CN103971030A - Method for predicting affinity of interaction between biomarker p53 and organic phosphate fire retardant - Google Patents

Abstract

The invention relates to a method for predicting affinity of the interaction between a biomarker p53 and an organic phosphate fire retardant. The method includes the following steps of establishing a quantitative structure-activity relationship QSAR model of the affinity KD and training set compound molecule descriptors through a partial least square method so as to obtain an affinity KD value, and predicting the affinity according to the affinity KD value. By the adoption of the method, interaction between the organic phosphate fire retardant and the biomarker p53 which are of different structures can be predicted, the method is low in cost and easy, convenient and rapid to achieve, and a large amount of labor, a large number of expenses and a large amount of time which are needed by experimental tests can be saved; the prediction result obtained through the method can provide the important data support for organic chemical ecological risk assessment and management, and the method has important theoretical and practical significance.

Description

A kind of method of predicting biomarker p53 and organophosphorus ester flame-proof agent interaction affinity

Technical field

The present invention relates to a kind of method of predicting biomarker p53 and organophosphorus ester flame-proof agent interaction affinity, belong to the Test Strategy field of ecology-oriented risk assessment.

Background technology

Along with the enhancing of people to fire disasters protection consciousness, fire retardant demand sharply rises.Organophosphorus ester flame-proof agent (OPFRs) is the conventional phosphorus flame retardant of a class, is widely used in the industries such as building materials, weaving, chemical industry and electronics.Along with brominated flame-retardant is worldwide progressively forbidden, as the production of the OPFRs fire retardant of its main substitute with use and all increase substantially.Owing to being additive flame retardant, the OPFRs being discharged in environment will produce a series of ecological risk, and their caused environmental problems just progressively obtain researcher's concern.Therefore, be necessary to further investigate the ecotoxicological effect of OPFRs, for pollution prevention and the control and management of this compounds provide foundation.

Tumor suppressor gene p53 is the gene the highest with cancer-related of finding up to now, and 50% tumour is relevant with p53 gene mutation.The effect of p53 tumor suppressor gene is the reaction to multiple crisis situation by integrator cell, controls the approach such as cell cycle chechpoint and active cell apoptosis, maintains genomic stability.The expression product p53 albumen of p53 gene is Growth of Cells " watch-dog ", and in the time of cell generation DNA damage, first p53 albumen can G occur inducing cell ₁phase retardance, suppresses cell proliferation, and p53 albumen also can pass through number of ways cell death inducing simultaneously.Given this, be necessary to study the interaction of OPFRs and p53.

Many based on experiment test for the interaction of chemicals and biomacromolecule at present, such as adopting interaction of biomacromolecules instrument detection etc.But rely on experiment test to obtain the total data of chemicals completely, there is huge financial burden.Meanwhile, the number of new synthetic organic chemicals used in everyday increases sharply, and these chemicals are carried out to experiment test one by one, cannot meet the demand of environment supervision, needs the low and chemicals appraisal procedure fast of development cost badly.Study on Quantitative Structure-Activity Relationship relevant (QSAR) is the effective ways of rapid evaluation chemicals ecological risks.QSAR technology is applied at aspects such as environmental photochemistry behavior, partition parameter and the ecotoxicological effects of organic contaminant.Also clearly regulation QSAR method can provide information support as the registration of chemicals to European Union's " chemicals is registered, assessment, mandate and statute of limitation ".The Organization for Economic Cooperation and Development (OECD) the QSAR model construction and the usage criteria regulation that propose in 2004, the QSAR model with following 5 standards can be for the risk assessment and management of chemicals: (1) has clearly defined environmental index; (2) there is clear and definite algorithm; (3) defined the application domain of model; (4) model has the suitable goodness of fit, robustness and predictive ability; (5) preferably can carry out mechanism explain.

The advantages such as that zebra fish has is small, easy raising, being known as is both at home and abroad a kind of idealized model animal of carrying out ecological toxicology research, in aspect widespread uses such as water quality monitoring, pollutant ecotoxicological effects.Between the many genes in zebra fish genome and human gene, there is corresponding relation, that a situation arises is also very similar with the mankind for tumour, and the cDNA sequence (NM_131327) of existing zebra fish p53 gene and the result to p53 Sectionalization of genes clone, sequencing in the DNA database Genbank setting up in American National biotechnology information center.Given this, the present invention, taking the p53 albumen of zebra fish as molecular model, studies the ecotoxicological effect of OPFRs compound.

In sum, OPFRs is distributed widely in the multiple surrounding medium in global range, will affect to the ecosystem.The present invention by experiment method records the interaction affinity of 10 kinds of OPFRs and p53 albumen, the interaction between OPFRs and p53 has been simulated in application molecular docking, disclose mechanism of action between the two, and set up the QSAR model that can predict OPFRs and p53 binding constant based on mechanism, illustrate and affected OPFRs and the interactional typical molecular characterization of p53.This research is significant for the ecological risk assessment of OPFRs compound; Also the pollution prevention and the control that can be this compounds provide theoretical foundation simultaneously.

Summary of the invention

The technical problem to be solved in the present invention is to provide a kind of easy, quick, method of efficiently predicting organic phosphoric acid fire retardant and biomarker p53 interaction affinity, and the method can be predicted its logK according to molecular structure of compounds _dvalue, and then the toxicological effect of compound is predicted and evaluated, for Risk Assessment of Chemicals and management provide necessary basic data.

The technical scheme that the present invention adopted is for achieving the above object:

A method of predicting biomarker p53 and organophosphorus ester flame-proof agent interaction affinity, comprises the following steps:

Adopt offset minimum binary method to build affinity K _dwith the quantitative structure activity relationship QSAR model of compound molecule descriptor, according to affinity K _dvalue realizes the prediction of affinity.

Described quantitative structure activity relationship QSAR model is:

logK _D=-4.76+5.67×10 ^-1X _5A+7.15×10 ^-1MATS _7v+1.67Mor _17m

Wherein X _5Aa topological index, the impact of the structure of sign molecule on organophosphorus ester flame-proof agent and p53 combination; MATS _7vbe a two-dimensional autocorrelation descriptor, obtain by the Van der waals volumes weighting of atom; Mor _17mfor 3D-MoRSE descriptor, obtain by the quality weighting of atom.

Described QSAR model can be used for predicting the genotoxic potential of OPFRs compound.

The present invention has following beneficial effect and advantage:

1. adopt the inventive method can predict the interaction of the agent of different structure organophosphorus ester flame-proof and biomarker p53, the method is with low cost, easy and quick, can save in a large number the required manpower of experiment test, expense and time.

2. the QSAR model development that the foundation of the interaction prediction method that this aspect relates to and checking strictly specify according to OECD and use directive/guide, therefore use predicting the outcome of patent of the present invention, can and manage for organic chemicals ecological risk assessment important Data support is provided, there is important theory and realistic meaning.

3. 10 kinds of OPFRs that the present invention adopts that interaction of biomacromolecules instrument records and the interaction affinity (logK of biomarker p53 _d) value filled up blank, contributes to further investigate environmental process and the toxicological effect of this compounds.

4. the method that adopts molecular docking and experiment in vitro to combine, has analyzed the key interactions of OPFRs and p53 receptor binding domain.

5. adopt partial least squares regression algorithm, choose descriptor based on Analysis on Mechanism and built forecast model, model simple, transparency is strong, is convenient to understand and practical application.

6. the directive/guide about QSAR model construction and use according to OECD, the QSAR model of foundation has the good goodness of fit, robustness and predictive ability.

7. this invention QSAR model, clear and definite application domain scope, can provide basic data for the risk assessment of persistence organic pollutant and management work.

Brief description of the drawings

Fig. 1 is method flow diagram of the present invention;

Fig. 2 is three kinds of representative OPFRs molecules and the p53 interaction schematic diagram at binding site;

Wherein (A) Tris (2-chloroethyl) phosphate; (B) Triethylphosphate; (C) Tri-n-propylphosphate;

Fig. 3 is OPFRs compound and p53 binding constant logK _dmeasured value and predicted value fitted figure;

Fig. 4 is model application domain phenogram.

Embodiment

Below in conjunction with drawings and Examples, the present invention is described in further detail.

The invention discloses a kind of interaction affinity (K that predicts biomarker p53 and organophosphorus ester flame-proof agent _d) method.The present invention has obtained zebra fish p53 albumen by recombinant expressed and purifying, and application protein macromolecule system with interaction has been measured the K of 10 kinds of organophosphate based flame retardants (OPFRs) with p53 albumen _dvalue; Combination based on molecular docking simulation OPFRs with p53, discovery hydrogen bond, hydrophobic and electrostatic interaction are the keys of both combinations; According to Study on Quantitative Structure-Activity Relationship relation (QSAR) model construction and the use directive/guide of the Organization for Economic Cooperation and Development (OECD), the Clear & Transparent partial least squares regression of uses algorithm is set up QSAR model with the quantum chemical descriptor of being convenient to understanding application.The application domain of model is clear and definite, has good fitting effect, robustness and predictive ability.Obtaining on the basis of molecular structure of compounds, only by the descriptor of computational representation architectural feature, the application QSAR model of building, can rapidly and efficiently predict the genotoxic potential of OPFRs compound, it is with low cost, simple and efficient, saves test required a large amount of manpower expenses and time, for ecological risk assessment and the management of this compounds provide important data filling, significant.

As shown in Figure 1, a kind of prediction biomarker p53 and organophosphorus ester flame-proof agent (OPFRs) interaction affinity (K _d) method, comprise the steps:

(1) p53 albumen is recombinant expressed: adopt TRIzol method to extract total RNA of zebra fish.Through design of primers, the steps such as recombinant expressed, purifying obtain p53 albumen.

(2) measuring K _dvalue: adopt protein macromolecule system with interaction to measure the association rate (k of 10 kinds of OPFRs and p53 albumen _a), dissociation rate (k _d), affinity (K _d)

(3) data collection and division: by the K of 10 kinds of OPFRs of measuring in step (2) _dvalue is training set and checking collection according to 4:1 random division;

(4) QSAR model construction: according to Analysis on action mechanism, calculate 19 kinds of theoretical chemistry descriptors, adopt offset minimum binary method to build K _dwith the quantitative structure activity relationship QSAR model of training set compound molecule descriptor, expression formula is:

logK _D=-4.76+5.67×10 ^-1X _5A+7.15×10 ^-1MATS _7v+1.67Mor _17m

Wherein X _5Aa topological index, the impact of the structure that can characterize molecule on OPFRs and p53 combination; MATS _7va two-dimensional autocorrelation descriptor, by the Van der waals volumes weighting of atom; Mor _17mbelong to 3D-MoRSE descriptor, by the quality weighting of atom; Above-mentioned three descriptors all obtain by DRAGON software, and the chemical molecular structure that input will be predicted in DRAGON software, just can obtain X _5A, MATS _7v, Mor _17m.

(5) checking of QSAR model and application domain characterize: square Q of outside prediction related coefficient for the result of QSAR model ² _eXTrMSE represents with root-mean-square error, and the compound application domain of QSAR model adopts the method for Williams figure to characterize.

Wherein, the QSAR model of setting up can be used for predicting the genotoxic potential of OPFRs compound.

The present invention realizes by following steps:

1. the construction and expression purifying of zebra fish p53 prokaryotic expression carrier;

(1) p53 expression vector establishment and qualification

Adopt TRIzol method to extract total RNA of zebra fish.The zebra fish p53 full length gene providing according to Genbank, the PCR primer of design pair for amplification scrambler.1.5%(quality volume fraction for PCR product) agarose gel electrophoresis detect.The PCR product that glue reclaims is connected under the effect of T4 ligase with carrier PET-28a, builds pMD18-T/z-p53 recombinant plasmid.Extract the cloned plasmids pMD18-T/z-p53 that order-checking is correct, with EcoRI, SalI double digestion, glue reclaims Insert Fragment, construction of expression vector PET-28a/z-p53, conversion DH5 α Host Strains, bacterium colony PCR and the qualification of plasmid double digestion.

(2) abduction delivering of p53 albumen

The expression vector pET28a/z-p53 building is transformed to e. coli bl21 (DE3) competent cell.Picking monoclonal colony inoculation is in LB nutrient culture media, and switching expansion is cultured to bacterium liquid OD ₆₀₀reach at 0.5～0.8 o'clock, adding final concentration is the IPTG of 1mmol/L, and after induction, thalline is dissolved in lysis buffer, ultrasonication thalline, and the centrifugal 20min of 12000r/min, gets respectively cleer and peaceful precipitation and carries out SDS-PAGE analysis.

(3) sex change purifying and the renaturation of p53 albumen

The p53 albumen that purifying is expressed with inclusion body form under Denaturing.Adopt Urea Gradient dialysis to carry out renaturation to the p53 albumen of purifying, after the p53 protein frozen of renaturation is dry after 12%SDS-PAGE electrophoresis detection in-80 DEG C of preservations.

The measuring of 2.OPFRs and p53 interaction affinity

This research adopts now widely used protein macromolecule system with interaction (Biacore T100, GE company) to study the interaction of organophosphate based flame retardant (OPFRs) and p53.BIACORE technology is owing to having without mark, and high sensitivity detects fast, and can real-time quantitative test etc. advantage, be widely used for the interaction of the biomolecule such as Study on Protein, nucleic acid, polypeptide, micromolecular compound.

2 passages are set on CM5 sensing chip, and a coupling p53 is as sense channel, and another does not fix p53 as blank reference channel.Using HBS solution as working fluid, after activation and sealing chip, more respectively with gradient concentration (0.156,0.3125,0.625,1.25,2.5,5 μ g/mL) 10 kinds of OPFRs of sample introduction respectively, each concentration rank replication 1 secondary response signal.Spr signal reaches Biacore T100 control software and obtains in conjunction with dynamic collection of illustrative plates from Biacore T100 instrument with signal graph form, after process of fitting treatment, carry out data processing and calculation of parameter with Biaevaluation analysis software, obtain the association rate (k of 10 kinds of OPFRs and p53 _a), dissociation rate (k _d) and affinity (K _d) value, K _dby k _dand k _aratio obtain.Referring to table one:

Table one

3. the interaction of molecular docking simulation OPFRs and p53

Employing is nested in CDOCKER module in Discovery studio2.5 simulates the interaction of OPFRs and p53.The docking schematic diagram discovery of being combined with p53 by analyzing OPFRs, the key position of the histidine residues (His282) of p53 and the active pocket of alanine residue (Ala275) in acceptor, can form stronger hydrogen bond action with compound.In addition, part also with binding pocket in another polar region interact, as valine residue (Val141), referring to (A), (B), (C) of Fig. 2.

Hydrogen bond and hydrophobic interaction are the key effects of both combinations.Between OPFRs molecule and p53, can form in the following way hydrogen bond: 1. the hydrogen atom of the oxygen atom of TCEP or TPrP molecule and alanine residue (Ala129) and histidine residues (His182) forms hydrogen bond; 2. the carbonylic oxygen atom of the hydrogen atom of TCEP or TEP molecule and valine residue (Val141) forms hydrogen bond; 3. the phenyl hydrogen atom of the chlorine atom of TCEP molecule and histidine residues (His282) and leucine residue (Leu162) forms hydrogen bond.Hydrogen bond action is firmly fixed on OPFRs molecule in acceptor pocket, more be conducive to OPFRs molecule and side chain valine residue (Val141) simultaneously, arginine residues (Arg181), isoleucine residue (Ile163), alanine residue (Ala129), the hydrophobic interaction between histidine residues (His182) and serine residue (Ser183).

In addition, find by electrostatic potential analysis, the surface major part of p53 receptor binding domain with positive electrostatic potential, this means in OPFRs compound molecule and is easier to and receptors bind with the position of negative electrostatic potential, also has electrostatic interaction between OPFRs molecule and p53.

The Establishment and evaluation of 4.QSAR model

(1) affinity (logK of OPFRs experiment being recorded and p53 _d) value is with the ratio random division training set of 7:3 and checking collection.

(2) choosing of mechanism anatomy and Molecular structure descriptor.By inference, OPFRs and p53 may and following two processes about the 1. distribution of compound between water and biologic facies; 2. the interaction between OPFRs molecule and p53.Therefore, choose and calculated 19 theoretical Molecular structure descriptors and describe said process,

Be respectively: octanol (logK _oW), molar volume (V), dipole moment (dipol), molecule highest occupied molecular orbital can (E _hOMO), the minimum track that do not occupy of molecule can (E _lUMO), the formal charge (q of the oxygen atom of the two keys of phosphorus oxygen _{p o} ), the most negative net charge (q of molecule ^-), electrophilicity index (ω), chemical potential (μ), compound hardness (η) and 9 DRAGON descriptor (HATS _0m, RDF _030v, X _5A, MATS _8v, E _1e, MATS _7v, MATS _8e, RDF _035mand Mor _17m).

(3) calculating of molecule descriptor.V is defined as the electron density volume in the space that electron density is 0.001 electronics/cube bohr.V and quantum chemical descriptor all adopt Gaussian09 program software to calculate.Detailed process is: first in Chemoffice, generate initial molecular structure, and utilize PM3 method wherein to carry out initial optimization, the graphic file obtaining is changed into Gaussian input file.Then use the B3LYP method in the density functional theory (DFT) in Gaussian09 routine package, compound molecule is carried out to structure optimization in 6-31G (d, p) base group level, obtain its stable molecular structure.Then the geometric configuration of having optimized is carried out to frequency analysis, to guarantee that system without void frequently.In the calculating of above-mentioned geometry optimization and DFT, all use self-consistent field (SCRF) and integral equation form polarization continuum Model (IEFPCM) to consider solvent (water) effect.In SCRF model, solute molecule is positioned in vacuum cavity, and solvent is regarded as continuous non-structure but have the medium of certain DIELECTRIC CONSTANT ε (ε=78.4 of water).ω is calculated by formula below:

$\mathrm{\ω}=\frac{{\mathrm{\μ}}^2}{2\mathrm{\η}}---\left(1\right)$

$\mathrm{\μ}=\frac{E_{\mathrm{LUMO}}+E_{\mathrm{HOMO}}}{2}---\left(2\right)$

$\mathrm{\η}=\frac{E_{\mathrm{LUMO}}-E_{\mathrm{HOMO}}}{2}---\left(3\right)$

Here, μ is chemical potential; η is Chemical hardness; E _hOMOit is the highest occupied molecular orbital energy of compound; E _lUMOthe minimum for occupying track energy of compound.

DRAGON descriptor, the configuration (molecule saves as .mol form) based on having optimized adopts Dragon software to calculate.

(4) foundation of QSAR model.Use the Molecular structure descriptor calculating in offset minimum binary (PLS) and step (3), obtain as drag:

logK _D=-4.76+5.67×10 ^-1X _5A+7.15×10 ^-1MATS _7v+1.67Mor _17m

Wherein X _5Ait is a topological index; MATS _7va two-dimensional autocorrelation descriptor (Moranautocorrelation-lag7/weighted by atomic van der Waals volumes), by the Van der waals volumes weighting of atom; Mor _17mbelong to 3D-MoRSE descriptor (signal17/weighted by atomicmasses).

In model, logK _dbe expressed as the function of 3 descriptor variables, training set data n=7.Related coefficient square (R ²) be 0.892, root-mean-square error (RMSE)=0.238, level of significance (p) <0.001, illustrates that model has the good goodness of fit.Accumulation cross validation coefficient (Q ² _cUM)=0.743, shows that model has good robustness, as shown in Figure 3.

(5) checking and the application domain of the QSAR model of setting up

The predictive ability of model adopts 3 external datas that have neither part nor lot in modeling to evaluate, by square (Q of outside prediction related coefficient ² _eXT) characterize Q ² _eXT=0.647, show the concrete predictive ability preferably of model.

${Q_{\mathrm{EXT}}}^2=1-\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(y_i-{\hat{y}}_i)}^2}{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(y_i-{\stackrel{\&OverBar;}{y}}_{\mathrm{EXT}})}^2}---\left(4\right)$

Wherein, the number of n representation compound, yi and represent respectively measured value and the predicted value of i compound; represent the mean value of external certificate collection predicted value.

Employing Williams figure carrys out the application domain of characterization model, as shown in Figure 4.Lever value (the h of integrated application Molecular structure descriptor _i) and the early warning value (h*=1.71) of molecular structure of compounds descriptor, taking standardized cross validation residual error as ordinate, lever value is the application domain of horizontal ordinate mapping characterization model, judges outlier.

Embodiment 1

Given compound trichloroethyl phosphate (TCEP): adopting Williams figure method to calculate its lever value is 0.2965<h* (early warning value)=1.71, residual (SE)=-1.31>-3, illustrates that this compound is in QSAR model application domain.Based on the mechanism of action, calculate three descriptor (X in model according to narration method in invention _5A, MATS _7v, Mor _17m) value, be respectively 0.121,0.047 ,-0.395.

TCEP and p53 interaction affinity logK _dmeasured value be :-5.55.As follows based on QSAR model prediction step:

logK _D=-4.76+0.567×(0.121)+0.715×(0.047)+1.67×(-0.395)=-5.24。Predicted value extremely conforms to measured value.

Embodiment 2

Given compound tricresyl phosphate (the chloro-2-propyl group of 1-) ester (TCCP): adopting Williams figure method to calculate its lever value is 0.503<h* (early warning value)=1.71, residual (SE)=-0.116>-3, illustrates that this compound is in QSAR model application domain.Based on the mechanism of action, calculate three descriptor (X in model according to narration method in invention _5A, MATS _7v, Mor _17m) value, be respectively 0.089 ,-0.303 ,-0.061.

TCEP and p53 interaction affinity logK _dmeasured value be :-5.02.As follows based on QSAR model prediction step:

logK _D=-4.76+0.567×(0.089)+0.715×(-0.303)+1.67×(-0.061)=-4.99。Predicted value extremely conforms to measured value.

Embodiment 3

Given compound triphenyl phosphate (TPP): adopting Williams figure method to calculate its lever value is 0.437<h* (early warning value)=1.71, residual (SE)=0.986<3, illustrates that this compound is in QSAR model application domain.Based on the mechanism of action, calculate three descriptor (X in model according to narration method in invention _5A, MATS _7v, Mor _17m) value, be respectively 0.084 ,-0.081,0.277.

TCEP and p53 interaction affinity logK _dmeasured value be :-4.15.As follows based on QSAR model prediction step:

logK _D=-4.76+0.567×(0.084)+0.715×(-0.081)+1.67×(0.277)=-4.38。Predicted value extremely conforms to measured value.

Claims (3)

1. a method of predicting biomarker p53 and organophosphorus ester flame-proof agent interaction affinity, is characterized in that comprising the following steps:

Adopt offset minimum binary method to build affinity K _dwith the quantitative structure activity relationship QSAR model of compound molecule descriptor, according to affinity K _dvalue realizes the prediction of affinity.

2. a kind of method of predicting biomarker p53 and organophosphorus ester flame-proof agent interaction affinity according to claim 1, is characterized in that described quantitative structure activity relationship QSAR model is:

logK _D=-4.76+5.67×10 ^-1X _5A+7.15×10 ^-1MATS _7v+1.67Mor _17m

Wherein X _5Aa topological index, the impact of the structure of sign molecule on organophosphorus ester flame-proof agent and p53 combination; MATS _7vbe a two-dimensional autocorrelation descriptor, obtain by the Van der waals volumes weighting of atom; Mor _17mfor 3D-MoRSE descriptor, obtain by the quality weighting of atom.

3. a kind of method of predicting biomarker p53 and organophosphorus ester flame-proof agent interaction affinity according to claim 1, is characterized in that described QSAR model can be used for predicting the genotoxic potential of OPFRs compound.