Methods and Applications of In Silico Aptamer Design and Modeling

Aptamers are nucleic acid analogues of antibodies with high affinity to different targets, such as cells, viruses, proteins, inorganic materials, and coenzymes. Empirical approaches allow the design of in vitro aptamers that bind particularly to a target molecule with high affinity and selectivity. Theoretical methods allow significant expansion of the possibilities of aptamer design. In this study, we review theoretical and joint theoretical-experimental studies dedicated to aptamer design and modeling. We consider aptamers with different targets, such as proteins, antibiotics, organophosphates, nucleobases, amino acids, and drugs. During nucleic acid modeling and in silico design, a full set of in silico methods can be applied, such as docking, molecular dynamics (MD), and statistical analysis. The typical modeling workflow starts with structure prediction. Then, docking of target and aptamer is performed. Next, MD simulations are performed, which allows for an evaluation of the stability of aptamer/ligand complexes and determination of the binding energies with higher accuracy. Then, aptamer/ligand interactions are analyzed, and mutations of studied aptamers made. Subsequently, the whole procedure of molecular modeling can be reiterated. Thus, the interactions between aptamers and their ligands are complex and difficult to understand using only experimental approaches. Docking and MD are irreplaceable when aptamers are studied in silico.     

Trastuzumab-Peptide Interactions: Mechanism and Application in Structure-Based Ligand Design

Understanding of protein-ligand interactions and its influences on protein stability is necessary in the research on all biological processes and correlative applications, for instance, the appropriate affinity ligand design for the purification of bio-drugs. In this study, computational methods were applied to identify binding site interaction details between trastuzumab and its natural receptor. Trastuzumab is an approved antibody used in the treatment of human breast cancer for patients whose tumors overexpress the HER2 (human epidermal growth factor receptor 2) protein. However, rational design of affinity ligands to keep the stability of protein during the binding process is still a challenge. Herein, molecular simulations and quantum mechanics were used on protein-ligand interaction analysis and protein ligand design. We analyzed the structure of the HER2-trastuzumab complex by molecular dynamics (MD) simulations. The interaction energies of the mutated peptides indicate that trastuzumab binds to ligand through electrostatic and hydrophobic interactions. Quantitative investigation of interactions shows that electrostatic interactions play the most important role in the binding of the peptide ligand. Prime/MM-GBSA calculations were carried out to predict the binding affinity of the designed peptide ligands. A high binding affinity and specificity peptide ligand is designed rationally with equivalent interaction energy to the wild-type octadecapeptide. The results offer new insights into affinity ligand design.     

Drug design strategies for targeting G-protein-coupled receptors

G-protein-coupled receptors (GPCRs) form a large protein family that plays an important role in many physiological and pathophysiological processes. Since the sequencing of the human genome has revealed several hundred new members of this receptor family, many new opportunities for developing novel therapeutics have emerged. The increasing knowledge of GPCRs (biological target space) and their ligands (chemical ligand space) enables novel drug design strategies to accelerate the finding and optimization of GPCR leads: The crystal structure of rhodopsin provides the first three-dimensional GPCR information, which now supports homology modeling studies and structure-based drug design approaches within the GPCR target family. On the other hand, the classical ligand-based design approaches (for example, virtual screening, pharmacophore modeling, quantitative structure-activity relationship (QSAR)) are still powerful methods for lead finding and optimization. In addition, the cross-target analysis of GPCR ligands has revealed more and more common structural motifs and three-dimensional pharmacophores. Such GPCR privileged structural motifs have been successfully used by many pharmaceutical companies to design and synthesize combinatorial libraries, which are subsequently tested against novel GPCR targets for lead finding. In the near future structural biology and chemogenomics might allow the mapping of the ligand binding to the receptor. The linking of chemical and biological spaces will aid in generating lead-finding libraries, which are tailor-made for their respective receptor.     

Fluorophore-Labeled Cyclic Nucleotides as Potent Agonists of Cyclic Nucleotide-Regulated Ion Channels

High-affinity fluorescent derivatives of cyclic adenosine and guanosine monophosphate are powerful tools for investigating their natural targets. Cyclic nucleotide-regulated ion channels belong to these targets and are vital for many signal transduction processes, such as vision and olfaction. The relation of ligand binding to activation gating is still challenging, and there is a need for fluorescent probes that enable the process to be broken down to the single-molecule level. This inspired us to prepare fluorophore-labeled cyclic nucleotides, which are composed of a bright dye and a nucleotide derivative with a thiophenol motif at position 8 that has already been shown to enable superior binding affinity. These bioconjugates were prepared by a novel cross-linking strategy that involves substitution of the nucleobase with a modified thiophenolate in good yield. Both fluorescent nucleotides are potent activators of different cyclic nucleotide-regulated ion channels with respect to the natural ligand and previously reported substances. Molecular docking of the probes excluding the fluorophore reveals that the high potency can be attributed to additional hydrophobic and cation-π interactions between the ligand and the protein. Moreover, the introduced substances have the potential to investigate related target proteins, such as cAMP- and cGMP-dependent protein kinases, exchange proteins directly activated by cAMP or phosphodiesterases.     

Thermodynamic Insight into the Effects of Water Displacement and Rearrangement upon Ligand Modifications using Molecular Dynamics Simulations

Computational methods, namely molecular dynamics (MD) simulations in combination with inhomogeneous fluid solvation theory (IFST) were used to retrospectively investigate various cases of ligand structure modifications that led to the displacement of binding site water molecules. Our findings are that water displacement per se is energetically unfavorable in the discussed examples, and that it is merely the fine balance between change in protein–ligand interaction energy, ligand solvation free energies, and binding site solvation free energies that determine if water displacement is favorable or not. We furthermore evaluated if we can reproduce experimental binding affinities by a computational approach combining changes in solvation free energies with changes in protein–ligand interaction energies and entropies. In two of the seven cases, this estimation led to large errors, implying that accurate predictions of relative binding free energies based on solvent thermodynamics is challenging. Nevertheless, MD simulations can provide insight regarding which water molecules can be targeted for displacement.     

In-Silico Calculations as a Helpful Tool for Designing New Extractants in Liquid-Liquid Extraction

Due to new challenges, new extraction solvents based on innovative extractants are needed in hydrometallurgy for specific tasks. Thus, the aim of the present article is to discuss the potential and limits of Quantitative Structure–Properties Relationship (QSPR) and molecular modeling for identifying new extractants. QSPR methods may have useful applications in such a complex problem as the design of ligands for metal separation. Nevertheless, the degree of reliability of the predictions is still limited and, in the present state of the art, these techniques are likely more useful for optimization within a given family of extractants than to build in-silico new reagents. The molecular modeling techniques provide binding energies between target metals and given ligands, as well as optimized chemical structures of the formed complexes. Thus, in principle, the information, which can be deduced from the molecular modeling computations are richer than that provided by QSPR methods. Nevertheless, an effort should be made to establish more tangible links between the calculated binding energies and the physical parameters used by the hydrometallurgists, such as the complexation constants in aqueous phase (β_MAn) or better the extraction constants (K_ex).     

Protonation patterns in tetracycline:tet repressor recognition: simulations and experiments

Resistance to the antibiotic tetracycline (Tc) is regulated by its binding as a Tc:Mg2+ complex to the Tet Repressor protein (TetR). Tc:TetR recognition is a complex problem, with the protein and ligand each having several possible conformations and protonation states, which are difficult to elucidate by experiment alone. We used a combination of free-energy simulations and crystallographic analysis to investigate the electrostatic interactions between protein and ligand and the possible role of induced fit in Tc binding. Tc in solution was described quantum mechanically, while Tc:TetR interactions were described by a recent, high-quality molecular-mechanics model. The orientations of the amide and imidazole groups were determined experimentally by a careful analysis of Debye-Waller factors in alternate crystallographic models. The agreement with experiment for these orientations suggested that the simulations and their more detailed, thermodynamic predictions were reliable. We found that the ligand prefers an extended, zwitterionic state both in solution and in complexation with the protein. Tc is thus preorganized for binding, while the protein combines lock-and-key behavior for regions close to the ligand's amide, enolate, and ammonium groups, with an induced fit for regions close to the Mg2+ ion. These insights and the modeling techniques employed should be of interest for engineering improved TetR ligands and improved TetR proteins for gene regulation, as well as for drug design.     

Engineering the Ligand Specificity of the Human Galectin-1 by Incorporation of Tryptophan Analogues

Galectin-1 is a β-galactoside-binding lectin with manifold biological functions. A single tryptophan residue (W68) in its carbohydrate binding site plays a major role in ligand binding and is highly conserved among galectins. To fine tune galectin-1 specificity, we introduced several non-canonical tryptophan analogues at this position of human galectin-1 and analyzed the resulting variants using glycan microarrays. Two variants containing 7-azatryptophan and 7-fluorotryptophan showed a reduced affinity for 3’-sulfated oligosaccharides. Their interaction with different ligands was further analyzed by fluorescence polarization competition assay. Using molecular modeling we provide structural clues that the change in affinities comes from modulated interactions and solvation patterns. Thus, we show that the introduction of subtle atomic mutations in the ligand binding site of galectin-1 is an attractive approach for fine-tuning its interactions with different ligands.     

Light-Control over Casein Kinase 1δ Activity with Photopharmacology: A Clear Case for Arylazopyrazole-Based Inhibitors

Protein kinases are responsible for healthy cellular processes and signalling pathways, and their dysfunction is the basis of many pathologies. There are numerous small molecule inhibitors of protein kinases that systemically regulate dysfunctional signalling processes. However, attaining selectivity in kinase inhibition within the complex human kinome is still a challenge that inspires unconventional approaches. One of those approaches is photopharmacology, which uses light-controlled bioactive molecules to selectively activate drugs only at the intended space and time, thereby avoiding side effects outside of the irradiated area. Still, in the context of kinase inhibition, photopharmacology has thus far been rather unsuccessful in providing light-controlled drugs. Here, we present the discovery and optimisation of a photoswitchable inhibitor of casein kinase 1δ (CK1δ), important for the control of cell differentiation, circadian rhythm, DNA repair, apoptosis, and numerous other signalling processes. Varying the position at which the light-responsive azobenzene moiety has been introduced into a known CK1δ inhibitor, LH846, revealed the preferred regioisomer for efficient photo-modulation of inhibitory activity, but the photoswitchable inhibitor suffered from sub-optimal (photo)chemical properties. Replacement of the bis-phenyl azobenzene group with the arylazopyrazole moiety yielded a superior photoswitch with very high photostationary state distributions, increased solubility and a 10-fold difference in activity between irradiated and thermally adapted samples. The reasons behind those findings are explored with molecular docking and molecular dynamics simulations. Results described here show how the evaluation of privileged molecular architecture, followed by the optimisation of the photoswitchable unit, is a valuable strategy for the challenging design of the photoswitchable kinase inhibitors.     

Dissimilar Ligands Bind in a Similar Fashion: A Guide to Ligand Binding-Mode Prediction with Application to CELPP Studies

The molecular similarity principle has achieved great successes in the field of drug design/discovery. Existing studies have focused on similar ligands, while the behaviors of dissimilar ligands remain unknown. In this study, we developed an intercomparison strategy in order to compare the binding modes of ligands with different molecular structures. A systematic analysis of a newly constructed protein–ligand complex structure dataset showed that ligands with similar structures tended to share a similar binding mode, which is consistent with the Molecular Similarity Principle. More importantly, the results revealed that dissimilar ligands can also bind in a similar fashion. This finding may open another avenue for drug discovery. Furthermore, a template-guiding method was introduced for predicting protein–ligand complex structures. With the use of dissimilar ligands as templates, our method significantly outperformed the traditional molecular docking methods. The newly developed template-guiding method was further applied to recent CELPP studies.     

A Molecular-Modeling Toolbox Aimed at Bridging the Gap between Medicinal Chemistry and Computational Sciences

In the current era of high-throughput drug discovery and development, molecular modeling has become an indispensable tool for identifying, optimizing and prioritizing small-molecule drug candidates. The required background in computational chemistry and the knowledge of how to handle the complex underlying protocols, however, might keep medicinal chemists from routinely using in silico technologies. Our objective is to encourage those researchers to exploit existing modeling technologies more frequently through easy-to-use graphical user interfaces. In this account, we present two innovative tools (which we are prepared to share with academic institutions) facilitating computational tasks commonly utilized in drug discovery and development: (1) the VirtualDesignLab estimates the binding affinity of small molecules by simulating and quantifying their binding to the three-dimensional structure of a target protein; and (2) the MD Client launches molecular dynamics simulations aimed at exploring the time-dependent stability of ligand–protein complexes and provides residue-based interaction energies. This allows medicinal chemists to identify sites of potential improvement in their candidate molecule. As a case study, we present the application of our tools towards the design of novel antagonists for the FimH adhesin.     

Computational design of an endo-1,4-beta-xylanase ligand binding site

The field of computational protein design has experienced important recent success. However, the de novo computational design of high-affinity protein-ligand interfaces is still largely an open challenge. Using the Rosetta program, we attempted the in silico design of a high-affinity protein interface to a small peptide ligand. We chose the thermophilic endo-1,4-β-xylanase from Nonomuraea flexuosa as the protein scaffold on which to perform our designs. Over the course of the study, 12 proteins derived from this scaffold were produced and assayed for binding to the target ligand. Unfortunately, none of the designed proteins displayed evidence of high-affinity binding. Structural characterization of four designed proteins revealed that although the predicted structure of the protein model was highly accurate, this structural accuracy did not translate into accurate prediction of binding affinity. Crystallographic analyses indicate that the lack of binding affinity is possibly due to unaccounted for protein dynamics in the 'thumb' region of our design scaffold intrinsic to the family 11 β-xylanase fold. Further computational analysis revealed two specific, single amino acid substitutions responsible for an observed change in backbone conformation, and decreased dynamic stability of the catalytic cleft. These findings offer new insight into the dynamic and structural determinants of the β-xylanase proteins.     

Molecular Recognition of the Catalytic Zinc(II) Ion in MMP-13: Structure-Based Evolution of an Allosteric Inhibitor to Dual Binding Mode Inhibitors with Improved Lipophilic Ligand Efficiencies

Matrix metalloproteinases (MMPs) are a class of zinc dependent endopeptidases which play a crucial role in a multitude of severe diseases such as cancer and osteoarthritis. We employed MMP-13 as the target enzyme for the structure-based design and synthesis of inhibitors able to recognize the catalytic zinc ion in addition to an allosteric binding site in order to increase the affinity of the ligand. Guided by molecular modeling, we optimized an initial allosteric inhibitor by addition of linker fragments and weak zinc binders for recognition of the catalytic center. Furthermore we improved the lipophilic ligand efficiency (LLE) of the initial inhibitor by adding appropriate zinc binding fragments to lower the clogP values of the inhibitors, while maintaining their potency. All synthesized inhibitors showed elevated affinity compared to the initial hit, also most of the novel inhibitors displayed better LLE. Derivatives with carboxylic acids as the zinc binding fragments turned out to be the most potent inhibitors (compound 3 (ZHAWOC5077): IC₅₀ = 134 nM) whereas acyl sulfonamides showed the best lipophilic ligand efficiencies (compound 18 (ZHAWOC5135): LLE = 2.91).     

Characterization of a Cyclic Nucleotide‐Activated K+ Channel and its Lipid Environment by Using Solid‐State NMR Spectroscopy

Voltage‐gated ion channels are large tetrameric multidomain membrane proteins that play crucial roles in various cellular transduction pathways. Because of their large size and domain‐related mobility, structural characterization has proved challenging. We analyzed high‐resolution solid‐state NMR data on different isotope‐labeled protein constructs of a bacterial cyclic nucleotide‐activated K⁺ channel (MlCNG) in lipid bilayers. We could identify the different subdomains of the 4×355 residue protein, such as the voltage‐sensing domain and the cyclic nucleotide binding domain. Comparison to ssNMR data obtained on isotope‐labeled cell membranes suggests a tight association of negatively charged lipids to the channel. We detected spectroscopic polymorphism that extends beyond the ligand binding site, and the corresponding protein segments have been associated with mutant channel types in eukaryotic systems. These findings illustrate the potential of ssNMR for structural investigations on large membrane‐embedded proteins, even in the presence of local disorder.     

Modeling and selection of flexible proteins for structure-based drug design: backbone and side chain movements in p38 MAPK

Receptor rearrangement upon ligand binding (induced fit) is a major stumbling block in docking and virtual screening. Even though numerous studies have stressed the importance of including protein flexibility in ligand docking, currently available methods provide only a partial solution to the problem. Most of these methods, being computer intensive, are often impractical to use in actual drug discovery settings. We had earlier shown that ligand-induced receptor side-chain conformational changes could be modeled statistically using data on known receptor-ligand complexes. In this paper, we show that a similar approach can be used to model more complex changes like backbone flips and loop movements. We have used p38 MAPK as a test case and have shown that a few simple structural features of ligands are sufficient to predict the induced variation in receptor conformations. Rigorous validation, both by internal resampling methods and on an external test set, corroborates this finding and demonstrates the robustness of the models. We have also compared our results with those from an earlier molecular dynamics simulation study on DFG loop conformations of p38 MAPK, and found that the results matched in the two cases. Our statistical approach enables one to predict the final ligand-induced conformation of the active site of a protein, based on a few ligand properties, prior to docking the ligand. We can do this without having to trace the step-by-step process by which this state is arrived at (as in molecular dynamics simulations), thereby drastically reducing computational effort.     

Ligand binding affinities from MD simulations

Simplified free energy calculations based on force field energy estimates of ligand-receptor interactions and thermal conformational sampling have emerged as a useful tool in structure-based ligand design. Here we give an overview of the linear interaction energy (LIE) method for calculating ligand binding free energies from molecular dynamics simulations. A notable feature is that the binding energetics can be predicted by considering only the intermolecular interactions of the ligand in the associated and dissociated states. The approximations behind this approach are examined, and different parametrizations of the model are discussed. LIE-type methods appear particularly promising for computational "lead optimization". Recent applications to protein-protein interactions and ion channel blocking are also discussed.     

Computational Modulation of the V3 Region of Glycoprotein gp125 of HIV-2

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.