A method for localizing ligand binding pockets in protein structures

The accurate identification of ligand binding sites in protein structures can be valuable in determining protein function. Once the binding site is known, it becomes easier to perform in silico and experimental procedures that may allow the ligand type and the protein function to be determined. For example, binding pocket shape analysis relies heavily on the correct localization of the ligand binding site. We have developed SURFNET-ConSurf, a modular, two-stage method for identifying the location and shape of potential ligand binding pockets in protein structures. In the first stage, the SURFNET program identifies clefts in the protein surface that are potential binding sites. In the second stage, these clefts are trimmed in size by cutting away regions distant from highly conserved residues, as defined by the ConSurf-HSSP database. The largest clefts that remain tend to be those where ligands bind. To test the approach, we analyzed a nonredundant set of 244 protein structures from the PDB and found that SURFNET-ConSurf identifies a ligand binding pocket in 75% of them. The trimming procedure reduces the original cleft volumes by 30% on average, while still encompassing an average 87% of the ligand volume. From the analysis of the results we conclude that for those cases in which the ligands are found in large, highly conserved clefts, the combined SURFNET-ConSurf method gives pockets that are a better match to the ligand shape and location. We also show that this approach works better for enzymes than for nonenzyme proteins.     

Landscape of protein–small ligand binding modes

Elucidating the mechanisms of specific small‐molecule (ligand) recognition by proteins is a long‐standing conundrum. While the structures of these molecules, proteins and ligands, have been extensively studied, protein–ligand interactions, or binding modes, have not been comprehensively analyzed. Although methods for assessing similarities of binding site structures have been extensively developed, the methods for the computational treatment of binding modes have not been well established. Here, we developed a computational method for encoding the information about binding modes as graphs, and assessing their similarities. An all‐against‐all comparison of 20,040 protein–ligand complexes provided the landscape of the protein–ligand binding modes and its relationships with protein‐ and chemical spaces. While similar proteins in the same SCOP Family tend to bind relatively similar ligands with similar binding modes, the correlation between ligand and binding similarities was not very high (R² = 0.443). We found many pairs with novel relationships, in which two evolutionally distant proteins recognize dissimilar ligands by similar binding modes (757,474 pairs out of 200,790,780 pairs were categorized into this relationship, in our dataset). In addition, there were an abundance of pairs of homologous proteins binding to similar ligands with different binding modes (68,217 pairs). Our results showed that many interesting relationships between protein–ligand complexes are still hidden in the structure database, and our new method for assessing binding mode similarities is effective to find them.     

Variations in the Binding Pocket of an Inhibitor of the Bacterial Division Protein FtsZ across Genotypes and Species

The recent increase in antibiotic resistance in pathogenic bacteria calls for new approaches to drug-target selection and drug development. Targeting the mechanisms of action of proteins involved in bacterial cell division bypasses problems associated with increasingly ineffective variants of older antibiotics; to this end, the essential bacterial cytoskeletal protein FtsZ is a promising target. Recent work on its allosteric inhibitor, PC190723, revealed in vitro activity on Staphylococcus aureus FtsZ and in vivo antimicrobial activities. However, the mechanism of drug action and its effect on FtsZ in other bacterial species are unclear. Here, we examine the structural environment of the PC190723 binding pocket using PocketFEATURE, a statistical method that scores the similarity between pairs of small-molecule binding sites based on 3D structure information about the local microenvironment, and molecular dynamics (MD) simulations. We observed that species and nucleotide-binding state have significant impacts on the structural properties of the binding site, with substantially disparate microenvironments for bacterial species not from the Staphylococcus genus. Based on PocketFEATURE analysis of MD simulations of S. aureus FtsZ bound to GTP or with mutations that are known to confer PC190723 resistance, we predict that PC190723 strongly prefers to bind Staphylococcus FtsZ in the nucleotide-bound state. Furthermore, MD simulations of an FtsZ dimer indicated that polymerization may enhance PC190723 binding. Taken together, our results demonstrate that a drug-binding pocket can vary significantly across species, genetic perturbations, and in different polymerization states, yielding important information for the further development of FtsZ inhibitors.     

Shape variation in protein binding pockets and their ligands

A common assumption about the shape of protein binding pockets is that they are related to the shape of the small ligand molecules that can bind there. But to what extent is that assumption true? Here we use a recently developed shape matching method to compare the shapes of protein binding pockets to the shapes of their ligands. We find that pockets binding the same ligand show greater variation in their shapes than can be accounted for by the conformational variability of the ligand. This suggests that geometrical complementarity in general is not sufficient to drive molecular recognition. Nevertheless, we show when considering only shape and size that a significant proportion of the recognition power of a binding pocket for its ligand resides in its shape. Additionally, we observe a "buffer zone" or a region of free space between the ligand and protein, which results in binding pockets being on average three times larger than the ligand that they bind.     

Prediction of volatile anesthetic binding sites in proteins

Computational methods designed to predict and visualize ligand protein binding interactions were used to characterize volatile anesthetic (VA) binding sites and unoccupied pockets within the known structures of VAs bound to serum albumin, luciferase, and apoferritin. We found that both the number of protein atoms and methyl hydrogen, which are within approximately 8 A of a potential ligand binding site, are significantly greater in protein pockets where VAs bind. This computational approach was applied to structures of calmodulin (CaM), which have not been determined in complex with a VA. It predicted that VAs bind to [Ca(2+)](4)-CaM, but not to apo-CaM, which we confirmed with isothermal titration calorimetry. The VA binding sites predicted for the structures of [Ca(2+)](4)-CaM are located in hydrophobic pockets that form when the Ca(2+) binding sites in CaM are saturated. The binding of VAs to these hydrophobic pockets is supported by evidence that halothane predominantly makes contact with aliphatic resonances in [Ca(2+)](4)-CaM (nuclear Overhauser effect) and increases the Ca(2+) affinity of CaM (fluorescence spectroscopy). Our computational analysis and experiments indicate that binding of VA to proteins is consistent with the hydrophobic effect and the Meyer-Overton rule.     

Model of glycoprotein hormone receptor ligand binding and signaling

Studies described here were initiated to develop a model of glycoprotein hormone receptor structure and function. We found that the region that links the lutropin receptor leucine-rich repeat domain (LRD) to its transmembrane domain (TMD) has substantial roles in ligand binding and signaling, hence we term it the signaling specificity domain (SSD). Theoretical considerations indicated the short SSDs in marmoset lutropin and salmon follitropin receptors have KH domain folds. We assembled models of lutropin, follitropin, and thyrotropin receptors by aligning models of their LRD, TMD, and shortened SSD in a manner that explains how substitutions in follitropin and thyrotropin receptors distant from their apparent ligand binding sites enable them to recognize lutropins. In these models, the SSD is parallel to the concave surface of the LRD and makes extensive contacts with TMD outer loops 1 and 2. The LRD appears to contact TMD outer loop 3 and a few residues in helices 1, 5, 6, and 7. We propose that signaling results from contacts of the ligands with the SSD and LRD that alter the LRD, which then moves TMD helices 6 and 7. The positions of the LRD and SSD support the notion that the receptor can be activated by hormones that dock with these domains in either of two different orientations. This would account for the abilities of some ligands and ligand chimeras to bind multiple receptors and for some receptors to bind multiple ligands. This property of the receptor may have contributed significantly to ligand-receptor co-evolution.     

Multiple diverse ligands binding at a single protein site: A matter of pre-existing populations

Here, we comment on the steadily increasing body of data showing that proteins with specificity actually bind ligands of diverse shapes, sizes, and composition. Such a phenomenon is not surprising when one considers that binding is a dynamic process with populations in equilibrium and that the shape of the binding site is strongly influenced by the molecular partner. It derives implicitly from the concept of populations. All proteins, specific and nonspecific, exist in ensembles of substates. If the library of ligands in solution is large enough, favorably matching ligands with altered shapes and sizes can be expected to bind, with a redistribution of the protein populations. Point mutations at spatially distant sites may exert large conformational rearrangements and hinge effects, consistent with mutations away from the binding site leading to population shifts and (cross-)drug resistance. A similar effect is observed in protein superfamilies, in which different sequences with similar topologies display similar large-scale dynamic motions. The hinges are frequently at analogous sites, yet with different substrate specificity. Similar topologies yield similar conformational isomers, although with different distributions of population times, owing to the change in the conditions, that is, the change in the sequences. In turn, different distributions relate to binding of different sizes and shapes. Hence, the binding site shape and size are defined by the ligand. They are not independent entities of fixed proportions and cannot be analyzed independently of the binding partner. Such a proposition derives from viewing proteins as dynamic distributions, presenting to the incoming ligands a range of binding site shapes. It illustrates how presumably specific binding molecules can bind multiple ligands. In terms of drug design, the ability of a single receptor to recognize many dissimilar ligands shows the need to consider more diverse molecules. It provides a rationale for higher affinity inhibitors that are not derived from substrates at their transition states and indicates flexible docking schemes.     

Topography for independent binding of alpha-helical and PPII-helical ligands to a peroxisomal SH3 domain

While the function of most small signaling domains is confined to binary ligand interactions, the peroxisomal Pex13p SH3 domain has the unique capacity of binding to two different ligands, Pex5p and Pex14p. We have used this domain as a model to decipher its structurally independent ligand binding sites. By the combined use of X-ray crystallography, NMR spectroscopy, and circular dichroism, we show that the two ligands bind in unrelated conformations to patches located at opposite surfaces of this SH3 domain. Mutations in the Pex13p SH3 domain that abolish interactions within the Pex13p-Pex5p interface specifically impair PTS1-dependent protein import into yeast peroxisomes.     

Identification of Distant Drug Off-Targets by Direct Superposition of Binding Pocket Surfaces

Correctly predicting off-targets for a given molecular structure, which would have the ability to bind a large range of ligands, is both particularly difficult and important if they share no significant sequence or fold similarity with the respective molecular target (“distant off-targets”). A novel approach for identification of off-targets by direct superposition of protein binding pocket surfaces is presented and applied to a set of well-studied and highly relevant drug targets, including representative kinases and nuclear hormone receptors. The entire Protein Data Bank is searched for similar binding pockets and convincing distant off-target candidates were identified that share no significant sequence or fold similarity with the respective target structure. These putative target off-target pairs are further supported by the existence of compounds that bind strongly to both with high topological similarity, and in some cases, literature examples of individual compounds that bind to both. Also, our results clearly show that it is possible for binding pockets to exhibit a striking surface similarity, while the respective off-target shares neither significant sequence nor significant fold similarity with the respective molecular target (“distant off-target”).     

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.     

Specificity of ligand binding to yeast hexokinase PII studied by STD-NMR

Hexokinase catalyzes the phosphorylation of glucose and is the first enzyme in glycolysis. To investigate enzyme–ligand interactions in yeast hexokinase isoform PII under physiological conditions, we utilized the technique of Saturation Transfer Difference NMR (STD NMR) to monitor binding modes and binding affinities of different ligands at atomic resolution. These experiments clearly show that hexokinase tolerates several changes at C-2 of its main substrate glucose, whereas epimerization of C-4 significantly reduces ligand binding. Although both glucose anomers bind to yeast hexokinase, the α-form is the preferred form for the phosphorylation reaction. These findings allow mapping of tolerated and prohibited modification sites on the ligand. Furthermore, competitive titration experiments show that mannose has the highest binding affinity of all examined sugars. As several naturally occurring sugars in cells show binding affinities in a similar range, hexokinase may be considered as an ‘emergency enzyme’ in yeast cells. Taken together, our results represent a comprehensive analysis of ligand–enzyme interactions in hexokinase PII and provide a valuable basis for inhibitor design and metabolic engineering.     

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.     

Detection of multiscale pockets on protein surfaces using mathematical morphology

Detection of pockets on protein surfaces is an important step toward finding the binding sites of small molecules. In a previous study, we defined a pocket as a space into which a small spherical probe can enter, but a large probe cannot. The radius of the large probes corresponds to the shallowness of pockets. We showed that each type of binding molecule has a characteristic shallowness distribution. In this study, we introduced fundamental changes to our previous algorithm by using a 3D grid representation of proteins and probes, and the theory of mathematical morphology. We invented an efficient algorithm for calculating deep and shallow pockets (multiscale pockets) simultaneously, using several different sizes of spherical probes (multiscale probes). We implemented our algorithm as a new program, ghecom (grid‐based HECOMi finder). The statistics of calculated pockets for the structural dataset showed that our program had a higher performance of detecting binding pockets, than four other popular pocket‐finding programs proposed previously. The ghecom also calculates the shallowness of binding ligands, R_inaccess (minimum radius of inaccessible spherical probes) that can be obtained from the multiscale molecular volume. We showed that each part of the binding molecule had a bias toward a specific range of shallowness. These findings will be useful for predicting the types of molecules that will be most likely to bind putative binding pockets, as well as the configurations of binding molecules. The program ghecom is available through the Web server ( http://biunit.naist.jp/ghecom ). Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Defining A Global Map of Functional Group-based 3D Ligand-binding Motifs

Uncovering conserved 3D protein–ligand binding patterns on the basis of functional groups (FGs) shared by a variety of small molecules can greatly expand our knowledge of protein–ligand interactions. Despite that conserved binding patterns for a few commonly used FGs have been reported in the literature, large-scale identification and evaluation of FG-based 3D binding motifs are still lacking. Here, we propose a computational method, Automatic FG-based Three-dimensional Motif Extractor (AFTME), for automatic mapping of 3D motifs to different FGs of a specific ligand. Applying our method to 233 naturally-occurring ligands, we define 481 FG-binding motifs that are highly conserved across different ligand-binding pockets. Systematic analysis further reveals four main classes of binding motifs corresponding to distinct sets of FGs. Combinations of FG-binding motifs facilitate the binding of proteins to a wide spectrum of ligands with various binding affinities. Finally, we show that our FG–motif map can be used to nominate FGs that potentially bind to specific drug targets, thus providing useful insights and guidance for rational design of small-molecule drugs.