Structure-Based Drug Discovery & Design: Strategies and Implementation
Structure-based drug discovery and design (SBDD) offers the most straightforward path to optimizing initial screening hits into lead compounds and further evolving those leads into clinical drug candidates with enhanced potency and selectivity profiles. In this discussion, we will explore the basic strategies involved in SBDD and outline the conditions and techniques required for implementing each strategy.
Protein Structure in Drug Discovery & Design
The benefits of using protein 3D structure in drug discovery and design (structure-based drug design) became evident in the early days of protein X-ray crystallography (Blundell et al. 2006). Two successful classic examples include the structure-based discovery of the AIDS drugs Agenerase and Viracept, developed using the crystal structure of HIV protease (Lapatto et al. 1989, Miller et al. 1989), and the influenza drug Relenza, designed using the structure of the virus neuraminidase (Varghese, 1998). At that time, most pharmaceutical companies deemed the structure-based method too costly and time-consuming for industrial application, thus relegating the responsibility to academic institutions.
During the following years, advances in methods and instrumentation for X-ray crystallography, the increased application of protein cloning and the development of new molecular biology methods and laboratory techniques, the arrival of dedicated synchrotron radiation facilities, and the establishment of large structural genomics consortia have completely transformed the field of structural biology. It would be safe to say that structure-based drug design is currently the state-of-the-art strategy at all large pharmaceutical and biotech companies.
Currently, structure-based drug design offers various strategies for initiating a project. A crucial requirement before choosing a strategy is a thorough analysis of available information about the target. For instance, having an experimental structure and establishing robust and reproducible crystallization conditions for the protein are essential steps before starting a structure-based drug design project. Only an experimental three-dimensional structure enables us to fully exploit this method, significantly accelerating the drug design process.
Below, we explore the basic strategies in structure-based drug design and the experimental data required to choose a particular strategy.
Planing a Structure-Based Drug Design Project: Basic Strategies
Analyzing available information about the target is crucial to planning and launching a structure-based discovery and design project. The image on the right illustrates possible strategies for structure-based drug design.
- Protein structure known / ligand known – structure-based drug design (SBDD) is straightforward.
- Known protein structure / unknown ligand – virtual screening, fragment-based screening, high-throughput screening (HTS), DNA-encoded library screening, and subsequent verification and optimization using structure-based methods.
- Protein structure unknown / ligand known – ligand-based drug design techniques. The protein structure needs to be determined to realize the full benefits of structure-based methods.
- Protein structure unknown / ligand unknown – de novo design: protein structure determination, drugability assessment of target, ligand binding assessment, virtual screening, fragment-based screening, high-throughput screening. Crystallization and structure determination of the target and its complex with a ligand are essential for the success of the project.
The image illustrates the basic strategies that can be followed in a structure-based drug design project. A key aspect of all strategies is the availability of an experimental three-dimensional structure of the drug target and its complex with a ligand. For further details, please refer to the text on the page.
In several company blog posts, we also discuss the applications of structural biology and structure-based drug design, starting from hit identification to lead generation.
Basic Strategy 1: Known Ligand and Protein Structures
This strategy, shown in the blue quadrant (image above), offers the most straightforward approach, leveraging the known structures of both the ligand and the protein target to enable efficient structure-based drug design. For instance, the structure of the bound ligand enables the construction of a pharmacophore model, which can be used in virtual screening of fragment and drug-like compound libraries, and the assessment of compound binding using docking methods. In addition, biophysical screening methods such as NMR spectroscopy, X-ray crystallography, and our proprietary weak-affinity chromatography (WAC™) technology are also commonly used to identify additional hits with new structures.
Upon identifying new hits, their binding details are verified using the protein-ligand complex structures determined, e.g., by X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy (cryo-EM). An advantage of the method of crystallographic fragment screening is that it provides direct insight into the mode of binding of fragment hits, while NMR spectroscopy can be used for low-affinity ligands (Kd > 10−6 M) or when the protein is in a non-ideal conformation for ligand binding during crystal soaking. Subsequent steps, including lead generation and optimization, require repeated cycles of X-ray structure determination of the target protein in complex with bound compounds. SAR (Structure-Activity Relationships)-based methods analyze and compare multiple hits and play a crucial role in lead optimization.
Basic Strategy 2: de novo Design: Known Protein Structure, Unknown Ligand
In the green quadrant of the image above, we focus on Basic Strategy 2: cases where the protein structure is known, but the ligand structure is unknown. Here, we need to conduct fragment or compound library screening at the start of a structure-based drug design. Since most proteins currently have AlphaFold-generated 3D structures, virtual screening can also be applied to the models. In addition, as in the Strategy 1 case, we can use biophysical screening methods like NMR spectroscopy or weak-affinity chromatography (WAC). Once we identify suitable binders, we may develop, e.g., a SAR model and, in addition, clone, express, purify, and crystallize the protein in a complex with the best ligands.
This approach validates the binding mode, provides direct insights into the stabilizing interactions in the protein-ligand complex, and facilitates structure-based discovery and design methods. We should also note that the presence of a ligand at the protein’s binding site often facilitates crystallization of the protein-ligand complex. When a structure of the ligand complex is obtained, we can proceed with Basic Strategy 1.
Basic Strategy 3: Ligand-Based Drug Design
The yellow quadrant in the image above relates to Basic Strategy 3, ligand-based drug design (LBDD). In this case, the ligand’s structure is already known (e.g., a substrate or inhibitor), but the protein target’s experimental structure is not available. The principle of this method suggests that knowing the structure of one or more ligands that bind to a protein allows us to create additional ligands with similar structures and equal or greater binding affinity. In this scenario, we can use the protein’s AlphaFold model to obtain details of the protein’s ligand-binding site, and use the known ligand (or ligands) structure to build a pharmacophore model based on the ligand’s shape and the presence of charged, polar, and hydrophobic groups. When several known ligands are available, their common features can be overlaid to improve the accuracy of the pharmacophore model.
The model can then be used, e.g., to perform virtual screening of a chemical library to identify similar compounds and to conduct quantitative structure-activity relationship (QSAR) analysis. Subsequently, the hits are ranked, and the best binders are selected for biochemical assay analysis. This process is repeated until good-quality lead compounds are obtained. It should be noted that computational methods, even when combined with an AlphaFold model, often yield inaccurate results. For this reason, experimental structures and SBDD remain the best and fastest alternatives for the discovery and design of new drugs.
The known ligand structure can also be used in the scaffold-hopping method. In this case, computational chemistry, machine learning, and AI methods, together with medicinal chemistry expertise, are employed to identify new chemical structures with similar activity. This method can help in identifying new chemotypes and, if necessary, new Intellectual Property (IP).
LBDD Challenges
The lack of experimental 3D structures in LBDD presents several challenges. One major limitation is that this method depends heavily on high-quality ligand data and does not provide direct insights into how ligands bind to their targets. Consequently, this can significantly slow down the optimization of initial hits. In contrast, traditional structure-based drug discovery (SBDD) offers direct mechanistic insights into ligand-binding interactions and facilitates the rational design of new ligands. LBDD can be useful in the early stages of a project, while the target’s experimental structure and its ligand complexes are being determined using traditional techniques such as X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy (cryo-EM). An experimental 3D structure in complex with a ligand will enable the implementation of Basic Strategy 1, ultimately accelerating the drug discovery process.
Basic Strategy 4: de novo Design: Unknown Ligand, no Experimental Protein Structure
Even in the absence of a known ligand and an experimental protein structure, we can still use virtual screening with an AlphaFold 3D model of the protein. However, before that, we need to determine the protein’s drugability and identify a potential ligand-binding site. Assuming that we have access to a purified protein, we can also run a high-throughput screen (HTS) in combination with a biochemical assay or use NMR spectroscopy to screen a fragment library.
If the target protein’s experimental structure is unavailable, SARomics Biostructures provides custom gene-to-protein-structure services. In such cases, the protein is cloned, expressed, purified, and crystallized, and its X-ray crystallographic structure is determined. In addition, our library of FastLane structures contains over 600 proteins for co-crystallization with the client’s ligands, with a turnaround time of a few weeks.
Hit to Lead & Lead Optimization
At SARomics Biostructures, as part of our structure-based drug design service packages, we include structure-based hit-to-lead and lead optimization (in collaboration with RGl Discovery). After identifying an active ligand (or ligands), structure-based optimization of ligand interactions with the target protein (hit expansion & lead generation) is run to obtain more efficient binders. Multiple protein-ligand complex structures and SAR modeling of the compounds provide detailed mapping of the binding site, enabling the design of new compounds. Structure-based hit-to-lead optimization typically requires several cycles. The result may be a series of compounds, which will be further optimized and tested to generate the final lead.
SARomics Biostructures’ fragment screening and fragment-based drug discovery and design services are tailored to meet each project’s and customer’s specific needs and requirements. Please do not hesitate to contact us to discuss your project.
How many structures are needed for a structure-based drug design project, from initial hits to a candidate drug? We recommend an excellent publication by the AstraZeneca team. H. Käck and T. Sjögren (2024). Macromolecular crystallography from an industrial perspective – the impact of synchrotron radiation on structure-based drug discovery. J. Synchrotron Rad. 32.