WO2010094300A1 - Procédé permettant de déterminer in silico un ensemble d'épitopes cibles sélectionnés - Google Patents

Procédé permettant de déterminer in silico un ensemble d'épitopes cibles sélectionnés Download PDF

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WO2010094300A1
WO2010094300A1 PCT/EP2009/001230 EP2009001230W WO2010094300A1 WO 2010094300 A1 WO2010094300 A1 WO 2010094300A1 EP 2009001230 W EP2009001230 W EP 2009001230W WO 2010094300 A1 WO2010094300 A1 WO 2010094300A1
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peptides
proteins
target
epitopes
protein
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Thomas Joos
Markus Templin
Dieter Stoll
Oliver Poetz
Andreas Zell
Hannes Planatscher
Jochen Supper
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NMI Naturwissenschaftliches und Medizinisches Institut
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/30—Detection of binding sites or motifs

Definitions

  • the present invention relates to a method for determining in silico a set of selected target epitopes suited for the identification of at least one protein of a set of proteins of interest within a pool of sample proteins.
  • the present invention relates to a method for detecting proteins of interest in a biological sample, to a set of selected target epitopes being suited for identifying proteins, to binding molecules that bind to target epitopes contained in such a set, and to the use of such binding molecules.
  • Protein expression profiling is becoming a key concept of clinical analytics and diagnosis. In the past decades, many examples have shown that human diseases are closely associated to changes in the activity of particular sets of genes and consecutively to changes in protein levels.
  • screening assays that allow identifying the presence or absence and respectively the level of different proteins contained in a proteome, especially of sets of biomarkers, which are related to a certain disease, is a powerful means to specifically diagnose certain conditions.
  • the condition of a cell, a tissue, an organ or an organism can be described, in particular by the quantitative profile of its proteome.
  • the expression of certain proteins is suppressed and the expression of other proteins is increased, or that certain proteins are not expressed at all, or that certain protein variants, isoforms or post-translational modifications are changed in level as compared to a normal or reference condition.
  • the protein profile is suitable as a direct indicator for the respective condition of cells or tissues, organs or the whole organism at a certain point in time and therefore is suited as an indicator for the health status of the organism.
  • 2D electrophoresis enables electrophoretic separation of up to 10.000 different protein species in a gel matrix in two dimensions followed by subsequent defined proteolysis of the separated proteins in spots cut from the gel, followed by the identification of the respective protein species via its specific peptide masses determined by mass spectrometry.
  • antibody-based methods are supplemented by antibody-based methods in which proteins are detected qualitatively and quantitatively through the specific binding to corresponding antibodies. Examples thereof are Western blots, ELISA or antibody microarrays.
  • MS-based strategies became the key technologies for identifying proteins in proteomic research within recent years.
  • Sample preparation and efficient fractionation of target analytes are the major bottleneck in all MS-based protein analysis technologies so far.
  • the complexity of biological samples needs to be greatly reduced and the protein of interest needs to be enriched significantly before low abundance proteins can be identified with MS.
  • Sample preparation strategies that reduce the complexity of protein samples or of peptide mixes from e.g. tryptic digests using immunoaffinity-based methods lead to a substantial increase in throughput and sensitivity.
  • the lack of thousands of appropriate peptide-spedfic capture reagents limits the application of such immunoaffinity-based approaches on a proteomic scale or for unbiased biomarker discovery.
  • these methods were mainly used for efficient and specific high abundant protein depletion or for low abundant diagnostic peptide enrichment followed by mass spectrometric quantification of single peptides from a proteome.
  • WO 2007/112927 of this assignee discloses a method wherein two different sets of binding molecules targeted against small terminal epitopes of proteolytic peptides are used in parallel or in succession to specifically identify each protein in a proteome.
  • Each set of binding molecules has to comprise up to 8000 binding molecules.
  • WO 2004/081575 discloses an array of binding molecules, which array allows a subdivision of a complex protein sample into subpopulations, whereupon a subsequent analysis of the subpopulations by mass spectrometry is performed.
  • binding molecules in particular antibodies are utilized that specifically bind to short epitopes.
  • the binding molecules are selected by rational selection of epitopes aiming to avoid an overlap and to increase the coverage between the entirety of the binding molecules used and the proteins recognized.
  • all possible C- terminal tetra-peptides are considered as the starting set for epitope selection.
  • These 160.000 sequences are thereafter reduced by deleting certain sequences to find a final set of epitopes, whereby the sequences are selected as to be likely to generate good epitopes, and are said to be also decided based on their frequency in naturally occurring proteins.
  • This document nevertheless describes methods for producing binding molecules that specifically bind to the set of epitopes, and methods for determining proteins of interest by using such set of binding molecules.
  • WO 02/060377 A2 discloses a classification procedure wherein the proteins within a complex protein sample are subdivided into subpopulations according to the binding to certain binding molecules which are referred to as "slinkers" that may be antibodies or binding partners for larger protein classes. By repeated binding procedures to different slinkers or slinker combinations individual proteins shall be isolated from a protein sample.
  • this object is achieved by a method for determining in silico a set of selected target epitopes suited for the detection of at least one protein of a set of proteins of interest within a pool of sample proteins, the method comprising the steps of:
  • a set of target epitopes can be determined that in turn can be used to generate a respective set of binding molecules which can be used to detect the proteins of interest in a given biological sample.
  • This invention is based on the surprising finding of the inventors that using the novel method allows to strongly reduce the number of binding molecules, which have to be generated in order to accomplish a full coverage of detection for a given group of proteins in a complex protein sample.
  • the second and first list may be identical lists or that the second list may be derived from the first list or otherwise compiled.
  • the “pool of sample proteins” may contain all peptides of a proteome, e.g. the human proteome, whereby the second list may cover all or several biomarkers.
  • the expression “less suited” in step d) shall mean that the excluded peptides are unsuited for or impeding the method applied for peptide identification and/or quantitation, e.g. are not suited for MS detection of the proteins of interest, possibly due to limited resolution of available MS equipment, or have unfavorable properties for envisaged measurements of any kind, e.g. bad solubility, chemical instability, and the like.
  • step e shall mean "minimal number of epitopes" only in the optimal case, whereby “less than the number of proteins of interest” is already an advantage over the prior art.
  • proteome is understood as the entirety of proteins present in a cell, a tissue, an organ or an organism including all expressed proteins, all their respective isoforms, polymorphisms and post-translational modifications. Proteome analyses may thereby address their respective concentrations at a certain point in time and under certain external conditions.
  • List of proteins of interest thereby means a certain list of proteins to be analyzed within a given sample. Such list can contain putative or confirmed biomarkers for e.g. cardiovascular disease and cancer to be identified or quantified within a given proteome.
  • Biomarkers means proteins that alone or in combination with other such proteins serve as markers for normal and abnormal physiology. Data about putative or confirmed biomarkers for cardiovascular disease and cancer are listed in Anderson, J Physiol 563.1 (2005) pp 23-60, listing 177 candidate biomarker proteins; in Polanski and Anderson, Biomarker Insights, 2006:2 1-48, listing 240 candidate biomarker proteins, and from the Human Biomarker Test Panel "HumanMAP” , Version 1.6, from Rules Based Medicine, Austin, Texas, listing 188 biomarkers. These publically available data-sources can be used as a data-base for in silico approaches to the definition of binding molecules and sets of binding molecules. After removal of redundant and high abundant proteins having concentrations in human plasma greater 0.3 mg/ml, the size of the Biomarker dataset is 300 proteins.
  • epitope herein refers to a chemical structure on a molecule that interacts with a binding molecule. More specifically, “epitope” means an amino acid sequence comprising 3 to 6 amino acids that is specific to a peptide/protein or a group of peptides/proteins. Preferably, the epitope is located within a protein sequence at the terminus of one or more peptides generated from a protein by proteolytic digest.
  • Binding molecule herein means any molecule or any substance which is able to bind to a peptide/protein or to which a peptide/protein can bind. It is known to the skilled person, that binding molecules may be for example antibodies, antibody fragments, aptamers, and recombinant binding molecules.
  • set is referred to as a group of epitopes comprising at least one C-terminal epitope and/or at least one N-terminal epitope.
  • set refers to at least one binding molecule capable of binding at least one of the epitopes from an epitope set.
  • peptide is defined as a linear chain of amino acids that may either represent a full-length protein, a truncation product, or a proteolytic fragment of a protein.
  • Detection and “detecting” in connection with a protein or peptide refers to binding, isolating, enriching, qualitatively and/or quantitatively identifying of one or more proteins or peptides within a complex sample.
  • support refers to a solid surface whereon binding molecules can be immobilized.
  • the surface may be formed from or coated with any material suitable for immobilizing binding molecules.
  • the surface may be either flat or comprising a number of cavities.
  • array herein refers to a set of binding molecules immobilized on a support or on addressable beads so that each binding molecule is present at a known or addressable location.
  • binding molecules may be arranged on the surface of a support in spatially defined manner, so that each binding molecule is located at a different and identifiable location.
  • identifying herein means the assignment of a specific known or hypothesized amino acid sequence to a peptide and/or protein.
  • Quantifying refers to measuring the total amount or concentration of a peptide in comparison to the total amount or concentration of another peptide or other compound or in relation to an otherwise specified parameter.
  • filter step means a selection step performed on a list of peptides by removing from said list peptides fulfilling certain criteria or not fulfilling certain criteria.
  • a filter is a piece of software or hardware that performs such filter steps in silico.
  • step e) can be further split into two or more sub- steps without departing from the scope of captioned invention. Although this is unlikely to happen, proteins of interest no longer represented in list 4 by at least one valid target peptide can be identified within the pool of sample proteins by standard assay techniques, e.g. by using a specific binding molecule not binding to any of the possible target peptides
  • the set of selected target epitopes in step d) can be used to detect any protein from the pool as long as list 1 equals list 2, whereby the number of binding molecules can be reduced when list 2 is smaller than list 1.
  • the method leads to a set of selected epitopes, wherein it is known which peptides carry the respective epitopes, and to which proteins these peptides belong, thereby eliminating false negative and false positive results.
  • WO 02/086081 discloses a method for identifying proteins of interest that are present as single isolates or mixtures of proteins, including cleaving the proteins of interest with a proteolytic agent to produce peptide fragments, contacting the peptides to an array having affixed thereto at discrete locations a set of binding molecules, detecting binding between the peptides and the binding molecules, and comparing the detected binding to a reference set.
  • Epitopes to be bound by the binding molecules are selected by applying a proteolytic digestion step to a protein pool for determining epitopes, and performing a randomized greedy algorithm to identify a set of epitopes that can distinguish the protein pool proteins.
  • the goal is to optimize such set of binding molecules, or epitopes associated with such binding molecules, such that many or all of the binding molecules recognize epitopes common to peptide fragments produced from cleavage of a plurality of proteins within a given mixture.
  • a concurrent goal is a minimum set that produces a unique binding pattern for the peptides of protein within the mixture.
  • each protein within the pool is compared with all other pool proteins for grouping the proteins into different groups thereof according to the occurrence of epitopes within the sequence of the proteins, thereby scoring the epitopes according to their occurrence.
  • the most abundant epitope is selected for the set of epitopes, and this algorithm is repeated with the remaining epitopes, and so on, until all proteins are represented in the selected set of epitopes.
  • an additional binding molecule may be added to the set.
  • the peptide fragments may be labeled after cleavage to allow e.g. fluorescent based detection of peptides bound on the array of binding molecules. If the binding molecules are designed such as to recognize C- or N-terminal epitopes, and the peptide fragments may not be labeled, Surface Plasmon Resonance shall be used to detect the binding pattern on the array of binding molecules.
  • the number of epitopes in the selected set thereof is reduced to such an amount that MS detection becomes suited for the detection of proteins of interest in a large pool of sample proteins.
  • the new method is a suitable method for circumventing the limitations of the prior art.
  • the antigen i.e. the epitope
  • the epitope was reduced to three or five amino acids and contains either a free N- or a free C-terminus.
  • Antibodies generated against these epitope therefore bind groups of peptides that share a common short motif at the N- or C-terminal end generated during the e.g. tryptic digestion of the biological sample.
  • TXP antibodies are also referred to as "TXP antibodies”.
  • the new binding molecules e.g. the TXP antibodies
  • the new binding molecules are capable of binding up to several hundred peptides derived from e.g. a fragmented proteome of any species. They can be used for the Lm- munofractionation of peptide classes.
  • the peptide fraction can be analyzed and deconvoluted by MS and MS/MS, which results in binding molecules that can be used for several dozens of targets and targets from different species.
  • TXP antibodies are enough to capture signature peptides that cover all human proteins in the Uniprot database (downloaded on 11 June 2007, http://www.uniprot.org/).
  • the application of TXP antibodies results in a ten-fold reduction of the number of capture antibodies required. Since the TXP epitopes are very short epitopes, amino acid differences occurring within the protein sequences of different species may become negligible.
  • TXP antibodies are a universal source for the analysis of signature peptides in human, animal, and plant pro- teomes using an immunoaffinity MS approach.
  • TXP antibodies can be used as a universal toolbox for the fast generation of sandwich immunoassays based on binding of a C-terminal and an N- terminal TXP-antibody generating a specific peptide recognition by 6 to 10 amino acids (dependent on epitope length of TXP-antibodies).
  • TXP-sandwich assays can be established without the need for antibody generation. This results in an enormous advantage in assay development, as concerns time and costs.
  • step d) is performed by applying at least one of the following target peptide removing filters to the possible target peptides in said third list:
  • Target Epitope Filter for removing target peptides comprising target epitopes with high abundance
  • Weight Filter for removing target peptides with similar weight and with the same target epitope, which cannot be separated by mass spectrometry
  • Length Filter for removing target peptides with a length not adapted to the resolution or the method of a subsequent detection technology, e.g. shorter than 15, 12 or 5 amino acids and/or longer than 30 amino acids,
  • Proteotypic Peptide Filter for removing target peptides not predicted to be pro- teotypic.
  • the "Met Filter” removes peptides having a methionine in at least one of its terminal sequences, since chemical modifications of methionine may hamper the recognition of the target epitope by a binding molecule, especially by an antibody.
  • the "Unknown AA Filter” removes peptides, selected based on database entries, containing unknown or uncertain amino acids. Thereby, the occurrence of false- negative results can be reduced irrespective of the detection method applied.
  • the "High Abundant Target Epitope filter” removes peptides containing epitopes present in preferably more than 700 peptides. Therefore, especially when applying detection methods with a strictly limited resolution, an exceeding of the resolvable peptide number can be avoided. This holds true especially for applications, in which peptides are detected by one-step Affinity-MS or by Fluorescence.
  • the "Length Filter” removes either peptides too small or too long for the read-out technology of the subsequent detection method. Small peptides e.g. often do not bind to HPLC-media and therefore are lost during HPLC-MS analysis. Large peptides might have signals outside the selected detection window of the mass spectrometer. The selected values can be adapted to the detection method applied. In case the subsequent detection is performed using fluorescent techniques such as Sandwich- Immunoassay, peptides that are shorter than 15 amino acids and might thus cause false-positive results.
  • the "High Abundant Protein Filter” removes peptides that bind the same binding molecule as peptides belonging to highly abundant proteins such as Actin or Tubulin. Therefore, false-negative results caused by sequestering out certain peptides from the binding molecules by other highly abundant peptides are avoided.
  • proteotypic Peptide Filter identifies peptides that are repeatedly and consistently identified for any given protein present in the mixture. All peptides which are not predicted to be proteotypic are removed.
  • Said target peptide removing filters are usually applied in a specific order, in which order the Met Filter, the Unknown AA Filter and the High Abundant Protein Filter are used at any point within the order.
  • the High Abundant Target Epitope Filter is applied within said order prior to the Weight Filter and prior to the Length Filter, and the Weight Filter is applied within said order prior to the Length Filter.
  • the preselection of epitopes is adjusted to the experimental setup, the novel method ensuring for example, a full coverage in the identification and quantification of a given group of proteins within a complex protein sample, thereby at the same time reducing the number of binding molecules required in the process.
  • this method offers the possibility to specifically adjust the search criteria for fitting binding molecules to the requirements and specifics of a given method for said identification and quantification of proteins within a sample.
  • step d it is further preferred, if in connection with step d) possible target peptides not belonging to a protein of interest are removed.
  • This step has the advantage that the set of selected target epitopes will be further minimized.
  • step d possible target peptides comprising a combination of N- and C-terminal possible target epitopes which is not unique amongst the possible target peptides are removed.
  • This step enhances the selectivity of the set of selected target epitopes.
  • the in silico fragmentation in step b) is performed according to the sequence characteristics of a sequence specific proteolytic digest. It is further preferred, if during step e) an optimization procedure is carried out for determining a globally optimal set of selected target epitopes, e.g. by using an ILP- solver, or by determining a locally optimal set of selected target epitopes, e.g. by applying a standard greedy approach.
  • the present invention also concerns a computer program for carrying out the new method, a computer programmed for carrying out the new method, and a data carrier containing information for carrying out this new program.
  • steps c) and d) are repeated in order to generate a second set of selected target epitopes, whereby for each possible target peptide in said third list, the selected target epitopes in the one set thereof comprise an N- terminal epitope and in the other set thereof comprise a C-terminal epitope.
  • the present invention also relates to a set of selected target epitopes, determined according to the new method, as well as to a set of selected target epitopes suited for identifying proteins of interest, wherein the proteins of interest serve as Biomarkers, and the set of selected target epitopes contains at least 30%, preferably 50%, more preferably at least 70% of the aforementioned set of selected target epitopes.
  • the present invention relates to a method for preparing a set of binding molecule suited for the detection of peptides or proteins of interest from a pool of proteins, comprising the steps of: a) determining a set of selected target epitopes according to the new method; and
  • the present invention relates to a set of binding molecule generated according to this method, and to a set of binding molecules, containing at least one binding molecule for each target epitope within the set of selected target epitopes, said binding molecules being preferably selected from the group consisting of antibodies, antibody fragments, antibody variants, aptamers, polynucleotides, recombinant binding molecules.
  • the present invention relates to a method for detecting proteins of interest in a biological sample, comprising the steps:
  • step d) preferably comprising the step of
  • target epitope is selected from one of the set of selected target epitopes mentioned above.
  • the protein of interest may be a biomarker protein.
  • the peptides are first harvested with the first set of binding molecules and thereafter, in a second step, eluted and incubated with the second set of binding molecules.
  • a first set of peptides is captured with the first terminus specific binding molecule, followed by detection of the specific ana- lyte peptide in said first set of captured peptides by binding of the second terminus specific antibody.
  • detection of the specific analyte peptide relies on binding of TXP antibodies to both termini at the same time.
  • step d) is preferably being performed using at least one of the following methods: mass spectroscopy, immunoassay, chromatography, electrophoresis, electrochemistry, surface plasmon resonance, oscillating crystal.
  • step c) and/or step e) is preferably performed by using binding molecules immobilized to a carrier, the carrier being selected from the group consisting of material for affinity chromatography, beads, microspheres, capillary surfaces, micro channel structures, sensory surfaces.
  • the present invention relates to the use of the above selected binding molecules for identifying proteins serving as biomarkers.
  • Fig. 1 and Fig. 2 show graphic representations of optimization procedures for 2 step Affinity MS Approach and Sandwich Immunoassay Approach.
  • peptide-specific antibodies that recognize peptide fragments generated by an enzymatic digest of a proteome is an effective possibility to enrich target peptides from highly complex samples. Combined with subsequent MS analysis it is a valuable tool for targeted proteomics.
  • the Triple X Proteomics approach of this embodiment is based on immunoaffinity enrichment of tryptic peptide groups subsequently identified in an MS read out.
  • the method is suitable for fractionating complex samples into classes of signature peptides that share the same terminal epitope, i.e. TXP epitope.
  • the enriched peptide classes can be deconvoluted through subsequent MS read-out. If the peptide signal turns out to be unique, then the peptides can be mapped back to their original proteins.
  • Different signature peptides can be used to identify a protein of interest in a digested sample. This gives rise to several protein targeting possibilities, wherein each TXP antibody is multispecific.
  • TXP antibodies can be generated from the epitopes of different signature peptides of the protein of interest enriching several other signature peptides at the same time. This is a unique possibility for reducing and optimising the number of antibodies required for an immunoaffinity approach on a proteome-wide scale.
  • TXP epitopes were defined and to determine the optimal set of TXP-type antibodies. All human proteins in the Uniprot database (downloaded on 11 June 2007, http://www.uniprot.org/ * ) were digested with trypsin - in silico. All TXP- epitopes enriching tryptic fragments that would impede the measurements had to be removed (Figure 3). This was achieved through multiple filtering steps. Based on incomplete sequencing information, the first filter removed the peptides that contained unknown amino acids. The second filter removed all TXP epitopes that were shared by more than 700 peptides. Larger peptide quantities may not be deconvoluted with an MS-based read-out.
  • the filter sets can be reduced further in order to only cover peptides derived from the proteins of interest.
  • the remaining peptides and antibodies constitute the basis for the optimization algorithm.
  • the goal is to minimize the number of antibodies to a degree that at least one peptide from every protein in the set is captured.
  • the minimum set cover problem is a difficult (NP-hard) combinatorial optimization problem.
  • Xj is a Boolean variable which is only true if antibody i is selected.
  • S 1 is true if protein j contains a peptide whose terminus matches antibody i.
  • P denominates the set of proteins, p, the j-th protein in a given set.
  • the number of selected antibodies is the objective function (1) to minimize.
  • TXP Antibodies are enough to capture signature peptides that cover 19072 human proteins deposited in the Uniprot database (downloaded on 11 June 2007, http://www.uniprot.org/). This would be a reduction by 12 compared to the possibility to generate one antibody per protein.
  • the TXP approach uses antibodies that are directed against short epitopes in order to fractionate peptide classes according to their common terminal sequences, the TXP epitopes. This requires the detailed characterization of the antibody binding activities. The specificity of the TXP epitope antibody needs to be assessed prior to applying the antibody in a bioanalytical workflow. The assessments can be done with peptide arrays.
  • peptide arrays were designed that contained positional scanning peptide libraries for the TXP epitopes.
  • Four different peptide libraries were synthesized for each TXP epitope. Within each library, one individual amino acid position was randomized; any of the 20 amino acids was possible. The sequence specificity is lost at the randomised position and the need for a specific amino acid is indicated by the loss of signal on a peptide bead made from such a positional scan peptide library.
  • Results have been obtained for an antibody that was generated against the C- terminus of a ⁇ -catenin signature peptide with the AMTR amino acid sequence.
  • the amino acids that contributed to the binding of the antigen were A, T and R.
  • the binding of the AMTR-specific antibody decreased to 5 % of the original signal. This was expected because A, T, and R were present at this position with a frequency of 5 %.
  • replacing methionine only had a marginal effect on the antigen-antibody binding.
  • the AXTR binding motif was shown to be specific for the AMTR antibody.
  • ⁇ -catenin was chosen as a model molecule to prove the feasibility of the TXP approach as the protein plays a key role in the WNT pathway and tumorigenesis. Mutations have been found at distinct phosphosites in several types of tumours.
  • Two TXP antibodies were chosen that were directed against the TXP epitopes that were present in the ⁇ -catenin signature peptides (GNPEEEDVDTSQVLYEWEQ- GFSQSFTQEQVADIDGQYAMTR, TEPMAWNETADLGLDIGAQGEALGYR).
  • the TXP antibodies were specific for the LGYR and AMTR termini and were used for the immunoaffinity fractionation of a tryptic HepFl mouse hepatoma cell line digest.
  • the two ⁇ -catenin signature peptides could be detected and verified within the LGYR and AMTR fractions using MALDI TOF/TOF MS. Mascot ion scores of 65 were obtained for the AMTR and 57 for the LGYR fragment. This shows that ⁇ -catenin could be detected independently by two different TXP antibodies. As expected, several other tryptic signature peptides were identified besides the ⁇ - catenin fragments.
  • the terminal sequences identified within the group of peptides enriched with the AMTR antibody correlated well with the binding motif AXTR, described in section 1.2.
  • the antibody was not very specific for the third C-terminal amino acid, where the amino acids alanine, phenylalanine, threonine, serine, glutamine, and tyrosine were found instead of methionine, which was used in the immunogen.
  • the antibody at the methionine position had no preference for side chains of a basic, acidic, large or hydrophobic nature.
  • Nine different proteins were found in the immunofraction of the AMTR antibody. 14 signature peptides could be identified for peptide classes enriched with the LGYR antibody. Two peptides were present in pyroglutamic acid or glutamine form.
  • the Triple X Proteomics approach broadens the applicability of immu- noaffinity MS.
  • a feasible number of antibodies would enable the analysis of entire proteomes of any given species.
  • TXP reduces the number of required antibodies tremendously, thereby bringing the coverage of proteomes into reach.
  • the TXP concept enables the combination of unbiased and biased proteomic approaches.
  • the biomarker workflow is simplified in so far that the tools of the discovery phase - the TXP antibodies - can be used in the validation phase. Once a potential biomarker is identified with TXP immunoaffinity MS, the same antibody can be applied to validate the biomarker candidate.
  • Antibodies could be produced that were specific for short peptide sequences with a length of 3 amino acids at the free terminus.
  • the properties of the generated antibodies enabled TXP protein profiling to be performed.
  • the generated antibodies could be integrated in a simple immunoaffinity workflow. It was shown for 23 proteins that the peptide classes could be enriched and the corresponding proteins be clearly identified. Thus, the difficulties experienced when developing antibodies for protein analysis on a proteomic scale are minor compared to the classical immunoaffinity MS approach.
  • the TXP concept is suitable for immunoaffinity MS approaches on a proteomic scale.
  • the speed and specificity of a single immunoaffinity enrichment step, combined with accurate mass determination, makes the combination of unbiased and biased proteomic approaches feasible.
  • the short epitopes of the TXP antibodies extend the application spectrum of immunoaffinity MS strategies to humans, animals and plants, as sequence heterologies become negligible. 2. Examples for optimizations procedures
  • the list of proteins of interest is a set of 5 G-protein coupled receptor families, comprising acetylcholine, alpha- adrenergic, beta-adrenergic, histamine and serotonin receptors.
  • Table 1 List of proteins of interest sorted by protein families
  • the complete list of potentially signature peptides contains 123 theoretically selectable C and N-terminal epitopes of a length of 4 to 5 amino acids.
  • the optimization task is to define the minimal number of epitopes, which cover all signature peptides of interest. If the number of epitopes is significantly smaller than the number of proteins of interest, this approach fits the goal of reducing the number of required antibodies to identify large sets of target proteins.
  • Table 2 Optimal Set-Cover, the smallest subset of columns (antibodies) with at least one 1 in every row protein.
  • AB4, 7 and 9 represent the optimal solution for the set of proteins (Protein 1-6) here.
  • the set-covering problem is NP- hard, which means that it is computationally hard to solve.
  • the problem can be formulated a classical linear programming problem with boolean variables. Boolean variables can take values of 0 (zero) or 1 (one). Variables are designated with the name "x”.
  • variable x is set to 1
  • the antibody number i in the list is selected, if the value is 0, the antibody is omitted.
  • a protein is covered if one or more selected antibodies capture at least one peptide of the tryptic digest of the protein.
  • the set of epitopes that would capture an identifiable peptide from the tryptic digest of Pl 1229 can be derived from the output of the filter pipeline (table 3). These are:
  • All terminal sequences have a corresponding variable Xi with an index i, e.g. X23 for ALCNK, x 6 7 for WIK, etc.
  • a constant designated Vi is introduced which has the value 1 if the terminal sequence corresponding to Xi is contained in the set of epitopes for the protein of interest.
  • V23 would have the value 1, because ALCNK is contained in the set.
  • Table 3 Valid targets in the tryptic digest of the muscarinic acetylcholine receptor Ml (Pl 1229)
  • Xis is the variable corresponding to the antibody specific for the C-terminal sequence EIER, which does not capture any peptide from Pl 1229. This would lead to a 0 value for yi 5 .
  • This program is then solved with a suitable solver (e.g. cplex), by exporting it in a standard format for linear programs (Ip, gmpl).
  • a suitable solver e.g. cplex
  • the used solvers are very efficient, state-of-the-art implementations of well-known algorithms like the simplex algorithm for linear programs, and branch-cut-techniques for integer programs.
  • a solver returns the values for x variables, which are equivalent to the optimal subset of antibodies: antibody i is selected if the variable X 1 has the value 1.
  • the optimal set for the 5 GPCR families contains 13 epitopes:
  • a data structure called ,,epitope combination graph" which contains all existing epitope combinations.
  • a "node” is defined in the data structure. If a peptide exhibits a certain epitope combination, the nodes corresponding to the epitopes are connected with a "peptide edge". These edges are directed, meaning that they have start and end epitope. The direction of an edge defines which epitope is used for capture (start) and detection (end). Only peptides (length > 15 AA) which can reliably be bound by two antibodies are added to the graph. If the data are prepared for sequential Immunoaffinity MS the length filter can be set to shorter peptides.
  • the left side shows the "epitope combination graph" before the application of the uniqueness filter.
  • the edges where inserted because two peptides show the same terminal epitopes .
  • the right side shows the "epitope combination graph” after the application of the uniqueness filter.
  • Parallel edges have been removed. If the optimization is done for 2 step Immunoaffinity MS, parallel edges are only removed if the weights of the peptides corresponding to the edges differ less than the resolution of the used mass spectrometry device/protocol/technology can discriminate (e.g. 4 Dalton).
  • the epitope combination graph is ready for the optimization step.
  • a score is assigned to every epitope.
  • the score is a weighted sum of
  • the node with the highest score is selected.
  • the weighed sum of this node is not used for selection anymore. If a protein has been identified, all peptide edges from other epitopes of this protein are removed. Then all residual scores are updated and the whole process is started again with the reduced set of epitopes.
  • FIGs 2a to 2d illustrate the in succession the first four iterations of the optimization process.
  • PGAR's score is the highest and therefore this epitope is selected.
  • the scores of the connected nodes (LLLL, EEEE, AFST) are updated (1 identified peptide) as shown in Fig. 2b.
  • AFST scores highest because it leads to the identification of peptide AFST-- -PGAR captured by PGAR in Fig. 2c.
  • the peptide corresponding to the peptide edge connecting PPPR and THTG belongs to the same protein as the identified peptide. This edge is therefore removed and the weighed sums are updated for SSSR as part of the identified AFST- SSSR peptide.
  • SSSS is selected by chance resulting in the modified scheme Fig. 2d, where the weighed sum of the corresponding epitope ASPR is updated as the peptide SSSS— ASPR is identified.
  • the method for in silico determination of a set of selected target epitopes can be used in combination with a multitude of different analytical methods.
  • the particular advantage of the new method is that in the preprocessing step, the set of epitopes determined can be tightly adjusted to the particular needs and constraints of the respective method.
  • the novel method thereby comprises a predefinition phase.
  • this phase it is determined, from which list the analytes are to be selected.
  • Examples for Protein lists are the Uniprot and HUPO databases as well as the Biomarker dataset, as described hereinabove.
  • the novel method comprises a preprocessing phase, in which an in silico proteolytic digest of the Proteins comprised in the list defined in the predefinition phase is performed.
  • the in silico digest thereby can be performed according to the specific features of a Tryptic digest, a LysC-digest or other known methods for sequence-specific proteolytic digests.
  • the remaining peptides are subjected to a filtering phase, in which peptides, which exhibit unfavorable features for the chosen analytic method, are eliminated from the epitope list.
  • Protein fragments - sharing the same terminus sequence - are enriched by using 3 to 5 amino acid specific terminal antibodies after a proteolytic digest. This step results in an enrichment of a group of peptides with the same N- or C-terminal sequence.
  • the detection of the proteins is performed in a following step with mass spectrometry.
  • a first set of binding molecules is immobilized on a support or on addressable beads.
  • a complex protein sample is subjected to protein fragmentation by for example tryptic digest.
  • the resulting peptide mixture is brought into contact with the support or the beads.
  • unbound peptides are removed in a washing step.
  • the bound peptide fractions are then released from the binding molecules and the peptide mixtures generated this way are subjected to MS- analysis.
  • a preprocessing phase is performed, in which the following filter criteria are applied in the following order:
  • the peptide Length Filter in accordance with the specifications of the MS procedures will sort out peptides with a length shorter than 5 amino acids and/or longer than 30 amino acids.
  • the optimization problem in case 1 can be formalized as a set cover problem.
  • the goal is to cover a set of proteins with a minimal set of antibodies.
  • Small instances, up to several hundred proteins, can be approached by solving an integer linear program with standard solver software. It is unfeasible to formulate and solve integer programs for large instances.
  • the inventors have applied heuristic algorithms to calculate epitope sets for coverage of the whole proteome - several thousand proteins. It has been shown in theory that a standard greedy approach gives generally satisfactory results.
  • the heuristic algorithm chooses the antibody that captures the most yet uncovered proteins in every iteration. This is repeated until every protein is covered by at least one antibody.
  • Versus the standard one antibody one antibody approach the terminus specific antibody approach reduces the required number of antibodies e.g. to cover the proteome, by the factor 12, see table 4 below.
  • Protein fragments - sharing the same terminus sequence - are enriched by using 3 to 5 amino acid specific terminal antibodies after a proteolytic digest. This step results in an enrichment of a group of peptides with the same N- or C-terminal sequence. With a second enrichment step using additional 3 to 5 amino acid specific terminal antibodies the complexity of this peptide population is further reduced, in general down to one species.
  • the detection of the proteins is performed in a following step with mass spectrometry.
  • a first set of binding molecules is immobilized on a support, e.g. beads.
  • a complex protein sample is subjected to protein fragmentation by for example tryptic digest.
  • the resulting peptide mixture is brought into contact with the support or the beads.
  • unbound peptides are removed in a washing step.
  • the bound peptides are then released from the first binding molecules and the resulting peptide mixture is brought into contact with a second set of binding molecules. After unbound peptides have been removed, the bound peptides are released from the second binding molecules and subjected to MS-analysis.
  • pairs of binding molecules have to be selected, that bind at least one, preferably a multiple of one, at most however only a comparably limited number of peptides from the initial peptide mixture.
  • Two independent filter processes are performed for a first and a second set of binding molecules, respectively.
  • peptide length filter in accordance with the specifications of the MS procedures will sort out peptides with a length shorter than 5 amino acids and/or longer than 30 amino acids.
  • the filter not contained in the filter process for the second set of binding molecules are excluded because peptides meeting the respective criteria will be removed from the theoretical peptide set during the first binding procedure.
  • the bioinformatics optimization of this assay can be separated into two steps. First, the generation of the data structure in which all combinations of first and second binding molecules which capture multiple one are stored. At this step a weight filter is applied, removing all combinations which capture isobaric peptides.
  • a weighted score for each binding molecule is calculated, this score returns the number of peptides it captures for which the corresponding first or second binding molecule already exists plus the number of peptides it captured for which the corresponding first or second binding molecule does not exist yet.
  • This score is the base for the iterative optimization scheme. In this scheme the set of binding molecules is incremented by the binding molecule which obtains the highest score.
  • the data structure is updated to account for inclusion of a new binding molecule into the assay.
  • This update procedure removes all peptide information of proteins which are already covered though a peptide. This procedure is repeated until for all proteins at least one peptide is captured by a pair of one first and one second binding molecule.
  • TXP antibodies in the 2 step Affinity MS Approach results in a dramatic reduction of required antibodies compared to the standard one antibody one analyte method.
  • the reduction is 9.3 in the case of a digest with LysC and the aim to cover the whole proteome, as can be taken from table 5 above.
  • TXP antibodies bind to short sequences they can be used in different species (animals, plants, microbials, etc.).
  • This transspecies application is a big advantage as the number of antibodies which have to be prepared for the study of the proteomes of different species is dramatically lower than today, where usually antibodies are applied which are specific for one or only some related species.
  • Protein fragments - sharing the same terminal sequence - are enriched by using 3 to 5 amino acid specific terminal antibodies after a proteolytic digest.
  • a detection step using a second 3-5 aminoacid specific terminal antibody the peptide is identified.
  • two antibodies bind simultaneously - not like in case 2 sequentially - to the peptide.
  • Sandwich assay In this method, a first set of binding molecules, the capture set, is immobilized on a support or on e.g. addressable beads. A complex protein sample is subjected to protein fragmentation by for example tryptic digest. Subsequently, the resulting peptide mixture is brought into contact with the support or the beads. Subsequently, unbound peptides are removed in a washing step.
  • second binding molecules the detection set
  • specific peptides or proteins can be detected.
  • the binding molecules bind to the termini of the detected peptides. More specifically, it is preferred, if the first binding molecules bind to the N-terminus of the peptides while the second binding molecules bind to the C-terminus of the peptides. Of course, this order can be reversed.
  • the prerequisite for the successful Sandwich-Immunoassay is the uniqueness of the combination of the first and the second binding molecules.
  • the advantage of the novel method is that a multitude of analytes can be analyzed successively or in parallel requiring only a relatively small number of binding molecules.
  • a process is performed, in which the following filter criteria are applied in the following order to the peptide pool from the preprocessing phase:
  • the high protein abundance filter has to be applied only to the epitope selection process for the first set of binding molecules. This is the case, because in the prefractionation step of the Sandwich-Immunoassay, epitopes, which match the criteria of these filters, will be selected out and can therefore not disturb the following analysis steps.
  • a prerequisite for a Sandwich-Immunoassay is that two antibodies bind to their target peptide. This is possible to a degree for peptides with a length larger than 12 amino acids, but reliable only for peptides with a length larger than 15 amino acids. The binding does not work for peptides with a length smaller than 12 amino acids. Because of this, only peptides with a length longer than 15 amino acids are considered as targets.
  • the optimization procedure of this bioinformatics workflow can be separated into two steps. First, the generation of the data structure in which all epitope combinations which capture multiple one are stored.
  • Target epitope-combinations which are not unique, and thus can not be assigned to a specific peptide and protein, have to be removed from the data structure .
  • the length considerations explained above have implications on the selection of peptides with unique terminal epitope combinations.
  • Uniqueness is defined by the hypothesis that there is no other peptide in the solution with the same terminal sequences, which is likely to bind both antibodies. As this is considered possible from a length of 12 amino acids, peptides from 12 to 15 amino acids have to be considered in the uniqueness filter, even if they are not considered as targets. These peptides can "interfere” with the detection process. Peptides with a length below 12 amino acids do not disturb the detection process, because they are too short to bind both antibodies, and hence are ignored in the uniqueness filter. This means e.g. that an epitope combination of a target (>15 amino acids) would be considered unique, even if a peptide shorter than 12 amino acids has the same combination. On the other hand, a peptide from 12 to 15 amino acids in length would not be used for optimization, even if the epitope combination is unique, because the binding of both antibodies is not considered to be reliable.
  • the structure contains additional information for each binding molecule.
  • the number of capture-detector pairs it takes part in is stored. Furthermore, it is stored if for a capture-detector pair one of the binding molecules is already contained in the assay.
  • a weighted score for each binding molecule is calculated, this score returns the number of peptides it captures for which the corresponding capture or detector binding molecule already exists plus the number of peptides it captures for which the corresponding capture or detector binding molecule does not exist yet.
  • This score is the base for the iterative optimization scheme. In this scheme the set of binding molecules is incremented by the antibody which obtains the highest score. This scoring function is corrected for the capturing of two or more peptides from the same protein.
  • the data structure is updated to account for inclusion of a new binding molecule into the assay.
  • This update procedure removes all peptide information of proteins which are already covered through a peptide. This procedure is repeated until for all proteins at least one peptide is captured by such a capture- detector pair.
  • TXP antibodies in the Sandwich Immunoassay approach results in a dramatic reduction of required antibodies compared to the standard one antibody one analyte method.
  • the reduction is 8 in the case of a digest with Trypsin with the aim to cover the whole proteome, as can be seen in table 6 below.
  • required antibodies used Proteins required TXP for one antibody one database / list Protease covered antibodies analyte approach reduction
  • TXP antibodies bind to short sequences they can be used in different t species - usually antibodies are specific for one or some species.
  • sandwich immunoassays can be established without the need for generation of capture molecules. This results in a tremendous saving of assay development time and costs for capture molecule generation.

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Abstract

Un procédé permettant de déterminer in silico un ensemble d'épitopes cibles sélectionnés adaptés à la détection d'au moins une protéine d'un ensemble de protéines d'intérêt au sein d'une réserve de protéines échantillons, comprend les étapes suivantes : fournir une première liste contenant les séquences desdites protéines échantillons; fournir une seconde liste contenant les séquences desdites protéines d'intérêt; appliquer une fragmentation in silico aux protéines échantillons dans ladite première liste pour générer une troisième liste contenant des séquences de peptides cibles possibles ayant des épitopes cibles possibles à leurs extrémités libres N-terminale et C-terminale, lesquels épitopes cibles possibles comprennent chacun trois à cinq acides aminés N-terminaux ou C-terminaux; générer une quatrième liste de séquences de peptides cibles valides en supprimant de ladite troisième liste de tels peptides cibles possibles qui sont moins adaptés à l'identification de ladite protéine d'intérêt; sélectionner, parmi lesdits épitopes cibles possibles sur lesdits peptides cibles valides dans ladite quatrième liste, un ensemble d'épitopes cibles sélectionnés, ledit ensemble d'épitopes cibles sélectionnés contenant au moins un épitope cible pour chaque protéine d'intérêt dans ladite seconde liste, tout en contenant un nombre aussi petit que possible d'épitopes cibles.
PCT/EP2009/001230 2009-02-20 2009-02-20 Procédé permettant de déterminer in silico un ensemble d'épitopes cibles sélectionnés Ceased WO2010094300A1 (fr)

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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012169911A1 (fr) * 2011-06-10 2012-12-13 Auckland Uniservices Limited Peptides, produits de recombinaison et leurs utilisations
WO2013013922A1 (fr) * 2011-07-28 2013-01-31 Nmi Naturwissenschaftliches Und Medizinisches Institut An Der Universitaet Tuebingen Procédé de détection de biomolécules
US10274485B2 (en) 2011-07-28 2019-04-30 NMI Naturwissenschaftliches und Medizinisches Institut an der Universität Tübingen Method for detecting biomolecules
US20240087675A1 (en) * 2021-03-15 2024-03-14 Amazon Technologies, Inc. Methods for optimizing tumor vaccine antigen coverage for heterogenous malignancies
WO2023046385A1 (fr) * 2021-09-22 2023-03-30 Asml Netherlands B.V. Systèmes et procédés de sélection de motif
CN115903394A (zh) * 2021-09-22 2023-04-04 Asml荷兰有限公司 图案选择系统和方法

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