                 Valine 
               
               
                 Cysteine 
                 UGU 
                 ACA 
                 Threonine 
                 Isoleucine 
                 AUA 
                 UAU 
                 Tyrosine 
               
               
                   
                 UGC 
                 ACG 
                 Threonine 
                   
                 AUC 
                 UAG 
                 Stop 
               
               
                   
                   
                   
                   
                   
                 AUU 
                 UAA 
                 Stop 
               
               
                 Glutamic 
                 GAA 
                 CUU 
                 Leucine 
                 Leucine 
                 CUG 
                 GAC 
                 Asp 
               
               
                 Acid 
                 GAG 
                 CUG 
                 Leucine 
                   
                 CUC 
                 GAG 
                 Glutamic 
               
               
                   
                   
                   
                   
                   
                 CUU 
                 GAA 
                 acid 
               
               
                   
                   
                   
                   
                   
                 UUA 
                 AAU 
                 Glutamic 
               
               
                   
                   
                   
                   
                   
                 CUA 
                 GAU 
                 Acid 
               
               
                   
                   
                   
                   
                   
                 UUG 
                 AAC 
                 Asparagine 
               
               
                   
                   
                   
                   
                   
                 CUG 
                 GAC 
                 Aspartic 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Acid 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Asparagine 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Aspartic 
               
               
                   
                   
                   
                   
                   
                   
                   
                 Acid 
               
               
                 Lysine 
                 AAA 
                 UUU 
                 Phenylalanine 
                 Threonine 
                 ACA 
                 UGU 
                 Cysteine 
               
               
                   
                 AAG 
                 UUC 
                 Phenylalanine 
                   
                 ACG 
                 UGC 
                 Cysteine 
               
               
                   
                   
                   
                   
                   
                 ACC 
                 UGG 
                 Tryptophan 
               
               
                   
                   
                   
                   
                   
                 ACU 
                 UGA 
                 Stop 
               
               
                 Methionine 
                 AUG 
                 UAC 
                 Tyrosine 
                 Tryptophan 
                 UGG 
                 ACC 
                 Threonine 
               
               
                 Phenylalanine 
                 UUU 
                 AAA 
                 Lysine 
                 Tyrosine 
                 UAC 
                 AUG 
                 Methionine 
               
               
                   
                 UUC 
                 AAG 
                 Lysine 
                   
                 UAU 
                 AUA 
                 Isoleucine 
               
               
                 Proline 
                 CCA 
                 GGU 
                 Glycine 
                 Valine 
                 GUA 
                 CAU 
                 Histidine 
               
               
                   
                 CCC 
                 GGG 
                 Glycine 
                   
                 GUG 
                 CAC 
                 Histidine 
               
               
                   
                 CCU 
                 GGA 
                 Glycine 
                   
                 GUC 
                 CAG 
                 Glutamine 
               
               
                   
                 CCG 
                 GGC 
                 Glycine 
                   
                 GUU 
                 CAA 
                 Glutamine 
               
               
                   
               
             
          
         
       
     
     A Model of Recognition Based on Hydropathy 
     Several investigations have been directed at gaining an understanding of how hydropathic profiles and binding constants between complementary peptides are connected. The most comprehensive of these was carried out by Fassina et al. (1989) who studied the relationship between a complementary peptide designed on a computer to maximize complementary hydropathy against a thirteen-residue section of a glycoprotein. The study demonstrates a positive correlation between binding constants, as determined by an affinity binding column assay, and the degree of hydropathic complementarity, implying that a peptide&#39;s hydropathic character is inextricably linked to the binding mechanism. 
     This interesting result suggests that binding between two complementary related peptides is determined solely by the hydropathicity. Importantly, it also suggests that the steric nature of the side chain alone does not directly influence the ability of peptides to recognise each other, for in general, residues with similar hydropathic character display a wide distribution of side chain shapes and sizes. 
     Approaches to Preparing Complementary Peptides 
     The generation of a complementary peptide is straightforward in cases where the DNA sequence information is available. The complementary base sequence is read in either the 5′-3′ or 3′-5′ direction and translated to the peptide sequence according to the genetic code. In the absence of knowledge of the nucleotide sequence of the sense peptide, many possible permutations of complementary sequences exist, in accordance with the degeneracy of the genetic code (as shown in Tables 2 and 4). 
     Several approaches to define complementary sequences in such instances have been proposed: 
     One such approach makes a series of educated guesses based on the use of preferred codon usage tables (Aota et al. 1988) which allows one to assess the probability of a particular codon to be used for each amino acid for a given sequence. 
     Another approach, where applicable, is to assign the complementary residue to the amino acid which is the most frequent out of all the theoretical complementary residues. 
     Thus, in a situation where the DNA sequence is unknown, the possible complementary amino acids for a leucine residue are glutamine (3 possible codons), stop (2 possible codons), glutamic acid (1 possible codons) and lysine (1 possible codon). In this case glutamine would be chosen on the basis of statistical weight. Information such as this, along with the use of codon usage tables leads to a consensus approach to limiting the number of possible combinations of complementary sequences. Bost and Blalock (1989), Omichinski et al. (1989) and Shai et al. (1989) have employed methods of this type. 
     A number of studies have demonstrated the value of this type of approach to designing peptides with real functional utility. 
     Although some very high affinities have been reported for these peptides (K d ˜10 −9  M), most are of moderate affinity (K d ˜10 −3 -10 −7 M). Their potential applications therefore would depend on the affinity attained in a particular system. Lower affinity complementary peptides may be useful for diagnostic tests or for purification of ligands. Higher affinity peptides may serve a purpose in the development of therapeutics, for example a complementary peptide to a coat protein of a virus may interfere with the virus-host interaction at the molecular level, thus providing a strategy to manage this type of disorder. 
     Although the importance of inverted hydropathy in protein-protein interactions has long been recognized (Blalock and Smith, 1984) there has been little activity to apply this method on a large scale to investigate the complementary peptide partners of many proteins. One such attempt is recorded in the literature. “In the design of computer-based mining tools, no attention has been paid to a unique feature in the genetic code that determines the basic physico-chemical character of the encoded amino acids” (Kohler and Blalock, 1998). They proposed a method to scan DNA sequence banks using the hydropathic binary code, U.S. Pat. No. 5,523,208. The method described differs from the current invention as outlined below. 
     The current invention finds regions of potentially interacting amino acid sequences by using the relationships outlined in Tables 2 and 4. U.S. Pat. No. 5,523,208 determines regions of potentially interacting peptides by an altogether different method, that of hydropathy scoring. The results of analyses are thus completely different. 
     The process (algorithms) in by which sequences are analysed are different in the current invention than described U.S. Pat. No. 5,523,208. In particular, the current invention describes different algorithms for the analysis of complementary regions between proteins, or within proteins. 
     Problems Addressed By the Invention 
     The current problems associated with design of complementary peptides are: 
     A lack of understanding of the forces of recognition between complementary peptides 
     An absence of software tools to facilitate searching and selecting complementary peptide pairs from within a protein database. 
     A lack of understanding of statistical relevance/distribution of naturally encoded complementary peptides and how this corresponds to functional relevance. 
     Based on these shortfalls, embodiments of the invention describes the following technological advances in this field: 
     A mini library approach to define forces of recognition between human Interleukin (IL) 1β and its complementary peptides; 
     A high throughput computer system to analyse an entire database for intra/inter-molecular complementary regions; and 
     A novel (computational) method of analyzing X-ray crystal files for potential discontinuous complementary binding sites. 
     The Innovation 
     Studies into preferred complementary peptide pairings between IL-1β and its complementary ligand reveal the importance of both the genetic code and complementary hydropathy for recognition. Specifically, for our example, the genetic code for a region of protein codes for the complementary peptide with the highest affinity. An important observation is that this complementary peptide maps spatially and by residue hydropathic character to the interacting portion of the IL-1R receptor, as elucidated by the X-ray crystal structure Brookhaven reference pdb2itb.ent. 
     Using these novel observations as guiding principles for analysis, we have developed a computational analysis system to evaluate the statistical and functional relevance of intra/inter- molecular complementary sequences. 
     This invention provides significant benefits for those interested in: 
     The analysis and acquisition of peptide sequences to be used in the understanding of protein-protein interactions. 
     The development of peptides or small molecules which could be used to manipulate these interactions. 
     The advantages of this invention to previous work in this field include: 
     Using a valid statistical model. Previously, complementary mappings within protein structures has been statistically validated by assuming that the occurrence of individual amino acids is equally weighted at 1/20 (Baranyi, 1995). Our statistical model takes into account the natural occurrence of amino acids and thus generates probabilities dependent on sequence rather than content per se. 
     Facilitation of batch searching of an entire database. Previously, investigations into the significance of naturally encoded complementary related sequences have been limited to small sample sizes with non-automated methods. The invention allows for analysis of an entire database at a time, overcoming the sampling problem, and providing for the first time an overview or ‘map’ of complementary peptide sequences within known protein sequences. 
     The ability to map complementary sequences as a function of frame size and percentage antisense amino acid content. Previously, no consideration has been given to the significance of the frame length of complementary sequences. Our invention produces a statistical map as a function of frame size and percentage complementary residue content such that the statistical importance of how nature selects these frames may be evaluated. 
    
    
     BRIEF DESCRIPTION OF DRAWINGS 
     The present invention is described with reference to accompanying drawings. In the drawings, like reference numbers indicate identical or functionally similar elements. 
     FIG. ( 1 ) shows a block diagram illustrating one embodiment of a method of the present invention; 
     FIG. ( 2 ) shows a block diagram illustrating one embodiment for carrying out Step  4  in FIG. ( 1 ); 
     FIG. ( 3 ) shows a block diagram illustrating one embodiment for carrying out Step  5  in FIG. ( 1 ); 
     FIG. ( 4 ) shows a block diagram illustrating one embodiment for carrying out Step  8  in FIG. ( 2 ) and ( 3 ); 
     FIG. ( 5 ) shows a block diagram illustrating one embodiment for carrying out Step  8  in FIG. ( 2 ) and ( 3 ); 
     FIG. ( 6 ) shows a block diagram illustrating one embodiment for carrying out Step  6  in FIG. ( 1 ); 
     FIG. ( 7 ) shows a block diagram illustrating one embodiment of a method of the present invention; 
     FIG. ( 8 ) shows a block diagram illustrating one embodiment for carrying out Step  29  in FIG. ( 7 ); 
     FIG. ( 9 ) shows a block diagram illustrating one embodiment for carrying out Step  30  in FIG. ( 7 ); 
     FIG. ( 10 ) shows a diagram illustrating one embodiment of software design required to implement the ALS program; 
     FIG. ( 11 ) shows a diagram illustrating the principle of complementary peptide derivation. The amino acid sequence encoded by the minus or ‘complementary’ strand on DNA, when read in the 5′-3′ direction, is known as a complementary peptide. The general scheme is illustrated in FIG. ( 11 ). 
     FIG. ( 12 ) shows a diagram to illustrate antisense amino acids pairings inherent in the genetic code; Amino acids are represented by single letter codes. ‘Stop’ indicates a stop codon. Solid lines connect sense—complementary amino acid (represented as one letter code) related residues. Non-polar residues are shaded, polar residues are in white (adapted from Mekler and Idlis, 1981). 
     FIG. ( 13 ) shows a representation of the Molecular Recognition Theory; and 
     FIG. ( 14 ) shows a graph and text illustrating biological data as an example of the utility of the ALS program. The program picked out antisense region LITVLNI (SEQ ID No. 8) in the IL- 1 R receptor. This peptide was shown to inhibit the biological activity of IL- 1 b in ESAP assay. The effect is dependent on the peptide sequence (see scrambled peptide LTILINV (SEQ ID No. 9)). The same effect is also seen in a Serum Amyloid IL- 1  assay (i.e. assay independence). The peptide was shown to bind directly to IL- 1  by using biosensing techniques. 
    
    
     A DESCRIPTION OF THE ANALYTICAL PROCESS OF THE INVENTION 
     The software, ALS (antisense ligand searcher), performs the following tasks: 
     Given the input of two amino acid sequences, calculates the position, number and probability of the existence of intra- (within a protein) and inter- (between proteins) molecular antisense regions. ‘Antisense’ refers to relationships between amino acids specified in Tables 2 and 4 (both 5′→3′ derived and 3′→5′ derived coding schemes). 
     Allows sequences to be inputted manually through a suitable user interface (UI) and also through a connection to a database such that automated, or batch, processing can be facilitated. 
     Provides a suitable database to store results and an appropriate interface to allow manipulation of this data. 
     Allows generation of random sequences to function as experimental controls. 
     Diagrams describing the algorithms involved in this software are shown in FIGS. 1-5. 
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
     1. Overview 
     The present invention is directed toward a computer-based process, a computer-based system and/or a computer program product for analysing antisense relationships between protein or DNA sequences. A scheme of software architecture of a preferred embodiment is shown in FIG.  13 . 
     The method of the embodiment provides a tool for the analysis of protein or DNA sequences for antisense relationships. This embodiment covers analysis of DNA or protein sequences for intramolecular (within the same sequence) antisense relationships or inter-molecular (between 2 different sequences) antisense relationships. This principle applies whether the sequence contains amino acid information (protein) or DNA information, since the former may be derived from the latter. 
     The overall process of the invention is to facilitate the batch analysis of an entire genome (collection of genes/and or protein sequences) for every possible antisense relationship of both inter- and intra-molecular nature. For the purpose of example it will be described here how a protein sequence database, SWISS PROT (Bairoch and Apweiler, 1999), may be analysed by the methods described. 
     SWISS PROT contains a list of protein sequences. The current invention does not specify in what format the input sequences are held—for this example we used a relational database to allow access to this data. 
     The program runs in two modes. The first mode (Intermolecular) is to select the first protein sequence in SWISS PROT and then analyse the antisense relationships between this sequence and all other protein sequences, one at a time. The program then selects the second sequence and repeats this process. This continues until all of the possible relationships have been analysed. The second mode (Intramolecular) is where each protein sequence is analysed for antisense relationships within the same protein and thus each sequence is loaded from the database and analysed in turn for these properties. Both operational modes use the same core algorithms for their processes. The core algorithms are described in detail below. 
     An example of the output from this process is shown in Table 7. Table 7 shows a list of proteins in the SWISS PROT database that contain highly improbable numbers of intramolecular antisense frames of size 10 (frame size is a section of the main sequence, it is described in more detail below). In Table 7 the total number of antisense frames are shown. Another way of representing this data is to list the actual sequence information itself. An example of the biological relevance of peptides derived from this method is shown in FIG.  14 . The embodiment can output the data in either of these formats as well as many others. 
     2. Method of the Present Invention 
     For the purpose of example protein sequence 1 is ATRGRDSRDERSDERTD (SEQ ID No. 1) and protein sequence 2 is GTFRTSREDSTYSGDTDFDE (SEQ ID No. 2) (universal 1 letter amino acid codes used). 
     In step  1  (see FIG.  1 ), a protein sequence, sequence 1, is loaded. The protein sequence consists of an array of universally recognised amino acid one letter codes, e.g. ‘ADTRGSRD’ (SEQ ID No. 3). The source of this sequence can be a database, or any other file type. Step  2 , is the same operation as for step  1 , except sequence 2 is loaded. Decision step  3  involves comparing the two sequences and determining whether they are identical, or whether they differ. If they differ, processing continues to step  4 , described in FIG. 2, otherwise processing continues to step  5 , described in FIG.  3 . 
     Step  6  analyses the data resulting from either step  4 , or step  5 , and involves an algorithm described in FIG.  6 . 
     
       
         
               
             
               
               
             
           
               
                 TABLE 5 
               
             
             
               
                   
               
               
                 Description of parameters used in FIG. 2 
               
             
          
           
               
                 Name 
                 Description 
               
               
                   
               
               
                 N 
                 Framesize - the number of amino acids that make up each ‘frame’ 
               
               
                 X 
                 Score threshold - the number of amino acids that have to fulfil 
               
               
                   
                 the antisense criteria within a given frame for that frame 
               
               
                   
                 to be stored for analysis 
               
               
                 Y 
                 Score of individual antisense comparison (either 1 or 0) 
               
               
                 IS 
                 Running score for frame - (sum of y for frame) 
               
               
                 Ip1 
                 Position marker for Sequence 1 - used to track location of selected 
               
               
                   
                 frame for sequence 1 
               
               
                 Ip2 
                 Position marker for Sequence 2 - used to track location of selected 
               
               
                   
                 frame for sequence 1 
               
               
                 F 
                 Current position in frame 
               
               
                   
               
             
          
         
       
     
     In Step  7 , a ‘frame’ is selected for each of the proteins selected in steps  1  and  2 . A ‘frame’ is a specific section of a protein sequence. For example, for sequence 1, the first frame of length ‘5’ would correspond to the characters ‘ATRGR’ (SEQ ID No. 4). The user of the program decides the frame length as an input value. This value corresponds to parameter ‘n’ in FIG. 2. A frame is selected from each of the protein sequences (sequence 1 and sequence 2). Each pair of frames that are selected are aligned and frame position parameter f is set to zero. The first pair of amino acids are ‘compared’ using the algorithm shown in FIG.  4 /FIG.  5 . The score output from this algorithm (y, either one or zero) is added to a aggregate score for the frame is. In decision step  9  it is determined whether the aggregate score iS is greater than the Score threshold value (x). If it is then the frame is stored for further analyisis. If it is not then decision step  10  is implemented. In decision step  10 , it is determined whether it is possible for the frame to yield the score threshold (x). If it can, the frame processing continues and f is incremented such that the next pair of amino acids are compared. If it cannot, the loop exits and the next frame is selected. The position that the frame is selected from the protein sequences is determined by the parameter ip1 for sequence 1 and ip2 for sequence 2 (refer to FIG.  2 ). Each time steps  7  to  10  or  7  to  11  are completed, the value of ip1 is zeroed and then incremented until all frames of sequence 1 have been analysed against the chosen frame of sequence 2. When this is done, ip2 is then incremented and the value of ip1 is incremented until all frames of sequence 1 have been analysed against the chosen frame of sequence 2. This process repeats and terminates when ip2 is equal to the length of sequence 2. Once this process is complete, sequence 1 is reversed programmatically and the same analysis as described above is repeated. The overall effect of repeating steps  7  to  11  using each possible frame from both sequences is to facilitate step  8 , the antisense scoring matrix for each possible combination of linear sequences at a given frame length. 
     FIG. 3 shows a block diagram of the algorithmic process that is carried out in the conditions described in FIG.  1 . Step  12  is the only difference between the algorithms FIG.  2  and FIG.  3 . In step  12 , the value of ip2 (the position of the frame in sequence 2) is set to at least the value of ip1 at all times since as sequence 1 and sequence 2 are identical, if ip2 is less than ip1 then the same sequences are being searched twice. 
     FIGS. 4 and 5 describe the process in which a pair of amino acids (FIG. 4) or a pair of triplet codons are assessed for an antisense relationship. The antisense relationships are listed in Tables 2 and 4. In step  13 , the currently selected amino acid from the current frame of sequence 1 and the currently selected amino acid from the current frame of sequence 2 (determined by parameter ‘f’ in FIGS.  2 / 3 ) are selected. For example, the first amino acid from the first frame of sequence 1 would be ‘A’ and the first amino acid from the first frame of sequence 2 would be ‘G’. In step  14 , the ASCII character codes for the selected single uppercase characters are determined and multiplied and, in step  15 , the product compared with a list of precalculated scores, which represent the antisense relationships in Tables 2 and 4. If the amino acids are deemed to fulfil the criteria for an antisense relationship (the product matches a value in the precalculated list) then an output parameter ‘T’ is set to 1, otherwise the output parameter is set to zero. 
     Steps  16 - 21  relate to the case where the input sequences are DNA/RNA code rather the protein sequence. For example sequence 1 could be AAATTTAGCATG (SEQ ID No. 5) and sequence 2 could be TTTAAAMGCATGC (SEQ ID No. 6). The domain of the current invention includes both of these types of information as input values, since the protein sequence can be decoded from the DNA sequence, in accordance with the genetic code. Steps  16 - 21  determine antisense relationships for a given triplet codon. In step  16 , the currently selected triplet codon for both sequences is ‘read’. For example, for sequence 1 the first triplet codon of the first frame would be ‘AAA’, and for sequence 2 this would be ‘TTT’. In step  17 , the second character of each of these strings is selected. In step  18 , the ASCII codes are multiplied and compared, in decision step  19 , to a list to find out if the bases selected are ‘complementary’, in accordance with the rules of the genetic code. If they are, the first bases are compared in step  20 , and subsequently the third bases are compared in step  21 . Step  18  then determines whether the bases are ‘complementary’ or not. If the comparison yields a ‘non-complementary’ value at any step the routine terminates and the output score ‘T’ is set to zero. Otherwise the triplet codons are complementary and the output score T=1. 
     FIG. 6 illustrates the process of rationalising the results after the comparison of 2 protein or 2 DNA sequences. In step  22 , the first ‘result’ is selected. A result consists of information on a pair of frames that were deemed ‘antisense’ in FIGS. 2 or  3 . This information includes location, length, score (i.e., the sum of scores for a frame) and frame type (forward or reverse, depending on orientation of sequences with respect to one another). In step  23 , the frame size, the score values and the length of the parent sequence are then used to calculate the probability of that frame existing. The statistics, which govern the probability of any frame existing, are described in the next section and refer to equations 1-4. If the probability is less than a user chosen value ‘p’, then the frame details are ‘stored’ for inclusion in the final result set (step  24 ). 
     Statistical Basis of Program Operation 
     The number of complementary frames in a protein sequence can be predicted from appropriate use of statistical theory. 
     The probability of any one residue fitting the criteria for a complementary relationship with any other is defined by the groupings illustrated in EXAMPLE 2. Thus, depending on the residue in question, there are varying probabilities for the selection of a complementary amino acid. This is a result of an uneven distribution of possible partners. For example possible complementary partners for a tryptophan residue include only proline whilst glycine, serine, cysteine and arginine all fulfil the criteria as complementary partners for threonine. The probabilities for these residues aligning with a complementary match are thus 0.05 and 0.2 respectively. The first problem in fitting an accurate equation to describe the expected number of complementary frames within any sequence is integrating these uneven probabilities into the model. One solution is to use an average value of the relative abundance of the different amino acids in natural sequences. This is calculated by equation 1 
     
       
           v=ΣR*N   1 
       
     
     Where v=probability sum, R=fractional abundance of amino acid in  e.coli  proteins, N=number of complementary partners specified by genetic code. 
     This value (v) is calculated as 2.98. The average probability (p) of selecting a complementary amino acid is thus 2.98/20=0.149. 
     For a single ‘frame’ of size (n) the probability (C) of pairing a number of complementary amino acids (r) can be described by the binomial distribution (equation 2)              C   =         n   !           (     n   -   r     )     !                     r   !                         p   r                       (     1   -   p     )       (     n   -   r     )               2                              
     With this information we can predict that the expected number (Ex) of complementary frames in a protein to be:              Ex   =     2                     (     S   -   n     )     2                       n   !           (     n   -   r     )     !                     r   !                         p   r                       (     1   -   p     )       (     n   -   r     )               3                              
     Where S=protein length, n=frame size, r=number of complementary residues required for a frame and p=0.149. If r=n, representing that all amino acids in a frame have to fulfil a complementary relationship, the above equation simplifies to: 
     
       
           Ex =2( S−n ) 2   p   n   4 
       
     
     For a population of randomly assembled amino acid chains of a predetermined length we would expect the number of frames fulfilling the complementary criteria in the search algorithm to vary in accordance with a normal distribution. 
     Importantly, it is possible to standardise results such that given a calculated mean (μ) and standard deviation (σ) for a population it is possible to determine the probability of any specific result occurring. Standardisation of the distribution model is facilitated by the following relation:              Z   =       X   -   μ     σ           5                              
     Where X is a single value (result) in a population. 
     If we are considering complementary frames with a single protein structure then the above statistical model requires further analysis. In particular, the possibility exists that a region may be complementary to itself, as indicated in the diagram below.                           
     Reverse turn motifs within proteins. A region of protein may be complementary to itself. In this scenario, A-S, L-K and V-D are complementary partners. A six amino acid wide frame would thus be reported (in reverse orientation). A frame of this type is only specified by half of the residues in the frame. Such a frame is called a reverse turn. 
     In this scenario, once half of the frame length has been selected with complementary partners, there is a finite probability that those partners are the sequential neighbouring amino acids to those already selected. The probability of this occurring in any protein of any sequence is: 
     
       
           Ex=p   f     12   ( S−f )  7 
       
     
     Where f is the frame size for analysis, and S is the sequence length and p is the average probability of choosing an antisense amino acid. 
     The software of the embodiment incorporates all of the statistical models reported above such that it may assess whether a frame qualifies as a forward frame, reverse frame, or reverse turn. 
     Antisense X-Ray Structure Analysis (AXRA) Software 
     Currently over 20 prokaryote and 1 eukaryote genomes have been completely sequenced and more than 3 times that number are in progress or nearing completion including the human genome. The wealth of information generated is providing the foundation for a new important initiative in structural biology. Protein fold assignment and homology modelling of related protein structures have become important research tools, providing structural insights for many different areas of biology and medicine, Burley et al., 1999. At present, however, despite large-scale protein structure analyses only a fraction of a protein can usually be modelled e.g. 18% of all residues, or domains in yeast proteins. 
     “The obvious solution to this problem is to obtain complete three-dimensional structural information for each distinct protein fold. De novo prediction of a protein structure from its sequence is simply not feasible at present”, Burley et al, 1999. 
     The current invention provides a novel method for aiding the determination of three dimensional structure. 
     This software performs the following tasks: 
     Reads an X-Ray structure file 
     Determines regions of complementary hydropathy and /or antisense pairings in 3D space, between 
     1) 2 discontinuous protein sequences 
     2) 1 discontinuous and 1 linear protein sequences 
     3) 2 linear protein sequences. 
     Inventive Aspect of Software 
     The observation that many receptor-ligand contact points within the IL-1β II-1R X-ray crystal structure involve an interchange of residues of opposite polarity, suggests that this may represent a general principle of protein contact points. In this vein, AXRA was designed to analyse X-ray data for regions of complementary hydropathy and/or antisense relationships between proximal residues. This software confers significant advantages in: 
     Prediction of tertiary and quaternary protein structures. 
     Prediction of intermolecular contact points 
     AXRA overcomes previous limitations of analysing protein sequences for antisense interactions by recognising for the first time that antisense pairings also exist in discontinuous regions of proteins, and thus antisense sequence searching can be expanded to 3 dimensional structures. 
     Program Operation 
     In overview, program functions by: 
     Reading an X-ray data file 
     Calculating which sets of residues, or ‘frames’ of user defined length, represent the greatest area of complementary hydropathy and/or antisense relationships. 
     User options allow control over searching parameters such as frame length, minimum distance for partner and number of neighbouring residues from the same chain to exclude from analysis. 
     
       
         
               
             
               
               
             
           
               
                 TABLE 6 
               
             
             
               
                   
               
               
                 Description of parameters used in AXRA process 
               
             
          
           
               
                 Name 
                 Description 
               
               
                   
               
               
                 N 
                 Framesize 
               
               
                 Pos 
                 Current index in a sequence array (one letter amino acid codes) 
               
               
                 Mind 
                 A minimum distance parameter - used with maxd to determine the 
               
               
                   
                 range of distance values that will be used to locate neighboring 
               
               
                   
                 amino acids. 
               
               
                 Maxd 
                 A maximum distance parameter - used with mind to determine the 
               
               
                   
                 range of distance values that will be used to locate neighboring 
               
               
                   
                 amino acids. 
               
               
                 X 
                 Maximum number of amino acids that can exist within a Nearest 
               
               
                   
                 Neighbour Sphere (NNS). 
               
               
                 ST 
                 Score Threshold - when analysing frames of the protein sequence 
               
               
                   
                 the score threshold determines the number of amino acids within a 
               
               
                   
                 frame that have to exist for that frame to be counted as a ‘hit’ 
               
               
                 S 
                 Score - the aggregate score of antisense relationships 
               
               
                   
                 within a frame 
               
               
                 HST 
                 HydropathyScoreThreshold - a user input value determining 
               
               
                   
                 the threshold hydropathy score (determining whether a frame is 
               
               
                   
                 saved or not). 
               
               
                 LR 
                 List of arrays of hydropathy scores. Each amino acid has a list 
               
               
                   
                 of hydropathy scores relating to the list of nearest neighbour 
               
               
                   
                 (NNS) amino acids. 
               
               
                 RF 
                 Result Frame - a frame containing hydropathy scores. 
               
               
                   
               
             
          
         
       
     
     Decision steps  25  to  30  are shown in FIG.  7 . In step  25 , the program reads a file containing the Cartesian x, y, z co-ordinates of a protein structure and these are stored by conventional programmatic means (step  26 ). The protein sequence (1 letter amino acid codes) is also read from this file and stored in memory as an array of characters. In step  27 , the distances between each alpha- carbon atom (as denoted in Brookhaven databank format CA) and all other carbon atoms that make up each amino acid (CB, c1, c2, cn) are calculated by vector mathematics from the cartesian co-ordinates. The program user chooses (through the UI) which atom type (e.g. CB, c1 etc) are used in the calculation of the distances between two amino acids. The x closest amino acids for each residue are stored for further analysis. The value x, the number of nearest amino acids to interrogate, is provided by the user from a suitable user interface (UI). For each amino acid in the protein structure we now have a list of proximal amino acids within distances mind and maxd between any carbon atoms that constitute the structure of that residue. The default maximum distance in this process is 15 angstroms; if less than x amino acids fall within this distance then only those within this distance will be stored. The user may change this value through the UI. This is known as the Nearest Neighbour Sphere (NNS). In decision step  28 , the program flow follows the user&#39;s choice (input through the UI) as to whether the analysis should be based on hydropathy (step  29 ) or whether the analysis should be based on antisense relationships (step  30 ). 
     Decision steps  31  to  35  are shown in FIG.  8 . In step  31 , the antisense relationships between the first amino acid in the protein sequence (stored in step  25 ) and the list of amino acids stored as the nearest neighbour sphere (NNS) are determined. (Programmatically, the NNS is a list of arrays—one array for each position in the protein sequence). To do this, each amino acid in the sequence is selected in turn and compared with each member of its NNS (stored in step  27 ) using the algorithm depicted in FIG.  5 . If none of the NNS members for a particular amino acid show an antisense relationship (i.e. output value of  1  from FIG. 5) then a zero value is scored at this position in a Result Array ‘R’, otherwise the details (sequence index) of the closest amino acid fulfilling an antisense relationship are stored in the result Array ‘R’ for further analysis. The user may specify input values determining the maximum (maxd) and minimum (mind) distances that antisense relationships must fall within to be accepted. This process is repeated for all amino acids in the protein sequence generating a Result Array ‘R’ containing sequence indexes of all amino acids that fulfil an antisense criteria within the NNS. The overall process here is to define which proximal amino acids have antisense relationships. 
     Decision step  32  routes the users selection (from the UI) of whether to find regions of antisense relationships between 2 continuous parts of the same sequence (step  33 ), 1 continuous and 1 discontinuous part of the same sequence (step  34 ) or 2 discontinuous parts of the same sequence (step  35 ). 
     In step  33 , the first ‘frame’ of length ‘n’ of the protein sequence is selected. The frame is a section of the total sequence, and the length of this frame (n) is chosen by the user through the UI. Also chosen through the UI is a ScoreThreshold&#39; ‘ST’ parameter. The first frame (of length ‘n’) is selected from the protein sequence. For each amino acid in this frame the NNS is analysed. If any continuous combinations of antisense relationships within the NNS are found where the aggregate score ‘S’ is greater than the user chosen ScoreThreshold ‘ST’ then the amino acids sequence locations are stored as a ‘hit’ frame. This is repeated for each frame in the protein sequence. When the process has finished the ‘hit frame’ results are then listed in an appropriate UI format. 
     In step  34 , the first ‘frame’ of length ‘n’ of the protein sequence is selected. The frame is a section of the total sequence, and the length of this frame (n) is chosen by the user through the UI. Also chosen through the UI is a ScoreThreshold&#39; ‘ST’ parameter. The first frame (of length ‘n’) is selected from the protein sequence. For each amino acid in each frame the NNS is analysed. If any discontinuous combinations of antisense relationships within the NNS are found where the aggregate score ‘S’ is greater than the user chosen ScoreThreshold ‘ST’ then the amino acids sequence locations are stored as a ‘hit’ frame. This is repeated for each frame of the protein sequence. When the process has finished the ‘hit frame’ results are then listed in an appropriate UI format. 
     In step  35 , the first amino acid of the protein sequence is selected. The list of antisense relationships determined in step  31  is listed in an appropriate UI format. 
     Decision steps  36  to  40  are shown in FIG.  9 . In step  40 , the hydropathic comparison scores between the first amino acid in the protein sequence (stored in step  25 ) and the list of amino acids stored as the nearest neighbour sphere (NNS) are determined using the following equation: 
     
       
           H =( a   1   +a   2 ) 2   
       
     
     Where a1 and a2 are the hydropathy scores of the amino acids selected as scored on the Kyte and Doolittle scale (Kyte and Doolittle, 1982). This equation is evaluated for each pair of amino acids specified by the currently selected amino acid and its partners in the NNS and the resulting H values are scored. 
     The user may specify input values determining the maximum (maxd) and minimum (maxd) distances that relationships must fall within to be processed further. This process is repeated for all amino acids in the protein sequence. The overall process here is to define the hydropathic relationships between proximal amino acids. Programmatically, we end up with a list of arrays where each array contains a list of hydropathic scores for amino acids neighbouring the amino acid specified by the index in the main list. This list of arrays LR is then used for steps  37 ,  38  or  39 . 
     Decision step  36  routes the users selection (from the UI) of whether to find regions of complementary hydropathy between 2 continuous parts of the same sequence (step  37 ), 1 continuous and 1 discontinuous part of the same sequence (step  38 ) or 2 discontinuous parts of the same sequence (step  39 ). 
     In step  37 , the frame is a section of the total sequence, and the length of this frame (n) is chosen by the user through the UI. Also chosen through the UI is a Hydropathy Score Threshold ‘HST’ parameter. The first ‘frame’ of length ‘n’ of the protein sequence is selected. In this first frame the first amino acid is selected. The LOWEST value of the list of hydropathy scores formed in step  40  is taken and written to a result frame RF. (The sequence indexes of the amino acids that are responsible for the lowest scores are written to another list SL such that a link between amino acid location and hydropathy is created.). This is repeated for each amino acid in the frame until we have a completed result Frame ‘RF’ that contains a list of the lowest hydropathy scores available for the specified amino acids. The average hydropathy for this frame is then determined by the following equation:              Ω   =           ∑                           H     L             10                              
     Where H is defined in the equation above, L is the frame length, denoting the length of the amino acid sequence that is used for the comparison. The lower the score (Ω), the greater the degree of hydropathic complementarity for the defined region. 
     Once the average hydropathy score is calculated, if that score is LOWER than the HST parameter the sequence indexes of the amino acids that were responsible for the hydropathy values used in equation 10 are analysed for continuity (i.e. are these amino acids continuous, such as pos 10, pos 11, pos 12 etc). If continuity is found, the frame is stored for further analysis. 
     This is repeated for each frame of the protein sequence (i.e. of frame length 7, 1-7, 2-8, 3-9 etc). When the process has finished the results are then listed in an appropriate UI format. 
     In step  39 , the frame is a section of the total sequence, and the length of this frame (n) is chosen by the user through the UI. Also chosen through the UI is a Hydropathy Score Threshold ‘HST’ parameter. The first ‘frame’ of length ‘n’ of the protein sequence is selected. In this first frame the first amino acid is selected. The LOWEST value of the list of hydropathy scores formed in step  40  is taken and written to a result frame RF. (The sequence indexes of the amino acids that are responsible for the lowest scores are written to another list SL such that a link between amino acid location and hydropathy is created.). This is repeated for each amino acid in the frame until we have a completed result Frame ‘RF’ that contains a list of the lowest hydropathy scores available for the specified amino acids. The average hydropathy for this frame is then determined by the following equation 10. 
     Once the average hydropathy score is calculated, if that score is LOWER than the HST parameter the sequence indexes of the amino acids that were responsible for the hydropathy values used in equation 10 are stored in a suitable programmatic container to display as results. This is repeated for each frame of the protein sequence (i.e. of frame length 7, 1-7, 2-8, 3-9 etc). When the process has finished the results are then listed in an appropriate UI format. 
     In step  38 , all hydropathic relationships (equation 10) between each amino acid and its NNS counterparts are written out to a display for further analysis. 
     The program flow is illustrated in FIG.  7 . 
     Specific Example of AXRA Output 
     The software was used to select regions of complementary hydropathy within the IL-1β IL-1R crystal structure. The program was run on the X-ray file (pdb2itb) and selected the most complementary region between the ligand and receptor as consisting of residues 47-54 of IL-1β (sequence QGEESND, SEQ ID No. 7) and residues 245, 244, 303, 298, 242, 249, 253 of the receptor (sequence W, S, V, I, G, Y, N). This demonstrates two things. Firstly, it shows that the software functions properly in that it can locate regions of hydropathic complementarity between a receptor-ligand pair. Secondly, it proves that the region of IL-1β which has the closest residues of greatest hydropathic inversion to the IL-1 type I receptor is the trigger loop region of IL-1β to which we have previously designed antisense peptides. 
     UTILITY OF THE INVENTION 
     This invention presents a novel informatics technology that greatly accelerates the pace for initial identification and subsequent optimization of small peptides that bind to protein-protein targets. Using this technology an operator can systematically produce large numbers or ‘catalogues’ of small peptides that are very useful and specific agonists/antagonists of protein-protein interactions. 
     These peptides are ideally suited for use in drug discovery programs as biological tools for probing gene function, or as a basis for configuring drug discovery screens or ba molecular scaffold for medicinal chemistry. In addition, peptides with a high affinity for a protein could form drugs in their own right. 
     Finally, these peptides are amenable to dramatic further improvement through various methods in addition to traditional medicinal chemistry. 
     The publications, patents, and patent applications listed herein are incorporated by reference in their entirety. 
     REFERENCES 
     The publications, patents, and patent applications cited are incorporated by reference herein in their entirety. 
     Aota S, Gojobori T, Ishibashi F, Marvyama T and Ilkarnea T. 1988. Codon usage tabulated from the GenBank Genetic Sequence Data. Nucleic Acid Res. 16: 315-391. 
     Bairoch A and Apweiler R. 1999. The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1999. Nucleic Acids Research. 27:49-54. 
     Biro J. 1981. Comparative analysis of specificity in protein-protein interactions. Part II.: 
     The complementary coding of some proteins as the possible source of specificity in protein-protein interactions. Med.Hypotheses 7: 981-993. 
     Blalock J E. 1995. Genetic origins of protein shape and interaction rules. Nature Medicine 1: 876-878. 
     Blalock J E and Smith E M. 1984. Hydropathic anti-complementarity of amino acids based on the genetic code. Biochem Biophys Res Commun. 12: 203-7. 
     Baranyi L, Campbell W, Ohshima K, Fujimoto S, Boros M and Okada H. 1995. The antisense homology box: a new motif within proteins that encodes biologically active peptides. Nature Medicine. 1:894-901. 
     Baranyi L, Campbell W and Okada H. 1996. Antisense homology boxes in C5a receptor and C5a anaphylatoxin: a new method for identification of potentially active peptides. J Immunol. 157:4591-601. 
     Bost K L, Smith E M. and Blalock J E.1985. Similarity between the corticotropin (ACTH) receptor and a peptide encoded by an RNA that is complementary to ACTH mRNA. Proc.Natl. Acad. Sci. USA 82: 1372-1375. 
     Bost K L and Blalock J E. 1989. Production of anti-idiotypic antibodies by immunization with a pair of complementary peptides. J. Molec. Recognit. 1: 179-183. 
     Burley S K, Almo S C, Bonanno J B, Capel M, Chance M R, Gaasterland T, Lin D, Sali A, Studier F W and Swaminathan S. 1999. Structural genomics: beyond the Human Genome Project. Nature Genetics 23: 151-157. 
     Fassina G, Zamai M, Burke M B, Chaiken, M. 1989. Recognition properties of antisense peptides to Arg8-vasopressin/bovine neurophysin II biosynthetic precursor sequences. Biochemistry 28, 8811-8818. 
     Fishman M and Adler F L. 1967 Cold Spring Harbour Symp. Quant. Biol. 32: 343-350 Gaasterland T. Structural genomics: Bioinformatics in the driver&#39;s seat. Nature Biotechnology 16: 645-627, 1998. 
     Goldberg D. E. 1989. Genetic algorithms in search optimisation and machine learning. Addison-Wesley. 
     Goldstein D J. 1998. An unacknowledged problem for structural genomics? Nature Biotechnology 16: 696-697. 
     Kohler H and Blalock E. 1998. The hydropathic binary code: a tool in genomic research? Nature Biotechnology 16: 601. 
     Kyte J and Doolittle RF. 1982. A simple method for displaying the hydropathic character of a protein. J Mol Biol 5:105-132. 
     Mekler L B. 1969 Specific selective interaction between amino acid groups of polypeptide chains Biofizika 14: 581-584. 
     Mekler L B and Idlis R G. 1981 Deposited Doc. VINITI 1476-81. 
     Root-Bernstein R S and Holsworthy D D. 1988. Antisense peptides: a critical mini-review. J. Theor. Biol. 190: 107-119. 
     Root-Bernstein R S. 1982. Amino acid pairing. J Theor Biol. 94:885-94. 
     Sansom C. 1998. Extending the boundaries of molecular modelling. Nature Biotechnology 16: 917-918. 
     Shai Y, Brunck T K and Chaiken I M. 1989. Antisense peptide recognition of sense peptides: sequence simplification and evaluation of forces underlying the interaction. Biochemistry. 28: 8804-11. 
     Stryer L. Biochmistry. 4 th  Edition. Freeman and Company, New York 1997. 
     Zull J E, Taylor R C, Michaels G S and Rushforth N B. 1994. Nucleic acid sequences coding for internal antisense peptides: are there implications for protein folding and evolution? Nucleic Acids Res. 22: 3373-80. 
     
       
         
               
             
               
               
               
               
               
               
               
               
             
           
               
                 TABLE 7 
               
             
             
               
                   
               
               
                 EXAMPLES OF PROTEINS TO WHICH COMPLEMENTARY PEPTIDES CAN BE 
               
               
                 IDENTIFIED BY ANTISENSE LIGAND SEARCHER (ALS) IN THE SWISSPROT 
               
               
                 DATABASE 
               
               
                 Frame Size 10: Swiss Prot DB: 50 significant proteins 
               
             
          
           
               
                 Accession No. 
                 Description 
                 Length 
                 No. 
                 No 
                 No. RT 
                 Total 
                 Ex( ) 
               
               
                   
               
               
                 SHEEP (P50415) 
                 BACTENECIN 7 PRECURSOR (BAC7) 
                 190 
                  8 
                  8 
                 4 
                 16 
                 9.89E−05 
               
               
                 CHICK (Q98937) 
                 TRANSCRIPTION FACTOR BF-2 
                 440 
                 22 
                 26 
                 0 
                 48 
                 0.00053 
               
               
                 HUMAN (P55316) 
                 TRANSCRIPTION FACTOR BF-2 
                 469 
                 12 
                  4 
                 0 
                 16 
                 0.000603 
               
               
                 MOUSE (Q61345) 
                 TRANSCRIPTION FACTOR BF-2 
                 456 
                 22 
                 18 
                 1 
                 40 
                 0.00057 
               
               
                 HUMAN (Q12837) 
                 BRAIN-SPECIFIC HOMEOBOX 
                 410 
                 40 
                 53 
                 1 
                 93 
                 0.000461 
               
               
                 MOUSE (Q63934) 
                 BRAIN-SPECIFIC HOMEOBOX 
                 411 
                 108  
                 127  
                 1 
                 235  
                 0.000463 
               
               
                 HUMAN (P20264) 
                 BRAIN-SPECIFIC HOMEOBOX 
                 500 
                 102  
                 103  
                 1 
                 205  
                 0.000685 
               
               
                 MOUSE (P31361) 
                 BRAIN-SPECIFIC HOMEOBOX 
                 495 
                 82 
                 83 
                 1 
                 165  
                 0.000671 
               
               
                 DROME (Q24266) 
                 TRANSCRIPTION FACTOR BTD 
                 644 
                 28 
                 32 
                 0 
                  6 
                 0.001 
               
               
                 GVCL (P41726) 
                 DNA-BINDING PROTEIN 
                  58 
                 48 
                 54 
                 10  
                 102 
                 9.22E−06 
               
               
                 HUMAN (P02452) 
                 PROCOLLAGEN ALPHA 1(I) 
                 1464  
                  6 
                 62 
                 4 
                 68 
                 0.005873 
               
               
                 HUMAN (P02458) 
                 PROCOLLAGEN ALPHA 1(II) 
                 1418  
                  6 
                 22 
                 2 
                 28 
                 0.005509 
               
               
                 MOUSE (P28481) 
                 PROCOLLAGEN ALPHA 1(II) 
                 1459  
                  8 
                 31 
                 3 
                 39 
                 0.005833 
               
               
                 BOVIN (P04258) 
                 COLLAGEN ALPHA 1(III) CHAIN 
                 1049  
                  8 
                 17 
                 3 
                 25 
                 0.003015 
               
               
                 HUMAN (P02461) 
                 PROCOLLAGEN ALPHA 1(III) 
                 1466  
                  8 
                 28 
                 2 
                 36 
                 0.005889 
               
               
                 BOVIN (Q28083) 
                 COLLAGEN ALPHA 1(XI) CHAIN 
                 911 
                  8 
                  4 
                 0 
                 12 
                 0.002274 
               
               
                 MOUSE (Q01149) 
                 PROCOLLAGEN ALPHA 2(I 
                 1373  
                 14 
                 84 
                 4 
                 98 
                 0.005165 
               
               
                 MOUSE (Q99020) 
                 CARG-BINDING FACTOR-A (CBF-A) 
                 285 
                  6 
                  9 
                 3 
                 15 
                 0.000223 
               
               
                 HUMAN (P22681) 
                 PROTO-ONCOGENE C-CBL 
                 906 
                 12 
                  2 
                 0 
                 14 
                 0.002249 
               
               
                 HUMAN (Q13319) 
                 CYCLIN-DEPENDENT KINASE 5 
                 367 
                  6 
                  1 
                 1 
                  7 
                 0.000369 
               
               
                 DROME (P17970) 
                 VOLTAGE-GATED POTASSIUM CH 
                 924 
                 162  
                 162  
                 18  
                 324  
                 0.002339 
               
               
                 DROME (Q02280) 
                 POTASSIUM CHANNEL PROTEIN E 
                 1174  
                 38 
                 63 
                 9 
                 101  
                 0.003776 
               
               
                 RAT (Q09167) 
                 SULIN-INDUCED GROWTH 
                 269 
                 32 
                 42 
                 8 
                 74 
                 0.000198 
               
               
                 CHICK (Q90611) 
                 72 KD TYPE IV COLLAGENASE 
                 663 
                  8 
                  8 
                 4 
                 16 
                 0.001204 
               
               
                 HPBVF (P29178) 
                 CORE ANTIGEN 
                 195 
                 14 
                 15 
                 3 
                 29 
                 0.000104 
               
               
                 DROME (P32027) 
                 FORK HEAD DOMAIN PROTEIN 
                 508 
                 50 
                 40 
                 0 
                 90 
                 0.000707 
               
               
                 CRYPA (P52753) 
                 CRYPARIN PRECURSOR 
                 118 
                 18 
                 18 
                 6 
                 36 
                 3.82E−05 
               
               
                 CANFA (P30803) 
                 ADENYLATE CYCLASE, TYPE V 
                 1184  
                 55 
                 34 
                 3 
                 89 
                 0.003841 
               
               
                 RABIT (P40144) 
                 ADENYLATE CYCLASE, TYPE V 
                 1264  
                 25 
                 21 
                 3 
                 46 
                 0.004378 
               
               
                 DICDI (P54639) 
                 CYSTEINE PROTEINASE 4 PRO . . . 
                 442 
                 84 
                 82 
                 14  
                 166  
                 0.000535 
               
               
                 ORYSA (P22913) 
                 DEHYDRIN RAB 16D 
                 151 
                  8 
                 22 
                 0 
                 30 
                 6.25E−05 
               
               
                 ORYSA (P12253) 
                 WATER-STRESS INDUCIBLE PRO . . . 
                 163 
                 14 
                 12 
                 4 
                 26 
                 7.28E−05 
               
               
                 RAPSA (P21298) 
                 LATE EMBRYOGENESIS ABUNDANT 
                 184 
                 24 
                 20 
                 0 
                 44 
                 9.2BE−05 
               
               
                 DROME (P23792) 
                 DISCONNECTED PROTEIN 
                 568 
                 27 
                 28 
                 8 
                 55 
                 0.000884 
               
               
                 DROME (Q24563) 
                 DOPAMINE RECEPTOR 2 
                 539 
                  8 
                  0 
                 0 
                  8 
                 0.000796 
               
               
                 DICDI (Q04503) 
                 PRESPORE PROTEIN DP87 PRE . . . 
                 555 
                 22 
                 17 
                 1 
                 39 
                 0.000844 
               
               
                 DROME (P23022) 
                 DOUBLESEX PROTEIN 
                 427 
                 56 
                 68 
                 0 
                 124  
                 0.0005 
               
               
                 DROME (P23023) 
                 DOUBLESEX PROTEIN, MALE-SP . . . 
                 549 
                 70 
                 88 
                 0 
                 158  
                 0.000826 
               
               
                 DROME (Q27368) 
                 TRANSCRIPTION FACTOR E2F 
                 805 
                  6 
                 11 
                 1 
                 17 
                 0.001776 
               
               
                 DROME (P20105) 
                 ECDYSONE-INDUCED PROTEIN 7 
                 829 
                  8 
                  5 
                 1 
                 13 
                 0.001883 
               
               
                 DROME (P11536) 
                 ECDYSONE-INDUCED PROTEIN 7 
                 883 
                 80 
                 83 
                 1 
                 163  
                 0.002136 
               
               
                 EBV (P12978) 
                 BNA-2 NUCLEAR PROTEIN 
                 487 
                 174  
                 178  
                 0 
                 352  
                 0.00065 
               
               
                 HUMAN (P18146) 
                 EARLY GROWTH RESPONSE PRO . . . 
                 543 
                 12 
                 17 
                 1 
                 29 
                 0.000808 
               
               
                 MOUSE (P49749) 
                 HOMEOBOX EVEN-SKIPPED HOM . . . 
                 475 
                 223  
                 208  
                 0 
                 431  
                 0.000618 
               
               
                 HUMAN (Q12947) 
                 FORKHEAD-RELATED TRANSCR . . . 
                 408 
                 10 
                 20 
                 0 
                 30 
                 0.000456 
               
               
                 HUMAN (Q16676) 
                 FORKHEAD-RELATED TRANSCR . . . 
                 465 
                 14 
                  7 
                 1 
                 21 
                 0.000592 
               
               
                 DROME (P33244) 
                 NUCLEAR HORMONE RECEPTOR 
                 1043  
                 104  
                 118  
                 12  
                 222  
                 0.002981 
               
               
                 BURCE (P24127) 
                 FUSARIC ACID RESISTANCE PRO . . . 
                 142 
                  6 
                  0 
                 0 
                  6 
                 5.52E−05 
               
               
                 SCHPO (P41891) 
                 GAR2 PROTEIN 
                 500 
                  8 
                  8 
                 0 
                 16 
                 0.000685 
               
               
                 HUMAN (P43694) 
                 TRANSCRIPTION FACTOR GATA-4 
                 442 
                 12 
                 12 
                 2 
                 24 
                 0.000535 
               
               
                   
               
             
          
         
       
     
     
       
         
           
             9 
           
           
             1 
             17 
             PRT 
             Unknown 
             
               Hypothetical sequence 
             
           
            1
Ala Thr Arg Gly Arg Asp Ser Arg Asp Glu Arg Ser Asp Glu Arg Thr
1               5                   10                  15
Asp
 
           
             2 
             20 
             PRT 
             Unknown 
             
               Hypothetical sequence 
             
           
            2
Gly Thr Phe Arg Thr Ser Arg Glu Asp Ser Thr Tyr Ser Gly Asp Thr
1               5                   10                  15
Asp Phe Asp Glu
            20
 
           
             3 
             8 
             PRT 
             Unknown 
             
               Hypothetical sequence 
             
           
            3
Ala Asp Thr Arg Gly Ser Arg Asp
1               5
 
           
             4 
             5 
             PRT 
             Unknown 
             
               Hypothetical sequence 
             
           
            4
Ala Thr Arg Gly Arg
1               5
 
           
             5 
             12 
             DNA 
             Unknown 
             
               Hypothetical sequence 
             
           
            5
aaatttagca tg                                                         12
 
           
             6 
             12 
             DNA 
             Unknown 
             
               Hypothetical sequence 
             
           
            6
tttaaagcat gc                                                         12
 
           
             7 
             7 
             PRT 
             Homo sapiens 
           
            7
Gln Gly Glu Glu Ser Asn Asp
1               5
 
           
             8 
             7 
             PRT 
             Homo sapiens 
           
            8
Leu Ile Thr Val Leu Asn Ile
1               5
 
           
             9 
             7 
             PRT 
             Unknown 
             
               Hypothetical sequence 
             
           
            9
Leu Thr Ile Leu Ile Asn Val
1               5