Abstract:
A system and methods for rapidly and accurately assessing ligand binding characteristics for diverse classes of protein molecules. Modeling methods are used to represent the protein molecules and simulate their interaction with ligand molecules. Protein/ligand interactions are characterized by a fingerprint analysis that permits grouping of the proteins based on predicted structural features and ligand reactivity rather than sequence similarities or homology alone.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS  
       [0001]    This application claims priority under 35 U.S.C. Section 119(e) to U.S. Provisional Application Serial No. 60/226,327, and filed on Aug. 18, 2000. 
     
    
     
       BACKGROUND OF THE INVENTION  
         [0002]    1. Field of the Invention  
           [0003]    The invention relates to the field of protein analysis and more particularly to a system and method for predicting protein function.  
           [0004]    2. Description of the Related Art  
           [0005]    Genomic scale protein and gene identification projects continue to generate an ever-increasing number of sequences. Although the complete genomic sequence for a number organisms, including humans, is currently known, a substantial number of identified proteins and genes remain biochemically uncharacterized with little or no knowledge of their biological significance. To this end, current research efforts have begun to increasingly focus on the development of methods for characterizing, categorizing, and associating these sequences.  
           [0006]    In an effort to advance knowledge of the biochemical function of expressed genes, large databases of sequence information have been made available. As shown in FIG. 1, such a system typically includes a genomics/proteonomics database  20  that stores information related to a plurality of proteins, nucleotides, or a combination thereof. The database  20  is further configured to interact with a bioinformatics search and computation engine  30  that retrieves and processes protein and/or nucleotide information from the database  20 . One example of a currently commercially available genomics/proteonomics database and search/computation engine includes the GeneAtlas and AtlasStore products available from Accelrys in San Diego, Calif.  
           [0007]    [0007]FIG. 2 illustrates one embodiment of data organization within the database  20 . A plurality of entries  50  which describe and associate information with particular sequences is stored within the genomics/proteomics database  20 . The entries  50  comprise an identifier  51 , such as a name, accession number, or other reference that is associated with a particular sequence  60  that encodes a protein or portion of a protein molecule. The sequence  60  is further associated with one or more annotations or descriptors  65  used to store additional information about the sequence  60 . The annotations  65  for the protein sequence  60  may be any of a number of different informational types and are representative of a characteristic, value, or property that is associated with the protein. Of the many possible types and combinations of annotations  65 , some exemplary annotations include name descriptors (which may include the identifier  60 ), physical property characterizations (molecular weight, charge, shape, 3-D structure), chemical property characterizations (enzymatic activity, cofactors, turnover) as well as other annotation types.  
           [0008]    The typical genome for even a relatively simple organism contains many thousands of genes, most of which are un-characterized and have not been previously investigated. Conventional experimental techniques used to assess these biological molecules are based largely on manual techniques and “wet chemistry” approaches and are limited with respect to the total number species which can be studied. The limited ability of these techniques to rapidly collect and associate biological and genetic information makes for slow and painstaking progress toward understanding the biological function of expressed genes and the genome as a whole.  
           [0009]    More recently, computational characterization of gene and protein products has become of increasing interest to researchers as a method for performing research and analysis leading to more rapid identification and functional characterization of biological pathways and their component elements. However, when it comes to the computational analysis of the information present in genomic databases, the techniques available have been limited in power. For example, protein functional associations are typically conducted on the basis of sequence comparisons. Sequence homologies are analyzed in an attempt to identify functional analogy between proteins. This method of functional assessment is limited in its predictive ability and is not useful in comparing or identifying functional analogy between proteins that have different sequences. In the absence of further experimentation, these methods are restricted in their ability to provide useful functional information.  
         SUMMARY OF THE INVENTION  
         [0010]    In one embodiment, the invention comprises a method of deriving sequence annotations for sequences in a genomics or proteomics database by modeling the three dimensional structure of at least one protein encoded by a sequence in the database. Furthermore, the method comprises modeling an interaction between at least one ligand and the modeled three dimensional structure, and deriving an annotation from calculated characteristics of the interaction.  
           [0011]    In another embodiment, the invention comprises a method of annotating sequences in a genomics or proteomics database by selecting a set of sequences from the database, obtaining a structural model of each protein encoded by the set of sequences, selecting a set of ligand molecules, separately modeling an interaction between each ligand and each structural protein model. Furthermore, the method comprises deriving a value indicative of the strength of interaction between each ligand molecule and each protein model, and storing the values in association with the sequences in the database.  
           [0012]    In still another embodiment, the invention comprises a method of making a functional association between first and second protein molecules by retrieving a first series of values representative of binding strength between the first protein and a set of ligand molecules, retrieving a second series of values representative of binding strength between the second protein and the set of ligand molecules, and comparing the first series of values with the second series of values.  
           [0013]    In a further embodiment, the invention comprises a computer readable medium storing a plurality of gene sequences, at least a first one of which has one or more annotations stored in association therewith, wherein the annotations comprise a set of values indicative of the predicted strength of binding between a protein encoded by the first gene sequence and a corresponding set of chemically diverse ligand molecules.  
           [0014]    In a still further embodiment, the invention comprises a method of characterizing a protein by modeling an interaction between the protein and a ligand molecule. The method derives a value indicative of binding strength between the protein and the ligand molecule, repeats the modeling and derives for one or more additional ligand molecules, and stores the values as an associated set so as to form an interaction fingerprint characterizing chemical behavior of the protein.  
           [0015]    In another embodiment, the invention comprises a method of comparing first and second protein molecules by retrieving a first set of values representative of binding strength between the first protein and a corresponding set of ligands. Furthermore, the method comprises retrieving a second set of values representative of binding strength between the second protein and the set of ligands, and comparing the first set of values to the second set of values.  
           [0016]    In still another embodiment, the invention comprises a method of identifying a target protein for pharmaceutical intervention comprising the steps of: (a) selecting a first potential target protein, (b) retrieving a first interaction fingerprint comprising a set of values representative of binding strength between the potential target protein and a corresponding set of ligands, (c) retrieving a different interaction fingerprint comprising a set of values representative of binding strength between a different protein and the set of ligands, (d) comparing the first interaction fingerprint with the second interaction fingerprint and (e) repeating steps (c) and (d) for a plurality of different proteins encoded by a selected genome.  
           [0017]    In yet another embodiment, the invention comprises a system for biological research comprising, a database storing both gene sequences and interaction fingerprints characterizing chemical behavior of at least some proteins encoded by the gene sequences, and a search and computation engine configured to retrieve and compare the interaction fingerprints.  
           [0018]    In a still further embodiment, the invention comprises a method of assessing ligand interactions by selecting a ligand, and modeling the interaction of the ligand with a plurality of protein models spanning substantially an entire genome. 
       
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0019]    These and other aspects, advantages, and novel features of the invention will become apparent upon reading the following detailed description and upon reference to the accompanying drawings. In the drawings, same elements have the same reference numerals in which:  
         [0020]    [0020]FIG. 1 is a prior art database of sequence information.  
         [0021]    [0021]FIG. 2 illustrates a prior art schema for data organization within the database of sequence information of FIG. 1.  
         [0022]    [0022]FIG. 3 illustrates a method for annotating entries in a genomic/proteomic database.  
         [0023]    [0023]FIG. 4 illustrates a process for calculating ligand interaction annotations.  
         [0024]    [0024]FIG. 5 illustrates exemplary ligand interaction annotation sets for two proteins. 
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0025]    Embodiments of the invention will now be described with reference to the accompanying Figures, wherein like numerals refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive manner, simply because it is being utilized in conjunction with a detailed description of certain specific embodiments of the invention. Furthermore, embodiments of the invention may include several novel features, no single one of which is solely responsible for its desirable attributes or which is essential to practicing the inventions herein described.  
         [0026]    The present invention relates to systems and methods for utilizing models of protein structures to produce structure derived annotations useful in identifying analogies in function and/or chemical behavior between proteins. The annotations are derived from a variety of categories of structural information and provide a novel mechanism for characterization of expressed protein.  
         [0027]    The methods of characterization and annotation presented herein provide improved protein characterization capabilities over conventional characterization methodologies that typically rely heavily on sequence-based comparisons. This method is also particularly useful in determining associations between dissimilar proteins that may have functional or behavioral analogies which are not obvious due to differences in the protein sequence.  
         [0028]    [0028]FIG. 3 illustrates one advantageous method for annotating entries contained in a genomic/proteomic database using annotations resulting from protein modeling and protein/ligand interaction simulations. The method starts at block  150  where sequences encoding expressed proteins are retrieved from a genomics database. Using the stored sequence information, three-dimensional protein structures are derived at block  160  using any of a variety of structural derivation/prediction methods well known to those in the art. These methods may include energy minimizing protein folding methods, comparisons to known structures having similar sequences, etc. In some cases, a relevant protein structure may be or may have been experimentally established, thus eliminating the need to produce a predicted structure from sequence or other information.  
         [0029]    Once a three dimensional structure has been derived, a plurality of ligand interaction annotations are created at block  180  based upon a docking simulation between each modeled protein and each one of a series of selected ligands. Creation and use of ligand interaction annotations will be described in further detail below. In some embodiments of the invention, plurality of structural annotations are also created at block  170  which describe various structural features and/or properties of the protein molecule. The structural annotations may include one or more of the following: a shape pattern annotation, an active/binding site annotation, or an electrostatic field pattern annotation.  
         [0030]    At block  190 , the collection of derived annotations, including ligand interaction annotations and structural annotations (if any), are stored within the genomics/proteomics database  20  for subsequent retrieval and analysis.  
         [0031]    1. Creation of Interaction Fingerprint  
         [0032]    [0032]FIG. 4 illustrates an advantageous process for calculating ligand interaction annotations. In this process, a plurality of proteins or protein fragments  205  which are encoded by selected genetic sequences are identified, and each protein  205  is represented by a three dimensional structural model  210 . As mentioned above, the model  210  of the protein  205  may be predicted computationally or determined in whole or in part based on experimental information. For example, x-ray crystallographic information may be used to identify the protein structure and provide necessary information used in the construction of the structural model of the protein  205 . Typically, the protein model  210  will be obtained using information derived from nucleotide sequences which code for the amino acid sequence of the protein or protein fragment  205 .  
         [0033]    The plurality of protein models  210  form a protein modeling set  215 , shown by way of example in FIG. 4 as being composed of Proteins A, B, C, D, and E. The modeling set  215  may comprise protein models  210  describing the constituent proteins from a partial or complete genome in a particular organism. In one embodiment of the invention, models are derived for every expressed sequence of the human genome.  
         [0034]    In the reverse high-throughput screening process  200 , a ligand set  220  is further identified which contains a plurality of modeled ligand molecules  225  whose interaction with the plurality of proteins  205  in the protein modeling set  215  is to be characterized. The type of the ligand molecules  225  may be any of a number of different compositions and may include for example, organic molecules, inorganic molecules, ions, proteins, protein fragments, nucleotides, RNA, DNA or other molecules. In one advantageous embodiment, the ligand set is a chemically diverse set of organic molecules.  
         [0035]    Upon formation of the protein modeling set  215  and the ligand set  225 , an interaction assessment or virtual screening assessment  230  is performed. Virtual assessment  230  entails modeling the interaction of each ligand  225  with each of the proteins  205  in the protein modeling set  215 . In the example shown in FIG. 4, the virtual assessment  230  comprises simulating the interaction between the four ligands (Ligands A-D) and the five proteins (Proteins A-E) and results in a total of twenty protein/ligand interactions which are modeled.  
         [0036]    The protein/ligand interaction comparisons  235  comprise identifying the nature of ligand interaction with each protein  205  in the comparison. Typically, the protein/ligand interactions are characterized by a bonding affinity between each protein  205  and each ligand  225 . This could be a binary characterization, e.g. does the ligand bind or not, or it could be a numerical variable such as an estimate of an equilibrium binding constant or binding energy value.  
         [0037]    Using the information obtained from the interaction assessment  230 , a plurality of annotations are associated with each protein  205 . The plurality of annotations for each protein  205  further form a pattern of interactions which may be used to compare or distinguish the proteins  205  from one another on the basis of the calculated ligand interactions. Using the fingerprint  115  as a metric for comparison, proteins  205  may be structurally or functionally associated when they share commonalities in the interaction fingerprints  115 . This feature of the high-throughput modeling process  200  provides a powerful mechanism to associate proteins which does not rely on sequence or homology matching/comparisons alone, and which is useful in predicting how seemingly dissimilar proteins may be functionally related.  
         [0038]    [0038]FIG. 5 illustrates an set of annotations for a first protein which form a first vector  510  of ligand interaction information. This vector may be referred to as an “interaction fingerprint” for the protein. In this example, the vector comprises a series of binary values, each one of which is representative of the presence or absence of binding between one of the test ligands and the protein. In the example of FIG. 5, a set of twenty ligands is used, although it will be appreciated that more or fewer than twenty may be used. A value of 1 in the vector denotes that the corresponding ligand will bind to the protein. A second vector  520  is also obtained for a second protein after docking simulations with the same set of test ligands.  
         [0039]    When the two vectors are aligned and compared, it can be seen that the two proteins bind to four common ligands. An “overlap” computation may therefore be performed which comprises a vector multiplication where corresponding entries are multiplied, and the results summed to form a scalar output value. In the example of FIG. 5, the scalar output is 4. It may be useful to normalize this by dividing by the square root of the number of 1s in the first vector  510  times the number of 1s in the second vector  520 . A normalized overlap value in this example is therefore slightly less than 0.8. It will be appreciated that analogous overlap values may also be computed if numerical variables such as estimated binding constants are used instead of binary 1 and 0 values.  
         [0040]    2. Uses of the Interaction Fingerprint  
         [0041]    a. Protein Functional Associations  
         [0042]    This overlap value may be used as an indication of protein similarity in the same way that sequence homology is used. Normalized overlap values closer to one indicate proteins with similar chemical response. Overlap values closer to zero indicate proteins with divergent chemical response. These overlap values provide a valuable supplement to sequence homologies because the chemical behavior of two proteins may be similar even if their sequences are quite different. The interaction fingerprints can therefore be used to resolve ambiguous function assignments and improve the accuracy of functional annotation transfer from one sequence to another in a genomic database.  
         [0043]    b. Toxicity Assessment  
         [0044]    Qualified drug candidates can be evaluated for toxicity by comparing the protein which is the target of the drug candidate to other proteins in the human genome. In this application, the interaction fingerprint of the target protein is compared with the interaction fingerprint of other proteins, typically all or substantially all other proteins, in the human genome. Proteins that share a similar interaction fingerprint to the drug substrate may be identified as possible sources of undesirable side effects of the drug candidate.  
         [0045]    Another form of toxicity assessment may be performed which may be termed “reverse high throughput screening.” In this method, a drug candidate is tested for binding affinity to substantially all of the modeled proteins of a desired genome. For example, a drug candidate with known or suspected desirable pharmaceutical activity is screened for affinity against substantially every expressed gene sequence in the human genome. Modeled binding events discovered during this screening process are possible sources of undesirable side effects. It will be appreciated that this is a reversal of conventional high throughput screening, where hundreds or thousands of ligands are tested for affinity to a single protein target. This procedure can be highly useful in drug discovery. For example, if a set of lead compounds have been identified, further testing can be focused on those leads which show the highest target selectivity and least likelihood of toxicity, thus reducing the amount of resources used to follow up on initially promising leads that later fail due to toxicity problems. A further benefit of performing this analysis is that an additional ligand interaction annotation can be added to the database for each protein, expanding the coverage of the stored interaction fingerprints.  
         [0046]    c. Identification of Targets for Pharmaceutical Intervention  
         [0047]    A variety of methods of fingerprint analysis can be used to improve the process of selecting targets for pharmaceutical intervention. The methods can be used to minimize the chances of selecting candidates having adverse side effects.  
         [0048]    In one embodiment, a biochemical pathway is identified for intervention. As one example, a metabolic pathway in a disease pathogen may be selected which involves the activity of ten different proteins. If any of these ten proteins are inactivated, the biochemical chain will be broken and the pathogen will be killed. With interaction fingerprint annotations, the interaction fingerprint overlap of each of these ten proteins with each protein of the human genome may be determined. To minimize the potential for adverse side effects, the ten proteins in the pathogen&#39;s metabolic pathway may be ranked according to their average chemical response similarity to the proteins of the human genome. They may also be ranked according to the maximum chemical response overlap found with any human protein. The proteins in the pathogen&#39;s metabolic pathway with lower average and/or maximum overlaps to human proteins are then identified as the best candidates for pharmaceutical intervention because a ligand which inactivates one of these pathogen proteins is less likely to bind to human proteins with resulting undesired side effects.  
         [0049]    d. Chemical Family Identification for Drug Candidates  
         [0050]    Once a protein target for pharmaceutical intervention has been identified, its chemical fingerprint can be analyzed to see if it tends to bind to ligands in a particular chemical family. The chemical fingerprint for a particular protein may, for example, indicate that sulfones tend to interact with the protein. In this case, drug discovery can be focused on candidates in the indicated family first, leading to faster identification of specific molecules with the desired pharmaceutical activity.  
         [0051]    e. Selectivity Identification  
         [0052]    Interaction fingerprints may also be used to focus drug discovery on molecules in chemical families that are more likely to exhibit a specific desired pharmaceutical activity without exhibiting other activities. For example, a family of related proteins may have been previously functionally characterized, and it may be desirable to inactivate one of these proteins in a specific biochemical pathway. Members of the kinase family are one possible example. Kinases are involved in a wide variety of biochemical reactions, a specific one of which may be the target of drug discovery research. Because the proteins in this family are functionally related, many members of the family are likely to have similar interaction fingerprints. However, there are still likely to be differences between them. The interaction fingerprints may advantageously be analyzed to identify a chemical family of ligands that is preferentially bound to the target, but not highly bound to other members of the protein family.  
         [0053]    f. Protein Family Profiling  
         [0054]    Biological research on proteins making up a functionally related family can also be facilitated with the added information provided by the interaction fingerprints. Subfamilies can be identified and family trees can be constructed based upon interaction fingerprint overlaps of the different family members. In this application, the interaction fingerprint overlaps are analyzed an a manner analogous to sequence homologies in phylogenetic profiling.  
         [0055]    As previously described, acquisition and analysis of the interaction fingerprint pattern produced by modeled protein/ligand interactions has numerous areas of application. The information obtained from the protein/ligand interaction assessments can be used in data mining applications to determine associations and relationships between diverse classes of both proteins and ligands to reveal previously unknown functional or structural similarities. In addition, drug discovery may be facilitated by increasing the likelihood of selective activity of leads, and by reducing the chance that a qualified candidate will exhibit toxic or otherwise adverse side effects.  
         [0056]    Although the foregoing description of the invention has shown, described and pointed out novel features of the invention, it will be understood that various omissions, substitutions, and changes in the form of the detail of the system and methods as illustrated may be made by those skilled in the art without departing from the spirit of the present invention. Consequently the scope of the invention should not be limited to the foregoing discussion but should be defined by the appended claims.