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WO1998029744A2 - Procede de classification de produits geniques - Google Patents

Procede de classification de produits geniques Download PDF

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Publication number
WO1998029744A2
WO1998029744A2 PCT/US1997/023762 US9723762W WO9829744A2 WO 1998029744 A2 WO1998029744 A2 WO 1998029744A2 US 9723762 W US9723762 W US 9723762W WO 9829744 A2 WO9829744 A2 WO 9829744A2
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WIPO (PCT)
Prior art keywords
proteins
protein
panel
ligand
multiplicity
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PCT/US1997/023762
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English (en)
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WO1998029744A3 (fr
Inventor
Lawrence M. Kauvar
Hugo O. Villar
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Telik, Inc.
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Application filed by Telik, Inc. filed Critical Telik, Inc.
Priority to AU59020/98A priority Critical patent/AU5902098A/en
Publication of WO1998029744A2 publication Critical patent/WO1998029744A2/fr
Publication of WO1998029744A3 publication Critical patent/WO1998029744A3/fr

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N15/00—Mutation or genetic engineering; DNA or RNA concerning genetic engineering, vectors, e.g. plasmids, or their isolation, preparation or purification; Use of hosts therefor
    • C12N15/09—Recombinant DNA-technology
    • C12N15/10—Processes for the isolation, preparation or purification of DNA or RNA
    • C12N15/1034—Isolating an individual clone by screening libraries
    • C12N15/1089—Design, preparation, screening or analysis of libraries using computer algorithms
    • C—CHEMISTRY; METALLURGY
    • C40—COMBINATORIAL TECHNOLOGY
    • C40B—COMBINATORIAL CHEMISTRY; LIBRARIES, e.g. CHEMICAL LIBRARIES
    • C40B30/00—Methods of screening libraries
    • C40B30/04—Methods of screening libraries by measuring the ability to specifically bind a target molecule, e.g. antibody-antigen binding, receptor-ligand binding
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6845—Methods of identifying protein-protein interactions in protein mixtures

Definitions

  • the invention relates to methods whereby the tens of thousands of proteins encoded by the genome of an organism can usefully be classified so as to provide a significant aid in design of therapeutic and diagnostic methods relative to the organism. More specifically, the invention concerns methods to define classes of genome-encoded proteins by evaluating their reactivities with ligands of known activity, especially those which are natural products, and with respect to reference panels of such ligands.
  • Binding of a ligand to a protein target can also be estimated indirectly through comparison of profiles obtained by reacting both a known binding ligand and a ligand of unknown binding ability with a panel of antibodies or other agents of differing reactivities as described in U.S. Patent Nos. 5,300,425; 5,340,474; 4,963,263; and 5,133,866. All of the foregoing publications are incorporated herein by reference.
  • the present invention offers methods both to identify protein targets of any arbitrarily chosen ligand, and in particular natural product ligands, and also methods to obtain a meaningful classification of the numerous proteins encoded by the genome of even a higher animal.
  • the classification is based on obtaining a novel database which is comprised of signatures of a multiplicity of proteins.
  • the signatures are binding profiles with respect to a maximally diverse panel of ligands.
  • the classification system for genome products provided by the invention which is based on ligand binding makes possible more efficient screening of combinatorial libraries for suitable therapeutic and diagnostic candidates. It also provides an appropriate basis for drug design.
  • the invention is directed to a signature database wherein each signature represents a binding profile of a particular protein with respect to a maximally diverse panel of ligands.
  • each signature represents a binding profile of a particular protein with respect to a maximally diverse panel of ligands.
  • the ligands may be natural products; the multiplicity of proteins may be that multiplicity encoded by the genome of an animal, or that portion of the encoded proteins that is expressed in a particular cell or tissue.
  • signature refers to a profile of binding affinities or other reactivities of a protein with respect to a diverse panel of ligands.
  • the invention is directed to a method to obtain a database consisting essentially of a signature for each of a multiplicity of proteins, said proteins representing a random collection of gene-encoded proteins.
  • the method comprises determining a signature for each protein by contacting each of said proteins with each member of a maximally diverse panel of ligands under conditions wherein the affinity of said protein for said member of the panel can be assessed, assessing the affinity of said protein for each member of the panel, and arranging the affinities assessed in a retrievable form so as to obtain said signature.
  • the signatures are assembled so as to provide said database.
  • the invention is also directed to the database thus obtained and to a method to classify proteins using the database. It is preferred that the database be set out in computer-readable form.
  • the invention is directed to a method to classify a multiplicity of proteins which method comprises contacting each individual protein in the multiplicity with each ligand in a panel of ligands which ligands bind in different degrees with respect to proteins; detecting the degree of binding of said individual protein to each of said ligands in the panel; recording the degree of binding of said protein to each of the ligands in the panel; and arranging said recorded degrees of binding so as to provide a characteristic signature of said individual protein; and comparing the signatures for each protein in said multiplicity; and classifying the proteins according to the similarity of their signatures.
  • the invention concerns a method to identify proteins with strong binding to a given compound, such as a natural product with useful pharmacological activity.
  • the method comprises (a) comparing measured or calculated properties of the compound of interest to corresponding properties of a maximally diverse reference set of ligands whose binding to a large multiplicity of proteins, such as a cDNA library, can be readily determined; (b) selecting proteins that bind best to those reference ligands that are most similar to the given compound; and (c) directly testing the selected proteins for binding to the compound itself.
  • affinity fingerprint i.e., the binding profile to a diverse set of proteins. If the binding properties of the reference panel of ligands to a large multiplicity of proteins has been previously recorded as a signature database, then a subset of those proteins can be used as a reference panel to define fingerprints. The more independent such proteins are in their binding characteristics, the more useful they will be.
  • a protein reactive with a specified ligand can be identified by (a) comparing the fingerprint of the ligand of interest with respect to a reference panel of proteins to the fingerprints of a reference set of ligands with respect to the same protein reference panel; (b) using the ligands in this reference set that have fingerprints similar to that of the ligand of interest as substitutes in screens of protein libraries.
  • the invention provides short cuts to identifying a group of proteins of interest with similar binding properties which does not necessarily resort to the database provided by the present invention. In general, this aspect takes advantage of the availability of the fingerprints only of the ligands in the panel.
  • the method comprises comparing the fingerprint of a compound of interest for which a class of binding proteins is desired to the fingerprints of the ligand reference panel against the same protein set, selecting ligands from the panel whose fingerprints most closely approximate that of the compound of interest, and using these compounds as probes of proteins generated by a cDNA library or a set of proteins from any other appropriate source.
  • Figure 1 represents an illustration of the terminology used herein ⁇ it illustrates a fingerprint database as compared to a signature database. The darkness of the rectangles shown in the tables represents the tightness of binding exhibited.
  • the multiplicity of proteins encoded in the human genome, or the genome of any organism, including a multicellular eukaryotic organism is believed to be responsible for the metabolic state of the organism and for the response of the organism to stimuli including administration of compounds or compositions, infection, metabolic imbalance, and the like.
  • the standard approach has been to seek some form of interaction with one or more of these proteins. Since the number of proteins involved in the organism is so large, cross-reactivities are bound to occur, and deliberate targeting of a particular enzyme or other protein may result in unattended consequences. Further, it is not always clear what the relevant target protein is, or target proteins are for a given bioactive compound.
  • each member of the collection of proteins is evaluated with respect to a panel of ligands so as to create a signature for each protein.
  • the signatures then provide a multiplicity of data points which can be compared to classify the proteins into categories by virtue of the similarity of their behavior with respect to the entire panel of ligands.
  • signature refers to a set of binding affinities of the protein in question with respect to a reference panel of maximally diverse ligands. This is the orthogonal set of data to that which is obtained when a ligand is tested against a reference panel of proteins, wherein a "fingerprint" is obtained.
  • Figure 1 illustrates one representation of fingerprint and signature databases, where the affinity of binding is shown by the darkness of each of the rectangles in the table.
  • the fingerprint database is represented by the vertical columns in the matrix on the lower left; each compound has a fingerprint represented by the four variously shaded sections, each of which represents its binding to the proteins labeled 5, 18, 92 and 873.
  • the signature database is shown in the lower right, and represents horizontal sets of binding data, one set of four boxes for each protein representing the binding of that protein to compounds nos. 10, 30, 812 and 11,262.
  • “fingerprints” and “signatures” are complementary representations, and the representative panel members in each case are chosen to obtain maximal diversity, and each fingerprint or signature represents a binding profile with respect to such a maximally diverse panel.
  • ligands which are obtained from natural products, since these materials have increased probabilities of interaction with one or more proteins in the collection by virtue of evolution.
  • the databases provided by the invention which permit classification of proteins and evaluation of binding similarities of these proteins in general are assembled from signatures of each of the proteins in the assembly with respect to a panel of ligands.
  • the signatures are most useful if the ligands in the panel are maximally diverse.
  • the panel will include 10-100 such maximally diverse ligands, more preferably 20-50. While maximum diversity is desirable, it may not be necessary in all instances, for example where evaluation of a range of activity across a group of natural products is desired. However, in order to achieve the most informative classification system for any given collection of proteins, the results will be more meaningful if maximal diversity in the panel is accomplished.
  • Maximal diversity can be evaluated according to a number of criteria, such as the completeness of coverage measured by the percentage of proteins recognized. Achieving completeness of coverage with the smallest possible set is also useful and this aspect of diversity can be assessed by comparing the number of compounds to the number of principal components.
  • most desirable are panels wherein the panel covers at least 90% of proteins under consideration and/or provides at least five principal components with respect to the range of the multiplicity of proteins, and/or wherein for the panel, the average of the differences between a profile for any given protein as compared to a second protein is at least three times the differences observed for repeated determinations of a single one of these proteins.
  • Panels of ligands of the desired diversity can be obtained in a number of ways.
  • One of these ways is set forth in PCT application WO95/18969, referenced above.
  • a panel of proteins is selected which can be used to obtain characteristic binding fingerprints with regard to itself for large arrays of ligands, including natural product ligands.
  • the availability of these fingerprints permits the construction of a panel of maximally diverse compounds based on similarities and differences of their fingerprints.
  • Each fingerprint can be summarized as a point in n-dimensional space, where n is the number of proteins in the reference panel, and the distance between the points in this n-dimensional space is inversely proportional to the similarity of their binding characteristics.
  • This application describes a method for predicting the binding of a candidate ligand to a protein target
  • a reference panel of maximally diverse proteins is used to obtain fingerprints of a training set of ligands.
  • the training set is also tested with respect to the target protein
  • a mathematical formula is then derived from the collection of fingerprints which best predicts the outcome of binding of the training set to the target based on their binding affinities to the reference panel
  • This formula can be used to predict the binding of a candidate compound to the target, also based on the results of testing its affinity for the members of the panel.
  • the maximally diverse reference panel of ligands When the maximally diverse reference panel of ligands has been assembled, it provides the basis for determining signatures of individual proteins in the multiplicity of proteins to be classified.
  • the signatures are determined by measuring the binding or reactivity of each protein in the multiplicity with each member of the panel, recording the assessed values of reactivity or binding, and arranging these reactivities or affinities into a profile or "signature.” Reactivity or binding can be measured in any suitable manner.
  • the ligand representing a panel member can be coupled to a solid support, optionally through a biotin/avidin or analogous linkage, and an extract of tissue containing the proteins of interest treated with the support.
  • the support is then eluted with solutions of varying strength so as to obtain a pattern of binding strength for the various proteins in the mixture.
  • Identification of the proteins can be accomplished by various means, such as by noting the position of the various proteins in a two-dimensional electrophoretic gel. Of course, the more exactly bound proteins can individually be identified, the more useful the signatures for these proteins are.
  • the small molecule to be analyzed for binding to a target protein is coupled to a small molecule member of a binding pair wherein the binding partner is a protein, such as the biotin/avidin binding pair.
  • the small molecule can thus be associated with a fusion protein containing, e.g., avidin as the protein binding pair member and a first portion of a severable protein whose functionality is dependent on association with its remaining second portion.
  • the second portion is produced in the same cell as a fusion protein with the putative target protein. Association of the ligand with the target protein thus results in association of the two portions of the functional protein, whose function can then be detected. Typically, the function of the associated portions is that of a transcription factor and a reporter gene is used to assess the results.
  • This system allows an entire cDNA library to be screened. The cDNA library is constructed so that the encoded proteins are expressed as fusion proteins containing one of the severable protein portions. Each successful target protein which binds to the ligand can be identified readily as each target protein is expressed separately in an individual yeast cell.
  • binding can be measured using "virtual" experiments fitting structures of ligands to known structures of proteins.
  • the use of this approach to classify ligands based on a protein panel was described by Briem, H. and Kuntz, I.D., J Med Chem (1996) 39:3401-3408.
  • the affinities of the protein for the panel members constitute a signature for the protein.
  • the signatures obtained for the individual proteins in the multiplicity can be used in a number of ways.
  • signatures for a large multiplicity of proteins can be assembled into a database so that the proteins can be classified by binding similarities. This can be done empirically or by computational methods; for example, each signature may be represented by a single numerical vector based on a plot of the affinities with respect to the panel members in a space having the number of dimensions corresponding to the number of panel members.
  • proteins with very similar signatures would be expected to show similar binding characteristics generally.
  • the ability of any particular protein to bind to a particular ligand can be calculated based on a formula derived from the reaction of a known group of proteins (analogous to the training set set forth in the PCT application noted above) with the panel.
  • a formula derived from this set of proteins based on signatures obtained from the reference panel and their manipulation to ascertain the binding of the training set to the ligand can be applied to any protein in the multiplicity.
  • the database can be used to select initiation structures or "scaffolds" for building combinatorial libraries by selecting base compounds that bind a desirable portion of the proteins in the multiplicity.
  • This task is simplified by permitting testing of a candidate scaffold model against only one or two members of each class.
  • a scaffold model which has suitable affinity for about 0.5-2% of all the proteins encoded by the genome provides a more effective general utility than compounds that bind more than 10% of the genome or less than 0.1%.
  • Compounds in this category have already been found in other ways — e.g., Cibacron blue binds about 2% of all known proteins and staurosporine binds a substantial variety of kinases.
  • the availability of the database of the invention will permit identification of additional suitable scaffolds.
  • Empirical Signature Comparison and Retrieval of Desired Protein Classes provides a means to classify proteins in a multiplicity based on similarity of signatures, however arrived at. Further, the database can be screened for signatures that are similar to the signature of a protein known to bind a given ligand, thus retrieving additional proteins in the database which also would be expected to bind the ligand.
  • there is an additional approach which does not require assembly of a massive database, but rather relies simply on the availability of fingerprints of the panel ligands with respect to a basic set of proteins.
  • proteins which bind a specific ligand can be identified even if the ligand is not available in sufficient quantity or in sufficient purity for manipulation directly. All that is required is sufficient ligand to obtain a fingerprint for it with respect to the same set of proteins as used to obtain the fingerprints for the reference ligand panel. By comparing fingerprints of the panel members with that of the ligand, panel members with the greatest binding similarity to the ligand can be selected. These ligands, which are available in sufficient quantity, can then be used as substitutes to "fish out" potential targets from a library of proteins, such as that generated by a cDNA library.
  • the methods described in copending application U.S. Serial No. 08/731,613, wherein a system analogous to the fields yeast "two- hybrid" system is employed to test the ability of a ligand to bind to members of a library can be performed using the similar panel members as substitutes for the ligand of interest.
  • proteins analogous to potential target proteins endogenous to a protein set to be investigated can be identified. For example, if there are a dozen viral proteins known to be essential for viral infection, this set can be tested for ability of each protein to bind an arbitrary ligand, such as a natural product. If the screen identifies a ligand which binds several of these proteins, the ligand is of interest as a potential prophylactic or therapeutic.
  • the full spectrum of proteins endogenous to the potential host to which the therapeutic or prophylactic compound binds can be identified as described above — i.e., either the identified compound is used directly to "fish out" binding proteins using a two-hybrid type assay, or the fingerprint of the identified compound is used to select ligand reference panel members for this purpose as described above.
  • Example 1 Multiple Protein Targets as an Aid in Drug Design It is common that a therapeutic compound, especially a compound which is a natural product, exerts its effects by interacting with multiple targets. In order to improve the performance of the drug, and to eliminate side effects, it is useful to ascertain what proteins are, in fact, subject to interaction with the drug.
  • the drug is readily obtainable by synthesis or isolation, it would be a straightforward matter to obtain this class of proteins by using, for example, the yeast "two-hybrid" assay or similar assay as described in copending application U.S. Serial No. 08/731,613, filed 16 October 1996, inco ⁇ orated herein by reference above. Quite often, however, the drug may be difficult to synthesize, available in limited quantities, or not available in pure form. Under these circumstances, the method of the invention can provide a means for identifying the relevant class of proteins by substituting, in such direct assays, compounds in the reference panel described herein whose fmge ⁇ rints are most similar to the fmge ⁇ rint of the drug as determined against a suitable protein reference panel.
  • the drug of interest would be tested against the panel of proteins against which finge ⁇ rints have been obtained for the ligand reference panel members.
  • An arbitrary number, perhaps two or three of the panel members with fmge ⁇ rints most similar to that of the drug are then used as substitutes for the drug in the physical assay. Proteins which bind to these substitute ligands under the conditions of the assay are likely to interact with the drug.
  • aspirin The ability of a drug to interact with a variety of proteins in a subject to which the drug is administered is illustrated by the example of aspirin. Aspirin, of course, is readily available and could be used directly in a two-hybrid type assay; however, the behavior of aspirin illustrates that to be expected from any arbitrary drug, which may not be thus available.
  • aspirin and other salicylates exhibit the following interactions which are synergistic in providing symptomatic relief for pain associated with minor infections:
  • pathogens upon infecting a subject, effect the expression of a multiplicity of proteins.
  • the set of proteins that a pathogen produces when it encounters a potential host can be identified by various techniques for evaluating new protein expression. Such techniques are described, for example, in Mahan, M.J. et al. Proc NatlAcad Sci USA (1995) 92:669-673. This defines a class that may be useful to inhibit, since drugs that target the class would represent new antibiotics with intrinsically low susceptibility to generating resistant strains. This is because mutations in several proteins concurrently would be needed to generate resistance. The class probably does not correspond to proteins of similar sequence, although the genes may be clustered on the chromosome.
  • a subset having similar signatures represents a subclass that has utility for drug discovery.
  • Compounds that target a member of this class of similar compounds will generally be useful antiinfective agents.
  • any member of the class can be used to find suitable drugs, for example, using direct high throughput screening of a chemical library. If some of the pathogen proteins in the class are available only in minute amounts, then another convenient protein in the same signature class can be used for the screening.
  • signatures are obtained against the reference panel of ligands for the pathogen-associated proteins.
  • the signatures can simply be matched, empirically or using mathematical techniques to the signatures associated with the proteins encoded in the genome of the target organism. If such a database is unavailable, however, the signatures among the pathogen-associated proteins can be compared to ascertain the most characteristic signature for the largest subset of these.
  • the maximally diverse ligand reference panel is then tested against these proteins and the ligands with the highest binding for these proteins are chosen as substitutes, as set forth in Example 1 above, to screen proteins generated from a cDNA library or genomic library prepared from the subject using the yeast "two-hybrid” assay or any other appropriate means.
  • Those proteins in the host which fall into the same signature class as the target proteins can be used to monitor specificity for the pathogen with respect to a candidate drug.
  • Example 3 Manipulation of Signatures and Finge ⁇ rints to Identify Target Proteins
  • the target proteins for a ligand of interest can be identified by taking advantage of the matrix described in this example.
  • the matrix set forth below represents a hypothetical matrix illustrating the generation of a formula as a substitute for a compound (ligand) of interest permitting deduction of whether a candidate target protein will bind the ligand even if the ligand itself is not available for testing.
  • LR1-LR5 represent ligand reference panel members.
  • the actual ligand of interest is represented by L.
  • Prl-Pr5 are five training proteins which bind or otherwise react in varying degrees with each of the reference ligand panel members The degree of reactivity is arbitrarily assigned a value on a scale of 1-10 where 10 indicates high reactivity and 1 indicates low reactivity. Generally, a logarithmic scale of measured values is used.
  • a ligand of interest may have a finge ⁇ rint that is very similar to that of a panel member — e.g., LR3.
  • LR3 could substitute for the ligand in physical assays for proteins that bind this ligand by finding proteins that bind LR3.
  • the ligand of interest may have a fmge ⁇ rint with a datapoint similar or identical to that of LR3 with respect to Prl and Pr2, but where the datapoints with respect to Pr4 and Pr5 are the same or similar to those obtained for LR4.
  • the class of proteins considered likely to bind the ligand of interest is in the class that binds equally well to LR3 and LR4. If a database of signatures is available, these proteins can be identified electronically.
  • LR3 and LR4 can be used to screen a cDNA library in replicates and only those proteins that bind both will be further studied.
  • the ligand of interest which may be available in limited quantity or only in impure form, shows a finge ⁇ rint against the training set with monotonically increasing reactivities over the Prl-Pr5 range, a pattern grossly different from any of the reference fmge ⁇ rints.
  • a formula is then generated by assigning weights to each of the elements of the five LR1-LR5 finge ⁇ rints to obtain a predicted "L" ligand finge ⁇ rint that matches that actually obtained for the ligand of interest.
  • the weighting values will need to be the same for each element of the finge ⁇ rints.
  • the weights applied to the Prl element with respect to how the values from LR1-LR5 are counted have to be the same as those applied to Pr2.
  • Each of the coefficients A-E will have a numerical value; some of the coefficients may be zero. This same equation, with the same values of A-E will be used to calculate the predicted reactivity with the ligand of interest for any individual candidate protein.
  • (+2)(8) + (+3)(9) + (-1)(4) + (-2)(7) + (+1)(5) P to provide a predicted reactivity value of 30.
  • Kits can be prepared which include, in separate containers, each of the members of the training set of proteins, each of the members of the ligand reference panel, and the ligand of interest, along with reagents for testing their reactivity. More commonly, however, the kit, for pu ⁇ oses of identifying whether a particular protein binds to a ligand of interest will need to contain only the ligand reference panel and the surrogate formula. To use the kit, the signature for a candidate protein is obtained against the reference panel members and the surrogate formula is used to predict the degree of interaction of the protein with the ligand of interest.

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Abstract

La présente invention concerne un procédé de classification d'un grand nombre de protéines contenues dans une collection déterminée. La collection peut représenter le répertoire de protéines codées par le génome d'un organisme comprenant un organisme supérieur ou des protéines exprimées par un tissu particulier ou un type de cellules. La classification est basée sur la possibilité de lier des ligands contenus dans un panel représentatif de la plage d'interactions physiologiques. Les procédés de l'invention peuvent également être utilisés de manière à évaluer une liaison relative des protéines dans un ensemble de protéines par rapport à un ligand significatif physiologiquement de façon à permettre la modification d'une spécificité d'un ligand désiré d'une interaction d'un récepteur.
PCT/US1997/023762 1997-01-03 1997-12-23 Procede de classification de produits geniques WO1998029744A2 (fr)

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AU59020/98A AU5902098A (en) 1997-01-03 1997-12-23 Method to classify gene products

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US78536097A 1997-01-03 1997-01-03
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000007024A3 (fr) * 1998-07-29 2000-05-11 Smithkline Beecham Plc Methode d'analyse de proteines

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DE69217497T2 (de) * 1991-09-18 1997-06-12 Affymax Tech Nv Verfahren zur synthese der verschiedenen sammlungen von oligomeren
DK0738390T3 (da) * 1994-01-06 2003-07-21 Telik Inc Surrogater for targetmolekyler og forbedrede referencepaneler

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2000007024A3 (fr) * 1998-07-29 2000-05-11 Smithkline Beecham Plc Methode d'analyse de proteines

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