Computational fragment-based drug design to explore the hydrophobic sub-pocket of the mitotic kinesin Eg5 allosteric binding site

Eg5, a mitotic kinesin exclusively involved in the formation and function of the mitotic spindle has attracted interest as an anticancer drug target. Eg5 is co-crystallized with several inhibitors bound to its allosteric binding pocket. Each of these occupies a pocket formed by loop 5/helix α2 (L5/α2). Recently designed inhibitors additionally occupy a hydrophobic pocket of this site. The goal of the present study was to explore this hydrophobic pocket with our MED-SuMo fragment-based protocol, and thus discover novel chemical structures that might bind as inhibitors. The MED-SuMo software is able to compare and superimpose similar interaction surfaces upon the whole protein data bank (PDB). In a fragment-based protocol, MED-SuMo retrieves MED-Portions that encode protein-fragment binding sites and are derived from cross-mining protein-ligand structures with libraries of small molecules. Furthermore we have excluded intra-family MED-Portions derived from Eg5 ligands that occupy the hydrophobic pocket and predicted new potential ligands by hybridization that would fill simultaneously both pockets. Some of the latter having original scaffolds and substituents in the hydrophobic pocket are identified in libraries of synthetically accessible molecules by the MED-Search software. Ksenia Oguievetskaia and Laetitia Martin-Chanas contributed equally to this work.     

Inhibition of protein interactions: co-crystalized protein–protein interfaces are nearly as good as holo proteins in rigid-body ligand docking

Modulating protein interaction pathways may lead to the cure of many diseases. Known protein–protein inhibitors bind to large pockets on the protein–protein interface. Such large pockets are detected also in the protein–protein complexes without known inhibitors, making such complexes potentially druggable. The inhibitor-binding site is primary defined by the side chains that form the largest pocket in the protein-bound conformation. Low-resolution ligand docking shows that the success rate for the protein-bound conformation is close to the one for the ligand-bound conformation, and significantly higher than for the apo conformation. The conformational change on the protein interface upon binding to the other protein results in a pocket employed by the ligand when it binds to that interface. This proof-of-concept study suggests that rather than using computational pocket-opening procedures, one can opt for an experimentally determined structure of the target co-crystallized protein–protein complex as a starting point for drug design.     

Coarse‐grained molecular dynamics simulations of protein–ligand binding

Coarse‐grained molecular dynamics (CGMD) simulations with the MARTINI force field were performed to reproduce the protein–ligand binding processes. We chose two protein–ligand systems, the levansucrase–sugar (glucose or sucrose), and LinB–1,2‐dichloroethane systems, as target systems that differ in terms of the size and shape of the ligand‐binding pocket and the physicochemical properties of the pocket and the ligand. Spatial distributions of the Coarse‐grained (CG) ligand molecules revealed potential ligand‐binding sites on the protein surfaces other than the real ligand‐binding sites. The ligands bound most strongly to the real ligand‐binding sites. The binding and unbinding rate constants obtained from the CGMD simulation of the levansucrase–sucrose system were approximately 10 times greater than the experimental values; this is mainly due to faster diffusion of the CG ligand in the CG water model. We could obtain dissociation constants close to the experimental values for both systems. Analysis of the ligand fluxes demonstrated that the CG ligand molecules entered the ligand‐binding pockets through specific pathways. The ligands tended to move through grooves on the protein surface. Thus, the CGMD simulations produced reasonable results for the two different systems overall and are useful for studying the protein–ligand binding processes. © 2014 Wiley Periodicals, Inc.     

Estimation of important binding sites in compounds that interact with proteins

Proteins are one of the important substances in understanding biological activity, and many of them express the function by binding to other proteins or small molecules (ligands) on the molecular surface. This interaction often occurs in the hollows (pockets) on the molecular surface of the protein. It is known that when pockets are similar in structure and physical properties, they are likely to express similar functions and to bind similar ligands. Therefore, exploring the similarity of the structure and physical properties in pockets is very useful because it leads to the discovery of new ligands that are likely to bind. In addition, exploring the important structure when binding to the protein significant spot in the ligand will provide useful knowledge for the development of new ligands.In this study, we propose a method to search for proteins containing pockets that are structurally and physically similar to significant spot in the pocket of the analyzed protein, and to extract significant spots in the ligands that bind to them. We use feature points as data. Feature points are the 3-dimensional points that are extracted from 3D structure data of proteins with feature values quantifying hydrophobicity and electrostatic potential. The corresponding feature points are extracted by comparing structurally and physically the pockets of the search target proteins with the significant spot of the analyzed protein. By evaluating the similarity based on the comparison results of the feature values given to the extracted feature points, we search for proteins that are similar to the analyzed protein. From the ligands that bind to the searched proteins, atoms that are near the protein pocket and similar to the atoms in ligand binding to the analyzed protein are extracted. The site constituted by the extracted atoms is defined as a significant spot in the ligand.As a result of classifying ligands binding to the protein by using the extracted significant spot in the ligand, the effectiveness of the proposed method was confirmed.     

Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose

We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.     

Clustering and classifying diverse HIV entry inhibitors using a novel consensus shape-based virtual screening approach: further evidence for multiple binding sites within the CCR5 extracellular pocket

HIV entry inhibitors have emerged as a new generation of antiretroviral drugs that block viral fusion with the CXCR4 and CCR5 membrane coreceptors. Several small molecule antagonists for these coreceptors have been developed, some of which are currently in clinical trials. However, because no crystal structures for the coreceptor proteins are available, the binding modes of the known inhibitors within the coreceptor extracellular pockets need to be analyzed by means of site-directed mutagenesis and computational experiments. Previous studies have indicated that there is more than one binding site within the CCR5 extracellular pocket. This article investigates and develops this hypothesis using a novel spherical harmonic-based consensus shape clustering approach. The consensus shape approach is evaluated using retrospective virtual screening of CXCR4 and CCR5 inhibitors. Multiple combinations of CCR5 ligands in multiple trial superpositions are constructed to find consensus queries that give high virtual screening enrichments. Receiver-operator-characteristic performance analyses for both CXCR4 and CCR5 inhibitors show that the new consensus shape matching approach gives better virtual screening enrichments than existing shape matching and docking virtual screening techniques. The results obtained also provide strong evidence to support the notion that there are three main binding sites within the CCR5 extracellular cavity.     

Evaluation of the searching abilities of HBOP and HBSITE for binding pocket detection

We evaluated the pocket‐searching abilities of the computer programs HBOP and HBSITE, in which hydrophobic potentials calculated on grid points generated around a protein function as an indicator of the pocket‐like property‐using a test set of 458 experimentally observed structures of protein–ligand complexes. The results were compared with those obtained using the alternative algorithms PASS and SiteID, which only consider geometric properties, and Q‐SiteFinder, which considers not only geometry but also physicochemical properties. The comparison revealed that HBOP and HBSITE could detect experimentally observed ligand‐binding pockets for more test complexes than PASS and SiteID. In addition, the success rate of HBOP for detecting binding pockets was higher than that of Q‐SiteFinder, and that of HBSITE was comparable with that of Q‐SiteFinder. Results of tests for ligand‐unbound state proteins indicated that HBSITE was more appropriate than Q‐SiteFinder for pocket searches of ligand‐unbound proteins, and HBSITE was more robust than Q‐SiteFinder against structural differences between ligand‐bound and ‐unbound state proteins. © 2009 Wiley Periodicals, Inc. J Comput Chem 2009     

Using consensus-shape clustering to identify promiscuous ligands and protein targets and to choose the right query for shape-based virtual screening

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, many authors have discussed how best to choose the initial query compounds and which of their conformations should be used. Furthermore, it is increasingly the case that pharmaceutical companies have multiple ligands for a given target and these may bind in different ways to the same pocket. Conversely, a given ligand can sometimes bind to multiple targets, and this is clearly of great importance when considering drug side-effects. We recently introduced the notion of spherical harmonic-based "consensus shapes" to help deal with these questions. Here, we apply a consensus shape clustering approach to the 40 protein-ligand targets in the DUD data set using PARASURF/PARAFIT. Results from clustering show that in some cases the ligands for a given target are split into two subgroups which could suggest they bind to different subsites of the same target. In other cases, our clustering approach sometimes groups together ligands from different targets, and this suggests that those ligands could bind to the same targets. Hence spherical harmonic-based clustering can rapidly give cross-docking information while avoiding the expense of performing all-against-all docking calculations. We also report on the effect of the query conformation on the performance of shape-based screening of the DUD data set and the potential gain in screening performance by using consensus shapes calculated in different ways. We provide details of our analysis of shape-based screening using both PARASURF/PARAFIT and ROCS, and we compare the results obtained with shape-based and conventional docking approaches using MSSH/SHEF and GOLD. The utility of each type of query is analyzed using commonly reported statistics such as enrichment factors (EF) and receiver-operator-characteristic (ROC) plots as well as other early performance metrics.     

Exploring Multi-Subsite Binding Pockets in Proteins: DEEP-STD NMR Fingerprinting and Molecular Dynamics Unveil a Cryptic Subsite at the GM1 Binding Pocket of Cholera Toxin B

Ligand-based NMR techniques to study protein–ligand interactions are potent tools in drug design. Saturation transfer difference (STD) NMR spectroscopy stands out as one of the most versatile techniques, allowing screening of fragments libraries and providing structural information on binding modes. Recently, it has been shown that a multi-frequency STD NMR approach, differential epitope mapping (DEEP)-STD NMR, can provide additional information on the orientation of small ligands within the binding pocket. Here, the approach is extended to a so-called DEEP-STD NMR fingerprinting technique to explore the binding subsites of cholera toxin subunit B (CTB). To that aim, the synthesis of a set of new ligands is presented, which have been subject to a thorough study of their interactions with CTB by weak affinity chromatography (WAC) and NMR spectroscopy. Remarkably, the combination of DEEP-STD NMR fingerprinting and Hamiltonian replica exchange molecular dynamics has proved to be an excellent approach to explore the geometry, flexibility, and ligand occupancy of multi-subsite binding pockets. In the particular case of CTB, it allowed the existence of a hitherto unknown binding subsite adjacent to the GM1 binding pocket to be revealed, paving the way to the design of novel leads for inhibition of this relevant toxin.     

<Emphasis Type="Italic">In-silico</Emphasis> Screening using Flexible Ligand Binding Pockets: A Molecular Dynamics-based Approach

Summary In-silico screening of flexible ligands against flexible ligand binding pockets (LBP) is an emerging approach in structure-based drug discovery. Here, we describe a molecular dynamics (MD) based docking approach to investigate the influence on the high-throughput in-silico screening of small molecules against flexible ligand binding pockets. In our approach, an ensemble of 51 energetically favorable structures of the LBP of human estrogen receptor α (hERα) were collected from 3 ns MD simulations. In-silico screening of 3500 endocrine disrupting compounds against these flexible ligand binding pockets resulted in thousands of ER–ligand complexes of which 582 compounds were unique. Detailed analysis of MD generated structures showed that only 17 of the LBP residues significantly contribute to the overall binding pocket flexibility. Using the flexible LBP conformations generated, we have identified 32 compounds that bind better to the flexible ligand-binding pockets compared to the crystal structure. These compounds, though chemically divergent, are structurally similar to the natural hormone. Our MD-based approach in conjunction with grid–based distributed computing could be applied routinely for in-silico screening of large databases against any given target.     

Standard free energy of releasing a localized water molecule from the binding pockets of proteins: double-decoupling method

Localized water molecules in the binding pockets of proteins play an important role in noncovalent association of proteins and small drug compounds. At times, the dominant contribution to the binding free energy comes from the release of localized water molecules in the binding pockets of biomolecules. Therefore, to quantify the energetic importance of these water molecules for drug design purposes, we have used the double-decoupling approach to calculate the standard free energy of tying up a water molecule in the binding pockets of two protein complexes. The double-decoupling approach is based on the underlying principle of statistical thermodynamics. We have calculated the standard free energies of tying up the water molecule in the binding pockets of these complexes to be favorable. These water molecules stabilize the protein-drug complexes by interacting with the ligands and binding pockets. Our results offer ideas that could be used in optimizing protein-drug interactions, by designing ligands that are capable of targeting localized water molecules in protein binding sites. The resulting free energy of ligand binding could benefit from the potential free energy gain accompanying the release of these water molecules. Furthermore, we have examined the theoretical background of the double-decoupling method and its connection to the molecular dynamics thermodynamic integration techniques.     

A pseudo-ligand approach to virtual screening

A virtual screening method is presented that is grounded on a receptor-derived pharmacophore model termed "virtual ligand" or "pseudo-ligand". The model represents an idealized constellation of potential ligand sites that interact with residues of the binding pocket. For rapid virtual screening of compound libraries the potential pharmacophore points of the virtual ligand are encoded as an alignment-free correlation vector, avoiding spatial alignment of pharmacophore features between the pharmacophore query (i.e., the virtual ligand) and the candidate molecule. The method was successfully applied to retrieving factor Xa inhibitors from a Ugi three-component combinatorial library, and yielded high enrichment of actives in a retrospective search for cyclooxygenase-2 (COX-2) inhibitors. The approach provides a concept for "de-orphanizing" potential drug targets and identifying ligands for hitherto unexplored or allosteric binding pockets.     

CrystalDock: a novel approach to fragment-based drug design

We present a novel algorithm called CrystalDock that analyzes a molecular pocket of interest and identifies potential binding fragments. The program first identifies groups of pocket-lining receptor residues (i.e., microenvironments) and then searches for geometrically similar microenvironments present in publically available databases of ligand-bound experimental structures. Germane fragments from the crystallographic or NMR ligands are subsequently placed within the novel binding pocket. These positioned fragments can be linked together to produce ligands that are likely to be potent; alternatively, they can be joined to an inhibitor with a known or suspected binding pose to potentially improve binding affinity. To demonstrate the utility of the algorithm, CrystalDock is used to analyze the principal binding pockets of influenza neuraminidase and Trypanosoma brucei RNA editing ligase 1, validated drug targets in the fight against pandemic influenza and African sleeping sickness, respectively. In both cases, CrystalDock suggests modifications to known inhibitors that may improve binding affinity.     

Mapping protein pockets through their potential small-molecule binding volumes: QSCD applied to biological protein structures

Previously we demonstrated a method, Quantized Surface Complementarity Diversity (QSCD), of defining molecular diversity by measuring shape and functional complementarity of molecules to a basis set of theoretical target surfaces [Wintner E.A. and Moallemi C.C., J. Med. Chem., 43 (2000) 1993]. In this paper we demonstrate a method of mapping actual protein pockets to the same basis set of theoretical target surfaces, thereby allowing categorization of protein pockets by their properties of shape and functionality. The key step in the mapping is a `dissection' algorithm that breaks any protein pocket into a set of potential small molecule binding volumes. It is these binding volumes that are mapped to the basis set of theoretical target surfaces, thus measuring a protein pocket not as a single surface but as a collection of molecular recognition environments.     

Voronoi-based detection of pockets in proteins defined by large and small probes

The function of enzymatic proteins is given by their ability to bind specific small molecules into their active sites. These sites can often be found in pockets on a hypothetical boundary between the protein and its environment. Detection, analysis, and visualization of pockets find its use in protein engineering and drug discovery. Many definitions of pockets and algorithms for their computation have been proposed. Kawabata and Go defined them as the regions of empty space into which a small spherical probe can enter but a large probe cannot and developed programs that can compute their approximate shape. In this article, this definition was slightly modified in order to capture the existence of large internal holes, and a Voronoi-based method for the computation of the exact shape of these modified regions is introduced. The method first puts a finite number of large probes on the protein exterior surface and then, considering both large probes and atomic balls as obstacles for the small probe, the method computes the exact shape of the regions for the small probe. This is all achieved with Voronoi diagrams, which help with the safe navigation of spherical probes among spherical obstacles. Detected regions are internally represented as graphs of vertices and edges describing possible movements of the center of the small probe on Voronoi edges. The surface bounding each region is obtained from this representation and used for visualization, volume estimation, and comparison with other approaches. © 2019 Wiley Periodicals, Inc.     

A new method for ligand docking to flexible receptors by dual alanine scanning and refinement (SCARE)

Protein binding sites undergo ligand specific conformational changes upon ligand binding. However, most docking protocols rely on a fixed conformation of the receptor, or on the prior knowledge of multiple conformations representing the variation of the pocket, or on a known bounding box for the ligand. Here we described a general induced fit docking protocol that requires only one initial pocket conformation and identifies most of the correct ligand positions as the lowest score. We expanded a previously used diverse "cross-docking" benchmark to thirty ligand-protein pairs extracted from different crystal structures. The algorithm systematically scans pairs of neighbouring side chains, replaces them by alanines, and docks the ligand to each 'gapped' version of the pocket. All docked positions are scored, refined with original side chains and flexible backbone and re-scored. In the optimal version of the protocol pairs of residues were replaced by alanines and only one best scoring conformation was selected from each 'gapped' pocket for refinement. The optimal SCARE (SCan Alanines and REfine) protocol identifies a near native conformation (under 2 angstroms RMSD) as the lowest rank for 80% of pairs if the docking bounding box is defined by the predicted pocket envelope, and for as many as 90% of the pairs if the bounding box is derived from the known answer with approximately 5 angstroms margin as used in most previous publications. The presented fully automated algorithm takes about 2 h per pose of a single processor time, requires only one pocket structure and no prior knowledge about the binding site location. Furthermore, the results for conformationally conserved pockets do not deteriorate due to substantial increase of the pocket variability.     

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank

One approach to estimating the "chemical tractability" of a candidate protein target where we know the atomic resolution structure is to examine the physical properties of potential binding sites. A number of other workers have addressed this issue. We characterize ~290,000 "pockets" from ~42,000 protein crystal structures in terms of a three parameter "pocket space": volume, buriedness, and hydrophobicity. A metric DLID (drug-like density) measures how likely a pocket is to bind a drug-like molecule. This is calculated from the count of other pockets in its local neighborhood in pocket space that contain drug-like cocrystallized ligands and the count of total pockets in the neighborhood. Surprisingly, despite being defined locally, a global trend in DLID can be predicted by a simple linear regression on log(volume), buriedness, and hydrophobicity. Two levels of simplification are necessary to relate the DLID of individual pockets to "targets": taking the best DLID per Protein Data Bank (PDB) entry (because any given crystal structure can have many pockets), and taking the median DLID over all PDB entries for the same target (because different crystal structures of the same protein can vary because of artifacts and real conformational changes). We can show that median DLIDs for targets that are detectably homologous in sequence are reasonably similar and that median DLIDs correlate with the "druggability" estimate of Cheng et al. (Nature Biotechnology 2007, 25, 71-75).     

Control of multivalent interactions by binding epitope density

Receptor clustering by multivalent ligands can activate signaling pathways. In principle, multivalent ligand features can control clustering and the downstream signals that result, but the influence of ligand structure on these processes is incompletely understood. Using a series of synthetic polymers that vary systematically, we studied the influence of multivalent ligand binding epitope density on the clustering of a model receptor, concanavalin A (Con A). We analyze three aspects of receptor clustering: the stoichiometry of the complex, rate of cluster formation, and receptor proximity. Our experiments reveal that the density of binding sites on a multivalent ligand strongly influences each of these parameters. In general, high binding epitope density results in greater numbers of receptors bound per polymer, faster rates of clustering, and reduced inter-receptor distances. Ligands with low binding epitope density, however, are the most efficient on a binding epitope basis. Our results provide insight into the design of ligands for controlling receptor-receptor interactions and can be used to illuminate mechanisms by which natural multivalent displays function.