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Protein-protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo- or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the challenge of distinguishing a natively bound complex from non-native forms. Current protein docking approaches address this problem by sampling multiple binding modes in proteins and scoring each mode, with the lowest-energy (or highest scoring) binding mode being regarded as a near-native complex. However, high-scoring modes often match poorly with the true bound form, suggesting a need for improvement of the scoring function. In this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein-protein interfaces. We tested KFC-E on four benchmark sets of unbound examples and two benchmark sets of bound examples, with the results demonstrating a clear improvement over scores that examine conservation and coevolution across the entire interface.

Selecting peptides that bind strongly to the major histocompatibility complex (MHC) for inclusion in a vaccine has therapeutic potential for infections and tumors. Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding predictions. Here, we train support vector machine classifier (SVMC) models on physicochemical sequence-based and structure-based descriptor sets to predict peptide binding to a well-studied model mouse MHC I allele, H-2Db. Recursive feature elimination and two-way forward feature selection were also performed. Although low on sensitivity compared to the current state-of-the-art algorithms, models based on physicochemical descriptor sets achieve specificity and precision comparable to the most popular sequence-based algorithms. The best-performing model is a hybrid descriptor set containing both sequence-based and structure-based descriptors. Interestingly, close to half of the physicochemical sequence-based descriptors remaining in the hybrid model were properties of the anchor positions, residues 5 and 9 in the peptide sequence. In contrast, residues flanking position 5 make little to no residue-specific contribution to the binding affinity prediction. The results suggest that machine-learned models incorporating both sequence-based descriptors and structural data may provide information on specific physicochemical properties determining binding affinities.

Proteins are essential elements of biological systems, and their function typically relies on their ability to successfully bind to specific partners. Recently, an emphasis of study into protein interactions has been on hot spots, or residues in the binding interface that make a significant contribution to the binding energetics. In this study, we investigate how conservation of hot spots can be used to guide docking prediction. We show that the use of evolutionary data combined with hot spot prediction highlights near‐native structures across a range of benchmark examples. Our approach explores various strategies for using hot spots and evolutionary data to score protein complexes, using both absolute and chemical definitions of conservation along with refinements to these strategies that look at windowed conservation and filtering to ensure a minimum number of hot spots in each binding partner. Finally, structure‐based models of orthologs were generated for comparison with sequence‐based scoring. Using two data sets of 22 and 85 examples, a high rate of top 10 and top 1 predictions are observed, with up to 82% of examples returning a top 10 hit and 35% returning top 1 hit depending on the data set and strategy applied; upon inclusion of the native structure among the decoys, up to 55% of examples yielded a top 1 hit. The 20 common examples between data sets show that more carefully curated interolog data yields better predictions, particularly in achieving top 1 hits. Proteins 2015; 83:1940–1946. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Summary: Protein–nucleic acid interactions are among the most important intermolecular interactions in the regulation of cellular events. Identifying residues involved in these interactions from protein structure alone is an important challenge. Here we introduce the webserver interface to DNA Binding Site Identifier (DBSI), a powerful structure-based SVM model for the prediction and visualization of DNA binding sites on protein structures. DBSI has been shown to be a top-performing model to predict DNA binding sites on the surface of a protein or peptide and shows promise in predicting RNA binding sites. Availability and Implementation: Server is available at http://dbsi.mitchell-lab.org Contact: jcmitchell@wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Synaptotagmins contain tandem C2 domains and function as Ca2+ sensors for vesicle exocytosis but the mechanism for coupling Ca2+ rises to membrane fusion remains undefined. Synaptotagmins bind SNAREs, essential components of the membrane fusion machinery, but the role of these interactions in Ca2+-triggered vesicle exocytosis has not been directly assessed. We identified sites on synaptotagmin−1 that mediate Ca2+-dependent SNAP25 binding by zero-length cross-linking. Mutation of these sites in C2A and C2B eliminated Ca2+-dependent synaptotagmin−1 binding to SNAREs without affecting Ca2+-dependent membrane binding. The mutants failed to confer Ca2+ regulation on SNARE-dependent liposome fusion and failed to restore Ca2+-triggered vesicle exocytosis in synaptotagmin-deficient PC12 cells. The results provide direct evidence that Ca2+-dependent SNARE binding by synaptotagmin is essential for Ca2+-triggered vesicle exocytosis and that Ca2+-dependent membrane binding by itself is insufficient to trigger fusion. A structure-based model of the SNARE-binding surface of C2A provided a new view of how Ca2+-dependent SNARE and membrane binding occur simultaneously.

Protein–protein interactions can be altered by mutating one or more “hot spots,” the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta–Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta–Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface. Proteins 2007. © 2007 Wiley-Liss, Inc.

Onconase (ONC), an amphibian member of the bovine pancreatic ribonuclease A (RNase A) superfamily, is in phase III clinical trials as a treatment for malignant mesothelioma. RNase A is a far more efficient catalyst of RNA cleavage than ONC but is not cytotoxic. The innate ability of ONC to evade the cytosolic ribonuclease inhibitor protein (RI) is likely to be a primary reason for its cytotoxicity. In contrast, the non-covalent interaction between RNase A and RI is one of the strongest known, with the RI.RNase A complex having a K(d) value in the femtomolar range. Here, we report on the use of the fast atomic density evaluation (FADE) algorithm to identify regions in the molecular interface of the RI.RNase A complex that exhibit a high degree of geometric complementarity. Guided by these "knobs" and "holes", we designed variants of RNase A that evade RI. The D38R/R39D/N67R/G88R substitution increased the K(d) value of the pRI.RNase A complex by 20 x 10(6)-fold (to 1.4 microM) with little change to catalytic activity or conformational stability. This and two related variants of RNase A were more toxic to human cancer cells than was ONC. Notably, these cytotoxic variants exerted their toxic activity on cancer cells selectively, and more selectively than did ONC. Substitutions that further diminish affinity for RI (which has a cytosolic concentration of 4 microM) are unlikely to produce a substantial increase in cytotoxic activity. These results demonstrate the utility of the FADE algorithm in the examination of protein-protein interfaces and represent a landmark towards the goal of developing chemotherapeutics based on mammalian ribonucleases.

Two methods for rapid characterization of molecular shape are presented. Both techniques are based on the density of atoms near the molecular surface. The Fast Atomic Density Evaluation (FADE) algorithm uses fast Fourier transforms to quickly estimate densities. The Pairwise Atomic Density Reverse Engineering (PADRE) method derives modified density measures from the relationship between atomic density and total potentials. While many shape-characterization techniques define shape relative to a surface, the descriptors returned by FADE and PADRE can measure local geometry from points within the three-dimensional space surrounding a molecule. The methods can be used to find crevices and protrusions near the surface of a molecule and to test shape complementarity at the interface between docking molecules.

The computer program DOT quickly finds low-energy docked structures for two proteins by performing a systematic search over six degrees of freedom. A novel feature of DOT is its energy function, which is the sum of both a Poisson-Boltzmann electrostatic energy and a van der Waals energy, each represented as a grid-based correlation function. DOT evaluates the energy of interaction for many orientations of the moving molecule and maintains separate lists scored by either the electrostatic energy, the van der Waals energy or the composite sum of both. The free energy is obtained by summing the Boltzmann factor over all rotations at each grid point. Three important findings are presented. First, for a wide variety of protein-protein interactions, the composite-energy function is shown to produce larger clusters of correct answers than found by scoring with either van der Waals energy (geometric fit) or electrostatic energy alone. Second, free-energy clusters are demonstrated to be indicators of binding sites. Third, the contributions of electrostatic and attractive van der Waals energies to the total energy term appropriately reflect the nature of the various types of protein-protein interactions studied.

We describe methods and results for four new types of challenge in the Critical Assessment of PRedicted Interactions (CAPRI). Two new challenges asked predictors to create models related to protein interface design. The first of these was to distinguish binding interfaces from designed nonbinding interfaces. The second was to predict the effects of all single-point mutations on hemagglutinin binding to two small designed proteins. Two additional challenges asked predictors to submit high-resolution structures for interface-bound crystallographic waters and for binding heparin to a putative glycosylase.

Protein-protein interactions are a fundamental aspect of many biological processes. The advent of recombinant protein and computational techniques has allowed for the rational design of proteins with novel binding capabilities. It is therefore desirable to predict which designed proteins are capable of binding in vitro. To this end, we have developed a learned classification model that combines energetic and non-energetic features. Our feature set is adapted from specialized potentials for aromatic interactions, hydrogen bonds, electrostatics, shape, and desolvation. A binding model built on these features was initially developed for CAPRI Round 21, achieving top results in the independent assessment. Here, we present a more thoroughly trained and validated model, and compare various support-vector machine kernels. The Gaussian kernel model classified both high-resolution complexes and designed nonbinders with 79-86% accuracy on independent test data. We also observe that multiple physical potentials for dielectric-dependent electrostatics and hydrogen bonding contribute to the enhanced predictive accuracy, suggesting that their combined information is much greater than that of any single energetics model. We also study the change in predictive performance as the model features or training data are varied, observing unusual patterns of prediction in designed interfaces as compared with other data types.

International audience; The CAPRI (Critical Assessment of Predicted Interactions) and CASP (Critical Assessment of protein Structure Prediction) experiments have demonstrated the power of community-wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community-wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting that there may be important physical chemistry missing in the energy calculations. A total of 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the nonpolar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were, on average, structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a nonbinder.

We present a computationally efficient method for flexible refinement of docking predictions that reflects observed motions within a protein's structural class. Using structural homologs, we derive deformation models that capture likely motions. The models or “replicates” typically align along a rigid core, with a handful of flexible loops, linkers and tails. A few replicates can generate a much larger number of conformers, by exchanging each flexible region independently of the others. In this way, 10 replicates of a protein having 6 flexible regions can be used to generate a million conformations of a molecule. While this has obvious advantages in terms of sampling, the cost of assessing energies at every conformer is prohibitive, particularly when both molecules are flexible. Our approach addresses this combinatorial explosion, using key assumptions to compress the sampling by many orders of magnitude. ReplicOpter can perform hierarchical clustering from a list of rigid docking predictions and find nearby structures to any promising cluster representatives. These predicted complexes can then be refined and rescored. ReplicOpter's scoring function includes a Lennard-Jones potential softened using the Anderson-Chandler-Weeks decomposition, a desolvation term derived from the Atomic Contact Energy function, Coulombic electrostatics, hydrogen bonding, and terms to model pi-pi and pi-cation interactions. ReplicOpter has performed well on several recent CAPRI systems. We are presently benchmarking ReplicOpter on the complete docking benchmark set to fully establish its utility in refining rigid docking predictions and identifying near-native solutions. © 2010 Wiley-Liss, Inc.

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues., Author Summary Allostery is the process whereby a molecule binds to one site in a protein and alters the function of a distant site. This phenomenon is ubiquitous, as proteins frequently must adapt their behavior to changes in the cellular milieu. The mechanism(s) underlying allostery remains incompletely understood. In particular, predictive models are needed that distinguish amino-acid residues that are critical to allostery, or “hotspots”, from non-hotspots. Here we have used data-mining approaches to infer rules that distinguish hotspots from non-hotspots. Starting with a data set of known hotspot and non-hotspot residues from a diverse set of allosteric proteins, the training data set, we applied machine learning to this data to “learn” models, or sets of rules, for distinguishing hotspots and non-hotspots by inferring associations between the classification (hotspot or non-hotspot) and an associated set of calculated attributes. Many models that showed the highest predictive power on the training data also exhibited high accuracy and sensitivity when applied to an independent data set. Moreover, the pattern of predicted hotspots in the proteins we studied was consistent with known structure/function relationships and previous work suggesting that a network of essential residues mediates the allosteric transition.

PDZ proteins coordinate assembly of protein complexes that participate in diverse biological processes. GIPC is a multifunctional PDZ protein that interacts with several soluble and membrane proteins. Unlike most PDZ proteins, GIPC contains single PDZ domain and the mechanisms by which GIPC mediates its actions remain unclear. We investigated the possibility that in lieu of multiple PDZ domains, GIPC forms multimers. Here, we demonstrate that GIPC can bind to itself and that the PDZ domain is involved in GIPC-GIPC interaction. Gel filtration, sucrose gradient centrifugation and chemical cross-linking showed that whereas bulk of cytosolic GIPC was present as monomer, oligomers with an estimated molecular mass corresponding to GIPC homotrimer were readily detectable in the membrane fraction. Modeling of GIPC PDZ domain showed feasibility of trimerization. Immunogold electron microscopy showed that GIPC is present in clusters near vesicles. Our data suggest that oligomers of GIPC mediate its functions in melanocytes.

In this study, we present the DNA-Binding Site Identifier (DBSI), a new structure-based method for predicting protein interaction sites for DNA binding. DBSI was trained and validated on a data set of 263 proteins (TRAIN-263), tested on an independent set of protein-DNA complexes (TEST-206) and data sets of 29 unbound (APO-29) and 30 bound (HOLO-30) protein structures distinct from the training data. We computed 480 candidate features for identifying protein residues that bind DNA, including new features that capture the electrostatic microenvironment within shells near the protein surface. Our iterative feature selection process identified features important in other models, as well as features unique to the DBSI model, such as a banded electrostatic feature with spatial separation comparable with the canonical width of the DNA minor groove. Validations and comparisons with established methods using a range of performance metrics clearly demonstrate the predictive advantage of DBSI, and its comparable performance on unbound (APO-29) and bound (HOLO-30) conformations demonstrates robustness to binding-induced protein conformational changes. Finally, we offer our feature data table to others for integration into their own models or for testing improved feature selection and model training strategies based on DBSI.