Mapping the binding sites of challenging drug targets

An increasing number of medically important proteins are challenging drug targets because their binding sites are too shallow or too polar, are cryptic and thus not detectable without a bound ligand or located in a protein–protein interface. While such proteins may not bind druglike small molecules with sufficiently high affinity, they are frequently druggable using novel therapeutic modalities. The need for such modalities can be determined by experimental or computational fragment based methods. Computational mapping by mixed solvent molecular dynamics simulations or the FTMap server can be used to determine binding hot spots. The strength and location of the hot spots provide very useful information for selecting potentially successful approaches to drug discovery.     

Receptor–ligand molecular docking

Docking methodology aims to predict the experimental binding modes and affinities of small molecules within the binding site of particular receptor targets and is currently used as a standard computational tool in drug design for lead compound optimisation and in virtual screening studies to find novel biologically active molecules. The basic tools of a docking methodology include a search algorithm and an energy scoring function for generating and evaluating ligand poses. In this review, we present the search algorithms and scoring functions most commonly used in current molecular docking methods that focus on protein–ligand applications. We summarise the main topics and recent computational and methodological advances in protein–ligand docking. Protein flexibility, multiple ligand binding modes and the free-energy landscape profile for binding affinity prediction are important and interconnected challenges to be overcome by further methodological developments in the docking field.     

Computational design of protein-ligand interfaces: potential in therapeutic development

Computational design of protein-ligand interfaces finds optimal amino acid sequences within a small-molecule binding site of a protein for tight binding of a specific small molecule. It requires a search algorithm that can rapidly sample the vast sequence and conformational space, and a scoring function that can identify low energy designs. This review focuses on recent advances in computational design methods and their application to protein-small molecule binding sites. Strategies for increasing affinity, altering specificity, creating broad-spectrum binding, and building novel enzymes from scratch are described. Future prospects for applications in drug development are discussed, including limitations that will need to be overcome to achieve computational design of protein therapeutics with novel modes of action.     

Application of surface plasmon resonance toward studies of low-molecular-weight antigen-antibody binding interactions

Methods for studying low-molecular-weight antigen-antibody binding interactions using surface plasmon resonance detection are presented. The experimental parameters most relevant to studies of low-molecular-weight antigen-antibody binding interactions are discussed. Direct kinetic analysis of the binding interactions is most informative, providing both apparent association and dissociation rate constants from which equilibrium constants can be calculated. Equilibrium analysis, including steady-state and solution affinity studies, offers an alternative approach to direct kinetic analysis when knowledge of the individual kinetic rate constants is not required or difficult to determine. The various methods are illustrated by studies of an anti-T(4) Fab fragment binding interaction with several thyroxine analogs. The methods utilized were dependent on the affinity of the interaction. The high-affinity anti-T(4) Fab fragment/l-T(4) binding interaction was evaluated using direct kinetic analysis. An intermediate affinity anti-T(4) Fab fragment/l-T(3) binding interaction was evaluated using a combination of direct kinetic analysis, steady-state analysis, and solution affinity analysis. The relatively weak anti-T(4) Fab fragment/l-T(2) binding interaction was evaluated using steady-state and solution affinity analysis protocols. Several thyroxine tracers that could not be immobilized to a biosensor surface were also evaluated via the solution affinity format. In cases where a given binding interaction was examined using multiple methods the results were comparable.     

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Inferring potential drug indications, for either novel or approved drugs, is a key step in drug development. Previous computational methods in this domain have focused on either drug repositioning or matching drug and disease gene expression profiles. Here, we present a novel method for the large‐scale prediction of drug indications (PREDICT) that can handle both approved drugs and novel molecules. Our method is based on the observation that similar drugs are indicated for similar diseases, and utilizes multiple drug–drug and disease–disease similarity measures for the prediction task. On cross‐validation, it obtains high specificity and sensitivity (AUC=0.9) in predicting drug indications, surpassing existing methods. We validate our predictions by their overlap with drug indications that are currently under clinical trials, and by their agreement with tissue‐specific expression information on the drug targets. We further show that disease‐specific genetic signatures can be used to accurately predict drug indications for new diseases (AUC=0.92). This lays the computational foundation for future personalized drug treatments, where gene expression signatures from individual patients would replace the disease‐specific signatures.     

Compressed images for affinity prediction-2 (CIFAP-2): an improved machine learning methodology on protein–ligand interactions based on a study on caspase 3 inhibitors

Abstract

The aim of this study is to propose an improved computational methodology, which is called Compressed Images for Affinity Prediction-2 (CIFAP-2) to predict binding affinities of structurally related protein–ligand complexes. CIFAP-2 method is established based on a protein–ligand model from which computational affinity information is obtained by utilizing 2D electrostatic potential images determined for the binding site of protein–ligand complexes. The quality of the prediction of the CIFAP-2 algorithm was tested using partial least squares regression (PLSR) as well as support vector regression (SVR) and adaptive neuro-fuzzy ?nference system (ANFIS), which are highly promising prediction methods in drug design. CIFAP-2 was applied on a protein–ligand complex system involving Caspase 3 (CASP3) and its 35 inhibitors possessing a common isatin sulfonamide pharmacophore. As a result, PLSR affinity prediction for the CASP3–ligand complexes gave rise to the most consistent information with reported empirical binding affinities (pIC₅₀) of the CASP3 inhibitors.     

DynaBiS: A hierarchical sampling algorithm to identify flexible binding sites for large ligands and peptides

Knowing the ligand or peptide binding site in proteins is highly important to guide drug discovery, but experimental elucidation of the binding site is difficult. Therefore, various computational approaches have been developed to identify potential binding sites in protein structures. However, protein and ligand flexibility are often neglected in these methods due to efficiency considerations despite the recognition that protein–ligand interactions can be strongly affected by mutual structural adaptations. This is particularly true if the binding site is unknown, as the screening will typically be performed based on an unbound protein structure. Herein we present DynaBiS, a hierarchical sampling algorithm to identify flexible binding sites for a target ligand with explicit consideration of protein and ligand flexibility, inspired by our previously presented flexible docking algorithm DynaDock. DynaBiS applies soft-core potentials between the ligand and the protein, thereby allowing a certain protein–ligand overlap resulting in efficient sampling of conformational adaptation effects. We evaluated DynaBiS and other commonly used binding site identification algorithms against a diverse evaluation set consisting of 26 proteins featuring peptide as well as small ligand binding sites. We show that DynaBiS outperforms the other evaluated methods for the identification of protein binding sites for large and highly flexible ligands such as peptides, both with a holo or apo structure used as input.     

Thermodynamic and kinetic parameters of lipase-catalyzed ester hydrolysis in biphasic systems with varying organic solvents

Kinetics of lipase-catalyzed hydrolysis of esters were modeled using reactant activities for aqueous-organic, biphasic systems. By using thermodynamic activities of the substrates in ordinary rate equations, the kinetic parameters were corrected for the contribution of substrate-solvent interactions and a uniform quantification of the substrates for lipase attached to the interface can be achieved. The kinetic parameters, on the basis of their thermodynamic activities, should be constant in different systems, provided that the solvents do not interfere with the binding of the substrates to the enzyme nor affect the catalytic mechanism. Experimental and computational methods on how to obtain the thermodynamic activities of the substrates are presented. Initial rates were determined for Pseudomonas cepacia lipase (PcL)-catalyzed hydrolysis of decyl chloroacetate in dynamic emulsions with various solvents. The thermodynamic equilibrium and corrected kinetic constants for this reaction appeared to be similar in various systems. The kinetics of PcL in an isooctane-aqueous biphasic system could be adequately described with the rate equation for a ping-pong mechanism. The observed inhibitory effect of decanol appeared to be a consequence of this mechanism, allowing the backreaction of the decanol with the chloroacetyl-enzyme complex. The kinetic performance of PcL in systems with toluene, dibutyl ether, and methyl isobutyl ketone could be less well described. The possible causes for this and for the remaining differences in corrected kinetic parameters are discussed. (c) 1995 John Wiley & Sons, Inc.     

Timing is everything the role of kinetics in G protein activation

The binding of a drug to a G-protein coupled receptor initiates a complex series of dynamic events that ultimately leads to a cellular response. In addition to the concentrations of receptor, drug and G-protein, important determinants of the cellular response are the rates at which these species interact. However, most models for G-protein coupled receptor signaling are equilibrium models that neglect the role of reaction kinetics. A kinetic ternary-complex model of signaling through G-protein coupled receptors is presented. We demonstrate that this kinetic model can make significantly different predictions than an equilibrium ternary complex model, which provides a different perspective on multiple aspects of the signal transduction cascade, such as agonist efficacy, the effect of precoupled receptors, and the role of RGS proteins. Incorporation of the reaction kinetics is critical for a complete understanding of signal transduction and will ultimately impact the fields of drug discovery and drug design.     

Surface plasmon resonance analysis of antifungal azoles binding to CYP3A4 with kinetic resolution of multiple binding orientations

The heme-containing cytochrome P450s (CYPs) are a major enzymatic determinant of drug clearance and drug-drug interactions. The CYP3A4 isoform is inhibited by antifungal imidazoles or triazoles, which form low-spin heme iron complexes via formation of a nitrogen-ferric iron coordinate bond. However, CYP3A4 also slowly oxidizes the antifungal itraconazole (ITZ) at a site that is approximately 25 A from the triazole nitrogens, suggesting that large antifungal azoles can adopt multiple orientations within the CYP3A4 active site. Here, we report a surface plasmon resonance (SPR) analysis with kinetic resolution of two binding modes of ITZ, and the related drug ketoconazole (KTZ). SPR reveals a very slow off-rate for one binding orientation. Multiphasic binding kinetics are observed, and one of the two binding components resolved by curve fitting exhibits "equilibrium overshoot". Preloading of CYP3A4 with the heme ligand imidazole abolishes this component of the antifungal azole binding trajectories, and it eliminates the conspicuously slow off-rate. The fractional populations of CYP3A4 complexes corresponding to different drug orientations can be manipulated by altering the duration of the pulse of drug exposure. UV-vis difference absorbance titrations yield low-spin spectra and K(D) values that are consistent with the high-affinity complex resolved by SPR. These results demonstrate that ITZ and KTZ bind in multiple orientations, including a catalytically productive mode and a slowly dissociating inhibitory mode. Most importantly, they provide the first example of a SPR-based method for the kinetic characterization of binding of a drug to any human CYP, including mechanistic insight not available from other methods.     

Biosensor-surface plasmon resonance: quantitative analysis of small molecule-nucleic acid interactions

Surface plasmon resonance (SPR)-biosensor techniques directly provide essential information for the study and characterization of small molecule-nucleic acid interactions, and the use of these methods is steadily increasing. The method is label-free and monitors the interactions in real time. Both dynamic and steady-state information can be obtained for a wide range of reaction rates and binding affinities. This article presents the basics of the SPR technique, provides suggestions for experimental design, and illustrates data processing and analysis of results. A specific example of the interaction of a well-known minor groove binding agent, netropsin, with DNA is evaluated by both kinetic and steady-state SPR methods. Three different experiments are used to illustrate different approaches and analysis methods. The three sets of results show the reproducibility of the binding constants and agreement from both steady-state and kinetic analyses. These experiments also show that reliable kinetic information can be obtained, even with difficult systems, if the experimental conditions are optimized to minimize mass transport effects. Limitations of the biosensor-SPR technique are also discussed to provide an awareness of the care needed to conduct a successful experiment.     

In vivo approaches and rationale for quantifying kinetics and imaging brain lipid metabolic pathways

Developing a kinetic strategy to examine rates of lipid metabolic pathways can help to elucidate the roles that lipids play in tissue function and structure in health and disease. This review summarizes such a strategy, and shows how it has been applied to quantify different kinetic aspects of brain lipid metabolism in animals and humans. Methods involve injecting intravenously a radioactive or heavy isotope labeled substrate that will be incorporated into a lipid metabolic pathway, and using chemical analytical and/or imaging procedures (e.g., quantitative autoradiography or positron emission tomography) to determine tracer distribution in brain regions and their lipid compartments as a function of time. From the measurements, fluxes, turnover rates, half-lives and ATP consumption rates can be calculated, and incorporation rates can be imaged. Experimental changes in these kinetic parameters can help to identify changes in the expression of regulatory enzymes, and thus aid in drug targeting. Cases that are discussed are arachidonic acid turnover and imaging of neuroreceptor-initiated phospholipase A2 activation, ether phospholipid biosynthesis, and kinetics of the phosphatidylinositol cycle.     

Integrating cell-level kinetic modeling into the design of engineered protein therapeutics

Functional genomics and proteomics are identifying many potential drug targets for novel therapeutic proteins, and both rational and combinatorial protein engineering methods are available for creating drug candidates. A central challenge is the definition of the most appropriate design criteria, which will benefit critically from computational kinetic models that incorporate integration from the molecular level to the whole systems level. Interpretation of these processes will require mathematical models that are refined in combination with relevant data derived from quantitative assays, to correctly set biophysical objectives for protein design.     

Structure-based computational analysis of protein binding sites for function and druggability prediction

Protein binding sites are the places where molecular interactions occur. Thus, the analysis of protein binding sites is of crucial importance to understand the biological processes proteins are involved in. Herein, we focus on the computational analysis of protein binding sites and present structure-based methods that enable function prediction for orphan proteins and prediction of target druggability. We present the general ideas behind these methods, with a special emphasis on the scopes and limitations of these methods and their validation. Additionally, we present some successful applications of computational binding site analysis to emphasize the practical importance of these methods for biotechnology/bioeconomy and drug discovery.     

The kinetic mechanism for cytochrome P450 metabolism of type II binding compounds: evidence supporting direct reduction

The metabolic stability of a drug is an important property that should be optimized during drug design and development. Nitrogen incorporation is hypothesized to increase the stability by coordination of nitrogen to the heme iron of cytochrome P450, a binding mode that is referred to as type II binding. However, we noticed that the type II binding compound 1 has less metabolic stability at sub-saturating conditions than a closely related type I binding compound 3. Three kinetic models will be presented for type II binder metabolism; (1) Dead-end type II binding, (2) a rapid equilibrium between type I and II binding modes before reduction, and (3) a direct reduction of the type II coordinated heme. Data will be presented on reduction rates of iron, the off rates of substrate (using surface plasmon resonance) and the catalytic rate constants. These data argue against the dead-end, and rapid equilibrium models, leaving the direct reduction kinetic mechanism for metabolism of the type II binding compound 1.     

Kinetics and mechanics of cell adhesion

Cell adhesion is mediated by specific interaction between receptors and ligands. Such interaction provides not only physical linkage but also communication between the cell and its environment. The kinetics and mechanics of cell adhesion are coupled, because force can influence the formation and dissociation of receptor-ligand bonds. The kinetic rates and their force dependence determine how likely, how rapidly and how strongly cells bind as well as how long they remain bound. Since adhesion molecules are linked to apposing cellular membranes, their interaction is governed by two-dimensional (2D) kinetics. This is in contrast to the three-dimensional (3D) binding of soluble ligands to cell surface receptors. Unlike the 3D case in which many methods are available for measuring kinetic rates, not until recently have the 2D kinetic rates become experimentally measurable. In this review, I will discuss the recent progress in the experimental methods that enable quantification of the relevant kinetic and mechanical parameters, the fundamental concepts that underlie the physics of the biological phenomena, and the mathematical models that relate functions to the intrinsic properties of the adhesion molecules.     

Parameterization of an effective potential for protein–ligand binding from host–guest affinity data

Force field accuracy is still one of the “stalemates” in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein–ligand binding, organic host–guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host–guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein–ligand binding in two drug targets against the HIV‐1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics‐based binding free energy models can be used to evaluate and optimize force fields for protein–ligand systems. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison analysis of primary ligand‐binding sites in seven‐helix membrane proteins

Seven‐helix transmembrane proteins, including the G‐protein‐coupled receptors (GPCRs), mediate a broad range of fundamental cellular activities through binding to a wide range of ligands. Understanding the structural basis for the ligand‐binding selectivity of these proteins is of significance to their structure‐based drug design. Comparison analysis of proteins' ligand‐binding sites provides a useful way to study their structure‐activity relationships. Various computational methods have been developed for the binding‐site comparison of soluble proteins. In this work, we applied this approach to the analysis of the primary ligand‐binding sites of 92 seven‐helix transmembrane proteins. Results of the studies confirmed that the binding site of bacterial rhodopsins is indeed different from all GPCRs. In the latter group, further comparison of the binding sites indicated a group of residues that could be responsible for ligand‐binding selectivity and important for structure‐based drug design. Furthermore, unexpected binding‐site dissimilarities were observed among adrenergic and adenosine receptors, suggesting that the percentage of the overall sequence identity between a target protein and a template protein alone is not sufficient for selecting the best template for homology modeling of seven‐helix membrane proteins. These results provided novel insight into the structural basis of ligand‐binding selectivity of seven‐helix membrane proteins and are of practical use to the computational modeling of these proteins. © 2010 Wiley Periodicals, Inc. Biopolymers 95: 31–38, 2011.     

Computational Chemistry in Lead Identification,Library Design and Lead Optimisation

Abstract

We describe a variety of the computational techniques which we use in the drug discovery and design process. Some of these computational methods are designed to support the new experimental technologies of high-throughput screening and combinatorial chemistry. We also consider some new approaches to problems of long-standing interest such as protein-ligand docking and the prediction of free energies of binding.