Since the completion of the Human Genome Project in 2003, researchers have been trying to find molecular targets related to specific phenotypes. Many experimental techniques have been developed for the confirmation of new disease targets and the discovery of lead compounds. In addition, the increase in the number of protein structures and the increase in computing power have made it possible for high-throughput computational screening of lead compounds. Molecular docking is a calculation method that searches for possible binding positions in a protein in a given ligand and calculates the binding affinity. Therefore, there is an increasing need to develop a computational tool to discover new targets. By discovering targets that are not expected to bind to lead compounds or existing drugs, one can try to eliminate undesirable side effects; or by repositioning drugs to expand the indications of drugs. Driven by this, the reverse docking method has attracted more and more attention.
Compared with traditional molecular docking, inverse or reverse docking is used to identify the target of a given ligand in a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain the molecular mechanism of drugs and relocate drugs to find alternative indications for drugs, as well as detect drug adverse reactions and drug toxicity.
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In reverse docking, the necessary process is similar to the forward docking method: preparing a data set, looking for ligand poses, and scoring and sorting complex structures. Several problems, including high computational cost and scoring bias between proteins, make the reverse docking process quite complicated.
The prerequisite for reverse docking is the collection of target structures with information about the potential ligand binding region. The correct construction of the target structure database is a key step to improve the accuracy and applicability of the reverse docking method. Many target databases for reverse docking have been developed; in addition, defining an appropriate binding site for each protein is also important for calculation efficiency and accuracy of docking results. The binding site or pocket of a receptor is a specific region of the receptor that binds to the ligand to form an interaction. The predefined binding pocket facilitates reverse docking because it reduces the time to search for the proper docking area between the ligand and the receptor.
In many previous studies, the binding pocket is usually defined by those residues that have at least one heavy atom within the distance of the ligand’s heavy atom. There are two main methods for obtaining binding pockets; one is to retrieve binding pockets from the complex structure of the protein database (PDB), and the other is to use a binding pocket search program. The automated process of defining the binding site is ideal because a large number of targets need to be examined during the reverse docking process.
The molecular docking procedure used in the reverse docking process is similar to the conventional docking method. Many popular docking programs have been developed, such as GOLD, DOCK, AutoDock or Glide; some modifications have been made on their basis to make the whole process more efficient and accurate in calculation. Compared with conventional docking methods, the reverse docking process is computationally demanding because it must handle a large number of protein targets. The noise of the docking score produces false positives in target confirmation. In particular, the target dependence of the docking score is more critical in reverse docking than in forward docking.
Many studies have used reverse docking as the primary method or as a secondary option for analyzing broad-spectrum targets related to small molecules. Through reverse docking, new disease targets can be identified, molecular mechanisms of substances can be explained, alternative indications for known drugs can be found through drug repositioning, and adverse drug reactions and drug toxicity can be detected. Researchers chose specific reverse docking tools and servers to meet their specific conditions and purposes.
The main components of green tea such as epigallocatechin gallate (EGCG), epigallocatechin (EGC), epicatechin gallate (ECG) and epicatechin (EC)) have a wide range of anti-tumor effects active. The discovery of its target plays an important role in revealing the anti-tumor mechanism. Therefore, in order to determine the potential target proteins of tea polyphenols, the researchers adopted a method of virtual screening of drug targets by reverse docking.
Prepare the three-dimensional crystal structure of tea polyphenol small molecules;
The PDTD protein database screened several clinically important proteins with anti-tumor effects as potential receptors and processed them;
Reverse docking screening based on AutoDock;
The results are analyzed and screened, such as further docking research on binding mode and molecular dynamics research on the mechanism of action.
Based on the results of reverse docking, the researchers found several potential targets of tea polyphenols, and on this basis, they carried out molecular docking studies to study the binding pockets and binding modes, and found that electrostatic interactions and hydrogen bonds are in the binding of tea polyphenols to the targets. An important role in the process.
Ginsenoside is the main component of ginseng. In traditional medicine, ginseng is considered to have therapeutic value for many diseases. In order to verify the empirically observed effects of ginseng, researchers used the reverse docking method to screen for target proteins related to specific diseases. The general process is as follows:
Construction of drug target database;
Selection of protein targets related to toxicity and side effects;
Preparation of ginsenoside ligand small molecule and target structure;
Use reverse docking tools for reverse docking screening;
Based on the docking scoring and binding mode analysis, the researchers found four potential target proteins of ginsenosides, and found four proteins that may be associated with side effects and toxicity.
Reverse docking provides a list of potential target proteins for further research. Drug target determination is the first step in drug discovery. As one of the strategies to supplement experimental methods, reverse docking has become one of the effective tools to determine the potential targets of a given compound. It is not only used for target confirmation, but also predicts toxicity and adverse effects. Side effects can also be used to discover unknown novel targets for drugs or natural compounds. Since reverse docking is a drug target identification method based on docking, for clear target confirmation, consideration should be given to constructing a potential target data set, detecting binding sites or cavities, and improving the accuracy of search algorithms and scoring functions.
Compared with other structure-based target discovery methods (such as pharmacophores, binding site similarity, and fingerprint-based interaction methods), reverse docking has direct advantages. As a result of docking, generating the binding mode of target and ligand is an effective method for lead compound optimization. However, there are some limitations and several problems related to the reverse docking method, such as the problem of target structure data set construction, the inability to consider receptor flexibility due to high computational cost, and docking score bias. If these limitations and problems are adequately resolved, then reverse docking becomes a truly useful tool for drug discovery.
1.Lee A, Lee K, Kim D. Using reverse docking for target identification and its applications for drug discovery[J]. Expert opinion on drug discovery, 2016, 11(7): 707-715.
2.Zheng R, Chen T, Lu T. A comparative reverse docking strategy to identify potential antineoplastic targets of tea functional components and binding mode[J]. International journal of molecular sciences, 2011, 12(8): 5200-5212.
3.Park K, Cho A E. Using reverse docking to identify potential targets for ginsenosides[J]. Journal of Ginseng Research, 2016.
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