A simple shape characteristic of protein protein recognition

Bioinformatics (2007) 23(7), 789–792 The authors wish to apologize for the omission     

Dissecting protein-protein recognition sites

The recognition sites in 70 pairwise protein-protein complexes of known three-dimensional structure are dissected in a set of surface patches by clustering atoms at the interface. When the interface buries <2000 A2 of protein surface, the recognition sites usually form a single patch on the surface of each component protein. In contrast, larger interfaces are generally multipatch, with at least one pair of patches that are equivalent in size to a single-patch interface. Each recognition site, or patch within a site, contains a core made of buried interface atoms, surrounded by a rim of atoms that remain accessible to solvent in the complex. A simple geometric model reproduces the number and distribution of atoms within a patch. The rim is similar in composition to the rest of the protein surface, but the core has a distinctive amino acid composition, which may help in identifying potential protein recognition sites on single proteins of known structures.     

The kinetics of protein-protein recognition

We examine a simple kinetic model for association that incorporates the basic features of protein-protein recognition within the rigid body approximation, that is, when no large conformation change occurs. Association starts with random collision at the rate k_coll predicted by the Einstein-Smoluchowski equation. This creates an encounter pair that can evolve into a stable complex if and only if the two molecules are correctly oriented and positioned, which has a probability p_r. In the absence of long-range interactions, the bimolecular rate of association is p_r k_coll. Long-range electrostatic interactions affect both k_coll and p_r. The collision rate is multiplied by q_t, a factor larger than 1 when the molecules carry net charges of opposite sign as coulombic attraction makes collisions more frequent, and less than 1 in the opposite case. The probability p_r is multiplied by a factor q_r that represents the steering effect of electric dipoles, which preorient the molecules before they collide. The model is applied to experimental data obtained by Schreiber and Fersht (Nat. Struct. Biol. 3:427–431, 1996) on the kinetics of barnase-barstar association. When long-range electrostatic interactions are fully screened or mutated away, q_tq_r ≈1, and the observed rate of productive collision is p_r k_coll ≈10⁵ M⁻¹ · s⁻¹. Under these conditions, p_r ≈1.5 · 10⁻⁵ is determined by geometric constraints corresponding to a loss of rotational freedom. Its value is compatible with computer docking simulations and implies a rotational entropy loss ΔS_rot ≈ 22 e.u. in the transition state. At low ionic strength, long-range electrostatic interactions accelerate barnase-barstar association by a factor q_tq_rof up to 10⁵ as favorable charge-charge and charge-dipole interactions work together to make it much faster than free diffusion would allow. Proteins 28:153–161, 1997. © 1997 Wiley-Liss Inc.     

A novel shape complementarity scoring function for protein-protein docking

Shape complementarity is the most basic ingredient of the scoring functions for protein-protein docking. Most grid-based docking algorithms use the total number of grid points at the binding interface to quantify shape complementarity. We have developed a novel Pairwise Shape Complementarity (PSC) function that is conceptually simple and rapid to compute. The favorable component of PSC is the total number of atom pairs between the receptor and the ligand within a distance cutoff. When applied to a benchmark of 49 test cases, PSC consistently ranks near-native structures higher and produces more near-native structures than the traditional grid-based function, and the improvement was seen across all prediction levels and in all categories of the benchmark. Without any post-processing or biological information about the binding site except the complementarity-determining region of antibodies, PSC predicts the complex structure correctly for 6 test cases, and ranks at least one near-native structure in the top 20 predictions for 18 test cases. Our docking program ZDOCK has been parallelized and the average computing time is 4 minutes using sixteen IBM SP3 processors. Both ZDOCK and the benchmark are freely available to academic users (http://zlab.bu.edu/~ rong/dock).     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement. This paper briefly introduced the original Resonant Recognition Model (RRM), and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM). The key characteristic of the new model is that it can predict directly the protein-protein interaction from the primary sequence, and the MRRM is more suitable than the RRM for this prediction. The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction. Translated from Journal of Shanghai University (Natural Science), 2006, 12(1): 69–73 [译自: 上海大学学报(自然科学版)]     

A role for surface hydrophobicity in protein-protein recognition.

The role of hydrophobicity as a determinant of protein-protein interactions is examined. Surfaces of apo-protein targets comprising 9 classes of enzymes, 7 antibody fragments, hirudin, growth hormone, and retinol-binding protein, and their associated ligands with available X-ray structures for their complexed forms, are scanned to determine clusters of surface-accessible amino acids. Clusters of surface residues are ranked on the basis of the hydrophobicity of their constituent amino acids. The results indicate that the location of the co-crystallized ligand is commonly found to correspond with one of the strongest hydrophobic clusters on the surface of the target molecule. In 25 of 38 cases, the correspondence is exact, with the position of the most hydrophobic cluster coinciding with more than one-third of the surface buried by the bound ligand. The remaining 13 cases demonstrate this correspondence within the top 6 hydrophobic clusters. These results suggest that surface hydrophobicity can be used to identify regions of a protein''s surface most likely to interact with a binding ligand. This fast and simple procedure may be useful for identifying small sets of well-defined loci for possible ligand attachment.     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model (RRM),and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM).The key characteristic of the new model is that it can predict directly the proteinprotein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.     

A docking analysis of the statistical physics of protein-protein recognition

We describe protein-protein recognition within the frame of the random energy model of statistical physics. We simulate, by docking the component proteins, the process of association of two proteins that form a complex. We obtain the energy spectrum of a set of protein-protein complexes of known three-dimensional structure by performing docking in random orientations and scoring the models thus generated. We use a coarse protein representation where each amino acid residue is replaced by its Vorono? cell, and derive a scoring function by applying the evolutionary learning program ROGER to a set of parameters measured on that representation. Taking the scores of the docking models to be interaction energies, we obtain energy spectra for the complexes and fit them to a Gaussian distribution, from which we derive physical parameters such as a glass transition temperature and a specificity transition temperature.     

A simple and efficient method for predicting protein-protein interaction sites

Computational methods for predicting protein-protein interaction sites based on structural data are characterized by an accuracy between 70 and 80%. Some experimental studies indicate that only a fraction of the residues, forming clusters in the center of the interaction site, are energetically important for binding. In addition, the analysis of amino acid composition has shown that residues located in the center of the interaction site can be better discriminated from the residues in other parts of the protein surface. In the present study, we implement a simple method to predict interaction site residues exploiting this fact and show that it achieves a very competitive performance compared to other methods using the same dataset and criteria for performance evaluation (success rate of 82.1%).     

Atomic contact vectors in protein-protein recognition

The ability to analyze and compare protein-protein interactions on the structural level is critical to our understanding of various aspects of molecular recognition and the functional interplay of components of biochemical networks. In this study, we introduce atomic contact vectors (ACVs) as an intuitive way to represent the physico-chemical characteristics of a protein-protein interface as well as a way to compare interfaces to each other. We test the utility of ACVs in classification by using them to distinguish between homodimers and crystal contacts. Our results compare favorably with those reported by other authors. We then apply ACVs to mine the PDB for all known protein-protein complexes and separate transient recognition complexes from permanent oligomeric ones. Getting at the basis of this difference is important for our understanding of recognition and we achieved a success rate of 91% for distinguishing these two classes of complexes. Although accessible surface area of the interface is a major discriminating feature, we also show that there are distinct differences in the contact preferences between the two kinds of complexes. Illustrating the superiority of ACVs as a basic comparison measure over a sequence-based approach, we derive a general rule of thumb to determine whether two protein-protein interfaces are redundant. With this method, we arrive at a nonredundant set of 209 recognition complexes--the largest set reported so far.     

An iterative knowledge-based scoring function for protein-protein recognition

Using an efficient iterative method, we have developed a distance-dependent knowledge-based scoring function to predict protein-protein interactions. The function, referred to as ITScore-PP, was derived using the crystal structures of a training set of 851 protein-protein dimeric complexes containing true biological interfaces. The key idea of the iterative method for deriving ITScore-PP is to improve the interatomic pair potentials by iteration, until the pair potentials can distinguish true binding modes from decoy modes for the protein-protein complexes in the training set. The iterative method circumvents the challenging reference state problem in deriving knowledge-based potentials. The derived scoring function was used to evaluate the ligand orientations generated by ZDOCK 2.1 and the native ligand structures on a diverse set of 91 protein-protein complexes. For the bound test cases, ITScore-PP yielded a success rate of 98.9% if the top 10 ranked orientations were considered. For the more realistic unbound test cases, the corresponding success rate was 40.7%. Furthermore, for faster orientational sampling purpose, several residue-level knowledge-based scoring functions were also derived following the similar iterative procedure. Among them, the scoring function that uses the side-chain center of mass (SCM) to represent a residue, referred to as ITScore-PP(SCM), showed the best performance and yielded success rates of 71.4% and 30.8% for the bound and unbound cases, respectively, when the top 10 orientations were considered. ITScore-PP was further tested using two other published protein-protein docking decoy sets, the ZDOCK decoy set and the RosettaDock decoy set. In addition to binding mode prediction, the binding scores predicted by ITScore-PP also correlated well with the experimentally determined binding affinities, yielding a correlation coefficient of R = 0.71 on a test set of 74 protein-protein complexes with known affinities. ITScore-PP is computationally efficient. The average run time for ITScore-PP was about 0.03 second per orientation (including optimization) on a personal computer with 3.2 GHz Pentium IV CPU and 3.0 GB RAM. The computational speed of ITScore-PP(SCM) is about an order of magnitude faster than that of ITScore-PP. ITScore-PP and/or ITScore-PP(SCM) can be combined with efficient protein docking software to study protein-protein recognition.     

A simple dependence between protein evolution rate and the number of protein-protein interactions

Background  

It has been shown for an evolutionarily distant genomic comparison that the number of protein-protein interactions a protein has correlates negatively with their rates of evolution. However, the generality of this observation has recently been challenged. Here we examine the problem using protein-protein interaction data from the yeast Saccharomyces cerevisiae and genome sequences from two other yeast species.     

Chirality-independent protein-protein recognition between transmembrane domains in vivo

Stereospecificity in protein-protein recognition and docking is an unchallenged dogma. Soluble proteins provide the main source of evidence for stereospecificity. In contrast, within the membrane little is known about the role of stereospecificity in the recognition process. Here, we have reassessed the stereospecificity of protein-protein recognition by testing whether it holds true for the well-defined glycophorin A (GPA) transmembrane domain in vivo. We found that the all-D amino acid GPA transmembrane domain and two all-D mutants specifically associated with an all-L GPA transmembrane domain, within the membrane milieu of Escherichia coli. Molecular dynamics techniques reveal a possible structural explanation to the observed interaction between all-D and all-L transmembrane domains. A very strong correlation was found between amino acid residues at the interface of both the all-L homodimer structure and the mixed L/D heterodimer structure, suggesting that the original interactions are conserved. The results suggest that GPA helix-helix recognition within the membrane is chirality-independent.     

Context shapes: Efficient complementary shape matching for protein-protein docking

We describe an efficient method for partial complementary shape matching for use in rigid protein-protein docking. The local shape features of a protein are represented using boolean data structures called Context Shapes. The relative orientations of the receptor and ligand surfaces are searched using precalculated lookup tables. Energetic quantities are derived from shape complementarity and buried surface area computations, using efficient boolean operations. Preliminary results indicate that our context shapes approach outperforms state-of-the-art geometric shape-based rigid-docking algorithms.     

Effect of local shape modifications of molecular surfaces on rigid-body protein-protein docking

Geometric complementarity is the most dominant term in protein-protein docking and therefore, a good geometric representation of the molecules, which takes into account the flexibility of surface residues, is desirable. We present a modified geometric representation of the molecular surface that down-weighs the contribution of specified parts of the surface to the complementarity score. We apply it to the mobile ends of the most flexible side chains: lysines, glutamines and arginines (trimming). The new representation systematically reduces the complementarity scores of the false-positive solutions, often more than the scores of the correct solutions, thereby improving significantly our ability to identify nearly correct solutions in rigid-body docking of unbound structures. The effect of trimming lysine residues is larger than trimming of glutamine or arginine residues. It appears to be independent of the conformations of the trimmed residues but depends on the relative abundance of such residues at the interface and on the non-interacting surface. Combining the modified geometric representation with electrostatic complementarity further improves the docking results.     

A simple test of vocal individual recognition in wild meerkats

Individual recognition is thought to be a crucial ability facilitating the evolution of animal societies. Given its central importance, much research has addressed the extent of this capacity across the animal kingdom. Recognition of individuals vocally has received particular attention due, in part, to the insights it provides regarding the cognitive processes that underlie this skill. While much work has focused on vocal individual recognition in primates, there is currently very little data showing comparable skills in non-primate mammals under natural conditions. This may be because non-primate mammal societies do not provide obvious contexts in which vocal individual recognition can be rigorously tested. We addressed this gap in understanding by designing an experimental paradigm to test for individual recognition in meerkats (Suricata suricatta) without having to rely on naturally occurring social contexts. Results suggest that when confronted with a physically impossible scenario-the presence of the same conspecific meerkat in two different places-subjects responded more strongly than during the control, a physically possible setup. We argue that this provides the first clear evidence for vocal individual recognition in wild non-primate mammals and hope that this novel experimental design will allow more systematic cross-species comparisons of individual recognition under natural settings.