Algorithms for computational solvent mapping of proteins

Computational mapping methods place molecular probes (small molecules or functional groups) on a protein surface to identify the most favorable binding positions by calculating an interaction potential. We have developed a novel computational mapping program called CS-Map (computational solvent mapping of proteins), which differs from earlier mapping methods in three respects: (i) it initially moves the ligands on the protein surface toward regions with favorable electrostatics and desolvation, (ii) the final scoring potential accounts for desolvation, and (iii) the docked ligand positions are clustered, and the clusters are ranked on the basis of their average free energies. To understand the relative importance of these factors, we developed alternative algorithms that use the DOCK and GRAMM programs for the initial search. Because of the availability of experimental solvent mapping data, lysozyme and thermolysin are considered as test proteins. Both DOCK and GRAMM speed up the initial search, and the combined algorithms yield acceptable mapping results. However, the DOCK-based approaches place the consensus site farther from its experimentally determined position than CS-Map, primarily because of the lack of a solvation term in the initial search. The GRAMM-based program also finds the correct consensus site for thermolysin. We conclude that good sampling is the most important requirement for successful mapping, but accounting for desolvation and clustering of ligand positions also help to reduce the number of false positives.     

Multiple solvent crystal structures: probing binding sites, plasticity and hydration

Multiple solvent crystal structures (MSCS) of porcine pancreatic elastase were used to map the binding surface the enzyme. Crystal structures of elastase in neat acetonitrile, 95% acetone, 55% dimethylformamide, 80% 5-hexene-1,2-diol, 80% isopropanol, 80% ethanol and 40% trifluoroethanol showed that the organic solvent molecules clustered in the active site, were found mostly unclustered in crystal contacts and in general did not bind elsewhere on the surface of elastase. Mixtures of 40% benzene or 40% cyclohexane in 50% isopropanol and 10% water showed no bound benzene or cyclohexane molecules, but did reveal bound isopropanol. The clusters of organic solvent probe molecules coincide with pockets occupied by known inhibitors. MSCS also reveal the areas of plasticity within the elastase binding site and allow for the visualization of a nearly complete first hydration shell. The pattern of organic solvent clusters determined by MSCS for elastase is consistent with patterns for hot spots in protein-ligand interactions determined from database analysis in general. The MSCS method allows probing of hot spots, plasticity and hydration simultaneously, providing a powerful complementary strategy to guide computational methods currently in development for binding site determination, ligand docking and design.     

Identification of the ligand binding sites on the molecular surface of proteins

Identification of protein biochemical functions based on their three-dimensional structures is now required in the post-genome-sequencing era. Ligand binding is one of the major biochemical functions of proteins, and thus the identification of ligands and their binding sites is the starting point for the function identification. Previously we reported our first trial on structure-based function prediction, based on the similarity searches of molecular surfaces against the functional site database. Here we describe the extension of our first trial by expanding the search database to whole heteroatom binding sites appearing within the Protein Data Bank (PDB) with the new analysis protocol. In addition, we have determined the similarity threshold line, by using 10 structure pairs with solved free and complex structures. Finally, we extensively applied our method to newly determined hypothetical proteins, including some without annotations, and evaluated the performance of our methods.     

Use of organic solvents and small molecules for locating binding sites on proteins in solution*

Application of a modified ePHOGSY and other novel NMR experiments to an H2O-DMSO solution of the protein FKBP12 identified the presence of one molecule of DMSO bound in the substrate binding site. It occupies the same spatial region occupied by the pipecolidine moiety of the immunosuppressive drugs FK506 and Rapamycin complexed to the protein. The binding constant KD for this DMSO molecule was only 275 mM. A substructure search of small molecules similar to DMSO resulted in the identification of molecules with improved binding affinity. This work represents a clear example of the powerful interplay of molecular modelling and NMR.     

A novel approach to local similarity of protein binding sites substantially improves computational drug design results

We present a novel notion of binding site local similarity based on the analysis of complete protein environments of ligand fragments. Comparison of a query protein binding site (target) against the 3D structure of another protein (analog) in complex with a ligand enables ligand fragments from the analog complex to be transferred to positions in the target site, so that the complete protein environments of the fragment and its image are similar. The revealed environments are similarity regions and the fragments transferred to the target site are considered as binding patterns. The set of such binding patterns derived from a database of analog complexes forms a cloud-like structure (fragment cloud), which is a powerful tool for computational drug design. It has been shown on independent test sets that the combined use of a traditional energy-based score together with the cloud-based score responsible for the quality of embedding of a ligand into the fragment cloud improves the self-docking and screening results dramatically. The usage of a fragment cloud as a source of positioned molecular fragments fitting the binding protein environment has been validated by reproduction of experimental ligand optimization results.     

Accessibility to internal cavities and ligand binding sites monitored by protein crystallographic thermal factors

Protein structures are flexible both in solution and in the solid state. X-ray crystallographically determined thermal factors monitor the flexibility of protein atoms. A method utilizing such factors is proposed to delineate protein regions through which a ligand can exchange between binding site and bulk solvent. It is based on the assumption that thermally excited protein regions are excellent candidates for opening a ligand channel. Computationally simple and inexpensive, the method analyzes directions from which thermal factors can propagate within the protein, resulting in thermal motion paths (TMPs). Applications to engineered T4 lysozymes, where an artificial internal cavity can host hydrophobic molecules, and to sperm whale myoglobins, where the active site is completely buried, yielded results in agreement with other independent structural observations and with previous hypotheses. Further new features could also be suggested. The proposed TMP analysis could aid molecular dynamics simulation studies as well as time-resolved and site-directed mutagenesis experimental studies, especially given its modest computational expense and its direct roots in experimental results based on thermal factors determined in high-resolution crystallographic studies. Proteins 31:201–213,1998. © 1998 Wiley-Liss, Inc.     

Molecular modeling of protein function regions

Experimental protein structures often provide extensive insight into the mode and specificity of small molecule binding, and this information is useful for understanding protein function and for the design of drugs. We have performed an analysis of the reliability with which ligand-binding information can be deduced from computer model structures, as opposed to experimentally derived ones. Models produced as part of the CASP experiments are used. The accuracy of contacts between protein model atoms and experimentally determined ligand atom positions is the main criterion. Only comparative models are included (i.e., models based on a sequence relationship between the protein of interest and a known structure). We find that, as expected, contact errors increase with decreasing sequence identity used as a basis for modeling. Analysis of the causes of errors shows that sequence alignment errors between model and experimental template have the most deleterious effect. In general, good, but not perfect, insight into ligand binding can be obtained from models based on a sequence relationship, providing there are no alignment errors in the model. The results support a structural genomics strategy based on experimental sampling of structure space so that all protein domains can be modeled on the basis of 30% or higher sequence identity.     

Crystallographic analysis of the pH-dependent binding of iminobiotin by streptavidin.

Streptavidin binds 2'-iminobiotin in a pH-dependent fashion--affinity decreases as the pH is lowered. This property makes the purification of compounds conjugated to streptavidin or immobiotin possible under mild conditions by affinity chromatography. In order to understand the molecular details of this pH-dependent binding, we analyzed the crystal structures of the complex of core streptavidin with 2'-iminobiotin at pH values 4.0 and 7.5. The two structures are very similar to each other even at their binding sites. Although the relative abundance of the protonated species of the ligand is increased more than 3,000-fold on going from pH 7.5 to pH 4.0, both structures contain only the nonprotonated from of the ligand. Streptavidin selects the nonprotonated form, which, at pH 4.0, is one part in 7.9 x 10(7).     

Method for comparing the structures of protein ligand-binding sites and application for predicting protein-drug interactions

Many drugs, even ones that are designed to act selectively on a target protein, bind unintended proteins. These unintended bindings can explain side effects or indicate additional mechanisms for a drug's medicinal properties. Structural similarity between binding sites is one of the reasons for binding to multiple targets. We developed a method for the structural alignment of atoms in the solvent-accessible surface of proteins that uses similarities in the local atomic environment, and carried out all-against-all structural comparisons for 48,347 potential ligand-binding regions from a nonredundant protein structure subset (nrPDB, provided by NCBI). The relationships between the similarity of ligand-binding regions and the similarity of the global structures of the proteins containing the binding regions were examined. We found 10,403 known ligand-binding region pairs whose structures were similar despite having different global folds. Of these, we detected 281 region pairs that had similar ligands with similar binding modes. These proteins are good examples of convergent evolution. In addition, we found a significant correlation between Z-score of structural similarity and true positive rate of "active" entries in the PubChem BioAssay database. Moreover, we confirmed the interaction between ibuprofen and a new target, porcine pancreatic elastase, by NMR experiment. Finally, we used this method to predict new drug-target protein interactions. We obtained 540 predictions for 105 drugs (e.g., captopril, lovastatin, flurbiprofen, metyrapone, and salicylic acid), and calculated the binding affinities using AutoDock simulation. The results of these structural comparisons are available at http://www.tsurumi.yokohama-cu.ac.jp/fold/database.html.     

Interactions in native binding sites cause a large change in protein dynamics

Cellular functions are regulated by molecules that interact with proteins and alter their activities. To enable such control, protein activity, and therefore protein conformational distributions, must be susceptible to alteration by molecular interactions at functional sites. Here we investigate whether interactions at functional sites cause a large change in the protein conformational distribution. We apply a computational method, called dynamics perturbation analysis (DPA), to identify sites at which interactions have a large allosteric potential D(x), which is the Kullback-Leibler divergence between protein conformational distributions with and without an interaction. In DPA, a protein is decorated with surface points that interact with neighboring protein atoms, and D(x) is calculated for each of the points in a coarse-grained model of protein vibrations. We use DPA to examine hundreds of protein structures from a standard small-molecule docking test set, and find that ligand-binding sites have elevated values of D(x): for 95% of proteins, the probability of randomly obtaining values as high as those in the binding site is 10(-3) or smaller. We then use DPA to develop a computational method to predict functional sites in proteins, and find that the method accurately predicts ligand-binding-site residues for proteins in the test set. The performance of this method compares favorably with that of a cleft analysis method. The results confirm that interactions at small-molecule binding sites cause a large change in the protein conformational distribution, and motivate using DPA for large-scale prediction of functional sites in proteins. They also suggest that natural selection favors proteins whose activities are capable of being regulated by molecular interactions.     

Crystal structures of the tryptophan repressor binding protein WrbA and complexes with flavin mononucleotide

The tryptophan repressor binding protein WrbA binds to the tryptophan repressor protein TrpR. Although the biological role of WrbA remains unclear, it has been proposed to function in enhancing the stability of TrpR-DNA complexes. Sequence database analysis has identified WrbA as a founding member of a flavodoxin-like family of proteins. Here we present crystal structures of WrbA from Deinococcus radiodurans and Pseudomonas aeruginosa and their complexes with flavin mononucleotide. The protomer structure is similar to that of previously determined long-chain flavodoxins; however, each contains a conserved inserted region unique to the WrbA family. Interestingly, each WrbA protein forms a homotetramer with 222 symmetry, unique among flavodoxin-like proteins, in which each protomer binds one flavin mononucleotide cofactor molecule.     

Identification and structural characterization of heme binding in a novel dye-decolorizing peroxidase, TyrA

TyrA is a member of the dye-decolorizing peroxidase (DyP) family, a new family of heme-dependent peroxidase recently identified in fungi and bacteria. Here, we report the crystal structure of TyrA in complex with iron protoporphyrin (IX) at 2.3 A. TyrA is a dimer, with each monomer exhibiting a two-domain, alpha/beta ferredoxin-like fold. Both domains contribute to the heme-binding site. Co-crystallization in the presence of an excess of iron protoporphyrin (IX) chloride allowed for the unambiguous location of the active site and the specific residues involved in heme binding. The structure reveals a Fe-His-Asp triad essential for heme positioning, as well as a novel conformation of one of the heme propionate moieties compared to plant peroxidases. Structural comparison to the canonical DyP family member, DyP from Thanatephorus cucumeris (Dec 1), demonstrates conservation of this novel heme conformation, as well as residues important for heme binding. Structural comparisons with representative members from all classes of the plant, bacterial, and fungal peroxidase superfamily demonstrate that TyrA, and by extension the DyP family, adopts a fold different from all other structurally characterized heme peroxidases. We propose that a new superfamily be added to the peroxidase classification scheme to encompass the DyP family of heme peroxidases.     

Evidence of new cadmium binding sites in recombinant horse L-chain ferritin by anomalous Fourier difference map calculation

We refined the structure of the tetragonal form of recombinant horse L-chain apoferritin to 2.0 Å and we compared it with that of the cubic form previously refined to the same resolution. The major differences between the two structures concern the cadmium ions bound to the residues E130 at the threefold axes of the molecule. Taking advantage of the significant anomalous signal (f′′ = 3.6 e⁻) of cadmium at 1.375 Å, the wavelength used here, we performed anomalous Fourier difference maps with the refined model phases. These maps reveal the positions of anomalous scatterers at different locations in the structure. Among these, some are found near residues that were known previously to bind metal ions, C48, E57, C126, D127, E130, and H132. But new cadmium binding sites are evidenced near residues E53, E56, E57, E60, and H114, which were suggested to be involved in the iron loading process. The quality of the anomalous Fourier difference map increases significantly with noncrystallographic symmetry map averaging. Such maps reveal density peaks that fit the positions of Met and Cys sulfur atoms, which are weak anomalous scatterers (f′′ = 0.44 e⁻). Proteins 31:477–485, 1998. © 1998 Wiley-Liss, Inc.     

Structural clusters of evolutionary trace residues are statistically significant and common in proteins

Given the massive increase in the number of new sequences and structures, a critical problem is how to integrate these raw data into meaningful biological information. One approach, the Evolutionary Trace, or ET, uses phylogenetic information to rank the residues in a protein sequence by evolutionary importance and then maps those ranked at the top onto a representative structure. If these residues form structural clusters, they can identify functional surfaces such as those involved in molecular recognition. Now that a number of examples have shown that ET can identify binding sites and focus mutational studies on their relevant functional determinants, we ask whether the method can be improved so as to be applicable on a large scale. To address this question, we introduce a new treatment of gaps resulting from insertions and deletions, which streamlines the selection of sequences used as input. We also introduce objective statistics to assess the significance of the total number of clusters and of the size of the largest one. As a result of the novel treatment of gaps, ET performance improves measurably. We find evolutionarily privileged clusters that are significant at the 5% level in 45 out of 46 (98%) proteins drawn from a variety of structural classes and biological functions. In 37 of the 38 proteins for which a protein-ligand complex is available, the dominant cluster contacts the ligand. We conclude that spatial clustering of evolutionarily important residues is a general phenomenon, consistent with the cooperative nature of residues that determine structure and function. In practice, these results suggest that ET can be applied on a large scale to identify functional sites in a significant fraction of the structures in the protein databank (PDB). This approach to combining raw sequences and structure to obtain detailed insights into the molecular basis of function should prove valuable in the context of the Structural Genomics Initiative.     

Analysis of protein structures reveals regions of rare backbone conformation at functional sites

Regions of rare conformation were located in 300 protein crystal structures representing seven major protein folds. A distance matrix algorithm was used to search rapidly for 9-residue fragments of rare backbone conformation using a comparison to a relational database of encoded fragments derived from the database of nonredundant structures. Rare fragments were found in 61% of the analyzed protein structures. Detailed analysis was performed for 78 proteins of different folds. The rare fragments were located near functional sites in 72% of the protein structures. The rare fragments often formed parts of ligand-binding sites (59%), protein-protein interfaces (8%), and domain-domain contacts (5%). Of the remaining structures, 5% had a high average B-factor or high local B-factors. Statistical analysis suggests that the association between ligands and rare regions does not occur by chance alone. The present study is likely to underestimate the number of functional sites, because not all analyzed protein structures contained a ligand. The results suggest that rapid searches for regions with rare local backbone conformations can assist in prediction of functional sites in novel proteins.     

Prediction of ligand-binding sites of proteins by molecular docking calculation for a random ligand library

A new approach to predicting the ligand-binding sites of proteins was developed, using protein-ligand docking computation. In this method, many compounds in a random library are docked onto the whole protein surface. We assumed that the true ligand-binding site would exhibit stronger affinity to the compounds in the random library than the other sites, even if the random library did not include the ligand corresponding to the true binding site. We also assumed that the affinity of the true ligand-binding site would be correlated to the docking scores of the compounds in the random library, if the ligand-binding site was correctly predicted. We call this method the molecular-docking binding-site finding (MolSite) method. The MolSite method was applied to 89 known protein-ligand complex structures extracted from the Protein Data Bank, and it predicted the correct binding sites with about 80-99% accuracy, when only the single top-ranked site was adopted. In addition, the average docking score was weakly correlated to the experimental protein-ligand binding free energy, with a correlation coefficient of 0.44.     

Experimental and computational studies on the binding of diazinon to human serum albumin

In the present research, the binding properties of diazinon (DZN), as an organophosphorus herbicide, to human serum albumin (HSA) were investigated using combination of spectroscopic, electrochemistry, and molecular modeling techniques. Changes in the UV–Vis and FT-IR spectra were observed upon ligand binding along with a significant degree of tryptophan fluorescence quenching on complex formation. The obtained results from spectroscopic and electrochemistry experiments along with the computational studies suggest that DZN binds to residues located in subdomains IIA of HSA with binding constant about 1410.9 M^?1 at 300 K. From the thermodynamic parameters calculated according to the van’t Hoff equation, the enthalpy change ΔH° and entropy change ΔS° were found to be ?16.695 and 0.116 KJ/mol K, respectively. The primary binding pattern is determined by hydrophobic interaction and hydrogen binding occurring in so-called site I of HSA. DZN could slightly alter the secondary structure of HSA. All of experimental results are supported by computational techniques such as docking and molecular dynamics simulation using a HSA crystal model.     

Prediction of transition metal-binding sites from apo protein structures

Metal ions are crucial for protein function. They participate in enzyme catalysis, play regulatory roles, and help maintain protein structure. Current tools for predicting metal-protein interactions are based on proteins crystallized with their metal ions present (holo forms). However, a majority of resolved structures are free of metal ions (apo forms). Moreover, metal binding is a dynamic process, often involving conformational rearrangement of the binding pocket. Thus, effective predictions need to be based on the structure of the apo state. Here, we report an approach that identifies transition metal-binding sites in apo forms with a resulting selectivity >95%. Applying the approach to apo forms in the Protein Data Bank and structural genomics initiative identifies a large number of previously unknown, putative metal-binding sites, and their amino acid residues, in some cases providing a first clue to the function of the protein.     

Automated identification of binding sites for phosphorylated ligands in protein structures

Phosphorylation is a crucial step in many cellular processes, ranging from metabolic reactions involved in energy transformation to signaling cascades. In many instances, protein domains specifically recognize the phosphogroup. Knowledge of the binding site provides insights into the interaction, and it can also be exploited for therapeutic purposes. Previous studies have shown that proteins interacting with phosphogroups are highly heterogeneous, and no single property can be used to reliably identify the binding site. Here we present an energy‐based computational procedure that exploits the protein three‐dimensional structure to identify binding sites involved in the recognition of phosphogroups. The procedure is validated on three datasets containing more than 200 proteins binding to ATP, phosphopeptides, and phosphosugars. A comparison against other three generic binding site identification approaches shows higher accuracy values for our method, with a correct identification rate in the 80–90% range for the top three predicted sites. Addition of conservation information further improves the performance. The method presented here can be used as a first step in functional annotation or to guide mutagenesis experiments and further studies such as molecular docking. Proteins 2012;. © 2012 Wiley Periodicals, Inc.