Dye-pair reporter systems for protein-peptide molecular interactions

Modifying a linear peptide near each terminus with a fluorescent dye can make it able to signal its own binding to a protein. As originally described, the dye pair is composed of fluorescein and tetramethylrhodamine [Wei, A.-P., Blumenthal, D. K., and Herron, J. N. (1994) Anal. Chem. 66, 1500-1506]. This paper shows that it may also be two molecules of tetramethylrhodamine. In aqueous solution, mutual affinity of the dyes causes fluorescence-quenching contact between them. When the peptide is bound by an antibody or cleaved by a proteinase, or when acetonitrile is added, dye-to-dye contact decreases and fluorescence increases 3-15-fold. When five peptides of 4-20 amino acid residues were doubly modified with tetramethylrhodamine, each product had the absorption spectrum of a tetramethylrhodamine dimer. As the peptides were not known to have special conformational features, self-affinity of the dye appeared to be the main cause of dimerization. Disruption of the dye dimers by acetonitrile suggested that dimerization of the dye(s) in aqueous solution was largely an effect of hydrophobicity. Dye-tagged peptides were used in fluorometric assays for two peptide-protein interactions. First, a peptide from type II collagen recognized by a monoclonal antibody was derivatized with two different dye pairs. The monoclonal bound each modified peptide, disrupting dye-to-dye contact and increasing fluorescence up to 4-fold. Second, a phosphopeptide recognized by an SH2 domain was tagged with fluorescein and tetramethylrhodamine, and its binding to the SH2 domain was detected through fluorescence. Doubly dye-tagged peptides offer a direct, solution-phase assay for protein-peptide binding.     

Structure-based prediction of protein-peptide specificity in Rosetta

Protein-peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein-peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure-based prediction of protein-peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide-binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide-binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure-based approaches can provide atomic-level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress.     

A simple physical model for the prediction and design of protein-DNA interactions

Protein-DNA interactions are crucial for many biological processes. Attempts to model these interactions have generally taken the form of amino acid-base recognition codes or purely sequence-based profile methods, which depend on the availability of extensive sequence and structural information for specific structural families, neglect side-chain conformational variability, and lack generality beyond the structural family used to train the model. Here, we take advantage of recent advances in rotamer-based protein design and the large number of structurally characterized protein-DNA complexes to develop and parameterize a simple physical model for protein-DNA interactions. The model shows considerable promise for redesigning amino acids at protein-DNA interfaces, as design calculations recover the amino acid residue identities and conformations at these interfaces with accuracies comparable to sequence recovery in globular proteins. The model shows promise also for predicting DNA-binding specificity for fixed protein sequences: native DNA sequences are selected correctly from pools of competing DNA substrates; however, incorporation of backbone movement will likely be required to improve performance in homology modeling applications. Interestingly, optimization of zinc finger protein amino acid sequences for high-affinity binding to specific DNA sequences results in proteins with little or no predicted specificity, suggesting that naturally occurring DNA-binding proteins are optimized for specificity rather than affinity. When combined with algorithms that optimize specificity directly, the simple computational model developed here should be useful for the engineering of proteins with novel DNA-binding specificities.     

Heat capacity changes for protein-peptide interactions in the ribonuclease S system.

Two fragments of pancreatic ribonuclease A, a truncated version of S-peptide (residues 1-15) and S-protein (residues 21-124), combine to give a catalytically active complex designated ribonuclease S. We have substituted the wild-type residue Met-13 with six other hydrophobic residues ranging in size from alanine to phenylalanine and have determined the thermodynamic parameters associated with binding of these analogues to S-protein by titration calorimetry in the temperature range 5-25 degrees C. The heat capacity change (delta Cp) associated with binding was obtained from a global analysis of the temperature dependences of the free energies and enthalpies of binding. The delta Cp's were not correlated in any simple fashion with the nonpolar surface area (delta Anp) buried upon binding.     

Re-evaluating the role of heat-shock protein-peptide interactions in tumour immunity

Early investigations into the immune surveillance of chemically-induced sarcomas led to two important concepts in tumour immunobiology: one, tumour rejection can be elicited by immune recognition of tumour antigens; and two, tumours express unique sets of antigens, which are known as tumour-specific antigens. The pioneering studies of Srivastava and colleagues led to the proposal that heat-shock proteins (HSPs) function as ubiquitous tumour-specific antigens, with the specificity residing in a population of bound peptides that identify the tissue of origin of the HSP. However, recent findings, including new data on the cell biology of peptide generation and trafficking, have called into question the specificity of tumour rejection that is induced by HSPs.     

Global discriminative learning for higher-accuracy computational gene prediction

Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM) in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.     

Incorporation of pairwise interactions into the Lifson-Roig model for helix prediction.

The helix/coil equilibrium of a peptide in solution can be modulated by a variety of side-chain interactions that are not incorporated into the standard statistical mechanical models for prediction of peptide helical content. In this report, we describe a recursive formulation of the Lifson-Roig model that facilitates incorporation of specific pairwise side-chain interactions as well as nonspecific individual side-chain capping interactions. Application of this extended model to a series of host/guest peptides indicates that the apparent delta G value for a pairwise apolar interaction is dependent upon the spacing and orientation but not the sequential location of the participating residues. The apparent delta G values for such interactions are about 40% greater than the corresponding apparent delta delta G values obtained from difference measurements.     

A discriminative approach for identifying domain–domain interactions from protein–protein interactions

Protein domains are functional and structural units of proteins. Therefore, identification of domain–domain interactions (DDIs) can provide insight into the biological functions of proteins. In this article, we propose a novel discriminative approach for predicting DDIs based on both protein–protein interactions (PPIs) and the derived information of non‐PPIs. We make a threefold contribution to the work in this area. First, we take into account non‐PPIs explicitly and treat the domain combinations that can discriminate PPIs from non‐PPIs as putative DDIs. Second, DDI identification is formalized as a feature selection problem, in which it tries to find out a minimum set of informative features (i.e., putative DDIs) that discriminate PPIs from non‐PPIs, which is plausible in biology and is able to predict DDIs in a systematic and accurate manner. Third, multidomain combinations including two‐domain combinations are taken into account in the proposed method, where multidomain cooperations may help proteins to interact with each other. Numerical results on several DDI prediction benchmark data sets show that the proposed discriminative method performs comparably well with other top algorithms with respect to overall performance, and outperforms other methods in terms of precision. The PPI data sets used for prediction of DDIs and prediction results can be found at http://csb.shu.edu.cn/dipd . Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Thermodynamics of protein-peptide interactions in the ribonuclease S system studied by titration calorimetry

Two fragments of pancreatic ribonuclease A, a truncated version of S-peptide (residues 1-15) and S-protein (residues 21-124), combine to give a catalytically active complex designated ribonuclease S. Residue 13 in the peptide is methionine. According to the X-ray structure of the complex of S-protein and S-peptide (1-20), this residue is almost fully buried. We have substituted Met-13 with seven other hydrophobic residues ranging in size from glycine to phenylalanine and have determined the thermodynamic parameters associated with the binding of these analogues to S-protein by titration calorimetry at 25 degrees C. These data should provide useful quantitative information for evaluating the contribution of hydrophobic interactions in the stabilization of protein structures.     

NMR spectroscopic and molecular modeling studies of protein-carbohydrate and protein-peptide interactions

Investigations of the conformations of carbohydrates, their analogues and their molecular mimics are described, with emphasis on structural and functional information that can be gained by NMR spectroscopic techniques in combination with molecular modeling. The transferred nuclear Overhauser effect (trNOE) has been employed to determine the bound conformations of carbohydrates and other bioactive molecules in complex with protein receptors. The corresponding experiments in the rotating frame (trROE) and selective editing experiments (e.g., QUIET-NOESY) are used to eliminate indirect cross-relaxation pathways (spin diffusion), thereby minimizing errors in the data used for calculation of conformations. Saturation transfer difference NMR experiments reveal detailed information about intermolecular contacts between ligand and protein. Computational techniques are integrated with NMR-derived information to construct structural models of these bioactive molecules and of their complexes with proteins. Recent investigations into the nature of molecular mimicry with regard to protein-ligand interactions are described, along with applications in determining the mode of action of enzyme inhibitors. The results are relevant for the design of the next generation of drug and vaccine candidates.     

Challenges for the prediction of macromolecular interactions

Macromolecular interactions are central to most cellular processes. Experimental methods generate diverse data on these interactions ranging from high throughput protein-protein interactions (PPIs) to the crystallised structures of complexes. Despite this, only a fraction of interactions have been identified and therefore predictive methods are essential to fill in the numerous gaps. Many predictive methods use information from related proteins. Accordingly, we review the conservation of interface and ligand binding sites within protein families and their association with conserved residues and Specificity Determining Positions. We then review recent developments in predictive methods for the identification of PPIs, protein interface sites and small molecule ligand binding sites. The challenges that are still faced by the community in these areas are discussed.     

A multigroup model for predator-prey interactions

The predator-prey interactions between the protozoan Tetrahymena pyriformis and the bacterium Aerobacter aerogenes have been studied experimentally and mathematically. A mathematical model for the ciliates defines the mass distribution of cells within the population. The resulting model equations are solved by the use of multigroup theory. Experimental data from batch and continuous flow reactors are compared with the results of the numerical integration.     

A lock-and-key model for protein-protein interactions

MOTIVATION: Protein-protein interaction networks are one of the major post-genomic data sources available to molecular biologists. They provide a comprehensive view of the global interaction structure of an organism's proteome, as well as detailed information on specific interactions. Here we suggest a physical model of protein interactions that can be used to extract additional information at an intermediate level: It enables us to identify proteins which share biological interaction motifs, and also to identify potentially missing or spurious interactions. RESULTS: Our new graph model explains observed interactions between proteins by an underlying interaction of complementary binding domains (lock-and-key model). This leads to a novel graph-theoretical algorithm to identify bipartite subgraphs within protein-protein interaction networks where the underlying data are taken from yeast two-hybrid experimental results. By testing on synthetic data, we demonstrate that under certain modelling assumptions, the algorithm will return correct domain information about each protein in the network. Tests on data from various model organisms show that the local and global patterns predicted by the model are indeed found in experimental data. Using functional and protein structure annotations, we show that bipartite subnetworks can be identified that correspond to biologically relevant interaction motifs. Some of these are novel and we discuss an example involving SH3 domains from the Saccharomyces cerevisiae interactome. AVAILABILITY: The algorithm (in Matlab format) is available (see http://www.maths.strath.ac.uk/~aas96106/lock_key.html).     

A mathematical model for photoreceptor interactions

The interactions between rods and cones in the retina have been the focus of innumerable experimental and theoretical biological studies in previous decades yet the understanding of these interactions is still incomplete primarily due to the lack of a unified concept of cone photoreceptor organization and its role in retinal diseases. The low abundance of cones in many of the non-primate mammalian models that have been studied make conclusions about the human retina difficult. A more complete knowledge of the human retina is crucial for counteracting the events that lead to certain degenerative diseases, in particular those associated with photoreceptor cell death (e.g., retinitis pigmentosa). In an attempt to gain important insight into the role and interactions of the rods and the cones we develop and analyze a set of mathematical equations that model a system of photoreceptors and incorporate a direct rod-cone interaction. Our results show that the system can exhibit stable oscillations, which correspond to the rhythmic renewal and shedding of the photoreceptors. In addition, our results show the mathematical necessity of this rod-cone direct interaction for survival of both and gives insight into this mechanism.     

Computational methods for the prediction of protein interactions

Establishing protein interaction networks is crucial for understanding cellular operations. Detailed knowledge of the 'interactome', the full network of protein-protein interactions, in model cellular systems should provide new insights into the structure and properties of these systems. Parallel to the first massive application of experimental techniques to the determination of protein interaction networks and protein complexes, the first computational methods, based on sequence and genomic information, have emerged.     

预测蛋白质间相互作用的生物信息学方法

后基因组时代的研究模式,已从原来的序列-结构-功能转向基因表达-系统动力学-生理功能。建立蛋白质间相互作用的完全网络,即蛋白质相互作用组(interactome),将有助于从系统角度加深对细胞结构和功能的认识,并为新药靶点的发现和药物设计提供理论基础。一系列系统分析蛋白质相互作用的实验方法已经建立,近年来,出现了多种预测蛋白质相互作用的生物信息学方法,这些方法不仅是对传统实验方法的有价值的补充,而且能够扩展实验方法的预测范围;同时,在开发这些方法的过程中建立了一些重要的分子进化和分子生物学慨念。本文综述了9种生物信息学方法的原理、方法评估、存在的问题．并分析了这个领域的发展前景。