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Computational approaches are useful tools to interpret and guide experiments to expedite the antibiotic drug design process. Structure based drug design (SBDD) and ligand based drug design (LBDD) are the two general types of computer-aided drug design (CADD) approaches in existence. SBDD methods analyze macromolecular target 3-dimensional structural information, typically of proteins or RNA, to identify key sites and interactions that are important for their respective biological functions. Such information can then be utilized to design antibiotic drugs that can compete with essential interactions involving the target and thus interrupt the biological pathways essential for survival of the microorganism(s). LBDD methods focus on known antibiotic ligands for a target to establish a relationship between their physiochemical properties and antibiotic activities, referred to as a structure-activity relationship (SAR), information that can be used for optimization of known drugs or guide the design of new drugs with improved activity. In this chapter, standard CADD protocols for both SBDD and LBDD will be presented with a special focus on methodologies and targets routinely studied in our laboratory for antibiotic drug discoveries.
Despite the fact that numerous antibiotic drugs are available and have been routinely used for a much longer time than most other drugs, the fight between humans and the surrounding bacteria responsible for infections are ongoing and will be so for the foreseeable future. Contributing to this is the steady rise of antibiotics drug resistance leading to the need for new antibiotics (1, 2). Toward the design of new antibiotics, computer-aided drug design (CADD) can be combined with wet-lab techniques to elucidate the mechanism of drug resistance, to search for new antibiotic targets and to design novel antibiotics for both known and new targets. Notably CADD methods can produce an atomic level structure-activity relationship (SAR) used to facilitate the drug design process thereby minimizing time and costs (3, 4).
Understanding the atomic-detailed mechanism behind the antibiotics resistance helps to reveal limitations in current antibiotics and shed light on the design of new drugs. For examples, Trylska et al. studied the effects of mutations at the bacterial ribosomal A-site using molecular dynamics (MD) simulations to reveal the origins of bacterial resistance to aminoglycosidic antibiotics (5). Our lab studied the impact of ribosomal modification on the binding of the antibiotic telithromycin using a combined Grand Canonical Monte Carlo (GCMC)/Molecular Dynamics (MD) simulation methodology (6, 7) and revealed atom-level details of how those modifications lead to resistance that will be of utility to improve the activity and spectrum of macrolide analogs thereby minimizing resistance (8).
An important alternative to solve the antibiotic resistance issue is the identification of new antibiotic targets that may represent novel mechanisms essential for bacterial survival. For example, researchers used bioinformatics approaches to screen various databases computationally and identified seven enzymes involved in bacterial metabolic pathways as well as 15 non-homologous proteins located on membranes in the gram positive bacterium Staphylococcus aureus (SA), thereby indicating them as potential targets (9). Such findings may help to overcome the resistance of this bacterium to common antibiotics such as methicillin, fluoroquinolones and oxazolidinones. An example of a recently identified novel antibiotic target is the protein heme oxygenase, involved in the metabolism of heme by bacteria as required to access iron (10–12). In collaborative studies with the Wilks lab, we have successfully applied CADD techniques to identify inhibitors of the bacterial heme oxygenases from Pseudomonas aeruginosa and Neisseria meningitides, thereby confirming the potential role of heme oxygenases as a novel antimicrobial targets (13, 14).
Researchers are also continuing to look for new antibiotics against existing targets and computational approaches have been successfully used in a number of studies. Using in silico database screening, Chang et al. found a new series of non-β-lactam antibiotics, the oxadiazoles, which can inhibit penicillin-binding protein 2a (PBP2a) of methicillin-resistant SA (MRSA), the cause of most infections in hospitals (15). Using ligand-based drug design (LBDD), our lab with Andrade and coworkers investigated analogs of the third-generation ketolide antibiotic telithromycin as a possible means to address the bacterial resistance problem associated with that class of antibiotics (16–18). In another study, based on the 3D structure of the complex of human defensin peptide HNP1 with Lipid II, which serves as precursor for bacterial cell wall biosynthesis and is a validated target for antibiotics, our lab designed a simple pharmacophore model and used it in a database screen to search for low weight defensin mimetics (19). From that effort, a lead compound was identified that targets Lipid II with high specificity and affinity. Notably, this is the first example of a small molecular weight compound that shows promising activity against Lipid II. Lead compound derivatives were subsequently identified again using CADD in combination with medicinal chemistry (20) and the accumulated SAR information will facilitate the development of next generation antibiotics targeting gram positive pathogenic bacteria.
Figure 1 illustrates the basic CADD workflow that can be interactively used with experimental techniques to identify novel lead compounds as well as direct iterative ligand optimization (3, 4, 21, 22). The process starts with the biological identification of a putative target to which ligand binding should lead to antimicrobial activity. In SDBB, the 3D structure of the target can be identified by X-ray crystallography or NMR or using homology modeling. This lays the foundation for CADD SBDD screening using the methods described below. LBDD is used in the absence of the target 3D structure with the central theme being the development of an SAR from which information on modification of the lead compound to improve activity can be obtained. Information from the CADD methods is then used to design compounds that are subjected to chemical synthesis and biological assay, with the information from those experiments used to further develop the SAR, yielding further improvements in the compounds with respect to activity as well as absorption, disposition, metabolism and excretion (ADME) considerations (23). Notably, CADD methods are evolving with researchers continually updating and implementing new CADD techniques with higher levels of accuracy and speed (24–26). In this chapter, we will present commonly used CADD approaches, including those used in our lab for the design of next-generation antibiotics.
CADD methods are mathematical tools to manipulate and quantify the properties of potential drug candidates as implemented in a number of programs. These include a range of publicly and commercially available software packages; the subset described below represents examples of fundamental tools for CADD with emphasis on those commonly used in our laboratory.
CADD can be separated into ligand or hit identification and ligand or hit optimization, with both SBDD and LBDD methods useful in the appropriate context. Database screening methods are often used for hit identification (59) while a number of methods may be used for hit optimization (4, 24, 60). These include the Site-identification by ligand competitive saturation (SILCS) methodology. Below we present a collection of methods that may be used for both ligand identification and optimization.
MD simulations can be used to study target-ligand interactions at an atomic level of detail (61), to generate conformational ensembles for the target or for the ligand to take flexibility into account for both SBDD and LBDD studies (see Note 1) and, in combination with other methods, used to estimate relative free energies of binding. Following are the steps required to perform a standard MD simulation (see Note 2 for additional MD techniques). A convenient web-based tool to perform a number of the steps below is the CHARMM-GUI at www.charmm-gui.org (62).
SILCS is a novel CADD protocol developed in our lab to facilitate ligand design (65). It uses all-atom explicit-solvent MD simulations that include small organic solutes, such as propane, methanol and others, to identify 3D functional-group binding patterns on the target. These patterns can be used qualitatively to direct ligand design and, when converted to free energies, termed grid free energy (GFE) FragMaps (66, 67), used to quantitatively estimate the relative binding affinities of ligands. The detailed protocol based on full MD simulations was described previously in this same book series (68). Here we present an updated protocol based on the use of oscillating μex Grand Canonical Monte Carlo/MD (GCMC/MD) simulations for SILCS (69). The GCMC/MD approach allows for the application of the SILCS method to target systems with deep or occluded pockets such as nuclear receptors and GPCRs (70).
VS against a database containing commercially available compounds, is an efficient way to find potential low-molecular weight binders to the target protein (59). While the ZINC database is available, researchers may want to prepare an in-house database for specific use.
Docking involves posing a compound in the putative binding site on the target in an optimal way defined by a scoring function in combination with a conformational sampling method (78). Various docking programs are available that differ based on the scoring function used to describe the interaction between small molecule and the target and the conformational sampling method used to generate the binding poses of the ligand on the protein. Here we present a docking protocol using the DOCK program (49) to illustrate the typical docking VS workflow.
An alternative to docking based VS is target-based pharmacophore VS (84). This approach can quickly filter a database for potential binders to a specific bacterial target. A pharmacophore model is defined as spatially distributed chemical features that are essential for specific ligand-target binding. It represents a simplification of the detailed energetic information used by docking methods and so its computational requirements are much lower. While multiple methods can be used to generate pharmacophores (84), we will present a method based on information from SILCS as described in section 3.2. The workflow for generation of a SILCS-based pharmacophore model (73, 74) is illustrated in Figure 2.
Once lead compounds are identified from experiments, LBDD methods can be utilized to start to develop an SAR or find more hit compounds. Of these, the similarity search method is the most straightforward and rapid approach (87). It can search for compounds that are chemically or physiochemically similar to the input compound, as described below. This approach may also be used as lead validation, as a compound that has multiple analogs with biological activity from which SAR can be developed is appropriate for further studies (88).
When multiple hits for a specific bacterial target with activity data are available, structure-activity relationship (SAR) models can be developed and used to predict new compounds with improved activity (93). LBDD SAR models use regression methods to relate a set of descriptors of the lead series of compounds to their activities. The developed regression model can then be used to quantitatively predict the activity of the modified compounds (93). The descriptors can be physical or chemical properties of compounds or even geometric parameters that are representative for the spatial distributions of important functional groups in the compounds, i.e. pharmacophore features. Knowledge of the relationship of these properties to activity (i.e. SAR) can be used by the medicinal chemist to qualitatively design new, synthetically-accessible compounds that can be quantitatively evaluated. When developing SAR using pharmacophore descriptors, the appropriate conformations of the compounds that are responsible for the biological activity must be used. Here we illustrate the development of SAR using our in-house developed conformationally sampled pharmacophore (CSP) protocol (94, 95).
Free energy perturbation (FEP) is a higher level, computationally demanding method with increased accuracy (see Note 5) that may be used to quantify the binding free energy change related to a modification in a compound (102). To save computational time, the single step FEP (SSFEP) may be applied (103). The approach uses a pre-computed MD simulation of the hit compound-target complex from which the free energy difference due to small, single non-hydrogen atom modifications (e.g. aromatic –H to –Cl or –OH) can be rapidly evaluated (103). This is in contrast to the need for many simulations in which the chemical modification is introduced in standard FEP methods (102). SSFEP has the ability to give rapid predictions of binding affinity changes related to modifications and, thus, is quite useful for lead optimization (104). The method may be applied using the following protocol with most simulations packages.
The utility of the SSFEP approach is that the ΔΔG values for many modifications may be rapidly evaluated as the same trajectories from the original MD simulations of the hit compound are used in each case. This approach may be of use during the fine tuning of ligand affinity or specificity for a target or as required to improve physiochemical and pharmacokinetic properties without significantly altering desirable properties such as affinity.
This work was supported by NIH grant CA107331, University of Maryland Center for Biomolecular Therapeutics, Samuel Waxman Cancer Research Foundation, and the Computer-Aided Drug Design (CADD) Center at the University of Maryland, Baltimore.
Conflict of interest: A.D.M. is Co-founder and CSO of SilcsBio LLC.
1Conformational flexibility of molecules is a very important feature no matter if it is a small ligand or a large protein. Thus conformational sampling of a protein or ligand that produces an ensemble of biological meaningful conformations is necessary either for SBDD or for LBDD. The CADD methods presented in the chapter such as SILCS for SBDD or CSP for LBDD take this issue into account and thus have advantages over other CADD methods that only rely on single crystal structure or limited ligand conformations.
2MD simulation is an efficient way to generate conformational ensembles. For larger system, more advanced MD techniques can be employed to enhance the sampling efficiency such as replica exchange methods. The protocols developed in our lab such as Hamiltonian replica exchange with biasing potentials (107) and replica exchange with concurrent solute scaling and Hamiltonian biasing in one dimension (108) are efficient replica exchange methods for use to enhance the MD efficiency. However, with all MD based methods the user must perform careful analysis to assure that the conformational ensemble is adequately converged for effective use in CADD.
3Protonation states of titratable residues at the targeted binding site and in the ligand being studied are quite important when setting up the CADD calculations. For example, different protonation states of histidine residues can offer different hydrogen bonding types to potential ligands. Available experimental observations and known complex structures are useful to determine the correct protonation state of protein residue upon ligand binding. Software such as Reduce can assign the most appropriate protonation state based on environment. Constant pH MD simulation (109) where protonation state of titratable residue can change during the simulation may also be useful. With respect to ligands, many computational tools for prediction of ionization state are available, though common sense by the user is often adequate to deal with the most common ionizable groups such as carboxylates.
4For VS, consensus scoring can be used instead of a single scoring scheme to rank hit compounds to allow more diversity of the identified compounds (86). For example, in our SILCS-Pharm protocol, LGFE and RMSD are used together to rank compounds that pass our pharmacophore model filtering. Additional scoring metrics can include the DOCK or AUTODOCK scores (49, 50), or the average interaction energies from MD simulations, with many other variations available.
5In the ligand optimization stage of CADD, as only a few compounds are under consideration, accuracy rather than computational efficiency is usually pursued. This means more sophisticated binding affinity evaluation methods should be used. These include the free energy methods such as SSFEP or the SILCS based LGFE scoring discussed above.
6When constructing the final list of compound for experimental assays from VS, in addition to the binding score, drug likeness can be another criterion to further filter the list. Potential bioavailability of a compound is often judged by the Lipinski’s rule of five (RO5) (82). The 4-dimensional bioavailability (4D-BA) descriptor (83) is a scalar term derived from the four criteria in RO5 and thus facilitates the selection of potential bioavailable compounds in an automatic fashion. Pan assay interference compounds (PAINS) filter (110) can also be used to remove compounds that are likely to interfere in experimental screening techniques mainly through potential reactivity leading to false positives.