CN103049678A - Molecular mechanism analytical method of homotherapy for heteropathy and based on protein interaction networks - Google Patents
Molecular mechanism analytical method of homotherapy for heteropathy and based on protein interaction networks Download PDFInfo
- Publication number
- CN103049678A CN103049678A CN2012104810140A CN201210481014A CN103049678A CN 103049678 A CN103049678 A CN 103049678A CN 2012104810140 A CN2012104810140 A CN 2012104810140A CN 201210481014 A CN201210481014 A CN 201210481014A CN 103049678 A CN103049678 A CN 103049678A
- Authority
- CN
- China
- Prior art keywords
- compound
- medicine
- diseases
- target
- network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 12
- 230000006916 protein interaction Effects 0.000 title claims abstract description 8
- 230000009456 molecular mechanism Effects 0.000 title abstract description 12
- 201000010099 disease Diseases 0.000 claims abstract description 112
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 112
- 150000001875 compounds Chemical class 0.000 claims abstract description 92
- 239000003814 drug Substances 0.000 claims abstract description 75
- 108090000623 proteins and genes Proteins 0.000 claims abstract description 65
- 230000007246 mechanism Effects 0.000 claims abstract description 27
- 238000000034 method Methods 0.000 claims description 49
- 102000004169 proteins and genes Human genes 0.000 claims description 49
- 238000013459 approach Methods 0.000 claims description 14
- 239000004615 ingredient Substances 0.000 claims description 13
- 238000010835 comparative analysis Methods 0.000 claims description 5
- 238000007621 cluster analysis Methods 0.000 claims description 4
- 238000012216 screening Methods 0.000 claims description 4
- 229940079593 drug Drugs 0.000 abstract description 9
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 238000011161 development Methods 0.000 abstract description 2
- 230000004931 aggregating effect Effects 0.000 abstract 1
- 206010008190 Cerebrovascular accident Diseases 0.000 description 13
- 208000006011 Stroke Diseases 0.000 description 13
- 208000029078 coronary artery disease Diseases 0.000 description 13
- 210000004556 brain Anatomy 0.000 description 11
- 230000006870 function Effects 0.000 description 11
- 230000010534 mechanism of action Effects 0.000 description 7
- 230000009471 action Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 230000008569 process Effects 0.000 description 5
- 239000002052 molecular layer Substances 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 230000003993 interaction Effects 0.000 description 3
- 230000002452 interceptive effect Effects 0.000 description 3
- 150000002632 lipids Chemical class 0.000 description 3
- 238000003012 network analysis Methods 0.000 description 3
- 101150037123 APOE gene Proteins 0.000 description 2
- 102100033715 Apolipoprotein A-I Human genes 0.000 description 2
- 102100040202 Apolipoprotein B-100 Human genes 0.000 description 2
- 102100029470 Apolipoprotein E Human genes 0.000 description 2
- 208000024172 Cardiovascular disease Diseases 0.000 description 2
- 101000733802 Homo sapiens Apolipoprotein A-I Proteins 0.000 description 2
- 101000889953 Homo sapiens Apolipoprotein B-100 Proteins 0.000 description 2
- 101001051093 Homo sapiens Low-density lipoprotein receptor Proteins 0.000 description 2
- 102100024640 Low-density lipoprotein receptor Human genes 0.000 description 2
- 208000026106 cerebrovascular disease Diseases 0.000 description 2
- 238000007418 data mining Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000029087 digestion Effects 0.000 description 2
- 230000002526 effect on cardiovascular system Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012827 research and development Methods 0.000 description 2
- 238000005211 surface analysis Methods 0.000 description 2
- KEWSCDNULKOKTG-UHFFFAOYSA-N 4-cyano-4-ethylsulfanylcarbothioylsulfanylpentanoic acid Chemical compound CCSC(=S)SC(C)(C#N)CCC(O)=O KEWSCDNULKOKTG-UHFFFAOYSA-N 0.000 description 1
- 101150018624 ARF6 gene Proteins 0.000 description 1
- 102100033312 Alpha-2-macroglobulin Human genes 0.000 description 1
- 102100037320 Apolipoprotein A-IV Human genes 0.000 description 1
- 102100030970 Apolipoprotein C-III Human genes 0.000 description 1
- 102000001893 Bone Morphogenetic Protein Receptors Human genes 0.000 description 1
- 108010040422 Bone Morphogenetic Protein Receptors Proteins 0.000 description 1
- 102000049320 CD36 Human genes 0.000 description 1
- 108010045374 CD36 Antigens Proteins 0.000 description 1
- 102100028914 Catenin beta-1 Human genes 0.000 description 1
- 102100037637 Cholesteryl ester transfer protein Human genes 0.000 description 1
- 101100379471 Drosophila melanogaster Arf51F gene Proteins 0.000 description 1
- 102100038595 Estrogen receptor Human genes 0.000 description 1
- 101000799972 Homo sapiens Alpha-2-macroglobulin Proteins 0.000 description 1
- 101000806793 Homo sapiens Apolipoprotein A-IV Proteins 0.000 description 1
- 101000793223 Homo sapiens Apolipoprotein C-III Proteins 0.000 description 1
- 101000916173 Homo sapiens Catenin beta-1 Proteins 0.000 description 1
- 101000880514 Homo sapiens Cholesteryl ester transfer protein Proteins 0.000 description 1
- 101000882584 Homo sapiens Estrogen receptor Proteins 0.000 description 1
- 101001078133 Homo sapiens Integrin alpha-2 Proteins 0.000 description 1
- 101001124991 Homo sapiens Nitric oxide synthase, inducible Proteins 0.000 description 1
- 101000741790 Homo sapiens Peroxisome proliferator-activated receptor gamma Proteins 0.000 description 1
- 101001094647 Homo sapiens Serum paraoxonase/arylesterase 1 Proteins 0.000 description 1
- 101000671637 Homo sapiens Upstream stimulatory factor 1 Proteins 0.000 description 1
- 102100025305 Integrin alpha-2 Human genes 0.000 description 1
- 108090001030 Lipoproteins Proteins 0.000 description 1
- 102000004895 Lipoproteins Human genes 0.000 description 1
- 101100490437 Mus musculus Acvrl1 gene Proteins 0.000 description 1
- 108010008858 Nitric Oxide Synthase Type I Proteins 0.000 description 1
- 102100022397 Nitric oxide synthase, brain Human genes 0.000 description 1
- 102100029438 Nitric oxide synthase, inducible Human genes 0.000 description 1
- 102100038825 Peroxisome proliferator-activated receptor gamma Human genes 0.000 description 1
- 102100035476 Serum paraoxonase/arylesterase 1 Human genes 0.000 description 1
- 102100040105 Upstream stimulatory factor 1 Human genes 0.000 description 1
- 108091008605 VEGF receptors Proteins 0.000 description 1
- 102100033177 Vascular endothelial growth factor receptor 2 Human genes 0.000 description 1
- 210000001367 artery Anatomy 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008236 biological pathway Effects 0.000 description 1
- 230000000857 drug effect Effects 0.000 description 1
- 239000003596 drug target Substances 0.000 description 1
- 239000000796 flavoring agent Substances 0.000 description 1
- 235000019634 flavors Nutrition 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000008604 lipoprotein metabolism Effects 0.000 description 1
- 230000004060 metabolic process Effects 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000002969 morbid Effects 0.000 description 1
- 230000036285 pathological change Effects 0.000 description 1
- 231100000915 pathological change Toxicity 0.000 description 1
- 230000008054 signal transmission Effects 0.000 description 1
- 238000000547 structure data Methods 0.000 description 1
Images
Landscapes
- Investigating Or Analysing Biological Materials (AREA)
- Medicines That Contain Protein Lipid Enzymes And Other Medicines (AREA)
Abstract
The invention discloses a molecular mechanism analytical method of homotherapy for heteropathy. The molecular mechanism analytical method of homotherapy for heteropathy is used for analyzing the molecular mechanism of the homotherapy for different diseases and includes steps: S1 building the protein interaction networks of different diseases correspondingly; S2 forecasting potential targets of all compounds contained in a homotherapy drug according to the compound content of the homotherapy drug, and reserving the compound with the target corresponding to genes related to the diseases; S3 aggregating all the compounds in the homotherapy drug according to distribution of the potential targets of the compounds contained in the homotherapy drug in the protein interaction networks of the diseases; S4 determining a target combination frequently appearing in each aggregated cluster; and S5 contrastively analyzing nodes corresponding to the targets frequently appearing in the protein interaction networks of different diseases to obtain the molecular mechanism of the molecular mechanism on at least two diseases. The molecular mechanism analytical method can be used for explaining the mechanism of the homotherapy principle for different diseases in terms of molecules, and is especially beneficial to application and development of the homotherapy for the heteropathy principle of the traditional Chinese medical science in the aspect of combination of Chinese traditional and Western medicine.
Description
Technical field
The invention belongs to the bioinformatics technique field, be specifically related to a kind of analytical approach for the treatment of different diseases with same method molecule mechanism, particularly a kind for the treatment of different diseases with same method molecule mechanism analytical approach based on protein reciprocation network.
Background technology
From ancient times to the present, " treating different diseases with same method " used by traditional Chinese medical science doctor, and becomes a large characteristic of Chinese pharmaceutical therapeutics, and existing a large amount of clinical reports is card.
The implication of " treating different diseases with same method " refers to different diseases, if impel the interpretation of the cause, onset and process of an illness of morbidity identical, and available same method treatment, namely card is ruled together same.The intension of " but treating different diseases with same method " again more than that, " card " of the traditional Chinese medical science is a kind of multisystem, many target spots, pathological change is comprehensive at many levels, Chinese medicine is to act on many target spot performance drug effects too, need to sufficient understanding understanding have been arranged to two aspects, find to support this theoretical material base and molecular mechanisms of action just can so that " treating different diseases with same method " theory better used and developed.
At present, although have correlative study that the similarities and differences between similar disease are analyzed abroad, reusing of drugs also do not have special research for treating different diseases with same method; Although domestic have some about the research for the treatment of different diseases with same method theory, its molecular mechanisms of action do not explained fully, this will be an impediment to related drugs further research and development and should the theory widespread use.
Chinese medicine has been developed multiple prescription aspect treating different diseases with same method,, and confirmed definite curative effect.It is complicated that but Chinese medicinal formulae forms compound, and Chinese medicine passes through polycomponent, the combined action of many target spots realizes effect, and its molecular mechanisms of action in the treating different diseases with same method process is not studied fully, thereby to carry out deep explaination very important in the face of the mechanism of action of this prescription from molecular layer.
At present, developed the interaction of several different methods for target molecules in drugs and the human body, wherein, network pharmacology has received increasing concern as important developing direction of medicament research and development.The method of Excavation Cluster Based on Network Analysis is widely used in the middle of the relevant research of medicine.Also set up individual about drug targets and the interactive database of protein simultaneously.
These methods and data are conducive to explain from the molecule aspect mechanism of action of medicine, do not go but use at present in the research for the treatment of different diseases with same method.Therefore, that how to utilize that these accumulate is a large amount of about medicine-target effect, and it is problem demanding prompt solution that the interactive data of protein are come from the mechanism of action of the complicated Chinese medicine system of molecular layer surface analysis.
Summary of the invention
The technical matters that (one) will solve
The technology of solution of the present invention asks that be that the interactive data of protein are come from the mechanism of action of the complicated medicine of molecular layer surface analysis next week.
(2) technical scheme
The present invention is directed to the molecule mechanism of Chinese medicine treating different diseases with same method, at first make up various disease protein reciprocation network separately; Then carry out cluster according to the distribution situation of potential target on protein network of compound composition in the medicine of ruling together, utilize the Apriori algorithm to find frequently target combination on the cluster basis, thereby obtain the molecule mechanism that this medicine of ruling together is realized treating different diseases with same method by frequent target and the characteristic in network thereof that obtains under the comparative analysis various disease at last.
Thus, the present invention proposes a kind for the treatment of different diseases with same method molecule mechanism analytical approach, for the molecule mechanism of analyzing the medicine realization treating different diseases with same method of ruling together, the described medicine of ruling together refers to treat simultaneously the medicine of a plurality of diseases, comprises the steps: S1, makes up at least two kinds of diseases protein reciprocation network separately; S2, according to the potential target of each compound in this medicine of ruling together of compound ingredient prediction of the described medicine of ruling together, keep the compound of answering target with the gene pairs of described disease association; S3, according to the distribution of potential target in the protein reciprocation network of each disease of the compound composition of the described medicine of ruling together to the medicine of ruling together in each compound carry out cluster; Determine the frequent target combination that occurs in S4, each class in described cluster bunch; S5, the node corresponding to target of the numerous appearance of protein reciprocation network intermediate frequency of described disease is analyzed, described two kinds of diseases realized the molecule mechanism for the treatment of different diseases with same method at least to obtain this medicine of ruling together.
According to a kind of embodiment of the present invention, described step S2 further comprises the screening step to the compound composition of the described medicine of ruling together: if the gene-correlation of neither one and described disease in the potential target of described compound, then it is screened out, otherwise keep this compound.
According to a kind of embodiment of the present invention, the cluster among the described step S3 is for the distance of potential target between the compound on the protein reciprocation network of described disease association compound to be carried out cluster analysis.
According to a kind of embodiment of the present invention, being defined as follows of described distance:
Distance wherein
IjRepresent two compound i, the distance between the j, G
i, G
jRepresent respectively compound i, the target set that j is corresponding, || the length of set is got in expression, max (| G
i|, | G
j|) expression set G
i, G
jThe length of middle maximum, m, n represent to gather G
i, G
jIn target; d
MnRepresent the distance of two targets in protein reciprocation network, utilize shortest path to represent the distance of two nodes in protein reciprocation network.
According to a kind of embodiment of the present invention, described step S4 comprises: utilize the Apriori algorithm to find frequently target combination, find out the coefficient target combination of principal ingredient institute of this medicine of ruling together from the molecule aspect.
According to a kind of embodiment of the present invention, the comparative analysis among the described step S5 refers to by being subject to the situation of the node of this pharmaceutical intervention of ruling together at least in the protein reciprocation network that contrasts described two types disease.
According to a kind of embodiment of the present invention, described step S5 comprises:
Analyze the coefficient target combination of principal ingredient institute of the described medicine of ruling together that described step S4 obtains, and described principal ingredient different effective object separately in described at least two kinds of diseases;
The characteristic of analyzing the neighbor node of the common target of described at least two kinds of diseases in described protein interaction network is analyzed the indirectly-acting of this medicine of ruling together;
In conjunction with the analysis of above-mentioned two aspects, analyze this medicine molecule mechanism for described at least two kinds of diseases of ruling together from the molecule aspect.
According to a kind of embodiment of the present invention, the described medicine of ruling together is Chinese medicinal formulae.
(3) beneficial effect
The present invention's combination on the basis of protein interaction network analysis method utilizes data mining algorithm, and by the comparative analysis impact of medical compounds composition on the protein interaction network of various disease of ruling together, face treating different diseases with same method from molecular layer, be that the passable identical treatment mechanism of various disease makes an explanation and analyzes, be particularly useful for utilization and the development for the treatment of different diseases with same method theory aspect clinical and the combination of Chinese tradiational and Western medicine.
Description of drawings
Fig. 1 is the process flow diagram of the molecule mechanism analytical approach based on protein reciprocation network of the present invention;
Fig. 2 is the rule together process flow diagram of molecule mechanism analytical approach of the brain heart based on protein reciprocation network of one embodiment of the present of invention;
Fig. 3 is the protein reciprocation network figure relevant with coronary heart disease that embodiments of the invention obtain;
Fig. 4 is the protein reciprocation network figure relevant with apoplexy that embodiments of the invention obtain.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in further detail.
The present invention is from the molecule aspect, in conjunction with utilizing the network analysis of protein reciprocation and data mining algorithm, analyze to explain the mechanism of action of medicine in the treating different diseases with same method process of ruling together at the protein reciprocation network of at least two kinds of disease associations respectively, the described medicine of ruling together refers to treat simultaneously the medicine of various disease.The overall flow of method of the present invention is seen Fig. 1.The present invention includes following several step:
Step S1: make up at least two kinds of diseases protein reciprocation network separately.
In order to study the molecular mechanisms of action for the treatment of different diseases with same method, at first need to understand disease related gene and interaction thereof separately, make up the protein reciprocation network with described disease association.In protein reciprocation network, the node of network represents a kind of protein, and internodal limit represents between two protein reciprocation is arranged.
Step S2: according to the potential target of each compound in this medicine of ruling together of compound ingredient prediction of the medicine of ruling together, keep the compound of answering target with the gene pairs of described disease association.
In view of the compound of analog structure often has identity function, therefore utilize the target of analog structure compound as the target of this compound.
Step S3: according to the distribution of potential target in the protein reciprocation network of each disease of the compound composition of the described medicine of ruling together to the medicine of ruling together in each compound carry out cluster.
Step S4: determine the frequent target combination that occurs in each class in described cluster bunch.
This step can utilize the apriori algorithm to carry out Association Rule Analysis.
Step S5: node corresponding to target to the numerous appearance of protein reciprocation network intermediate frequency of each disease is analyzed, and described two kinds of diseases realized the molecule mechanism for the treatment of different diseases with same method at least to obtain this medicine of ruling together.
On the one hand according to the frequent target combination of each disease, find out main compound coefficient target combination at least two kinds of diseases of the medicine of ruling together, and these compounds different effective object separately at least two kinds of diseases; On the other hand, by analyzing topological property and the characteristic of the neighbor node effect of further analyzing this rule together medicine thereof of the common gene of at least two kinds of diseases in the various disease network.In conjunction with the analysis of two aspects, thereby explain the mechanism of action for the treatment of different diseases with same method from the molecule aspect.
Further specify technical scheme of the present invention at reference accompanying drawing 2 and by embodiment down.
Step S1: the protein reciprocation network that makes up at least two kinds of diseases.
In this embodiment, we have determined two kinds of representational cardiovascular and cerebrovascular diseases as shown in Figure 2: apoplexy and coronary heart disease.In order to study the molecular mechanisms of action for the treatment of different diseases with same method, at first need to understand related gene and the interaction thereof separately of two kinds of diseases.This embodiment finds relevant disease gene, wherein coronary heart disease for coronary heart disease with apoplexy respectively in omim database: 131 genes, apoplexy: 165 genes.With gene shine in the protein reciprocation network of GeneMania.Wherein, the node of network represents a kind of protein, and internodal limit represents between two protein reciprocation is arranged.Obtain at last the relevant protein reciprocation network of coronary heart disease and see Fig. 3, the protein reciprocation network that apoplexy is relevant is seen Fig. 4.
Among this embodiment, we analyze Chinese medicinal formulae " the step-length brain heart is logical ".At first, need to collect the compound composition of every flavor Chinese medicine in the logical prescription of the step-length brain heart: traditional Chinese medicine ingredients can come from Chinese medicine innovation net (http://www.tcm120.com/tcm/q_tcd/asp).The compound structure data come from the PubChem database.
Step S2: according to the potential target of each compound in this medicine of ruling together of compound ingredient prediction of the medicine of ruling together, keep the compound of answering target with the gene pairs of described disease association.
The compound target forecast function that the present invention utilizes MetaDrug software to provide is input as the two-dimensional structure file of compound, finds analogue compounds according to the two-dimensional structure of each compound, and with the target of analogue compounds as the potential target of this compound.Described method for screening compound refers to screen according to the potential target set of each compound, if the gene-correlation of neither one and disease in the potential target of a compound thinks that then this compound is invalid for this disease, screens out it; Otherwise keep this compound, namely only keep the compound that those contain the corresponding target of disease related gene.After the screening, for 68 kinds of compounds of coronary heart disease residue; For 64 kinds of compounds of apoplexy residue.
Step S3: each compound of the medicine of ruling together is carried out cluster according to the distribution of potential target in the protein reciprocation network of each disease of the compound composition of the described medicine of ruling together.
For the distance of potential target between the compound on the protein reciprocation network of disease association compound is carried out cluster analysis.Here the distance of two compounds refers to that two two targets corresponding to compound are integrated into the distance on the protein reciprocation network, are defined as follows:
Distance wherein
IjRepresent two compound i, the distance between the j, G
i, G
jRepresent respectively compound i, the target set that j is corresponding, || the length of set is got in expression, max (| G
i|, | G
j|) expression set G
i, G
jThe length of middle maximum, m, n represent to gather G
i, G
jIn target; d
MnRepresent the distance of two targets in protein reciprocation network, utilize shortest path to represent the distance of two nodes in protein reciprocation network.Utilize the distance value between this formula calculating compound, utilize hierarchical clustering algorithm to carry out cluster analysis according to the distance between the compound.
Cluster result such as table 1 under the coronary heart disease condition, the cluster result under the apoplexy condition sees Table 2.Wherein, the compound that the compound of larger class bunch correspondence is wanted for this drug main, its corresponding target has also embodied main function.
The compound cluster result that table 1 obtains based on the relevant protein network of coronary heart disease
The compound cluster result that table 2 obtains based on the relevant protein network of apoplexy
Step S4: determine the frequent target combination that occurs in each class in described cluster bunch.
Through after the cluster, the compound within each class of cluster bunch is having higher similarity aspect the potential target, and the compound in the inhomogeneity bunch is often differing larger aspect the potential target, and different functions is arranged; It is bunch also corresponding a kind of function type of each class; The more class of inclusion compound bunch then illustrates the large percentage that the compound of this class function in this medicine accounts for, and the major function of this medicine is corresponding to the function of compound in the larger class bunch so.The set of the corresponding potential target of each compound in the class bunch, the present invention's potential target that each compound is corresponding is gathered as a record, the combination of the target of described frequent appearance is that the potential target of compound in each class bunch is analyzed, utilize the apriori algorithm to find frequently target combination, the maximum kind bunch target set that corresponding frequent target combination is a plurality of principal ingredient effects in the prescription, corresponding to the target that has embodied this prescription major function, finding out the coefficient target combination of principal ingredient institute of prescription from the molecule aspect, is exactly the coefficient target set of a plurality of main compound in " the step-length brain heart is logical " prescription.Like this, both find out the compound combination that plays major function the medicine from the molecule aspect, also determined which target in the human body that compound frequently acts in this medicine of ruling together.The results are shown in Table 3,4.
Table 3 is for the frequent target combination of table 1 cluster result
Table 4 is for the frequent target combination of table 2 cluster result
Step S5: node corresponding to target to the numerous appearance of protein reciprocation network intermediate frequency of each disease is analyzed, and described two kinds of diseases realized the molecule mechanism for the treatment of different diseases with same method at least to obtain this medicine of ruling together.
This embodiment carries out as above analysis for coronary heart disease and apoplexy respectively, at last the result under two kinds of diseases is compared.On the one hand according to the frequent assortment of genes that obtains under each disease, find out the coefficient target combination in two kinds of diseases of " the step-length brain heart is logical " main compound, and these compounds different effective object separately in two kinds of diseases; On the other hand, further analyze the party's indirectly-acting by the characteristic of analyzing the common neighbor node of gene in the various disease network of two kinds of diseases.In conjunction with the analysis of two aspects, thereby explain material base and the mechanism of action that " the step-length brain heart is logical " square brain heart is ruled together from the molecule aspect.
The node that the Chinese medicine main compound is applied in the protein network of two kinds of disease associations of comparative analysis and the adjacent node of these nodes: the main compound of discovery " the step-length brain heart is logical " is for coronary heart disease or apoplexy all acts on LDLR simultaneously, APOA1, APOE, APOB, these 5 targets of LPL, main and the lipid of these several targets, the metabolism of lipoprotein and mediation lipid, the path of transportation digestion is relevant; And these 5 targets belong to the product of the atherosis related gene of omim database medium sized artery, also accord with relevant result of study.From network, analyze these 5 targets, calculate to find that its degree average in two networks (being 6 in the coronary heart disease of path association and apoplexy network) is all obviously greater than the average degree (average degree of all nodes is 3 in coronary heart disease and the apoplexy network) of whole network, as seen these 5 targets occupy relative consequence in network, and can find out that these five targets all relatively close in corresponding network, and be arranged in relatively closely sub-network of connection.Simultaneously, frequently acting on two kinds of diseases jointly in the relevant target, the main compound of this prescription is directed to different diseases and also frequently acts on each autocorrelative target, main compound such as this prescription also frequently acts on the relevant HGPS of coronary heart disease, CETP, PON1, CD36, CTNNB1, the AR target frequently acts on the NOS1 relevant with apoplexy, NOS2A simultaneously, ITGA2, the APP target.As seen this prescription can also affect respectively each autocorrelative target of two kinds of diseases when acting on the common target of two kinds of various disease, thereby has realized that the brain heart rules together.
In order to contrast the relation between the result who obtains in two kinds of disease association networks, the present invention has added up LDLR, APOA1 in addition, APOE, APOB, the degree of the neighbor node of these 5 targets of LPL in Fig. 2 and Fig. 3 also carries out rank: the neighbor node of the rank front three that obtains according to Fig. 1 is PPARG, ESR1, USF1, these several targets all and ALK1, bmp receptor, Arf6, the ErbB acceptor, c-Met, the signal transmission path that VEGFR etc. are relevant is relevant; The neighbor node of the rank front three that obtains according to Fig. 2 is APOA4, APOC3, and A2M, and these several relevant biological pathways all relate to the metabolism of lipid and lipoprotein, and transportation, digestion, its function class is similar to 5 top targets.In the time of can finding out " the step-length brain heart is logical " treatment cardiovascular and cerebrovascular disease from this result, when acting on the common target of two kinds of diseases, can also indirectly intervene different paths, thereby different morbid states there is different impacts, reaches the purpose for the treatment of simultaneously various disease.
Although above explanation to embodiments of the invention is to describe with the rule together analysis of medicine of the brain heart, the present invention is not limited to this, and the present invention is applicable equally for the treating different diseases with same method molecule mechanism analysis of other medicines of ruling together.
Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; be understood that; the above only is specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of making, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (8)
1. treating different diseases with same method molecule mechanism analytical approach is used for analyzing the medicine of ruling together and realizes the molecule mechanism for the treatment of different diseases with same method, and the described medicine of ruling together refers to treat simultaneously the medicine of a plurality of diseases, it is characterized in that, comprises the steps:
S1, at least two kinds of diseases of structure protein reciprocation network separately;
S2, according to the potential target of each compound in this medicine of ruling together of compound ingredient prediction of the described medicine of ruling together, keep the compound of answering target with the gene pairs of described disease association;
S3, according to the distribution of potential target in the protein reciprocation network of each disease of the compound composition of the described medicine of ruling together to the medicine of ruling together in each compound carry out cluster;
Determine the frequent target combination that occurs in S4, each class in described cluster bunch;
S5, the node corresponding to target of the numerous appearance of protein reciprocation network intermediate frequency of described disease is analyzed, described two kinds of diseases realized the molecule mechanism for the treatment of different diseases with same method at least to obtain this medicine of ruling together.
2. treating different diseases with same method molecule mechanism analytical approach as claimed in claim 1, it is characterized in that, described step S2 further comprises the screening step to the compound composition of the described medicine of ruling together: if the gene-correlation of neither one and described disease in the potential target of described compound, then it is screened out, otherwise keep this compound.
3. treating different diseases with same method molecule mechanism analytical approach as claimed in claim 1, it is characterized in that, the cluster among the described step S3 is for the distance of potential target between the compound on the protein reciprocation network of described disease association compound to be carried out cluster analysis.
4. treating different diseases with same method molecule mechanism analytical approach as claimed in claim 3 is characterized in that, being defined as follows of described distance:
Distance wherein
IjRepresent two compound i, the distance between the j, G
i, G
jRepresent respectively compound i, the target set that j is corresponding, || the length of set is got in expression, max (| G
i|, | G
j|) expression set G
i, G
jThe length of middle maximum, m, n represent to gather G
i, G
jIn target; d
MnRepresent the distance of two targets in protein reciprocation network, utilize shortest path to represent the distance of two nodes in protein reciprocation network.
5. treating different diseases with same method molecule mechanism analytical approach as claimed in claim 1, it is characterized in that, described step S4 comprises: utilize the Apriori algorithm to find frequently target combination, find out the coefficient target combination of principal ingredient institute of this medicine of ruling together from the molecule aspect.
6. treating different diseases with same method molecule mechanism analytical approach as claimed in claim 5, it is characterized in that, the comparative analysis among the described step S5 refers to by being subject to the situation of the node of this pharmaceutical intervention of ruling together at least in the protein reciprocation network that contrasts described two types disease.
7. treating different diseases with same method molecule mechanism analytical approach as claimed in claim 6 is characterized in that, described step S5 comprises:
Analyze the coefficient target combination of principal ingredient institute of the described medicine of ruling together that described step S4 obtains, and described principal ingredient different effective object separately in described at least two kinds of diseases;
The characteristic of analyzing the neighbor node of the common target of described at least two kinds of diseases in described protein interaction network is analyzed the indirectly-acting of this medicine of ruling together;
In conjunction with the analysis of above-mentioned two aspects, analyze this medicine molecule mechanism for described at least two kinds of diseases of ruling together from the molecule aspect.
8. such as each described treating different diseases with same method molecule mechanism analytical approach among the claim 1-7, it is characterized in that, the described medicine of ruling together is Chinese medicinal formulae.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210481014.0A CN103049678B (en) | 2012-11-23 | 2012-11-23 | Based on the treating different diseases with same method molecule mechanism analytical approach of protein reciprocation network |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210481014.0A CN103049678B (en) | 2012-11-23 | 2012-11-23 | Based on the treating different diseases with same method molecule mechanism analytical approach of protein reciprocation network |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103049678A true CN103049678A (en) | 2013-04-17 |
| CN103049678B CN103049678B (en) | 2015-09-09 |
Family
ID=48062312
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210481014.0A Active CN103049678B (en) | 2012-11-23 | 2012-11-23 | Based on the treating different diseases with same method molecule mechanism analytical approach of protein reciprocation network |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN103049678B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480467A (en) * | 2016-06-07 | 2017-12-15 | 王�忠 | A kind of differentiation or the method for comparative drug effort module |
| WO2020253221A1 (en) * | 2019-06-19 | 2020-12-24 | 江南大学 | Method for analyzing relationship between communication path and heat resistance of lipase |
| CN112309505A (en) * | 2020-11-05 | 2021-02-02 | 湖南大学 | Anti-neocoronal inflammation drug discovery method based on network characterization |
| CN115458061A (en) * | 2022-10-13 | 2022-12-09 | 南开大学 | Drug-protein interaction prediction method and system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1653454A (en) * | 2002-03-11 | 2005-08-10 | 株式会社医药分子设计研究所 | Method of forming molecule function network |
| CN101002206A (en) * | 2004-07-09 | 2007-07-18 | 惠氏公司 | Methods and systems for predicting protein-ligand coupling specificities |
| JP2007323209A (en) * | 2006-05-30 | 2007-12-13 | Fujitsu Ltd | Protein complex interaction evaluation program, recording medium with program recorded, protein complex interaction evaluation apparatus, and protein complex interaction evaluation method |
| CN101110095A (en) * | 2006-07-20 | 2008-01-23 | 中国科学院自动化研究所 | Method for batch detecting susceptibility gene of common brain disease |
| CN101881766A (en) * | 2009-05-08 | 2010-11-10 | 复旦大学 | Quantitative analysis method of structure domain mapping protein interaction groups |
| CN102298674A (en) * | 2010-06-25 | 2011-12-28 | 清华大学 | Method for determining medicament target and/or medicament function based on protein network |
-
2012
- 2012-11-23 CN CN201210481014.0A patent/CN103049678B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1653454A (en) * | 2002-03-11 | 2005-08-10 | 株式会社医药分子设计研究所 | Method of forming molecule function network |
| CN101002206A (en) * | 2004-07-09 | 2007-07-18 | 惠氏公司 | Methods and systems for predicting protein-ligand coupling specificities |
| JP2007323209A (en) * | 2006-05-30 | 2007-12-13 | Fujitsu Ltd | Protein complex interaction evaluation program, recording medium with program recorded, protein complex interaction evaluation apparatus, and protein complex interaction evaluation method |
| CN101110095A (en) * | 2006-07-20 | 2008-01-23 | 中国科学院自动化研究所 | Method for batch detecting susceptibility gene of common brain disease |
| CN101881766A (en) * | 2009-05-08 | 2010-11-10 | 复旦大学 | Quantitative analysis method of structure domain mapping protein interaction groups |
| CN102298674A (en) * | 2010-06-25 | 2011-12-28 | 清华大学 | Method for determining medicament target and/or medicament function based on protein network |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480467A (en) * | 2016-06-07 | 2017-12-15 | 王�忠 | A kind of differentiation or the method for comparative drug effort module |
| CN107480467B (en) * | 2016-06-07 | 2020-11-03 | 王�忠 | Method for distinguishing or comparing drug action modules |
| WO2020253221A1 (en) * | 2019-06-19 | 2020-12-24 | 江南大学 | Method for analyzing relationship between communication path and heat resistance of lipase |
| CN112309505A (en) * | 2020-11-05 | 2021-02-02 | 湖南大学 | Anti-neocoronal inflammation drug discovery method based on network characterization |
| CN115458061A (en) * | 2022-10-13 | 2022-12-09 | 南开大学 | Drug-protein interaction prediction method and system |
| CN115458061B (en) * | 2022-10-13 | 2024-01-23 | 南开大学 | Medicine-protein interaction prediction method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN103049678B (en) | 2015-09-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Loneliness among the rural older people in Anhui, China: prevalence and associated factors | |
| Kuhn et al. | The effects of children’s migration on elderly kin’s health: A counterfactual approach | |
| Salehitali et al. | Quality of life and its effective factors in tuberculosis patients receiving directly observed treatment short-course (DOTS) | |
| Queenan et al. | Representativeness of patients and providers in the Canadian Primary Care Sentinel Surveillance Network: a cross-sectional study | |
| Sánchez Romero et al. | Relationship between the gut microbiome and osteoarthritis pain: review of the literature | |
| Fouladbakhsh | Gender, symptom experience, and use of complementary and alternative medicine practices among cancer survivors in the US cancer population | |
| Ad Hoc Committee on Systemic Lupus Erythematosus Response Criteria for Fatigue | Measurement of fatigue in systemic lupus erythematosus: a systematic review | |
| Miaskowski | Subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: a cluster analysis | |
| Huang et al. | Record linkage research and informed consent: who consents? | |
| Tao et al. | The anatomy of health-supportive neighborhoods: A multilevel analysis of built environment, perceived disorder, social interaction and mental health in Beijing | |
| Yi et al. | Health-related quality of life and influencing factors among rural left-behind wives in Liuyang, China | |
| CN103049678B (en) | Based on the treating different diseases with same method molecule mechanism analytical approach of protein reciprocation network | |
| Ohta et al. | Improvement in quality of life through self-management of mild symptoms during the COVID-19 pandemic: a prospective cohort study | |
| Vu et al. | Interventions to improve the quality of life of patients with chronic obstructive pulmonary disease: a global mapping during 1990–2018 | |
| Fischer et al. | The long shadow of an infection: COVID-19 and performance at work | |
| Kumar et al. | Inequity in health care delivery in India: the problem of rural medical practitioners | |
| Barcella et al. | Identification of a transcriptome profile associated with improvement of organ function in septic shock patients after early supportive therapy | |
| Ille et al. | Non-invasive mapping for effective preoperative guidance to approach highly language-eloquent gliomas—a large scale comparative cohort study using a new classification for language eloquence | |
| Tabašević et al. | Diversity of Ruderal Communities in Urban Environments—A Case Study from Serbia (SE Europe) | |
| da Silva et al. | Thrombophilia and immune-related genetic markers in long COVID | |
| Casula et al. | Impact of the COVID-19 pandemic on the therapeutic continuity among outpatients with chronic cardiovascular therapies | |
| Pan et al. | Dual trajectories of depression and social participation among Chinese older adults | |
| Godfrey et al. | The symptom of pain in individuals recently hospitalized for heart failure | |
| Lee et al. | High-throughput 3D tumor spheroid array platform for evaluating sensitivity of proton-drug combinations | |
| Kanoute et al. | Current status of oral health research in Africa: an overview |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |