Investigation of cation–π interactions in sugar-binding proteins

Cation–π interaction is a non-covalent binding force that plays a significant role in protein stability and drug–receptor interactions. In this work, we have investigated the structural role of cation–π interactions in sugar-binding proteins (SBPs). We observed 212 cation–π interactions in 53 proteins out of 59 SBPs in dataset. There is an average one energetically significant cation–π interaction for every 66 residues in SBPs. In addition, Arg is highly preferred to form cation–π interactions, and the average energy of Arg-Trp is high among six pairs. Long-range interactions are predominant in the analyzed cation–π interactions. Comparatively, all interaction pairs favor to accommodate in strand conformations. The analysis of solvent accessible area indicates that most of the aromatic residues are found on buried or partially buried whereas cationic residues were found mostly on the exposed regions of protein. The cation–π interactions forming residues were found that around 43% of cation–π residues had highly conserved with the conservation score ≥6. Almost cationic and π-residues equally share in the stabilization center. Sugar-binding site analysis in available complexes showed that the frequency of Trp and Arg is high, suggesting the potential role of these two residues in the interactions between proteins and sugar molecules. Our observations in this study could help to further understand the structural stability of SBPs.     

Spectroscopic study on the communication between a heme a3 propionate, Asp399 and the binuclear center of cytochrome c oxidase from Paracoccus denitrificans

The proton pumping mechanism of cytochrome c oxidase on a molecular level is highly disputed. Recently theoretical calculations and real time electron transfer measurements indicated the involvement of residues in the vicinity of the ring A propionate of heme a3, including Asp399 and the CuB ligands His 325, 326. In this study we probed the interaction of Asp399 with the binuclear center and characterize the protonation state of its side chain. Redox induced FTIR difference spectra of mutations at the site in direct comparison to wild type, indicate that below pH 5 Asp 399 displays signals typical for the deprotonation of the acidic residue with reduction of the enzyme. Interestingly at a pH higher than 5, no contributions from Asp 399 are evident. In order to probe the interaction of the site with the binuclear center we followed the rebinding of CO by infrared spectroscopy for mutations on residue Asp399 to Glu, Asn and Leu. Previously different CO conformers have been identified for bacterial cytochrome c oxidases, and its pH dependent behaviour discussed to be relevant for catalysis. Interestingly we observe the lack of this pH dependency and a strong influence on the observable conformers for all mutants studied here, clearly suggesting a communication of the site with the heme-copper center and the nearby histidine residues.     

RNA binding site of R17 coat protein

The specific interaction between R17 coat protein and its target of translational repression at the initiation site of the R17 replicase gene was studied by synthesizing variants of the RNA binding site and measuring their affinity to the coat protein by using a nitrocellulose filter binding assay. Substitution of two of the seven single-stranded residues by other nucleotides greatly reduced the Ka, indicating that they are essential for the RNA-protein interaction. In contrast, three other single-stranded residues can be substituted without altering the Ka. When several of the base-paired residues in the binding site are altered in such a way that pairing is maintained, little change in Ka is observed. However, when the base pairs are disrupted, coat protein does not bind. These data suggest that while the hairpin loop structure is essential for protein binding, the base-paired residues do not contact the protein directly. On the basis of these and previous data, a model for the structural requirements of the R17 coat protein binding site is proposed. The model was successfully tested by demonstrating that oligomers with sequences quite different from the replicase initiator were able to bind coat protein.     

Protein-protein interaction site prediction based on conditional random fields

MOTIVATION: We are motivated by the fast-growing number of protein structures in the Protein Data Bank with necessary information for prediction of protein-protein interaction sites to develop methods for identification of residues participating in protein-protein interactions. We would like to compare conditional random fields (CRFs)-based method with conventional classification-based methods that omit the relation between two labels of neighboring residues to show the advantages of CRFs-based method in predicting protein-protein interaction sites. RESULTS: The prediction of protein-protein interaction sites is solved as a sequential labeling problem by applying CRFs with features including protein sequence profile and residue accessible surface area. The CRFs-based method can achieve a comparable performance with state-of-the-art methods, when 1276 nonredundant hetero-complex protein chains are used as training and test set. Experimental result shows that CRFs-based method is a powerful and robust protein-protein interaction site prediction method and can be used to guide biologists to make specific experiments on proteins. AVAILABILITY: http://www.insun.hit.edu.cn/~mhli/site_CRFs/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Spectroscopic study on the communication between a heme a3 propionate, Asp399 and the binuclear center of cytochrome c oxidase from Paracoccus denitrificans

The proton pumping mechanism of cytochrome c oxidase on a molecular level is highly disputed. Recently theoretical calculations and real time electron transfer measurements indicated the involvement of residues in the vicinity of the ring A propionate of heme a₃, including Asp399 and the Cu_B ligands His 325, 326. In this study we probed the interaction of Asp399 with the binuclear center and characterize the protonation state of its side chain. Redox induced FTIR difference spectra of mutations at the site in direct comparison to wild type, indicate that below pH 5 Asp 399 displays signals typical for the deprotonation of the acidic residue with reduction of the enzyme. Interestingly at a pH higher than 5, no contributions from Asp 399 are evident. In order to probe the interaction of the site with the binuclear center we followed the rebinding of CO by infrared spectroscopy for mutations on residue Asp399 to Glu, Asn and Leu. Previously different CO conformers have been identified for bacterial cytochrome c oxidases, and its pH dependent behaviour discussed to be relevant for catalysis. Interestingly we observe the lack of this pH dependency and a strong influence on the observable conformers for all mutants studied here, clearly suggesting a communication of the site with the heme-copper center and the nearby histidine residues.     

Residue centrality, functionally important residues, and active site shape: analysis of enzyme and non-enzyme families

The representation of protein structures as small-world networks facilitates the search for topological determinants, which may relate to functionally important residues. Here, we aimed to investigate the performance of residue centrality, viewed as a family fold characteristic, in identifying functionally important residues in protein families. Our study is based on 46 families, including 29 enzyme and 17 non-enzyme families. A total of 80% of these central positions corresponded to active site residues or residues in direct contact with these sites. For enzyme families, this percentage increased to 91%, while for non-enzyme families the percentage decreased substantially to 48%. A total of 70% of these central positions are located in catalytic sites in the enzyme families, 64% are in hetero-atom binding sites in those families binding hetero-atoms, and only 16% belong to protein-protein interfaces in families with protein-protein interaction data. These differences reflect the active site shape: enzyme active sites locate in surface clefts, hetero-atom binding residues are in deep cavities, while protein-protein interactions involve a more planar configuration. On the other hand, not all surface cavities or clefts are comprised of central residues. Thus, closeness centrality identifies functionally important residues in enzymes. While here we focus on binding sites, we expect to identify key residues for the integration and transmission of the information to the rest of the protein, reflecting the relationship between fold and function. Residue centrality is more conserved than the protein sequence, emphasizing the robustness of protein structures.     

4,4'-Bis dimethylaminodiphenylcarbinol: a new reagent for selective chemical modification. Interaction with porcine malate dehydrogenase

4,4-bis Dimethylaminodiphenylcarbinol (BDC-OH) has recently been reported to be a highly sensitive reagent for the quantitative determination of sulfhydryl residues in biological materials (1). In this communication the effectiveness of BDC-OH as a reagent for selective chemical modification of “active center” cysteine residues was investigated. The supernatant and mitochondrial forms of malate dehydrogenase were chosen for investigation by this reagent. Supernatant malate dehydrogenase which has never been found to contain an “active center” cysteine is unaffected by this reagent. Mitochondrial malate dehydrogenase (L malate: NAD⁺ oxidoreductase, EC 1.1.1.37) from porcine heart can be irreversibly inactivated by a 20 fold M excess of the reagent. Chemical modification of two essential sulfhydryl residues is prevented by the presence of the coenzyme, NAD⁺, suggesting that the site of interaction is located at or near the coenzyme binding site and hence at or near the enzymatic center of this enzyme.     

Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction

Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as “feedback” to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.     

Crystal Structure of Metal-Dependent Allantoinase from Escherichia coli

Allantoinase acts as a key enzyme for the biogenesis and degradation of ureides by catalyzing the conversion of (S)-allantoin into allantoate, the final step in the ureide pathway. Despite limited sequence similarity, biochemical studies of the enzyme suggested that allantoinase belongs to the amidohydrolase family. In this study, the crystal structure of allantoinase from Escherichia coli was determined at 2.1 Å resolution. The enzyme consists of a homotetramer in which each monomer contains two domains: a pseudo-triosephosphate-isomerase barrel and a β-sheet. Analogous to other enzymes in the amidohydrolase family, allantoinase retains a binuclear metal center in the active site, embedded within the barrel fold. Structural analyses demonstrated that the metal ions in the active site ligate one hydroxide and six residues that are conserved among allantoinases from other organisms. Functional analyses showed that the presence of zinc in the metal center is essential for catalysis and enantioselectivity of substrate. Both the metal center and active site residues Asn94 and Ser317 play crucial roles in dictating enzyme activity. These structural and functional features are distinctively different from those of the metal-independent allantoinase, which was very recently identified.     

Crystallographic analysis of active site contributions to regiospecificity in the diiron enzyme toluene 4-monooxygenase

Crystal structures of toluene 4-monooxygenase hydroxylase in complex with reaction products and effector protein reveal active site interactions leading to regiospecificity. Complexes with phenolic products yield an asymmetric μ-phenoxo-bridged diiron center and a shift of diiron ligand E231 into a hydrogen bonding position with conserved T201. In contrast, complexes with inhibitors p-NH(2)-benzoate and p-Br-benzoate showed a μ-1,1 coordination of carboxylate oxygen between the iron atoms and only a partial shift in the position of E231. Among active site residues, F176 trapped the aromatic ring of products against a surface of the active site cavity formed by G103, E104 and A107, while F196 positioned the aromatic ring against this surface via a π-stacking interaction. The proximity of G103 and F176 to the para substituent of the substrate aromatic ring and the structure of G103L T4moHD suggest how changes in regiospecificity arise from mutations at G103. Although effector protein binding produced significant shifts in the positions of residues along the outer portion of the active site (T201, N202, and Q228) and in some iron ligands (E231 and E197), surprisingly minor shifts (<1 ?) were produced in F176, F196, and other interior residues of the active site. Likewise, products bound to the diiron center in either the presence or absence of effector protein did not significantly shift the position of the interior residues, suggesting that positioning of the cognate substrates will not be strongly influenced by effector protein binding. Thus, changes in product distributions in the absence of the effector protein are proposed to arise from differences in rates of chemical steps of the reaction relative to motion of substrates within the active site channel of the uncomplexed, less efficient enzyme, while structural changes in diiron ligand geometry associated with cycling between diferrous and diferric states are discussed for their potential contribution to product release.     

Identification of selectivity-determining residues in cytochrome P450 monooxygenases: a systematic analysis of the substrate recognition site 5

The large and diverse family of cytochrome P450 monooxygenases (CYPs) was systematically analyzed to identify selectivity- and specificity-determining residues in the substrate recognition site 5, which is located in close vicinity to the heme center. A positively charged heme-interacting residue was identified in the structures of 29 monooxygenases and in 97.7% of the 6379 CYP sequences investigated here. This heme-interacting residue restricts the conformation of the substrate recognition site 5 and is preferentially located at position 10 or 11 after the conserved ExxR motif (in 94.4% of the sequences), in 3.3% of the sequences at position 9 or 12. As a result, a classification by the position of the heme-interacting residue allows to predict residues that are closest to the heme center and restrict its accessibility. In 98.4% of all CYP sequences a preferentially hydrophobic residue is located at position 5 after the ExxR motif that is predicted to point close to the heme center. Replacing this residue by hydrophobic residues of different size has been shown to change substrate specificity and regioselectivity for CYPs of different superfamilies. Twenty-seven percent of all CYPs are predicted to contain a second selectivity-determining residue at position 9 after the ExxR motif that can be identified by the pattern EXXR-X(7)-{P}-x-P-[HKR].     

Partial Order Optimum Likelihood (POOL): Maximum Likelihood Prediction of Protein Active Site Residues Using 3D Structure and Sequence Properties

A new monotonicity-constrained maximum likelihood approach, called Partial Order Optimum Likelihood (POOL), is presented and applied to the problem of functional site prediction in protein 3D structures, an important current challenge in genomics. The input consists of electrostatic and geometric properties derived from the 3D structure of the query protein alone. Sequence-based conservation information, where available, may also be incorporated. Electrostatics features from THEMATICS are combined with multidimensional isotonic regression to form maximum likelihood estimates of probabilities that specific residues belong to an active site. This allows likelihood ranking of all ionizable residues in a given protein based on THEMATICS features. The corresponding ROC curves and statistical significance tests demonstrate that this method outperforms prior THEMATICS-based methods, which in turn have been shown previously to outperform other 3D-structure-based methods for identifying active site residues. Then it is shown that the addition of one simple geometric property, the size rank of the cleft in which a given residue is contained, yields improved performance. Extension of the method to include predictions of non-ionizable residues is achieved through the introduction of environment variables. This extension results in even better performance than THEMATICS alone and constitutes to date the best functional site predictor based on 3D structure only, achieving nearly the same level of performance as methods that use both 3D structure and sequence alignment data. Finally, the method also easily incorporates such sequence alignment data, and when this information is included, the resulting method is shown to outperform the best current methods using any combination of sequence alignments and 3D structures. Included is an analysis demonstrating that when THEMATICS features, cleft size rank, and alignment-based conservation scores are used individually or in combination THEMATICS features represent the single most important component of such classifiers.     

A mixed mechanistic-electrostatic model to explain pH dependence of glycosyl hydrolase enzyme activity

Glycosyl hydrolases are a vast group of enzymes that share a common topology at their active site with two acidic residues that are responsible for activity. In spite of their similarity, they exhibit a wide range of pH optima that must depend on other factors. Using structural and mechanistic knowledge about glycosyl hydrolases from families 7, 10, and 16, we have formulated a new mathematical model that can include not only the ionization behavior of the catalytic residues but also as many ionizable residues as desired in the active site. In addition, the model can incorporate electrostatic influences via acid dissociation equilibrium constants and chemical relationships such as hydrogen bonds. The results of the simulations indicate a clear shift in the pH dependence of activity for the enzymes only when a close interrelation (hydrogen bond) between the catalytic and auxiliary residues in the active site is taken into account. This explains the observations from mutagenesis studies that show this type of shift and cannot be explained by a purely electrostatic interaction theory. Moreover, the presence of the kind of chemical interaction proposed could provide stabilization of the activity in the presence of environmental, structural, pH and electrostatic variations. These findings and the implications for the design of new mutagenesis strategies are discussed. The results suggest a way to modify, via site-directed mutagenesis, the acid dissociation of one of the catalytic residues in the active site independently of the other, which could have clear advantages over the purely electrostatic modifications that usually affect both residues simultaneously.     

Text mining improves prediction of protein functional sites

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.     

Distinct agonist- and antagonist-binding sites on the glycine receptor.

The distinction between receptor-binding sites for agonists and antagonists underpins the pharmacological differences between these two classes of ligands. In the glycine receptor, antagonist (strychnine) binding requires an interaction with residues Lys-200 and Tyr-202. We now demonstrate that the agonist-binding site of this receptor is located at the residue Thr-204. The agonist-binding site interaction is thus likely to be mediated by hydrogen bonding and not by ionic interactions. Our results demonstrate that, in contrast to other studies of ligand-gated ion channel receptors, agonist- and antagonist-binding sites are composed of distinct amino acid residues.     

Development of a protein–ligand-binding site prediction method based on interaction energy and sequence conservation

We present a new method for predicting protein–ligand-binding sites based on protein three-dimensional structure and amino acid conservation. This method involves calculation of the van der Waals interaction energy between a protein and many probes placed on the protein surface and subsequent clustering of the probes with low interaction energies to identify the most energetically favorable locus. In addition, it uses amino acid conservation among homologous proteins. Ligand-binding sites were predicted by combining the interaction energy and the amino acid conservation score. The performance of our prediction method was evaluated using a non-redundant dataset of 348 ligand-bound and ligand-unbound protein structure pairs, constructed by filtering entries in a ligand-binding site structure database, LigASite. Ligand-bound structure prediction (bound prediction) indicated that 74.0 % of predicted ligand-binding sites overlapped with real ligand-binding sites by over 25 % of their volume. Ligand-unbound structure prediction (unbound prediction) indicated that 73.9 % of predicted ligand-binding residues overlapped with real ligand-binding residues. The amino acid conservation score improved the average prediction accuracy by 17.0 and 17.6 points for the bound and unbound predictions, respectively. These results demonstrate the effectiveness of the combined use of the interaction energy and amino acid conservation in the ligand-binding site prediction.     

Prediction of metal ion-binding sites in proteins using the fragment transformation method

The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion-binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion-binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion-binding sites. Six types of metal ion-binding templates- those involving Ca(2+), Cu(2+), Fe(3+), Mg(2+), Mn(2+), and Zn(2+)-were constructed using the residues within 3.5 ? of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion-binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.     

SiteMotif: A graph-based algorithm for deriving structural motifs in Protein Ligand binding sites

Studying similarities in protein molecules has become a fundamental activity in much of biology and biomedical research, for which methods such as multiple sequence alignments are widely used. Most methods available for such comparisons cater to studying proteins which have clearly recognizable evolutionary relationships but not to proteins that recognize the same or similar ligands but do not share similarities in their sequence or structural folds. In many cases, proteins in the latter class share structural similarities only in their binding sites. While several algorithms are available for comparing binding sites, there are none for deriving structural motifs of the binding sites, independent of the whole proteins. We report the development of SiteMotif, a new algorithm that compares binding sites from multiple proteins and derives sequence-order independent structural site motifs. We have tested the algorithm at multiple levels of complexity and demonstrate its performance in different scenarios. We have benchmarked against 3 current methods available for binding site comparison and demonstrate superior performance of our algorithm. We show that SiteMotif identifies new structural motifs of spatially conserved residues in proteins, even when there is no sequence or fold-level similarity. We expect SiteMotif to be useful for deriving key mechanistic insights into the mode of ligand interaction, predict the ligand type that a protein can bind and improve the sensitivity of functional annotation.     

Detection of outlier residues for improving interface prediction in protein heterocomplexes

Sequence-based understanding and identification of protein binding interfaces is a challenging research topic due to the complexity in protein systems and the imbalanced distribution between interface and noninterface residues. This paper presents an outlier detection idea to address the redundancy problem in protein interaction data. The cleaned training data are then used for improving the prediction performance. We use three novel measures to describe the extent a residue is considered as an outlier in comparison to the other residues: the distance of a residue instance from the center instance of all residue instances of the same class label (Dist), the probability of the class label of the residue instance (PCL), and the importance of within-class and between-class (IWB) residue instances. Outlier scores are computed by integrating the three factors; instances with a sufficiently large score are treated as outliers and removed. The data sets without outliers are taken as input for a support vector machine (SVM) ensemble. The proposed SVM ensemble trained on input data without outliers performs better than that with outliers. Our method is also more accurate than many literature methods on benchmark data sets. From our empirical studies, we found that some outlier interface residues are truly near to noninterface regions, and some outlier noninterface residues are close to interface regions.