CN115116561A - Construction method and application of drug-target protein-schizophrenia interaction network - Google Patents
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Abstract
The invention belongs to the technical field of biomedicine, and discloses a construction method and application of a drug-target protein-schizophrenia interaction network. The invention screens out new drugs which can be used for relieving or even treating schizophrenia from the drug-target protein-disease interaction network by constructing the drug-target protein-disease interaction network and collecting candidate genes of schizophrenia. The construction method of the drug-target protein-schizophrenia interaction network provides a basis for repositioning related drugs for preventing or treating schizophrenia and detecting new clinical application of the existing drugs by the constructed interaction network. Can accelerate the drug development process and save a great deal of manpower and financial resources.
Description
Technical Field
The invention relates to the technical field of biomedicine, in particular to a construction method and application of a drug-target protein-schizophrenia interaction network.
Background
Schizophrenia is a serious mental disease with a worldwide prevalence of about 0.4% for life. It can have a devastating effect on the patient and its care givers and can bring significant costs to the healthcare system. Schizophrenia is a heterogeneous disease with positive symptoms (delusions, hallucinations, thought disorder); negative symptoms (anhedonia, depreciation, social withdrawal, poor thoughts) and cognitive dysfunction. With the understanding and clinical research on schizophrenia, schizophrenia is gradually classified into different types, such as early-onset schizophrenia and late-onset schizophrenia according to the onset age of schizophrenia, and defective schizophrenia and non-defective schizophrenia according to the dimension differences of signs and symptoms, the course of disease, pathophysiological relevance, risk and etiological factors, treatment response and the like.
The etiology of schizophrenia is still poorly understood and over the past few years, many different antipsychotics have been developed and tested for their effectiveness and safety in reducing the positive symptoms and maintaining stability of schizophrenia. Although a variety of antipsychotic drugs are available to alleviate the symptoms of schizophrenia, the response rates to these drugs are lower than expected, they act slowly, and often produce severe adverse side effects.
Current antipsychotic drug therapy relies primarily on targeting the brain dopamine D2 receptor, but new drugs are being developed that act through glutamate receptors, glycine transporters, or α -7-nicotinic acetylcholine receptors. However, to date, none of these new approaches have brought about therapeutic breakthroughs. Although antipsychotics have undisputed efficacy in treating the positive symptoms of schizophrenia, for most patients, the drug is not effective for all symptoms, including negative and cognitive symptoms, and serious side effects persist. While subsequently developed new drugs can mitigate the occurrence of motor side effects, other safety and tolerability issues arise. Since the advent of antipsychotic treatment in the early 1950 s, the subsequent progress has been modest. However, screening of drugs for treatment aiming at different symptoms and disease modes of different types of schizophrenia still lacks at present, and the heterogeneity of pathophysiology in various fields of schizophrenia requires a plurality of treatment methods, so that finding out effective drugs aiming at different types is one of the most important research problems in the field of schizophrenia.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a construction method and application of a drug-target protein-schizophrenia interaction network.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for constructing a drug-target protein-schizophrenia interaction network, comprising the following steps:
(1) collecting drug-target protein interaction relationship through a drug database, and screening human target spots and drugs meeting FDA standard;
(2) mapping the diseases to schizophrenia related genes through a disease database, and integrating disease-pathogenic gene interrelations;
(3) and (3) integrating the drug-target protein interaction relation and the disease-pathogenic gene interaction relation by using Python to construct a drug-protein-schizophrenia interaction network.
The drug-protein-schizophrenia interaction network of the present invention provides a universal method of systematically understanding how drugs treat diseases, systematically identifying proteins relevant to treatment, predicting which schizophrenia-related genes will alter drug efficacy or cause severe adverse reactions to drug treatment, and the multidimensional interaction group can be easily expanded to add other node types relevant to diseases.
The medicine-target protein-schizophrenia interaction network can start from different expression modes of different schizophrenia types, and find medicines influencing corresponding targets according to different table genes, so that the medicine-target protein-schizophrenia interaction network has a treatment effect on different schizophrenia types.
As a preferred embodiment of the method for constructing a drug-target protein-schizophrenia interaction network according to the present invention, in the step (2), schizophrenia candidate genes are collected and sequenced to obtain defective schizophrenia difference genes and early-onset schizophrenia difference genes; the candidate gene, the defective schizophrenia difference gene and the early-onset schizophrenia difference gene are pathogenic genes; mapping the pathogenic genes on a human protein interaction network through Cytoscape and R, and screening out the schizophrenia-pathogenic gene interaction relationship from the pathogenic genes.
As a preferred embodiment of the method for constructing a drug-target protein-schizophrenia interaction network according to the present invention, the schizophrenia candidate gene includes ABL2, ALDOA, ARHGAP1, ABR, SLC25a6, ARNT, ACACA, APAF1, STS, ACVR2A, APBA2, ASCL1, ADRA1A, BIRC3, serpin 1, JAG1, APOE, rer, AGT, FAS, ATP2a2, AK4, AQP6, ATP2B4, AKT1, AR, ATRX, ALDH1a1, ARL4D, and KIF 1A; the defect schizophrenia differential gene comprises ABR, ALAD, ARHGAP1, ACLY, ALAS1, ARNT, ACO2, ABCD1, ARRB1, ACTG1, ALDH1A1, GET3, ACTN4, ALDH3B1, ATIC, ACVR1B, ALOX15B, ATOX1, ACVR2B, APLP2, TNFRSF17, ADCY7, APP, ZFP36L1, AP2A1, APRT, ZFP36L2, AKT1, ARF3, ARHGAP 1; the early-onset schizophrenia differential gene comprises ABCF1, AQP3, POLR3D, ACVR18, ARHGAPS, BNIP1, PLIN2, ARHGDIA, KLF9, ADORA2A, ARHGDIB, C3AR1, PARP1, ATF3, TMEM258, AGER, ATP5F1D, CFAP410, AHR, ATPSF1E, CBFA2T3, ALDOA, ATP6V1A, SLC25A6, ATP6V1C1, CCNE1, FAS, BMI1 and CCNH.
In a second aspect, the invention applies the drug-target protein-schizophrenia interaction network obtained by the construction method to screening drugs for preventing or treating schizophrenia.
As a preferred embodiment of the application of the invention, the weight and the restart probability are given according to the node type in the drug-target protein-schizophrenia interaction network, random walk is restarted from the disease and drug node, and the influence of the drug and the disease on the nodes in the multidimensional interaction group is calculated through the frequency of the walk node to calculate the diffusion curve of the drug and the disease; and respectively calculating diffusion curves of the drugs and the diseases and a diffusion curve SIM of the diseases related to the schizophrenia, comparing the diffusion curves with the SIM and respectively calculating Euclidean distances between every two diffusion curves, and predicting drug sequencing for the given schizophrenia according to the distances.
As a preferred embodiment of the application described in the present invention, the node is given a weight W { 'indication':3.541889556309463, 'protein':4.396695660380823, 'drug':3.2071696595616364}, and the probability a that the walker continues walking at a given step rather than restarting is 0.8595436247434408.
As a preferred embodiment of the application described in the present invention, the diffusion curves of the drug and the disease and the diffusion curve of the schizophrenia-related disease SIM are calculated by L2 norm, respectively.
The diffusion profile of the present invention provides predictive power and interpretability in drug-disease therapy modeling, and allows identification of proteins associated with treatment of schizophrenia.
As a preferred embodiment of the use according to the invention, said medicament for the treatment of schizophrenia comprises Rilonacept, Tetrabenazine, lsomethetene, Zuclopenthxol, Droperidol, Acetohenazine, lloperidine, Dopamine, Ketanserin, Enzastaurin, Vilazolone, Epicriptine, Dihydrexidine, Flupentixol, Taurine, Benzphetamine, Piceatannol, Citalopram, Butarbibaral and Cinnarizine.
As a preferred embodiment of the use according to the invention, the medicament for the treatment of early schizophrenia comprises Veliparib, Talazoparib, Niraparib, Fingolimod, E-2012, Rucaparib, Olaparib, mexiletin, Nadroparin, Aldleukin, Denileukin difitox, Asparagine, Vinblast, Auranofin, Rivaniline, Mesalazine, Dupirumab, Aspirin, Muromonab and Ribavirin.
As a preferred embodiment of the use according to the invention, said medicament for the treatment of defective schizophrenia comprises Tasonerm, A-674563, Castanosporine, Belatamab prolin, Bexarotene, lpilimumab, Thiyropin alfa, Reversine, Puromycin, Anisomycin, Botulin toxin type A, Glycolic acid, Pazopanib, Aminolecinic acid, Alfacalcolide, Dimethyl fumarate, Pexidartinib, Wortmannin, Befibrat and Cediranib.
Compared with the prior art, the invention has the beneficial effects that:
the invention screens out new drugs which can be used for relieving or even treating schizophrenia from the drug-target protein-disease interaction network by constructing the drug-target protein-disease interaction network and collecting candidate genes of schizophrenia. The construction method of the drug-target protein-schizophrenia interaction network provides a basis for repositioning related drugs for preventing or treating schizophrenia and detecting new clinical application of the existing drugs by the constructed interaction network. Can accelerate the drug development process and save a great deal of manpower and financial resources.
Drawings
FIG. 1 is a flow chart of the construction of a drug-target protein-disease interaction network;
FIG. 2 is a graph of drug-protein-disease interaction network;
fig. 3 is a graph showing the diffusion curve of drugs and diseases and the disease diffusion curve SIM.
Detailed Description
Protein interaction plays an important role in a cell molecular signaling pathway network, a molecular mechanism of a disease is researched through a protein interaction network of pathogenic genes, a disease risk gene of schizophrenia may act on a common molecular network from the viewpoint of system biology, such a common molecular interaction network may involve a plurality of signaling pathways to perform related cellular functions, and it is important to understand the pathogenic mechanism of schizophrenia and find out therapeutic drugs through the schizophrenia network. However, as the pharmacological understanding is deepened, the mode of ' replacing ' one target and one drug ' by the mode of ' multiple targets and multiple drugs ' is widely accepted. Drugs often target multiple proteins, not just one. Furthermore, drugs may interact with other proteins in addition to the primary therapeutic target, i.e., off-target effects. And the pathogenic gene of a disease is usually more than one, but includes a few major genes and many minor genes.
Therefore, identifying Drug-Target interaction (DTI) is an important prerequisite for the relevant fields of pharmacology, Drug relocation, Drug discovery, side effect prediction, and Drug resistance.
To better illustrate the objects, aspects and advantages of the present invention, the present invention will be further described with reference to specific examples. It will be understood by those skilled in the art that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The test methods used in the examples are all conventional methods unless otherwise specified; the materials, reagents and the like used are commercially available unless otherwise specified.
Example 1: construction method of drug-target protein-disease interaction network
Schizophrenia is a complex multifactorial disease, and it seems unlikely that a single target drug would be used to treat all symptoms of the disease, and a more ideal treatment would be to find different drugs and drug combinations for different types of schizophrenia based on their different expression patterns. In view of the time, money, resources, and other costs involved in developing new drugs. By utilizing known interaction data and unpaired small molecule compounds stored in various databases, new therapeutic effects and uses of existing or obsolete drugs for disease, i.e., drug relocation, are discovered. The flow is shown in FIG. 1.
The method comprises the following specific steps:
(1) data collection
Drug-target protein interaction relationships were collected via Drug databases (Drug library, Drug replicating Hub), and human targets and drugs meeting FDA standards (4,622 nodes, 11,959 edges) were screened for 2,268 drugs and 2,354 target proteins.
Diseases are mapped to genes influenced by the diseases through Cytoscape through the effects of genome change, expression change or posttranslational modification of a disease database (DisGeNet), and disease-pathogenic gene interrelations (27,092 nodes, 673,412 edges) are integrated, wherein 21,878 diseases and 5,214 pathogenic genes are included.
(2) Multi-dimensional interactive group construction
The three interactions were integrated using Python to construct a drug-protein-disease interaction network, as shown in figure 2.
(3) Calculating drug and disease diffusion curves
By spreading the effects of each drug and disease in the multidimensional interaction group using a re-start random walk, the drug or disease spreading profile knows the proteins most affected by each drug or disease. Each drug or disease diffusion curve is calculated by biased random walks starting from the drug or disease node. At each step, the random walker may resume its walk or jump to a neighboring node based on the optimized edge weights. After multiple walks, the diffusion curve measures the frequency of visits to each node, representing the effect of a drug or disease on that node.
Assigning weights W { W } by different types of nodes drug ,w protein ,w indication Relative likelihood of hopping from one node type to another. Alpha represents the probability that the pedestrian will continue walking at a given step rather than restarting.
The probability of walker jumping to a different type of node is first calculated. And secondly, calculating the probability of the walker jumping to different nodes of the same type. And finally, calculating diffusion distribution through power iteration.
(4) Drug prediction
For a drug to treat a disease, it must affect proteins that are similar to the proteins destroyed by the disease and biological functions. The diffusion profile of drugs and diseases encodes the effect of drugs and diseases on proteins.
Comparing the diffusion curves of the drug and the disease predicts which drugs can treat a given disease. For each drug, its similarity to the disease diffusion curve SIM (similarity) was calculated separately from the L2 norm, and the drugs most likely to treat the disease were ranked together as shown in fig. 3.
Example 2: construction method of drug-target protein-schizophrenia interaction network
(1) Data collection
Drug-target protein interaction relationships were collected via Drug databases (Drug library, Drug replicating Hub), and human targets and drugs meeting FDA standards (4,622 nodes, 11,959 edges) were screened for 2,268 drugs and 2,354 target proteins.
Diseases are mapped to genes that they affect by Cytoscape through effects such as genomic changes, expression changes or post-translational modifications in the disease database (DisGeNet), integrating disease-pathogenic gene interrelations (27,092 nodes, 673,412 edges) including 21,878 diseases and 5,214 pathogenic genes.
1,720 schizophrenia candidate genes were collected, mapped on a human protein interaction network (18,276 nodes, 817,086 edges) by Cytoscape and R, and an interaction subnet of schizophrenia-causing genes (1,501 nodes, 9,084 edges) was selected therefrom. And obtaining the defective and early schizophrenia difference gene by sequencing.
The first 30 candidate causative genes for schizophrenia (n-1501) are shown in table 1:
TABLE 1 schizophrenia candidate causative genes
The first 30 early-onset schizophrenia differentially expressed genes (n ═ 1206) are shown in table 2:
TABLE 2 genes differentially expressed in early onset schizophrenia
The first 30 defective schizophrenia differentially expressed genes (n ═ 1161) are shown in table 3:
TABLE 3 defective schizophrenia differentially expressed genes
(2) Multi-dimensional interaction group construction
The three interactions were integrated using Python to construct a drug-protein-schizophrenia interaction network. In order to find out a medicine for treating the schizophrenia with different types, the schizophrenia and candidate genes thereof are added into diseases and pathogenic genes in the process of constructing a multidimensional interaction group, and the interaction relationship between the schizophrenia with different types and different genes thereof is realized.
(3) Calculating drug and disease diffusion curves
And giving a weight and a restart probability according to the node type, restarting random walk from the disease and medicine nodes, and calculating the influence of the medicine and the disease on the nodes in the multi-dimensional interaction group according to the frequency of the walk nodes.
Weights W { ' indication ':3.541889556309463, ' protein ':4.396695660380823, ' drug ':3.2071696595616364} and α { ' 0.8595436247434408 are assigned by different types of nodes.
The probability of walker jumping to a different type of node is first calculated. And secondly, calculating the probability of the walker jumping to different nodes of the same type. And finally, calculating diffusion distribution through power iteration.
(4) Drug prediction
The diffusion curves of the drugs and the diseases and the diffusion curve SIM (similarity) of the related diseases of the schizophrenia are respectively calculated through the L2 norm, the Euclidean distance between the drugs and the diffusion curves of the diseases is respectively calculated through the SIM comparison of the drugs and the diffusion curves of the diseases, the drugs of each given disease are respectively predicted and sorted through the distance, and the drugs with the top rank of 20 are screened out.
The drugs for predicting the schizophrenia are analyzed in an integrated mode, the drugs which are closer to each other indicate that the drugs are more related to the protein regulation mechanism of the schizophrenia typing and the related diseases and are more likely to be used for treating the schizophrenia, and the predicted drugs for relieving or even treating the schizophrenia are obtained by sequencing and annotating the drugs according to the calculated Euclidean distance.
The first 20 drugs predicted by the multiscale action group to treat schizophrenia, including drug ID, name, treatment disease in drug bank, are shown in table 4:
table 4 predicted first 20 drugs for schizophrenia treatment
And further replacing the protein interaction relation network with an interaction network among the differentially expressed genes of the schizophrenia in different types, and performing medicine prediction on the schizophrenia in different types. The results are as follows:
the first 20 drugs predicted by the multiscale action group to treat early onset schizophrenia, including drug ID, name, treatment disease in drug bank, as shown in table 5:
table 5 predicted first 20 drugs for treatment of early-onset schizophrenia
The first 20 drugs of treatment-deficient schizophrenia predicted by the multiscale action group, including drug ID, name, treatment disease in drug bank, are shown in table 6:
table 6 predicted first 20 drugs for treatment-deficient schizophrenia
The predicted medicines are partially the existing medicines which are already used for treating schizophrenia, and the feasibility of the construction method of the medicine-target protein-schizophrenia interaction network is proved.
The multidimensional interaction group of the present invention provides a general method of systematically understanding how a drug treats a disease, systematically identifying proteins relevant to treatment, predicting which genes will alter the efficacy of a drug or cause severe adverse reactions to drug treatment, and can be easily expanded to add other node types relevant to a disease. Diffusion curves provide predictive power and interpretability in drug-disease therapy modeling, and proteins associated with treating a given disease can be determined.
The medicine-target protein-schizophrenia interaction network can start from different expression modes of different schizophrenia types, and find medicines influencing corresponding targets according to different table genes, so that the medicine-target protein-schizophrenia interaction network has a treatment effect on different schizophrenia types.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the protection scope of the present invention, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for constructing a drug-target protein-schizophrenia interaction network is characterized by comprising the following steps:
(1) collecting drug-target protein interaction relationship through a drug database, and screening human target spots and drugs meeting FDA standard;
(2) mapping the diseases to schizophrenia related genes through a disease database, and integrating disease-pathogenic gene interrelations;
(3) and (3) integrating the drug-target protein interaction relation and the disease-pathogenic gene interaction relation by using Python to construct a drug-protein-schizophrenia interaction network.
2. The method for constructing a drug-target protein-schizophrenia interaction network according to claim 1, wherein in the step (2), schizophrenia candidate genes are collected and sequenced to obtain defective schizophrenia difference genes and early-onset schizophrenia difference genes; the candidate gene, the defective schizophrenia difference gene and the early-onset schizophrenia difference gene are pathogenic genes; mapping the pathogenic genes on a human protein interaction network through Cytoscape and R, and screening out the schizophrenia-pathogenic gene interaction relationship from the pathogenic genes.
3. The method of constructing a drug-target protein-schizophrenia interaction network according to claim 2, wherein the schizophrenia candidate genes comprise ABL2, ALDOA, ARHGAP1, ABR, SLC25A6, ARNT, ACACA, APAF1, STS, ACVR2A, APBA2, ASCL1, ADRA1A, BIRC3, SERPINC1, JAG1, APOE, RERE, AGT, FAS, ATP2a2, AK4, AQP6, ATP2B4, AKT1, AR, ATRX, ALDH1a1, ARL4D and KIF 1A; the defective schizophrenia-differential gene comprises ABR, ALAD, ARHGAP1, ACLY, ALAS1, ARNT, ACO2, ABCD1, ARRB1, ACTG1, ALDH1A1, GET3, ACTN4, ALDH3B1, ATIC, ACVR1B, ALOX15B, ATOX1, ACVR2B, APLP2, TNFRSF17, ADCY7, APP, ZFP36L1, AP2A1, APRT, ZFP36L2, AKT1, ARF3, ARHGAP 1; the early-onset schizophrenia differential gene comprises ABCF1, AQP3, POLR3D, ACVR18, ARHGAPS, BNIP1, PLIN2, ARHGDIA, KLF9, ADORA2A, ARHGDIB, C3AR1, PARP1, ATF3, TMEM258, AGER, ATP5F1D, CFAP410, AHR, ATPSF1E, CBFA2T3, ALDOA, ATP6V1A, SLC25A6, ATP6V1C1, CCNE1, FAS, BMI1 and CCNH.
4. The use of the drug-target protein-schizophrenia interaction network obtained by the construction method of any one of claims 1 to 3 in screening drugs for preventing or treating schizophrenia.
5. The use according to claim 4, characterized in that weights and restart probabilities are assigned according to node types in the drug-target protein-schizophrenia interaction network, random walks are restarted from disease and drug nodes, and drug and disease diffusion curves are calculated by calculating the influence of drugs and diseases on nodes in a multidimensional interaction set through the frequency of the nodes of the walks; and respectively calculating diffusion curves of the drugs and the diseases and a diffusion curve SIM of the diseases related to the schizophrenia, comparing the diffusion curves with the SIM and respectively calculating Euclidean distances between every two diffusion curves, and predicting drug sequencing for the given schizophrenia according to the distances.
6. The application of claim 4, wherein the nodes are given weights of W { 'indication':3.541889556309463, 'protein':4.396695660380823, 'drug':3.2071696595616364} and the probability a of the walker continuing walking at a given step rather than restarting is 0.8595436247434408.
7. The use according to claim 4, characterized in that the diffusion curves of drugs and diseases and the schizophrenia-related disease diffusion curve SIM are calculated by the L2 norm, respectively.
8. The use according to claim 4, wherein the medicament for the treatment of schizophrenia comprises Rilonacept, Tetrabenazine, lsomettene, Zuclofenthxol, Droperidol, Acetopenazine, lloperidone, Dopamine, Ketanserin, Enzastaurin, Vilazodane, Epitriptine, Dihydrexidine, Flupentixol, Taurine, Benzphetamine, Piceatannol, Citalopram, Butababital and Cinnarizine.
9. The use according to claim 4, wherein the medicament for the treatment of early-onset schizophrenia comprises Veliparib, Talazopab, Niraparib, Fingolimod, E-2012, Rucaparib, Olaparib, mexiletin, Nadroparin, Aldesukin, Denileukin difitox, Asparagine, Vinblast, Auranofin, Rivanilline, Mesalazine, Dupilumab, Aspiab, Muromonand Ribavirin.
10. The use according to claim 4, wherein said medicament for the treatment of defective schizophrenia comprises Tasoneremin, A-674563, Castanosporine, Belantamab malonatin, Bexarotene, lpilimumab, Thyrotropin alfa, Reversine, Puromycin, Anisomycin, Botulin toxin type A, Glycolic acid, Pazopanib, Aminolytic acid, Alfacalcolide, Dimethyl fumarate, Pexidartinib, Wortmannin, Bezafibrate and Cediranib.
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