Phage display screening against a set of targets to establish peptide-based sugar mimetics and molecular docking to predict binding site

A novel selection approach is presented to screen phage display peptide libraries against sets of receptors that share specificity for the same ligand. This strategy was applied to the discovery of glycomimetic peptides. Through these screens, a number of peptide clones were discovered that bind the lectins used in the screen, in a sugar competitive manner. In addition, the majority of the selected peptides demonstrate sugar type mimicry consistent with lectin specificity. Docking studies were conducted to establish whether the mimetic peptides bind to the lectin ConA at the sugar binding site or to a nearby, alternative site shown to bind to YPY-containing peptides previously discovered from single-target screens. Of the three cyclic peptides subjected to computational docking, CNTPLTSRC had the highest predicted affinity and CSRILTAAC demonstrated specificity for the sugar binding site comparable to the natural ligand itself.     

An aggregate analysis of many predicted structures to reduce errors in protein structure comparison caused by conformational flexibility

Background

Conformational flexibility creates errors in the comparison of protein structures. Even small changes in backbone or sidechain conformation can radically alter the shape of ligand binding cavities. These changes can cause structure comparison programs to overlook functionally related proteins with remote evolutionary similarities, and cause others to incorrectly conclude that closely related proteins have different binding preferences, when their specificities are actually similar. Towards the latter effort, this paper applies protein structure prediction algorithms to enhance the classification of homologous proteins according to their binding preferences, despite radical conformational differences.

Methods

Specifically, structure prediction algorithms can be used to "remodel" existing structures against the same template. This process can return proteins in very different conformations to similar, objectively comparable states. Operating on close homologs exploits the accuracy of structure predictions on closely related proteins, but structure prediction is often a nondeterministic process. Identical inputs can generate subtly different models with very different binding cavities that make structure comparison difficult. We present a first method to mitigate such errors, called "medial remodeling", that examines a large number of predicted structures to eliminate extreme models of the same binding cavity.

Results

Our results, on the enolase and tyrosine kinase superfamilies, demonstrate that remodeling can enable proteins in very different conformations to be returned to states that can be objectively compared. Structures that would have been erroneously classified as having different binding preferences were often correctly classified after remodeling, while structures that would have been correctly classified as having different binding preferences almost always remained distinct. The enolase superfamily, which exhibited less sequential diversity than the tyrosine kinase superfamily, was classified more accurately after remodeling than the tyrosine kinases. Medial remodeling reduced errors from models with unusual perturbations that distort the shape of the binding site, enhancing classification accuracy.

Conclusions

This paper demonstrates that protein structure prediction can compensate for conformational variety in the comparison of protein-ligand binding sites. While protein structure prediction introduces new uncertainties into the structure comparison problem, our results indicate that unusual models can be ignored through an analysis of many models, using techniques like medial remodeling. These results point to applications of protein structure comparison that extend beyond existing crystal structures.

     

Mapping the Binding Region on the Low Density Lipoprotein Receptor for Blood Coagulation Factor VIII

Low density lipoprotein receptor (LDLR) was shown to mediate clearance of blood coagulation factor VIII (FVIII) from the circulation. To elucidate the mechanism of interaction of LDLR and FVIII, our objective was to identify the region of the receptor necessary for binding FVIII. Using surface plasmon resonance, we found that LDLR exodomain and its cluster of complement-type repeats (CRs) bind FVIII in the same mode. This indicated that the LDLR site for FVIII is located within the LDLR cluster. Similar results were obtained for another ligand of LDLR, α-2-macroglobulin receptor-associated protein (RAP), a common ligand of receptors from the LDLR family. We further generated a set of recombinant fragments of the LDLR cluster and assessed their structural integrity by binding to RAP and by circular dichroism. A number of fragments overlapping CR.2-5 of the cluster were positive for binding RAP and FVIII. The specificity of these interactions was tested by site-directed mutagenesis of conserved tryptophans within the LDLR fragments. For FVIII, the specificity was also tested using a single-chain variable antibody fragment directed against the FVIII light chain as a competitor. Both cases resulted in decreased binding, thus confirming its specificity. The mutagenic study also showed an importance of the conserved tryptophans in LDLR for both ligands, and the competitive binding results showed an involvement of the light chain of FVIII in its interaction with LDLR. In conclusion, the region of CR.2-5 of LDLR was defined as the binding site for FVIII and RAP.     

Pre-docking filter for protein and ligand 3D structures

Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.     

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.     

Characterization of Group A Streptococcal M23 Protein and Comparison of the M3 and M23 Protein’s Ligand-Binding Domains

The present study concerns the properties for binding of human plasma and extracellular matrix proteins and the relationship between M3 and M23 molecules. Here, it is demonstrated that M23 protein shows a multiple binding to fibrinogen (FG), fibronectin (FN), human serum albumin (HSA), immunoglobulin G (IgG), kininogen, and collagen type I (CI) in Western blot analysis. Some sets of truncated-recombinant M3 or M23 protein fragments were assayed for their capacity to bind FN, FG, IgG, HSA, and CI. The HSA binding activity resided in the C-repeat region of M3 protein, whereas fibrinogen-binding activity resided in the A-repeat region. The FG, FN, and IgG binding sites were mapped to the N-terminal portion of M23 protein, whereas HSA binding was localized in the B-repeat domain, which has homology with C-repeat domain in M3 molecule. Therefore, it is concluded that the FN, FG, and IgG binding regions in the M3 and M23 proteins are quite dissimilar at the amino acid sequence level, whereas HSA binding is localized to the conserved C-repeat domain in the M3 and M23 proteins.     

Predicting molecular interactions and inducible complementarity: Fragment docking of fab-peptide complexes

Antibody-antigen interactions are representative of a broad class of receptor-ligand interactions involving both specificity and potential inducible complementarity. To test possible mechanisms of antigenantibody recognition and specificity computationally, we have used a Metropolis Monte Carlo algorithm to dock fragments of the epitope Glu-Val-Val-Pro-His-Lys-Lys to the X-ray structures of both the free and the complexed Fab of the antibody B13I2 (raised against the C-helix of myohemerythri). The fragments Pro-His and Val-Pro-His, which contain residues experimentally identified as important for binding, docked correctly to both structures, but all tetrapeptide and larger fragments docked correctly only to the complexed Fab, even when torsional flexibility was added to the ligand. However, only tetrapeptide and larger fragments showed significantly more favorable energies when docked to the complexed Fab coordinates than when docked to either the free Fab or a non-specific site remote from the combining site. Comparison of the free and complexed B13I2 structures revealed that atoms within 5 Å of Val-Pro-His showed little movement upon peptide binding, but atoms within 5 Å of the other four epitope residues showed greater movements. These results computationally distinguish recognition and binding processes with practical implications for drug design strategies. Overall, this new fragment docking approach establishes distinct roles for the “lock-and-key” (recognition) and the “handshake” (binding) paradigms in antibody-antigen interaction, suggests an incremental approach to incorporating flexibility in computational docking, and identifies critical regions within receptor binding sites for ligand recognition. © 1994 Wiley-Liss, Inc.     

Knowledge-based scoring function to predict protein-ligand interactions

The development and validation of a new knowledge-based scoring function (DrugScore) to describe the binding geometry of ligands in proteins is presented. It discriminates efficiently between well-docked ligand binding modes (root-mean-square deviation <2.0 A with respect to a crystallographically determined reference complex) and those largely deviating from the native structure, e.g. generated by computer docking programs. Structural information is extracted from crystallographically determined protein-ligand complexes using ReLiBase and converted into distance-dependent pair-preferences and solvent-accessible surface (SAS) dependent singlet preferences for protein and ligand atoms. Definition of an appropriate reference state and accounting for inaccuracies inherently present in experimental data is required to achieve good predictive power. The sum of the pair preferences and the singlet preferences is calculated based on the 3D structure of protein-ligand binding modes generated by docking tools. For two test sets of 91 and 68 protein-ligand complexes, taken from the Protein Data Bank (PDB), the calculated score recognizes poses generated by FlexX deviating <2 A from the crystal structure on rank 1 in three quarters of all possible cases. Compared to FlexX, this is a substantial improvement. For ligand geometries generated by DOCK, DrugScore is superior to the "chemical scoring" implemented into this tool, while comparable results are obtained using the "energy scoring" in DOCK. None of the presently known scoring functions achieves comparable power to extract binding modes in agreement with experiment. It is fast to compute, regards implicitly solvation and entropy contributions and produces correctly the geometry of directional interactions. Small deviations in the 3D structure are tolerated and, since only contacts to non-hydrogen atoms are regarded, it is independent from assumptions of protonation states.     

Size versus polarizability in protein-ligand interactions: binding of noble gases within engineered cavities in phage T4 lysozyme

To investigate the relative importance of size and polarizability in ligand binding within proteins, we have determined the crystal structures of pseudo wild-type and cavity-containing mutant phage T4 lysozymes in the presence of argon, krypton, and xenon. These proteins provide a representative sample of predominantly apolar cavities of varying size and shape. Even though the volumes of these cavities range up to the equivalent of five xenon atoms, the noble gases bind preferentially at highly localized sites that appear to be defined by constrictions in the walls of the cavities, coupled with the relatively large radii of the noble gases. The cavities within pseudo wild-type and L121A lysozymes each bind only a single atom of noble gas, while the cavities within mutants L133A and F153A have two independent binding sites, and the L99A cavity has three interacting sites. The binding of noble gases within two double mutants was studied to characterize the additivity of binding at such sites. In general, when a cavity in a protein is created by a "large-to-small" substitution, the surrounding residues relax somewhat to reduce the volume of the cavity. The binding of xenon and, to a lesser degree, krypton and argon, tend to expand the volume of the cavity and to return it closer to what it would have been had no relaxation occurred. In nearly all cases, the extent of binding of the noble gases follows the trend xenon>krypton>argon. Pressure titrations of the L99A mutant have confirmed that the crystallographic occupancies accurately reflect fractional saturation of the binding sites. The trend in noble gas affinity can be understood in terms of the effects of size and polarizability on the intermolecular potential. The plasticity of the protein matrix permits repulsion due to increased ligand size to be more than compensated for by attraction due to increased ligand polarizability. These results have implications for the mechanism of general anesthesia, the migration of small ligands within proteins, the detection of water molecules within apolar cavities and the determination of crystallographic phases.     

Opioid receptor three-dimensional structures from distance geometry calculations with hydrogen bonding constraints.

Three-dimensional structures of the transmembrane, seven alpha-helical domains and extracellular loops of delta, mu, and kappa opioid receptors, were calculated using the distance geometry algorithm, with hydrogen bonding constraints based on the previously developed general model of the transmembrane alpha-bundle for rhodopsin-like G-protein coupled receptors (Biophys. J. 1997. 70:1963). Each calculated opioid receptor structure has an extensive network of interhelical hydrogen bonds and a ligand-binding crevice that is partially covered by a beta-hairpin formed by the second extracellular loop. The binding cavities consist of an inner "conserved region" composed of 18 residues that are identical in delta, mu, and kappa opioid receptors, and a peripheral "variable region," composed of 19 residues that are different in delta, mu, and kappa subtypes and are responsible for the subtype specificity of various ligands. Sixteen delta-, mu-, or kappa-selective, conformationally constrained peptide and nonpeptide opioid agonists and antagonists and affinity labels were fit into the binding pockets of the opioid receptors. All ligands considered have a similar spatial arrangement in the receptors, with the tyramine moiety of alkaloids or Tyr1 of opioid peptides interacting with conserved residues in the bottom of the pocket and the tyramine N+ and OH groups forming ionic interactions or H-bonds with a conserved aspartate from helix III and a conserved histidine from helix VI, respectively. The central, conformationally constrained fragments of the opioids (the disulfide-bridged cycles of the peptides and various ring structures in the nonpeptide ligands) are oriented approximately perpendicular to the tyramine and directed toward the extracellular surface. The results obtained are qualitatively consistent with ligand affinities, cross-linking studies, and mutagenesis data.     

Ligand-mediated dimerization of a carbohydrate-binding molecule reveals a novel mechanism for protein-carbohydrate recognition

The structural and thermodynamic basis for carbohydrate-protein recognition is of considerable importance. NCP-1, which is a component of the Piromyces equi cellulase/hemicellulase complex, presents a provocative model for analyzing how structural and mutational changes can influence the ligand specificity of carbohydrate-binding proteins. NCP-1 contains two "family 29" carbohydrate-binding modules designated CBM29-1 and CBM29-2, respectively, that display unusually broad specificity; the proteins interact weakly with xylan, exhibit moderate affinity for cellulose and mannan, and bind tightly to the beta-1,4-linked glucose-mannose heteropolymer glucomannan. The crystal structure of CBM29-2 in complex with cellohexaose and mannohexaose identified key residues involved in ligand recognition. By exploiting this structural information and the broad specificity of CBM29-2, we have used this protein as a template to explore the evolutionary mechanisms that can lead to significant changes in ligand specificity. Here, we report the properties of the E78R mutant of CBM29-2, which displays ligand specificity that is different from that of wild-type CBM29-2; the protein retains significant affinity for cellulose but does not bind to mannan or glucomannan. Significantly, E78R exhibits a stoichiometry of 0.5 when binding to cellohexaose, and both calorimetry and ultracentrifugation show that the mutant protein displays ligand-mediated dimerization in solution. The three-dimensional structure of E78R in complex with cellohexaose reveals the intriguing molecular basis for this "dimeric" binding mode that involves the lamination of the oligosaccharide between two CBM molecules. The 2-fold screw axis of the ligand is mirrored in the orientation of the two protein domains with adjacent sugar rings stacking against the equivalent aromatic residues in the binding site of each protein molecule of the molecular sandwich. The sandwiching of an oligosaccharide chain between two protein modules, leading to ligand-induced formation of the binding site, represents a completely novel mechanism for protein-carbohydrate recognition that may mimic that displayed by naturally dimeric protein-carbohydrate interactions.     

VASP-E: Specificity Annotation with a Volumetric Analysis of Electrostatic Isopotentials

Algorithms for comparing protein structure are frequently used for function annotation. By searching for subtle similarities among very different proteins, these algorithms can identify remote homologs with similar biological functions. In contrast, few comparison algorithms focus on specificity annotation, where the identification of subtle differences among very similar proteins can assist in finding small structural variations that create differences in binding specificity. Few specificity annotation methods consider electrostatic fields, which play a critical role in molecular recognition. To fill this gap, this paper describes VASP-E (Volumetric Analysis of Surface Properties with Electrostatics), a novel volumetric comparison tool based on the electrostatic comparison of protein-ligand and protein-protein binding sites. VASP-E exploits the central observation that three dimensional solids can be used to fully represent and compare both electrostatic isopotentials and molecular surfaces. With this integrated representation, VASP-E is able to dissect the electrostatic environments of protein-ligand and protein-protein binding interfaces, identifying individual amino acids that have an electrostatic influence on binding specificity. VASP-E was used to examine a nonredundant subset of the serine and cysteine proteases as well as the barnase-barstar and Rap1a-raf complexes. Based on amino acids established by various experimental studies to have an electrostatic influence on binding specificity, VASP-E identified electrostatically influential amino acids with 100% precision and 83.3% recall. We also show that VASP-E can accurately classify closely related ligand binding cavities into groups with different binding preferences. These results suggest that VASP-E should prove a useful tool for the characterization of specific binding and the engineering of binding preferences in proteins.     

The roles of contact residue disorder and domain composition in characterizing protein-ligand binding specificity and promiscuity

Most protein chains interact with only one ligand but a small number of protein chains can bind several ligands, and many examples are available in the protein-ligand complex database of PDB. Among these proteins, some show preferences for the ligands or types of ligands they bind; however, so far we have only poor understanding of what determines protein-ligand binding and its specificity. Here we investigate the structural and functional properties of proteins in protein-ligand complexes. Analysis of the protein-ligand complex dataset from the PDB structure database reveals that proteins with more interactions have more disordered contact residues. Those proteins containing few disordered contact residues that bind multiple ligands have a tendency to consist of several domains. Analysis of physicochemical properties of hub contact residues binding multiple ligands indicates that they are enriched for hydrophilic, charged, polar and His-Asp catalytic triad residues. Finally, in order to differentiate proteins binding different classes of ligands, we mapped the three most prominent classes of ligands onto different superfamily domains. Our results demonstrate that contact residue disorder and ordered multiple domains are complementary factors that play a crucial role in determining ligand binding specificity and promiscuity.     

Computer-aided molecular modeling of the binding site architecture for eight monoclonal antibodies that bind a high potency guanidinium sweetener

Computer-aided molecular modeling of the antibody binding site of eight different monoclonal antibodies (mAb) that bind the intense sweetener ligand (N-(p-cyanophenyl)-N'-diphenylmethyl) guanidine acetic acid was completed using canonical loop structures and framework regions from known immuno globulins as “parent structures” for the molecular scaffoldings. The models of the fragment variable (Fv) region of the mAb were analyzed/or the presence and location of residues predicted to be involved in ligand binding. Several binding site tryptophan residues in these models were located in positions that support previous flurospectroscopic observations of the mAb-ligand complexation. Computer-aided renderings of the electrostatic potential at the van der Waals surface of the Fv region were compared and found to be consistent with the ligand binding specificity profiles for the different mAb. The Fv model of mAb NC6.8 was consistent with the binding site features determined in the Fab structure recently solved by X-ray diffraction techniques. These Fv models should provide an adequate basis for site-directed mutagenesis experiments in order to characterize interactive motifs in the mAb binding site.     

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.