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.     

Exhaustive comparison and classification of ligand‐binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ～2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.     

Merging chemical and biological space: Structural mapping of enzyme binding pocket space

Structure‐based drug design tries to mutually map pharmacological space populated by putative target proteins onto chemical space comprising possible small molecule drug candidates. Both spaces are connected where proteins and ligands recognize each other: in the binding pockets. Therefore, it is highly relevant to study the properties of the space composed by all possible binding cavities. In the present contribution, a global mapping of protein cavity space is presented by extracting consensus cavities from individual members of protein families and clustering them in terms of their shape and exposed physicochemical properties. Discovered similarities indicate common binding epitopes in binding pockets independent of any possibly given similarity in sequence and fold space. Unexpected links between remote targets indicate possible cross‐reactivity of ligands and suggest putative side effects. The global clustering of cavity space is compared to a similar clustering of sequence and fold space and compared to chemical ligand space spanned by the chemical properties of small molecules found in binding pockets of crystalline complexes. The overall similarity architecture of sequence, fold, and cavity space differs significantly. Similarities in cavity space can be mapped best to similarities in ligand binding space indicating possible cross‐reactivities. Most cross‐reactivities affect co‐factor and other endogenous ligand binding sites. Proteins 2009. © 2008 Wiley‐Liss, Inc.     

Cofactor‐binding sites in proteins of deviating sequence: Comparative analysis and clustering in torsion angle,cavity, and fold space

Small molecules are recognized in protein‐binding pockets through surface‐exposed physicochemical properties. To optimize binding, they have to adopt a conformation corresponding to a local energy minimum within the formed protein–ligand complex. However, their conformational flexibility makes them competent to bind not only to homologous proteins of the same family but also to proteins of remote similarity with respect to the shape of the binding pockets and folding pattern. Considering drug action, such observations can give rise tounexpected and undesired cross reactivity. In this study, datasets of six different cofactors (ADP, ATP, NAD(P)(H), FAD, and acetyl CoA, sharing an adenosine diphosphate moiety as common substructure), observed in multiple crystal structures of protein–cofactor complexes exhibiting sequence identity below 25%, have been analyzed for the conformational properties of the bound ligands, the distribution of physicochemical properties in the accommodating protein‐binding pockets, and the local folding patterns next to the cofactor‐binding site. State‐of‐the‐art clustering techniques have been applied to group the different protein–cofactor complexes in the different spaces. Interestingly, clustering in cavity (Cavbase) and fold space (DALI) reveals virtually the same data structuring. Remarkable relationships can be found among the different spaces. They provide information on how conformations are conserved across the host proteins and which distinct local cavity and fold motifs recognize the different portions of the cofactors. In those cases, where different cofactors are found to be accommodated in a similar fashion to the same fold motifs, only a commonly shared substructure of the cofactors is used for the recognition process. Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Using multiple microenvironments to find similar ligand-binding sites: application to kinase inhibitor binding

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.     

Structural studies on ligand–DNA systems: A robust approach in drug design

Molecular docking, molecular mechanics, molecular dynamics and relaxation matrix simulation protocols have been extensively used to generate the structural details of ligand-receptor complexes in order to understand the binding interactions between the two entities. Experimental methods like NMR spectroscopy and X-ray crystallography are known to provide structural information about ligand-receptor complexes. In addition, fluorescence spectroscopy, circular dichroism (CD) spectroscopy and molecular docking have also been utilized to decode the phenomenon of the ligand-DNA interactions, with good correlation between experimental and computational results. The DNA binding affinity was demonstrated by analysing fluorescence spectral data. Structural rigidity of DNA upon ligand binding was identified by CD spectroscopy. Docking is carried out using the DNA-Dock program which results in the binding affinity data along with structural information like interatomic distances and H-bonding, etc. The complete structural analyses of various drug-DNA complexes have afforded results that indicate a specific DNA binding pattern of these ligands. It also exhibited that certain structural features of ligands can make a ligand to be AT- or GC-specific. It was also demonstrated that changing specificity from AT base pairs to GC base pairs further improved the DNA topoisomerase inhibiting activity in certain ligands. Thus, a specific molecular recognition signature encrypted in the structure of ligand can be decoded and can be effectively employed in designing more potent antiviral and antitumour agents.     

SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites

Studying similarities in protein molecules has become a fundamental activity in much of biology and biomedical research, for which methods such as multiple sequence alignments are widely used. Most methods available for such comparisons cater to studying proteins which have clearly recognizable evolutionary relationships but not to proteins that recognize the same or similar ligands but do not share similarities in their sequence or structural folds. In many cases, proteins in the latter class share structural similarities only in their binding sites. While several algorithms are available for comparing binding sites, there are none for deriving structural motifs of the binding sites, independent of the whole proteins. We report the development of SiteMotif, a new algorithm that compares binding sites from multiple proteins and derives sequence-order independent structural site motifs. We have tested the algorithm at multiple levels of complexity and demonstrate its performance in different scenarios. We have benchmarked against 3 current methods available for binding site comparison and demonstrate superior performance of our algorithm. We show that SiteMotif identifies new structural motifs of spatially conserved residues in proteins, even when there is no sequence or fold-level similarity. We expect SiteMotif to be useful for deriving key mechanistic insights into the mode of ligand interaction, predict the ligand type that a protein can bind and improve the sensitivity of functional annotation.     

Updates to Binding MOAD (Mother of All Databases): Polypharmacology Tools and Their Utility in Drug Repurposing

The goal of Binding MOAD is to provide users with a data set focused on high-quality x-ray crystal structures that have been solved with biologically relevant ligands bound. Where available, experimental binding affinities (K_a, K_d, K_i, IC₅₀) are provided from the primary literature of the crystal structure. The database has been updated regularly since 2005, and this most recent update has added nearly 7000 new structures (growth of 21%). MOAD currently contains 32,747 structures, composed of 9117 protein families and 16,044 unique ligands. The data are freely available on www.BindingMOAD.org. This paper outlines updates to the data in Binding MOAD as well as improvements made to both the website and its contents. The NGL viewer has been added to improve visualization of the ligands and protein structures. MarvinJS has been implemented, over the outdated MarvinView, to work with JChem for small molecule searching in the database. To add tools for predicting polypharmacology, we have added information about sequence, binding-site, and ligand similarity between entries in the database. A main premise behind polypharmacology is that similar binding sites will bind similar ligands. The large amount of protein–ligand information available in Binding MOAD allows us to compute pairwise ligand and binding-site similarities. Lists of similar ligands and similar binding sites have been added to allow users to identify potential polypharmacology pairs. To show the utility of the polypharmacology data, we detail a few examples from Binding MOAD of drug repurposing targets with their respective similarities.     

Resolving protein structure‐function‐binding site relationships from a binding site similarity network perspective

Functional annotation is seldom straightforward with complexities arising due to functional divergence in protein families or functional convergence between non‐homologous protein families, leading to mis‐annotations. An enzyme may contain multiple domains and not all domains may be involved in a given function, adding to the complexity in function annotation. To address this, we use binding site information from bound cognate ligands and catalytic residues, since it can help in resolving fold‐function relationships at a finer level and with higher confidence. A comprehensive database of 2,020 fold‐function‐binding site relationships has been systematically generated. A network‐based approach is employed to capture the complexity in these relationships, from which different types of associations are deciphered, that identify versatile protein folds performing diverse functions, same function associated with multiple folds and one‐to‐one relationships. Binding site similarity networks integrated with fold, function, and ligand similarity information are generated to understand the depth of these relationships. Apart from the observed continuity in the functional site space, network properties of these revealed versatile families with topologically different or dissimilar binding sites and structural families that perform very similar functions. As a case study, subtle changes in the active site of a set of evolutionarily related superfamilies are studied using these networks. Tracing of such similarities in evolutionarily related proteins provide clues into the transition and evolution of protein functions. Insights from this study will be helpful in accurate and reliable functional annotations of uncharacterized proteins, poly‐pharmacology, and designing enzymes with new functional capabilities. Proteins 2017; 85:1319–1335. © 2017 Wiley Periodicals, Inc.     

Investigating the binding behaviour of two avidin‐based testosterone binders using molecular recognition force spectroscopy

Molecular recognition force spectroscopy, a biosensing atomic force microscopy technique allows to characterise the dissociation of ligand–receptor complexes at the molecular level. Here, we used molecular recognition force spectroscopy to study the binding capability of recently developed testosterone binders. The two avidin‐based proteins called sbAvd‐1 and sbAvd‐2 are expected to bind both testosterone and biotin but differ in their binding behaviour towards these ligands. To explore the ligand binding and dissociation energy landscape of these proteins, we tethered biotin or testosterone to the atomic force microscopy probe while the testosterone‐binding protein was immobilized on the surface. Repeated formation and rupture of the ligand–receptor complex at different pulling velocities allowed determination of the loading rate dependence of the complex‐rupturing force. In this way, we obtained the molecular dissociation rate (k_off) and energy landscape distances (x_β) of the four possible complexes: sbAvd‐1‐biotin, sbAvd‐1‐testosterone, sbAvd‐2‐biotin and sbAvd‐2‐testosterone. It was found that the kinetic off‐rates for both proteins and both ligands are similar. In contrast, the x_β values, as well as the probability of complex formations, varied considerably. In addition, competitive binding experiments with biotin and testosterone in solution differ significantly for the two testosterone‐binding proteins, implying a decreased cross‐reactivity of sbAvd‐2. Unravelling the binding behaviour of the investigated testosterone‐binding proteins is expected to improve their usability for possible sensing applications. Copyright © 2014 John Wiley & Sons, Ltd.     

Fragment-based modeling of NAD binding to the catalytic subunits of diphtheria and pertussis toxins

We describe a novel application of a fragment-based ligand docking technique; similar methods are commonly applied to the de novo design of ligands for target protein binding sites. We have used several new flexible docking and superposition tools, as well as a more conventional rigid-body (fragment) docking method, to examine NAD binding to the catalytic subunits of diphtheria (DT) and pertussis (PT) toxins, and to propose a model of the NAD–PT complex. Docking simulations with the rigid NAD fragments adenine and nicotinamide revealed that the low-energy dockings clustered in three distinct sites on the two proteins. Two of the sites were common to both fragments and were related to the structure of NAD bound to DT in an obvious way; however, the adenine subsite of PT was shifted relative to that of DT. We chose adenine/nicotinamide pairs of PT dockings from these clusters and flexibly superimposed NAD onto these pairs. A Monte Carlo–based flexible docking procedure and energy minimization were used to refine the modeled NAD–PT complexes. The modeled complex accounts for the sequence and structural similarities between PT and DT and is consistent with many results that suggest the catalytic importance of certain residues. A possible functional role for the structural difference between the two complexes is discussed. Proteins 31:282–298, 1998. © 1998 Wiley-Liss, Inc.     

Steric and thermodynamic limits of design for the incorporation of large unnatural amino acids in aminoacyl‐tRNA synthetase enzymes

Orthogonal aminoacyl‐tRNA synthetase/tRNA pairs from archaea have been evolved to facilitate site specific in vivo incorporation of unnatural amino acids into proteins in Escherichia coli. Using this approach, unnatural amino acids have been successfully incorporated with high translational efficiency and fidelity. In this study, CHARMM‐based molecular docking and free energy calculations were used to evaluate rational design of specific protein–ligand interactions for aminoacyl‐tRNA synthetases. A series of novel unnatural amino acid ligands were docked into the p‐benzoyl‐L ‐phenylalanine tRNA synthetase, which revealed that the binding pocket of the enzyme does not provide sufficient space for significantly larger ligands. Specific binding site residues were mutated to alanine to create additional space to accommodate larger target ligands, and then mutations were introduced to improve binding free energy. This approach was used to redesign binding sites for several different target ligands, which were then tested against the standard 20 amino acids to verify target specificity. Only the synthetase designed to bind Man‐α‐O‐Tyr was predicted to be sufficiently selective for the target ligand and also thermodynamically stable. Our study suggests that extensive redesign of the tRNA synthatase binding pocket for large bulky ligands may be quite thermodynamically unfavorable. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

<Emphasis Type="Italic">In Silico</Emphasis> Study on Retinoid-binding Modes in Human RBP and ApoD Lipocalins

Lipocalins are proteins with highly homologous structures but diverse sequences that are potential candidates for scaffold protein engineering with novel ligand-binding functions. Numerous crystal structures of lipocalin-ligand complexes have been identified and used in the study of their binding modes. On the other hand, crystallization studies cannot meet the increasing demand for novel lipocalin-ligand complexes in scaffold engineering, which requires rapid computational analyses of their binding modes in parallel. Human retinol-binding protein (RBP) and apolipoprotein D (apoD) are sequentially very distant proteins, but they show tight binding against retinoids, such as retinol and retinoic acid. In the present study, complexes of the two lipocalins with retinol and retinoic acid were modeled computationally by a molecular docking simulation, and their ligand-binding modes were analyzed at a molecular level. The models identified the crucial residues of lipocalins that interact with the ligands and revealed the similarities and differences in their retinoid-binding modes as well as in the specific interactions of the retinoid species within the same lipocalin. An analysis of the amino acid propensity of the retinoid-binding residues suggested that the evolutionary preference of the residues is restricted to the binding pocket rather than the entire protein. The distribution of charged residues around the terminus of retinoic acid showed a huge difference between RBP and ApoD, which might be a factor for the different binding affinities of lipocalins against retinoic acid. This in silico study is expected to be applied to scaffold protein engineering for novel retinoid-binding lipocalins.     

Molecular recognition of Fc‐specific ligands binding onto the consensus binding site of IgG: insights from molecular simulation

Immunoglobulin G (IgG) plays an important role in clinical diagnosis and therapeutics. Meanwhile, the consensus binding site (CBS) on the Fc domain of IgG is responsible for ligand recognition, especially for Fc‐specific ligands. In this study, molecular simulation methods were used to investigate molecular interactions between the CBS of the Fc domain and seven natural Fc‐specific ligands. The analysis on the binding energy of the Fc–ligand complex indicated that hydrophobic interactions provide the main driving force for the Fc–ligand binding processes. The hot spots on the ligands and Fc were identified with the computational alanine scanning approach. It was found that the residues of tryptophan and tyrosine on the ligands have significant contributions for the Fc–ligand binding, while Met252, Ile253, Asn434, His435, and Tyr436 are the key residues of Fc. Moreover, two binding modes based on tryptophan or tyrosine were summarized and constructed according to the pairwise interaction analysis. Guidelines for the rational design of CBS‐specific ligands with high affinity and specificity were proposed. Copyright © 2014 John Wiley & Sons, Ltd.     

Structural motifs recurring in different folds recognize the same ligand fragments

Background  

The structural analysis of protein ligand binding sites can provide information relevant for assigning functions to unknown proteins, to guide the drug discovery process and to infer relations among distant protein folds. Previous approaches to the comparative analysis of binding pockets have usually been focused either on the ligand or the protein component. Even though several useful observations have been made with these approaches they both have limitations. In the former case the analysis is restricted to binding pockets interacting with similar ligands, while in the latter it is difficult to systematically check whether the observed structural similarities have a functional significance.     

Multiple binding modes for palmitate to barley lipid transfer protein facilitated by the presence of proline 12

Molecular dynamics simulations have been used to characterise the binding of the fatty acid ligand palmitate in the barley lipid transfer protein 1 (LTP) internal cavity. Two different palmitate binding modes (1 and 2), with similar protein–ligand interaction energies, have been identified using a variety of simulation strategies. These strategies include applying experimental protein–ligand atom–atom distance restraints during the simulation, or protonating the palmitate ligand, or using the vacuum GROMOS 54B7 force‐field parameter set for the ligand during the initial stages of the simulations. In both the binding modes identified the palmitate carboxylate head group hydrogen bonds with main chain amide groups in helix A, residues 4 to 19, of the protein. In binding mode 1 the hydrogen bonds are to Lys 11, Cys 13, and Leu 14 and in binding mode 2 to Thr 15, Tyr 16, Val 17, Ser 24 and also to the OH of Thr 15. In both cases palmitate binding exploits irregularity of the intrahelical hydrogen‐bonding pattern in helix A of barley LTP due to the presence of Pro 12. Simulations of two variants of barley LTP, namely the single mutant Pro12Val and the double mutant Pro12Val Pro70Val, show that Pro 12 is required for persistent palmitate binding in the LTP cavity. Overall, the work identifies key MD simulation approaches for characterizing the details of protein–ligand interactions in complexes where NMR data provide insufficient restraints.     

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. Copyright © 2014 John Wiley & Sons, Ltd.     

Predicting structural effects in HIV-1 protease mutant complexes with flexible ligand docking and protein side-chain optimization

We present a computational approach for predicting structures of ligand-protein complexes and analyzing binding energy landscapes that combines Monte Carlo simulated annealing technique to determine the ligand bound conformation with the dead-end elimination algorithm for side-chain optimization of the protein active site residues. Flexible ligand docking and optimization of mobile protein side-chains have been performed to predict structural effects in the V32I/I47V/V82I HIV-1 protease mutant bound with the SB203386 ligand and in the V82A HIV-1 protease mutant bound with the A77003 ligand. The computational structure predictions are consistent with the crystal structures of these ligand-protein complexes. The emerging relationships between ligand docking and side-chain optimization of the active site residues are rationalized based on the analysis of the ligand-protein binding energy landscape. Proteins 33:295–310, 1998. © 1998 Wiley-Liss, Inc.