Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design.

Identification and size characterization of surface pockets and occluded cavities are initial steps in protein structure-based ligand design. A new program, CAST, for automatically locating and measuring protein pockets and cavities, is based on precise computational geometry methods, including alpha shape and discrete flow theory. CAST identifies and measures pockets and pocket mouth openings, as well as cavities. The program specifies the atoms lining pockets, pocket openings, and buried cavities; the volume and area of pockets and cavities; and the area and circumference of mouth openings. CAST analysis of over 100 proteins has been carried out; proteins examined include a set of 51 monomeric enzyme-ligand structures, several elastase-inhibitor complexes, the FK506 binding protein, 30 HIV-1 protease-inhibitor complexes, and a number of small and large protein inhibitors. Medium-sized globular proteins typically have 10-20 pockets/cavities. Most often, binding sites are pockets with 1-2 mouth openings; much less frequently they are cavities. Ligand binding pockets vary widely in size, most within the range 10(2)-10(3)A3. Statistical analysis reveals that the number of pockets and cavities is correlated with protein size, but there is no correlation between the size of the protein and the size of binding sites. Most frequently, the largest pocket/cavity is the active site, but there are a number of instructive exceptions. Ligand volume and binding site volume are somewhat correlated when binding site volume is < or =700 A3, but the ligand seldom occupies the entire site. Auxiliary pockets near the active site have been suggested as additional binding surface for designed ligands (Mattos C et al., 1994, Nat Struct Biol 1:55-58). Analysis of elastase-inhibitor complexes suggests that CAST can identify ancillary pockets suitable for recruitment in ligand design strategies. Analysis of the FK506 binding protein, and of compounds developed in SAR by NMR (Shuker SB et al., 1996, Science 274:1531-1534), indicates that CAST pocket computation may provide a priori identification of target proteins for linked-fragment design. CAST analysis of 30 HIV-1 protease-inhibitor complexes shows that the flexible active site pocket can vary over a range of 853-1,566 A3, and that there are two pockets near or adjoining the active site that may be recruited for ligand design.     

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.     

Accurate detection of protein:ligand binding sites using molecular dynamics simulations

Accurate prediction of location of cavities and surface grooves in proteins is important, as these are potential sites for ligand binding. Several currently available programs for cavity detection are unable to detect cavities near the surface or surface grooves. In the present study, an optimized molecular dynamics based procedure is described for detection and quantification of interior cavities as well as surface pockets. This is based on the observation that the mobility of water in such pockets is significantly lower than that of bulk water. The algorithm efficiently detects surface grooves that are sites of protein-ligand and protein-protein interaction. The algorithm was also used to substantially improve the performance of an automated docking procedure for docking monomers of nonobligate protein-protein complexes. In addition, it was applied to predict key residues involved in the binding of the E. coli toxin CcdB with its inhibitor. Predictions were subsequently validated by mutagenesis experiments.     

Mapping of ligand‐binding cavities in proteins

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand‐binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between‐species differences and flexibility upon ligand‐binding. The presented results show that information on ligand‐binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand‐binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of “orphan structures”, selection of protein structures for docking studies in structure‐based design, and identification of proteins for selectivity screens in drug design programs. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Identification of protein functional surfaces by the concept of a split pocket

The function of a protein is often fulfilled via molecular interactions on its surfaces, so identifying the functional surface(s) of a protein is helpful for understanding its function. Here, we introduce the concept of a split pocket, which is a pocket that is split by a cognate ligand. We use a geometric approach that is site‐specific. Specifically, we first compute a set of all pockets in the protein with its ligand(s) and a set of all pockets with the ligand(s) removed and then compare the two sets of pockets to identify the split pocket(s) of the protein. To reduce the search space and expedite the process of surface partitioning, we design probe radii according to the physicochemical textures of molecules. Our method achieves a success rate of 96% on a benchmark test set. We conduct a large‐scale computation to identify ～19,000 split pockets from 11,328 structures (1.16 million potential pockets); for each pocket, we obtain residue composition, solvent‐accessible area, and molecular volume. With this database of split pockets, our method can be used to predict the functional surfaces of unbound structures. Indeed, the functional surface of an unbound protein may often be found from its similarity to remotely related bound forms that belong to distinct folds. Finally, we apply our method to identify glucose‐binding proteins, including unbound structures. Our study demonstrates the power of geometric and evolutionary matching for studying protein functional evolution and provides a framework for classifying protein functions by local spatial patterns of functional surfaces. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Screening and docking chemical ligands onto pocket cavities of a protease for designing a biocatalyst

A detailed study of the trypsin surface has been carried out to gain insight into its biological functions and interactions which helped to determine the binding specificity. Twenty-four cavity pockets were automatically identified on trypsin from PDB file entry 1AUJ using CASTp (Computed Atlas of Surface Topography of proteins). Molecular docking was exploited as an efficient in silico screening tool for studying protein–ligand interactions. A systematic docking study using Autodock 3.05 has been performed on the five largest binding pockets in trypsin. A set of ten putative chemical ligands was used to dock into selected binding pockets. Docking of ligands into the five largest pockets in trypsin showed that 1,10-phenanthroline and ethanolamine preferentially bound at pocket 24 and benzamidine at pocket 22. Thermodynamically, we also found that ethanol, propanol, propandiol and phosphoethanolamine preferentially bound at pocket 21 whereas p-aminobenzamidine, phenylacetic acid and phenylalanine interacted mainly at pocket 20 based on their lowest interaction free energy.     

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.     

Identifying continuous pores in protein structures with PROPORES by computational repositioning of gating residues

Proteins containing concavities such as pockets, cavities, and tunnels or pores perform important functions in ligand‐induced signal transduction, enzymatic catalysis, and in facilitating the permeation of small molecules through membranes. Computational algorithms for identifying such shapes are therefore of great use for studying the mechanisms of these reactions. We developed the novel toolkit PROPORES for pore identification and applied our program to the systems aquaporin, tryptophan synthase, leucine transporter, and acetylcholinesterase. As a novel feature, the program checks whether access to occluded ligand binding pockets or blocked channels can be achieved by systematically rotating side chains of the gating residues. In this way, we obtain a more flexible view of the putative structural adaptability of protein structures. For the four systems mentioned, the new method was able to identify connections between pores that are separated in the X‐ray structures or to connect internal pores with the protein surrounding. The software is available from http://gepard.bioinformatik.uni‐saarland.de/software/propores/ . Proteins 2012. © 2011 Wiley Periodicals, Inc.     

Pocketome via comprehensive identification and classification of ligand binding envelopes

We developed a new computational algorithm for the accurate identification of ligand binding envelopes rather than surface binding sites. We performed a large scale classification of the identified envelopes according to their shape and physicochemical properties. The predicting algorithm, called PocketFinder, uses a transformation of the Lennard-Jones potential calculated from a three-dimensional protein structure and does not require any knowledge about a potential ligand molecule. We validated this algorithm using two systematically collected data sets of ligand binding pockets from complexed (bound) and uncomplexed (apo) structures from the Protein Data Bank, 5616 and 11,510, respectively. As many as 96.8% of experimental binding sites were predicted at better than 50% overlap level. Furthermore 95.0% of the asserted sites from the apo receptors were predicted at the same level. We demonstrate that conformational differences between the apo and bound pockets do not dramatically affect the prediction results. The algorithm can be used to predict ligand binding pockets of uncharacterized protein structures, suggest new allosteric pockets, evaluate feasibility of protein-protein interaction inhibition, and prioritize molecular targets. Finally the data base of the known and predicted binding pockets for the human proteome structures, the human pocketome, was collected and classified. The pocketome can be used for rapid evaluation of possible binding partners of a given chemical compound.     

Dewetting transitions in protein cavities

In a previous analysis of the solvation of protein active sites, a drying transition was observed in the narrow hydrophobic binding cavity of Cox‐2. With the use of a crude metric that often seems able to discriminate those protein cavities that dry from those that do not, we made an extensive search of the PDB, and identified five other proteins that, in molecular dynamics simulations, undergo drying transitions in their active sites. Because such cavities need not desolvate before binding hydrophobic ligands they often exhibit very large binding affinities. This article gives evidence that drying in protein cavities is not unique to Cox‐2. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Analysis and prediction of calcium-binding pockets from apo-protein structures exhibiting calcium-induced localized conformational changes

Calcium binding in proteins exhibits a wide range of polygonal geometries that relate directly to an equally diverse set of biological functions. The binding process stabilizes protein structures and typically results in local conformational change and/or global restructuring of the backbone. Previously, we established the MUG program, which utilized multiple geometries in the Ca²⁺‐binding pockets of holoproteins to identify such pockets, ignoring possible Ca²⁺‐induced conformational change. In this article, we first report our progress in the analysis of Ca²⁺‐induced conformational changes followed by improved prediction of Ca²⁺‐binding sites in the large group of Ca²⁺‐binding proteins that exhibit only localized conformational changes. The MUG^SR algorithm was devised to incorporate side chain torsional rotation as a predictor. The output from MUG^SR presents groups of residues where each group, typically containing two to five residues, is a potential binding pocket. MUG^SR was applied to both X‐ray apo structures and NMR holo structures, which did not use calcium distance constraints in structure calculations. Predicted pockets were validated by comparison with homologous holo structures. Defining a “correct hit” as a group of residues containing at least two true ligand residues, the sensitivity was at least 90%; whereas for a “correct hit” defined as a group of residues containing at least three true ligand residues, the sensitivity was at least 78%. These data suggest that Ca²⁺‐binding pockets are at least partially prepositioned to chelate the ion in the apo form of the protein.     

3- and 19-oximes of 16alpha,17alpha-cyclohexanoprogesterone derivatives: synthesis and interactions with progesterone receptor and other proteins

Series of 3- and 19-oximes of 16alpha,17alpha-cyclohexanoprogesterone derivatives (pregna-d'-pentaranes) have been synthesized with the aim of probing the surfaces of progesterone receptor's and two other protein ligand binding pockets neighboring to 3- and 19-positions of steroid core. The same derivatives were also studied as possible intermediates for attachment to matrixes. The data on affinity constants suggest the presence of hydrophobic cavities with hydrophilic necks in the progesterone receptor and serum pentaranophylin near C19 of bound ligand and the lack of such a cavity in uterine pentaranophylin. Any of 3-oxime substitutions were found to significantly diminish the ligand affinity for the progesterone receptor. It was also found that some of these modifications, in the Z-configuration particularly, might increase the affinity for serum and uterine pentaranophylins. The latter finding suggests the presence of large cavities near C3 of bound ligand in these proteins and interchangeability between 3-keto and 3-oxime groups in ligand-protein interactions.     

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.     

Structural analysis of protein‐ligand interactions: the binding of endogenous compounds and of synthetic drugs

The large number of macromolecular structures deposited with the Protein Data Bank (PDB) describing complexes between proteins and either physiological compounds or synthetic drugs made it possible a systematic analysis of the interactions occurring between proteins and their ligands. In this work, the binding pockets of about 4000 PDB protein‐ligand complexes were investigated and amino acid and interaction types were analyzed. The residues observed with lowest frequency in protein sequences, Trp, His, Met, Tyr, and Phe, turned out to be the most abundant in binding pockets. Significant differences between drug‐like and physiological compounds were found. On average, physiological compounds establish with respect to drugs about twice as many hydrogen bonds with protein atoms, whereas drugs rely more on hydrophobic interactions to establish target selectivity. The large number of PDB structures describing homologous proteins in complex with the same ligand made it possible to analyze the conservation of binding pocket residues among homologous protein structures bound to the same ligand, showing that Gly, Glu, Arg, Asp, His, and Thr are more conserved than other amino acids. Also in the cases in which the same ligand is bound to unrelated proteins, the binding pockets showed significant conservation in the residue types. In this case, the probability of co‐occurrence of the same amino acid type in the binding pockets could be up to thirteen times higher than that expected on a random basis. The trends identified in this study may provide an useful guideline in the process of drug design and lead optimization. Copyright © 2014 John Wiley & Sons, Ltd.     

Ligand entry into the calyx of β‐lactoglobulin

Although the thermodynamic principles that control the binding of drug molecules to their protein targets are well understood, the detailed process of how a ligand reaches a protein binding site has been an intriguing question over decades. The short time interval between the encounter between a ligand and its receptor to the formation of the stable complex has prevented experimental observations. Bovine β‐lactoglobulin (βlg) is a lipocalin member that carries fatty acids (FAs) and other lipids in the cellular environment. Βlg accommodates a FA molecule in its highly hydrophobic cavity and exhibits the capability of recognizing a wide variety of hydrophobic ligands. To elucidate the ligand entry process on βlg, we report molecular dynamics simulations of the encounter between palmitate (PA) or laurate (LA) and βlg. Our results show that residues localized in loops at the cavity entrance play an important role in the ligand penetration process. Analysis of the short‐term interaction energies show that the forces operating on the systems lead to average conformations very close to the crystallographic holo‐forms. Whereas the binding free energy analysis using the molecular mechanics Generalized Born surface area method shows that these conformations were thermodynamically favorable. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 744–757, 2014.     

A guest molecule-host cavity fitting algorithm to mine PDB for small molecule targets

Inhaled anesthetic molecule occupancy of a protein internal cavity depends in part on the volumes of the guest molecule and the host site. Current algorithms to determine volume and surface area of cavities in proteins whose structures have been determined and cataloged make no allowance for shape or small degrees of shape adjustment to accommodate a guest. We developed an algorithm to determine spheroid dimensions matching cavity volume and surface area and applied it to screen the cavities of 6,658 nonredundant structures stored in the Protein Data Bank (PDB) for potential targets of halothane (2-bromo-2-chloro-1,1,1-trifluoroethane). Our algorithm determined sizes of prolate and oblate spheroids matching dimensions of each cavity found. If those spheroids could accommodate halothane (radius 2.91 A) as a guest, we determined the packing coefficient. 394,766 total cavities were identified. Of 58,681 cavities satisfying the fit criteria for halothane, 11,902 cavities had packing coefficients in the range of 0.46-0.64. This represents 20.3% of cavities large enough to hold halothane, 3.0% of all cavities processed, and found in 2,432 protein structures. Our algorithm incorporates shape dependence to screen guest-host relationships for potential small molecule occupancy of protein cavities. Proteins with large numbers of such cavities are more likely to be functionally altered by halothane.     

VASP: A Volumetric Analysis of Surface Properties Yields Insights into Protein-Ligand Binding Specificity

Many algorithms that compare protein structures can reveal similarities that suggest related biological functions, even at great evolutionary distances. Proteins with related function often exhibit differences in binding specificity, but few algorithms identify structural variations that effect specificity. To address this problem, we describe the Volumetric Analysis of Surface Properties (VASP), a novel volumetric analysis tool for the comparison of binding sites in aligned protein structures. VASP uses solid volumes to represent protein shape and the shape of surface cavities, clefts and tunnels that are defined with other methods. Our approach, inspired by techniques from constructive solid geometry, enables the isolation of volumetrically conserved and variable regions within three dimensionally superposed volumes. We applied VASP to compute a comparative volumetric analysis of the ligand binding sites formed by members of the steroidogenic acute regulatory protein (StAR)-related lipid transfer (START) domains and the serine proteases. Within both families, VASP isolated individual amino acids that create structural differences between ligand binding cavities that are known to influence differences in binding specificity. Also, VASP isolated cavity subregions that differ between ligand binding cavities which are essential for differences in binding specificity. As such, VASP should prove a valuable tool in the study of protein-ligand binding specificity.