Patent Application: US-57185400-A

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 to an iterative procedure . 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:
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 fig1 . 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 fig1 . the embodiment can output the data in either of these formats as well as many others . 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 fig1 ), 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 fig2 otherwise processing continues to step 5 , described in fig3 . step 6 analyses the data resulting from either step 4 , or step 5 , and involves an algorithm described in fig6 . 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 fig2 . 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 fig4 / fig5 . 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 fig2 ). 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 . fig3 shows a block diagram of the algorithmic process that is carried out in the conditions described in fig1 . step 12 is the only difference between the algorithms fig2 and fig3 . 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 . fig4 and 5 describe the process in which a pair of amino acids ( fig4 ) 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 fig2 / 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 . fig6 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 fig2 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 ). 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 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 : 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 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 : 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 . 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 . determines regions of complementary hydropathy and / or antisense pairings in 3d space , between 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 : 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 . 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 . decision steps 25 to 30 are shown in fig7 . 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 fig8 . 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 fig5 . if none of the nns members for a particular amino acid show an antisense relationship ( i . e . output value of 1 from fig5 ) 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 fig9 . 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 : 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 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 . 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 . 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 - 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