A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

Abstract

BackgroundTraditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target.MethodologyWe developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery.ConclusionsThis article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

Platelet Aggregation Pathway Network-Based Approach for Evaluating Compounds Efficacy

“…Due to a global topological property of a 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 [13, 14, 25]. The NE of a graph G is measured by the shortest paths between pairs of nodes with the following $\begin{array}{c}\text{NE}=\sum _{i\ne j\in G}\frac{\mathrm{1}}{d_{ij}},\end{array}$ where d ij is the length of the shortest path between nodes i and j and the sum is over all N ( N − 1)/2 pairs of nodes with a total number N of nodes in the graph G .…”

“…In recent years, computational polypharmacology approaches have been developed [11–13]. Encouraged by the successes of using computational approaches to tackle various problems in different biological systems, we have developed a novel and valuable computational approach to evaluate the efficacy of ligands by calculating the influence on pathway network [14]. …”

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

“…Network efficiency (Latora & Marchiori, 2001), or critical node detection (Boginski & Commander, 2009) may serve as a starting measure to judge network integrity after multi-target action (Agoston et al, 2005; Csermely et al, 2005; Li et al, 2011c). Pathway analysis of molecular networks gives a more complex picture, and may reveal multiple intervention points affecting pathway-encoded functions, utilizing pathway cross-talks, or switching off compensatory circuits of network robustness.…”

Network-based drug discovery by integrating systems biology and computational technologies

“…Also, a biological network-based multi-target computational estimation scheme was used for screening anticoagulant activity of a series of argatroban intermediates and eight natural products based on affinity predictions from their multi-target docking scores and network efficiency analysis [76]. This scheme has been evolved from the traditional single agent virtual screening method which relies on evaluating binding affinity of the agent to single target, on the other hand, this model has focused on a network screening strategy based on phenotypic data of drug molecules against a complex disease by general network estimation.…”