A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. Protein–Protein Interaction (PPI) Network
2.1.2. Drug-Target Interactions (DTIs) Network
2.1.3. Risk Genes
2.2. The Maximum Flow Algorithm for Drug Repurposing
2.2.1. Constructing the Maximum Flow Network
2.2.2. Push-Relabel Maximum Flow Algorithm
Algorithm 1 Push-Relabel_MaximumFlow_Algorithm [28]. |
Input: PPI, Capacity = C, N = unique nodes of PPI, start_node = SDN, destination_node = DDN. Output: Maximum flow between SDN and DDN (1) FOR i = 1 to length [N]: a. HeightV [i] = 0//HeightV is height of every vertex b. FlowV [i] = 0//FlowV is the flow of every vertex (2) HeightV [start_node] = length [N] (3) FOR i = 1 to length [PPI]: a. FlowE [i] = 0//FlowE is the flow of every edge in the PPI (4) V = adjacentVetex[start_node] (5) FOR i = 1 to length [V]: a. FlowV [V[i]] = Capacity [V[i]] b. excessFlow [V[i]] = Capacity [V[i]] (6) PUSH: FOR i = 1 to length [N]: If excessFlow [N[i]] ≠ 0: (in the residual graph) tmpV = adjacentVetex[N[i]] if HeightV [N[i]] > lowest_height[tmpV] Push_flow from N[i] to lower height vertices (7) RELABEL: FOR i = 1 to length [N]: If excessFlow [N[i]] ≠ 0: (in the residual graph) tmpV = adjacentVetex[N[i]] if HeightV [N[i]] ≤ lowest_height[tmpV] HeightV [N[i]] = minimumHeight[tmp] |
2.2.3. Drug Repurposing from Maximum Flow Values
Algorithm 2 Pipeline of the maximum flow-based drug repurposing. |
Input: PPI = all the PPIs, FDA_DT = all the FDA approved DTs in PPIs network, DTI = DTIs for FDA_DT, RG = risk genes, W = flow capacity of edges. Output: CD = candidate drugs for repurposing for the treatment of breast cancer. 1. FOR i = 1 to length [PPI]: a. Calculate flow capacity of the edge using Equation (1): C[i] = TOMSimilarity (PPI[i]) 2. CREATE two dummy nodes: a. source dummy node = SDN and destination dummy node = DDN 3. FOR i = 1 to length [FDA_DT]: a. Index = length [PPI] + 1 b. CONNECT SDN to FDA_DT[i] and add this interaction in PPI[index] c. W[index] = sum of the capacities of the outgoing edges from PPI[index] 4. FOR i = 1 to the length of RG: a. Index = length of PPI + 1 b. CONNECT RG[i] to DDN and add this interaction in PPI[index] c. C[index] = sum of the capacities of the incoming edges from PPI[index] 5. The nodes in PPIs and their associated outgoing flow value = Push-Relabel_MaximumFlow_Algorithm (PPI, C, SDN, DDN) 6. prioritized_DTs = sort the nodes in PPI in decreasing order of their outgoing flows 7. CD = sort drugs in DTI using prioritized_DTs |
3. Experimental Results
3.1. Mapping Drug Targets and Disease-Specific Risk Genes to the PPIs Network
3.2. Weights of the Interactions in PPIs Network
3.3. Formulating Drug Repurposing as a Maximum Flow Network
3.4. Drug Repurposing for Breast Cancer, IBD, and COPD
3.5. Performance Evaluation
3.6. Performance Comparison with Other Methods
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Properties | Values |
---|---|
Number of nodes | 13,368 |
Number of edges | 140,899 |
Transitivity | 0.292 |
Average clustering coefficient | 0.173 |
Edge density | 0.002 |
Average degree | 21.08 |
Total triangles | 4,105,272 |
Drug Name | Target Protein | Target Gene | Flow Value | Status | Reference |
---|---|---|---|---|---|
Guanidine | P78352 | DLG4 | 0.0489 | Confirmed | [30] |
Phenethyl Isothiocyanate | P31946 | YWHAB | 0.0389 | Confirmed | [31] |
Caffeine | P78527 | PRKDC | 0.0363 | Confirmed | [32] |
Tamoxifen | Q05655 | PRKCD | 0.0363 | Confirmed | [33] |
(2S)-2-({6-[(3-Amino-5-chlorophenyl)amino]-9-isopropyl-9H-purin-2-yl}amino)-3-methyl-1-butanol | Q00534 | CDK6 | 0.03319202 |
Drug Name | Target Protein | Target Gene | Flow Value | Status | Reference |
---|---|---|---|---|---|
Dasatinib | P12931 | SRC | 0.08292133 | Confirmed | [34] |
Phenethyl Isothiocyanate | P31946 | YWHAB | 0.06112281 | Confirmed | [35] |
Adenosine-5′ | P00558 | PGK1 | 0.04545455 | Confirmed | [36] |
Acetylsalicylic acid | P54646 | PRKAA2 | 0.03627599 | ||
Glutamic Acid | P07814 | EPRS | 0.03527291 | Confirmed | [37] |
Drug Name | Target Protein | Target Gene | Flow Value | Status | Reference |
---|---|---|---|---|---|
Phenethyl Isothiocyanate | P31946 | YWHAB | 0.05054656 | Confirmed | [38] |
Minocycline | P42574 | CASP3 | 0.03767546 | Confirmed | [39] |
Pseudoephedrine | P15336 | ATF2 | 0.03201844 | Confirmed | [38] |
Methyl 4,6-O-[(1R)-1-carboxyethylidene]-beta-D-galactopyranoside | P02743 | APCS | 0.03150388 | ||
NADH | O43920 | NDUFS5 | 0.02409639 | Confirmed | [40] |
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Islam, M.M.; Wang, Y.; Hu, P. A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases. Life 2021, 11, 1115. https://doi.org/10.3390/life11111115
Islam MM, Wang Y, Hu P. A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases. Life. 2021; 11(11):1115. https://doi.org/10.3390/life11111115
Chicago/Turabian StyleIslam, Md. Mohaiminul, Yang Wang, and Pingzhao Hu. 2021. "A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases" Life 11, no. 11: 1115. https://doi.org/10.3390/life11111115