Relationships between structure and interaction kinetics for HIV-1 protease inhibitors

Predictive modeling and regression analysis of diverse sulfonamide compounds employed in cancer therapy

Topological indices (TIs) have rich applications in various biological contexts, particularly in therapeutic strategies for cancer. Predicting the performance of compounds in the treatment of cancer is one such application, wherein TIs offer insights into the molecular structures and related properties of compounds. By examining, various compounds exhibit different degree-based TIs, analysts can pinpoint the treatments that are most efficient for specific types of cancer. This paper specifically delves into the topological indices (TIs) implementations in forecasting the biological and physical attributes of innovative compounds utilized in addressing cancer through therapeutic interventions. The analysis being conducted to derivatives of sulfonamides, namely, 4-[(2,4-dichlorophenylsulfonamido)methyl]cyclohexanecarboxylic acid (1), ethyl 4-[(naphthalene-2-sulfonamido)methyl]cyclohexanecarboxylate (2), ethyl 4-[(2,5-dichlorophenylsulfonamido)methyl]cyclohexanecarboxylate (3), 4-[(naphthalene-2-sulfonamido)methyl]cyclohexane-1-carboxylic acid (4) and (2S)-3-methyl-2-(naphthalene-1-sulfonamido)-butanoic acid (5), is performed by utilizing edge partitioning for the computation of degree-based graph descriptors. Subsequently, a linear regression-based model is established to forecast characteristics, like, melting point and formula weight in a quantitative structure-property relationship. The outcomes emphasize the effectiveness or capability of topological indices as a valuable asset for inventing and creating of compounds within the realm of cancer therapy.

Bacterial Glycolipid Acting on Protein Transport Across Membranes

The process of protein transport across membranes involves a variety of factors and has been extensively investigated. Traditionally, proteinaceous translocons and chaperones have been recognized as crucial factors in this process. However, recent studies have highlighted the significant roles played by lipids and a glycolipid present in biological membranes in membrane protein transport. Membrane lipids can influence transport efficiency by altering the physicochemical properties of membranes. Notably, our studies have revealed that diacylglycerol (DAG) attenuates mobility in the membrane core region, leading to a dramatic suppression of membrane protein integration. Conversely, a glycolipid in Escherichia coli inner membranes, named membrane protein integrase (MPIase), enhances integration not only through the alteration of membrane properties but also via direct interactions with membrane proteins. This review explores the mechanisms of membrane protein integration mediated by membrane lipids, specifically DAG, and MPIase. Our results, along with the employed physicochemical analysis methods such as fluorescence measurements, nuclear magnetic resonance, surface plasmon resonance, and docking simulation, are presented to elucidate these mechanisms.

Transient states during the annealing of mismatched and bulged oligonucleotides

Oligonucleotide hybridization is crucial in various biological, prebiotic and nanotechnological processes, including gene regulation, non-enzymatic primer extension and DNA nanodevice assembly. Although extensive research has focused on the thermodynamics and kinetics of nucleic acid hybridization, the behavior of complex mixtures and the outcome of competition for target binding remain less well understood. In this study, we investigate the impact of mismatches and bulges in a 12 bp DNA or RNA duplex on its association (kon) and dissociation (koff) kinetics. We find that such defects have relatively small effects on the association kinetics, while the dissociation kinetics vary in a position-dependent manner by up to 6 orders of magnitude. Building upon this observation, we explored a competition scenario involving multiple oligonucleotides, and observed a transient low specificity of probe hybridization to fully versus partially complementary targets in solution. We characterize these long-lived metastable states and their evolution toward equilibrium, and show that sufficiently long-lived mis-paired duplexes can serve as substrates for prebiotically relevant chemical copying reactions. Our results suggest that transient low accuracy states may spontaneously emerge within all complex nucleic acid systems comprising a large enough number of competing strands, with potential repercussions for gene regulation in the realm of modern biology and the prebiotic preservation of genetic information.

Structures of NF-κB p52 homodimer-DNA complexes rationalize binding mechanisms and transcription activation

The mammalian NF-κB p52:p52 homodimer together with its cofactor Bcl3 activates transcription of κB sites with a central G/C base pair (bp), while it is inactive toward κB sites with a central A/T bp. To understand the molecular basis for this unique property of p52, we have determined the crystal structures of recombinant human p52 protein in complex with a P-selectin(PSel)-κB DNA (5'-GGGGTGACCCC-3') (central bp is underlined) and variants changing the central bp to A/T or swapping the flanking bp. The structures reveal a nearly two-fold widened minor groove in the central region of the DNA as compared to all other currently available NF-κB-DNA complex structures, which have a central A/T bp. Microsecond molecular dynamics (MD) simulations of free DNAs and p52 bound complexes reveal that free DNAs exhibit distinct preferred conformations, and p52:p52 homodimer induces the least amount of DNA conformational changes when bound to the more transcriptionally active natural G/C-centric PSel-κB, but adopts closed conformation when bound to the mutant A/T and swap DNAs due to their narrowed minor grooves. Our binding assays further demonstrate that the fast kinetics favored by entropy is correlated with higher transcriptional activity. Overall, our studies have revealed a novel conformation for κB DNA in complex with NF-κB and pinpoint the importance of binding kinetics, dictated by DNA conformational and dynamic states, in controlling transcriptional activation for NF-κB.

Intermolecular Interactions between a Membrane Protein and a Glycolipid Essential for Membrane Protein Integration

Inducing newly synthesized proteins to appropriate locations is an indispensable biological function in every organism. Integration of proteins into biomembranes in Escherichia coli is mediated by proteinaceous factors, such as Sec translocons and an insertase YidC. Additionally, a glycolipid named MPIase (membrane protein integrase), composed of a long sugar chain and pyrophospholipid, was proven essential for membrane protein integration. We reported that a synthesized minimal unit of MPIase possessing only one trisaccharide, mini-MPIase-3, involves an essential structure for the integration activity. Here, to elucidate integration mechanisms using MPIase, we analyzed intermolecular interactions of MPIase or its synthetic analogs with a model substrate, the Pf3 coat protein, using physicochemical methods. Surface plasmon resonance (SPR) analyses revealed the importance of a pyrophosphate for affinity to the Pf3 coat protein. Compared with mini-MPIase-3, natural MPIase showed faster association and dissociation due to its long sugar chain despite the slight difference in affinity. To focus on more detailed MPIase substructures, we performed docking simulations and saturation transfer difference-nuclear magnetic resonance. These experiments yielded that the 6-O-acetyl group on glucosamine and the phosphate of MPIase play important roles leading to interactions with the Pf3 coat protein. The high affinity of MPIase to the hydrophobic region and the basic amino acid residues of the protein was suggested by docking simulations and proven experimentally by SPR using protein mutants devoid of target regions. These results demonstrated the direct interactions of MPIase with a substrate protein and revealed detailed mechanisms of membrane protein integration.

Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations

We develop an approach to characterize the effects of gating by a multiconformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterize them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multistate gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open, and wide-open) and two inaccessible (closed and a newly identified, "parted") macrostate conformations. The parted conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ = 0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease─having a wide range of experimental association constants (∼10⁴-10¹⁰ M^-1 s^-1)─yields gated rate constants in the range of ∼0.5-5.7 × 10⁸ M^-1 s^-1. This indicates that, whereas the association rate of some inhibitors could be described by the model, for many inhibitors either subsequent conformational transitions or alternate binding mechanisms may be rate-limiting. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.

Influence of molecular rebinding on the reaction rate of complex formation

We simulated Brownian diffusion and reaction-diffusion processes to study the influence of molecular rebinding on the reaction rates of bimolecular reactions. We found that the number of rebinding events, N_reb, is proportional to the target's size and inversely proportional to the diffusion coefficient D and simulation time-step Δt. We found the proportionality constant close to π^-1/2. We confirmed that N_reb is defined as a ratio of the activation-limited rate constant k_a to the diffusion-limited rate constant, k_D. We provide the formula describing the reactivity coefficient κ, modelling the transient-native complex transition for the activation-controlled reaction rates. We show that κ is proportional to (D/Δt)^1/2. Finally, we apply our rebinding-including reaction rate model to the real reactions of photoacid dissociation and protein association. Based on literature data for both types of reactions, we found the Δt time-scale. We show that for the photodissociation of a proton, the Δt is equal to 171 ± 18 fs and the average number of rebinding events is approximately equal to 40. For proteins, Δt is of the order of 100 ps with around 20 rebinding events. In both cases the timescale is similar to the timescale of fluctuation of the solvent molecules surrounding the reactants; vibrations and bending in the case of photoacid dissociation and diffusional motion for proteins.

Evolution of the drug-target residence time model

IntroductionThe pharmacological action of a drug is linked to its affinity for a specific molecular target as quantified by in vitro equilibrium measurements. However, it is clear that for many highly effective drugs, interactions with their molecular targets do not conform to simple, equilibrium conditions in vivo and this results in a temporal discordance between pharmacokinetics and pharmacodynamics. The drug-target residence time model was developed to provide a theoretical framework with which to understand cases in which very slow dissociation of the drug-target complex in vivo results in durable PD effects even after systemic concentrations of drug have waned.Area coveredIn this article, the author provides a brief description of the drug-target residence time model and focuses on the refinements that have been made to the original model to incorporate the influences of compound rebinding in cells and pharmacokinetic properties of drug molecules.Expert opinionThere is now overwhelming evidence for the utility of the drug-target residence time model as a framework for understanding in vivo drug action. The in vitro measured residence time (τ_R) must be used in concert with equilibrium measures of drug-target affinity (e.g. IC₅₀) and with in vivo measures of pharmacokinetic half-life, to afford the researcher a powerful approach to compound optimization for clinical effect. Despite the significant use and refinement of this model, continued studies are required to better understand the dynamic interplay between residence time, target pathobiology, drug distribution and drug pharmacokinetics.

Modern Sensing Approaches for Predicting Toxicological Responses of Food- and Drug-Based Bioactives on Microbiomes of Gut Origin

Recent scientific findings have correlated the gut microbes with homeostasis of human health by delineating their role in pathogen resistance, bioactive metabolization, and immune responses. Foreign materials, like xenobiotics, that induce an altering effect to the human body also influence the gut microbiome to some extent and often limit their use as a result of significant side effects. Investigating the xenobiotic effect of new therapeutic material or edible could be quite painstaking and economically non-viable. Thus, the use of predictive toxicology methods can be an innovative strategy in the food, pharma, and agriculture industries. There are reported in silico, ex vivo, in vitro, and in vivo methods to evaluate such effects but with added drawbacks, such as lower predictability, physiological dissimilarities, and high cost of associated invasive procedures. This review highlights the current and future possibilities with newer modern sensing approaches of economic and time-scale advantages for predicting toxicological responses on gut microbiomes.

Molecular Mechanics Study of Flow and Surface Influence in Ligand-Protein Association

Ligand–protein association is the first and critical step for many biological and chemical processes. This study investigated the molecular association processes under different environments. In biology, cells have different compartments where ligand–protein binding may occur on a membrane. In experiments involving ligand–protein binding, such as the surface plasmon resonance and continuous flow biosynthesis, a substrate flow and surface are required in experimental settings. As compared with a simple binding condition, which includes only the ligand, protein, and solvent, the association rate and processes may be affected by additional ligand transporting forces and other intermolecular interactions between the ligand and environmental objects. We evaluated these environmental factors by using a ligand xk263 binding to HIV protease (HIVp) with atomistic details. Using Brownian dynamics simulations, we modeled xk263 and HIVp association time and probability when a system has xk263 diffusion flux and a non-polar self-assembled monolayer surface. We also examined different protein orientations and accessible surfaces for xk263. To allow xk263 to access to the dimer interface of immobilized HIVp, we simulated the system by placing the protein 20Å above the surface because immobilizing HIVp on a surface prevented xk263 from contacting with the interface. The non-specific interactions increased the binding probability while the association time remained unchanged. When the xk263 diffusion flux increased, the effective xk263 concentration around HIVp, xk263–HIVp association time and binding probability decreased non-linearly regardless of interacting with the self-assembled monolayer surface or not. The work sheds light on the effects of the solvent flow and surface environment on ligand–protein associations and provides a perspective on experimental design.

Drug Re-positioning Studies for Novel HIV-1 Inhibitors Using Binary QSAR Models and Multi-target-driven In Silico Studies

Current antiretroviral therapies against HIV involve the usage of at least two drugs that target different stages of HIV life cycle. However, potential drug interactions and side effects pose a problem. A promising concept for complex disease treatment is 'one molecule-multiple target' approach to overcome undesired effects of multiple drugs. Additionally, it is beneficial to consider drug re-purposing due to the cost of taking a drug into the market. Taking these into account, here potential anti-HIV compounds are suggested by virtually screening small approved drug molecules and clinical candidates. Initially, binary QSAR models are used to predict the therapeutic activity of around 7900 compounds against HIV and to predict the toxicity of molecules with high therapeutic activities. Selected compounds are considered for molecular docking studies against two targets, HIV-1 protease enzyme, and chemokine co-receptor CCR5. The top docking poses for all 549 molecules are then subjected to short (1 ns) individual molecular dynamics (MD) simulations and they are ranked based on their calculated relative binding free energies. Finally, 25 molecules are selected for long (200 ns) MD simulations, and 5 molecules are suggested as promising multi-target HIV agents. The results of this study may open new avenues for the designing of new dual HIV-1 inhibitor scaffolds.

Quantitative Structure Activity/Pharmacokinetics Relationship Studies of HIV-1 Protease Inhibitors Using Three Modelling Methods

BACKGROUND

HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application.

OBJECTIVE

We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities.

METHODS

We collected experimental values of k_off/K_i and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN).

RESULTS

For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r² of 0.688, 0.768 and 0.787, respectively, and r²_pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r² of 0.952, 0.869 and 0.960, respectively, and the r²_pred of 0.852, 0.628 and 0.814, respectively.

CONCLUSION

Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.

Kinetics of Drug Binding and Residence Time

The kinetics of drug binding and unbinding is assuming an increasingly crucial role in the long, costly process of bringing a new medicine to patients. For example, the time a drug spends in contact with its biological target is known as residence time (the inverse of the kinetic constant of the drug-target unbinding, 1/ koff). Recent reports suggest that residence time could predict drug efficacy in vivo, perhaps even more effectively than conventional thermodynamic parameters (free energy, enthalpy, entropy). There are many experimental and computational methods for predicting drug-target residence time at an early stage of drug discovery programs. Here, we review and discuss the methodological approaches to estimating drug binding kinetics and residence time. We first introduce the theoretical background of drug binding kinetics from a physicochemical standpoint. We then analyze the recent literature in the field, starting from the experimental methodologies and applications thereof and moving to theoretical and computational approaches to the kinetics of drug binding and unbinding. We acknowledge the central role of molecular dynamics and related methods, which comprise a great number of the computational methods and applications reviewed here. However, we also consider kinetic Monte Carlo. We conclude with the outlook that drug (un)binding kinetics may soon become a go/no go step in the discovery and development of new medicines.

Heterogeneous CPU+GPU-Enabled Simulations for DFTB Molecular Dynamics of Large Chemical and Biological Systems

We introduce a new heterogeneous CPU+GPU-enhanced DFTB approach for the routine and efficient simulation of large chemical and biological systems. Compared to homogeneous computing with conventional CPUs, heterogeneous computing approaches exhibit substantial performance with only a modest increase in power consumption, both of which are essential to upcoming exascale computing initiatives. We show that DFTB-based molecular dynamics is a natural candidate for heterogeneous computing, since the computational bottleneck in these simulations is the diagonalization of the Hamiltonian matrix, which is performed several times during a single molecular dynamics trajectory. To thoroughly test and understand the performance of our heterogeneous CPU+GPU approach, we examine a variety of algorithmic implementations, benchmarks of different hardware configurations, and applications of this methodology on several large chemical and biological systems. Finally, to demonstrate the capability of our implementation, we conclude with a large-scale DFTB MD simulation of explicitly solvated HIV protease (3974 atoms total) as a proof-of-concept example of an extremely large/complex system which, to the best of our knowledge, is the first time that an entire explicitly solvated protein has been treated at a quantum-based MD level of detail.

Recent Advances in Biosensors Based Nanostructure for Pharmaceutical Analysis

Background:

The development of novel nanostructures for pharmaceutical analysis has received great attention. Biosensors are a class of analytical techniques competent in the rapid quantification of drugs. Recently, the nanostructures have been applied for modification of biosensors.

Objective:

The goal of the present study is to review novel nanostructures for pharmaceutical analysis by biosensors.

Method:

In this review, the application of different biosensors was extensively discussed.

Results:

Biosensors based nanostructures are a powerful alternative to conventional analytical techniques, enabling highly sensitive, real-time, and high-frequency monitoring of drugs without extensive sample preparation. Several examples of their application have been reported.

Conclusion:

The present paper reviews the recent advances on the pharmaceutical analysis of biosensor based nanostructures.

Diverse structural assemblies of U-shaped hydrazinyl-sulfonamides: experimental and theoretical analysis of non-covalent interactions stabilizing solid state conformations

The preparation and structures of five new U-shaped hydrazinyl-sulfonamides are reported.

Prediction of Drug-Target Binding Kinetics by Comparative Binding Energy Analysis

A growing consensus is emerging that optimizing the drug–target affinity alone under equilibrium conditions does not necessarily translate into higher potency in vivo and that instead binding kinetic parameters should be optimized to ensure better efficacy. Therefore, in silico methods are needed to predict the kinetic parameters and the mechanistic determinants of drug–protein binding. Here we demonstrate the application of COMparative BINding Energy (COMBINE) analysis to derive quantitative structure–kinetics relationships (QSKRs) for the dissociation rate constants (k_off) of inhibitors of heat shock protein 90 (HSP90) and HIV-1 protease. We derived protein-specific scoring functions by correlating k_off rate constants with a subset of weighted interaction energy components determined from the energy-minimized structures of drug–protein complexes. As the QSKRs derived for these sets of chemically diverse compounds have good predictive ability and provide insights into important drug–protein interactions for optimizing k_off, COMBINE analysis offers a promising approach for binding kinetics-guided lead optimization.

Single-round isolation of diverse RNA aptamers from a random sequence pool

Aptamers are oligonucleotide ligands with specific binding affinity to target molecules. Generally, RNA aptamers are selected from an RNA pool with random sequences, using the technique termed SELEX, in which the target-binding RNA molecules are repeatedly isolated and exponentially amplified. Despite several advantages, SELEX often produces uncertain results during the iterative amplifications of the rare target-binding RNA molecules. Here, we develop a non-repeated, primer-less and target immobilization-free isolation method for generating RNA aptamers, which is robust to experimental noise. Uniquely, this method focuses on finding and removal of non-aptamer sequences from the RNA pool by RNase digestion leaving target-bound aptamer molecules, and thus is independent of aptamer types. The undigested RNA sequences remaining are so few in number that they must be mixed with a large excess of a known sequence for further manipulations and this sequence is then removed by restriction digestion followed by high-throughput sequencing analysis to identify aptamers. Using this method, we generated multiple RNA aptamers targeting α-thrombin and TGFβ1 proteins, independently. This method potentially generates thousands of sequences as aptamer candidates, which may enable us to predict a common average sequence or structural property of these aptamers that is different from input RNA.

Ligand Binding Pathways and Conformational Transitions of the HIV Protease

It is important to determine the binding pathways and mechanisms of ligand molecules to target proteins to effectively design therapeutic drugs. Molecular dynamics (MD) is a promising computational tool that allows us to simulate protein-drug binding at an atomistic level. However, the gap between the time scales of current simulations and those of many drug binding processes has limited the usage of conventional MD, which has been reflected in studies of the HIV protease. Here, we have applied a robust enhanced simulation method, Gaussian accelerated molecular dynamics (GaMD), to sample binding pathways of the XK263 ligand and associated protein conformational changes in the HIV protease. During two of 10 independent GaMD simulations performed over 500-2500 ns, the ligand was observed to successfully bind to the protein active site. Although GaMD-derived free energy profiles were not fully converged because of insufficient sampling of the complex system, the simulations still allowed us to identify relatively low-energy intermediate conformational states during binding of the ligand to the HIV protease. Relative to the X-ray crystal structure, the XK263 ligand reached a minimum root-mean-square deviation (RMSD) of 2.26 Å during 2.5 μs of GaMD simulation. In comparison, the ligand RMSD reached a minimum of only ∼5.73 Å during an earlier 14 μs conventional MD simulation. This work highlights the enhanced sampling power of the GaMD approach and demonstrates its wide applicability to studies of drug-receptor interactions for the HIV protease and by extension many other target proteins.

Rapid measurement of inhibitor binding kinetics by isothermal titration calorimetry

Although drug development typically focuses on binding thermodynamics, recent studies suggest that kinetic properties can strongly impact a drug candidate’s efficacy. Robust techniques for measuring inhibitor association and dissociation rates are therefore essential. To address this need, we have developed a pair of complementary isothermal titration calorimetry (ITC) techniques for measuring the kinetics of enzyme inhibition. The advantages of ITC over standard techniques include speed, generality, and versatility; ITC also measures the rate of catalysis directly, making it ideal for quantifying rapid, inhibitor-dependent changes in enzyme activity. We used our methods to study the reversible covalent and non-covalent inhibitors of prolyl oligopeptidase (POP). We extracted kinetics spanning three orders of magnitude, including those too rapid for standard methods, and measured sub-nM binding affinities below the typical ITC limit. These results shed light on the inhibition of POP and demonstrate the general utility of ITC-based enzyme inhibition kinetic measurements.

There is growing evidence that the kinetics of interactions between inhibitors and their targets can strongly impact therapeutic efficacy. Here the authors describe an isothermal titration calorimetry-based method that can rapidly quantify inhibition kinetics and measure sub-nM binding affinities.

Streamlined circular proximity ligation assay provides high stringency and compatibility with low-affinity antibodies

Proximity ligation assay (PLA) is a powerful tool for quantitative detection of protein biomarkers in biological fluids and tissues. Here, we present the circular proximity ligation assay (c-PLA), a highly specific protein detection method that outperforms traditional PLA in stringency, ease of use, and compatibility with low-affinity reagents. In c-PLA, two proximity probes bind to an analyte, providing a scaffolding that positions two free oligonucleotides such that they can be ligated into a circular DNA molecule. This assay format stabilizes antigen proximity probe complexes and enhances stringency by reducing the probability of random background ligation events. Circle formation also increases selectivity, since the uncircularized DNA can be removed enzymatically. We compare this method with traditional PLA on several biomarkers and show that the higher stringency for c-PLA improves reproducibility and enhances sensitivity in both buffer and human plasma. The limit of detection ranges from femtomolar to nanomolar concentrations for both methods. Kinetic analyses using surface plasmon resonance (SPR) and biolayer interferometry (BLI) reveal that the variation in limit of detection is due to the variation in antibody affinity and that c-PLA outperforms traditional PLA for low-affinity antibodies. The lower background signal can be used to increase proximity probe concentration while maintaining a high signal-to-noise ratio, thereby enabling the use of low-affinity reagents in a homogeneous assay format. We anticipate that the advantages of c-PLA will be useful in a variety of clinical protein detection applications where high-affinity reagents are lacking.

New approaches for computing ligand-receptor binding kinetics

The recent and growing evidence that the efficacy of a drug can be correlated to target binding kinetics has seeded the development of a multitude of novel methods aimed at computing rate constants for receptor-ligand binding processes, as well as gaining an understanding of the binding and unbinding pathways and the determinants of structure-kinetic relationships. These new approaches include various types of enhanced sampling molecular dynamics simulations and the combination of energy-based models with chemometric analysis. We assess these approaches in the light of the varying levels of complexity of protein-ligand binding processes.

Structure-Affinity Relationships and Structure-Kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A3 Receptor Antagonists

We expanded on a series of pyrido[2,1-f]purine-2,4-dione derivatives as human adenosine A₃ receptor (hA₃R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA₃R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [³⁵S]GTPγS binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a k_on–k_off–K_D kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA₃R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds’ binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA₃R antagonists in the early phase of drug discovery.

Kinetics for Drug Discovery: an industry-driven effort to target drug residence time

A considerable number of approved drugs show non-equilibrium binding characteristics, emphasizing the potential role of drug residence times for in vivo efficacy. Therefore, a detailed understanding of the kinetics of association and dissociation of a target-ligand complex might provide crucial insight into the molecular mechanism-of-action of a compound. This deeper understanding will help to improve decision making in drug discovery, thus leading to a better selection of interesting compounds to be profiled further. In this review, we highlight the contributions of the Kinetics for Drug Discovery (K4DD) Consortium, which targets major open questions related to binding kinetics in an industry-driven public-private partnership.

Rationalizing the Binding Kinetics for the Inhibition of the Burkholderia pseudomallei FabI1 Enoyl-ACP Reductase

There is growing awareness of the link between drug-target residence time and in vivo drug activity, and there are increasing efforts to determine the molecular factors that control the lifetime of a drug-target complex. Rational alterations in the drug-target residence time require knowledge of both the ground and transition states on the inhibition reaction coordinate, and we have determined the structure-kinetic relationship for 22 ethyl- or hexyl-substituted diphenyl ethers that are slow-binding inhibitors of bpFabI1, the enoyl-ACP reductase FabI1 from Burkholderia pseudomallei. Analysis of enzyme inhibition using a two-dimensional kinetic map demonstrates that the ethyl and hexyl diphenyl ethers fall into two distinct clusters. Modifications to the ethyl diphenyl ether B ring result in changes to both on and off rates, where residence times of up to ∼700 min (∼11 h) are achieved by either ground state stabilization (PT444) or transition state destabilization (slower on rate) (PT404). By contrast, modifications to the hexyl diphenyl ether B ring result in residence times of 300 min (∼5 h) through changes in only ground state stabilization (PT119). Structural analysis of nine enzyme:inhibitor complexes reveals that the variation in structure-kinetic relationships can be rationalized by structural rearrangements of bpFabI1 and subtle changes to the orientation of the inhibitor in the binding pocket. Finally, we demonstrate that three compounds with residence times on bpFabI1 from 118 min (∼2 h) to 670 min (∼11 h) have in vivo efficacy in an acute B. pseudomallei murine infection model using the virulent B. pseudomallei strain Bp400.

Mechanism of the Association Pathways for a Pair of Fast and Slow Binding Ligands of HIV-1 Protease

Equilibrium constants, together with kinetic rate constants of binding, are key factors in the efficacy and safety of drug compounds, informing drug design. However, the association pathways of protein-ligand binding, which contribute to their kinetic behaviors, are little understood. In this work, we used unbiased all-atom molecular dynamics (MD) simulations with an explicit solvent model to study the association processes of protein-ligand binding. Using the HIV protease (HIVp)-xk263 and HIVp-ritonavir protein-ligand systems as cases, we observed that ligand association is a multistep process involving diffusion, localization, and conformational rearrangements of the protein, ligand, and water molecules. Moreover, these two ligands preferred different routes of binding, which reflect two well-known binding mechanisms: induced-fit and conformation selection models. Our study shows that xk263 has a stronger capacity for desolvating surrounding water molecules, thereby inducing a semiopen conformation of the HIVp flaps (induced-fit model). In contrast, the slow dehydration characteristic of ritonavir allows for gradual association with the binding pocket of HIVp when the protein's flap conformation is fully open (conformation selection model). By studying the mechanism of ligand association and understanding the role of solvent molecules during the binding event, we can obtain a different perspective on the mechanism of macromolecule recognition, providing insights into drug discovery.

Constructing Interconsistent, Reasonable, and Predictive Models for Both the Kinetic and Thermodynamic Properties of HIV-1 Protease Inhibitors

Accumulated evidence suggests that the in vivo biological potency of a ligand is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of the protein-ligand interaction (PLI). However, the existing experimental and computational techniques are largely insufficient and limited in large-scale measurements or accurate predictions of the kinetic properties of PLI. In this work, elaborate efforts have been made to develop interconsistent, reasonable, and predictive models of the association rate constant (k_on), dissociation rate constant (k_off), and equilibrium dissociation constant (K_D) of a series of HIV protease inhibitors with different structural skeletons. The results showed that nine Volsurf descriptors derived from water (OH2) and hydrophobic (DRY) probes are key molecular determinants for the kinetic and thermodynamic properties of HIV-1 protease inhibitors. To the best of our knowledge, this is the first time that interconsistent and reasonable models with strong prediction power have been established for both the kinetic and thermodynamic properties of HIV protease inhibitors.

Binding Kinetics in Drug Discovery

Over the last years, researchers have increasingly become interested in measuring and understanding drugs' binding kinetics, namely the time in which drug and its target associate and dissociate. Historically, drug discovery programs focused on the optimization of target affinity as a proxy of in-vivo efficacy. However, often the efficacy of a ligand is not appropriately described by the in-vitro measured drug-receptor affinity, but rather depends on the lifetime of the in-vivo drug-receptor interaction. In this review we review recent works that highlight the importance of binding kinetics, molecular determinants for rational optimization and the recent emergence of computational methods as powerful tools in measuring and understanding binding kinetics.

Toward High-Throughput Predictive Modeling of Protein Binding/Unbinding Kinetics

One of the unaddressed challenges in drug discovery is that drug potency determined in vitro is not a reliable indicator of drug activity in vivo. Accumulated evidence suggests that in vivo activity is more strongly correlated with the binding/unbinding kinetics than the equilibrium thermodynamics of protein-ligand interactions (PLIs). However, existing experimental and computational techniques are insufficient in studying the molecular details of kinetics processes of PLIs on a large scale. Consequently, we not only have limited mechanistic understanding of the kinetic processes but also lack a practical platform for high-throughput screening and optimization of drug leads on the basis of their kinetic properties. For the first time, we address this unmet need by integrating coarse-grained normal mode analysis with multitarget machine learning (MTML). To test our method, HIV-1 protease is used as a model system. We find that computational models based on the residue normal mode directionality displacement of PLIs can not only recapitulate the results from all-atom molecular dynamics simulations but also predict protein-ligand binding/unbinding kinetics accurately. When this is combined with energetic features, the accuracy of combined kon and koff prediction reaches 74.35%. Furthermore, our integrated model provides us with new insights into the molecular determinants of the kinetics of PLIs. We propose that the coherent coupling of conformational dynamics and thermodynamic interactions between the receptor and the ligand may play a critical role in determining the kinetic rate constants of PLIs. In conclusion, we demonstrate that residue normal mode directionality displacement can serve as a kinetic fingerprint to capture long-time-scale conformational dynamics of the binding/unbinding kinetics. When this is coupled with MTML, it is possible to screen and optimize compounds on the basis of their binding/unbinding kinetics in a high-throughput fashion. The further development of such computational tools will bridge one of the critical missing links between in vitro compound screening and in vivo drug activity.

The drug-target residence time model: a 10-year retrospective

The drug-target residence time model was first introduced in 2006 and has been broadly adopted across the chemical biology, biotechnology and pharmaceutical communities. While traditional in vitro methods view drug-target interactions exclusively in terms of equilibrium affinity, the residence time model takes into account the conformational dynamics of target macromolecules that affect drug binding and dissociation. The key tenet of this model is that the lifetime (or residence time) of the binary drug-target complex, and not the binding affinity per se, dictates much of the in vivo pharmacological activity. Here, this model is revisited and key applications of it over the past 10 years are highlighted.

Anti-HIV Drug Discovery and Development: Current Innovations and Future Trends

The early effectiveness of combinatorial antiretroviral therapy (cART) in the treatment of HIV infection has been compromised to some extent by rapid development of multidrug-resistant HIV strains, poor bioavailability, and cumulative toxicities, and so there is a need for alternative strategies of antiretroviral drug discovery and additional therapeutic agents with novel action modes or targets. From this perspective, we first review current strategies of antiretroviral drug discovery and optimization, with the aid of selected examples from the recent literature. We highlight the development of phosphate ester-based prodrugs as a means to improve the aqueous solubility of HIV inhibitors, and the introduction of the substrate envelope hypothesis as a new approach for overcoming HIV drug resistance. Finally, we discuss future directions for research, including opportunities for exploitation of novel antiretroviral targets, and the strategy of activation of latent HIV reservoirs as a means to eradicate the virus.

On-rate based optimization of structure-kinetic relationship--surfing the kinetic map

In the lead discovery process residence time has become an important parameter for the identification and characterization of the most efficacious compounds in vivo. To enable the success of compound optimization by medicinal chemistry toward a desired residence time the understanding of structure-kinetic relationship (SKR) is essential. This article reviews various approaches to monitor SKR and suggests using the on-rate as the key monitoring parameter. The literature is reviewed and examples of compound series with low variability as well as with significant changes in on-rates are highlighted. Furthermore, findings of kinetic on-rate changes are presented and potential underlying rationales are discussed.

Surface plasmon resonance as a high throughput method to evaluate specific and non-specific binding of nanotherapeutics

Surface plasmon resonance (SPR) is a powerful analytical technique used to quantitatively examine the interactions between various biomolecules, such as proteins and nucleic acids. The technique has been particularly useful in screening and evaluating binding affinity of novel small molecule and biomolecule-derived therapeutics for various diseases and applications including lupus medications, thrombin inhibitors, HIV protease inhibitors, DNA gyrase inhibitors and many others. Recently, there has been increasing interest in nanotherapeutics (nanoRx), due to their unique properties and potential for controlled release of encapsulated drugs and structure-specific targeting to diseased tissues. NanoRx offer the potential to solve many drug delivery challenges by enabling, specific interactions between molecules on the surface of the nanoparticle and molecules in the diseased tissue, while minimizing off-target interactions toward non-diseased tissues. These properties are largely dependent upon careful control and balance of nanoRx interactions and binding properties with tissues in vivo. Given the great promise of nanoRx with regard to engineering specific molecular interactions, SPR can rapidly quantify small aliquots of nanoRx formulations for desired and undesired molecular interactions. Moving forward, we believe that utilization of SPR in the screening and design of nanoRx has the potential to greatly improve the development of targeted nanoRx formulations and eventually lead to improved therapeutic efficacy. In this review, we discuss (1) the fundamental principles of SPR and basic quantitative analysis of SPR data, (2) previous applications of SPR in the study of non-particulate therapeutics and nanoRx, and (3) future opportunities for the use of SPR in the evaluation of nanoRx.

Switches of hydrogen bonds during ligand-protein association processes determine binding kinetics

Revealing the processes of ligand-protein associations deepens our understanding of molecular recognition and binding kinetics. Hydrogen bonds (H-bonds) play a crucial role in optimizing ligand-protein interactions and ligand specificity. In addition to the formation of stable H-bonds in the final bound state, the formation of transient H-bonds during binding processes contributes binding kinetics that define a ligand as a fast or slow binder, which also affects drug action. However, the effect of forming the transient H-bonds on the kinetic properties is little understood. Guided by results from coarse-grained Brownian dynamics simulations, we used classical molecular dynamics simulations in an implicit solvent model and accelerated molecular dynamics simulations in explicit waters to show that the position and distribution of the H-bond donor or acceptor of a drug result in switching intermolecular and intramolecular H-bond pairs during ligand recognition processes. We studied two major types of HIV-1 protease ligands: a fast binder, xk263, and a slow binder, ritonavir. The slow association rate in ritonavir can be attributed to increased flexibility of ritonavir, which yields multistep transitions and stepwise entering patterns and the formation and breaking of complex H-bond pairs during the binding process. This model suggests the importance of conversions of spatiotemporal H-bonds during the association of ligands and proteins, which helps in designing inhibitors with preferred binding kinetics.

Induced maturation of human immunodeficiency virus

HIV-1 assembles at the plasma membrane of virus-producing cells as an immature, noninfectious particle. Processing of the Gag and Gag-Pol polyproteins by the viral protease (PR) activates the viral enzymes and results in dramatic structural rearrangements within the virion--termed maturation--that are a prerequisite for infectivity. Despite its fundamental importance for viral replication, little is currently known about the regulation of proteolysis and about the dynamics and structural intermediates of maturation. This is due mainly to the fact that HIV-1 release and maturation occur asynchronously both at the level of individual cells and at the level of particle release from a single cell. Here, we report a method to synchronize HIV-1 proteolysis in vitro based on protease inhibitor (PI) washout from purified immature virions, thereby temporally uncoupling virus assembly and maturation. Drug washout resulted in the induction of proteolysis with cleavage efficiencies correlating with the off-rate of the respective PR-PI complex. Proteolysis of Gag was nearly complete and yielded the correct products with an optimal half-life (t(1/2)) of ~5 h, but viral infectivity was not recovered. Failure to gain infectivity following PI washout may be explained by the observed formation of aberrant viral capsids and/or by pronounced defects in processing of the reverse transcriptase (RT) heterodimer associated with a lack of RT activity. Based on our results, we hypothesize that both the polyprotein processing dynamics and the tight temporal coupling of immature particle assembly and PR activation are essential for correct polyprotein processing and morphological maturation and thus for HIV-1 infectivity.

IMPORTANCE

Cleavage of the Gag and Gag-Pol HIV-1 polyproteins into their functional subunits by the viral protease activates the viral enzymes and causes major structural rearrangements essential for HIV-1 infectivity. This proteolytic maturation occurs concomitant with virus release, and investigation of its dynamics is hampered by the fact that virus populations in tissue culture contain particles at all stages of assembly and maturation. Here, we developed an inhibitor washout strategy to synchronize activation of protease in wild-type virus. We demonstrated that nearly complete Gag processing and resolution of the immature virus architecture are accomplished under optimized conditions. Nevertheless, most of the resulting particles displayed irregular morphologies, Gag-Pol processing was not faithfully reconstituted, and infectivity was not recovered. These data show that HIV-1 maturation is sensitive to the dynamics of processing and also that a tight temporal link between virus assembly and PR activation is required for correct polyprotein processing.

Trends in protein-based biosensor assemblies for drug screening and pharmaceutical kinetic studies

The selection of natural and chemical compounds for potential applications in new pharmaceutical formulations constitutes a time-consuming procedure in drug screening. To overcome this issue, new devices called biosensors, have already demonstrated their versatility and capacity for routine clinical diagnosis. Designed to perform analytical analysis for the detection of a particular analyte, biosensors based on the coupling of proteins to amperometric and optical devices have shown the appropriate selectivity, sensibility and accuracy. During the last years, the exponential demand for pharmacokinetic studies in the early phases of drug development, along with the need of lower molecular weight detection, have led to new biosensor structure materials with innovative immobilization strategies. The result has been the development of smaller, more reproducible biosensors with lower detection limits, and with a drastic reduction in the required sample volumes. Therefore in order to describe the main achievements in biosensor fields, the present review has the main aim of summarizing the essential strategies used to generate these specific devices, that can provide, under physiological conditions, a credible molecule profile and assess specific pharmacokinetic parameters.

FLiK: a direct-binding assay for the identification and kinetic characterization of stabilizers of inactive kinase conformations

Despite the hundreds of kinase inhibitors currently in discovery and preclinical phases, the number of FDA-approved kinase inhibitors remains very low by comparison, a discrepancy which reflects the challenges which accompanies kinase inhibitor development. Targeting protein kinases with ATP-competitive inhibitors has been the classical approach to inhibit kinase activity, but the highly conserved nature of the ATP-binding site often contributes to the poor inhibitor selectivity. To address this problem, we developed a high-throughput screening technology that can discriminate for inhibitors, which stabilize inactive kinase conformations by binding within allosteric pockets in the kinase domain. Here, we describe how to use the Fluorescence Labels in Kinases approach to measure the K(d) of ligands as well as how to kinetically characterize the binding and dissociation of ligands to the kinase. We also describe how this technology can be used to rapidly screen small molecule libraries in high throughput.

Subtle Mitsunobu couplings under super-heating: the role of high-throughput continuous flow and microwave strategies

Fragile Mitsunobu reaction can efficiently be performed under super-heating.

Detecting ligand interactions with G protein-coupled receptors in real-time on living cells

G protein-coupled receptors (GPCRs) are a large group of receptors of great biological and clinical relevance. Despite this, the tools for a detailed analysis of ligand-GPCR interactions are limited. The aim of this paper was to demonstrate how ligand binding to GPCRs can be followed in real-time on living cells. This was conducted using two model systems, the radiolabeled porcine peptide YY (pPYY) interacting with transfected human Y2 receptor (hY2R) and the bombesin antagonist RM26 binding to the naturally expressed gastrin-releasing peptide receptor (GRPR). By following the interaction over time, the affinity and kinetic properties such as association and dissociation rate were obtained. Additionally, data were analyzed using the Interaction Map method, which can evaluate a real-time binding curve and present the number of parallel interactions contributing to the curve. It was found that pPYY binds very slowly with an estimated time to equilibrium of approximately 12h. This may be problematic in standard end-point assays where equilibrium is required. The RM26 binding showed signs of heterogeneity, observed as two parallel interactions with unique kinetic properties. In conclusion, measuring binding in real-time using living cells opens up for a better understanding of ligand interactions with GPCRs.