JP2003263465A - Method for designing/selecting protein-bonding peptide - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To design and select a peptide, which bonds specifically and highly affinitively to a specific protein, as conveniently and rationally as possible through the assistance of a computer. <P>SOLUTION: According to this method for designing/selecting the protein-bonding peptide, the amino-acid sequence of a target protein and its three-dimensional molecular structure are inputted into the computer, hydrophobic potential (MHP) of surface points uniformly disposed on a solvent contacting surface of the molecular structure is calculated while this is two-dimensionally developed by a self-organization characterized map method, further an aggregating area of a hydrophobic amino-acid is identified by removing high frequency areas through Fourier transformation, and a peptide having a bonding property to an amino-acid sequence on the molecular structure corresponding to this aggregating area is designed/selected by using a qualitative structure activity correlation program of the computer. As the correlation program, it is possible to employ a program including processes for quantifying and Fourier- transforming the degree of hydrophobicity of each amino-acid residue on the molecular structure corresponding to the aggregating area of the hydrophobic amino-acid and selecting an amino-acid sequence expressing a complementary hydrophobicity period as against a displayed period. <P>COPYRIGHT: (C)2003,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for designing and selecting a novel peptide using a computer, and in particular, it identifies a region (epitope) to which a peptide in a target protein (receptor) can bind and identifies this epitope sequence. The present invention relates to a method for designing / selecting a protein-binding peptide, including designing one or more novel candidate peptides (ligands) capable of binding to and selecting appropriate ones, and including a binding simulation step by geometric / energetic evaluation.

[0002]

2. Description of the Related Art When attempting to bind a protein with a physiologically active peptide, if a peptide that can reliably bind with a specific protein can be designed, the drug can reach the affected area or the immune system. It will be easier and will be very useful for treatment and prevention of diseases.

Conventional methods for designing novel candidate peptides (ligands) capable of binding to an epitope sequence include the sense-antisense theory proposed by Blalock group and the theory proposed by Fassina group. Examples have been reported.

Sense-antisense theory proposed by Bralock et al. [Biochem. Biophys. Res. Commum., 121; 203-20]
7 (1984)] was empirically derived that a peptide (antisense peptide) encoded by a nucleotide sequence complementary to the nucleotide sequence of mRNA encoding a peptide (sense peptide) interacts with the sense peptide. It is a theory.

The theory proposed by Fasina et al. [Arch. Bio
chem. biophys., 296; 137-143 (1992)], a peptide consisting of amino acids whose absolute values are the same and whose positive and negative are the same as the hydrophobicity of each amino acid of a certain peptide interacts with the original peptide. Is an empirically derived theory.

[0006] Further, a method of designing a novel candidate peptide (ligand) capable of binding to an epitope sequence is described in the hypervariable region sequence (CD) of an antibody specific to a target protein obtained by animal immunization.
There is also a method of designing by imitating R).

[0007] In many cases, the geometrical evaluation of binding simulation sets a receptor and a ligand in a rigid body model. The rigid body model is a model in which the main chain or side chain of a protein or peptide, which is originally flexible and capable of undergoing various conformational changes, is intentionally fixed.

In the geometrical evaluation, in many cases, a rigid body model is derived by using a three-dimensional three-dimensional structure as a standard for proteins, and an energy-optimized structure as a standard for peptides, and using this rigid body model, receptors are used. / We are performing a ligand binding simulation.

Ideally, it is preferable to perform a receptor / ligand binding simulation using a soft body model, but in that case, the computational cost becomes enormous due to the structural diversity that can be taken by proteins and ligands. Sex decreases.

To evaluate energetically binding simulations, one looks at the stability of the free energy of the receptor / ligand complex in a solvent. Specifically, hydrogen bonds, ionic bonds (electrostatic bonds), hydrophobic bonds, and van der Waals bonds are energetically involved in the binding simulation of the receptor / ligand complex. Calculate and evaluate free energy.

In the energy evaluation, it is ideal to use the soft body model as in the geometric evaluation. The binding simulation that fills the entire complex space is at least 6 even if the protein is assumed to be a rigid body model.
This is because it is necessary to take the degree of freedom of direction into consideration.

However, when the simulation is performed using a soft body model, the amount of calculation exponentially increases by the degree of freedom of the side chain, and the calculation cost becomes enormous, which is not realistic.

Therefore, in most cases, the ligand is used as a low molecular weight compound, and few systems can evaluate the ligand as a peptide. In addition, many systems evaluate by using a rigid body model or by giving degrees of freedom only to some side chains.

[0014]

As described above, in the prior art, there are many parts that depend on the experience of the operator, and it is often impossible to logically guide them, or it is often impossible to identify a valid region with reproducibility. .

Further, in order to actually obtain the target protein-binding peptide, a method of screening from a peptide library or the like, which is an expensive and long-term experimental means, is adopted, or from the empirical judgment of the experimenter. Selection of the peptide sequence is performed.

At the geometrical evaluation stage, a rigid body model is often used during simulation, and therefore there are cases where the results of the in-vitro experiment do not reflect the results of the simulation. At the stage of energetic evaluation, there are elements that cannot be covered by simulation, including the solvent environment, and the results of in vitro experiments may differ from the results of simulation or the experimental environment may not be taken into consideration.

Thus, it is not easy to design and select a peptide that specifically binds to a specific protein with high affinity, and a peptide having a certain function can be designed by a simple means. However, there was a demand for a method of further selection.

Therefore, an object of the present invention is to solve the above-mentioned problems and design a peptide that binds specifically and with high affinity to a specific protein as easily and rationally as possible with the aid of a computer. However, the subject of the obtained peptides is to design and select a peptide sequence that can be put to practical use.

[0019]

In order to solve the above-mentioned problems, in the present invention, the amino acid sequence of a target protein and its three-dimensional molecular structure are input to a computer, and the three-dimensional molecular structure is evenly distributed on the solvent contact surface. The hydrophobic potential (MHP) of the surface points arranged on the surface is calculated, and it is two-dimensionally developed by the self-organizing feature map method, and the high-frequency region is removed by Fourier transform to determine the aggregate region of the hydrophobic amino acids. Design and selection of a protein-binding peptide, which comprises identifying and designing a peptide having a binding property to the amino acid sequence on the above-mentioned three-dimensional molecular structure corresponding to this region by a computer-based quantitative structure-activity relationship program It was a method.

The above-mentioned quantitative structure-activity relationship program quantifies the hydrophobicity of each amino acid residue of the amino acid sequence in the three-dimensional molecular structure corresponding to the aggregated region of hydrophobic amino acids, Fourier transforms it, and displays it in the displayed cycle. It is preferable to employ a program including a step of selecting an amino acid sequence having a complementary hydrophobicity cycle.

Further, the above-mentioned hydrophobic potential (MH
P) is a value represented by the following formula (2).

[0022]

[Equation 2]

The computer-based quantitative structure-activity relationship program is preferably a quantitative structure-activity relationship program incorporating a genetic algorithm.

The binding simulation of the protein-peptide complex is preferably carried out on a rigid body model by geometrical evaluation, and then on the protein-peptide interaction energy, and a step of selecting peptides evaluated by these simulations. A method of designing and selecting a protein-binding peptide containing

[0025]

BEST MODE FOR CARRYING OUT THE INVENTION A basic flow chart of each step for designing and selecting a protein-binding peptide is shown in FIG.

The program on the computer can be created using C language, and the development environment can be UNIX (R). The program mainly runs on Pentium (R) II 500MHz, memory 256M, FreeBSD 2.2.8 RELEASE, SunOS,
Operation on Solaris and IRIX has been confirmed.

Identification of a region to which a peptide on the surface of the target protein shown in FIG. 2 (b) can bind (SD-LIME) is carried out by the inventors of the present application, SD-LIME (Search Domain-Leading Interacti).
on system for molecular evaluation), but this method focuses on the fact that a region having physicochemical characteristics (hydrophobic characteristic, electrostatic characteristic) on the surface of the target protein is likely to become an epitope. , It is possible to identify with high accuracy a region to which a peptide on the surface of a target protein can bind. The performance of the SD-LIME method can be suggested by comparison with the X-ray crystal structure analysis model in the binding surface inference test of protein-protein interaction.

First, when a peptide is designed using the SD-LIME method, in order to identify a region (epitope) to which the peptide in the target protein (receptor) can bind,
It requires information on the three-dimensional structure of the target protein.

The three-dimensional structure data of many proteins are
It has been reported by X-ray crystal analysis technology, NMR structure analysis technology, etc., and the registration is open to the public, and three-dimensional information can be freely obtained.

If the three-dimensional structure of the protein has not been published, a homology modeling method, a 3D-1D method or the like, which is a commonly used three-dimensional structure prediction method,
The three-dimensional structure of the target protein (receptor) may be acquired.

The region to which the peptide can bind is on the surface of the target protein (receptor), that is, in the plane in contact with the solvent. In order to find a region that meets such conditions, a region that forms a groove (Groove) or a hole (Cavity) on the solvent-contacting surface is searched visually or programmatically from the three-dimensional structure of the protein. The region to which the peptide can bind. A region in which amino acids having hydrophobic properties are relatively aggregated in the surface region, or a region recognized as an epitope by an antibody prepared by immunizing an animal can be a region to which a peptide can bind.

As a preferred example, as shown in FIG.
Using the two-point method, the radius of the solvent molecule is set to 1.4 Å, the molecular surface point of the receptor is calculated, and the solvent contact surface (Fig. 2) is calculated.
Create grids that are evenly arranged in (a) (Fig. 2).
(B)). In order to calculate the hydrophobic potential (MHP) on the surface point (point M), follow [Equation 3] of the following equation [J. Biol. Chem., 266 (24), 16120-16127,
1991.].

[0033]

[Equation 3]

[0034]

[Table 1]

The learning number is set to 2000, and a self-organizing feature map in which the positions on the surface points (grid points of the grid) are associated with the MHP values is created.

Then, as shown in FIG. 3 (a), it is converted into a two-dimensional shape by the self-organizing feature map method, and converted into a spectrum if necessary.

The self-organizing feature map method is also referred to as a competitive learning type neural network method (Kohonen, T .:
Self-organization and Associative Memory, Springer
-Verlag (1988), Kohonen, T .: The Self-Organizing Ma
p, Proc. IEEE 78 (9), 1464-1480 (1990)), etc., which is a type of pattern recognition method well known.

The neural network method is characterized by learning from data, and is a method capable of extracting a common feature hidden in a variety of data by learning.

In this method, since the neurons are arranged in a one-dimensional or two-dimensional connection, it is possible to perform pattern recognition in which not only the neurons that have survived due to the competition but also the neurons in the vicinity of the neurons simultaneously learn. Is characterized by. According to this method, it is possible to map the mutual relationship of the input data in the original space into a map having one-dimensional or two-dimensional connections by the neurons.

Next, FFT processing is performed with a characteristic portion up to a predetermined level of frequency (FIG. 3 (b)), that is, an area above a predetermined threshold value (for example, an average value), that is, an area that tends to be hydrophilic. By removing and classifying only the highly hydrophobic region, a clustered map can be obtained (FIG. 3C).

By inversely mapping the clustered self-organizing feature map into three-dimensional coordinates, the regions on the surface of the target protein to which peptides can bind (black portions in FIG. 2 (b) are densely gathered. Area) can be identified.

Next, a peptide having a binding property to the amino acid sequence on the stereomolecular structure corresponding to the specified region is designed and selected by a computer-based quantitative structure-activity relationship program.

Here, as a method for designing a peptide candidate capable of specifically binding to an amino acid sequence, PC-LIM is used.
E (Phase C-Leading Interaction system for Molecula
r Evaluation) method can be adopted.

Usually, each amino acid residue has different properties, and these properties govern the action of the peptide or protein. Since the characteristics of each amino acid residue can be seen from various points of view such as electrical or geometric,
Numerical quantification of the characteristics of one amino acid is possible.

In the PC-LIME method, the hydrophobicity of each amino acid residue given as peptide information is quantified, Fourier transformed, and the result is displayed as a periodicity graph. The reason for evaluating the degree of hydrophobicity is that it has become clear that hydrophobic residues and hydrophilic residues are present periodically due to their physicochemical properties. In designing a candidate peptide, a sequence showing a cycle complementary to this cycle is selected.

The quantitative structure-activity relationship program is preferably a quantitative structure-activity relationship program incorporating a genetic algorithm. Specifically, when selecting from peptide sequences showing abundant complementarity to the cycle of a candidate peptide, a genetic algorithm that mimics the evolution of the living world is incorporated into the calculation formula to efficiently select candidates. Narrow down the peptides.

According to the genetic algorithm, selection (selection of highly evaluated peptides to be evolved, information is continued to the next generation), crossover (separation into two out of the selected peptides and mating) To create a new peptide) and mutation (create a new peptide by replacing a residue in the amino acid sequence of the selected peptide with another residue with a certain probability), and select a candidate peptide. To do.

Another feature of the PC-LIME method is that it is possible to evolve its own process by feeding back experimental data so that it can respond well to the target protein.

Specifically, when the target protein or target primary sequence has some unique physicochemical characteristics, a QSAR (quantitative structure activity relationship) model is prepared by a technique well known to those skilled in the art, Can be adapted.

For example, if the presence of an important frequency for binding between a receptor / ligand is found from the QSAR model, then in the step of the genetic algorithm of the present method,
It is possible to change the target frequency to a process that is not preferentially selected. If this work is performed, more optimal peptide design can be performed for each target protein.

Next, it is preferable that the binding simulation of the protein-peptide complex is first performed by a rigid body model by geometrical evaluation, and then the interaction energy between the protein and peptide is evaluated. A method for designing and selecting a protein-binding peptide including a step of selecting a peptide.

The binding simulation of the protein-peptide complex performed on the rigid body model by the geometrical evaluation in the present invention was carried out by the inventors of the present invention by GD-LIME (Geometrical D
ocking-Leading Interaction system for Molecular Ev
The method referred to as “aluation” method can be adopted, but other well-known coupling simulation can also be adopted. The binding simulation of the protein-peptide complex performed on the rigid body model is important because it also helps to search for the initial position when performing energy evaluation in the next step.

In the present invention, TD-LIME (Total
Docking-Leading Interaction system for Molecular E
valuation) method, which is a simulation of binding of a protein-peptide complex. This method gives a degree of freedom to both the main chain and side chain of the ligand, and gives a degree of freedom to the side chain on the receptor side to perform energy evaluation. Is.

Some proteins form covalent bonds such as S—S bonds found between cysteines during their interaction, but in many cases, a non-covalent bond forms a complex structure. To be done. The main non-covalent bonds in protein-protein interactions are hydrogen bonds, ionic bonds (electrostatic bonds), hydrophobic bonds, and van der Waals bonds.

Although these bonds have a weaker binding force than covalent bonds, many of these interactions constitute a stable complex structure. Furthermore, such coupling is noticeable at very short distances (10 Å or less). This fact reflects many of the unique features of biopolymer interactions.

The present invention proposes a new receptor / ligand interaction model in which the interaction between a protein and a solvent is clearly taken into consideration as a simulation for the interaction energy between a protein and a peptide, and a complex in a solvent is proposed. Seeking energy.

The energy produced by the pressure exerted by the solvent on the protein surface can be calculated by evaluating pΔV. ΔV is the change in solvent excluded volume due to the change in the structure of the complex. This energy in the present invention is calculated by a method incorporating an analytical algorithm for calculating the excluded volume.

First, ΔV (change in solvent excluded volume) of atoms in a given complex structure is calculated.

These values are then used to calculate the kinetic energy term. The energy generated by the pressure from the solvent on the surface of the complex is calculated as follows.

[0060]

[Equation 4]

Here, k _B is the Boltzmann constant, T is the absolute temperature, ρ is the number concentration of atoms, G (r) is the free energy of cavity formation, and G (r ⁱ _c ) is the energy of cavity formation by atom i.
Let the cavity radius be r ⁱ _c Let the change in cavity volume be ΔV ⁱ _c .

The hydrophobic interaction energy consists of pressure on the surface of the complex, van der Waals interaction between all atom pairs between the proteins constituting the complex, and van der Waals interaction between the solvent and the protein. The van der Waals interaction energy between proteins is calculated by the following potential.

[0063]

[Equation 5]

In the equation, N is the total number of atoms A _ij and B _ij are parameters depending on the atom pair.

To reduce the calculation cost of the van der Waals interaction energy between the complex and the solvent, the following approximate expression may be used.

[0066]

[Equation 6]

“A” represents an atomic species, and SASA represents a solvent contact area. Here, the parameters proposed by the inventors of the present application using the PLS method, which is one of the regression analysis methods, were used.

[0068]

[Table 2]

This indirectly calculates G _vdw as an experimental value based on the following equation.

[0070]

[Equation 7]

This is expressed as follows.

[0072]

[Equation 8]

G _sol , G _cav , and G _pol represent solvation energy, cavity formation energy, and electrostatic interaction energy, respectively. For G _sol , experimental values were used, and the remaining terms were calculated. By this, G _vdw can be indirectly calculated by assuming that G _cav and G _pol are correct. From the above, all E _vdw are as follows.

[0074]

[Equation 9]

Further, the hydrophobic interaction is defined as the sum of the solvent pressure and the Van der Waals interaction as follows.

[0076]

[Equation 10]

In the present invention, the following formula is adopted to calculate the electrostatic interaction potential generated by the solvent molecules and the atoms forming all the complexes.

[0078]

[Equation 11]

Here, G _el represents electrostatic interaction energy. N is the total number of atoms forming the complex, q _i q _j is the point charge of the atom, d _i is the interatomic distance, α _i is the cavity of atom i or Bo
rn is the radius. The first term of the above equation can be divided into two terms, one showing Coulomb's law in vacuum and the second term showing the dielectric effect of charged particle pairs.

[0080]

[Equation 12]

Incidentally, the second and third terms of the above equation 12 are the Solution Chemistry.
General Born (GB) formula or G _pol complex in the _field-
It can be expressed as the electrostatic polarization energy between solvents as follows. The meaning of each symbol is the same as above.

[0082]

[Equation 13]

The following potential was used as the hydrogen bond energy. This potential has the advantage that a very short hydrogen bond and a very strong hydrogen bond can be prevented by the repulsive term.

[0084]

[Equation 14]

C _ij and D _ij are parameters depending on the hydrogen bond pair.

The binding of biopolymers is brought about by conformational changes between the monomers. This term was adopted to take into account changes in internal energy between interacting proteins. However, this section applies only to the side chains of protein binding sites. The backbone of the protein is fixed. As a result, bond and bending energy are not considered at this stage. The twisting energy used is that of the MM3 force field.

[0087]

[Equation 15]

Here, V is a parameter that depends on the atomic species forming the dihedral angle. Protein solvation energy,
Desolvation energies have been proposed.

[0089]

[Equation 16]

Where Δσ is an atomic species dependent parameter (Atomic Solvation Parameters (ASP)),
N is the total number of atoms.

In the present invention, the hydrophobic interaction energy is derived from the pressure on the surface of the complex, the van der Waals interaction between all the atom pairs between the proteins constituting the complex, and the van der Waals interaction between the solvent and the protein. Become. That is, _assuming that the energy generated by the pressure applied from the solvent on the protein surface is E _px and the van der Waals interaction energy between the receptor / ligand is E _vdw , the hydrophobic interaction energy E _hy is the sum E _hy = E _px. + E
It can be represented by _vdw .

The interaction between the receptor / ligand is as follows: hydrophobic interaction energy is E _hy , electrostatic interaction energy is E _el , hydrogen bond energy is E _hb ,
_Letting E _tor be the energy produced by the intramolecular conformational change and E _{desol the} solvation / desolvation energy, the receptor / ligand interaction energy ΔG
Can be expressed by the following formula.

ΔG = E _hy + E _el + E _hb + E _tor + E _desol Thus, the quantitative comparison evaluation of ΔG can _{select the} target peptide (for example, one in a low energy state) from the candidate peptide group. it can.

The present invention improves the conventional method for designing a peptide from only a primary amino acid sequence and the method for designing a peptide from the three-dimensional structure of a target protein, and combines these methods for peptide design. Is the way to do it. By designing the peptides in a combinatorial manner, the problems occurring at each stage can be complemented.

For example, the method of designing only from the primary array cannot perform geometrical evaluation in a three-dimensional space,
This can be complemented by a combination of methods for designing peptides from a three-dimensional structure. In the method of designing a peptide from a three-dimensional structure, the continuous physicochemical properties of the primary sequence cannot be taken into consideration, but this can be compensated for in the method of designing only the primary sequence. By the above method, a peptide that binds to a specific protein in a logical and rational manner with high specificity and specificity can be designed with the aid of a computer.

The present invention can also be applied to research reagents using peptides, therapeutic and diagnostic pharmaceuticals, addition to foods, compounds derived using this peptide as a lead compound, and other well-known techniques utilizing peptides. .

[0097]

EXAMPLE As an example of the present invention, a peptide design specific to enterotoxin A will be described.

The computer programs used in the examples were created using C language, and the development environment was UNIX (R). The programs were mainly Pentium (R) II 500 MHz, memory 256M, FreeBSD 2.2. .8 RELEASE
Ran on

The primary sequence for Enterotoxin A is shown in SEQ ID NO: 1, which is Gen
It can be obtained from the bank's accession number (SAENTAB),
Furthermore, the three-dimensional structure was analyzed using a PDB file (1D5X) (Fig. 5).

Incidentally, enterotoxin has a molecular weight of 27
It is a simple protein of about 000, is resistant to digestive enzymes such as trypsin and heat, and is currently known to have A to L types due to the difference in antigenicity. The enterotoxin type, which is the most common cause of food poisoning, is type A, and the combined number of cases of food poisoning due to A + B type and A + D type accounts for over 80%.

Further, enterotoxin has the action of specifically activating T cells and producing a large amount of various types of cytokines in a short time, and is also called "bacterial superantigen". The amount required for the onset of enterotoxin in humans was considered to be 5 to 50 μg for enterotoxin B, but in cases such as processed milk, there were cases where enterotoxin A of 200 ng or less also developed. Food poisoning by Staphylococcus occurs by ingesting enterotoxins produced by Staphylococcus aureus when they grow in food together with food.

To determine the target sequence, SD-LIME (Sea
rch Domain-Leading Interaction system for Molecula
r Evaluation). It was revealed that two regions clearly forming hydrophobic clusters were generated.

As shown in FIG. 4, this result was inversely mapped to a three-dimensional structure (solvent contact surface 1). Regions forming hydrophobic clusters are dark black areas 2 and 2'in two places.
I was able to observe. Based on this data, the amino acid sequence corresponding to the black portion 2 was targeted for peptide design.

The target sequence is shown in SEQ ID NO: 2. This region is the 65th to 78th portion of the amino acid sequence of SEQ ID NO: 1 and is a region involved in binding to MHC and is a valid candidate as a peptide binding surface.

Next, a peptide sequence was designed from the above target sequence by the PC-LIME method. The peptide sequence is shown in SEQ ID NO: 3. Next, in order to investigate whether or not this peptide should be selected, energy optimization was performed, and analysis was performed by entrotoxin A and geometric docking (GD-LIME), and its positioning was indicated.

As shown in FIG. 5, peptide 3 of SEQ ID NO: 3 appeared as a gray ring-shaped portion along a part of enterotoxin A (three-dimensional structure of dark black ribbon-like structure) 4. As a result of evaluating the energy of two molecules from this position, the total energy ΔG is −25.545 kc
It was a stable result of al and very low energy state.

From these results, it was evaluated that the designed peptide is a peptide that can obtain a bond with high specificity and affinity that is geometrically and energetically stable and should be selected. .

[0108]

Industrial Applicability As described above, the present invention specifies a region where hydrophobic amino acids are assembled, and a computer quantitative determination of a peptide having a binding property to the amino acid sequence in the stereomolecular structure corresponding to this region. Since it was designed and selected by a dynamic structure-activity relationship program, it is possible to design and select a peptide that binds specifically and with high affinity to a specific protein as easily and rationally as possible with the aid of a computer. There are advantages.

[0109]

[Sequence list]                               SEQUENCE LISTING <110> Kabushikikaisha enkaku-iryou kenkyusho <120> Method for designing and selecting protein-binding peptides <130> KPO5586-27 <160> 3 <210> 1 <211> 257 <212> PRT <220> staphylococcus aureus <400> 1 Met Lys Lys Thr Ala Phe Thr Leu Leu Leu Phe Ile Ala Leu Thr Leu  1 5 10 15 Thr Thr Ser Pro Leu Val Asn Gly Ser Glu Lys Ser Glu Glu Ile Asn             20 25 30 Glu Lys Asp Leu Arg Lys Lys Ser Glu Leu Gln Gly Thr Ala Leu Gly          35 40 45 Asn Leu Lys Gln Ile Tyr Tyr Tyr Asn Glu Lys Ala Lys Thr Glu Asn      50 55 60 Lys Glu Ser His Asp Gln Phe Leu Gln His Thr Ile Leu Phe Lys Gly  65 70 75 80 Phe Phe Thr Asp His Ser Trp Tyr Asn Asp Leu Leu Val Asp Phe Asp                  85 90 95 Ser Lys Asp Ile Val Asp Lys Tyr Lys Gly Lys Lys Val Asp Leu Tyr             100 105 110 Gly Ala Tyr Tyr Gly Tyr Gln Cys Ala Gly Gly Thr Pro Asn Lys Thr         115 120 125 Ala Cys Met Tyr Gly Gly Val Thr Leu His Asp Asn Asn Arg Leu Thr     130 135 140 Glu Glu Lys Lys Val Pro Ile Asn Leu Trp Leu Asp Gly Lys Gln Asn 145 150 155 160 Thr Val Pro Leu Glu Thr Val Lys Thr Asn Lys Lys Asn Val Thr Val                 165 170 175 Gln Glu Leu Asp Leu Gln Ala Arg Arg Tyr Leu Gln Glu Lys Tyr Asn             180 185 190 Leu Tyr Asn Ser Asp Val Phe Asp Gly Lys Val Gln Arg Gly Leu Ile         195 200 205 Val Phe His Thr Ser Thr Glu Pro Ser Val Asn Tyr Asp Leu Phe Gly     210 215 220 Ala Gln Gly Gln Tyr Ser Asn Thr Leu Leu Arg Ile Tyr Arg Asp Asn 225 230 235 240 Lys Thr Ile Asn Ser Glu Asn Met His Ile Asp Ile Tyr Leu Tyr Thr Ser                 245 250 255 <110> Kabushikikaisha enkaku-iryou kenkyusho <120> Method for designing and selecting protein-binding peptides <130> KPO5586-27 <160> 3 <210> 2 <211> 14 <212> PRT <220> staphylococcus aureus <400> 2 Lys Glu Ser His Asp Gln Phe Leu Gln His Thr Ile Leu Phe  1 5 10 <110> Kabushikikaisha enkaku-iryou kenkyusho <120> Method for designing and selecting protein-binding peptides <130> KPO5586-27 <160> 3 <210> 3 <211> 14 <212> PRT <400> 3 His Asp Arg Ser Ile Ser Asp His Leu Cys Thr Leu Val Val   1 5 10

[Brief description of drawings]

FIG. 1 is a flow chart illustrating a process of designing and selecting a protein-binding peptide according to an embodiment.

FIG. 2A is an explanatory view of a solvent contact surface of a molecule. FIG. 2B is a schematic view showing a three-dimensional structure of a solvent contact surface of a protein molecule.

FIG. 3A is a self-organizing feature map in which a hydrophobic potential on a solvent-contacting surface is converted into a two-dimensional shape. FIG. 3B is a self-organizing feature map after FFT processing.

FIG. 4 is a three-dimensional structure diagram showing a state in which a peptide is bound to the solvent-contact surface of enterotoxin A.

FIG. 5 is a computer simulation image showing a geometrical binding state of enterotoxin A of peptides designed and selected by the method of Example.

[Explanation of symbols]

1 Solvent contact surface 2, 2'black part 3 peptides 4 Enterotoxin A

Claims (5)

[Claims] 1. An amino acid sequence of a protein and its three-dimensional molecular structure are input to a computer to calculate a hydrophobic potential (MHP) of surface points evenly arranged on the solvent contact surface of the three-dimensional molecular structure, A hydrophobic amino acid assembly region is identified by two-dimensional expansion by the self-organizing feature map method, and a high frequency region is removed by Fourier transform, and the amino acid sequence corresponding to this region in the three-dimensional molecular structure is identified. A method for designing and selecting a protein-binding peptide, which comprises designing and selecting a peptide having a binding property by a computer-based quantitative structure-activity relationship program. 2. The quantitative structure-activity relationship program quantifies the degree of hydrophobicity of each amino acid residue of the amino acid sequence on the three-dimensional molecular structure corresponding to the aggregated region of hydrophobic amino acids, Fourier transforms it, and displays it in the displayed cycle. The method for designing and selecting a protein-binding peptide according to claim 1, which is a quantitative structure-activity relationship program including a step of selecting an amino acid sequence having a complementary hydrophobicity cycle. 3. The method for designing / selecting a protein-binding peptide according to claim 1, wherein the hydrophobic potential (MHP) is a value represented by the following formula (1). [Equation 1] 4. The method for designing and selecting a protein-binding peptide according to claim 1, wherein the computer-based quantitative structure-activity relationship program is a quantitative structure-activity relationship program incorporating a genetic algorithm. . 5. A step of performing a binding simulation of a protein-peptide complex on a rigid body model by geometrical evaluation, then on a protein-peptide interaction energy, and selecting peptides evaluated by these simulations. The method for designing and selecting a protein-binding peptide according to any one of claims 1 to 4, which comprises: