Molecular Docking Technique and Methods

Molecular docking methods are widely used in modern drug design to explore the conformation of ligands in binding sites of large molecular targets. This method also estimates the ligand-receptor binding free energy by evaluating key phenomena involved in the intermolecular recognition process. With the emergence of various docking algorithms, it is important to understand the principle and method of molecular docking to formulate effective strategies. The purpose of this paper is to study the current common molecular docking strategies, to facilitate users to understand and design correct strategies and save time and experimental costs.

What is Molecular Docking?

Molecular docking is a computational biology method widely used in drug discovery to predict how a small molecule (usually a drug) interacts with a target receptor, such as a protein or DNA. This technique simulates the binding process between molecules and their receptors to identify the optimal ligand-receptor configurations, also known as ligand postures. During this process, the interactions between the ligand and the receptor are quantified through a scoring system, which allows researchers to evaluate the binding affinity and stability of the complex.

Since its inception in the mid-1970s, molecular docking has become an essential tool in structure-based drug design, helping researchers understand how drug molecules bind to their targets. By using docking simulations, researchers can optimize drug designs, improving their effectiveness and selectivity. This method not only aids in discovering lead compounds but also plays a crucial role in designing and optimizing therapeutically significant compounds.

The following article will introduce some important concepts in docking to help you better understand molecular docking.

Components of Molecular Docking

Ligand and Receptor

• Ligand: Ligand is a small molecule that interacts with a target molecule (usually a protein or other large molecule), usually a drug molecule or a candidate molecule.
• Receptor: A receptor is a target molecule, usually a protein, enzyme, or nucleic acid, that binds to a ligand and can form a complex with the ligand through specific chemical bonds and non-covalent interactions.

Binding Sites

Binding Sites are specific regions of the receptor molecule that bind to the ligand. This region is usually composed of a group of amino acid residues that can bind to ligands through van der Waals forces, hydrogen bonding, electrostatic interactions, etc.

Docking Scores

Docking Scores are numerical indicators used to quantify the binding affinity of ligands to receptors. This score is based on computer-simulated docking results that assess the stability and affinity of the ligand binding to the receptor. A lower (or higher, depending on the algorithm) molecular docking score generally indicates that the ligand is more tightly bound to the receptor and the affinity is stronger. This score helps researchers screen out possible candidate molecules for further experimental validation and drug design.

After understanding the above basic concepts, users can refer to the previous Molecular Docking Software and Tools to conduct preliminary molecular docking experiments.

Molecular Docking Applications

Molecular docking technology plays an important role in drug discovery, especially for large-scale compound screening against molecular target groups, and traditional high-throughput screening (HTVS) has become the gold standard for the discovery of bioactive compounds. However, the high cost of establishing and maintaining these screening platforms is often a major barrier to their widespread application. With the rapid development of computer technology and the accumulation of more and more target structures, chemical properties, and biological data, molecular docking, as a computer simulation method, provides an efficient and low-cost alternative. It involves predicting the preferred orientation and binding affinity of a ligand (such as a small molecule or drug) to a receptor (typically a protein or enzyme), providing valuable insights into molecular interactions. Below are some key applications of molecular docking:

Drug Discovery and Design

The application of molecular docking in drug discovery is particularly prominent. It helps screen out potential drug candidates by predicting small molecules or drug interactions with target receptors. By simulating the ligand-receptor binding process, molecular docking can identify lead compounds with strong binding forces to the receptor, thereby identifying them as promising drug candidates. This process enables researchers to screen large libraries of compounds in a virtual environment, significantly reducing costly and time-consuming experimental screening efforts.

Figure 1. Molecular docking is a key step in drug design (Saikia et al.,2019)

In the evolving field of drug discovery, the process of identifying and developing effective therapeutic drugs has become more complex and precise. One of the most powerful tools at the forefront of this revolution is molecular docking, a computational technique that plays a key role in the early stages of drug development. Molecular docking allows scientists to model how small molecules - such as potential drug candidates - interact with target proteins, enzymes, or other biomolecules, providing valuable insights into the binding affinity, stability, and potential efficacy of these compounds.

Read on for Molecular Docking in Drug Discovery as we delve into the key concepts of molecular docking in drug discovery to better understand how this innovative approach is changing the way we develop life-saving drugs.

Protein-Protein and Protein-DNA Interactions

Molecular docking is also increasingly being applied to understand non-covalent interactions between proteins or between proteins and nucleic acids, such as DNA or RNA. Docking allows researchers to study interactions in signal transduction, gene regulation, and immune responses, which are critical for developing treatments for diseases such as cancer, viral infections, and autoimmune diseases.

HDOCK, established by Yan, Yumeng, et al., is a novel Web server that supports protein-protein and protein-DNA /RNA docking and accepts sequence and structural inputs for proteins.

Enzyme inhibition and design

Docking is widely used in the design of enzyme inhibitors, especially in drug development targeting enzymes involved in disease processes, such as proteases in HIV, kinases in cancer, and acetylcholinesterase in neurodegenerative diseases. By simulating how inhibitors bind to enzymes, docking helps design molecules that can block enzyme activity and prevent disease progression.

An example of this comes from a study by Ring et al., in which several structure-based approaches to drug design, including docking, were conducted to identify novel non-peptide inhibitors of serine and cysteine protease family enzymes.

Toxicology and safety assessment

Molecular docking can predict potential off-target interactions and toxicity by simulating how drug candidates interact with unexpected targets. This application is critical to minimize the risk of adverse reactions in drug development and to ensure the safety of new compounds before they enter clinical trials.

An example of this comes from a study by He, Junhui et al., which aimed to explore the toxicity and potential mechanisms of thiabendazole using novel network toxicology and molecular docking techniques.

Principles of Molecular Docking Techniques

The basic principle of molecular docking consists of two key components: the docking process (an event that simulates the binding of a ligand to a receptor) and the evaluation of binding strength through various scoring functions.

Docking Algorithms

Molecular docking algorithm is a technique that computationally predicts how small molecules (ligands) will bind to larger molecules (receptors, such as proteins). The basic principle can be simply understood as the computer simulates the binding process between ligand and receptor to find the best way for them to bind in space. The core task of the docking algorithm is to find the optimal position and posture of ligand binding to the receptor. This process is usually done by "searching" in the space around the binding site of the receptor. Ligands can be rotated and translated to find the most suitable way to bind.

Algorithms based on the flexibility of ligands and receptors

• Rigid Docking algorithm: Rigid docking assumes that the receptor and ligand remain rigid during the docking process, that is, no structural changes occur. The docking algorithm searches for the optimal binding position and posture of the ligand near the binding site of the receptor.
• Flexible Docking algorithm: Flexible docking allows some or all of the structure of the ligand and receptor to change, taking into account the flexibility of the molecular structure. The optimal binding mode of ligand and receptor was found by introducing the changes of rotation and distortion within the molecule.

Algorithms based on Search strategy and optimization method

• Simulated Annealing algorithm: The simulated annealing algorithm simulates the annealing process of the physical system, and the ligand continuously reduces the energy by changing its position and posture during the search process, so as to find the lowest energy state (i.e. the most stable binding mode). The global optimal solution is found by simulating the gradual reduction of temperature.
• Genetic Algorithm (Genetic Algorithm): The Genetic algorithm simulates the principles of natural selection and genetics, and gradually improves the binding mode of ligands and receptors through the operation of selection, crossover, and mutation, so as to find the most suitable binding mode.
• Monte Carlo algorithm (Monte Carlo): The Monte Carlo algorithm generates ligand and receptor binding modes by random sampling and selects the optimal binding mode by evaluating the energy of these bindings (score function). The method relies on randomness to avoid falling into local optimal solutions.
• Grid-based Algorithm: The lattice algorithm calculates the interaction between the ligand and each grid point by dividing the binding site of the receptor into a three-dimensional grid. The ligands slide across the grid, evaluating their interactions with various points in the grid to find the best binding location.

Different molecular docking algorithms are suitable for different research needs and computational resources, and selecting the right algorithm can significantly improve the efficiency of drug screening and molecular design. The comparison of different algorithms is shown in Table 1.

Table 1. Comparison and application of common docking algorithms

Scoring Functions

In the field of molecular docking, Scoring Functions are used to evaluate ligand-receptor binding affinity. They calculate the likelihood and stability of molecular docking based on the intermolecular forces and help determine the optimal binding mode of ligand and receptor. The scoring function quantifies the energy or affinity of each ligand-receptor binding pattern by providing a numerical score that is often used to predict the outcome of molecular docking.

The goal of the scoring function is to predict the energy state of the ligand and receptor after binding, that is, to calculate the binding energy. By simulating molecular interactions, the scoring function helps determine which ligand-receptor binding mode has the lowest free energy and is most likely to be stable in an organism. The scoring function usually works in the following ways:

• Binding energy: The scoring function calculates the total energy based on the interaction between ligand and acceptor, including hydrogen bonds, van der Waals forces, electrostatic forces, etc.
• Free energy variation: The scoring function sometimes also includes the effects of solvent effects and temperature effects to estimate the free energy of ligand-receptor binding.
• Optimization goal: The goal of the scoring function is to determine the optimal binding pattern by maximizing affinity (or minimizing binding energy).

Molecular Docking Methods

There are a variety of molecular docking methods, and each method has different advantages and disadvantages according to its principle and application scenario:

Grid-based docking

Grid-based docking is a method to simplify the calculation of the interaction between ligand and receptor by gridding the receptor surface. The basic idea is to build a spatial grid, and then calculate the interaction of each grid point with the ligand on this grid. Computation-efficient, suitable for fast screening, but with limited accuracy.

Molecular Dynamics (MD) simulation docking

Molecular dynamics (MD) simulation docking methods capture the dynamic interaction between ligand and receptor by simulating the movement of molecules over a certain period of time. This method not only considers the static mechanical interaction between molecules, but also simulates the flexibility and deformation of molecules in the binding process. Considering molecular flexibility and dynamic behavior, high accuracy, but large computational resource requirements.

Template-based docking

Template-based docking method is also called homologous docking, its basic idea is to use the known receptor-ligand binding pattern as a template, by comparing the structure of the receptor and ligand to predict the new binding pattern. Suitable for receptor-ligand binding with known structures, fast, but unable to deal with completely unknown docking problems.

Machine learning docking

Machine learning (ML) technology has become a new method in molecular docking in recent years, mainly used to improve the accuracy and efficiency of docking. Machine learning can analyze the characteristics of molecular docking through big data, and learn new rules and patterns from it, so as to predict the results of molecular docking more accurately. Can improve the accuracy and efficiency of docking, especially in the big data environment, and can handle complex non-linear relationships, but requires a large number of training data.

By combining different docking methods, researchers can more efficiently and accurately predict the binding patterns of drugs to target receptors, thus facilitating the progress of the drug discovery process.

Advantages and Challenges of Molecular Docking

Advantages of Molecular Docking

Molecular docking is an important technique in drug design and molecular biology research with many advantages. It can efficiently screen a large number of ligand-receptor binding modes, save experimental time and resources, and is especially suitable for high-throughput screening. Through docking, structural information of drugs and targets can be obtained, providing guidance for drug optimization, and predicting various protein-protein and ligand-receptor interactions.

Challenges in Molecular Docking

However, molecular docking also faces many challenges. The accuracy of the scoring function is still a problem, especially when dealing with molecular flexibility and complex interactions, where it is often difficult for existing methods to accurately simulate the solvent effect and the flexibility of the receptor. The high demand for computational resources, especially for molecular dynamics simulations and flexible docking, makes these methods have certain limitations in high-throughput screening because of the long computation time. In addition, virtual screening may have false positive and false negative problems, resulting in inconsistent docking results with experimental data.

Nevertheless, as the technology continues to advance and optimize, the application of molecular docking in drug discovery still has important potential.