Novel Protein-Protein Interactions Inferred from Literature Context

We have developed a method that predicts Protein-Protein Interactions (PPIs) based on the similarity of the context in which proteins appear in literature. This method outperforms previously developed PPI prediction algorithms that rely on the conjunction of two protein names in MEDLINE abstracts. We show significant increases in coverage (76% versus 32%) and sensitivity (66% versus 41% at a specificity of 95%) for the prediction of PPIs currently archived in 6 PPI databases. A retrospective analysis shows that PPIs can efficiently be predicted before they enter PPI databases and before their interaction is explicitly described in the literature. The practical value of the method for discovery of novel PPIs is illustrated by the experimental confirmation of the inferred physical interaction between CAPN3 and PARVB, which was based on frequent co-occurrence of both proteins with concepts like Z-disc, dysferlin, and alpha-actinin. The relationships between proteins predicted by our method are broader than PPIs, and include proteins in the same complex or pathway. Dependent on the type of relationships deemed useful, the precision of our method can be as high as 90%. The full set of predicted interactions is available in a downloadable matrix and through the webtool Nermal, which lists the most likely interaction partners for a given protein. Our framework can be used for prioritizing potential interaction partners, hitherto undiscovered, for follow-up studies and to aid the generation of accurate protein interaction maps.     

Enhancements to the Rosetta Energy Function Enable Improved Identification of Small Molecules that Inhibit Protein-Protein Interactions

Protein-protein interactions are among today’s most exciting and promising targets for therapeutic intervention. To date, identifying small-molecules that selectively disrupt these interactions has proven particularly challenging for virtual screening tools, since these have typically been optimized to perform well on more “traditional” drug discovery targets. Here, we test the performance of the Rosetta energy function for identifying compounds that inhibit protein interactions, when these active compounds have been hidden amongst pools of “decoys.” Through this virtual screening benchmark, we gauge the effect of two recent enhancements to the functional form of the Rosetta energy function: the new “Talaris” update and the “pwSHO” solvation model. Finally, we conclude by developing and validating a new weight set that maximizes Rosetta’s ability to pick out the active compounds in this test set. Looking collectively over the course of these enhancements, we find a marked improvement in Rosetta’s ability to identify small-molecule inhibitors of protein-protein interactions.     

Identification of Novel Protein-Protein Interactions of Yersinia pestis Type III Secretion System by Yeast Two Hybrid System

Type III secretion system (T3SS) of the plague bacterium Y. pestis encodes a syringe-like structure consisting of more than 20 proteins, which can inject virulence effectors into host cells to modulate the cellular functions. Here in this report, interactions among the possible components in T3SS of Yersinia pestis were identified using yeast mating technique. A total of 57 genes, including all the pCD1-encoded genes except those involved in plasmid replication and partition, pseudogenes, and the putative transposase genes, were subjected to yeast mating analysis. 21 pairs of interaction proteins were identified, among which 9 pairs had been previously reported and 12 novel pairs were identified in this study. Six of them were tested by GST pull down assay, and interaction pairs of YscG-SycD, YscG-TyeA, YscI-YscF, and YopN-YpCD1.09c were successfully validated, suggesting that these interactions might play potential roles in function of Yersinia T3SS. Several potential new interactions among T3SS components could help to understand the assembly and regulation of Yersinia T3SS.     

蛋白质相互作用的生物信息学研究进展

生命过程的分子基础在于生物分子之间的相互作用,其中蛋白质分子之间的相互作用占有极其重要的地位。研究蛋白质相互作用对于理解生命的真谛、探讨致病微生物的致病机理,以及研究新药提高人们的健康水平具有重要的作用。用生物信息学的方法研究蛋白质的相互作用已经取得了许多重要的成果,但也有很多问题还需解决。本文从蛋白质相互作用的数据库、预测方法、可预测蛋白质相互作用的网上服务、蛋白质相互作用网络等几方面,对蛋白质相互作用的生物信息学研究成果及其存在的问题做了概述。     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Evolution of Specificity in Protein-Protein Interactions

Hub proteins are proteins that maintain promiscuous molecular recognition. Because they are reported to play essential roles in cellular control, there has been a special interest in the study of their structural and functional properties, yet the mechanisms by which they evolve to maintain functional interactions are poorly understood. By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms. We focus on two types of hub proteins: Multi hubs, which interact with their partners through different interfaces, and Singlish hubs, which do so through a single interface. We show that loss of structural stability is required for the evolution of protein-protein-interaction (PPI) networks, and it is more profound in Singlish hub systems. In addition, different ratios of hydrophobic to electrostatic interfacial amino acids are shown to support distinct network topologies (i.e., Singlish and Multi systems), and therefore underlie a fundamental design principle of PPI in a crowded environment. We argue that the physical nature of hydrophobic and electrostatic interactions, in particular, their favoring of either same-type interactions (hydrophobic-hydrophobic), or opposite-type interactions (negatively-positively charged) plays a key role in maintaining the network topology while allowing the protein amino acid sequence to evolve.     

Identification of Structure-Stabilizing Interactions in Enzymes: A Novel Mechanism to Impact Enzyme Activity

Cruzain, a cysteine protease in the cathepsin family, is pivotal to the life-cycle of Trypanosoma cruzi, the etiological agent in Chagas disease. Current inhibitors of cruzain suffer from drawbacks involving gastrointestinal and neurological side effects and as a result have spurred the search for alternative anti-trypanocidals. Through sequence alignment studies and intra-residue interaction analysis of the pro-protein of cruzain (pro-cruzain), we have identified a host of non-active site residues that are conserved among the cathepsins. We hypothesize that these conserved amino acids play a critical role in structure-stabilizing interactions among the cathepsins and are therefore crucial for eventually gaining protease activity. As predicted, mutation of selected conserved non-active site amino-acid candidates in cruzain resulted in a compromised structural stability and a corresponding loss in enzymatic activity relative to wild-type enzyme. By advancing the discovery of novel, non-active-site-based targets to arrest enzymatic activity our results potentially open the field of alternative inhibitor design. The advantages of defining such a non-active-site inhibitor design space is discussed.     

Computational Identification of Protein-Protein Interactions in Rice Based on the Predicted Rice Interactome Network

Plant protein-protein interaction networks have not been identified by large-scale experiments. In order to better understand the protein interactions in rice, the Predicted Rice Interactome Network (PRIN; http://bis.zju.edu.cn/ prin/) presented 76,585 predicted interactions involving 5,049 rice proteins. After mapping genomic features of rice (GO annotation, subcellular localization prediction, and gene expression), we found that a well-annotated and biologically significant network is rich enough to capture many significant functional linkages within higher-order biological systems, such as pathways and biological processes. Furthermore, we took MADS-box do- main-containing proteins and circadian rhythm signaling pathways as examples to demonstrate that functional protein complexes and biological pathways could be effectively expanded in our predicted network. The expanded molecular network in PRIN has considerably improved the capability of these analyses to integrate existing knowledge and provide novel insights into the function and coordination of genes and gene networks.     

How Structure Defines Affinity in Protein-Protein Interactions

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins.     

Independence of Glucagon Receptors and Glucagon Inactivation in Liver Cell Membranes

THERE is increasing evidence that receptors for polypeptide hormones are localized on the cell membrane. Hormone-receptor interactions have been studied primarily by measuring the bmding of ¹²⁵I-labelled hormones to intact¹ or broken-cell preparations^2–6. Peptide hormones, however, are often inactivated after exposure to the cell extract and numerous enzymes reported as specific hormone-degrading have been described. With some hormones, such as insulin^1,6,7, biologically significant receptor interactions have been demonstrated in the absence of hormone degradation, but with other hormones, such as glucagon, it has not been possible to dissociate the processes of specific receptor binding and of hormone inactivation³, which suggests that these two processes may be functionally or structurally related. Until this question is resolved, it will not be possible to characterize properly the kinetics of the hormone-receptor interaction or to isolate and purify the receptor.     

Characterization of the Hepatic Glucagon Receptor

Abstract

The hepatic glucagon receptor was covalently labeled with [¹²⁵I-Tyr¹⁰]-monoiodoglucagon by use of the heterobifunctional crosslinker hydroxysuccini-midyl-p-azidobenzoate and analyzed by SDS-gel electrophoresis. The autoradio-gram of the gel showed one band at M_r=63,000 that was sensitive to excess unlabeled glucagon and GTP. The labeled receptor was solubilized with Lubrol-PX and the hydrodynamic characteristics of the receptor were determined. The molecular parameters of the solubilized receptor are S_{20, w} = 4.3 ± 0.1, Stokes radius = 6.3 ± 0.1 nm, frictional coefficient f/f° = 1.8 and a calculated M_r = 33,000 fragment, that retains guanine nucleotide sensitivity. Elastase treatment of vacant receptors results in a M_r = 24,000 fragment that binds hormone in a GTP-sensitive manner. The M_r = 24,000 fragment is contained within the M_r = 33,000 fragment. The M_r = 63,000 receptor upon treatment with endo-β-N-acetylglucosamine F for 4 h yields four fragments of apparent M_r = 61,000, 56,000, 51,000, and 45,000; 24 h treatment results in the accumulation of the last two fragments. Neither M_r = 33,000 and 24,000 fragment appear to be substrates for endo-β-N-acetylglucosaminidase F.

These data allow us to conclude that the hepatic glucagon receptor in the membrane is a dimer of ～ 60,000 dalton hormone binding subunit which is a glycoprotein containing at least four N-linked glycans accounting for 18,000 daltons of its mass. Both the hormone binding function and the capacity for the interaction with the stimulatory regulator of adenylyl cyclase are contained within a fragment of only ～ 21,000 daltons that does not contain any N-linked glycans.     

Identification of the Classical Androgen Receptor in Male Rat Liver

Abstract

Male rat liver contains components in both cytosol and nucleosol which bind the synthetic testosterone derivative, mibolerone, with a high affinity, low capacity and a high specificity for androgens. Gel filtration chromatography shows two binding components. A high molecular weight component (M.Wt 230,000) present in cytosol alone and a low molecular weight component (M.Wt 60,000) present in cytosol and nucleosol. Male rat liver contains the classical androgen receptor.     

Comparative Analysis of Protein-Protein Interactions in Cancer-Associated Genes 25

Protein-protein interactions (PPIs) have been widely studied to understand the bi-ological processes or molecular functions associated with different disease systems like cancer. While focused studies on individual cancers have generated valuable in-formation, global and comparative analysis of datasets from different cancer types has not been done. In this work, we carried out bioinformatic analysis of PPIs corresponding to differentially expressed genes from microarrays of various tumor tissues (belonging to bladder, colon, kidney and thyroid cancers) and compared their associated biological processes and molecular functions (based on Gene On-tology terms). We identified a set of processes or functions that are common to all these cancers, as well as those that are specific to only one or partial cancer types. Similarly, protein interaction networks in nucleic acid metabolism were compared to identify the common/specific clusters of proteins across different cancer types. Our results provide a basis for further experimental investigations to study protein interaction networks associated with cancer. The methodology developed in this work can also be applied to study similar disease systems.     

Computational Prediction of Protein-Protein Interactions in Leishmania Predicted Proteomes

The Trypanosomatids parasites Leishmania braziliensis, Leishmania major and Leishmania infantum are important human pathogens. Despite of years of study and genome availability, effective vaccine has not been developed yet, and the chemotherapy is highly toxic. Therefore, it is clear just interdisciplinary integrated studies will have success in trying to search new targets for developing of vaccines and drugs. An essential part of this rationale is related to protein-protein interaction network (PPI) study which can provide a better understanding of complex protein interactions in biological system. Thus, we modeled PPIs for Trypanosomatids through computational methods using sequence comparison against public database of protein or domain interaction for interaction prediction (Interolog Mapping) and developed a dedicated combined system score to address the predictions robustness. The confidence evaluation of network prediction approach was addressed using gold standard positive and negative datasets and the AUC value obtained was 0.94. As result, 39,420, 43,531 and 45,235 interactions were predicted for L. braziliensis, L. major and L. infantum respectively. For each predicted network the top 20 proteins were ranked by MCC topological index. In addition, information related with immunological potential, degree of protein sequence conservation among orthologs and degree of identity compared to proteins of potential parasite hosts was integrated. This information integration provides a better understanding and usefulness of the predicted networks that can be valuable to select new potential biological targets for drug and vaccine development. Network modularity which is a key when one is interested in destabilizing the PPIs for drug or vaccine purposes along with multiple alignments of the predicted PPIs were performed revealing patterns associated with protein turnover. In addition, around 50% of hypothetical protein present in the networks received some degree of functional annotation which represents an important contribution since approximately 60% of Leishmania predicted proteomes has no predicted function.