Posted at 10.14.2018
A Novel strategy to analyzing anticancer drug effectiveness predicated on biochemical pathway network
Abstract and Keywords
As cancer tumor is a sophisticated disease, recent cancers research is progressively carried out as systems-level. Which conducts novel polypharmacological strategies to assess anticancer drugs that may alter the complete pathway network alternatively than inhibit or activate single aim for. In current work, a novel way was developed by integrating multi-target binding free energy calculation and biological network efficiency analysis to estimate the biological strength. The obvious advantage of the network centered prediction method is that it can evaluate the role of different protein by natural pathway network analysis. This process can evaluate the drugs' efficiency more comprehensively than traditional one target analysis methods and would be very useful for discovering novel drugs against complicated disease like malignancy.
Keywords: anticancer drug, network efficiency, cancer tumor pathway, system biology
Translational research promotes basic biological discoveries from the fundamental research into center applications, and it uses the clinic observations to point future guidelines for important research (1). A great portion of biological molecules with specific physiological changes such as proteins, nucleic acids and small ligands have important impact on the elimination and treatment of diseases. Because so many sophisticated diseases are related to natural pathways, the studies on related pathways could uncover the relation between the biological molecules, pathways, mobile entities and medical clinic goals (2).
Abiological pathwayis some actions among natural molecules in a biological process that are sorted out in a particular manner and is capable of doing certain biological functions (3). As cancers is a complex disease, recent cancer research is progressively completed as systems-level (4). And this conducts novel polypharmacological strategies to assess anticancer drugs which will alter the complete pathway network alternatively than inhibit or stimulate single concentrate on (5). Because so many protein or ligands whose biological functions are not explored completely in the tumor pathways, it is time-consuming and costly to determine biological functions through biological experiments for each and every (6). Moreover, it can scarcely to evaluate a drug's effect on tens of targets and pathways by experimental techniques. Therefore, it is immediate need of developing a computational approach to solve this issue.
With the improvement of system biology and bionetwork, we realize that the natural potency of an excellent drug may well not merely determined by the inhibition of an individual target, but rather by the rebalancing of several protein or occurrences, which contribute to the etiology, pathogeneses, and progression of a complicated disease (7). The available methodologies of in silico testing based on a single target seem not effective in learning ligands' results on biological process comprehensively for complicated disease like tumor (8-9). In current work, a novel strategy originated by integrating multi-target binding free energy calculation and biological network efficiency research to estimate the biological strength. The obvious benefit of the network centered prediction method is that it can determine the role of different protein by biological pathway network analysis. This approach can measure the drugs' effectiveness more comprehensively than traditional single target analysis methods and would be very useful for discovering novel drugs against complicated disease like cancer tumor.
Constructing the biochemical pathway network
The network was constructed by by hand curating clinical literatures (10-41), and information from Reactome (42) and KEGG (43) knowledgebase. In order to construct an effective network for anticancer drug analysis, only proteins and their relationships that have been reported directly relate to tumor were included. The proteins which take part in the malignancy pathway were suggested as nodes. The relationships of these proteins were proposed as connections between nodes. The relations means that protein in upstream increases the function of the downstream health proteins.
Constructing the drug data source established on Chinese Pharmacopoeia
The drug library for performing biochemical pathway network established anticancer drug evaluation includes 683 drugs in the Chinese Pharmacopoeia 2010. The constructions of these drugs were built by Marvin Sketch (ChemAxon). And, all of these structures were minimized with the MMFF force field by openbabel (44). Within the minimization procedure, the threshold of root mean square deviation (RMSD) of potential energy was placed to 0. 01 kcal/mol. The optimized structures of most drugs were saved as a Multi-MoleculeSDF data file and ready for further binding energy computation.
Binding free energy calculations
The binding free energy calculations were performed by Autodock 4. 2 program (45). For every protein buildings, all drinking water molecules were removed. From then on, polar hydrogen atoms were added and non-polar hydrogen atoms were merged using the Hydrogen component in Autodock Tools (ADT). All of the atoms were assigned Kollman incomplete charges. Grid maps of the pre-defined lively site for every atom type were determined by AutoGrid. The grid map of the molecular docking was set up with a 454545 box devoted to the pre-defined effective site, with a spacing of 0. 375 between each grid tips. Molecular docking and calculation of drug/proteins dissociation constant based on Lamarckian genetic algorithm (LGA) were performed by Autodock. The docking parameters were set as follows: the ligand translation step was placed to 1 1. 0, the ligand quaternion and torsion step were both set to 60 levels, the maximum volume of energy assessments was set to 1 1. 0 107, the amount of individuals in inhabitants of hereditary algorithm was set to 150, the utmost number of hereditary algorithm procedures was establish to 2. 5 105, the rate of mutation and crossover were arranged to 0. 02 and 0. 8, respectively. All the parameters were arranged to defaultvalues. When looking the conformational and orientational spots of the ligand with rotatable bonds full versatility, the framework of the protein was held rigid. For each docking works, 10 indie goes were performed to evaluated different ligand poses in support of the most favourable pose was dumped to the effect file.
Network efficiency calculation
The harm induced by the disorders on the network is characterized by the network efficiency (NE), which is defined as the total of the reciprocals of the shortest avenue lengths between all pairs of nodes. Because of a worldwide topological property of your network which could be applied to measure the integrity of the network, the network efficiency was assumed to be used as a measure for drug efficiency. The NE of the graph G is assessed by the shortest paths between pairs of nodes with the following expression:
where dij is the length of the shortest path between node i and j and the sum is over all N(N 1)/2 pairs of nodes with final number N of nodes in the graph G. In the event the network is weighted, dij is the path with the least weight. The original line values of every border were arbitrarily set to 10. To give comparative network efficiency, this variety NE is divided by the initial network efficiency. Thus we considered the network efficiency of the initial network as 100% and assessed the relative network efficiency after every attack. We have chosen the clotting cascade network as the network models.
The network efficiency was calculated for each medicine. The drugs' results to the network rely on the binding capacity to the prospective protein. We supposed that one drug could inhibit the target protein well as the binding ability was relatively high. For a drug, we changed the binding energy to network flux that straight downstream the target proteins of the network and then computed the network efficiency. In other term, the network flux of all edges, which point to the other focuses on protein from this target protein, was re-assigned based on the binding energy between the drug and the target protein. For each target health proteins, the medication with the highest binding energy was chosen as the reference standard. Network flux of each border in the network was computed with the appearance:
ОGs signifies the binding free energy of the very most potent drug, ОG presents the binding free energy of other drugs, and Fedge is the flux of the ends emerge from the proteins in the network. Default flux value of the sides between nodes was set to 10.
Results & Discussion
Regulation network in cancer is composed of alternativepathways
The cancers related biochemical pathway network was made by by hand curating clinical literatures. By analyzing these literatures, 50 important cancer-associated proteins or ligands have been figured out. At the same time, some traditional cancer-associated signaling pathways and their relationships were also included. All of the proteins and their connections have been reported straight relate to malignancy by scientific literatures. The schematic diagram of the network we've designed is shown in Shape 1. In such a regulation network, all the 50 important cancer-associated proteins or ligands act as positive factors for tumor progression. Although these protein or ligands straight relate to cancer, almost all of them can not promote their function independently. You will find 54 pairs of relationships between these protein make them connect to others and form an integrated regulation network.
On the other side, in the legislation network of cancers we have built, there are some alternative processes can boost cancer development independently. For example, a series of receptor tyrosine kinases such as EGFR, FGFR, HGFR, c-kit, IGFR and PDGFR, can be activated by their extracellular health proteins ligands (expansion factors) and then elicits downstream activation and signaling by phosphorylated tyrosines through the Grb2 phosphotyrosine-binding SH2 domains. Among these receptor tyrosine kinases, complete single-target inhibition cannot turn off the downstream activation and signaling by phosphorylated tyrosine because other receptor tyrosine kinases can trigger downstream activation simultaneously.
Degree syndication and characteristics of nodes with different degree values
As the nodes in the pathway network covered most of quite protein or ligands relate to cancer, we performed level analysis for each and every node in the malignancy network (Body 2). The results of level distribution analysis showed a great portion of the nodes (74. 5%) inside our cancer network have lower certifications (equivalent or less than 2). On the other hand, there are also several nodes with higher levels which web page link the nodes with lower diplomas and make the whole cancer network integrated alongside one another. These results exhibited that the tumors network made up of multi-level necessary protein or ligands with different features. Firstly, nothing of the nodes in our cancer tumor network with degree value equal to 0. This end result implied that protein or ligands in the network cannot exert their function without others. They revealed a multi-pathway network manner. Second of all, the nodes which degree value add up to 1 were always growth factors or ligands that can result in different subpathway activation. It is worth noting that these nodes exert their function by activating their downstream subpathway and finally enhance cancer progression respectively. Elimination of one node cannot inhibit cancer progression because other node will attenuate the inhibition impact by their functions. Finally, the nodes which degree value add up to 2 are always signal transduction related protein or ligands. They always act as linking nodes and can copy flux from an upstream node to downstream node. Last but not least, the nodes with higher degrees in the network mainly act as linking centers which combine different subpathway along.
Network efficiency based prediction as a book approach to analyzing anticancer medicine efficacy
In order to proceed with the network efficiency based mostly prediction, a three step process has been setup. Firstly, every drug that should be examined was docked into the proteins in our tumors pathway network and received its binding free energies to all or any the tumor related protein in the pathway network. Second, the binding free energies were converted to flux of edges in the network. Finally, the network efficiency was calculated from each flux of all the ends in the pathway network. Because every one of the nodes in our malignancy pathway network were protein or ligands which become positive enhancer for malignancy development, drugs which can reduce the network efficiency more significantly were examined as drugs with better anticancer efficacy. We have examined 683 drugs molecules from the Chinese Pharmacopoeia 2010 because all of these molecules were medically used drugs. The drugs were sorted by their capabilities of lowering the network efficiency. The top and last 20 drugs were taken to further analysis (Number 3). Among top 20 drugs which reduced the network efficiency most significantly, 8 out of 20 drugs (40%) were professional medical used anticancer drugs or drugs that reported have anticancer activities. In contrast, among the previous 20 drugs, none of them was scientific used anticancer drugs or has anticancer activities. The proportion of anticancer drugs was significantly different between your top and last 20 drugs. Through the use of our malignancy pathway related network efficiency prediction standard protocol, chemical substances with anticancer activities could be evidently distinguished from those without.
Like all molecular analysis method based on computational strategy, our strategy has many advantages as well as some limitations. Among the obvious advantages of the network based prediction method is that it considers the role of different protein by biological pathway network examination. Alternatively, the disadvantage of the method is that the reliability of the analysis technique relies on the consistency of network building and the accuracy of binding free energy diagnosis.
We developed a novel approach to evaluating anticancer medicine efficacy based on biochemical pathway network. This technique blended multi-target binding free energy computation, network flux and network efficiency for the prediction of anticancer drugs effectively. The obvious good thing about the network established prediction method is it can determine the role of different protein by natural pathway network analysis. This approach can measure the drugs' efficiency more comprehensively than traditional single target analysis methods and would be very useful for learning about novel drugs against sophisticated disease like cancer tumor. It remains to be motivated that what different structure and complexity of the cancer pathway network calls for effect to the experience, and the relevant work is underway.