CN115925536B - Small molecule inhibitors targeting DksA and their applications - Google Patents

Abstract

The invention provides a small molecular inhibitor for targeted inhibition of DksA and application thereof, wherein the small molecular inhibitor for targeted inhibition of DksA is applied to preparation of medicines for treating colibacillosis and salmonella, and 9 small molecular inhibitors for targeted inhibition of DksA are screened out by means of a high-throughput virtual screening technology, so that the small molecular inhibitor has the advantages of wide application range and strong targeting. Has antibacterial effect on various escherichia coli and salmonella, and has influence on bacterial biofilm formation and antibiotic resistance. The small molecule inhibitor for targeted inhibition of DksA plays an important role in the field of infection treatment of food-borne diseases caused by escherichia coli and salmonella, and provides a novel targeted therapeutic drug for clinical control of colibacillosis and salmonella.

Description

Small molecule inhibitor for targeted inhibition of DksA and application thereof

Technical Field

The invention belongs to the technical field of biological medicines and molecules, and particularly relates to a small molecule inhibitor for targeted inhibition of DksA and application thereof.

Background

Food-borne pathogenic bacteria have been closely related to public health problems with food safety which is a widely international concern. According to statistics, in bacterial food poisoning events of various countries of the world, the drug resistance problem of bacteria is more and more serious, about 70 thousands of death cases caused by drug-resistant bacteria are estimated to be about the world each year, and it is estimated that up to 1000 ten thousand cases can be caused in 2050, so that serious drug resistance problems lead human beings to face serious challenges in the fields of medical clinic, veterinary clinic, food animal breeding, food safety and the like, at present, animal-derived bacterial drug resistance phenomena of China are very common, multiple drug resistance and even universal drug resistance strains are continuously generated, and the food safety and public health are seriously endangered.

Salmonella disease (Salmonellosis) is an infectious disease co-affected by man and various animals caused by Salmonella infection, and causes serious harm to the breeding industry. In addition, the rising rate of infection of people also causes the salmonella to be a public health problem affecting the world. Salmonella has a rich serotype due to the diversity of O and H antigens, currently over 2600. Salmonella typhimurium (Salmonella Typhimurium) and Salmonella enteritidis (Salmonella enteritidis) in the intestinal subspecies of Salmonella enterica are two major serotypes responsible for human and animal Salmonella disease. Salmonella typhimurium is the most common serotype causing food-borne diseases, can cause acute gastroenteritis in humans, and can cause mice to develop systemic typhoid diseases. As facultative intracellular parasitic bacteria, salmonella proliferate and spread inside the cell by forming endocytosis that encapsulates the cell. Salmonella can cause the morbidity of various animals, and can cause recessive infection and persistent infection of adult animals with the duration of several months, thereby bringing great difficulty to the prevention and treatment of the disease. Worldwide, salmonella causes about 1.15 million people to die and about 37 tens of thousands of people each year. In the case of bacterial food poisoning in our country, 70-80% are caused by salmonella infection. In addition, the variety of drug-resistant genes carried by salmonella is increased, the drug resistance is increased continuously, and the cross transfer of the drug-resistant genes between farmed animals, environment and human beings generates serious harm to public health and food safety.

Coli (ESCHERICHIA COLI) is largely non-pathogenic as a human intestinal symbiotic, but is converted into pathogenic escherichia coli after capturing pathogenic or virulence factors from the outside, and can cause various zoonotic diseases. The escherichia coli infection of the cultured animals can not only lead to the increase of the morbidity and mortality of the animals, but also reduce the egg yield of the poultry, the meat quality of the livestock and the poultry is poor, and the cost of cultivation and treatment is increased. Infection of pathogenic escherichia coli can cause calf diarrhea, hemorrhagic enteritis, septicemia and other diseases, and poultry pathogenic escherichia coli can also cause local or systemic infection of poultry, so that the infection not only causes huge economic loss to animal breeding, but also causes serious harm to food safety, public health and human health once people are infected or people and other animals are indirectly infected after the surrounding environment is polluted by excrement.

Since the 40 s of the 20 th century, antibacterial drugs such as natural and chemically synthesized antibiotics have been considered as smart drugs for treating infectious diseases, greatly improving the quality of life of people and remarkably reducing the mortality caused by bacterial infection. However, with the massive clinical use and abuse of antibacterial drugs, multi-drug resistant strains are constantly emerging and widely prevalent and spread worldwide, and have posed a serious threat to human health. In particular to infection caused by gram-negative bacteria such as enterobacteria, pseudomonas aeruginosa, klebsiella pneumoniae, acinetobacter baumannii and the like, and few effective therapeutic drugs which can be selected clinically are available.

In E.coli and Salmonella, the RNA polymerase-binding transcription factor DksA is a pleiotropic regulator that controls the metabolism and virulence of bacteria under non-stringent growth conditions. However, small molecule inhibitors using DksA as targets are rarely reported at present.

Disclosure of Invention

Aiming at the defects in the prior art, the invention aims to provide a small molecule inhibitor for targeted inhibition of DksA and application thereof.

To achieve the above object, the solution of the present invention is:

In one aspect, a small molecule inhibitor for targeted inhibition of DksA is provided, the small molecule inhibitor having a structure that is any one of the following structures:

on the other hand, the application of the small molecule inhibitor for targeted inhibition of DksA in preparing medicines for treating colibacillosis and salmonella is provided.

Preferably, dksA has the amino acid sequence (SEQ ID NO. 1):

MQEGQNRKTSSLSILAIAGVEPYQEKPGEEYMNEAQLAHFRRILEAWRNQLRDEVDRTVTHMQDEAANFPDPVDRAAQEEEFSLELRNRDRERKLIKKIEKTLKKVEDEDFGYCESCGVEI GIRRLEARPTADLCIDCKTLAEIREKQMAG.

In yet another aspect, a medicament for treating colibacillosis and salmonella is provided, wherein the medicament comprises a small molecule inhibitor targeted to inhibit DksA as an active ingredient.

Preferably, the drug is used for targeted inhibition of DksA.

Preferably, the medicament further comprises a pharmaceutically acceptable carrier.

By adopting the scheme, the invention has the beneficial effects that:

The invention screens out 9 small molecule inhibitors targeted to inhibit DksA by means of a high-flux virtual screening technology, and has the advantages of wide application range and strong targeting. Has antibacterial effect on various escherichia coli and salmonella, and has influence on bacterial biofilm formation and antibiotic resistance. The small molecular inhibitor of the targeted DksA plays an important role in the field of infection treatment of food-borne diseases caused by escherichia coli and salmonella, and provides a novel targeted therapeutic drug for controlling clinical escherichia coli diseases and salmonella diseases.

Drawings

FIG. 1 is a 3D structure analysis chart of DksA in example 1 of the present invention (wherein, A is DksA structure obtained based on 7KHE model, B is interaction of ppGpp (sphere) with three amino acids in DksA (based on 7KHE model), C/D is molecular superposition chart of AF model, 1TJL and 7KHE model and RMSD value).

FIG. 2 is a graph showing the molecular docking pocket and analysis of DksA protein in example 1 of the present invention (wherein pocket means a small molecular binding pocket on DksA protein, 2 docking pockets are shown on DksA protein by molecular docking, respectively pocket1 and pocket2; size means the Size of small molecular binding pocket, PLB means phosphate receptor means protein ligand binding affinity, and is arranged in descending order, and pocket position is generally determined comprehensively by actual pocket position and PLB value; hyd means pocket quality score; side means pocket site; residues means site information containing amino acids).

FIG. 3 is a graph showing scoring functions of Drug Score and Lipinski Score in example 1 of the present invention (wherein log S: water solubility; HIA: oral availability; log P: lipid solubility; hERG pIC50: cardiotoxicity half-inhibitory concentration; 2D6 affinity category: risk index of Drug interactions, refers to the effects of cytochrome P450 enzyme metabolism and absorption of P protein (P-gp category), 2C9 pKi:2C9 protein kinase inhibitor, P-gp category: P protein; PPB90: binding rate of antibacterial Drug to plasma protein; BBB category: blood brain barrier; HBA: number of hydrogen bond acceptors; HBD: number of hydrogen bond donors; MW: molecular weight).

FIG. 4 is a graph showing the inhibitory effect of 9 compounds and 2 control drugs (Norfloxacin and Enrofloxacin) on 5 strains in example 3 of the present invention.

FIG. 5 is a graph showing the OD _620nm of the present invention after the effect of the 9 compounds and 2 control drugs (Norfloxacin and Enrofloxacin) on 5 strains, respectively, on crystal violet staining.

FIG. 6 is a graph showing the results of crystal violet staining after the 9 compounds and 2 control drugs (Norfloxacin and Enrofloxacin) of example 6 of the present invention were applied to 5 strains, respectively.

FIG. 7 is a graph showing the interaction pattern between compound No.5T13748 and DksA protein in example 6 of the present invention.

FIG. 8 is a graph showing the interaction pattern between compound No.6T6822 and DksA protein in example 6 of the present invention.

FIG. 9 is a graph showing the interaction pattern between compound No.7T7449 and DksA protein in example 6 of the present invention.

FIG. 10 is a graph showing the interaction pattern between compound No.8T7845 and DksA protein in example 6 of the present invention.

FIG. 11 is a graph showing the interaction pattern between compound No.10T8854 and DksA protein in example 6 of the present invention.

FIG. 12 is a graph showing the interaction pattern between compound No.12T7691 and DksA protein in example 6 of the present invention.

FIG. 13 is a graph showing the interaction pattern between compound No.19T4434 and DksA protein in example 6 of the present invention.

FIG. 14 is a graph showing the interaction pattern between compound No.25T2525 and DksA protein in example 6 of the present invention.

FIG. 15 is a graph showing the interaction pattern between compound No.40T9285 and DksA protein in example 6 of the present invention.

Detailed Description

The invention provides a small molecule inhibitor for targeted inhibition of DksA and application thereof.

DksA is a conserved RNA polymerase binding protein that is commonly found in many gastrointestinal pathogens, such as E.coli, salmonella, proteus, and the like. Studies have shown that DksA plays an important regulatory role in bacterial resistance generation and pathogenicity. The RNA polymerase is combined to play a global regulation role, so that the interaction between the polymerase and DNA is blocked, the formation of an initial complex is inhibited, the ppGpp effect is enhanced, and the transcription of NTP on rRNA is started. DksA is involved in a variety of biological functions such as negative regulation of rRNA expression, inhibition of transcript elongation, inhibition of exonuclease RNA cleavage, inhibition of pyrophosphorylation, and increase of intrinsic termination. Studies show that DksA is involved in regulation of bacterial movement and biofilm formation, and through forward regulation of SPI-1 gene, invasion of Salmonella typhimurium into epithelial cells is promoted. In addition, dksA expressed in the intestinal tract infection process is necessary for gastrointestinal tract colonization and systemic infection of pathogenic bacteria, so the invention aims to screen novel targeted inhibition DksA small molecular inhibitors so as to obtain targeted therapeutic drugs for treating colibacillosis and salmonella, and the antibacterial effect of the compound is expected to be superior to the sensitivity or activity of other antibiotics of the same class, the toxic and side effects on animals and the environment thereof are extremely low, and the drug resistance is not easy to generate.

Example 1:

DksA protein structural analysis consisting of 151 amino acids, containing a zinc finger structure, in which four cysteines are involved in the binding of metallic zinc ions (C114, C117, C135, C138). The 3D structure of the DksA protein is shown in figure 1, panel a, which consists of a G domain consisting of one irregular turn at the N-terminus, two long alpha helices, CCtip and a C-terminal alpha helix. Panel B of FIG. 1 shows DksA binding to the second channel of RNA polymerase, the regions involved in the interaction being the G domain (helix), CC tip and C-terminal alpha helix. In the structural analysis, two models (1 TJL and 7 KHE) from the crystal structure and the cryo-electron microscope structure were simultaneously selected and a 3D structure (AF model) thereof was constructed using AlphaFold, and superimposed images of the three models showed that RMSD values were 0.00, and that the structures were highly similar (fig. 1, C and D), meaning stability of the structure in different states. RMSD is root mean square deviation (Root Mean Square Deviation, RMSD), defined as follows:

Where N is the number of atoms, M _i is the mass of atom i, X _i is the coordinate vector of target atom i, Y _i is the coordinate vector of reference atom i, and M is the total mass. When RMSD does not employ quality weighting, all M _i =1 and m=n. In Amber, the unit of RMSD is emmi (Angstrom, )。

RMSD was used in molecular modeling to measure the degree of conformational aberration or the degree of track stability. In molecular docking, ligands crystallized on proteins are often extracted, the docking software is used for docking again, then the RMSD values between the two ligands before and after docking are compared, the smaller the value is, the higher the accuracy of the docking software is, RMSD=0.0 means perfect overlapping, and the larger the RMSD is, the greater the deviation degree of target molecules from reference molecules is, and the more unstable the structure is.

2. High throughput virtual screening of small molecule inhibitors targeted to inhibit DksA:

(1) Molecular docking the library of compounds screened in this time was targetmol library of compounds (T001). The compound library contains 38969 compounds. And (3) performing molecular cleaning treatment on the compound library, removing salt ions, metal ions, small fragments and the like, and obtaining a 3D structure of the compound through energy minimization treatment, so as to ensure the global conformation of the small molecular compound in the virtual screening process, and performing multi-conformation generation on the small molecular compound. Batch molecular docking of each conformation was performed on a given target screening region using virtual screening software FRED (Ver 3.2.0.2) to obtain affinity scoring values and interaction patterns for each compound with three different DksA protein models.

The possible presence of small molecule compound binding pockets (pockets) on these model surfaces were probed by the SITEFINDER insert in the MOE and analysis found that all three model surfaces contained two small molecule binding pockets whose positions and their properties (size, number of hydrophobic atoms, pocket mass scoring values, containing amino acids, etc.) are shown in fig. 2, where pocket1 exhibited superior properties to pocket2 (e.g., PLB values) in all three models. Although the three models are very similar in structure, the sizes, the properties and the contained amino acid sites of the three models are different, in order to increase the hit rate of screening compounds as much as possible, the three models are screened based on the Pocket1 region simultaneously in the screening process, wherein Pocket1 and Pocket2 of the AF model are adjacent, and Pocket2 contains ppGpp binding sites (K98, R129 and K139), so that the Pocket1 and Pocket2 regions of the models can be combined into one site for molecular screening.

(2) Compound screening, namely, screening 340 compounds by carrying out pharmaceutical property screening on the compounds. By using Stardrop software (Version 6.5.0) to calculate and analyze properties of small molecular compounds, such as water solubility (log s), lipid water distribution coefficient (log p), molecular weight, molecular flexibility, hydrogen bond property, surface accessibility area (TPSA), CYP2C9 enzyme degradation level (CYP 2C9 is an enzyme in human metabolism, is a membrane-bound hemoglobin, can hydroiyse drugs with many different properties, mainly acidic substrates, about 16% of clinical drugs are responsible for metabolism by CYP2C 9), hERG inhibition index (hERG: human ether-a-go-go related genes, potassium channel and human EAG related genes, hERG inhibition problem, still the most direct and effective strategy for improving cardiac toxicity), oral availability (HIA), drug interaction risk (2D 6) and the like. The 2D6 index has the effects of cytochrome P450 enzyme metabolism and P protein absorption. The evaluation, scoring and screening results were performed by using the screening criteria (Drug Score) for oral non-CNS drugs, see FIG. 3. The scoring function is a mathematical statistic of the above-described attributes, with a scoring value ranging from 0 to 1, with higher values indicating better proprietary attributes of the compound. Screening out 40 compounds with higher scoring and better proprietary properties, and performing primary screening on the compounds for inhibiting bacteria of escherichia coli, salmonella standard strains and multiple clinical isolates. The Lipinski rule is used for preliminary screening of a compound database to eliminate molecules unsuitable for becoming medicines, reduce the screening range and reduce the research and development cost of medicines, and is called as Lipinski rule, wherein a small-molecule medicine has the following properties of having a molecular weight of less than 500, having a number of hydrogen bond donors of less than 5, having a number of hydrogen bond acceptors of less than 10, having a lipid water distribution coefficient of less than 5 and having a number of rotatable bonds of not more than 10. Compounds that meet the Lipinski Score principle will have better pharmacokinetic properties and higher bioavailability during in vivo metabolism.

Example 2:

analysis of E.coli and Salmonella resistance

27 Common antibiotics were selected for drug susceptibility testing, and resistance analysis was performed on E.coli standard strain (E), salmonella standard Strain (ST) and clinical isolates E.coli (E-10), salmonella (ST-477), salmonella (ST-3), salmonella (ST-7), salmonella (ST-6), salmonella (ST-1) and Salmonella (ST-71) by the paper sheet method. Selecting single bacterial colony of each strain, adding the single bacterial colony into 1mL of LB, shaking the strain at 37 ℃ and 180rpm overnight, performing 1:100 expansion culture on the next day until OD _620nm is 0.5, adjusting the concentration of bacterial liquid to be 0.5 McFabry turbidity by using LB culture medium, taking 100 mu L of each bacterial liquid, respectively dripping each bacterial liquid on an LB plate, coating the plate by using a glass rod, standing for 15min, sticking a drug sensitive tablet, culturing the bacterial colony in a 37 ℃ incubator for 16-18h, measuring the diameter of a bacteriostasis ring (as shown in table 1), and counting the number of antibiotics sensitive to each strain.

TABLE 19 sensibility test results of strains to 27 antibiotic drug sensitive tablets

Note that S is sensitive, I is moderately sensitive, R is drug resistant

By analysis of the data in Table 1, E.coli standard strains (resistant to 11 antibiotics) and Salmonella standard strains (resistant to 12 antibiotics) were selected, and clinically isolated E.coli E-10 (resistant to 18 antibiotics), salmonella ST-7 (resistant to 14 antibiotics) and Salmonella ST-6 (resistant to 14 antibiotics) were subjected to subsequent intrinsic antibacterial tests.

Example 3:

intrinsic bacteriostasis assay for small molecule inhibitor compounds

Preparation of compound mother liquor 40 compounds were dissolved in dimethyl sulfoxide (DMSO) according to their respective solubilities to stock solutions, and according to pre-experiments, compound mother liquor was diluted with LB medium to working solution at a concentration of 800 μm. Norfloxacin (Norfloxacin) and enrofloxacin (Enrofloxacin) were selected as control compounds, norfloxacin was dissolved in Dimethylsulfoxide (DMSO) to a 10mM stock solution, the stock solution was diluted in LB medium to a working solution stock at a concentration of 800 μm, enrofloxacin hydrochloride was dissolved in water to a 10mM stock solution, and the stock solution was diluted in LB medium to a working solution stock at a concentration of 800 μm.

Coli (E) and Salmonella (ST), and the bacterial solutions of the clinically isolated E.coli (E-10), salmonella (ST-6) and Salmonella (ST-7) were grown to OD _620nm of 0.5, counted, diluted with LB to a bacterial solution concentration of 1X10 ⁵ CFU/mL, and cultured with 40 compounds and 2 control drugs (800. Mu.M) at 37℃for 24 hours, respectively, followed by determination of OD _620nm. The colony count was calculated by the dilution plating method, and the inhibition ratio of each compound to bacteria was counted.

Control colony count = average colony count without added small molecule inhibitor

Experimental group colony count = average colony count with small molecule inhibitor added

Bacterial inhibition = (number of control group colonies-number of experimental group colonies) ×100%/number of control group colonies

The inhibition rate results corresponding to the 40 small molecule inhibitors and the 2 control drugs are calculated based on the calculation formula shown in tables 2, 3,4, 5 and 6.

Wherein the structure of the 9 small molecule inhibitors is any one of the following structures:

TABLE 2 inhibition of E.coli (E) by 40 small molecule inhibitors and 2 control drugs

From table 2, 9 kinds of the 40 kinds of small molecule inhibitors were excellent in antibacterial effect, and the inhibition ratios to escherichia coli (E) were more than 50% of No.5 (antibacterial ratio 51.23%), no.6 (antibacterial ratio 60.01%), no.7 (antibacterial ratio 90.13%), no.8 (antibacterial ratio 66.35%), no.10 (antibacterial ratio 91.57%), no.12 (antibacterial ratio 79.19%), no.19 (antibacterial ratio 89.16%), no.25 (antibacterial ratio 89.74%) and No.40 (antibacterial ratio 54.12%), control drugs Norfloxacin (antibacterial ratio 84.66%) and Enrofloxacin (antibacterial ratio 80.46%).

TABLE 3 inhibition of E.coli (E-10) by 40 small molecule inhibitors and 2 control drugs

As is clear from Table 3, the inhibition ratios to E-10 were more than 50% for No.5 (inhibition ratio 52.66%), no.6 (inhibition ratio 60.28%), no.7 (inhibition ratio 91.25%), no.8 (inhibition ratio 69.22%), no.10 (inhibition ratio 81.23%), no.12 (inhibition ratio 74.25%), no.19 (inhibition ratio 89.55%), no.25 (inhibition ratio 85.22%) and No.40 (inhibition ratio 51.26%), and for the control drugs Norfloxacin (inhibition ratio 74.88%) and Enrofloxacin (inhibition ratio 90.01%).

TABLE 4 inhibition of Salmonella (ST) by 40 small molecule inhibitors and 2 control drugs

As is clear from Table 4, there were No.5 (inhibition ratio 76.38%), no.6 (inhibition ratio 65.24%), no.7 (inhibition ratio 94.56%), no.8 (inhibition ratio 74.85%), no.10 (inhibition ratio 95.63%), no.12 (inhibition ratio 77.78%), no.19 (inhibition ratio 94.58%), no.25 (inhibition ratio 98.24%) and No.40 (inhibition ratio 50.01%), control drugs Norfloxacin (inhibition ratio 82.47%) and Enrofloxacin (inhibition ratio 77.56%) which have inhibition ratios to Salmonella (ST) exceeding 50%.

TABLE 5 inhibition of Salmonella (ST-6) by 40 small molecule inhibitors and 2 control drugs

As is clear from Table 5, the inhibition ratios for Salmonella ST-6 exceeded 50% with No.5 (inhibition ratio 54.27%), no.6 (inhibition ratio 61.57%), no.7 (inhibition ratio 90.48%), no.8 (inhibition ratio 70.21%), no.10 (inhibition ratio 67.22%), no.12 (inhibition ratio 72.13%), no.19 (inhibition ratio 91.27%), no.25 (inhibition ratio 91.21%) and No.40 (inhibition ratio 50.58%), and control drugs Norfloxacin (inhibition ratio 88.10%) and Enrofloxacin (inhibition ratio 86.42%).

TABLE 6 inhibition of Salmonella (ST-7) by 40 small molecule inhibitors and 2 control drugs

As is clear from Table 6, there were No.5 (bacteriostatic 51.02%), no.6 (bacteriostatic 60.14%), no.7 (bacteriostatic 94.33%), no.8 (bacteriostatic 65.11%), no.10 (bacteriostatic 89.66%), no.12 (bacteriostatic 79.11%), no.19 (bacteriostatic 90.06%), no.25 (bacteriostatic 90.24%) and No.40 (bacteriostatic 50.44%), and control drugs Norfloxacin (bacteriostatic 85.93%) and Enrofloxacin (bacteriostatic 78.43%) which had a rate of inhibition of salmonella (ST-7) exceeding 50%.

In conclusion, the small molecule inhibitor with better activity for targeted inhibition of DksA is screened out according to the standard that the inhibition rate of the small molecule inhibitor is higher than 50% at the final concentration of 400 mu M. The results of the 9 small molecule inhibitors with the inhibition ratio exceeding 50% at the final concentration of 400 mu M are shown in FIG. 4, wherein the inhibition ratio of the compound 5 to the salmonella is highest (76.38%), the inhibition ratio of the compound 6 to 5 strains is equivalent, the inhibition ratio of the compound 7 to 5 strains is near 61.44%, the inhibition ratio of the compound 8 to the ST is highest 74.85%, the inhibition ratio of the compound 10 to the ST is highest 95.63%, the inhibition ratio of the compound 12 to the E coli (79.19%) and the inhibition ratio of the salmonella ST-7 (79.11%) are equivalent, the inhibition ratio of the compound 19 to the salmonella ST is highest 94.58%, the inhibition ratio of the compound 25 to the salmonella ST is highest 98.24%, the inhibition ratio of the compound 40 to the 5 strains is relatively average, the inhibition ratio of the control drug Norfloxacin to the salmonella ST-3824 is near 51.28%, and the inhibition ratio of the control drug Norfloxacin to the E-88.16% is highest to the E of the E coli and the inhibition ratio of the salmonella ST-7 is highest (79.11%).

Example 4:

Antibacterial sensitization test of small molecule inhibitor

The standard strain ST of salmonella is selected for an antibacterial sensitization test, a single colony of salmonella ST is added into 1mL of LB, a shaking table is used for carrying out 37 ℃ and 180rpm overnight, the concentration of bacterial liquid is adjusted to be 1x10 ⁵ CFU/mL by 1:100 expansion culture OD _620nm the next day, the antibacterial rate of the compound on the salmonella ST is about 65.00% according to test data of table 4, 9 compounds with different final concentrations are respectively added for carrying out overnight culture at 37 ℃, 100 mu L of each bacterial liquid is measured on LB plates and coated with glass rods, and the obtained product is subjected to standing for 15min and then is pasted with a drug sensitive tablet, and is subjected to incubation in a 37 ℃ incubator for 16-18 h. The results are shown in Table 7.

Table 79 results of antimicrobial sensitization of small molecule inhibitors to Salmonella ST

As can be seen from Table 7, the compound No.6 enhances the bacteriostatic effect of gentamicin on Salmonella ST, and the compound No.12 enhances the bacteriostatic effect of tetracycline on Salmonella ST.

Example 5:

effect of small molecule inhibitors on biofilm

The biological envelope significantly enhances the pathogenicity of salmonella typhimurium to mice, and the extracellular matrix of the salmonella biological envelope mainly comprises frizzled pili, cellulose, bapA (biological envelope related protein), O capsular antigen, lipopolysaccharide (LPS), extracellular DNA (eDNA) and the like. Crystal violet is a basic dye that binds to DNA in the nucleus, thereby staining the nucleus, and the crystal violet staining method quantifies the ability of the test bacteria to form a biofilm.

E.coli standard strain (E) and salmonella standard Strain (ST), clinically separating E.coli (E-10), salmonella (ST-6) and salmonella (ST-7) bacterial solutions, performing expansion culture until OD _620nm is 0.5, counting bacteria, diluting bacterial solution with LB (liquid concentration of 1x10 ⁵ CFU/mL), taking 200 mu L of diluted bacterial solution, inoculating onto a 96-well culture plate, repeating 3 multiple wells for each strain, taking culture medium as blank control, taking bacterial solution culture as positive control, and culturing for 24 hours at 37 ℃. After the cultivation is finished, the culture medium is discarded, the culture medium is washed 3 times by PBS to wash out impurities and plankton bacteria, 200 mu L of methanol is added into each hole to fix the biofilm, the methanol is discarded after 15min, 200 mu L of 0.1% crystal violet is added after airing for 5min, the excessive dye solution is washed out, airing is carried out, 200 mu L of 33% glacial acetic acid is added into each hole, an enzyme-labeling instrument is oscillated for 20min at high speed, OD _620nm is measured, and blank holes without inoculated strains are dyed and used as blank controls. The OD value reflects the firmness of the adhesion of the biofilm to the contact surface, and the biofilm can be classified according to a critical ODc value (OD value obtained by adding 3 times of standard deviation to the average value of blank holes in ODc), wherein OD is less than or equal to ODc and is not adhered (0), OD is less than or equal to 2ODc and is less than or equal to ODc and is less than or equal to 2, OD is less than or equal to 4, ODc is medium adhesion (++), and OD is more than or equal to 4, ODc is strong adhesion (++). The OD _620nm of the 5 test strains after crystal violet staining by 9 compounds as shown in fig. 5, the strain biofilms after the action of compounds 7, 12, 19, 25 and control drugs (Norfloxacin) and Enrofloxacin) were weakly adherent, the strain biofilms after the action of compounds 5, 6, 8, 10 and 40 were moderately adherent, the positive control biofilms of each strain were moderately adherent, but the amount of biofilms was greater than the strain to which the compound was added. The crystal violet staining photograph is shown in fig. 6, and the color depth indicates that the amount of the biofilm is large, which indicates that the bacterial amount is large and the antibacterial rate of the small molecule inhibitor is small.

The calculation formula is as follows:

Wherein, Is the average of blank wells x ₁、x₂ and x ₃, n is the number of samples.

Example 6:

mode of interaction of 9 Compounds with DksA protein, respectively

Through the experiment, the interaction pattern diagram of 9 compounds and targets is predicted, through high-throughput virtual screening, a DksA-based 3D structure model AF is adopted, after the structure is treated and optimized through protonation, small molecular compound binding pockets on the surface of the model AF are analyzed, the corresponding small molecular compound binding pockets in each model are determined to be docking areas of virtual screening, a compound library file with multiple conformations is screened through OEDock software of OpenEye company, affinity of each compound with targets is evaluated through a Chemgauss scoring function, and finally, a molecular docking model of the compound affinity and a protein target complex is obtained, and the proprietary nature of the compound is analyzed. And (3) further screening out proper compounds through affinity, drug forming property and the like, and screening out 9 small molecule inhibitors with better activity for targeted inhibition of DksA. All 9 compounds interact with amino acids in the DksA molecular pocket, not with ppGpp binding sites. The results are shown in fig. 7, 8, 9, 10, 11, 12, 13, 14 and 15.

The affinity scores with DksA were observed for compounds No.5, no.6, no.7, no.8, no.10, no.12, no.19, no.25 and No.40, where the affinity scores in the AF model and these compounds were all bound to pocket2 region, the compounds formed hydrogen bond interactions and pi hydrogen bond interactions with which amino acids, around which the positively charged amino acids were predominantly distributed. The 9 compounds have relatively ideal affinity scoring values and good drug forming property, and FIG. 7 shows interaction modes of the compound No.5T13748 and DksA protein, namely a complex structure of the DksA protein/T13748, wherein the compound is shown in green, and the N end to the C end of the DksA protein are marked in blue to red, and B shows interaction modes of the compound and important amino acids, wherein the compound is combined in a pocket2 region, and amino acids R125 and L134 form hydrogen bond interaction with the compound. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-6.94 kcal/mol.

FIG. 8 is a diagram showing interaction pattern of compound No.6T6822 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region, wherein amino acid L95, A128 forms hydrogen bond interaction with the compound, B. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-7.43 kcal/mol.

FIG. 9 is a diagram showing interaction pattern of compound No.7T7449 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region wherein amino acids K98, R129 form hydrogen bond interactions with the compound, wherein B is a diagram showing interaction pattern of compound No.7T7449 with DksA protein, the structure of DksA protein/T7449, the interaction pattern of compound with important amino acids. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-5.57 kcal/mol.

FIG. 10 is a diagram showing interaction pattern of compound No.8T7845 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region wherein amino acid K98 wherein amino acid R129 forms hydrogen bond interactions with the compound, B. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-6.31 kcal/mol.

FIG. 11 is a diagram showing interaction pattern of compound No.10T8854 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region wherein amino acid K98 wherein amino acids R129, E143 form hydrogen bond interactions with the compound, wherein B is a diagram showing interaction pattern of compound No.10T8854 with DksA protein, dksA protein/T8854. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-6.22 kcal/mol.

FIG. 12 is a diagram showing interaction pattern of compound No.12T7691 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region wherein amino acid K98 wherein amino acid R129 forms hydrogen bond interactions with the compound, B. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-6.11 kcal/mol.

FIG. 13 is a diagram showing interaction pattern of compound No.19T4434 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region wherein amino acid K98, R129 hydrogen bond interactions with the compound, wherein the structure of the complex of compound No.19T4434 with DksA protein is shown in green. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-6.63 kcal/mol.

FIG. 14 is a diagram showing interaction pattern of compound No.25T2525 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and B, interaction pattern of compound binding to pocket2 region wherein amino acid K98 wherein amino acid R129 forms hydrogen bond interactions with the compound. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the compound, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-5.52 kcal/mol.

FIG. 15 is a diagram showing interaction pattern of compound No.40T9285 with DksA protein, wherein the compound is shown in green and the N-to C-terminus of DksA protein is shown in blue to red, and the interaction pattern of compound binding to pocket2 region, wherein amino acid K98 wherein amino acid R129 forms hydrogen bond interactions with the compound, B. C, 2D mode diagram of complex interaction and amino acid around the compound, wherein the amino acid with positive charges is mainly distributed around the amino acid, D, 2D structure of the compound, and affinity scoring value of the compound and AF model is-5.19 kcal/mol.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.

Claims (1)

1. Application of a small molecule inhibitor targeting DksA in the preparation of drugs for the treatment of Escherichia coli and Salmonella simultaneously; The small molecule inhibitor is: The amino acid sequence of DksA is shown in SEQ ID NO.1.