New trends in affinity sensing: aptamers for ligand binding

Aptamers are artificial nucleic acid ligands that can be generated against amino acids, drugs, proteins and other molecules. They are isolated from complex libraries of synthetic nucleic acids by an iterative process of adsorption, recovery and amplification. This review described the in vitro process to obtain aptamers (SELEX). It mentions the main characteristics of these molecules (i.e. affinity, specificity and stability). Moreover, it discusses advantages over antibodies. It reports potential applications of aptamers in analytical and diagnostic assays as biocomponents of biosensors (aptasensors) and allosteric ribozymes (aptazymes).     

Ensembling machine learning models to boost molecular affinity prediction

This study unites six popular machine learning approaches to enhance the prediction of a molecular binding affinity between receptors (large protein molecules) and ligands (small organic molecules). Here we examine a scheme where affinity of ligands is predicted against a single receptor – human thrombin, thus, the models consider ligand features only. However, the suggested approach can be repurposed for other receptors. The methods include Support Vector Machine, Random Forest, CatBoost, feed-forward neural network, graph neural network, and Bidirectional Encoder Representations from Transformers. The first five methods use input features based on physico-chemical properties of molecules, while the last one is based on textual molecular representations. All approaches do not rely on atomic spatial coordinates, avoiding a potential bias from known structures, and are capable of generalizing for compounds with unknown conformations. Within each of the methods, we have trained two models that solve classification and regression tasks. Then, all models are grouped into a pipeline of two subsequent ensembles. The first ensemble aggregates six classification models which vote whether a ligand binds to a receptor or not. If a ligand is classified as active (i.e., binds), the second ensemble predicts its binding affinity in terms of the inhibition constant K_i.     

Construction of 3D models of the CYP11B family as a tool to predict ligand binding characteristics

Aldosterone is synthesised by aldosterone synthase (CYP11B2). CYP11B2 has a highly homologous isoform, steroid 11β-hydroxylase (CYP11B1), which is responsible for the biosynthesis of aldosterone precursors and glucocorticoids. To investigate aldosterone biosynthesis and facilitate the search for selective CYP11B2 inhibitors, we constructed three-dimensional models for CYP11B1 and CYP11B2 for both human and rat. The models were constructed based on the crystal structure of Pseudomonas Putida CYP101 and Oryctolagus Cuniculus CYP2C5. Small steric active site differences between the isoforms were found to be the most important determinants for the regioselective steroid synthesis. A possible explanation for these steric differences for the selective synthesis of aldosterone by CYP11B2 is presented. The activities of the known CYP11B inhibitors metyrapone, R-etomidate, R-fadrazole and S-fadrazole were determined using assays of V79MZ cells that express human CYP11B1 and CYP11B2, respectively. By investigating the inhibitors in the human CYP11B models using molecular docking and molecular dynamics simulations we were able to predict a similar trend in potency for the inhibitors as found in the in vitro assays. Importantly, based on the docking and dynamics simulations it is possible to understand the enantioselectivity of the human enzymes for the inhibitor fadrazole, the R-enantiomer being selective for CYP11B2 and the S-enantiomer being selective for CYP11B1.     

eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands

Molecular structures and functions of the majority of proteins across different species are yet to be identified. Much needed functional annotation of these gene products often benefits from the knowledge of protein–ligand interactions. Towards this goal, we developed eFindSite, an improved version of FINDSITE, designed to more efficiently identify ligand binding sites and residues using only weakly homologous templates. It employs a collection of effective algorithms, including highly sensitive meta-threading approaches, improved clustering techniques, advanced machine learning methods and reliable confidence estimation systems. Depending on the quality of target protein structures, eFindSite outperforms geometric pocket detection algorithms by 15–40 % in binding site detection and by 5–35 % in binding residue prediction. Moreover, compared to FINDSITE, it identifies 14 % more binding residues in the most difficult cases. When multiple putative binding pockets are identified, the ranking accuracy is 75–78 %, which can be further improved by 3–4 % by including auxiliary information on binding ligands extracted from biomedical literature. As a first across-genome application, we describe structure modeling and binding site prediction for the entire proteome of Escherichia coli. Carefully calibrated confidence estimates strongly indicate that highly reliable ligand binding predictions are made for the majority of gene products, thus eFindSite holds a significant promise for large-scale genome annotation and drug development projects. eFindSite is freely available to the academic community at http://www.brylinski.org/efindsite.     

D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors

The Drug Design Data Resource (D3R) Grand Challenges present an opportunity to assess, in the context of a blind predictive challenge, the accuracy and the limits of tools and methodologies designed to help guide pharmaceutical drug discovery projects. Here, we report the results of our participation in the D3R Grand Challenge 4 (GC4), which focused on predicting the binding poses and affinity ranking for compounds targeting the $$\beta$$-amyloid precursor protein (BACE-1). Our ligand similarity-based protocol using HYBRID (OpenEye Scientific Software) successfully identified poses close to the native binding mode for most of the ligands with less than 2 Å RMSD accuracy. Furthermore, we compared the performance of our HYBRID-based approach to that of AutoDock Vina and DOCK 6 and found that using a reference ligand to guide the docking process is a better strategy for pose prediction and helped HYBRID to perform better here. We also conducted end-point free energy estimates on molecules dynamics based ensembles of protein-ligand complexes using molecular mechanics combined with generalized Born surface area method (MM-GBSA). We found that the binding affinity ranking based on MM-GBSA scores have poor correlation with the experimental values. Finally, the main lessons from our participation in D3R GC4 are: (i) the generation of the macrocyclic conformers is a key step for successful pose prediction, (ii) the protonation states of the BACE-1 binding site should be treated carefully, (iii) the MM-GBSA method could not discriminate well between different predicted binding poses, and (iv) the MM-GBSA method does not perform well at predicting protein–ligand binding affinities here.     

Solvent models for protein–ligand binding: Comparison of implicit solvent poisson and surface generalized born models with explicit solvent simulations

Solvent effects play a crucial role in mediating the interactions between proteins and their ligands. Implicit solvent models offer some advantages for modeling these interactions, but they have not been parameterized on such complex problems, and therefore, it is not clear how reliable they are. We have studied the binding of an octapeptide ligand to the murine MHC class I protein using both explicit solvent and implicit solvent models. The solvation free energy calculations are more than 10³ faster using the Surface Generalized Born implicit solvent model compared to FEP simulations with explicit solvent. For some of the electrostatic calculations needed to estimate the binding free energy, there is near quantitative agreement between the explicit and implicit solvent model results; overall, the qualitative trends in the binding predicted by the explicit solvent FEP simulations are reproduced by the implicit solvent model. With an appropriate choice of reference system based on the binding of the discharged ligand, electrostatic interactions are found to enhance the binding affinity because the favorable Coulomb interaction energy between the ligand and protein more than compensates for the unfavorable free energy cost of partially desolvating the ligand upon binding. Some of the effects of protein flexibility and thermal motions on charging the peptide in the solvated complex are also considered. © 2001 John Wiley & Sons, Inc. J Comput Chem 22: 591–607, 2001     

Multiple-step ligand injection affinity capillary electrophoresis for determining binding constants of ligands to receptors

This work demonstrates the use of multiple-step ligand injection affinity capillary electrophoresis (ACE) using two model systems: vancomycin from Streptomyces orientalis and carbonic anhydrase B (CAB, EC 4.2.1.1). In this technique a sample plug of receptor and non-interacting standards is injected by pressure and electrophoresed in a buffer containing a given concentration of ligand. The sequence is repeated for all concentrations of ligand generating a single electropherogram containing a series of individual sample plugs superimposed on environments of buffer containing increasing concentrations of ligand. Analysis of the change in the relative migration time ratio, RMTR, relative to the non-interacting standards, as a function of the concentration of the ligand, yields a value for the binding constant. A competitive assay using the technique is also demonstrated using neutral ligands for CAB. These values agree well with those estimated using other binding and ACE techniques. Data demonstrating the quantitative potential of this method are presented.     

Experimental design and measurement uncertainty in ligand binding studies by affinity capillary electrophoresis

In all life sciences ligand binding assays (LBAs) play a crucial role. Unfortunately these are very error prone. One part of this uncertainty results from the unavoidable random measurement uncertainty, another part can be attributed to the experimental design. To investigate the latter, uncertainty propagation was evaluated as a function of the given experimental design. A design space including the normalized maximum response range (nMRR), the data point position (DPP), the data point range (DPR) and the number of data points (NoDP) was defined. Based on ten measured ms ACE source data sets 20 specific parameter sets were selected by Design of Experiments. Monte Carlo simulations using 100 000 repeats for every parameter set were employed. The resulting measurement uncertainty propagation factors (measurement uncertainty multiplier: MUM) were used to describe the whole design space by polynomial regression. The resulting 5‐dimensional response surface was investigated to evaluate the design parameter's influence and to find the minimal uncertainty propagation. It could be shown, that the nMRR is of highest importance, followed by DPP and DPR. Interestingly, the NoDP is less relevant. However, the interactions of the four parameters need to be carefully considered during design optimization. Using at least five data points which cover over 40% of the upper part of the binding hyperbola (DPP > 0.57) the MUM will be minimized (MUM approximately 1.5) when the nMRR is appropriate. It is possible to reduce the measurement uncertainty propagation more than one order of magnitude.     

Distance geometry analysis of ligand binding to drug receptor sites

Summary The method known as distance geometry approach for receptor mapping procedures is discussed. In this method a ligand binding to a certain receptor is considered as a collection of ligand points. Binding sites of the receptor are either empty or filled site points; a ligand point might bind to an empty site point; filled site points indicate that at that point no binding is possible. A binding mode of a ligand is a list of which ligand points coincide with which empty binding sites. The applicability of the method for QSAR studies is discussed; as examples are mentioned the dihydrofolate reductase, ₁- and ₂-receptors. Finally, some ideas on future developments in receptor mapping are discussed.DEDICATION This article is dedicated to the late Dr. Teake Bultsma who introduced the distance geometry approach into our department.     

On-column ligand synthesis coupled to partial-filling affinity capillary electrophoresis to estimate binding constants of ligands to a receptor

This paper describes a two-step procedure whereby on-column ligand synthesis and partial-filling affinity capillary electrophoresis (PFACE) are sequentially coupled to each other to determine the binding constants of 9-fluorenylmethoxy carbonyl (Fmoc)-amino acid-D-Ala-D-Ala species to vancomycin (Van) from Streptomyces orientalis. In this technique four separate plugs of sample are injected onto the capillary column and electrophoresed. The initial sample plug contains a D-Ala-D-Ala terminus peptide and two non-interacting standards. Plugs two and three contain solutions of Fmoc-amino acid-N-hydroxysuccinimide (NHS) ester and running buffer, respectively. The fourth sample plug contains an increasing concentration of Van partially-filled onto the capillary column. Upon electrophoresis the initial D-Ala-D-Ala peptide reacts with the Fmoc-amino acid NHS ester yielding the Fmoc-amino acid D-Ala-D-Ala peptide. Continued electrophoresis results in the overlap of the plugs of Van and Fmoc-amino acid-D-Ala-D-Ala peptide and non-interacting markers. Analysis of the change in the relative migration time ratio of the Fmoc-amino acid-D-Ala-D-Ala peptide relative to the non-interacting standards, as a function of the concentration of Van, yields a value for the binding constant. These values agree well with those estimated using other binding and ACE techniques.     

Predicting ligand binding affinity using on- and off-rates for the SAMPL6 SAMPLing challenge

Interest in ligand binding kinetics has been growing rapidly, as it is being discovered in more and more systems that ligand residence time is the crucial factor governing drug efficacy. Many enhanced sampling methods have been developed with the goal of predicting ligand binding rates (\(k_{\text {on}}\)) and/or ligand unbinding rates (\(k_{\text {off}}\)) through explicit simulation of ligand binding pathways, and these methods work by very different mechanisms. Although there is not yet a blind challenge for ligand binding kinetics, here we take advantage of experimental measurements and rigorously computed benchmarks to compare estimates of \(K_D\) calculated as the ratio of two rates: \(k_{\text {off}}/k_{\text {on}}\). These rates were determined using a new enhanced sampling method based on the weighted ensemble framework that we call “REVO”: Reweighting of Ensembles by Variance Optimization. This is a further development of the WExplore enhanced sampling method, in which trajectory cloning and merging steps are guided not by the definition of sampling regions, but by maximizing trajectory variance. Here we obtain estimates of \(k_{\text {on}}\) and \(k_{\text {off}}\) that are consistent across multiple simulations, with an average log10-scale standard deviation of 0.28 for on-rates and 0.56 for off-rates, which is well within an order of magnitude and far better than previously observed for previous applications of the WExplore algorithm. Our rank ordering of the three host–guest pairs agrees with the reference calculations, however our predicted \(\Delta G\) values were systematically lower than the reference by an average of 4.2 kcal/mol. Using tree network visualizations of the trajectories in the REVO algorithm, and conformation space networks for each system, we analyze the results of our sampling, and hypothesize sources of discrepancy between our \(K_D\) values and the reference. We also motivate the direct inclusion of \(k_{\text {on}}\) and \(k_{\text {off}}\) challenges in future iterations of SAMPL, to further develop the field of ligand binding kinetics prediction and modeling.     

Estimation of binding constants between ristocetin and teicoplanin and peptides using on-column ligand derivatization coupled to affinity capillary electrophoresis

This work utilizes on-column ligand synthesis and affinity capillary electrophoresis (ACE) to determine binding constants (K_b) of 9-flourenylmethyloxy carbonyl (Fmoc)-amino acid derivatives to the glycopeptide antibiotics ristocetin (Rist) and teicoplanin (Teic). In this technique, two separate plugs of sample are injected on to the capillary column and electrophoresed. The initial sample plug contains a d-Ala-d-Ala terminus peptide and either one or two non-interacting standard(s). The second plug contains a Fmoc-amino acid-N-hydroxysuccinimide (NHS) ester. The electrophoresis is then carried out with an increasing concentration of Rist or Teic in the running buffer. Upon electrophoresis the initial d-Ala-d-Ala peptide reacts with the Fmoc-amino acid yielding a new Fmoc-amino acid-d-Ala-d-Ala peptide derivative. Continued electrophoresis results in the binding of Rist or Teic to the Fmoc-amino acid-d-Ala-d-Ala peptide derivatives. Analysis of the change in the relative migration time ratio (RMTR) or electrophoretic mobility () of the Fmoc-amino acid-d-Ala-d-Ala peptide derivatives relative to the non-interacting standards, as a function of the concentration of Rist and Teic, yields a value for K_b. These findings demonstrate the advantage of coupling on-column ligand synthesis to ACE for estimating binding parameters between antibiotics and ligands.Abbreviations Rist Ristocetin - Teic Teicoplanin - ACE Affinity capillary electrophoresis - RMTR Relative migration time ratio     

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.     

Kinetics of ligand binding to membrane receptors from equilibrium fluctuation analysis of single binding events

Equilibrium fluctuation analysis of single binding events has been used to extract binding kinetics of ligand interactions with cell-membrane bound receptors. Time-dependent total internal reflection fluorescence (TIRF) imaging was used to extract residence-time statistics of fluorescently stained liposomes derived directly from cell membranes upon their binding to surface-immobilized antibody fragments. The dissociation rate constants for two pharmaceutical relevant antibodies directed against different B-cell expressed membrane proteins was clearly discriminated, and the affinity of the interaction could be determined by inhibiting the interaction with increasing concentrations of soluble antibodies. The single-molecule sensitivity made the analysis possible without overexpressed membrane proteins, which makes the assay attractive in early drug-screening applications.     

Multiple ligand detection and affinity measurement by ultrafiltration and mass spectrometry analysis applied to fragment mixture screening

Binding affinity of a small molecule drug candidate to a therapeutically relevant biomolecular target is regarded the first determinant of the candidate's efficacy. Although the ultrafiltration-LC/MS (UF-LC/MS) assay enables efficient ligand discovery for a specific target from a mixed pool of compounds, most previous analysis allowed for relative affinity ranking of different ligands. Moreover, the reliability of affinity measurement for multiple ligands with UF-LC/MS has hardly been strictly evaluated. In this study, we examined the accuracy of K_d determination through UF-LC/MS by comparison with classical ITC measurement. A single-point K_d calculation method was found to be suitable for affinity measurement of multiple ligands bound to the same target when binding competition is minimized. A second workflow based on analysis of the unbound fraction of compounds was then developed, which simplified sample preparation as well as warranted reliable ligand discovery. The new workflow implemented in a fragment mixture screen afforded rapid and sensitive detection of low-affinity ligands selectively bound to the RNA polymerase NS5B of hepatitis C virus. More importantly, ligand identification and affinity measurement for mixture-based fragment screens by UF-LC/MS were in good accordance with single ligand evaluation by conventional SPR analysis. This new approach is expected to become a valuable addition to the arsenal of high-throughput screening techniques for fragment-based drug discovery.     

Rate constant for diffusion-influenced ligand binding to receptors of arbitrary shape on a cell surface

The theory of ligand binding to receptors on a cell surface suggested by Berg and Purcell and generalized by Zwanzig and Szabo uses the assumption that receptors are circular absorbing disks on an otherwise reflecting sphere. One of the key ingredients of this theory is a solution for the rate constant for ligand binding to a single circular receptor on a reflecting plane. We give an exact solution for the rate constant for binding to a single elliptic receptor and an approximate solution for binding to a single receptor of more general shape. The latter was tested by Brownian dynamics simulations. We found that the approximate formula predicted the rate constant with better than 10% accuracy for all studied receptor shapes. Using our solutions one can find the rate constant for ligand binding to a cell covered by N noncircular receptors by means of the Zwanzig-Szabo formula.     

Betulinic acid binding to human serum albumin: a study of protein conformation and binding affinity

Betulinic acid (BA) has anti cancer and anti-HIV activity and has been proved to be therapeutically effective against cancerous and HIV-infected cells. Human serum albumin (HSA) is the predominant protein in the blood. Most drugs that bind to HSA will be transported to other parts of the body. Using micro TOF-Q mass spectrometry, we have shown, for the first time that BA isolated from a plant (Tephrosia calophylla) binds to HSA. The binding constant of BA to HSA was calculated from fluorescence data and found to be K(BA)=1.685+/-0.01 x 10(6) M(-1), indicating a strong binding affinity. The secondary structure of the HSA-BA complex was determined by circular dichroism. The results indicate that the HSA in this complex is partially unfolded. Further, binding of BA at nanomolar concentrations of BA to free HSA was detected using micro TOF-Q mass spectrometry. The study revealed a mass increase from 65199 Da (free HSA) to 65643 Da (HSA+drug), where the additional mass of 444 Da was due to bound BA. Based on the results of this study, it is suggested that micro TOF-Q mass spectrometry is useful technique for drug binding studies.     

Molecular determinants for ligand binding at Nav1.4 and Nav1.7 channels: Experimental affinity results analyzed by molecular modeling

Here, we focused on exploring the selectivity mechanism against Nav1.7 over Nav1.4 due to different binding modes of two selected inhibitors. By the superposition of Nav1.7 and Nav1.4 proteins, we selected the most homologous chain of Nav1.7 with Nav1.4, defining the active site of Nav1.4-VSD4 based on the aryl sulfonamide binding site of Nav1.7-VSD4. Comparison of the conformations exhibited by Tyr1386 (Nav1.4) and Tyr1537 (Nav1.7) suggested that the steric hindrance caused by Tyr1386 owned primary influence on inhibition selectivity, which was further verified through molecular docking and MD simulation of two representative inhibitors. Our finding would be helpful for discovery of selective Nav1.7 inhibitors over Nav1.4.     

The investigation of argon plasma surface modification to polyethylene: Quantitative ATR-FTIR spectroscopic analysis

This paper studied the surface modification of argon plasma to polyethylene by using ATR-FTIR analysis. The mass loss ratio has maximum value at discharge time of 70-120 s or discharge power of 62 W by using argon plasma treatment for polyethylene. New surface structure was formed after polyethylene was treated by argon plasma. The peroxide bond peak area also has maximum value at discharge time of 70-120 s or discharge power of 62 W. The CC nonsaturated double bond absorb peaks were appeared at 1640 cm⁻¹, 1549 cm⁻¹ and 1528 cm⁻¹ after polyethylene treated by argon plasma. The CC nonsaturated double bond absorb peak area has minimum value at discharge time of 60-70 s and the power of 65 W. The CC nonsaturated double bond absorb peak area has maximum value at discharge power of 62-72 W and the discharge time of 2 min. The absorption peak intensity of 2916 cm⁻¹ methylene nonsymmetry stretch vibration, 2848 cm⁻¹ methylene symmetry stretch vibration, 1463 cm⁻¹ methylene nonsymmetry changing angle vibration, and 719 cm⁻¹ methylene swing in plane vibration was decreased greatly. The four absorption peaks intensity has maximum value at discharge time of 120 s or discharge power of 62 W.