A Universal Strategy for Aptamer‐Based Nanopore Sensing through Host–Guest Interactions inside α‐Hemolysin

Nanopore emerged as a powerful single‐molecule technique over the past two decades, and has shown applications in the stochastic sensing and biophysical studies of individual molecules. Here, we report a versatile strategy for nanopore sensing by employing the combination of aptamers and host–guest interactions. An aptamer is first hybridized with a DNA probe which is modified with a ferrocene?cucurbit[7]uril complex. The presence of analytes causes the aptamer–probe duplex to unwind and release the DNA probe which can quantitatively produce signature current events when translocated through an α‐hemolysin nanopore. The integrated use of magnetic beads can further lower the detection limit by approximately two to three orders of magnitude. Because aptamers have shown robust binding affinities with a wide variety of target molecules, our proposed strategy should be universally applicable for sensing different types of analytes with nanopore sensors.     

Druggability Assessment of Allosteric Proteins by Dynamics Simulations in the Presence of Probe Molecules

Druggability assessment of a target protein has emerged in recent years as an important concept in hit-to-lead optimization. A reliable and physically relevant measure of druggability would allow informed decisions on the risk of investing in a particular target. Here, we define "druggability" as a quantitative estimate of binding sites and affinities for a potential drug acting on a specific protein target. In the present study, we describe a new methodology that successfully predicts the druggability and maximal binding affinity for a series of challenging targets, including those that function through allosteric mechanisms. Two distinguishing features of the methodology are (i) simulation of the binding dynamics of a diversity of probe molecules selected on the basis of an analysis of approved drugs and (ii) identification of druggable sites and estimation of corresponding binding affinities on the basis of an evaluation of the geometry and energetics of bound probe clusters. The use of the methodology for a variety of targets such as murine double mutant-2, protein tyrosine phosphatase 1B (PTP1B), lymphocyte function-associated antigen 1, vertebrate kinesin-5 (Eg5), and p38 mitogen-activated protein kinase provides examples for which the method correctly captures the location and binding affinities of known drugs. It also provides insights into novel druggable sites and the target's structural changes that would accommodate, if not promote and stabilize, drug binding. Notably, the ability to identify high affinity spots even in challenging cases such as PTP1B or Eg5 shows promise as a rational tool for assessing the druggability of protein targets and identifying allosteric or novel sites for drug binding.     

Large scale free energy calculations for blind predictions of protein–ligand binding: the D3R Grand Challenge 2015

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein–ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.     

A Multivalent Structure‐Specific RNA Binder with Extremely Stable Target Binding but Reduced Interaction with Nonspecific RNAs

By greatly enhancing binding affinities against target biomolecules, multivalent interactions provide an attractive strategy for biosensing. However, there is also a major concern for increased binding to nonspecific targets by multivalent binding. A range of charge‐engineered probes of a structure‐specific RNA binding protein PAZ as well as multivalent forms of these PAZ probes were constructed by using diverse multivalent avidin proteins (2‐mer, 4‐mer, and 24‐mer). Increased valency vastly enhanced the binding stability of PAZ to structured target RNA. Surprisingly, nonspecific RNA binding of multivalent PAZ can be reduced even below that of the PAZ monomer by controlling negative charges on both PAZ and multivalent avidin scaffolds. The optimized 24‐meric PAZ showed nearly irreversible binding to target RNA with negligible binding to nonspecific RNA, and this ultra‐specific 24‐meric PAZ probe allowed SERS detection of intact microRNAs at an attomolar level.     

Determining affinity-selected ligands and estimating binding affinities by online size exclusion chromatography/liquid chromatography-mass spectrometry

Size exclusion chromatography (SEC) isolation of affinity-selected ligands combined with reverse phase liquid chromatography-mass spectrometry (LC-MS) is an effective means for identifying members of mixtures which form tightly bound noncovalent complexes with target proteins. A potential liability of the approach is that the SEC isolation is carried out under nonequilibrium conditions favoring protein/ligand complex dissociation. At long SEC isolation times and/or for complexes with fast off-rates the extent of dissociation can jeopardize the ability to detect the affinity-selected components. Additionally, equilibrium binding affinities cannot be exactly determined from the measured distribution of isolated ligands. We present here an online SEC/LC-MS system for determining affinity-selected members of active mixtures which reduces this liability. A kinetic model of the SEC isolation process is developed to determine the practical limits for the application of the method and to extrapolate equilibrium binding affinities from the nonequilibrium data. The utility of online SEC/LC-MS for identifying affinity-selected ligands and for estimating binding affinities is demonstrated for a small molecule mixture of compounds with known binding affinities and for a simple combinatorial mixture.     

Structural and Kinetic Profiling of Allosteric Modulation of Duplex DNA Induced by DNA-Binding Polyamide Analogues

A combined structural and quantitative biophysical profile of the DNA binding affinity, kinetics and sequence-selectivity of hairpin polyamide analogues is described. DNA duplexes containing either target polyamide binding sites or mismatch sequences are immobilized on a microelectrode surface. Quantitation of the DNA binding profile of polyamides containing N-terminal 1-alkylimidazole (Im) units exhibit picomolar binding affinities for their target sequences, whereas 5-alkylthiazole (Nt) units are an order of magnitude lower (low nanomolar). Comparative NMR structural analyses of the polyamide series shows that the steric bulk distal to the DNA-binding face of the hairpin iPr-Nt polyamide plays an influential role in the allosteric modulation of the overall DNA duplex structure. This combined kinetic and structural study provides a foundation to develop next-generation hairpin designs where the DNA-binding profile of polyamides is reconciled with their physicochemical properties.     

Endonuclease-based Method for Detecting the Sequence Specific DNA Binding Protein on Double-stranded DNA Microarray

The double-stranded DNA (dsDNA) probe contains two different protein binding sites. One is for DNA- binding proteins to be detected and the other is for a DNA restriction enzyme. The two sites were arranged together with no base interval. The working principle of the capturing dsDNA probe is described as follows: the capturing probe can be cut with the DNA restriction enzyme (such as EcoR I) to cause a sticky terminal, if the probe is not bound with a target protein, and the sticky terminal can be extended and labeled with Cy3-dUTP by DNA polymerase. When the probe is bound with a target protein, the probe is not capable to be cut by the restriction enzyme because of space obstruction. The amount of the target DNA binding proteins can be measured according to the variations of fluorescent signals of the corresponding probes.     

Surface-grafted poly(acrylic acid) brushes as a precursor layer for biosensing applications: effect of graft density and swellability on the detection efficiency

Carboxyl groups along poly(acrylic acid) (PAA) brushes attached to the surface of a gold-coated substrate served as the precursor moieties for the covalent immobilization of amino-functionalized biotin or bovine serum albumin (BSA) to form a sensing probe for streptavidin (SA) or anti-BSA detection, respectively. Surface-grafted PAA brushes were obtained by acid hydrolysis of poly(tert-butyl acrylate) brushes, formerly prepared by surface-initiated atom transfer radical polymerization of tert-butyl acrylate. As determined by surface plasmon resonance, the PAA brushes immobilized with functionalized biotin or BSA probes not only showed good binding with the designated target analytes but also maintained a high resistance to nonspecific protein adsorption, especially those PAA brushes with a high surface graft density. Although the probe binding capacity can be raised as a function of the graft density of the PAA brushes or the amount of carboxyl groups along the PAA chains, the accessibility of the target analyte to the immobilized probe was limited at the high graft density of the PAA brushes. The effect was far more apparent for the BSA-anti-BSA probe-analyte pair than for the much smaller biotin-SA probe-analyte pair. The impact of the swellability of the PAA brushes, as tailored by the degree of carboxyl group activation, on both the sensing probe immobilization and analyte detection was also addressed. This investigation demonstrated that PAA brushes having a defined graft density have a promising potential as a precursor layer for biosensing applications.     

Significant enhancement of docking sensitivity using implicit ligand sampling

The efficient and accurate quantification of protein-ligand interactions using computational methods is still a challenging task. Two factors strongly contribute to the failure of docking methods to predict free energies of binding accurately: the insufficient incorporation of protein flexibility coupled to ligand binding and the neglected dynamics of the protein-ligand complex in current scoring schemes. We have developed a new methodology, named the 'ligand-model' concept, to sample protein conformations that are relevant for binding structurally diverse sets of ligands. In the ligand-model concept, molecular-dynamics (MD) simulations are performed with a virtual ligand, represented by a collection of functional groups that binds to the protein and dynamically changes its shape and properties during the simulation. The ligand model essentially represents a large ensemble of different chemical species binding to the same target protein. Representative protein structures were obtained from the MD simulation, and docking was performed into this ensemble of protein conformation. Similar binding poses were clustered, and the averaged score was utilized to rerank the poses. We demonstrate that the ligand-model approach yields significant improvements in predicting native-like binding poses and quantifying binding affinities compared to static docking and ensemble docking simulations into protein structures generated from an apo MD simulation.     

Supervised scoring models with docked ligand conformations for structure-based virtual screening

Protein-ligand docking programs have been used to efficiently discover novel ligands for target proteins from large-scale compound databases. However, better scoring methods are needed. Generally, scoring functions are optimized by means of various techniques that affect their fitness for reproducing X-ray structures and protein-ligand binding affinities. However, these scoring functions do not always work well for all target proteins. A scoring function should be optimized for a target protein to enhance enrichment for structure-based virtual screening. To address this problem, we propose the supervised scoring model (SSM), which takes into account the protein-ligand binding process using docked ligand conformations with supervised learning for optimizing scoring functions against a target protein. SSM employs a rough linear correlation between binding free energy and the root mean square deviation of a native ligand for predicting binding energy. We applied SSM to the FlexX scoring function, that is, F-Score, with five different target proteins: thymidine kinase (TK), estrogen receptor (ER), acetylcholine esterase (AChE), phosphodiesterase 5 (PDE5), and peroxisome proliferator-activated receptor gamma (PPARgamma). For these five proteins, SSM always enhanced enrichment better than F-Score, exhibiting superior performance that was particularly remarkable for TK, AChE, and PPARgamma. We also demonstrated that SSM is especially good at enhancing enrichments of the top ranks of screened compounds, which is useful in practical drug screening.     

Diffraction-based assay for detecting multiple analytes

The principles of diffraction are utilized to enable the simultaneous detection of multiple analytes in solution, forming the basis of a multi-analyte sensor. Probe molecules are immobilized on a substrate such that each type of molecule defines a specific pattern within the same region of substrate. The binding of a target molecule to its complementary probe is heralded by a characteristic diffraction image. This principle is demonstrated using antibody conjugates.     

On the magnitude of the chelate effect for the recognition of proteins by pharmacophores scaffolded by self-assembling oligonucleotides

The simultaneous interaction of the binding moieties of a bidentate ligand on adjacent epitopes of a target protein represents an attractive avenue for the discovery of specific, high-affinity binders. We used short DNA fragments in heteroduplex format to scaffold pairs of binding molecules with defined spatial arrangements. Iminobiotin derivates were coupled either via bifunctional linkers or by using various oligonucleotides, thus allowing monovalent or bivalent binding to streptavidin. We determined the binding affinities of the synthesized constructs in solution. We also investigated the efficiency of recovery of superior bidentate ligands in affinity capture experiments, by using both radioactive counts and DNA microarrays as readouts. This analysis confirmed the suitability of the DNA heteroduplex as a scaffold for the identification of synergistic pairs of binding moieties, capable of a high-affinity interaction with protein targets by virtue of the chelate effect.     

Borazine as a sensor for fluoride ion: a computational and experimental study

The computational and experimental studies have revealed that even simple molecule like borazine can act as a sensor for fluoride ions. This study further reported the various binding modes of analytes using quantum chemical calculations and the nature of such interactions have been examined using electron density surface analysis. Total charge transfer analysis (q_CT) correlates well with the binding affinities of analytes with the borazine receptor.     

Systematic mutational analysis of an ubiquitin ligase (MDM2)-binding peptide: computational studies

To understand the importance of amino acids that comprise the peptide PMI (p53-MDM2/MDMX inhibitor), a p53-mimicking peptide with high affinity for the ubiquitin ligase MDM2, computational alanine scanning has been carried out using various protocols. This approach is very useful for identifying regions of a peptide that can be mutated to yield peptides that bind to their targets with higher affinities. Computational alanine scanning is a very useful technique that involves mutating each amino acid of the peptide in its complex with its target (MDM2 in the current study) to alanine, running short simulations on the mutated complex and computing the difference in interaction energies between the mutant peptides and the target protein (MDM2 in the current study) relative to the interaction energy of the original (wild-type) peptide and the target protein (MDM2 in the current study). We find that running multiple short simulations yield values of computed binding affinities (enthalpies) that are similar to those obtained from a long simulation and are well correlated with the trends in the data available from experiments that used Surface Plasmon Resonance to obtain dissociation constants. The p53-mimicking peptides contain three amino acids (F19, W23 and L26) that are major determinants of the interactions between the peptides and MDM2 and form an essential motif. We find in the current study that the trends amongst the contributions to experimental binding affinities of the hydrophobic residues F19, W23 and L26 are the best reproduced in all the computational protocols examined here. This study suggests that running such short simulations may provide a rapid method to redesign peptides to obtain high-affinity variants against a target protein. We further observe that modelling an extended conformation at the C-terminus of the helical PMI peptides, in accord with the conformation of the p53-peptide complexed to MDM2, reproduces the trends seen amongst the experimental affinities of the peptides that carry the alanine mutations at their C-termini. This suggests that some of the mutant peptides possibly interconvert between helical and extended states and can bind to MDM2 in either conformation. This novel feature, not obvious from the crystallographic data, if factored into modelling protocols, may yield novel high-affinity peptides. Our findings suggest that such protocols may enable rapid investigations of at least certain types of amino acid mutations, notably from large to small amino acids.     

Evaluation of alternatives to warfarin as probes for Sudlow site I of human serum albumin: characterization by high-performance affinity chromatography

Warfarin is often used as a site-specific probe for examining the binding of drugs and other solutes to Sudlow site I of human serum albumin (HSA). However, warfarin has strong binding to HSA and the two chiral forms of warfarin have slightly different binding affinities for this protein. Warfarin also undergoes a slow change in structure when present in common buffers used for binding studies. This report examined the use of four related, achiral compounds (i.e., coumarin, 7-hydroxycoumarin, 7-hydroxy-4-methylcoumarin, and 4-hydroxycoumarin) as possible alternative probes for Sudlow site I in drug binding studies. High-performance affinity chromatography and immobilized HSA columns were used to compare and evaluate the binding properties of these probe candidates. Binding for each of the tested probe candidates to HSA was found to give a good fit to a two-site model. The first group of sites had moderate-to-high affinities for the probe candidates with association equilibrium constants that ranged from 6.4 x 10(3)M(-1) (coumarin) to 5.5 x 10(4)M(-1) (4-hydroxycoumarin) at pH 7.4 and 37 degrees C. The second group of weaker, and probably non-specific, binding regions, had association equilibrium constants that ranged from 3.8 x 10(1)M(-1) (7-hydroxy-4-methylcoumarin) to 7.3 x 10(2)M(-1) (coumarin). Competition experiments based on zonal elution indicated that all of these probe candidates competed with warfarin at their high affinity regions. Warfarin also showed competition with coumarin, 7-hydroxycoumarin and 7-hydroxy-4-methycoumarin for their weak affinity sites but appeared to not bind and/or compete for all of the weak sites of 4-hydroxycoumarin. It was found from this group that 4-hydroxycoumarin was the best alternative to warfarin for examining the interactions of drugs at Sudlow site I on HSA. These results also provided information on how the major structural components of warfarin contribute to the binding of this drug at Sudlow site I.     

Development of a fluorescent chalcone library and its application in the discovery of a mouse embryonic stem cell probe

We report the first fluorescent diamino-chalcone library and its application in the discovery of a mouse embryonic stem cell (mESC) probe. CDg4, a novel green fluorescent mESC probe was discovered through a high-content image based screening of 160 members of the chalcone library. Interestingly, the molecular binding target of CDg4 was identified as the glycogen of the stem cell colony surface, rather than a conventional protein target from an intracellular source.     

Two-dimensional turbulent flow chromatography coupled on-line to liquid chromatography–mass spectrometry for solution-based ligand screening against multiple proteins

We present herein a novel bioseparation/chemical analysis strategy for protein–ligand screening and affinity ranking in compound mixtures, designed to increase screening rates and improve sensitivity and ruggedness in performance. The strategy is carried out by combining on-line two-dimensional turbulent flow chromatography (2D-TFC) with liquid chromatography–mass spectrometry (LC–MS), and accomplished through the following steps: (1) a reversed-phase TFC stage to separate the protein/ligand complex from the unbound free molecules, (2) an on-line dissociation process to release the bound ligands from the complexes, and (3) a second mixed-mode cation-exchange/reversed-phase TFC stage to trap the bound ligands and to remove the proteins and salts, followed by LC–MS analysis for identification and determination of the binding affinities. The technique can implement an ultra-fast isolation of protein/ligand complex with the retention time of a complex peak in about 5 s, and on-line prepare the “clean” sample to be directly compatible with the LC–MS analysis. The improvement in performance of this 2D-TFC/LC–MS approach over the conventional approach has been demonstrated by determining affinity-selected ligands of the target proteins acetylcholinesterase and butyrylcholinesterase from a small library with known binding affinities and a steroidal alkaloid library composed of structurally similar compounds. Our results show that 2D-TFC/LC–MS is a generic and efficient tool for high-throughput screening of ligands with low-to-high binding affinities, and structure-activity relationship evaluation.     

Analytical assays based on detecting conformational changes of single molecules.

One common strategy for the detection of biomolecules is labeling either the target itself or an antibody that binds to it. Herein, a different approach, based on detecting the conformational change of a probe molecule induced by binding of the target is discussed. That is, what is being detected is not the presence of the target or the probe, but the conformational change of the probe. Recently, a single-molecule sensor has been developed that exploits this mechanism to detect hybridization of a single DNA oligomer to a DNA probe, as well as specific binding of a single protein to a DNA probe. Biomolecular recognition often involves large conformational changes of the molecules involved, and therefore this strategy may be applicable to other assays.     

Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge

We continued prospective assessments of the Wilma–solvated interaction energy (SIE) platform for pose prediction, binding affinity prediction, and virtual screening on the challenging SAMPL4 data sets including the HIV-integrase inhibitor and two host–guest systems. New features of the docking algorithm and scoring function are tested here prospectively for the first time. Wilma–SIE provides good correlations with actual binding affinities over a wide range of binding affinities that includes strong binders as in the case of SAMPL4 host–guest systems. Absolute binding affinities are also reproduced with appropriate training of the scoring function on available data sets or from comparative estimation of the change in target’s vibrational entropy. Even when binding modes are known, SIE predictions lack correlation with experimental affinities within dynamic ranges below 2 kcal/mol as in the case of HIV-integrase ligands, but they correctly signaled the narrowness of the dynamic range. Using a common protein structure for all ligands can reduce the noise, while incorporating a more sophisticated solvation treatment improves absolute predictions. The HIV-integrase virtual screening data set consists of promiscuous weak binders with relatively high flexibility and thus it falls outside of the applicability domain of the Wilma–SIE docking platform. Despite these difficulties, unbiased docking around three known binding sites of the enzyme resulted in over a third of ligands being docked within 2 Å from their actual poses and over half of the ligands docked in the correct site, leading to better-than-random virtual screening results.     

Prediction of ligand binding affinity using a multiple-conformations-multiple-protonation scheme: application to estrogen receptor α

A fast method that can predict the binding affinities of chemicals to a target protein with a high degree of accuracy will be very useful in drug design and regulatory science. We have been developing a scoring function for affinity prediction, which can be applied to extensive protein systems, and also trying to generate a prediction scheme that specializes in each target protein, with as high a predictive power as possible. In this study, we have constructed a prediction scheme with target-specific scores for estimating ligand-binding affinities to human estrogen receptor α (ERα), considering the major conformational change between agonist- and antagonist-bound forms and the change in protonation states of histidine at the ligand-binding site. The generated scheme calibrated with fewer training compounds (23 for the agonist-bound form, 17 for the antagonist-bound form) demonstrated good predictive power (a predictive r(2) of 0.83 for 154 validation compounds); this was also true for compounds with frameworks that were quite different from those of the training compounds. Our prediction scheme will be useful in drug development targeting ERα and in primary screening of endocrine disruptors, and provides a successful method of affinity prediction considering the major conformational changes in a protein.