Progress in molecular docking

Abstract

Background: In recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode.

Results: This study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning.

Conclusion: Limited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

Graphical abstract

Keywords

molecular docking / numerical analysis / optimization / data mining

INTRODUCTION

Molecular docking [¹] is such a structure-based drug design method that simulates the molecular interaction and predicts the binding mode and affinity between receptors and ligands. In recent years, this technology has been widely used in drug design research field. Using the compounds database to screen the potential pharmacophores is not only convenient for researchers to purchase, synthesize and complete follow-up pharmacological tests, but also greatly improves the efficiency and reduces the research cost. In addition, the emergence of the reverse molecular docking technology [²] could significantly improve the drug target predictive capacity and understand the related molecular mechanism for drug design. Finally, this review briefly introduces the latest progress and applications of molecular docking technology.

THE INTRODUCTION OF MOLECULAR DOCKING TECHNOLOGY

The basic theory of molecular docking

Molecular docking is to simulate the optimal conformation according to the complementarity and pre-organization, which could predict and obtain the binding affinity and interactive mode between ligand and receptor [¹].

Figure 1A shows the first proposed “lock-and-key model” [³], which refers to the rigid docking of receptors and ligands to find the correct orientation for the “key” to open up the “lock”. This model emphasizes the importance of geometric complementarity.

However, the real docking process is so flexible that receptors and ligands have to change their conformation to fit each other well. Thus, we develop “induced fit model” (Figure 1B) [⁴]. Based on geometric complementarity, the energy complementarity and pre-organization guarantee that receptors and ligands would obtain the most stable structure in such a manner that minimizes the free energy [⁵].

As shown in Figure 2, the molecular docking software can help us to find the optimal conformation and orientation according to complementarity and pre-organization with specific algorithm, followed by applying a scoring function to predict the binding affinity and analyze the interactive mode. Figure 3 shows the protein-DNA docking with Autodock Vina [⁶] displayed in PyMOL [⁷].

Molecular docking software

Figure 4 lists the main three types of software for molecular docking. Flexible-rigid docking has been widely used. However, since flexible docking is usually more accurate, the relevant researches have become the hot studying spot in recent years. Table 1 lists the widely-used molecular docking software and its algorithms, evaluation methods, features and application areas.

Molecular docking databases

The most popular protein structure database is the public database Protein Data Bank (PDB) [⁸]. Also, the public databases such as PubChem Compound Database [⁹] and ZINC [¹⁰] are free to use. Besides, there are many important commercial databases, such as Compound Database (AcD) [¹¹], Cambridge Structural Database (CSD) [¹²].

THE APPLICATIONS OF MOLECULAR DOCKING

Virtual screening to discover the lead compound and hit compound

Virtual screening [¹³] is to find the lead compound and hit compound from the molecular databases according to the scoring function, which has tremendously improved the screening efficiency compared with the traditional screen method (Figure 5).

The applications of virtual screening are commonly used. Notably, given the exponential growth of high-throughput [¹⁴], high-performance computing [¹⁵], machine learning [¹⁶] and deep learning [¹⁷] techniques, the integrated method flourishes quickly. For example, Pereira et al. [¹⁸] applied deep learning approach in virtual screening, which extracts relevant features from molecular docking data to create the distributed vector representations for protein-ligand complexes. Also, Pyzerknapp et al. [¹⁹] proposed the virtual high-throughput screening.

Prediction of potential targets

It is noted that the aforementioned methods are all general docking methods which use the different ligands in the database to dock with the same receptor. However, current commonly used reverse docking technique is different from them. Here, we employed Figure 6 to describe the reverse docking technique [²]. The reverse docking technique identifies the novel targets by assigning a single small-molecule ligand as the probe to dock with multiple receptors to discover potential binding cavities. In this way, the potential targets of drug can be predicted. For example, Grinter et al. [²⁰] explored the potential target oxidized squalene cyclase (OSC) of PRIMA-1 by using the reverse docking software package Mdock. Also, Chen et al. [²¹] applied reverse docking technique to discover targeted proteins of marine compounds with anti-tumor activity. Furthermore, Chen et al. [²¹] also indicated that reverse docking can be complementary with in vitro assays as an effective method of target fishing. Finally, we considered that exploring relevant mechanism of action or side effect profile by structural biology analysis [²²] such as the pocket analysis [²³], could significantly benefit the novel drug design.

CONCLUSION

Considering the approximation capacity of the scoring function and incomplete collection of conformations, the molecular docking score of inactive molecules will be improperly so high that implicates false positive [²⁴–²⁶]. Furthermore, if the actual compound and the compound in database are significantly different in physical properties, the molecular docking score will be abnormal [²⁷]. Therefore, it is necessary to take thermodynamic features into account [²⁸], or use retrospective verification to evaluate the reliability of the prediction of affinity [²⁹]. In addition, as the three-dimensional structure used for molecular docking will be away from its original environment resulting in a change in conformation, the docking result cannot truly reflect the state of the experimental docking. In the distant future, we are optimizing the conformational search algorithm by taking more flexible bonds, solvent states and integrating recent biological data mining algorithms [³⁰–³²] in consideration. In general, we believe that molecular docking technique will become such a reliable drug-design tool that integrate the big biological data by optimizing the scoring function and upgrading the relevant search algorithms.

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