WO2005103679A2 - Method for affinity scoring of peptide/protein complexes - Google Patents
Method for affinity scoring of peptide/protein complexes Download PDFInfo
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- WO2005103679A2 WO2005103679A2 PCT/BE2005/000052 BE2005000052W WO2005103679A2 WO 2005103679 A2 WO2005103679 A2 WO 2005103679A2 BE 2005000052 W BE2005000052 W BE 2005000052W WO 2005103679 A2 WO2005103679 A2 WO 2005103679A2
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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
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
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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
- G16B15/00—ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
- G16B15/30—Drug targeting using structural data; Docking or binding prediction
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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
- G16B20/30—Detection of binding sites or motifs
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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
- G16B20/50—Mutagenesis
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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
Definitions
- the present invention is related to a quantitative structure-based affinity scoring method for ligand/protein complexes such as peptide/protein complexes. More specifically, the present invention comprises a method that operates on the basis of a highly specific force field function (e.g. CHARMM) that is applied to all-atom structural representations of peptide/receptor complexes. Peptide side-chain contributions to total affinity are scored after detailed rotameric sampling followed by controlled energy refinement.
- the method of the invention further comprises a de novo approach to estimate dehydration energies from the simulation of individual amino acids in a solvent box filled with explicit water molecules and applying the same force field function as used to evaluate peptide/receptor complex interactions.
- Peptides are important regulatory molecules involved in a variety of biological mechanisms . Their function is generally determined by processing kinetics, interaction specificity and, more fundamentally, binding affinity. A thorough understanding of the contributions relevant for stable complex formation may form the basis of experimental rationalization, detection of novel ligands and optimization of- lead compounds. Predictive structure- based methods can be very helpful , provided that they are of sufficiently high accuracy. [0003] Structure-based binding studies face two major technical barriers. The first resides in the prediction of accurate 3D-structures for peptide/receptor complexes. Peptides are conformationally very flexible since most of their chemical bonds are subject to free rotation. A partial solution is to perform flexible docking using predefined rotamers .
- binding affinity depends on thermodynamic properties of the bound and free states of the molecules involved.
- structure- based affinity scoring methods invariably include approximations and/or a parameterization step wherein physically relevant effects are captured into tunable parameters.
- Statistical, or knowledge-based scoring methods operate on the basis of atom (or group) contact potentials derived from known protein structures.
- Empirical, or partitioning methods work with predefined physical energy terms, represented by parameterized mathematical equations that are optimized against experimental data.
- validation on independent data is required.
- it is not uncommon that methods performing relatively well on data similar to the training set are significantly less accurate on more divergent datasets or must even be retrained.
- Transferability therefore remains an important and delicate matter. Transferability is defined herein as the use of one and the same scoring function for different receptor/ligand systems.
- the present invention is related to the binding characteristics of anchor residues within peptide ligands of receptor molecules, e.g. human leukocyte antigen (HLA) complexes.
- HLA class I molecules are immunologically important receptors involved in specific recognition between cytotoxic T lymphocytes and pathogen-infected cells.
- Pathogen-derived peptides known as antigens, are mostly 8-10 residues long.
- Structural information from the Protein Data Bank (PDB) is available for an increasing number of receptor subtypes (at present about ten) .
- the present invention relates to the identification of the physico-chemically most relevant affinity determinants. Possible contributions like contact- based potentials, weight-adapted conformational energy terms, shape complementarity, hydrophobic corrections and different entropical components may yield good results but poor transferability. Because of the danger of over- parameterization, erroneous assignment of either false or redundant contributions is likely. Underparameterization, in particular of conformational strain, is another problem. The inventors therefore examined the possibility to develop a scoring function based exclusively on an established force field function. The principal advantage of force field based approaches is that different physico-chemical interactions can be computed in a consistent way.
- the present invention concerns a method for determining an affinity score of a binding protein/ligand complex such as a MHC receptor/ligand complex, said score advantageously being based, advantageously being primarily or solely based on structural data and a force field.
- An affinity score can equally be determined for the anchor residues of said binding protein/ligand complex such as a MHC receptor/ligand complex.
- the present invention comprises the method according to any of the previous embodiments whereby said affinity score represents a combination of desolvation energy and protein-ligand complex energy, advantageously receptor-ligand complex energy, wherein both said energies advantageously are derived from the same force field.
- the affinity score and method of the present invention is transferable to different protein/ligand systems, advantageously to different receptor/ligand systems, without reparameterisation.
- the present invention in particular relates to a method for determining an affinity score of a protein/ligand complex, characterised in that said score is based on, advantageously primarily based on, advantageously solely based on structural data and a force field, this method comprising the steps of (a) calculating a ligand-solvent interaction energy from a structural representation of the ligand placed in a box filled with explicit solvent molecules ,- (b) calculating a ligand-protein interaction energy from a structural representation of the ligand placed in the binding site of the protein; and (c) calculating said affinity score by subtracting the ligand-solvent interaction energy of step (a) from the ligand-protein interaction energy of step (b) .
- the present invention further relates to a method for determining an affinity score of a protein/ligand complex, characterised in that said score is based on, advantageously primarily based on, advantageously solely based on structural data and a force field, this method comprising the steps of (a) calculating a ligand-solvent interaction energy from a structural representation of the ligand placed in a box filled with explicit solvent molecules; (b) calculating a ligand-protein interaction energy from a structural representation of the ligand placed in the binding site of the protein; (c) calculating a conformational strain energy for the protein/ligand representation of step b; and (d) calculating said affinity score by subtracting the ligand-solvent interaction energy of step (a) and the conformational strain energy of step (c) from the ligand- protein interaction energy of step (b) .
- the conformational strain energy of step (c) herein is calculated as the difference between, on the one hand, the sum of the conformational energies of the ligand and protein in an unbound reference state and, on the other hand, the sum of the conformational energies of the ligand and the protein in the protein/ligand representation of step (b) . It is thus possible to calculate conformational strain energies sufficiently fast and in a way that they are compatible with the force field used. [0014] In a method according to the invention an affinity score advantageously is calculated (solely) from structural data and a force field.
- the force field may be any force field known in the art .
- ligand-protein interaction energies are used to derive ligand-protein interaction energies; ligand-solvent interaction energies, also called (de) solvation energies, (de) hydration energies; and possibly also conformational strain energies.
- (De) solvation energies are advantageously derived from simulations of amino acid model compounds in an explicit solvent environment, for instance an explicit water environment. Potential inconsistencies between solvent terms derived from experimental data and intra-complex terms based on the force field are thus avoided.
- the solvent in step (a) of the above methods exclusively consists of water molecules.
- the protein/ligand representation of step (b) in a method according to the invention is derived from an experimentally determined structure .
- the protein/ligand representation of step (b) may be generated by computer modelling.
- the computer modelling comprises an amino acid side-chain modelling step and/or an energy minimisation step.
- the representation of the ligand in the solvent box of step (a) of a method according to the invention is derived from an experimentally determined structure .
- the representation of the ligand in the solvent box of step (a) may be generated by computer modelling.
- the computer modelling comprises an amino acid side-chain modelling step and/or an energy minimisation step.
- the methods of the invention are highly suitable for affinity scoring of protein-ligand complexes or receptor-ligand complexes.
- the protein to which the ligand binds can be a receptor such as e.g. a MHC receptor, a HLA receptor, but it may also be an antibody.
- the antibody may be a polyclonal or a monoclonal antibody or a fragment thereof that is capable of forming a 3-D structure
- the ligand is a peptide, a small molecule or a pharmacophore.
- the term "ligand” in the context of the present invention can refer to part of a ligand, such as the anchor residues of a ligand.
- the term "ligand” can refer in particular to any part of a peptide, including a side chain, backbone moiety, chemical group, ... via which the ligand can interact with/bind to its binding protein or receptor.
- a method of the invention is highly suited to calculate an affinity score for the anchor residues of a ligand, but may also be used to determine affinity scores for non-anchor residues so that a score can be determined for the whole of a ligand.
- FIG. 1 represents computed hydration energies for amino acid side-chains in a water box. Contributions for van der Waals (black bars) , H-bonding (gray) and electrostatic interactions (white) are given separately. Values are further subdivided per chemical group present in a side-chain and are therefore useful as group solvation parameters (GSP) .
- GSP group solvation parameters
- each side-chain is composed of maximally three groups.
- Values for the aliphatic moieties (all residue types) are indicated by thin outlines.
- the second chemical moiety, if any, is indicated by thick outlines.
- the third moiety (only Tyr, Trp and His) again by thin outlines.
- the figure 2A represents energy contributions calculated for different side-chains placed at P2 in the pAla/A2 complex. White bars, desolvation energies; light gray bars, strain terms; dark gray bars, sum of intra- complex interactions. Total energies are indicated by tiny black bars; they correspond to the scores in column 3 of Table I .
- the Figure 2B represents a detail of Gin interactions with the A2 receptor; 1, first group (aliphatic moiety); 2, second group (amide function); V, van der Waals; H, H-bonds; E, electrostatic energy. Numeric values are computed interaction energies in kcal/mol units.
- the Figure 3 represents a comparison of anchor profiles for HLA-A1 between PepScope and Bimas predictions. Amino acid preferences for the anchor positions indicated at the left are listed in decreasing order of preference. Strong and non-preferred residues are indicated in bold and italics, respectively. The corresponding cutoffs are -2.5 and 0 kcal/mol for PepScope and 2 and 0.1 units for Bimas. [0029] Table I gives Predicted vs Experimental Affinities of F SKQYMTL Mutants. [0030] Table II shows HLA-B7 Anchor Specificity.
- the present invention describes the development of a novel affinity scoring method here named "PepScope” .
- the latter forms part of the EpiBaseTM platform for T-cell epitope identification (pending patent application WO03/105058 which is incorporated herein by reference) .
- the CHARMM force field was selected as the sole input function to PepScope. No parameterization steps were performed, except at the level of the protocol (e.g. the number of energy minimization steps, cutoff distances and model preparation strategy) . Exactly the same force field function was used to derive (de) hydration energies from simulations of amino acid model compounds in explicit water environment.
- the PepScope method uses only structural data and a standard force field function to score anchor residues in HLA complexes. This approach offers several advantages: (i) its independence from experimental binding data guarantees unbiased analysis with a greater sensitivity, (ii) the method is equally well applicable in cases where experimental information is scarce, and (iii) computed affinities can be thoroughly rationalized either by dissection into physical contributions or by structural inspection. Furthermore, since the method does not contain a training step, it is characterized by a great transferability. The inventors have demonstrated this by validation on four HLA receptors with divergent physical properties.
- the results presented in this work may therefore significantly affect T-cell epitope discovery programs applied in the field of peptide vaccine development.
- the PepScope method has an original approach to quantify desolvation effects. This is accomplished by in silico submersion of amino acid model compounds in explicit water, followed by standard energy minimization and proper rotameric sampling, energy calculation, averaging and selection. Combination of solvent terms with intra-complex energies results in scores devoid of systematic errors . Thus, desolvation energies derived by the said in silico method are compatible with intra-complex terms.
- the PepScope scoring function basically consists of three energetic components: desolvation, direct ligand/receptor interactions and intra-complex strain (whereby the two latter components are two types of intra- complex energy) . These terms are strongly affected by local conditions in a complex, the latter forming the basis of specificity. In order for a residue to be contributive, its local interactions have to compensate for unfavorable desolvation and strain. The net balance can be very delicate, especially when aromatic or polar side-chains are involved. From a modeling point of view, this imposes very high demands on the accuracy of complex models . The inventors have demonstrated that such high level of accuracy is attainable for buried anchors. It is obvious for the man skilled in the art that scoring of non-anchor residues or full peptide sequences lies in the extension of the methodology of the present invention. The PepScope method is therefore to be seen as an affinity scoring method for complete ligands.
- Affinity scoring algorithm Affinity scoring of anchor residues is accomplished basically by a combined side-chain rotameric search and energy refinement approach. The following steps are performed individually for all amino acid side-chains at each anchor position in all pAla/HLA complex models. [0040] The side-chain is introduced in standard geometry. Then, all rotamers from the same library as used in the model preparation are applied consecutively to the mutated side-chain. Dummy rotamers are used for Gly, Ala and Pro. Each rotameric variant is submitted to 800 steps conjugated gradient energy minimization. Moderate positional restraints (1 kcal/A 2 ) are applied to the full backbone of the complex except the substituted residue and its flanking peptidic groups. The cutoff for non-bonded interactions is set to 14 A. The following analyses are carried out on the minimized structures: computation of accessible surface area (ASA) of the side-chain rotamer, conversion of ASA into percentage buried surface area
- ASA accessible surface area
- Affinity scores are calculated for all possible amino acid substitutions at the anchor positions P2 and P9 in pAla/A2, -A24 and -B7, and positions P3 and P9 in pAla/Al .
- Noise effects due to imperfections in individual models or fluctuations in the minimization path are reduced by taking the average E tota i value over three models constructed per receptor type.
- the PepScope scoring function by default subdivides each of the three global energy terms into more elementary components, primarily for the sake of comprehensibility. In the case of the desolvation terms, this also enables working with functional group solvation parameters (GSPs) rendering desolvation terms conformation sensitive.
- GSPs functional group solvation parameters
- Edeso l v - ⁇ i %BSA ( i ) x GSP ( i ) ( 2 )
- E inter ⁇ i ⁇ E vdw ( i) + E h bo ( i) + Eeie ( i) ⁇ (3 )
- Estrain F rec + E pep + E se lf (4 )
- i denotes one of the chemical functions present in the mutated side-chain (defined in the legend to figure 1) . Any given side-chain type is described by maximally three chemical groups. All side-chain types comprise group 1, defined as the aliphatic moiety.
- E deso iv and Einter into group contributions are justified in view of the additive nature of surface areas and nonbonded energy terms .
- An important aspect of PepScope is that GSP values are derived de novo, i.e. from simulations of amino acid reference conformations in a spherical water box filled with explicit water molecules. This approach is expected to yield E deso iv energies that are maximally consistent with the intra-complex terms Ei nte r and E st r a i / because both types of energy are based on the same parameters and equations from the CHARMM force field.
- the PepScope solvent model is therefore designated “internal” as opposed to “external” models based on experimental group or atomic solvation parameters.
- the direct side-chain/receptor interactions (Einter) are the most obvious contributions. They consist of van der Waals interactions quantified by a "6-12" Lennard-
- E vdw (i) Jones potential
- Ebo(i) electrostatic interactions calculated by a Coulombic equation with a distance-dependent dielectric constant (E e ⁇ e (i))- Van der Waals and H-bond interactions are computed with a cutoff distance of 16 A while 25 A is used for electrostatic energy.
- the strain term E stra i n stands for "every increment in energy due to the mutant side-chain, except direct interactions" .
- strain contributions are computed on the mutated and minimized structure and compared with the same terms derived from the minimized pAla/HLA structure ("mutant" strain minus "Ala” strain) .
- a first component of E str ain is E reC / the strain energy residing in the receptor, more precisely within the set of atoms closer than 15 A from the C ⁇ -atom of the mutated position.
- a second component is E pep , the strain felt by the entire poly-Ala peptide (i.e., the full peptide minus the substituted residue) .
- This term includes both the self energy of the peptide and its interactions with the receptor.
- the third component is E se if, the self tension of the mutation, including all bonded and nonbonded energies within the mutated residue (side-chain, main-chain and flanking peptide groups) .
- E se ⁇ _- is measured relative to the self energy of the same amino acid in the water box, and not relative to the minimized pAla/HLA structure.
- the full score is always calculated first (Eq. 1) and then, if higher, truncated at 3.0 kcal/mol.
- Acetylated and aminomethylated amino acids are placed at the center of a spherical water box with a radius of 37 A and containing 6840 molecules in a TIP4P configuration.
- Side-chain conformations are retrieved from the same rotamer library as used in the preparation of complex models. Rotamers are considered one by one, for all 20 natural residue types. For each rotamer, the dimensions of the system are reduced by retaining only the water molecules in a 20 A layer around the solute. Next, all overlapping water molecules are removed. Overlap is defined as a distance between any solute-water atom pair smaller than the sum of their respective van der Waals radii, minus a tolerance of 1 A.
- the system is subsequently energy minimized by performing 200 steps conjugated gradient minimization using a 14 A cutoff and 10 kcal/A 2 positional restraint on the water oxygen atoms (not on the hydrogens, nor on the solute atoms) .
- the whole procedure is repeated 100 times per rotamer using slightly different initial placements (a uniform random offset relative to the center of the sphere was applied to the X- , Y- and Z-coordinates of the solute, sampled from the interval -2, +2 A) .
- the 25 solutions having the best total side- chain/water interaction are retained and their values are averaged.
- the rotamer with the lowest average energy is retained for each residue type.
- Figure 1 shows the computed energies, subdivided into van der Waals, H- bonding and electrostatic interactions for each chemical group .
- a r (i, ) and A c ⁇ i,j) are the group ASAs in the reference state (here, the rotamer selected in the water box simulations) and in a complex, respectively.
- Affinity scoring issues [0054] Accurate modeling of the conformation of anchor residues in peptide/receptor complexes is not very complicated, provided that models are prepared with state- of-the-art methodology, that the conformational space of the anchor side-chains is thoroughly explored, and that the structures are adequately refined using appropriate restraints. A greater problem however is to derive affinities from structural data. It is a real challenge to develop a robust, generally applicable scoring method that does not require reparameterization for different systems, i.e. a transferable method. [0055] The invention has contributed to the affinity scoring problem in several ways .
- the inventors have demonstrated that a standard force field is useful as the basis of a scoring function, provided that it is supplemented with a suitable solvent function.
- a method was developed to derive desolvation energies on the basis of the same force field that is used to evaluate intra-complex energetic contributions. The hypothesis that such "internal" model would be preferred over “external” approaches because of a higher consistency in the calculated energies, is confirmed by the results.
- Yet another embodiment of the present invention applies a fully additive energy model, wherein diverse contributions from van der Waals interactions, H- bonds, electrostatics, and different types of bonded energy are calculated separately and then simply added up in order to obtain final scores.
- Another embodiment of the present invention demonstrates that the net ligand binding free energy is fundamentally a delicate balance between very large components: free energy of desolvation and free energy of binding . When decomposing the latter into direct ligand/receptor interactions and induced strain, one arrives at two positive and one negative contribution.
- Figure 2 illustrates their mutual proportions, computed for all amino acid side-chains at position P2 in HLA-A2. Given the good correlation with experiment (Tables I and II) , the individual terms should be meaningful. However, the implications from the point of view of a modeler are, to put it mildly, enormous. The net binding energy of an "average" side-chain in a "typical” receptor pocket amounts at best about 25%, but mostly around 10%, or less, of the corresponding intra-complex interaction terms. This means that a 10% error in any of the independent contributions is likely to destroy the correlation with experiment .
- H-bonding Failure to simulate a single, relevant H-bond (or the generation of a false H-bond) distorts the computed interaction energy by 1.5 kcal/mol in H-bonding and roughly an equal amount in electrostatic energy; this is equivalent to more than 1 log-order in Kd.
- the present invention focuses on anchor residues, of which the conformation is strongly imposed by the receptor.
- full-length (octa- to decameric) HLA class I binding peptides are characterized by a significant degree of flexibility near the middle part. Scoring of full peptide sequences is possible on the basis of the scoring function of the present invention.
- Non- anchor residues are in general contributing weakly to total affinity. Exposed residues remain largely hydrated but cannot benefit from strong interactions.
- Buried side-chains are more difficult: by definition, they pay the full dehydration price but they are usually located in regions of lower atomic density, which leads to reduced (van der Waals) interactions. Assuming that they can bind free of strain, the interactions and hydration energies of polar side-chains are, as a rule of thumb, similar in size. For aromatic and aliphatic side-chains there usually remains a small net profit. This explains the overall slight preference for apolar residues at buried non-anchor positions (mostly P3 and P7) . Thus, buried polar side- chains in low-density regions generally stand no chance against non-polar ones, unless they are further stabilized by favorable electrostatics or H-bonds, potentially also mediated by structured water molecules. The technical complexity of modeling and scoring non-anchor residues is therefore very high, but fundamentally the same rules apply. The present invention on conformationally more restrained anchor residues can therefore be of great help in quantifying the contributions that are relevant to total affinity.
- Example 1 The preparation of model complexes depends on the availability of template structures in the Protein Data Bank. If one or more tertiary structures for a given HLA subtype are known, a selection is made primarily on the basis of crystallographic resolution. Additional criteria such as R-factor, length of the bound peptide, experimental affinity of the latter, width of the binding groove, etc., are considered as well . For A2 1DUZ was selected as the starting template. Since no exact templates were available for Al, A24 and B7, these had to be modeled. First, a structure was selected from the PDB using the same criteria as for A2 , considering also the sequence similarity of receptor residues in contact with the peptide.
- a high affinity peptide was docked into each HLA model by means of a flexible peptide docking algorithm, as described in Desmet et al (FASEB J) . Briefly, the poly-Ala peptide was replaced by the sequence YTAWPLVY for Al, FLSKQYMTL for A2 and A24, and FPVRPQVPL for B7. Bond lengths and angles, also for the main-chain, were initialized in standard geometry to avoid structural bias . The docking algorithm was then instructed to rebuild the peptide from the N- towards the C-terminus. Limited translation (max. 1 A) was allowed.
- IC 50 values were determined using a cell- based assay, largely according to van der Burg et al, Hum Immunol 1995;44:189-98 and Kessler et al, Hum Immunol 2003;64:245-55.
- immortalized B-cells displaying HLA-A*0201 or HLA-A*2402 homozygously VOSE EBV (A*0201, B*4402, Cw*050l/0711) and HATT EBV (A*2402, B*4801, Cw*080l/1202) are stripped of their self peptides, followed by equilibrium binding of test peptide in competition with fluorescent reference peptide (FLPSDC (5Fluorescein) FPSV for A2 and RYLKC (5Fluorescein) QQLL for A24) .
- FLPSDC fluorescent reference peptide
- test peptide A 10-point concentration range of test peptide is used for each measurement, typically in 2-fold increments from 62.5 nM to 32 ⁇ M, in a constant background of 30 nM reference peptide. Adapted ranges were used for excellent binders (minimal concentration 7.8 nM) and weak binders (maximal concentration 128 ⁇ M) . 50% inhibitory concentrations (IC 50 - values) were calculated as averages obtained from at least 3 independent measurements, i.e. from different cell preparations and peptide dilutions. Test peptides >95% pure (Thermo Electron GmbH) were stored at 10 mM in DMSO at -20 °C.
- Cysteine-containing peptides were stored at 10 mM in 1 mM DTT DMSO. DTT did not affect binding of FLSKQYMTL control peptide but significantly improved binding and reproducibility for FCSKQYMTL and FLSKQYMTC on both A2 and A24 (not shown) .
- Kd ref and c ref are the dissociation constant and formal concentration of the reference peptide, respectively.
- the Kd ref values were derived from an independent binding assay in which the fluorescence intensity was measured as a function of increasing concentrations of reference peptide. Non-linear curve fitting using a single-site binding scheme gave approximate Kd' s of 3nM for the A2 and 30 nM for the A24 reference peptides, respectively.
- HLA-A*0201 (A2) is one of the most extensively studied peptide binding receptor molecules. It is known to show a strong preference for peptide ligands having Leu at position P2 and Val or Leu at P9. The modeling of all 20 natural amino acid residues at the anchor positions P2 and P9 was performed systematically for the three pAla-A2 models.
- Figure 2 shows the averaged scoring values for position P2 in pAla-A2.
- Total affinity scores have been dissected into the three major energetic components: desolvation energy, side-chain/complex interaction (including interactions with all pAla residues but the mutated one) and "local strain” (including strain within the mutant residue) . All values are expressed in units of kcal/mol, i.e. the units of the force field equations .
- Gin is the sole polar side-chain that is reasonably well accepted in the receptor pocket at P2.
- Figure 2 shows that Gin can be accommodated almost free of strain, but the desolvation cost is large (17.2 kcal/mol). Yet, Gin seems to have no problem in compensating for this by making exceptionally favorable interactions .
- Figure 2B shows more detail. The aliphatic part of its side-chain interacts favorably with the pocket residues, almost exclusively through van der Waals terms.
- the amide group contributes even better: -9.6 (2V) , -2.6 (2H) and -2.9 (2E) kcal/mol from van der Waals, H-bonds and electrostatic interactions.
- the total van der Waals interaction in the complex is -15.7 kcal/mol or roughly three times stronger than in the water box. In itself this is not an exceptional situation for buried, well-packed side-chains, including the polar ones . What really makes the difference is the ability of Gin to form two nearly ideal H-bonds, one between the first of its two amide H ⁇ atoms and Glu A63.0 ⁇ and the other between the second amide H ⁇ and the backbone A63.0 atom.
- Trp and Phe Underpredicted (in the false negative sense) are Trp and Phe which experienced a relatively high strain in the models (12.1 and 5.6 kcal/mol, equivalent to 29.5 and 13.7 kJ/mol, respectively; the models were probably not fully relaxed) . All other residue types were predicted with an error less than -0.5 logKd. The global standard error was 3.1 kJ/mol . Interestingly, many features that have not been recognized before were correctly predicted: (i) Val is not the best possible residue at P9, (ii) Met, Leu, Ala and lie are almost equally preferred, (iii) 11 other residue types (50%) bind with lower but nonprohibitive strength.
- HLA-A*2402 (A24) is another abundant MHC class I receptor with similar hydrophobicity as A2 but a significantly different (presumed) specificity profile.
- the anchor positions also appear at positions P2 and P9 but the consensus motif is P2 (Y/F) , P9 (L/F/l/M) or P2 (Y/F) ,P9 (F/W/I/L) .
- Both specificity pockets are considerably larger than in A2. For the pocket at P2 , this is mainly due to the Phe-»Ser mutation in the receptor at residue A9, while the Leu—>Ala mutation at A81 can be held responsible for the larger F-pocket at the peptide C- terminus.
- Trp is the "winner" at both positions P2 and P9, which is in agreement with the predictions but not with the presumed A24 motif. It is possible that the lower intrinsic amino acid frequence of Trp can be held responsible for the gaps in motifs based on experimental data. For that matter, the same fact could also explain the often underestimated preference of Met at various positions in different receptors. [0081] A number of deviations between theoretical and experimental data exist.
- the tiny side-chains Ala and Gly are underpredicted at P2 by about 1 order of magnitude in Kd. This is often seen at other positions as well. A possible explanation could be the presence of structured water molecules which are ignored in the simulations.
- the underpredicted Gin might be explained in the same way. Structural analysis showed that Gin at P2 adopts an identical conformation as in A2 , but its van der Waals interactions are reduced by -1 kcal/mol (equivalent to -2.5 kJ/mol) , due to the mutation Phe-»Ser at receptor position A9. Given the constitution of the pocket, it is likely that one or more water molecules further stabilize the free O ⁇ atom of Gin.
- Trp can take full advantage of the Leu—Ala mutation at A81 but even here it suffers from a significant compensatory strain (ranging from 4.6 to 8.5 kcal/mol in the three models) . Phe experiences less strain but the latter is also strongly fluctuating (0.7 to 5.5 kcal/mol) . All in all, both Trp and Phe receive similar scores, in agreement with experiment, but both are somewhat underestimated presumably due to incomplete relaxation in the simulations.
- the P9 position in A24 is also special for another reason, namely its enormous diversity in binding affinity.
- the experimental affinity range spans 27 kJ/mol or -5 orders of magnitude in Kd. As much as 10 of the 20 residue types (50%) cause very poor binding. The reason for this pronounced specificity is related to the weird shape of the F-pocket. Side-chains larger than Ala tend to bump into the relatively rigid wall formed by A77-Asn, A116-Tyr and A147-Trp. This causes a residual strain of 1 kcal/mol (-2.5 kJ/mol) or higher. More important, however, is the loss of interaction with residue A81 (Ala i.s.o. Leu) for all side-chains that do not properly fill the pocket.
- Example 5 Binding specificity of B7 [0085]
- the inventors wanted to examine the performance of PepScope on HLA-B*0702 (B7) , a receptor with a pronounced preference for Pro at position P2 and a P9 specificity with shared features from A2 and A24.
- P2 profile seemed interesting: according to a point mutation analysis by Sidney et al . on the HIV nef 84- 92 peptide (FPVRPQVPL) , all P2 mutants were binding at least 100 times weaker than the native peptide sequence, thus only the wt Pro would be allowed at P2.
- Ala can also bind free of strain but interacts more weakly (-3.6 kcal/mol) .
- Val makes identical van der Waals interactions as Pro (-8.2 kcal/mol) but induces 4.4 kcal/mol of strain; together with the desolvation term (3.7 kcal/mol) and the minor electrostatics (-0.3 kcal/mol), the balance becomes slightly favorable (-0.5 kcal/mol).
- Sidney et al . do not provide data for the Val mutant but the Bimas matrix takes it up as a feasible residue.
- Several other side-chains can make relatively strong interactions but they invariably pay a very high "strain" price for it (e.g. Leu) .
- Example 6 Binding specificity of Al [0089] Having examined the anchor specificity of two typically hydrophobic (A2 and A24) and one sterically driven receptor (B7) , the inventors decided to test
- HLA-A*0101 (Al) is such a receptor, showing marked preference for Asp and Glu at P3 (not P2) and Tyr, Lys, Arg and Phe, at P9. Position P2 prefers small, polar side-chains like Thr and Ser but a pronounced motif has not been identified. Hydrophobic side- chains seem to play an inferior role in general .
- the Al system was therefore considered as an important test case for the validation of the solvent model and the scoring of H-bonds and electrostatic interactions. [0090] Since no studies on systematic anchor substitutions are available, the scores of this invention were compared with Bimas data ( Figure 3) . The first impression is that the results from both methods are in fair agreement.
- a common feature for Thr/Ser at P2 , Asp/Glu at P3 and Lys/Arg at P9 is their ability to bind relatively free of strain (the highest tensions were -1.5 kcal/mol for Asp/Glu at P3 and 2.5 kcal/mol for Arg at P9) .
- the interactions made by these residues in the complex are compensated only for desolvation energy ( Figure 1) .
- the OH-functions of both Ser and Thr at P2 can form two nearly ideal H-bonds (one as donor to Glu-A63 and one as acceptor from Asn-A66) for which they receive -2.4 kcal/mol. They also receive an equal (Ser) or slightly better (Thr: -2.9 kcal/mol) amount of electrostatic energy.
- Asp and Glu are true anchor residues at position P3. Asp can form a double bifurcated H-bond with Arg-A156 (-3.1 kcal/mol) and receives an extra -6.2 kcal/mol electrostatic energy, mainly from the same Arg but also from the peptide backbone NH dipoles of residues P3 and P5.
- Glu forms two suboptimal H-bonds with Arg-A156 and another weak H-bond with His-A70, for which it receives a total of -2.7 and -5.2 kcal/mol in H-bonding and electrostatic energy, respectively.
- Lys and Arg at P9 can both form a single H- bond with Asp-A116 (yielding -1.4 and -1.9 kcal/mol, respectively) , but the electrostatics are only of moderate quality (-1.2 and -1.8 kcal/mol, respectively). However, Lys can bind in a totally relaxed way, while Arg induces 2.5 kcal/mol of strain.
- Kessler JH Mommaas B, Mutis T, Huijbers I, Vissers D, Benckhuijsen WE, Schreuder GM, Offringa R, Goulmy E, Melief
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WO2007120834A2 (en) | 2006-04-13 | 2007-10-25 | Peptimmune, Inc. | Methods for designing and synthesizing directed sequence polymer compositions via the directed expansion of epitope permeability |
EP2586460A1 (en) | 2007-10-16 | 2013-05-01 | Peptimmune, Inc. | Method for designing and preparing vaccines comprising directed sequence polymer composition via the directed expansion of epitopes |
CN114420198A (en) * | 2022-01-04 | 2022-04-29 | 香港中文大学(深圳) | Protein-protein binding affinity prediction method, computer and storage medium |
CN115083515A (en) * | 2022-06-30 | 2022-09-20 | 北京工业大学 | protein-DNA binding affinity prediction method considering interface information and interaction energy |
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US7756674B2 (en) * | 2007-08-03 | 2010-07-13 | The Trustees Of Columbia University In The City Of New York | Methods of calculating differences of binding affinities between congeneric pairs of ligands by way of a displaced solvent functional |
KR20160128288A (en) * | 2013-10-07 | 2016-11-07 | 리서치 파운데이션 오브 더 시티 유니버시티 오브 뉴욕 | Method of using a water-based pharmacophore |
CN111809246A (en) * | 2020-07-23 | 2020-10-23 | 金华职业技术学院 | Method for screening small-molecule peptide mimetic inhibitor and application thereof |
CN114219775B (en) * | 2021-11-23 | 2023-08-15 | 南京应用数学中心 | Ellipsoid parameterization algorithm based on energy minimization criterion and application |
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AU6465598A (en) | 1998-03-13 | 1999-09-27 | Epimmune, Inc. | Hla-binding peptides and their uses |
AU2155401A (en) * | 1999-11-03 | 2001-05-30 | Algonomics Nv | Apparatus and method for structure-based prediction of amino acid sequences |
US7702465B2 (en) | 2002-06-10 | 2010-04-20 | Algonomics N.V. | Method, computing routine, device for predicting properties of MHC/peptide complexes, and data and peptides produced therefrom |
WO2004074505A2 (en) * | 2003-02-14 | 2004-09-02 | Eidogen Inc. | Method for determining functional sites in a protein |
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WO2007120834A2 (en) | 2006-04-13 | 2007-10-25 | Peptimmune, Inc. | Methods for designing and synthesizing directed sequence polymer compositions via the directed expansion of epitope permeability |
EP2586460A1 (en) | 2007-10-16 | 2013-05-01 | Peptimmune, Inc. | Method for designing and preparing vaccines comprising directed sequence polymer composition via the directed expansion of epitopes |
CN114420198A (en) * | 2022-01-04 | 2022-04-29 | 香港中文大学(深圳) | Protein-protein binding affinity prediction method, computer and storage medium |
CN115083515A (en) * | 2022-06-30 | 2022-09-20 | 北京工业大学 | protein-DNA binding affinity prediction method considering interface information and interaction energy |
CN115083515B (en) * | 2022-06-30 | 2024-06-11 | 北京工业大学 | Protein-DNA binding affinity prediction method considering interface information and interaction energy |
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