Andrew J. Bordner

Statistical analysis and prediction of protein-protein interfaces

Predicting protein–protein interfaces from a three‐dimensional structure is a key task of computational structural proteomics. In contrast to geometrically distinct small molecule binding sites, protein–protein interface are notoriously difficult to predict. We generated a large nonredundant data set of 1494 true protein–protein interfaces using biological symmetry annotation where necessary. The data set was carefully analyzed and a Support Vector Machine was trained on a combination of a new robust evolutionary conservation signal with the local surface properties to predict protein–protein interfaces. Fivefold cross validation verifies the high sensitivity and selectivity of the model. As much as 97% of the predicted patches had an overlap with the true interface patch while only 22% of the surface residues were included in an average predicted patch. The model allowed the identification of potential new interfaces and the correction of mislabeled oligomeric states. Proteins 2005.

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide‐MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T‐cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased‐Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all‐atom MHC model. Our method was tested by a comparison of the results of cross‐docking 14 peptides into HLA‐A*0201 and 9 peptides into H‐2Kb as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 Å backbone RMSD) for cross‐docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 Å illustrate the methods effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA‐A*0201 binders or nonbinders. In contrast with sequence‐based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H‐2Kb), not used for training, with comparable prediction accuracy. Proteins 2006.

Functional Importance of a Structurally Distinct Homodimeric Complex of the Family B G Protein-Coupled Secretin Receptor

Oligomerization of G protein-coupled receptors has been described, but its structural basis and functional importance have been inconsistent. Here, we demonstrate that the agonist occupied wild-type secretin receptor is predominantly in a guanine nucleotide-sensitive high-affinity state and exhibits negative cooperativity, whereas the monomeric receptor is primarily in a guanine nucleotide-insensitive lower affinity state. We previously demonstrated constitutive homodimerization of this receptor through the lipid-exposed face of transmembrane (TM) IV. We now use cysteine-scanning mutagenesis of 14 TM IV residues, bioluminescence resonance energy transfer (BRET), and functional analysis to map spatial approximations and functional importance of specific residues in this complex. All, except for three helix-facing mutants, trafficked to the cell surface, where secretin was shown to bind and elicit cAMP production. Cells expressing complementary-tagged receptors were treated with cuprous phenanthroline to establish disulfide bonds between spatially approximated cysteines. BRET was measured as an indication of receptor oligomerization and was repeated after competitive disruption of oligomers with TM IV peptide to distinguish covalent from noncovalent associations. Although all constructs generated a significant BRET signal, this was disrupted by peptide in all except for single-site mutants replacing five residues with cysteine. Of these, covalent stabilization of receptor homodimers through positions of Gly243, Ile247, and Ala250 resulted in a GTP-sensitive high-affinity state of the receptor, whereas the same procedure with Ala246 and Phe240 mutants resulted in a GTP-insensitive lower affinity state. We propose the existence of a functionally important, structurally specific high-affinity dimeric state of the secretin receptor, which may be typical of family B G protein-coupled receptors.

Predicting small ligand binding sites in proteins using backbone structure

Motivation: Specific non-covalent binding of metal ions and ligands, such as nucleotides and cofactors, is essential for the function of many proteins. Computational methods are useful for predicting the location of such binding sites when experimental information is lacking. Methods that use structural information, when available, are particularly promising since they can potentially identify non-contiguous binding motifs that cannot be found using only the amino acid sequence. Furthermore, a prediction method that can utilize low-resolution models is advantageous because high-resolution structures are available for only a relatively small fraction of proteins. Results: SitePredict is a machine learning-based method for predicting binding sites in protein structures for specific metal ions or small molecules. The method uses Random Forest classifiers trained on diverse residue-based site properties including spatial clustering of residue types and evolutionary conservation. SitePredict was tested by cross-validation on a set of known binding sites for six different metal ions and five different small molecules in a non-redundant set of protein–ligand complex structures. The prediction performance was good for all ligands considered, as reflected by AUC values of at least 0.8. Furthermore, a more realistic test on unbound structures showed only a slight decrease in the accuracy. The properties that contribute the most to the prediction accuracy of each ligand were also examined. Finally, examples of predicted binding sites in homology models and uncharacterized proteins are discussed. Availability: Binding site prediction results for all PDB protein structures and human protein homology models are available at http://sitepredict.org/. Contact: bordner.andrew@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

BackgroundThe binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC.ResultsWe have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides.ConclusionsThe RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/.

Comprehensive inventory of protein complexes in the Protein Data Bank from consistent classification of interfaces

BackgroundProtein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB).ResultsWe have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section).ConclusionOur method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method.

MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

BackgroundThe binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.ResultsWe fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes.ConclusionsThe MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.

Protein docking using surface matching and supervised machine learning.

Computational prediction of protein complex structures through docking offers a means to gain a mechanistic understanding of protein interactions that mediate biological processes. This is particularly important as the number of experimentally determined structures of isolated proteins exceeds the number of structures of complexes. A comprehensive docking procedure is described in which efficient sampling of conformations is achieved by matching surface normal vectors, fast filtering for shape complementarity, clustering by RMSD, and scoring the docked conformations using a supervised machine learning approach. Contacting residue pair frequencies, residue propensities, evolutionary conservation, and shape complementarity score for each docking conformation are used as input data to a Random Forest classifier. The performance of the Random Forest approach for selecting correctly docked conformations was assessed by cross‐validation using a nonredundant benchmark set of X‐ray structures for 93 heterodimer and 733 homodimer complexes. The single highest rank docking solution was the correct (near‐native) structure for slightly more than one third of the complexes. Furthermore, the fraction of highly ranked correct structures was significantly higher than the overall fraction of correct structures, for almost all complexes. A detailed analysis of the difficult to predict complexes revealed that the majority of the homodimer cases were explained by incorrect oligomeric state annotation. Evolutionary conservation and shape complementarity score as well as both underrepresented and overrepresented residue types and residue pairs were found to make the largest contributions to the overall prediction accuracy. Finally, the method was also applied to docking unbound subunit structures from a previously published benchmark set. Proteins 2007.

Predicting protein-protein binding sites in membrane proteins

BackgroundMany integral membrane proteins, like their non-membrane counterparts, form either transient or permanent multi-subunit complexes in order to carry out their biochemical function. Computational methods that provide structural details of these interactions are needed since, despite their importance, relatively few structures of membrane protein complexes are available.ResultsWe present a method for predicting which residues are in protein-protein binding sites within the transmembrane regions of membrane proteins. The method uses a Random Forest classifier trained on residue type distributions and evolutionary conservation for individual surface residues, followed by spatial averaging of the residue scores. The prediction accuracy achieved for membrane proteins is comparable to that for non-membrane proteins. Also, like previous results for non-membrane proteins, the accuracy is significantly higher for residues distant from the binding site boundary. Furthermore, a predictor trained on non-membrane proteins was found to yield poor accuracy on membrane proteins, as expected from the different distribution of surface residue types between the two classes of proteins. Thus, although the same procedure can be used to predict binding sites in membrane and non-membrane proteins, separate predictors trained on each class of proteins are required. Finally, the contribution of each residue property to the overall prediction accuracy is analyzed and prediction examples are discussed.ConclusionGiven a membrane protein structure and a multiple alignment of related sequences, the presented method gives a prioritized list of which surface residues participate in intramembrane protein-protein interactions. The method has potential applications in guiding the experimental verification of membrane protein interactions, structure-based drug discovery, and also in constraining the search space for computational methods, such as protein docking or threading, that predict membrane protein complex structures.

Importance of Each Residue within Secretin for Receptor Binding and Biological Activity

Secretin is a linear 27-residue peptide hormone that stimulates pancreatic and biliary ductular bicarbonate and water secretion by acting at its family B G protein-coupled receptor. While, like other family members, the carboxyl-terminal region of secretin is most important for high affinity binding and its amino-terminal region is most important for receptor selectivity and receptor activation, determinants for these activities are distributed throughout the entire length of this peptide. In this work, we have systematically investigated changing each residue within secretin to alanine and evaluating the impact on receptor binding and biological activity. The residues most critical for receptor binding were His1, Asp3, Gly4, Phe6, Thr7, Ser8, Leu10, Asp15, Leu19, and Leu23. The residues most critical for biological activity included His1, Gly4, Thr7, Ser8, Glu9, Leu10, Leu19, Leu22, and Leu23, with Asp3, Phe6, Ser11, Leu13, Asp15, Leu26, and Val27 also contributing. While the importance of residues in positions analogous to His1, Asp3, Phe6, Thr7, and Leu23 is conserved for several closely related members of this family, Leu19 is uniquely important for secretin. We, therefore, have further studied this residue by molecular modeling and molecular dynamics simulations. Indeed, the molecular dynamics simulations showed that mutation of Leu19 to alanine was destabilizing, with this effect greater than that observed for the analogous position in the other close family members. This could reflect reduced contact with the receptor or an increase in the solvent-accessible surface area of the hydrophobic residues in the carboxyl terminus of secretin as bound to its receptor.