Genome wide approaches to identify protein-DNA interactions.

PubMed

Ma, Tao; Ye, Zhenqing; Wang, Liguo

2018-05-29

Transcription factors are DNA-binding proteins that play key roles in many fundamental biological processes. Unraveling their interactions with DNA is essential to identify their target genes and understand the regulatory network. Genome-wide identification of their binding sites became feasible thanks to recent progress in experimental and computational approaches. ChIP-chip, ChIP-seq, and ChIP-exo are three widely used techniques to demarcate genome-wide transcription factor binding sites. This review aims to provide an overview of these three techniques including their experiment procedures, computational approaches, and popular analytic tools. ChIP-chip, ChIP-seq, and ChIP-exo have been the major techniques to study genome-wide in vivo protein-DNA interaction. Due to the rapid development of next-generation sequencing technology, array-based ChIP-chip is deprecated and ChIP-seq has become the most widely used technique to identify transcription factor binding sites in genome-wide. The newly developed ChIP-exo further improves the spatial resolution to single nucleotide. Numerous tools have been developed to analyze ChIP-chip, ChIP-seq and ChIP-exo data. However, different programs may employ different mechanisms or underlying algorithms thus each will inherently include its own set of statistical assumption and bias. So choosing the most appropriate analytic program for a given experiment needs careful considerations. Moreover, most programs only have command line interface so their installation and usage will require basic computation expertise in Unix/Linux. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Proteins interacting with cloning scars: a source of false positive protein-protein interactions.

PubMed

Banks, Charles A S; Boanca, Gina; Lee, Zachary T; Florens, Laurence; Washburn, Michael P

2015-02-23

A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine "cloning scar" present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected.

Proteins interacting with cloning scars: a source of false positive protein-protein interactions

PubMed Central

Banks, Charles A. S.; Boanca, Gina; Lee, Zachary T.; Florens, Laurence; Washburn, Michael P.

2015-01-01

A common approach for exploring the interactome, the network of protein-protein interactions in cells, uses a commercially available ORF library to express affinity tagged bait proteins; these can be expressed in cells and endogenous cellular proteins that copurify with the bait can be identified as putative interacting proteins using mass spectrometry. Control experiments can be used to limit false-positive results, but in many cases, there are still a surprising number of prey proteins that appear to copurify specifically with the bait. Here, we have identified one source of false-positive interactions in such studies. We have found that a combination of: 1) the variable sequence of the C-terminus of the bait with 2) a C-terminal valine “cloning scar” present in a commercially available ORF library, can in some cases create a peptide motif that results in the aberrant co-purification of endogenous cellular proteins. Control experiments may not identify false positives resulting from such artificial motifs, as aberrant binding depends on sequences that vary from one bait to another. It is possible that such cryptic protein binding might occur in other systems using affinity tagged proteins; this study highlights the importance of conducting careful follow-up studies where novel protein-protein interactions are suspected. PMID:25704442

The malaria parasite RhopH protein complex interacts with erythrocyte calmyrin identified from a comprehensive erythrocyte protein library.

PubMed

Miura, Toyokazu; Takeo, Satoru; Ntege, Edward H; Otsuki, Hitoshi; Sawasaki, Tatsuya; Ishino, Tomoko; Takashima, Eizo; Tsuboi, Takafumi

2018-06-02

Malaria merozoite apical organelles; microneme and rhoptry secreted proteins play functional roles during and following invasion of host erythrocytes. Among numerous proteins, the rhoptries discharge high molecular weight proteins known as RhopH complex. Recent reports suggest that the RhopH complex is essential for growth and survival of the malaria parasite within erythrocytes. However, an in-depth understanding of the host-parasite molecular interactions is indispensable. Here we utilized a comprehensive mouse erythrocyte protein library consisting of 443 proteins produced by a wheat germ cell-free system, combined with AlphaScreen technology to identify mouse erythrocyte calmyrin as an interacting molecule of the rodent malaria parasite Plasmodium yoelii RhopH complex (PyRhopH). The PyRhopH interaction was dependent on the calmyrin N-terminus and divalent cation capacity. The finding unveils a recommendable and invaluable usefulness of our comprehensive mouse erythrocyte protein library together with the AlphaScreen technology in investigating a wide-range of host-parasite molecular interactions. Copyright © 2018 Elsevier Inc. All rights reserved.

The Protein Identifier Cross-Referencing (PICR) service: reconciling protein identifiers across multiple source databases.

PubMed

Côté, Richard G; Jones, Philip; Martens, Lennart; Kerrien, Samuel; Reisinger, Florian; Lin, Quan; Leinonen, Rasko; Apweiler, Rolf; Hermjakob, Henning

2007-10-18

Each major protein database uses its own conventions when assigning protein identifiers. Resolving the various, potentially unstable, identifiers that refer to identical proteins is a major challenge. This is a common problem when attempting to unify datasets that have been annotated with proteins from multiple data sources or querying data providers with one flavour of protein identifiers when the source database uses another. Partial solutions for protein identifier mapping exist but they are limited to specific species or techniques and to a very small number of databases. As a result, we have not found a solution that is generic enough and broad enough in mapping scope to suit our needs. We have created the Protein Identifier Cross-Reference (PICR) service, a web application that provides interactive and programmatic (SOAP and REST) access to a mapping algorithm that uses the UniProt Archive (UniParc) as a data warehouse to offer protein cross-references based on 100% sequence identity to proteins from over 70 distinct source databases loaded into UniParc. Mappings can be limited by source database, taxonomic ID and activity status in the source database. Users can copy/paste or upload files containing protein identifiers or sequences in FASTA format to obtain mappings using the interactive interface. Search results can be viewed in simple or detailed HTML tables or downloaded as comma-separated values (CSV) or Microsoft Excel (XLS) files suitable for use in a local database or a spreadsheet. Alternatively, a SOAP interface is available to integrate PICR functionality in other applications, as is a lightweight REST interface. We offer a publicly available service that can interactively map protein identifiers and protein sequences to the majority of commonly used protein databases. Programmatic access is available through a standards-compliant SOAP interface or a lightweight REST interface. The PICR interface, documentation and code examples are available at

The Protein Identifier Cross-Referencing (PICR) service: reconciling protein identifiers across multiple source databases

PubMed Central

Côté, Richard G; Jones, Philip; Martens, Lennart; Kerrien, Samuel; Reisinger, Florian; Lin, Quan; Leinonen, Rasko; Apweiler, Rolf; Hermjakob, Henning

2007-01-01

Background Each major protein database uses its own conventions when assigning protein identifiers. Resolving the various, potentially unstable, identifiers that refer to identical proteins is a major challenge. This is a common problem when attempting to unify datasets that have been annotated with proteins from multiple data sources or querying data providers with one flavour of protein identifiers when the source database uses another. Partial solutions for protein identifier mapping exist but they are limited to specific species or techniques and to a very small number of databases. As a result, we have not found a solution that is generic enough and broad enough in mapping scope to suit our needs. Results We have created the Protein Identifier Cross-Reference (PICR) service, a web application that provides interactive and programmatic (SOAP and REST) access to a mapping algorithm that uses the UniProt Archive (UniParc) as a data warehouse to offer protein cross-references based on 100% sequence identity to proteins from over 70 distinct source databases loaded into UniParc. Mappings can be limited by source database, taxonomic ID and activity status in the source database. Users can copy/paste or upload files containing protein identifiers or sequences in FASTA format to obtain mappings using the interactive interface. Search results can be viewed in simple or detailed HTML tables or downloaded as comma-separated values (CSV) or Microsoft Excel (XLS) files suitable for use in a local database or a spreadsheet. Alternatively, a SOAP interface is available to integrate PICR functionality in other applications, as is a lightweight REST interface. Conclusion We offer a publicly available service that can interactively map protein identifiers and protein sequences to the majority of commonly used protein databases. Programmatic access is available through a standards-compliant SOAP interface or a lightweight REST interface. The PICR interface, documentation and

Affinity purification combined with mass spectrometry to identify herpes simplex virus protein-protein interactions.

PubMed

Meckes, David G

2014-01-01

The identification and characterization of herpes simplex virus protein interaction complexes are fundamental to understanding the molecular mechanisms governing the replication and pathogenesis of the virus. Recent advances in affinity-based methods, mass spectrometry configurations, and bioinformatics tools have greatly increased the quantity and quality of protein-protein interaction datasets. In this chapter, detailed and reliable methods that can easily be implemented are presented for the identification of protein-protein interactions using cryogenic cell lysis, affinity purification, trypsin digestion, and mass spectrometry.

Inferring Domain-Domain Interactions from Protein-Protein Interactions with Formal Concept Analysis

PubMed Central

Khor, Susan

2014-01-01

Identifying reliable domain-domain interactions will increase our ability to predict novel protein-protein interactions, to unravel interactions in protein complexes, and thus gain more information about the function and behavior of genes. One of the challenges of identifying reliable domain-domain interactions is domain promiscuity. Promiscuous domains are domains that can occur in many domain architectures and are therefore found in many proteins. This becomes a problem for a method where the score of a domain-pair is the ratio between observed and expected frequencies because the protein-protein interaction network is sparse. As such, many protein-pairs will be non-interacting and domain-pairs with promiscuous domains will be penalized. This domain promiscuity challenge to the problem of inferring reliable domain-domain interactions from protein-protein interactions has been recognized, and a number of work-arounds have been proposed. This paper reports on an application of Formal Concept Analysis to this problem. It is found that the relationship between formal concepts provides a natural way for rare domains to elevate the rank of promiscuous domain-pairs and enrich highly ranked domain-pairs with reliable domain-domain interactions. This piggybacking of promiscuous domain-pairs onto less promiscuous domain-pairs is possible only with concept lattices whose attribute-labels are not reduced and is enhanced by the presence of proteins that comprise both promiscuous and rare domains. PMID:24586450

Enhancing interacting residue prediction with integrated contact matrix prediction in protein-protein interaction.

PubMed

Du, Tianchuan; Liao, Li; Wu, Cathy H

2016-12-01

Identifying the residues in a protein that are involved in protein-protein interaction and identifying the contact matrix for a pair of interacting proteins are two computational tasks at different levels of an in-depth analysis of protein-protein interaction. Various methods for solving these two problems have been reported in the literature. However, the interacting residue prediction and contact matrix prediction were handled by and large independently in those existing methods, though intuitively good prediction of interacting residues will help with predicting the contact matrix. In this work, we developed a novel protein interacting residue prediction system, contact matrix-interaction profile hidden Markov model (CM-ipHMM), with the integration of contact matrix prediction and the ipHMM interaction residue prediction. We propose to leverage what is learned from the contact matrix prediction and utilize the predicted contact matrix as "feedback" to enhance the interaction residue prediction. The CM-ipHMM model showed significant improvement over the previous method that uses the ipHMM for predicting interaction residues only. It indicates that the downstream contact matrix prediction could help the interaction site prediction.

Computational prediction of host-pathogen protein-protein interactions.

PubMed

Dyer, Matthew D; Murali, T M; Sobral, Bruno W

2007-07-01

Infectious diseases such as malaria result in millions of deaths each year. An important aspect of any host-pathogen system is the mechanism by which a pathogen can infect its host. One method of infection is via protein-protein interactions (PPIs) where pathogen proteins target host proteins. Developing computational methods that identify which PPIs enable a pathogen to infect a host has great implications in identifying potential targets for therapeutics. We present a method that integrates known intra-species PPIs with protein-domain profiles to predict PPIs between host and pathogen proteins. Given a set of intra-species PPIs, we identify the functional domains in each of the interacting proteins. For every pair of functional domains, we use Bayesian statistics to assess the probability that two proteins with that pair of domains will interact. We apply our method to the Homo sapiens-Plasmodium falciparum host-pathogen system. Our system predicts 516 PPIs between proteins from these two organisms. We show that pairs of human proteins we predict to interact with the same Plasmodium protein are close to each other in the human PPI network and that Plasmodium pairs predicted to interact with same human protein are co-expressed in DNA microarray datasets measured during various stages of the Plasmodium life cycle. Finally, we identify functionally enriched sub-networks spanned by the predicted interactions and discuss the plausibility of our predictions. Supplementary data are available at http://staff.vbi.vt.edu/dyermd/publications/dyer2007a.html. Supplementary data are available at Bioinformatics online.

Effectively identifying compound-protein interactions by learning from positive and unlabeled examples.

PubMed

Cheng, Zhanzhan; Zhou, Shuigeng; Wang, Yang; Liu, Hui; Guan, Jihong; Chen, Yi-Ping Phoebe

2016-05-18

Prediction of compound-protein interactions (CPIs) is to find new compound-protein pairs where a protein is targeted by at least a compound, which is a crucial step in new drug design. Currently, a number of machine learning based methods have been developed to predict new CPIs in the literature. However, as there is not yet any publicly available set of validated negative CPIs, most existing machine learning based approaches use the unknown interactions (not validated CPIs) selected randomly as the negative examples to train classifiers for predicting new CPIs. Obviously, this is not quite reasonable and unavoidably impacts the CPI prediction performance. In this paper, we simply take the unknown CPIs as unlabeled examples, and propose a new method called PUCPI (the abbreviation of PU learning for Compound-Protein Interaction identification) that employs biased-SVM (Support Vector Machine) to predict CPIs using only positive and unlabeled examples. PU learning is a class of learning methods that leans from positive and unlabeled (PU) samples. To the best of our knowledge, this is the first work that identifies CPIs using only positive and unlabeled examples. We first collect known CPIs as positive examples and then randomly select compound-protein pairs not in the positive set as unlabeled examples. For each CPI/compound-protein pair, we extract protein domains as protein features and compound substructures as chemical features, then take the tensor product of the corresponding compound features and protein features as the feature vector of the CPI/compound-protein pair. After that, biased-SVM is employed to train classifiers on different datasets of CPIs and compound-protein pairs. Experiments over various datasets show that our method outperforms six typical classifiers, including random forest, L1- and L2-regularized logistic regression, naive Bayes, SVM and k-nearest neighbor (kNN), and three types of existing CPI prediction models. Source code, datasets and

Quantifying protein-protein interactions in high throughput using protein domain microarrays.

PubMed

Kaushansky, Alexis; Allen, John E; Gordus, Andrew; Stiffler, Michael A; Karp, Ethan S; Chang, Bryan H; MacBeath, Gavin

2010-04-01

Protein microarrays provide an efficient way to identify and quantify protein-protein interactions in high throughput. One drawback of this technique is that proteins show a broad range of physicochemical properties and are often difficult to produce recombinantly. To circumvent these problems, we have focused on families of protein interaction domains. Here we provide protocols for constructing microarrays of protein interaction domains in individual wells of 96-well microtiter plates, and for quantifying domain-peptide interactions in high throughput using fluorescently labeled synthetic peptides. As specific examples, we will describe the construction of microarrays of virtually every human Src homology 2 (SH2) and phosphotyrosine binding (PTB) domain, as well as microarrays of mouse PDZ domains, all produced recombinantly in Escherichia coli. For domains that mediate high-affinity interactions, such as SH2 and PTB domains, equilibrium dissociation constants (K(D)s) for their peptide ligands can be measured directly on arrays by obtaining saturation binding curves. For weaker binding domains, such as PDZ domains, arrays are best used to identify candidate interactions, which are then retested and quantified by fluorescence polarization. Overall, protein domain microarrays provide the ability to rapidly identify and quantify protein-ligand interactions with minimal sample consumption. Because entire domain families can be interrogated simultaneously, they provide a powerful way to assess binding selectivity on a proteome-wide scale and provide an unbiased perspective on the connectivity of protein-protein interaction networks.

A proteomics method using immunoaffinity fluorogenic derivatization-liquid chromatography/tandem mass spectrometry (FD-LC-MS/MS) to identify a set of interacting proteins.

PubMed

Nakata, Katsunori; Saitoh, Ryoichi; Ishigai, Masaki; Imai, Kazuhiro

2018-02-01

Biological functions in organisms are usually controlled by a set of interacting proteins, and identifying the proteins that interact is useful for understanding the mechanism of the functions. Immunoprecipitation is a method that utilizes the affinity of an antibody to isolate and identify the proteins that have interacted in a biological sample. In this study, the FD-LC-MS/MS method, which involves fluorogenic derivatization followed by separation and quantification by HPLC and finally identification of proteins by HPLC-tandem mass spectrometry, was used to identify proteins in immunoprecipitated samples, using heat shock protein 90 (HSP90) as a model of an interacting protein in HepaRG cells. As a result, HSC70 protein, which was known to form a complex with HSP90, was isolated, together with three different types of HSP90-beta. The results demonstrated that the proposed immunoaffinity-FD-LC-MS/MS method could be useful for simultaneously detecting and identifying the proteins that interact with a certain protein. Copyright © 2017 John Wiley & Sons, Ltd.

Mapping of Chikungunya Virus Interactions with Host Proteins Identified nsP2 as a Highly Connected Viral Component

PubMed Central

Bouraï, Mehdi; Lucas-Hourani, Marianne; Gad, Hans Henrik; Drosten, Christian; Jacob, Yves; Tafforeau, Lionel; Cassonnet, Patricia; Jones, Louis M.; Judith, Delphine; Couderc, Thérèse; Lecuit, Marc; André, Patrice; Kümmerer, Beate Mareike; Lotteau, Vincent; Desprès, Philippe; Vidalain, Pierre-Olivier

2012-01-01

Chikungunya virus (CHIKV) is a mosquito-transmitted alphavirus that has been responsible for an epidemic outbreak of unprecedented magnitude in recent years. Since then, significant efforts have been made to better understand the biology of this virus, but we still have poor knowledge of CHIKV interactions with host cell components at the molecular level. Here we describe the extensive use of high-throughput yeast two-hybrid (HT-Y2H) assays to characterize interactions between CHIKV and human proteins. A total of 22 high-confidence interactions, which essentially involved the viral nonstructural protein nsP2, were identified and further validated in protein complementation assay (PCA). These results were integrated to a larger network obtained by extensive mining of the literature for reports on alphavirus-host interactions. To investigate the role of cellular proteins interacting with nsP2, gene silencing experiments were performed in cells infected by a recombinant CHIKV expressing Renilla luciferase as a reporter. Collected data showed that heterogeneous nuclear ribonucleoprotein K (hnRNP-K) and ubiquilin 4 (UBQLN4) participate in CHIKV replication in vitro. In addition, we showed that CHIKV nsP2 induces a cellular shutoff, as previously reported for other Old World alphaviruses, and determined that among binding partners identified by yeast two-hybrid methods, the tetratricopeptide repeat protein 7B (TTC7B) plays a significant role in this activity. Altogether, this report provides the first interaction map between CHIKV and human proteins and describes new host cell proteins involved in the replication cycle of this virus. PMID:22258240

HVint: A Strategy for Identifying Novel Protein-Protein Interactions in Herpes Simplex Virus Type 1*

PubMed Central

Hernandez, Anna; Buch, Anna; Sodeik, Beate; Cristea, Ileana Mihaela

2016-01-01

Human herpesviruses are widespread human pathogens with a remarkable impact on worldwide public health. Despite intense decades of research, the molecular details in many aspects of their function remain to be fully characterized. To unravel the details of how these viruses operate, a thorough understanding of the relationships between the involved components is key. Here, we present HVint, a novel protein-protein intraviral interaction resource for herpes simplex virus type 1 (HSV-1) integrating data from five external sources. To assess each interaction, we used a scoring scheme that takes into consideration aspects such as the type of detection method and the number of lines of evidence. The coverage of the initial interactome was further increased using evolutionary information, by importing interactions reported for other human herpesviruses. These latter interactions constitute, therefore, computational predictions for potential novel interactions in HSV-1. An independent experimental analysis was performed to confirm a subset of our predicted interactions. This subset covers proteins that contribute to nuclear egress and primary envelopment events, including VP26, pUL31, pUL40, and the recently characterized pUL32 and pUL21. Our findings support a coordinated crosstalk between VP26 and proteins such as pUL31, pUS9, and the CSVC complex, contributing to the development of a model describing the nuclear egress and primary envelopment pathways of newly synthesized HSV-1 capsids. The results are also consistent with recent findings on the involvement of pUL32 in capsid maturation and early tegumentation events. Further, they open the door to new hypotheses on virus-specific regulators of pUS9-dependent transport. To make this repository of interactions readily accessible for the scientific community, we also developed a user-friendly and interactive web interface. Our approach demonstrates the power of computational predictions to assist in the design of

Protein-protein interactions: an application of Tus-Ter mediated protein microarray system.

PubMed

Sitaraman, Kalavathy; Chatterjee, Deb K

2011-01-01

In this chapter, we present a novel, cost-effective microarray strategy that utilizes expression-ready plasmid DNAs to generate protein arrays on-demand and its use to validate protein-protein interactions. These expression plasmids were constructed in such a way so as to serve a dual purpose of synthesizing the protein of interest as well as capturing the synthesized protein. The microarray system is based on the high affinity binding of Escherichia coli "Tus" protein to "Ter," a 20 bp DNA sequence involved in the regulation of DNA replication. The protein expression is carried out in a cell-free protein synthesis system, with rabbit reticulocyte lysates, and the target proteins are detected either by labeled incorporated tag specific or by gene-specific antibodies. This microarray system has been successfully used for the detection of protein-protein interaction because both the target protein and the query protein can be transcribed and translated simultaneously in the microarray slides. The utility of this system for detecting protein-protein interaction is demonstrated by a few well-known examples: Jun/Fos, FRB/FKBP12, p53/MDM2, and CDK4/p16. In all these cases, the presence of protein complexes resulted in the localization of fluorophores at the specific sites of the immobilized target plasmids. Interestingly, during our interactions studies we also detected a previously unknown interaction between CDK2 and p16. Thus, this Tus-Ter based system of protein microarray can be used for the validation of known protein interactions as well as for identifying new protein-protein interactions. In addition, it can be used to examine and identify targets of nucleic acid-protein, ligand-receptor, enzyme-substrate, and drug-protein interactions.

A protein interaction network analysis for yeast integral membrane protein.

PubMed

Shi, Ming-Guang; Huang, De-Shuang; Li, Xue-Ling

2008-01-01

Although the yeast Saccharomyces cerevisiae is the best exemplified single-celled eukaryote, the vast number of protein-protein interactions of integral membrane proteins of Saccharomyces cerevisiae have not been characterized by experiments. Here, based on the kernel method of Greedy Kernel Principal Component analysis plus Linear Discriminant Analysis, we identify 300 protein-protein interactions involving 189 membrane proteins and get the outcome of a highly connected protein-protein interactions network. Furthermore, we study the global topological features of integral membrane proteins network of Saccharomyces cerevisiae. These results give the comprehensive description of protein-protein interactions of integral membrane proteins and reveal global topological and robustness of the interactome network at a system level. This work represents an important step towards a comprehensive understanding of yeast protein interactions.

Co-evolutionary Analysis of Domains in Interacting Proteins Reveals Insights into Domain–Domain Interactions Mediating Protein–Protein Interactions

PubMed Central

Jothi, Raja; Cherukuri, Praveen F.; Tasneem, Asba; Przytycka, Teresa M.

2006-01-01

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein–protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the noninteracting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain–domain interactions. Given a protein–protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain–domain interactions, and used known domain–domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain–domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites. PMID:16949097

Identifying interacting proteins of a Caenorhabditis elegans voltage-gated chloride channel CLH-1 using GFP-Trap and mass spectrometry.

PubMed

Zhou, Zi-Liang; Jiang, Jing; Yin, Jiang-An; Cai, Shi-Qing

2014-06-25

Chloride channels belong to a superfamily of ion channels that permit passive passage of anions, mainly chloride, across cell membrane. They play a variety of important physiological roles in regulation of cytosolic pH, cell volume homeostasis, organic solute transport, cell migration, cell proliferation, and differentiation. However, little is known about the functional regulation of these channels. In this study, we generated an integrated transgenic worm strain expressing green fluorescence protein (GFP) fused CLC-type chloride channel 1 (CLH-1::GFP), a voltage-gated chloride channel in Caenorhabditis elegans (C. elegans). CLH-1::GFP was expressed in some unidentified head neurons and posterior intestinal cells of C. elegans. Interacting proteins of CLH-1::GFP were purified by GFP-Trap, a novel system for efficient isolation of GFP fusion proteins and their interacting factors. Mass spectrometry (MS) analysis revealed that a total of 27 high probability interacting proteins were co-trapped with CLHp-1::GFP. Biochemical evidence showed that eukaryotic translation elongation factor 1 (EEF-1), one of these co-trapped proteins identified by MS, physically interacted with CLH-1, in consistent with GFP-Trap experiments. Further immunostaining data revealed that the protein level of CLH-1 was significantly increased upon co-expression with EEF-1. These results suggest that the combination of GFP-Trap purification with MS is an excellent tool to identify novel interacting proteins of voltage-gated chloride channels in C. elegans. Our data also show that EEF-1 is a regulator of voltage-gated chloride channel CLH-1.

Identification of structural protein-protein interactions of herpes simplex virus type 1.

PubMed

Lee, Jin H; Vittone, Valerio; Diefenbach, Eve; Cunningham, Anthony L; Diefenbach, Russell J

2008-09-01

In this study we have defined protein-protein interactions between the structural proteins of herpes simplex virus type 1 (HSV-1) using a LexA yeast two-hybrid system. The majority of the capsid, tegument and envelope proteins of HSV-1 were screened in a matrix approach. A total of 40 binary interactions were detected including 9 out of 10 previously identified tegument-tegument interactions (Vittone, V., Diefenbach, E., Triffett, D., Douglas, M.W., Cunningham, A.L., and Diefenbach, R.J., 2005. Determination of interactions between tegument proteins of herpes simplex virus type 1. J. Virol. 79, 9566-9571). A total of 12 interactions involving the capsid protein pUL35 (VP26) and 11 interactions involving the tegument protein pUL46 (VP11/12) were identified. The most significant novel interactions detected in this study, which are likely to play a role in viral assembly, include pUL35-pUL37 (capsid-tegument), pUL46-pUL37 (tegument-tegument) and pUL49 (VP22)-pUS9 (tegument-envelope). This information will provide further insights into the pathways of HSV-1 assembly and the identified interactions are potential targets for new antiviral drugs.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

PubMed Central

Vinayagam, Arunachalam; Gibson, Travis E.; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

2016-01-01

The protein–protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as “indispensable,” “neutral,” or “dispensable,” which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network’s control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets. PMID:27091990

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

PubMed Central

Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

2012-01-01

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with

Detection of protein complex from protein-protein interaction network using Markov clustering

NASA Astrophysics Data System (ADS)

Ochieng, P. J.; Kusuma, W. A.; Haryanto, T.

2017-05-01

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Ecto-Fc MS identifies ligand-receptor interactions through extracellular domain Fc fusion protein baits and shotgun proteomic analysis

PubMed Central

Savas, Jeffrey N.; De Wit, Joris; Comoletti, Davide; Zemla, Roland; Ghosh, Anirvan

2015-01-01

Ligand-receptor interactions represent essential biological triggers which regulate many diverse and important cellular processes. We have developed a discovery-based proteomic biochemical protocol which couples affinity purification with multidimensional liquid chromatographic tandem mass spectrometry (LCLC-MS/MS) and bioinformatic analysis. Compared to previous approaches, our analysis increases sensitivity, shortens analysis duration, and boosts comprehensiveness. In this protocol, receptor extracellular domains are fused with the Fc region of IgG to generate fusion proteins that are purified from transfected HEK293T cells. These “ecto-Fcs” are coupled to protein A beads and serve as baits for binding assays with prey proteins extracted from rodent brain. After capture, the affinity purified proteins are digested into peptides and comprehensively analyzed by LCLC-MS/MS with ion trap mass spectrometers. In four working days, this protocol can generate shortlists of candidate ligand-receptor protein-protein interactions. Our “Ecto-Fc MS” approach outperforms antibody-based approaches and provides a reproducible and robust framework to identify extracellular ligand – receptor interactions. PMID:25101821

A protein isolated from human oviductal tissue in vitro secretion, identified as human lactoferrin, interacts with spermatozoa and oocytes and modulates gamete interaction.

PubMed

Zumoffen, C M; Gil, R; Caille, A M; Morente, C; Munuce, M J; Ghersevich, S A

2013-05-01

Is lactoferrin (LF) (detected in oviductal secretion) able to bind to oocytes and sperm and modulate gamete interaction? LF binds to zona pellucida (ZP) and spermatozoa (depending upon the capacitation stage and acrosome status) and inhibits gamete interaction in vitro. Proteins from human oviductal tissue secretion modulate gamete interaction and parameters of sperm function in vitro and some of them bind to sperm, but they remain to be isolated and identified. Proteins were isolated from human oviductal tissue secretion using their sperm membrane binding ability. One of the isolated proteins was identified as human LF and immunolocalized in tubal tissues. LF expression was analyzed in native oviductal fluid and oviduct epithelial cells (at different phases of the menstrual cycle: proliferative, periovulatory and secretory). In addition, the LF binding sites on spermatozoa (at different capacitation and acrosome reaction stages) and on ZP and the dose-dependent effect of LF on gamete interaction were investigated. All experiments were performed at least three times. Tubal tissues obtained from premenopausal patients (scheduled for hysterectomy, n = 23) were cultured in DMEM/Ham's F12 medium and conditioned media (CM) were collected. Motile spermatozoa were obtained by swim-up from normozoospermic semen samples from healthy donors (n = 4). An affinity chromatography with sperm membrane extracts was used to isolate proteins from CM. Isolated proteins were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophresis and further identified by nano liquid chromatography tandem mass spectrometry peptide sequencing. The presence of LF in oviductal tissue was investigated by immunohistochemistry and immunofluorescence and was detected in native oviductal fluid and oviduct epithelial cells homogenates by western blot. LF binding sites on gametes were investigated by incubating gametes with the protein coupled to fluorescein isothiocyanate (FITC). The acrosome

Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions

PubMed Central

Roy, Sushmita; Martinez, Diego; Platero, Harriett; Lane, Terran; Werner-Washburne, Margaret

2009-01-01

Background Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information. Results AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins. Conclusion AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains. PMID:19936254

Predicting Physical Interactions between Protein Complexes*

PubMed Central

Clancy, Trevor; Rødland, Einar Andreas; Nygard, Ståle; Hovig, Eivind

2013-01-01

Protein complexes enact most biochemical functions in the cell. Dynamic interactions between protein complexes are frequent in many cellular processes. As they are often of a transient nature, they may be difficult to detect using current genome-wide screens. Here, we describe a method to computationally predict physical interactions between protein complexes, applied to both humans and yeast. We integrated manually curated protein complexes and physical protein interaction networks, and we designed a statistical method to identify pairs of protein complexes where the number of protein interactions between a complex pair is due to an actual physical interaction between the complexes. An evaluation against manually curated physical complex-complex interactions in yeast revealed that 50% of these interactions could be predicted in this manner. A community network analysis of the highest scoring pairs revealed a biologically sensible organization of physical complex-complex interactions in the cell. Such analyses of proteomes may serve as a guide to the discovery of novel functional cellular relationships. PMID:23438732

UDoNC: An Algorithm for Identifying Essential Proteins Based on Protein Domains and Protein-Protein Interaction Networks.

PubMed

Peng, Wei; Wang, Jianxin; Cheng, Yingjiao; Lu, Yu; Wu, Fangxiang; Pan, Yi

2015-01-01

Prediction of essential proteins which are crucial to an organism's survival is important for disease analysis and drug design, as well as the understanding of cellular life. The majority of prediction methods infer the possibility of proteins to be essential by using the network topology. However, these methods are limited to the completeness of available protein-protein interaction (PPI) data and depend on the network accuracy. To overcome these limitations, some computational methods have been proposed. However, seldom of them solve this problem by taking consideration of protein domains. In this work, we first analyze the correlation between the essentiality of proteins and their domain features based on data of 13 species. We find that the proteins containing more protein domain types which rarely occur in other proteins tend to be essential. Accordingly, we propose a new prediction method, named UDoNC, by combining the domain features of proteins with their topological properties in PPI network. In UDoNC, the essentiality of proteins is decided by the number and the frequency of their protein domain types, as well as the essentiality of their adjacent edges measured by edge clustering coefficient. The experimental results on S. cerevisiae data show that UDoNC outperforms other existing methods in terms of area under the curve (AUC). Additionally, UDoNC can also perform well in predicting essential proteins on data of E. coli.

Identifying Hierarchical and Overlapping Protein Complexes Based on Essential Protein-Protein Interactions and “Seed-Expanding” Method

PubMed Central

Ren, Jun; Zhou, Wei; Wang, Jianxin

2014-01-01

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and “seed-expanding.” First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes. PMID:25143945

Integrated web visualizations for protein-protein interaction databases.

PubMed

Jeanquartier, Fleur; Jean-Quartier, Claire; Holzinger, Andreas

2015-06-16

Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. We selected M=10 out of N=53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.

Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology.

PubMed

DeBlasio, Stacy L; Chavez, Juan D; Alexander, Mariko M; Ramsey, John; Eng, Jimmy K; Mahoney, Jaclyn; Gray, Stewart M; Bruce, James E; Cilia, Michelle

2016-02-15

Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection-hallmarks of host-pathogen interactions-were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used protein interaction

Identifying Protein-protein Interaction in Drosophila Adult Heads by Tandem Affinity Purification (TAP)

PubMed Central

Tian, Xiaolin; Zhu, Mingwei; Li, Long; Wu, Chunlai

2013-01-01

Genetic screens conducted using Drosophila melanogaster (fruit fly) have made numerous milestone discoveries in the advance of biological sciences. However, the use of biochemical screens aimed at extending the knowledge gained from genetic analysis was explored only recently. Here we describe a method to purify the protein complex that associates with any protein of interest from adult fly heads. This method takes advantage of the Drosophila GAL4/UAS system to express a bait protein fused with a Tandem Affinity Purification (TAP) tag in fly neurons in vivo, and then implements two rounds of purification using a TAP procedure similar to the one originally established in yeast1 to purify the interacting protein complex. At the end of this procedure, a mixture of multiple protein complexes is obtained whose molecular identities can be determined by mass spectrometry. Validation of the candidate proteins will benefit from the resource and ease of performing loss-of-function studies in flies. Similar approaches can be applied to other fly tissues. We believe that the combination of genetic manipulations and this proteomic approach in the fly model system holds tremendous potential for tackling fundamental problems in the field of neurobiology and beyond. PMID:24335807

Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology

PubMed Central

DeBlasio, Stacy L.; Chavez, Juan D.; Alexander, Mariko M.; Ramsey, John; Eng, Jimmy K.; Mahoney, Jaclyn; Gray, Stewart M.; Bruce, James E.

2015-01-01

ABSTRACT Demonstrating direct interactions between host and virus proteins during infection is a major goal and challenge for the field of virology. Most protein interactions are not binary or easily amenable to structural determination. Using infectious preparations of a polerovirus (Potato leafroll virus [PLRV]) and protein interaction reporter (PIR), a revolutionary technology that couples a mass spectrometric-cleavable chemical cross-linker with high-resolution mass spectrometry, we provide the first report of a host-pathogen protein interaction network that includes data-derived, topological features for every cross-linked site that was identified. We show that PLRV virions have hot spots of protein interaction and multifunctional surface topologies, revealing how these plant viruses maximize their use of binding interfaces. Modeling data, guided by cross-linking constraints, suggest asymmetric packing of the major capsid protein in the virion, which supports previous epitope mapping studies. Protein interaction topologies are conserved with other species in the Luteoviridae and with unrelated viruses in the Herpesviridae and Adenoviridae. Functional analysis of three PLRV-interacting host proteins in planta using a reverse-genetics approach revealed a complex, molecular tug-of-war between host and virus. Structural mimicry and diversifying selection—hallmarks of host-pathogen interactions—were identified within host and viral binding interfaces predicted by our models. These results illuminate the functional diversity of the PLRV-host protein interaction network and demonstrate the usefulness of PIR technology for precision mapping of functional host-pathogen protein interaction topologies. IMPORTANCE The exterior shape of a plant virus and its interacting host and insect vector proteins determine whether a virus will be transmitted by an insect or infect a specific host. Gaining this information is difficult and requires years of experimentation. We used

Monitoring protein-protein interactions using split synthetic renilla luciferase protein-fragment-assisted complementation.

PubMed

Paulmurugan, R; Gambhir, S S

2003-04-01

In this study we developed an inducible synthetic renilla luciferase protein-fragment-assisted complementation-based bioluminescence assay to quantitatively measure real time protein-protein interactions in mammalian cells. We identified suitable sites to generate fragments of N and C portions of the protein that yield significant recovered activity through complementation. We validate complementation-based activation of split synthetic renilla luciferase protein driven by the interaction of two strongly interacting proteins, MyoD and Id, in five different cell lines utilizing transient transfection studies. The expression level of the system was also modulated by tumor necrosis factor alpha through NFkappaB-promoter/enhancer elements used to drive expression of the N portion of synthetic renilla luciferase reporter gene. This new system should help in studying protein-protein interactions and when used with other split reporters (e.g., split firefly luciferase) should help to monitor different components of an intracellular network.

Scoring functions for protein-protein interactions.

PubMed

Moal, Iain H; Moretti, Rocco; Baker, David; Fernández-Recio, Juan

2013-12-01

The computational evaluation of protein-protein interactions will play an important role in organising the wealth of data being generated by high-throughput initiatives. Here we discuss future applications, report recent developments and identify areas requiring further investigation. Many functions have been developed to quantify the structural and energetic properties of interacting proteins, finding use in interrelated challenges revolving around the relationship between sequence, structure and binding free energy. These include loop modelling, side-chain refinement, docking, multimer assembly, affinity prediction, affinity change upon mutation, hotspots location and interface design. Information derived from models optimised for one of these challenges can be used to benefit the others, and can be unified within the theoretical frameworks of multi-task learning and Pareto-optimal multi-objective learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evolutionary diversification of protein-protein interactions by interface add-ons.

PubMed

Plach, Maximilian G; Semmelmann, Florian; Busch, Florian; Busch, Markus; Heizinger, Leonhard; Wysocki, Vicki H; Merkl, Rainer; Sterner, Reinhard

2017-10-03

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein-protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein-protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein-protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein-protein interactions.

The binary protein-protein interaction landscape of Escherichia coli

PubMed Central

Rajagopala, Seesandra V.; Vlasblom, James; Arnold, Roland; Franca-Koh, Jonathan; Pakala, Suman B.; Phanse, Sadhna; Ceol, Arnaud; Häuser, Roman; Siszler, Gabriella; Wuchty, Stefan; Emili, Andrew; Babu, Mohan; Aloy, Patrick; Pieper, Rembert; Uetz, Peter

2014-01-01

Efforts to map the Escherichia coli interactome have identified several hundred macromolecular complexes, but direct binary protein-protein interactions (PPIs) have not been surveyed on a large scale. Here we performed yeast two-hybrid screens of 3,305 baits against 3,606 preys (~70% of the E. coli proteome) in duplicate to generate a map of 2,234 interactions, approximately doubling the number of known binary PPIs in E. coli. Integration of binary PPIs and genetic interactions revealed functional dependencies among components involved in cellular processes, including envelope integrity, flagellum assembly and protein quality control. Many of the binary interactions that could be mapped within multi-protein complexes were informative regarding internal topology and indicated that interactions within complexes are significantly more conserved than those interactions connecting different complexes. This resource will be useful for inferring bacterial gene function and provides a draft reference of the basic physical wiring network of this evolutionarily significant model microbe. PMID:24561554

A Library of Plasmodium vivax Recombinant Merozoite Proteins Reveals New Vaccine Candidates and Protein-Protein Interactions

PubMed Central

Hostetler, Jessica B.; Sharma, Sumana; Bartholdson, S. Josefin; Wright, Gavin J.; Fairhurst, Rick M.; Rayner, Julian C.

2015-01-01

Background A vaccine targeting Plasmodium vivax will be an essential component of any comprehensive malaria elimination program, but major gaps in our understanding of P. vivax biology, including the protein-protein interactions that mediate merozoite invasion of reticulocytes, hinder the search for candidate antigens. Only one ligand-receptor interaction has been identified, that between P. vivax Duffy Binding Protein (PvDBP) and the erythrocyte Duffy Antigen Receptor for Chemokines (DARC), and strain-specific immune responses to PvDBP make it a complex vaccine target. To broaden the repertoire of potential P. vivax merozoite-stage vaccine targets, we exploited a recent breakthrough in expressing full-length ectodomains of Plasmodium proteins in a functionally-active form in mammalian cells and initiated a large-scale study of P. vivax merozoite proteins that are potentially involved in reticulocyte binding and invasion. Methodology/Principal Findings We selected 39 P. vivax proteins that are predicted to localize to the merozoite surface or invasive secretory organelles, some of which show homology to P. falciparum vaccine candidates. Of these, we were able to express 37 full-length protein ectodomains in a mammalian expression system, which has been previously used to express P. falciparum invasion ligands such as PfRH5. To establish whether the expressed proteins were correctly folded, we assessed whether they were recognized by antibodies from Cambodian patients with acute vivax malaria. IgG from these samples showed at least a two-fold change in reactivity over naïve controls in 27 of 34 antigens tested, and the majority showed heat-labile IgG immunoreactivity, suggesting the presence of conformation-sensitive epitopes and native tertiary protein structures. Using a method specifically designed to detect low-affinity, extracellular protein-protein interactions, we confirmed a predicted interaction between P. vivax 6-cysteine proteins P12 and P41, further

A Laboratory-Intensive Course on the Experimental Study of Protein-Protein Interactions

ERIC Educational Resources Information Center

Witherow, D. Scott; Carson, Sue

2011-01-01

The study of protein-protein interactions is important to scientists in a wide range of disciplines. We present here the assessment of a lab-intensive course that teaches students techniques used to identify and further study protein-protein interactions. One of the unique elements of the course is that students perform a yeast two-hybrid screen…

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

PubMed

Suratanee, Apichat; Plaimas, Kitiporn

2017-01-01

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

Specificity of molecular interactions in transient protein-protein interaction interfaces.

PubMed

Cho, Kyu-il; Lee, KiYoung; Lee, Kwang H; Kim, Dongsup; Lee, Doheon

2006-11-15

In this study, we investigate what types of interactions are specific to their biological function, and what types of interactions are persistent regardless of their functional category in transient protein-protein heterocomplexes. This is the first approach to analyze protein-protein interfaces systematically at the molecular interaction level in the context of protein functions. We perform systematic analysis at the molecular interaction level using classification and feature subset selection technique prevalent in the field of pattern recognition. To represent the physicochemical properties of protein-protein interfaces, we design 18 molecular interaction types using canonical and noncanonical interactions. Then, we construct input vector using the frequency of each interaction type in protein-protein interface. We analyze the 131 interfaces of transient protein-protein heterocomplexes in PDB: 33 protease-inhibitors, 52 antibody-antigens, 46 signaling proteins including 4 cyclin dependent kinase and 26 G-protein. Using kNN classification and feature subset selection technique, we show that there are specific interaction types based on their functional category, and such interaction types are conserved through the common binding mechanism, rather than through the sequence or structure conservation. The extracted interaction types are C(alpha)-- H...O==C interaction, cation...anion interaction, amine...amine interaction, and amine...cation interaction. With these four interaction types, we achieve the classification success rate up to 83.2% with leave-one-out cross-validation at k = 15. Of these four interaction types, C(alpha)--H...O==C shows binding specificity for protease-inhibitor complexes, while cation-anion interaction is predominant in signaling complexes. The amine ... amine and amine...cation interaction give a minor contribution to the classification accuracy. When combined with these two interactions, they increase the accuracy by 3.8%. In the case of

Structural Interface Parameters Are Discriminatory in Recognising Near-Native Poses of Protein-Protein Interactions

PubMed Central

Malhotra, Sony; Sankar, Kannan; Sowdhamini, Ramanathan

2014-01-01

Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets. PMID:24498255

Structural interface parameters are discriminatory in recognising near-native poses of protein-protein interactions.

PubMed

Malhotra, Sony; Sankar, Kannan; Sowdhamini, Ramanathan

2014-01-01

Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.

Towards a map of the Populus biomass protein-protein interaction network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beers, Eric; Brunner, Amy; Helm, Richard

Biofuels can be produced from a variety of plant feedstocks. The value of a particular feedstock for biofuels production depends in part on the degree of difficulty associated with the extraction of fermentable sugars from the plant biomass. The wood of trees is potentially a rich source fermentable sugars. However, the sugars in wood exist in a tightly cross-linked matrix of cellulose, hemicellulose, and lignin, making them largely recalcitrant to release and fermentation for biofuels production. Before breeders and genetic engineers can effectively develop plants with reduced recalcitrance to fermentation, it is necessary to gain a better understanding of themore » fundamental biology of the mechanisms responsible for wood formation. Regulatory, structural, and enzymatic proteins are required for the complicated process of wood formation. To function properly, proteins must interact with other proteins. Yet, very few of the protein-protein interactions necessary for wood formation are known. The main objectives of this project were to 1) identify new protein-protein interactions relevant to wood formation, and 2) perform in-depth characterizations of selected protein-protein interactions. To identify relevant protein-protein interactions, we cloned a set of approximately 400 genes that were highly expressed in the wood-forming tissue (known as secondary xylem) of poplar (Populus trichocarpa). We tested whether the proteins encoded by these biomass genes interacted with each other in a binary matrix design using the yeast two-hybrid (Y2H) method for protein-protein interaction discovery. We also tested a subset of the 400 biomass proteins for interactions with all proteins present in wood-forming tissue of poplar in a biomass library screen design using Y2H. Together, these two Y2H screens yielded over 270 interactions involving over 75 biomass proteins. For the second main objective we selected several interacting pairs or groups of interacting proteins for in

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

PubMed

Yang, Lei; Zhao, Xudong; Tang, Xianglong

2014-01-01

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is conductive to uncovering disease mechanisms. This paper proposes an approach of predicting disease proteins based on cliques in a protein-protein interaction network. To tolerate false positive and negative interactions in protein networks, extending cliques and scoring predicted disease proteins with gene ontology terms are introduced to the clique-based method. Precisions of predicted disease proteins are verified by disease phenotypes and steadily keep to more than 95%. The predicted disease proteins associated with cliques can partly complement mapping between genotype and phenotype, and provide clues for understanding the pathogenesis of serious diseases.

Ion-dipole interactions and their functions in proteins.

PubMed

Sippel, Katherine H; Quiocho, Florante A

2015-07-01

Ion-dipole interactions in biological macromolecules are formed between atomic or molecular ions and neutral protein dipolar groups through either hydrogen bond or coordination. Since their discovery 30 years ago, these interactions have proven to be a frequent occurrence in protein structures, appearing in everything from transporters and ion channels to enzyme active sites to protein-protein interfaces. However, their significance and roles in protein functions are largely underappreciated. We performed PDB data mining to identify a sampling of proteins that possess these interactions. In this review, we will define the ion-dipole interaction and discuss several prominent examples of their functional roles in nature. © 2015 The Protein Society.

A Luciferase-fragment Complementation Assay to Detect Lipid Droplet-associated Protein-Protein Interactions*

PubMed Central

Kolkhof, Petra; Werthebach, Michael; van de Venn, Anna; Poschmann, Gereon; Chen, Lili; Welte, Michael; Stühler, Kai; Beller, Mathias

2017-01-01

A critical challenge for all organisms is to carefully control the amount of lipids they store. An important node for this regulation is the protein coat present at the surface of lipid droplets (LDs), the intracellular organelles dedicated to lipid storage. Only limited aspects of this regulation are understood so far. For the probably best characterized case, the regulation of lipolysis in mammals, some of the major protein players have been identified, and it has been established that this process crucially depends on an orchestrated set of protein-protein interactions. Proteomic analysis has revealed that LDs are associated with dozens, if not hundreds, of different proteins, most of them poorly characterized, with even fewer data regarding which of them might physically interact. To comprehensively understand the mechanism of lipid storage regulation, it will likely be essential to define the interactome of LD-associated proteins. Previous studies of such interactions were hampered by technical limitations. Therefore, we have developed a split-luciferase based protein-protein interaction assay and test for interactions among 47 proteins from Drosophila and from mouse. We confirmed previously described interactions and identified many new ones. In 1561 complementation tests, we assayed for interactions among 487 protein pairs of which 92 (19%) resulted in a successful luciferase complementation. These results suggest that a prominent fraction of the LD-associated proteome participates in protein-protein interactions. In targeted experiments, we analyzed the two proteins Jabba and CG9186 in greater detail. Jabba mediates the sequestration of histones to LDs. We successfully applied our split luciferase complementation assay to learn more about this function as we were e.g. able to map the interaction between Jabba and histones. For CG9186, expression levels affect the positioning of LDs. Here, we reveal the ubiquitination of CG9186, and link this

Potato leafroll virus structural proteins manipulate overlapping, yet distinct protein interaction networks during infection.

PubMed

DeBlasio, Stacy L; Johnson, Richard; Sweeney, Michelle M; Karasev, Alexander; Gray, Stewart M; MacCoss, Michael J; Cilia, Michelle

2015-06-01

Potato leafroll virus (PLRV) produces a readthrough protein (RTP) via translational readthrough of the coat protein amber stop codon. The RTP functions as a structural component of the virion and as a nonincorporated protein in concert with numerous insect and plant proteins to regulate virus movement/transmission and tissue tropism. Affinity purification coupled to quantitative MS was used to generate protein interaction networks for a PLRV mutant that is unable to produce the read through domain (RTD) and compared to the known wild-type PLRV protein interaction network. By quantifying differences in the protein interaction networks, we identified four distinct classes of PLRV-plant interactions: those plant and nonstructural viral proteins interacting with assembled coat protein (category I); plant proteins in complex with both coat protein and RTD (category II); plant proteins in complex with the RTD (category III); and plant proteins that had higher affinity for virions lacking the RTD (category IV). Proteins identified as interacting with the RTD are potential candidates for regulating viral processes that are mediated by the RTP such as phloem retention and systemic movement and can potentially be useful targets for the development of strategies to prevent infection and/or viral transmission of Luteoviridae species that infect important crop species. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The protein interaction map of bacteriophage lambda

PubMed Central

2011-01-01

Background Bacteriophage lambda is a model phage for most other dsDNA phages and has been studied for over 60 years. Although it is probably the best-characterized phage there are still about 20 poorly understood open reading frames in its 48-kb genome. For a complete understanding we need to know all interactions among its proteins. We have manually curated the lambda literature and compiled a total of 33 interactions that have been found among lambda proteins. We set out to find out how many protein-protein interactions remain to be found in this phage. Results In order to map lambda's interactions, we have cloned 68 out of 73 lambda open reading frames (the "ORFeome") into Gateway vectors and systematically tested all proteins for interactions using exhaustive array-based yeast two-hybrid screens. These screens identified 97 interactions. We found 16 out of 30 previously published interactions (53%). We have also found at least 18 new plausible interactions among functionally related proteins. All previously found and new interactions are combined into structural and network models of phage lambda. Conclusions Phage lambda serves as a benchmark for future studies of protein interactions among phage, viruses in general, or large protein assemblies. We conclude that we could not find all the known interactions because they require chaperones, post-translational modifications, or multiple proteins for their interactions. The lambda protein network connects 12 proteins of unknown function with well characterized proteins, which should shed light on the functional associations of these uncharacterized proteins. PMID:21943085

Non-interacting surface solvation and dynamics in protein-protein interactions.

PubMed

Visscher, Koen M; Kastritis, Panagiotis L; Bonvin, Alexandre M J J

2015-03-01

Protein-protein interactions control a plethora of cellular processes, including cell proliferation, differentiation, apoptosis, and signal transduction. Understanding how and why proteins interact will inevitably lead to novel structure-based drug design methods, as well as design of de novo binders with preferred interaction properties. At a structural and molecular level, interface and rim regions are not enough to fully account for the energetics of protein-protein binding, even for simple lock-and-key rigid binders. As we have recently shown, properties of the global surface might also play a role in protein-protein interactions. Here, we report on molecular dynamics simulations performed to understand solvent effects on protein-protein surfaces. We compare properties of the interface, rim, and non-interacting surface regions for five different complexes and their free components. Interface and rim residues become, as expected, less mobile upon complexation. However, non-interacting surface appears more flexible in the complex. Fluctuations of polar residues are always lower compared with charged ones, independent of the protein state. Further, stable water molecules are often observed around polar residues, in contrast to charged ones. Our analysis reveals that (a) upon complexation, the non-interacting surface can have a direct entropic compensation for the lower interface and rim entropy and (b) the mobility of the first hydration layer, which is linked to the stability of the protein-protein complex, is influenced by the local chemical properties of the surface. These findings corroborate previous hypotheses on the role of the hydration layer in shielding protein-protein complexes from unintended protein-protein interactions. © 2014 Wiley Periodicals, Inc.

Light-scattering studies of protein solutions: role of hydration in weak protein-protein interactions.

PubMed

Paliwal, A; Asthagiri, D; Abras, D; Lenhoff, A M; Paulaitis, M E

2005-09-01

We model the hydration contribution to short-range electrostatic/dispersion protein interactions embodied in the osmotic second virial coefficient, B(2), by adopting a quasi-chemical description in which water molecules associated with the protein are identified through explicit molecular dynamics simulations. These water molecules reduce the surface complementarity of highly favorable short-range interactions, and therefore can play an important role in mediating protein-protein interactions. Here we examine this quasi-chemical view of hydration by predicting the interaction part of B(2) and comparing our results with those derived from light-scattering measurements of B(2) for staphylococcal nuclease, lysozyme, and chymotrypsinogen at 25 degrees C as a function of solution pH and ionic strength. We find that short-range protein interactions are influenced by water molecules strongly associated with a relatively small fraction of the protein surface. However, the effect of these strongly associated water molecules on the surface complementarity of short-range protein interactions is significant, and must be taken into account for an accurate description of B(2). We also observe remarkably similar hydration behavior for these proteins despite substantial differences in their three-dimensional structures and spatial charge distributions, suggesting a general characterization of protein hydration.

Construction of reliable protein-protein interaction networks with a new interaction generality measure.

PubMed

Saito, Rintaro; Suzuki, Harukazu; Hayashizaki, Yoshihide

2003-04-12

Recent screening techniques have made large amounts of protein-protein interaction data available, from which biologically important information such as the function of uncharacterized proteins, the existence of novel protein complexes, and novel signal-transduction pathways can be discovered. However, experimental data on protein interactions contain many false positives, making these discoveries difficult. Therefore computational methods of assessing the reliability of each candidate protein-protein interaction are urgently needed. We developed a new 'interaction generality' measure (IG2) to assess the reliability of protein-protein interactions using only the topological properties of their interaction-network structure. Using yeast protein-protein interaction data, we showed that reliable protein-protein interactions had significantly lower IG2 values than less-reliable interactions, suggesting that IG2 values can be used to evaluate and filter interaction data to enable the construction of reliable protein-protein interaction networks.

An ensemble framework for identifying essential proteins.

PubMed

Zhang, Xue; Xiao, Wangxin; Acencio, Marcio Luis; Lemke, Ney; Wang, Xujing

2016-08-25

Many centrality measures have been proposed to mine and characterize the correlations between network topological properties and protein essentiality. However, most of them show limited prediction accuracy, and the number of common predicted essential proteins by different methods is very small. In this paper, an ensemble framework is proposed which integrates gene expression data and protein-protein interaction networks (PINs). It aims to improve the prediction accuracy of basic centrality measures. The idea behind this ensemble framework is that different protein-protein interactions (PPIs) may show different contributions to protein essentiality. Five standard centrality measures (degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and subgraph centrality) are integrated into the ensemble framework respectively. We evaluated the performance of the proposed ensemble framework using yeast PINs and gene expression data. The results show that it can considerably improve the prediction accuracy of the five centrality measures individually. It can also remarkably increase the number of common predicted essential proteins among those predicted by each centrality measure individually and enable each centrality measure to find more low-degree essential proteins. This paper demonstrates that it is valuable to differentiate the contributions of different PPIs for identifying essential proteins based on network topological characteristics. The proposed ensemble framework is a successful paradigm to this end.

Monitoring Ligand-Activated Protein-Protein Interactions Using Bioluminescent Resonance Energy Transfer (BRET) Assay.

PubMed

Coriano, Carlos; Powell, Emily; Xu, Wei

2016-01-01

The bioluminescent resonance energy transfer (BRET) assay has been extensively used in cell-based and in vivo imaging systems for detecting protein-protein interactions in the native environment of living cells. These protein-protein interactions are essential for the functional response of many signaling pathways to environmental chemicals. BRET has been used as a toxicological tool for identifying chemicals that either induce or inhibit these protein-protein interactions. This chapter focuses on describing the toxicological applications of BRET and its optimization as a high-throughput detection system in live cells. Here we review the construction of BRET fusion proteins, describe the BRET methodology, and outline strategies to overcome obstacles that may arise. Furthermore, we describe the advantage of BRET over other resonance energy transfer methods for monitoring protein-protein interactions.

PodNet, a protein-protein interaction network of the podocyte.

PubMed

Warsow, Gregor; Endlich, Nicole; Schordan, Eric; Schordan, Sandra; Chilukoti, Ravi K; Homuth, Georg; Moeller, Marcus J; Fuellen, Georg; Endlich, Karlhans

2013-07-01

Interactions between proteins crucially determine cellular structure and function. Differential analysis of the interactome may help elucidate molecular mechanisms during disease development; however, this analysis necessitates mapping of expression data on protein-protein interaction networks. These networks do not exist for the podocyte; therefore, we built PodNet, a literature-based mouse podocyte network in Cytoscape format. Using database protein-protein interactions, we expanded PodNet to XPodNet with enhanced connectivity. In order to test the performance of XPodNet in differential interactome analysis, we examined podocyte developmental differentiation and the effect of cell culture. Transcriptomes of podocytes in 10 different states were mapped on XPodNet and analyzed with the Cytoscape plugin ExprEssence, based on the law of mass action. Interactions between slit diaphragm proteins are most significantly upregulated during podocyte development and most significantly downregulated in culture. On the other hand, our analysis revealed that interactions lost during podocyte differentiation are not regained in culture, suggesting a loss rather than a reversal of differentiation for podocytes in culture. Thus, we have developed PodNet as a valuable tool for differential interactome analysis in podocytes, and we have identified established and unexplored regulated interactions in developing and cultured podocytes.

Evidence for the interaction of the regulatory protein Ki-1/57 with p53 and its interacting proteins

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nery, Flavia C.; Departamento de Genetica e Evolucao, Universidade Estadual de Campinas, Campinas, SP; Rui, Edmilson

Ki-1/57 is a cytoplasmic and nuclear phospho-protein of 57 kDa and interacts with the adaptor protein RACK1, the transcription factor MEF2C, and the chromatin remodeling factor CHD3, suggesting that it might be involved in the regulation of transcription. Here, we describe yeast two-hybrid studies that identified a total of 11 proteins interacting with Ki-1/57, all of which interact or are functionally associated with p53 or other members of the p53 family of proteins. We further found that Ki-1/57 is able to interact with p53 itself in the yeast two-hybrid system when the interaction was tested directly. This interaction could bemore » confirmed by pull down assays with purified proteins in vitro and by reciprocal co-immunoprecipitation assays from the human Hodgkin analogous lymphoma cell line L540. Furthermore, we found that the phosphorylation of p53 by PKC abolishes its interaction with Ki-1/57 in vitro.« less

In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

PubMed

Folador, Edson Luiz; de Carvalho, Paulo Vinícius Sanches Daltro; Silva, Wanderson Marques; Ferreira, Rafaela Salgado; Silva, Artur; Gromiha, Michael; Ghosh, Preetam; Barh, Debmalya; Azevedo, Vasco; Röttger, Richard

2016-11-04

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.

Identifying protein complexes based on brainstorming strategy.

PubMed

Shen, Xianjun; Zhou, Jin; Yi, Li; Hu, Xiaohua; He, Tingting; Yang, Jincai

2016-11-01

Protein complexes comprising of interacting proteins in protein-protein interaction network (PPI network) play a central role in driving biological processes within cells. Recently, more and more swarm intelligence based algorithms to detect protein complexes have been emerging, which have become the research hotspot in proteomics field. In this paper, we propose a novel algorithm for identifying protein complexes based on brainstorming strategy (IPC-BSS), which is integrated into the main idea of swarm intelligence optimization and the improved K-means algorithm. Distance between the nodes in PPI network is defined by combining the network topology and gene ontology (GO) information. Inspired by human brainstorming process, IPC-BSS algorithm firstly selects the clustering center nodes, and then they are separately consolidated with the other nodes with short distance to form initial clusters. Finally, we put forward two ways of updating the initial clusters to search optimal results. Experimental results show that our IPC-BSS algorithm outperforms the other classic algorithms on yeast and human PPI networks, and it obtains many predicted protein complexes with biological significance. Copyright © 2016 Elsevier Inc. All rights reserved.

UV-triggered Affinity Capture Identifies Interactions between the Plasmodium falciparum Multidrug Resistance Protein 1 (PfMDR1) and Antimalarial Agents in Live Parasitized Cells*

PubMed Central

Brunner, Ralf; Ng, Caroline L.; Aissaoui, Hamed; Akabas, Myles H.; Boss, Christoph; Brun, Reto; Callaghan, Paul S.; Corminboeuf, Olivier; Fidock, David A.; Frame, Ithiel J.; Heidmann, Bibia; Le Bihan, Amélie; Jenö, Paul; Mattheis, Corinna; Moes, Suzette; Müller, Ingrid B.; Paguio, Michelle; Roepe, Paul D.; Siegrist, Romain; Voss, Till; Welford, Richard W. D.; Wittlin, Sergio; Binkert, Christoph

2013-01-01

A representative of a new class of potent antimalarials with an unknown mode of action was recently described. To identify the molecular target of this class of antimalarials, we employed a photo-reactive affinity capture method to find parasite proteins specifically interacting with the capture compound in living parasitized cells. The capture reagent retained the antimalarial properties of the parent molecule (ACT-213615) and accumulated within parasites. We identified several proteins interacting with the capture compound and established a functional interaction between ACT-213615 and PfMDR1. We surmise that PfMDR1 may play a role in the antimalarial activity of the piperazine-containing compound ACT-213615. PMID:23754276

The Ser/Thr Protein Kinase Protein-Protein Interaction Map of M. tuberculosis.

PubMed

Wu, Fan-Lin; Liu, Yin; Jiang, He-Wei; Luan, Yi-Zhao; Zhang, Hai-Nan; He, Xiang; Xu, Zhao-Wei; Hou, Jing-Li; Ji, Li-Yun; Xie, Zhi; Czajkowsky, Daniel M; Yan, Wei; Deng, Jiao-Yu; Bi, Li-Jun; Zhang, Xian-En; Tao, Sheng-Ce

2017-08-01

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis, the leading cause of death among all infectious diseases. There are 11 eukaryotic-like serine/threonine protein kinases (STPKs) in Mtb, which are thought to play pivotal roles in cell growth, signal transduction and pathogenesis. However, their underlying mechanisms of action remain largely uncharacterized. In this study, using a Mtb proteome microarray, we have globally identified the binding proteins in Mtb for all of the STPKs, and constructed the first STPK protein interaction (KPI) map that includes 492 binding proteins and 1,027 interactions. Bioinformatics analysis showed that the interacting proteins reflect diverse functions, including roles in two-component system, transcription, protein degradation, and cell wall integrity. Functional investigations confirmed that PknG regulates cell wall integrity through key components of peptidoglycan (PG) biosynthesis, e.g. MurC. The global STPK-KPIs network constructed here is expected to serve as a rich resource for understanding the key signaling pathways in Mtb, thus facilitating drug development and effective control of Mtb. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

Building blocks for protein interaction devices

PubMed Central

Grünberg, Raik; Ferrar, Tony S.; van der Sloot, Almer M.; Constante, Marco; Serrano, Luis

2010-01-01

Here, we propose a framework for the design of synthetic protein networks from modular protein–protein or protein–peptide interactions and provide a starter toolkit of protein building blocks. Our proof of concept experiments outline a general work flow for part–based protein systems engineering. We streamlined the iterative BioBrick cloning protocol and assembled 25 synthetic multidomain proteins each from seven standardized DNA fragments. A systematic screen revealed two main factors controlling protein expression in Escherichia coli: obstruction of translation initiation by mRNA secondary structure or toxicity of individual domains. Eventually, 13 proteins were purified for further characterization. Starting from well-established biotechnological tools, two general–purpose interaction input and two readout devices were built and characterized in vitro. Constitutive interaction input was achieved with a pair of synthetic leucine zippers. The second interaction was drug-controlled utilizing the rapamycin-induced binding of FRB(T2098L) to FKBP12. The interaction kinetics of both devices were analyzed by surface plasmon resonance. Readout was based on Förster resonance energy transfer between fluorescent proteins and was quantified for various combinations of input and output devices. Our results demonstrate the feasibility of parts-based protein synthetic biology. Additionally, we identify future challenges and limitations of modular design along with approaches to address them. PMID:20215443

Interaction entropy for protein-protein binding

NASA Astrophysics Data System (ADS)

Sun, Zhaoxi; Yan, Yu N.; Yang, Maoyou; Zhang, John Z. H.

2017-03-01

Protein-protein interactions are at the heart of signal transduction and are central to the function of protein machine in biology. The highly specific protein-protein binding is quantitatively characterized by the binding free energy whose accurate calculation from the first principle is a grand challenge in computational biology. In this paper, we show how the interaction entropy approach, which was recently proposed for protein-ligand binding free energy calculation, can be applied to computing the entropic contribution to the protein-protein binding free energy. Explicit theoretical derivation of the interaction entropy approach for protein-protein interaction system is given in detail from the basic definition. Extensive computational studies for a dozen realistic protein-protein interaction systems are carried out using the present approach and comparisons of the results for these protein-protein systems with those from the standard normal mode method are presented. Analysis of the present method for application in protein-protein binding as well as the limitation of the method in numerical computation is discussed. Our study and analysis of the results provided useful information for extracting correct entropic contribution in protein-protein binding from molecular dynamics simulations.

Identifying Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

PubMed Central

Li, Min; Chen, Weijie; Wang, Jianxin; Pan, Yi

2014-01-01

Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures. PMID:24963481

Using peptide array to identify binding motifs and interaction networks for modular domains.

PubMed

Li, Shawn S-C; Wu, Chenggang

2009-01-01

Specific protein-protein interactions underlie all essential biological processes and form the basis of cellular signal transduction. The recognition of a short, linear peptide sequence in one protein by a modular domain in another represents a common theme of macromolecular recognition in cells, and the importance of this mode of protein-protein interaction is highlighted by the large number of peptide-binding domains encoded by the human genome. This phenomenon also provides a unique opportunity to identify protein-protein binding events using peptide arrays and complementary biochemical assays. Accordingly, high-density peptide array has emerged as a useful tool by which to map domain-mediated protein-protein interaction networks at the proteome level. Using the Src-homology 2 (SH2) and 3 (SH3) domains as examples, we describe the application of oriented peptide array libraries in uncovering specific motifs recognized by an SH2 domain and the use of high-density peptide arrays in identifying interaction networks mediated by the SH3 domain. Methods reviewed here could also be applied to other modular domains, including catalytic domains, that recognize linear peptide sequences.

Kinetic Measurements Reveal Enhanced Protein-Protein Interactions at Intercellular Junctions

PubMed Central

Shashikanth, Nitesh; Kisting, Meridith A.; Leckband, Deborah E.

2016-01-01

The binding properties of adhesion proteins are typically quantified from measurements with soluble fragments, under conditions that differ radically from the confined microenvironment of membrane bound proteins in adhesion zones. Using classical cadherin as a model adhesion protein, we tested the postulate that confinement within quasi two-dimensional intercellular gaps exposes weak protein interactions that are not detected in solution binding assays. Micropipette-based measurements of cadherin-mediated, cell-cell binding kinetics identified a unique kinetic signature that reflects both adhesive (trans) bonds between cadherins on opposing cells and lateral (cis) interactions between cadherins on the same cell. In solution, proposed lateral interactions were not detected, even at high cadherin concentrations. Mutations postulated to disrupt lateral cadherin association altered the kinetic signatures, but did not affect the adhesive (trans) binding affinity. Perturbed kinetics further coincided with altered cadherin distributions at junctions, wound healing dynamics, and paracellular permeability. Intercellular binding kinetics thus revealed cadherin interactions that occur within confined, intermembrane gaps but not in solution. Findings further demonstrate the impact of these revealed interactions on the organization and function of intercellular junctions. PMID:27009566

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

PubMed

Wei, Qing; La, David; Kihara, Daisuke

2017-01-01

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

A Brief Review of RNA–Protein Interaction Database Resources

PubMed Central

Yi, Ying; Zhao, Yue; Huang, Yan; Wang, Dong

2017-01-01

RNA–Protein interactions play critical roles in various biological processes. By collecting and analyzing the RNA–Protein interactions and binding sites from experiments and predictions, RNA–Protein interaction databases have become an essential resource for the exploration of the transcriptional and post-transcriptional regulatory network. Here, we briefly review several widely used RNA–Protein interaction database resources developed in recent years to provide a guide of these databases. The content and major functions in databases are presented. The brief description of database helps users to quickly choose the database containing information they interested. In short, these RNA–Protein interaction database resources are continually updated, but the current state shows the efforts to identify and analyze the large amount of RNA–Protein interactions. PMID:29657278

Genome-Wide Protein Interaction Screens Reveal Functional Networks Involving Sm-Like Proteins

PubMed Central

Fromont-Racine, Micheline; Mayes, Andrew E.; Brunet-Simon, Adeline; Rain, Jean-Christophe; Colley, Alan; Dix, Ian; Decourty, Laurence; Joly, Nicolas; Ricard, Florence; Beggs, Jean D.

2000-01-01

A set of seven structurally related Sm proteins forms the core of the snRNP particles containing the spliceosomal U1, U2, U4 and U5 snRNAs. A search of the genomic sequence of Saccharomyces cerevisiae has identified a number of open reading frames that potentially encode structurally similar proteins termed Lsm (Like Sm) proteins. With the aim of analysing all possible interactions between the Lsm proteins and any protein encoded in the yeast genome, we performed exhaustive and iterative genomic two-hybrid screens, starting with the Lsm proteins as baits. Indeed, extensive interactions amongst eight Lsm proteins were found that suggest the existence of a Lsm complex or complexes. These Lsm interactions apparently involve the conserved Sm domain that also mediates interactions between the Sm proteins. The screens also reveal functionally significant interactions with splicing factors, in particular with Prp4 and Prp24, compatible with genetic studies and with the reported association of Lsm proteins with spliceosomal U6 and U4/U6 particles. In addition, interactions with proteins involved in mRNA turnover, such as Mrt1, Dcp1, Dcp2 and Xrn1, point to roles for Lsm complexes in distinct RNA metabolic processes, that are confirmed in independent functional studies. These results provide compelling evidence that two-hybrid screens yield functionally meaningful information about protein–protein interactions and can suggest functions for uncharacterized proteins, especially when they are performed on a genome-wide scale. PMID:10900456

Identification of host proteins, Spata3 and Dkk2, interacting with Toxoplasma gondii micronemal protein MIC3.

PubMed

Wang, Yifan; Fang, Rui; Yuan, Yuan; Pan, Ming; Hu, Min; Zhou, Yanqin; Shen, Bang; Zhao, Junlong

2016-07-01

As an obligate intracellular protozoan, Toxoplasma gondii is a successful pathogen infecting a variety of animals, including humans. As an adhesin involving in host invasion, the micronemal protein MIC3 plays important roles in host cell attachment, as well as modulation of host EGFR signaling cascade. However, the specific host proteins that interact with MIC3 are unknown and the identification of such proteins will increase our understanding of how MIC3 exerts its functions. This study was designed to identify host proteins interacting with MIC3 by yeast two-hybrid screens. Using MIC3 as bait, a library expressing mouse proteins was screened, uncovering eight mouse proteins that showed positive interactions with MIC3. Two of which, spermatogenesis-associated protein 3 (Spata3) and dickkopf-related protein 2 (Dkk2), were further confirmed to interact with MIC3 by additional protein-protein interaction tests. The results also revealed that the tandem repeat EGF domains of MIC3 were critical in mediating the interactions with the identified host proteins. This is the first study to show that MIC3 interacts with host proteins that are involved in reproduction, growth, and development. The results will provide a clearer understanding of the functions of adhesion-associated micronemal proteins in T. gondii.

Identifying Interactions that Determine Fragment Binding at Protein Hotspots.

PubMed

Radoux, Chris J; Olsson, Tjelvar S G; Pitt, Will R; Groom, Colin R; Blundell, Tom L

2016-05-12

Locating a ligand-binding site is an important first step in structure-guided drug discovery, but current methods do little to suggest which interactions within a pocket are the most important for binding. Here we illustrate a method that samples atomic hotspots with simple molecular probes to produce fragment hotspot maps. These maps specifically highlight fragment-binding sites and their corresponding pharmacophores. For ligand-bound structures, they provide an intuitive visual guide within the binding site, directing medicinal chemists where to grow the molecule and alerting them to suboptimal interactions within the original hit. The fragment hotspot map calculation is validated using experimental binding positions of 21 fragments and subsequent lead molecules. The ligands are found in high scoring areas of the fragment hotspot maps, with fragment atoms having a median percentage rank of 97%. Protein kinase B and pantothenate synthetase are examined in detail. In each case, the fragment hotspot maps are able to rationalize a Free-Wilson analysis of SAR data from a fragment-based drug design project.

Toward a molecular understanding of nanoparticle-protein interactions.

PubMed

Treuel, Lennart; Nienhaus, Gerd Ulrich

2012-06-01

Wherever nanoparticles (NPs) come in contact with a living organism, physical and chemical interactions take place between the surfaces of the NPs and biomatter, in particular proteins. When NP are exposed to biological fluids, an adsorption layer of proteins, a "protein corona" forms around the NPs. Consequently, living systems interact with the protein-coated NP rather than with a bare NP. To anticipate biological responses to NPs, we thus require comprehensive knowledge of the interactions at the bio-nano interface. In recent years, a wide variety of biophysical techniques have been employed to elucidate mechanistic aspects of NP-protein interactions. In this brief review, we present the latest findings regarding the composition of the protein corona as it forms on NPs in the blood stream. We also discuss molecular aspects of this adsorption layer and its time evolution. The current state of knowledge is summarized, and issues that still need to be addressed to further advance our understanding of NP-protein interactions are identified.

Protein-protein interaction network-based detection of functionally similar proteins within species.

PubMed

Song, Baoxing; Wang, Fen; Guo, Yang; Sang, Qing; Liu, Min; Li, Dengyun; Fang, Wei; Zhang, Deli

2012-07-01

Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent. Copyright © 2012 Wiley Periodicals, Inc.

Identifying Bacterial Immune Evasion Proteins Using Phage Display.

PubMed

Fevre, Cindy; Scheepmaker, Lisette; Haas, Pieter-Jan

2017-01-01

Methods aimed at identification of immune evasion proteins are mainly rely on in silico prediction of sequence, structural homology to known evasion proteins or use a proteomics driven approach. Although proven successful these methods are limited by a low efficiency and or lack of functional identification. Here we describe a high-throughput genomic strategy to functionally identify bacterial immune evasion proteins using phage display technology. Genomic bacterial DNA is randomly fragmented and ligated into a phage display vector that is used to create a phage display library expressing bacterial secreted and membrane bound proteins. This library is used to select displayed bacterial secretome proteins that interact with host immune components.

Relevance of protein-protein interactions on the biological identity of nanoparticles.

PubMed

Vasti, Cecilia; Bonnet, Laura V; Galiano, Mauricio R; Rojas, Ricardo; Giacomelli, Carla E

2018-06-01

Considering that the use of nanoparticles (NPs) as carriers of therapeutic or theranostic agents has increased in the last years, it is mandatory to understand the interaction between NPs and living systems. In contact with biological fluids, the NPs (synthetic identity) are covered with biomolecules that form a protein corona, which defines the biological identity. It is well known that the protein corona formation is mediated by non-specific physical interactions, but protein-protein interactions (PPI), involving specific recognition sites of the polypeptides, are also involved. This work explores the relationship between the synthetic and biological identities of layered double hydroxides nanoparticles (LDH-NPs) and the effect of the protein corona on the cellular response. With such a purpose, the synthetic identity was modified by coating LDH-NPs with either a single protein or a complex mixture of them, followed by the characterization of the protein corona formed in a commonly used cell culture medium. A proteomic approach was used to identify the protein corona molecules and the PPI network was constructed with a novel bioinformatic tool. The coating on LDH-NPs defines the biological identity in such a way that the composition of the protein corona as well as PPI are changed. Electrostatic interactions appear not to be the only driving force regulating the interactions between NPs, proteins and cells since the specific recognition also play a fundamental role. However, the biological identity of LDH-NPs does not affect the interactions with cells that shows negligible cytotoxicity and high internalization levels. Copyright © 2018 Elsevier B.V. All rights reserved.

Prediction of Heterodimeric Protein Complexes from Weighted Protein-Protein Interaction Networks Using Novel Features and Kernel Functions

PubMed Central

Ruan, Peiying; Hayashida, Morihiro; Maruyama, Osamu; Akutsu, Tatsuya

2013-01-01

Since many proteins express their functional activity by interacting with other proteins and forming protein complexes, it is very useful to identify sets of proteins that form complexes. For that purpose, many prediction methods for protein complexes from protein-protein interactions have been developed such as MCL, MCODE, RNSC, PCP, RRW, and NWE. These methods have dealt with only complexes with size of more than three because the methods often are based on some density of subgraphs. However, heterodimeric protein complexes that consist of two distinct proteins occupy a large part according to several comprehensive databases of known complexes. In this paper, we propose several feature space mappings from protein-protein interaction data, in which each interaction is weighted based on reliability. Furthermore, we make use of prior knowledge on protein domains to develop feature space mappings, domain composition kernel and its combination kernel with our proposed features. We perform ten-fold cross-validation computational experiments. These results suggest that our proposed kernel considerably outperforms the naive Bayes-based method, which is the best existing method for predicting heterodimeric protein complexes. PMID:23776458

Surface energetics and protein-protein interactions: analysis and mechanistic implications

PubMed Central

Peri, Claudio; Morra, Giulia; Colombo, Giorgio

2016-01-01

Understanding protein-protein interactions (PPI) at the molecular level is a fundamental task in the design of new drugs, the prediction of protein function and the clarification of the mechanisms of (dis)regulation of biochemical pathways. In this study, we use a novel computational approach to investigate the energetics of aminoacid networks located on the surface of proteins, isolated and in complex with their respective partners. Interestingly, the analysis of individual proteins identifies patches of surface residues that, when mapped on the structure of their respective complexes, reveal regions of residue-pair couplings that extend across the binding interfaces, forming continuous motifs. An enhanced effect is visible across the proteins of the dataset forming larger quaternary assemblies. The method indicates the presence of energetic signatures in the isolated proteins that are retained in the bound form, which we hypothesize to determine binding orientation upon complex formation. We propose our method, BLUEPRINT, as a complement to different approaches ranging from the ab-initio characterization of PPIs, to protein-protein docking algorithms, for the physico-chemical and functional investigation of protein-protein interactions. PMID:27050828

Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

PubMed Central

Tan, Dan; Li, Qiang; Zhang, Mei-Jun; Liu, Chao; Ma, Chengying; Zhang, Pan; Ding, Yue-He; Fan, Sheng-Bo; Tao, Li; Yang, Bing; Li, Xiangke; Ma, Shoucai; Liu, Junjie; Feng, Boya; Liu, Xiaohui; Wang, Hong-Wei; He, Si-Min; Gao, Ning; Ye, Keqiong; Dong, Meng-Qiu; Lei, Xiaoguang

2016-01-01

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction. DOI: http://dx.doi.org/10.7554/eLife.12509.001 PMID:26952210

Studying Protein-Protein Interactions by Biotin AP-Tagged Pulldown and LTQ-Orbitrap Mass Spectrometry.

PubMed

Xie, Zhongqiu; Jia, Yuemeng; Li, Hui

2017-01-01

The study of protein-protein interactions represents a key aspect of biological research. Identifying unknown protein binding partners using mass spectrometry (MS)-based proteomics has evolved into an indispensable strategy in drug discovery. The classic approach of immunoprecipitation with specific antibodies against the proteins of interest has limitations, such as the need for immunoprecipitation-qualified antibody. The biotin AP-tag pull-down system has the advantage of high specificity, ease of use, and no requirement for antibody. It is based on the high specificity, high affinity interaction between biotin and streptavidin. After pulldown, in-gel tryptic digestion and tandem mass spectrometry (MS/MS) analysis of sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein bands can be performed. In this work, we provide protocols that can be used for the identification of proteins that interact with FOXM1, a protein that has recently emerged as a potential biomarker and drug target in oncotherapy, as an example. We focus on the pull-down procedure and assess the efficacy of the pulldown with known FOXM1 interactors such as β-catenin. We use a high performance LTQ Orbitrap MSn system that combines rapid LTQ ion trap data acquisition with high mass accuracy Orbitrap analysis to identify the interacting proteins.

PREFACE: Protein protein interactions: principles and predictions

NASA Astrophysics Data System (ADS)

Nussinov, Ruth; Tsai, Chung-Jung

2005-06-01

Proteins are the `workhorses' of the cell. Their roles span functions as diverse as being molecular machines and signalling. They carry out catalytic reactions, transport, form viral capsids, traverse membranes and form regulated channels, transmit information from DNA to RNA, making possible the synthesis of new proteins, and they are responsible for the degradation of unnecessary proteins and nucleic acids. They are the vehicles of the immune response and are responsible for viral entry into the cell. Given their importance, considerable effort has been centered on the prediction of protein function. A prime way to do this is through identification of binding partners. If the function of at least one of the components with which the protein interacts is known, that should let us assign its function(s) and the pathway(s) in which it plays a role. This holds since the vast majority of their chores in the living cell involve protein-protein interactions. Hence, through the intricate network of these interactions we can map cellular pathways, their interconnectivities and their dynamic regulation. Their identification is at the heart of functional genomics; their prediction is crucial for drug discovery. Knowledge of the pathway, its topology, length, and dynamics may provide useful information for forecasting side effects. The goal of predicting protein-protein interactions is daunting. Some associations are obligatory, others are continuously forming and dissociating. In principle, from the physical standpoint, any two proteins can interact, but under what conditions and at which strength? The principles of protein-protein interactions are general: the non-covalent interactions of two proteins are largely the outcome of the hydrophobic effect, which drives the interactions. In addition, hydrogen bonds and electrostatic interactions play important roles. Thus, many of the interactions observed in vitro are the outcome of experimental overexpression. Protein disorder

Monitoring Protein–Protein Interactions Using Split Synthetic Renilla Luciferase Protein-Fragment-Assisted Complementation

PubMed Central

Paulmurugan, R.; Gambhir, S. S.

2014-01-01

In this study we developed an inducible synthetic renilla luciferase protein-fragment-assisted complementation-based bioluminescence assay to quantitatively measure real time protein–protein interactions in mammalian cells. We identified suitable sites to generate fragments of N and C portions of the protein that yield significant recovered activity through complementation. We validate complementation-based activation of split synthetic renilla luciferase protein driven by the interaction of two strongly interacting proteins, MyoD and Id, in five different cell lines utilizing transient transfection studies. The expression level of the system was also modulated by tumor necrosis factor α through NFκB-promoter/enhancer elements used to drive expression of the N portion of synthetic renilla luciferase reporter gene. This new system should help in studying protein–protein interactions and when used with other split reporters (e.g., split firefly luciferase) should help to monitor different components of an intracellular network. PMID:12705589

Detecting protein-protein interactions using Renilla luciferase fusion proteins.

PubMed

Burbelo, Peter D; Kisailus, Adam E; Peck, Jeremy W

2002-11-01

We have developed a novel system designated the luciferase assay for protein detection (LAPD) to study protein-protein interactions. This method involves two protein fusions, a soluble reporter fusion and a fusion for immobilizing the target protein. The soluble reporter is an N-terminal Renilla luciferase fusion protein that exhibits high Renilla luciferase activity. Crude cleared lysates from transfected Cos1 cells that express the Renilla luciferase fusion protein can be used in binding assays with immobilized target proteins. Following incubation and washing, target-bound Renilla luciferase fusion proteins produce light from the coelenterazine substrate, indicating an interaction between the two proteins of interest. As proof of the principle, we reproduced known, transient protein-protein interactions between the Cdc42 GTPase and its effector proteins. GTPase Renilla fusion proteins produced in Cos1 cells were tested with immobilized recombinant GST-N-WASP and CEP5 effector proteins. Using this assay, we could detect specific interactions of Cdc42 with these effector proteins in approximately 50 min. The specificity of these interactions was demonstrated by showing that they were GTPase-specific and GTP-dependent and not seen with other unrelated target proteins. These results suggest that the LAPD method, which is both rapid and sensitive, may have research and practical applications.

Comparative analysis of protein-protein interactions in the defense response of rice and wheat.

PubMed

Cantu, Dario; Yang, Baoju; Ruan, Randy; Li, Kun; Menzo, Virginia; Fu, Daolin; Chern, Mawsheng; Ronald, Pamela C; Dubcovsky, Jorge

2013-03-12

Despite the importance of wheat as a major staple crop and the negative impact of diseases on its production worldwide, the genetic mechanisms and gene interactions involved in the resistance response in wheat are still poorly understood. The complete sequence of the rice genome has provided an extremely useful parallel road map for genetic and genomics studies in wheat. The recent construction of a defense response interactome in rice has the potential to further enhance the translation of advances in rice to wheat and other grasses. The objective of this study was to determine the degree of conservation in the protein-protein interactions in the rice and wheat defense response interactomes. As entry points we selected proteins that serve as key regulators of the rice defense response: the RAR1/SGT1/HSP90 protein complex, NPR1, XA21, and XB12 (XA21 interacting protein 12). Using available wheat sequence databases and phylogenetic analyses we identified and cloned the wheat orthologs of these four rice proteins, including recently duplicated paralogs, and their known direct interactors and tested 86 binary protein interactions using yeast-two-hybrid (Y2H) assays. All interactions between wheat proteins were further tested using in planta bimolecular fluorescence complementation (BiFC). Eighty three percent of the known rice interactions were confirmed when wheat proteins were tested with rice interactors and 76% were confirmed using wheat protein pairs. All interactions in the RAR1/SGT1/ HSP90, NPR1 and XB12 nodes were confirmed for the identified orthologous wheat proteins, whereas only forty four percent of the interactions were confirmed in the interactome node centered on XA21. We hypothesize that this reduction may be associated with a different sub-functionalization history of the multiple duplications that occurred in this gene family after the divergence of the wheat and rice lineages. The observed high conservation of interactions between proteins that

Identification of interacting proteins of the TaFVE protein involved in spike development in bread wheat.

PubMed

Zheng, Yong-Sheng; Lu, Yu-Qing; Meng, Ying-Ying; Zhang, Rong-Zhi; Zhang, Han; Sun, Jia-Mei; Wang, Mu-Mu; Li, Li-Hui; Li, Ru-Yu

2017-05-01

WD-40 repeat-containing protein MSI4 (FVE)/MSI4 plays important roles in determining flowering time in Arabidopsis. However, its function is unexplored in wheat. In the present study, coimmunoprecipitation and nanoscale liquid chromatography coupled to MS/MS were used to identify FVE in wheat (TaFVE)-interacting or associated proteins. Altogether 89 differentially expressed proteins showed the same downregulated expression trends as TaFVE in wheat line 5660M. Among them, 62 proteins were further predicted to be involved in the interaction network of TaFVE and 11 proteins have been shown to be potential TaFVE interactors based on curated databases and experimentally determined in other species by the STRING. Both yeast two-hybrid assay and bimolecular fluorescence complementation assay showed that histone deacetylase 6 and histone deacetylase 15 directly interacted with TaFVE. Multiple chromatin-remodelling proteins and polycomb group proteins were also identified and predicted to interact with TaFVE. These results showed that TaFVE directly interacted with multiple proteins to form multiple complexes to regulate spike developmental process, e.g. histone deacetylate, chromatin-remodelling and polycomb repressive complex 2 complexes. In addition, multiple flower development regulation factors (e.g. flowering locus K homology domain, flowering time control protein FPA, FY, flowering time control protein FCA, APETALA 1) involved in floral transition were also identified in the present study. Taken together, these results further elucidate the regulatory functions of TaFVE and help reveal the genetic mechanisms underlying wheat spike differentiation. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed Central

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-01-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. PMID:21666777

A Protein Preparation Method for the High-throughput Identification of Proteins Interacting with a Nuclear Cofactor Using LC-MS/MS Analysis.

PubMed

Tsuchiya, Megumi; Karim, M Rezaul; Matsumoto, Taro; Ogawa, Hidesato; Taniguchi, Hiroaki

2017-01-24

Transcriptional coregulators are vital to the efficient transcriptional regulation of nuclear chromatin structure. Coregulators play a variety of roles in regulating transcription. These include the direct interaction with transcription factors, the covalent modification of histones and other proteins, and the occasional chromatin conformation alteration. Accordingly, establishing relatively quick methods for identifying proteins that interact within this network is crucial to enhancing our understanding of the underlying regulatory mechanisms. LC-MS/MS-mediated protein binding partner identification is a validated technique used to analyze protein-protein interactions. By immunoprecipitating a previously-identified member of a protein complex with an antibody (occasionally with an antibody for a tagged protein), it is possible to identify its unknown protein interactions via mass spectrometry analysis. Here, we present a method of protein preparation for the LC-MS/MS-mediated high-throughput identification of protein interactions involving nuclear cofactors and their binding partners. This method allows for a better understanding of the transcriptional regulatory mechanisms of the targeted nuclear factors.

A systems wide mass spectrometric based linear motif screen to identify dominant in-vivo interacting proteins for the ubiquitin ligase MDM2.

PubMed

Nicholson, Judith; Scherl, Alex; Way, Luke; Blackburn, Elizabeth A; Walkinshaw, Malcolm D; Ball, Kathryn L; Hupp, Ted R

2014-06-01

Linear motifs mediate protein-protein interactions (PPI) that allow expansion of a target protein interactome at a systems level. This study uses a proteomics approach and linear motif sub-stratifications to expand on PPIs of MDM2. MDM2 is a multi-functional protein with over one hundred known binding partners not stratified by hierarchy or function. A new linear motif based on a MDM2 interaction consensus is used to select novel MDM2 interactors based on Nutlin-3 responsiveness in a cell-based proteomics screen. MDM2 binds a subset of peptide motifs corresponding to real proteins with a range of allosteric responses to MDM2 ligands. We validate cyclophilin B as a novel protein with a consensus MDM2 binding motif that is stabilised by Nutlin-3 in vivo, thus identifying one of the few known interactors of MDM2 that is stabilised by Nutlin-3. These data invoke two modes of peptide binding at the MDM2 N-terminus that rely on a consensus core motif to control the equilibrium between MDM2 binding proteins. This approach stratifies MDM2 interacting proteins based on the linear motif feature and provides a new biomarker assay to define clinically relevant Nutlin-3 responsive MDM2 interactors. Copyright © 2014 Elsevier Inc. All rights reserved.

Insight into bacterial virulence mechanisms against host immune response via the Yersinia pestis-human protein-protein interaction network.

PubMed

Yang, Huiying; Ke, Yuehua; Wang, Jian; Tan, Yafang; Myeni, Sebenzile K; Li, Dong; Shi, Qinghai; Yan, Yanfeng; Chen, Hui; Guo, Zhaobiao; Yuan, Yanzhi; Yang, Xiaoming; Yang, Ruifu; Du, Zongmin

2011-11-01

A Yersinia pestis-human protein interaction network is reported here to improve our understanding of its pathogenesis. Up to 204 interactions between 66 Y. pestis bait proteins and 109 human proteins were identified by yeast two-hybrid assay and then combined with 23 previously published interactions to construct a protein-protein interaction network. Topological analysis of the interaction network revealed that human proteins targeted by Y. pestis were significantly enriched in the proteins that are central in the human protein-protein interaction network. Analysis of this network showed that signaling pathways important for host immune responses were preferentially targeted by Y. pestis, including the pathways involved in focal adhesion, regulation of cytoskeleton, leukocyte transendoepithelial migration, and Toll-like receptor (TLR) and mitogen-activated protein kinase (MAPK) signaling. Cellular pathways targeted by Y. pestis are highly relevant to its pathogenesis. Interactions with host proteins involved in focal adhesion and cytoskeketon regulation pathways could account for resistance of Y. pestis to phagocytosis. Interference with TLR and MAPK signaling pathways by Y. pestis reflects common characteristics of pathogen-host interaction that bacterial pathogens have evolved to evade host innate immune response by interacting with proteins in those signaling pathways. Interestingly, a large portion of human proteins interacting with Y. pestis (16/109) also interacted with viral proteins (Epstein-Barr virus [EBV] and hepatitis C virus [HCV]), suggesting that viral and bacterial pathogens attack common cellular functions to facilitate infections. In addition, we identified vasodilator-stimulated phosphoprotein (VASP) as a novel interaction partner of YpkA and showed that YpkA could inhibit in vitro actin assembly mediated by VASP.

Split luciferase complementation assay to detect regulated protein-protein interactions in rice protoplasts in a large-scale format

PubMed Central

2014-01-01

Background The rice interactome, in which a network of protein-protein interactions has been elucidated in rice, is a useful resource to identify functional modules of rice signal transduction pathways. Protein-protein interactions occur in cells in two ways, constitutive and regulative. While a yeast-based high-throughput method has been widely used to identify the constitutive interactions, a method to detect the regulated interactions is rarely developed for a large-scale analysis. Results A split luciferase complementation assay was applied to detect the regulated interactions in rice. A transformation method of rice protoplasts in a 96-well plate was first established for a large-scale analysis. In addition, an antibody that specifically recognizes a carboxyl-terminal fragment of Renilla luciferase was newly developed. A pair of antibodies that recognize amino- and carboxyl- terminal fragments of Renilla luciferase, respectively, was then used to monitor quality and quantity of interacting recombinant-proteins accumulated in the cells. For a proof-of-concept, the method was applied to detect the gibberellin-dependent interaction between GIBBERELLIN INSENSITIVE DWARF1 and SLENDER RICE 1. Conclusions A method to detect regulated protein-protein interactions was developed towards establishment of the rice interactome. PMID:24987490

Manipulating fatty acid biosynthesis in microalgae for biofuel through protein-protein interactions.

PubMed

Blatti, Jillian L; Beld, Joris; Behnke, Craig A; Mendez, Michael; Mayfield, Stephen P; Burkart, Michael D

2012-01-01

Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP) and thioesterase (TE) govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr) as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes.

Manipulating Fatty Acid Biosynthesis in Microalgae for Biofuel through Protein-Protein Interactions

PubMed Central

Blatti, Jillian L.; Beld, Joris; Behnke, Craig A.; Mendez, Michael; Mayfield, Stephen P.; Burkart, Michael D.

2012-01-01

Microalgae are a promising feedstock for renewable fuels, and algal metabolic engineering can lead to crop improvement, thus accelerating the development of commercially viable biodiesel production from algae biomass. We demonstrate that protein-protein interactions between the fatty acid acyl carrier protein (ACP) and thioesterase (TE) govern fatty acid hydrolysis within the algal chloroplast. Using green microalga Chlamydomonas reinhardtii (Cr) as a model, a structural simulation of docking CrACP to CrTE identifies a protein-protein recognition surface between the two domains. A virtual screen reveals plant TEs with similar in silico binding to CrACP. Employing an activity-based crosslinking probe designed to selectively trap transient protein-protein interactions between the TE and ACP, we demonstrate in vitro that CrTE must functionally interact with CrACP to release fatty acids, while TEs of vascular plants show no mechanistic crosslinking to CrACP. This is recapitulated in vivo, where overproduction of the endogenous CrTE increased levels of short-chain fatty acids and engineering plant TEs into the C. reinhardtii chloroplast did not alter the fatty acid profile. These findings highlight the critical role of protein-protein interactions in manipulating fatty acid biosynthesis for algae biofuel engineering as illuminated by activity-based probes. PMID:23028438

Interaction entropy for protein-protein binding.

PubMed

Sun, Zhaoxi; Yan, Yu N; Yang, Maoyou; Zhang, John Z H

2017-03-28

Protein-protein interactions are at the heart of signal transduction and are central to the function of protein machine in biology. The highly specific protein-protein binding is quantitatively characterized by the binding free energy whose accurate calculation from the first principle is a grand challenge in computational biology. In this paper, we show how the interactionentropy approach, which was recently proposed for protein-ligand binding free energy calculation, can be applied to computing the entropic contribution to the protein-protein binding free energy. Explicit theoretical derivation of the interactionentropy approach for protein-protein interaction system is given in detail from the basic definition. Extensive computational studies for a dozen realistic protein-protein interaction systems are carried out using the present approach and comparisons of the results for these protein-protein systems with those from the standard normal mode method are presented. Analysis of the present method for application in protein-protein binding as well as the limitation of the method in numerical computation is discussed. Our study and analysis of the results provided useful information for extracting correct entropic contribution in protein-protein binding from molecular dynamics simulations.

Do cancer proteins really interact strongly in the human protein-protein interaction network?

PubMed

Xia, Junfeng; Sun, Jingchun; Jia, Peilin; Zhao, Zhongming

2011-06-01

Protein-protein interaction (PPI) network analysis has been widely applied in the investigation of the mechanisms of diseases, especially cancer. Recent studies revealed that cancer proteins tend to interact more strongly than other categories of proteins, even essential proteins, in the human interactome. However, it remains unclear whether this observation was introduced by the bias towards more cancer studies in humans. Here, we examined this important issue by uniquely comparing network characteristics of cancer proteins with three other sets of proteins in four organisms, three of which (fly, worm, and yeast) whose interactomes are essentially not biased towards cancer or other diseases. We confirmed that cancer proteins had stronger connectivity, shorter distance, and larger betweenness centrality than non-cancer disease proteins, essential proteins, and control proteins. Our statistical evaluation indicated that such observations were overall unlikely attributed to random events. Considering the large size and high quality of the PPI data in the four organisms, the conclusion that cancer proteins interact strongly in the PPI networks is reliable and robust. This conclusion suggests that perturbation of cancer proteins might cause major changes of cellular systems and result in abnormal cell function leading to cancer. © 2011 Elsevier Ltd. All rights reserved.

A Physical Interaction Network of Dengue Virus and Human Proteins*

PubMed Central

Khadka, Sudip; Vangeloff, Abbey D.; Zhang, Chaoying; Siddavatam, Prasad; Heaton, Nicholas S.; Wang, Ling; Sengupta, Ranjan; Sahasrabudhe, Sudhir; Randall, Glenn; Gribskov, Michael; Kuhn, Richard J.; Perera, Rushika; LaCount, Douglas J.

2011-01-01

Dengue virus (DENV), an emerging mosquito-transmitted pathogen capable of causing severe disease in humans, interacts with host cell factors to create a more favorable environment for replication. However, few interactions between DENV and human proteins have been reported to date. To identify DENV-human protein interactions, we used high-throughput yeast two-hybrid assays to screen the 10 DENV proteins against a human liver activation domain library. From 45 DNA-binding domain clones containing either full-length viral genes or partially overlapping gene fragments, we identified 139 interactions between DENV and human proteins, the vast majority of which are novel. These interactions involved 105 human proteins, including six previously implicated in DENV infection and 45 linked to the replication of other viruses. Human proteins with functions related to the complement and coagulation cascade, the centrosome, and the cytoskeleton were enriched among the DENV interaction partners. To determine if the cellular proteins were required for DENV infection, we used small interfering RNAs to inhibit their expression. Six of 12 proteins targeted (CALR, DDX3X, ERC1, GOLGA2, TRIP11, and UBE2I) caused a significant decrease in the replication of a DENV replicon. We further showed that calreticulin colocalized with viral dsRNA and with the viral NS3 and NS5 proteins in DENV-infected cells, consistent with a direct role for calreticulin in DENV replication. Human proteins that interacted with DENV had significantly higher average degree and betweenness than expected by chance, which provides additional support for the hypothesis that viruses preferentially target cellular proteins that occupy central position in the human protein interaction network. This study provides a valuable starting point for additional investigations into the roles of human proteins in DENV infection. PMID:21911577

Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

PubMed Central

Mazloom, Amin R.; Dannenfelser, Ruth; Clark, Neil R.; Grigoryan, Arsen V.; Linder, Kathryn M.; Cardozo, Timothy J.; Bond, Julia C.; Boran, Aislyn D. W.; Iyengar, Ravi; Malovannaya, Anna; Lanz, Rainer B.; Ma'ayan, Avi

2011-01-01

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/. PMID:22219718

Novel protein-protein interaction between spermidine synthase and S-adenosylmethionine decarboxylase from Leishmania donovani.

PubMed

Mishra, Arjun K; Agnihotri, Pragati; Srivastava, Vijay Kumar; Pratap, J Venkatesh

2015-01-09

Polyamine biosynthesis pathway has long been considered an essential drug target for trypanosomatids including Leishmania. S-adenosylmethionine decarboxylase (AdoMetDc) and spermidine synthase (SpdSyn) are enzymes of this pathway that catalyze successive steps, with the product of the former, decarboxylated S-adenosylmethionine (dcSAM), acting as an aminopropyl donor for the latter enzyme. Here we have explored the possibility of and identified the protein-protein interaction between SpdSyn and AdoMetDc. The protein-protein interaction has been identified using GST pull down assay. Isothermal titration calorimetry reveals that the interaction is thermodynamically favorable. Fluorescence spectroscopy studies also confirms the interaction, with SpdSyn exhibiting a change in tertiary structure with increasing concentrations of AdoMetDc. Size exclusion chromatography suggests the presence of the complex as a hetero-oligomer. Taken together, these results suggest that the enzymes indeed form a heteromer. Computational analyses suggest that this complex differs significantly from the corresponding human complex, implying that this complex could be a better therapeutic target than the individual enzymes. Copyright © 2014 Elsevier Inc. All rights reserved.

Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

PubMed

Zhang, Shu-Bo; Tang, Qiang-Rong

2016-07-21

Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. Copyright © 2016 Elsevier Ltd. All rights reserved.

HitPredict version 4: comprehensive reliability scoring of physical protein-protein interactions from more than 100 species.

PubMed

López, Yosvany; Nakai, Kenta; Patil, Ashwini

2015-01-01

HitPredict is a consolidated resource of experimentally identified, physical protein-protein interactions with confidence scores to indicate their reliability. The study of genes and their inter-relationships using methods such as network and pathway analysis requires high quality protein-protein interaction information. Extracting reliable interactions from most of the existing databases is challenging because they either contain only a subset of the available interactions, or a mixture of physical, genetic and predicted interactions. Automated integration of interactions is further complicated by varying levels of accuracy of database content and lack of adherence to standard formats. To address these issues, the latest version of HitPredict provides a manually curated dataset of 398 696 physical associations between 70 808 proteins from 105 species. Manual confirmation was used to resolve all issues encountered during data integration. For improved reliability assessment, this version combines a new score derived from the experimental information of the interactions with the original score based on the features of the interacting proteins. The combined interaction score performs better than either of the individual scores in HitPredict as well as the reliability score of another similar database. HitPredict provides a web interface to search proteins and visualize their interactions, and the data can be downloaded for offline analysis. Data usability has been enhanced by mapping protein identifiers across multiple reference databases. Thus, the latest version of HitPredict provides a significantly larger, more reliable and usable dataset of protein-protein interactions from several species for the study of gene groups. Database URL: http://hintdb.hgc.jp/htp. © The Author(s) 2015. Published by Oxford University Press.

Structure and Protein-Protein Interaction Studies on Chlamydia trachomatis Protein CT670 (YscO Homolog)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lorenzini, Emily; Singer, Alexander; Singh, Bhag

2010-07-28

Comparative genomic studies have identified many proteins that are found only in various Chlamydiae species and exhibit no significant sequence similarity to any protein in organisms that do not belong to this group. The CT670 protein of Chlamydia trachomatis is one of the proteins whose genes are in one of the type III secretion gene clusters but whose cellular functions are not known. CT670 shares several characteristics with the YscO protein of Yersinia pestis, including the neighboring genes, size, charge, and secondary structure, but the structures and/or functions of these proteins remain to be determined. Although a BLAST search withmore » CT670 did not identify YscO as a related protein, our analysis indicated that these two proteins exhibit significant sequence similarity. In this paper, we report that the CT670 crystal, solved at a resolution of 2 {angstrom}, consists of a single coiled coil containing just two long helices. Gel filtration and analytical ultracentrifugation studies showed that in solution CT670 exists in both monomeric and dimeric forms and that the monomer predominates at lower protein concentrations. We examined the interaction of CT670 with many type III secretion system-related proteins (viz., CT091, CT665, CT666, CT667, CT668, CT669, CT671, CT672, and CT673) by performing bacterial two-hybrid assays. In these experiments, CT670 was found to interact only with the CT671 protein (YscP homolog), whose gene is immediately downstream of ct670. A specific interaction between CT670 and CT671 was also observed when affinity chromatography pull-down experiments were performed. These results suggest that CT670 and CT671 are putative homologs of the YcoO and YscP proteins, respectively, and that they likely form a chaperone-effector pair.« less

NOXclass: prediction of protein-protein interaction types.

PubMed

Zhu, Hongbo; Domingues, Francisco S; Sommer, Ingolf; Lengauer, Thomas

2006-01-19

Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.

Toward a rigorous network of protein-protein interactions of the model sulfate reducer Desulfovibrio vulgaris Hildenborough

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chhabra, S.R.; Joachimiak, M.P.; Petzold, C.J.

Protein–protein interactions offer an insight into cellular processes beyond what may be obtained by the quantitative functional genomics tools of proteomics and transcriptomics. The aforementioned tools have been extensively applied to study E. coli and other aerobes and more recently to study the stress response behavior of Desulfovibrio 5 vulgaris Hildenborough, a model anaerobe and sulfate reducer. In this paper we present the first attempt to identify protein-protein interactions in an obligate anaerobic bacterium. We used suicide vector-assisted chromosomal modification of 12 open reading frames encoded by this sulfate reducer to append an eight amino acid affinity tag to themore » carboxy-terminus of the chosen proteins. Three biological replicates of the 10 ‘pulled-down’ proteins were separated and analyzed using liquid chromatography-mass spectrometry. Replicate agreement ranged between 35% and 69%. An interaction network among 12 bait and 90 prey proteins was reconstructed based on 134 bait-prey interactions computationally identified to be of high confidence. We discuss the biological significance of several unique metabolic features of D. vulgaris revealed by this protein-protein interaction data 15 and protein modifications that were observed. These include the distinct role of the putative carbon monoxide-induced hydrogenase, unique electron transfer routes associated with different oxidoreductases, and the possible role of methylation in regulating sulfate reduction.« less

DiffSLc: A graph centrality method to detect essential proteins of a protein-protein interaction network

USDA-ARS?s Scientific Manuscript database

Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures have been helpful in identifying critical genes and proteins in biomolecular networks. The proposed centrality measure DiffSLc uses the number of interactions of a protein and gen...

Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.

Huntingtin-associated protein-1 (HAP1) regulates endocytosis and interacts with multiple trafficking-related proteins.

PubMed

Mackenzie, Kimberly D; Lim, Yoon; Duffield, Michael D; Chataway, Timothy; Zhou, Xin-Fu; Keating, Damien J

2017-07-01

Huntingtin-associated protein 1 (HAP1) was initially identified as a binding partner of huntingtin, mutations in which underlie Huntington's disease. Subcellular localization and protein interaction data indicate that HAP1 may be important in vesicle trafficking, cell signalling and receptor internalization. In this study, a proteomics approach was used for the identification of novel HAP1-interacting partners to attempt to shed light on the physiological function of HAP1. Using affinity chromatography with HAP1-GST protein fragments bound to Sepharose columns, this study identified a number of trafficking-related proteins that bind to HAP1. Interestingly, many of the proteins that were identified by mass spectrometry have trafficking-related functions and include the clathrin light chain B and Sec23A, an ER to Golgi trafficking vesicle coat component. Using co-immunoprecipitation and GST-binding assays the association between HAP1 and clathrin light chain B has been validated in vitro. This study also finds that HAP1 co-localizes with clathrin light chain B. In line with a physiological function of the HAP1-clathrin interaction this study detected a dramatic reduction in vesicle retrieval and endocytosis in adrenal chromaffin cells. Furthermore, through examination of transferrin endocytosis in HAP1 -/- cortical neurons, this study has determined that HAP1 regulates neuronal endocytosis. In this study, the interaction between HAP1 and Sec23A was also validated through endogenous co-immunoprecipitation in rat brain homogenate. Through the identification of novel HAP1 binding partners, many of which have putative trafficking roles, this study provides us with new insights into the mechanisms underlying the important physiological function of HAP1 as an intracellular trafficking protein through its protein-protein interactions. Copyright © 2017 Elsevier Inc. All rights reserved.

Mining Host-Pathogen Protein Interactions to Characterize Burkholderia mallei Infectivity Mechanisms

PubMed Central

Memišević, Vesna; Zavaljevski, Nela; Rajagopala, Seesandra V.; Kwon, Keehwan; Pieper, Rembert; DeShazer, David; Reifman, Jaques; Wallqvist, Anders

2015-01-01

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections

Mining host-pathogen protein interactions to characterize Burkholderia mallei infectivity mechanisms.

PubMed

Memišević, Vesna; Zavaljevski, Nela; Rajagopala, Seesandra V; Kwon, Keehwan; Pieper, Rembert; DeShazer, David; Reifman, Jaques; Wallqvist, Anders

2015-03-01

Burkholderia pathogenicity relies on protein virulence factors to control and promote bacterial internalization, survival, and replication within eukaryotic host cells. We recently used yeast two-hybrid (Y2H) screening to identify a small set of novel Burkholderia proteins that were shown to attenuate disease progression in an aerosol infection animal model using the virulent Burkholderia mallei ATCC 23344 strain. Here, we performed an extended analysis of primarily nine B. mallei virulence factors and their interactions with human proteins to map out how the bacteria can influence and alter host processes and pathways. Specifically, we employed topological analyses to assess the connectivity patterns of targeted host proteins, identify modules of pathogen-interacting host proteins linked to processes promoting infectivity, and evaluate the effect of crosstalk among the identified host protein modules. Overall, our analysis showed that the targeted host proteins generally had a large number of interacting partners and interacted with other host proteins that were also targeted by B. mallei proteins. We also introduced a novel Host-Pathogen Interaction Alignment (HPIA) algorithm and used it to explore similarities between host-pathogen interactions of B. mallei, Yersinia pestis, and Salmonella enterica. We inferred putative roles of B. mallei proteins based on the roles of their aligned Y. pestis and S. enterica partners and showed that up to 73% of the predicted roles matched existing annotations. A key insight into Burkholderia pathogenicity derived from these analyses of Y2H host-pathogen interactions is the identification of eukaryotic-specific targeted cellular mechanisms, including the ubiquitination degradation system and the use of the focal adhesion pathway as a fulcrum for transmitting mechanical forces and regulatory signals. This provides the mechanisms to modulate and adapt the host-cell environment for the successful establishment of host infections

Molecular simulations of lipid-mediated protein-protein interactions.

PubMed

de Meyer, Frédérick Jean-Marie; Venturoli, Maddalena; Smit, Berend

2008-08-01

Recent experimental results revealed that lipid-mediated interactions due to hydrophobic forces may be important in determining the protein topology after insertion in the membrane, in regulating the protein activity, in protein aggregation and in signal transduction. To gain insight into the lipid-mediated interactions between two intrinsic membrane proteins, we developed a mesoscopic model of a lipid bilayer with embedded proteins, which we studied with dissipative particle dynamics. Our calculations of the potential of mean force between transmembrane proteins show that hydrophobic forces drive long-range protein-protein interactions and that the nature of these interactions depends on the length of the protein hydrophobic segment, on the three-dimensional structure of the protein and on the properties of the lipid bilayer. To understand the nature of the computed potentials of mean force, the concept of hydrophilic shielding is introduced. The observed protein interactions are interpreted as resulting from the dynamic reorganization of the system to maintain an optimal hydrophilic shielding of the protein and lipid hydrophobic parts, within the constraint of the flexibility of the components. Our results could lead to a better understanding of several membrane processes in which protein interactions are involved.

Mass spectrometric identification of proteins that interact through specific domains of the poly(A) binding protein

PubMed Central

Zhang, Chongxu; Nielsen, Maria E. O.; Chiang, Yueh-Chin; Kierkegaard, Morten; Wang, Xin; Lee, Darren J.; Andersen, Jens S.; Yao, Gang

2013-01-01

Poly(A) binding protein (PAB1) is involved in a number of RNA metabolic functions in eukaryotic cells and correspondingly is suggested to associate with a number of proteins. We have used mass spectrometric analysis to identify 55 non-ribosomal proteins that specifically interact with PAB1 from Saccharomyces cerevisiae. Because many of these factors may associate only indirectly with PAB1 by being components of the PAB1-mRNP structure, we additionally conducted mass spectrometric analyses on seven metabolically defined PAB1 deletion derivatives to delimit the interactions between these proteins and PAB1. These latter analyses identified 13 proteins whose associations with PAB1 were reduced by deleting one or another of PAB1’s defined domains. Included in this list of 13 proteins were the translation initiation factors eIF4G1 and eIF4G2, translation termination factor eRF3, and PBP2, all of whose previously known direct interactions with specific PAB1 domains were either confirmed, delimited, or extended. The remaining nine proteins that interacted through a specific PAB1 domain were CBF5, SLF1, UPF1, CBC1, SSD1, NOP77, yGR250c, NAB6, and GBP2. In further study, UPF1, involved in nonsense-mediated decay, was confirmed to interact with PAB1 through the RRM1 domain. We additionally established that while the RRM1 domain of PAB1 was required for UPF1-induced acceleration of deadenylation during nonsense-mediated decay, it was not required for the more critical step of acceleration of mRNA decapping. These results begin to identify the proteins most likely to interact with PAB1 and the domains of PAB1 through which these contacts are made. PMID:22836166

Mass spectrometric identification of proteins that interact through specific domains of the poly(A) binding protein.

PubMed

Richardson, Roy; Denis, Clyde L; Zhang, Chongxu; Nielsen, Maria E O; Chiang, Yueh-Chin; Kierkegaard, Morten; Wang, Xin; Lee, Darren J; Andersen, Jens S; Yao, Gang

2012-09-01

Poly(A) binding protein (PAB1) is involved in a number of RNA metabolic functions in eukaryotic cells and correspondingly is suggested to associate with a number of proteins. We have used mass spectrometric analysis to identify 55 non-ribosomal proteins that specifically interact with PAB1 from Saccharomyces cerevisiae. Because many of these factors may associate only indirectly with PAB1 by being components of the PAB1-mRNP structure, we additionally conducted mass spectrometric analyses on seven metabolically defined PAB1 deletion derivatives to delimit the interactions between these proteins and PAB1. These latter analyses identified 13 proteins whose associations with PAB1 were reduced by deleting one or another of PAB1's defined domains. Included in this list of 13 proteins were the translation initiation factors eIF4G1 and eIF4G2, translation termination factor eRF3, and PBP2, all of whose previously known direct interactions with specific PAB1 domains were either confirmed, delimited, or extended. The remaining nine proteins that interacted through a specific PAB1 domain were CBF5, SLF1, UPF1, CBC1, SSD1, NOP77, yGR250c, NAB6, and GBP2. In further study, UPF1, involved in nonsense-mediated decay, was confirmed to interact with PAB1 through the RRM1 domain. We additionally established that while the RRM1 domain of PAB1 was required for UPF1-induced acceleration of deadenylation during nonsense-mediated decay, it was not required for the more critical step of acceleration of mRNA decapping. These results begin to identify the proteins most likely to interact with PAB1 and the domains of PAB1 through which these contacts are made.

Application of linker technique to trap transiently interacting protein complexes for structural studies

PubMed Central

Reddy Chichili, Vishnu Priyanka; Kumar, Veerendra; Sivaraman, J.

2016-01-01

Protein-protein interactions are key events controlling several biological processes. We have developed and employed a method to trap transiently interacting protein complexes for structural studies using glycine-rich linkers to fuse interacting partners, one of which is unstructured. Initial steps involve isothermal titration calorimetry to identify the minimum binding region of the unstructured protein in its interaction with its stable binding partner. This is followed by computational analysis to identify the approximate site of the interaction and to design an appropriate linker length. Subsequently, fused constructs are generated and characterized using size exclusion chromatography and dynamic light scattering experiments. The structure of the chimeric protein is then solved by crystallization, and validated both in vitro and in vivo by substituting key interacting residues of the full length, unlinked proteins with alanine. This protocol offers the opportunity to study crucial and currently unattainable transient protein interactions involved in various biological processes. PMID:26985443

Predicting Human Protein Subcellular Locations by the Ensemble of Multiple Predictors via Protein-Protein Interaction Network with Edge Clustering Coefficients

PubMed Central

Du, Pufeng; Wang, Lusheng

2014-01-01

One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods. PMID:24466278

Protein profile and protein interaction network of Moniliophthora perniciosa basidiospores.

PubMed

Mares, Joise Hander; Gramacho, Karina Peres; Dos Santos, Everton Cruz; Santiago, André da Silva; Silva, Edson Mário de Andrade; Alvim, Fátima Cerqueira; Pirovani, Carlos Priminho

2016-06-24

Witches' broom, a disease caused by the basidiomycete Moniliophthora perniciosa, is considered to be the most important disease of the cocoa crop in Bahia, an area in the Brazilian Amazon, and also in the other countries where it is found. M. perniciosa germ tubes may penetrate into the host through intact or natural openings in the cuticle surface, in epidermis cell junctions, at the base of trichomes, or through the stomata. Despite its relevance to the fungal life cycle, basidiospore biology has not been extensively investigated. In this study, our goal was to optimize techniques for producing basidiospores for protein extraction, and to produce the first proteomics analysis map of ungerminated basidiospores. We then presented a protein interaction network by using Ustilago maydis as a model. The average pileus area ranged from 17.35 to 211.24 mm(2). The minimum and maximum productivity were 23,200 and 6,666,667 basidiospores per basidiome, respectively. The protein yield in micrograms per million basidiospores were approximately 0.161; 2.307, and 3.582 for germination times of 0, 2, and 4 h after germination, respectively. A total of 178 proteins were identified through mass spectrometry. These proteins were classified according to their molecular function and their involvement in biological processes such as cellular energy production, oxidative metabolism, stress, protein synthesis, and protein folding. Furthermore, to better understand the expression pattern, signaling, and interaction events of spore proteins, we presented an interaction network using orthologous proteins from Ustilago maydis as a model. Most of the orthologous proteins that were identified in this study were not clustered in the network, but several of them play a very important role in hypha development and branching. The quantities of basidiospores 7 × 10(9); 5.2 × 10(8), and 6.7 × 10(8) were sufficient to obtain enough protein mass for the three 2D-PAGE replicates, for

Prediction of physical protein protein interactions

NASA Astrophysics Data System (ADS)

Szilágyi, András; Grimm, Vera; Arakaki, Adrián K.; Skolnick, Jeffrey

2005-06-01

Many essential cellular processes such as signal transduction, transport, cellular motion and most regulatory mechanisms are mediated by protein-protein interactions. In recent years, new experimental techniques have been developed to discover the protein-protein interaction networks of several organisms. However, the accuracy and coverage of these techniques have proven to be limited, and computational approaches remain essential both to assist in the design and validation of experimental studies and for the prediction of interaction partners and detailed structures of protein complexes. Here, we provide a critical overview of existing structure-independent and structure-based computational methods. Although these techniques have significantly advanced in the past few years, we find that most of them are still in their infancy. We also provide an overview of experimental techniques for the detection of protein-protein interactions. Although the developments are promising, false positive and false negative results are common, and reliable detection is possible only by taking a consensus of different experimental approaches. The shortcomings of experimental techniques affect both the further development and the fair evaluation of computational prediction methods. For an adequate comparative evaluation of prediction and high-throughput experimental methods, an appropriately large benchmark set of biophysically characterized protein complexes would be needed, but is sorely lacking.

An Evolutionarily Conserved Innate Immunity Protein Interaction Network*

PubMed Central

De Arras, Lesly; Seng, Amara; Lackford, Brad; Keikhaee, Mohammad R.; Bowerman, Bruce; Freedman, Jonathan H.; Schwartz, David A.; Alper, Scott

2013-01-01

The innate immune response plays a critical role in fighting infection; however, innate immunity also can affect the pathogenesis of a variety of diseases, including sepsis, asthma, cancer, and atherosclerosis. To identify novel regulators of innate immunity, we performed comparative genomics RNA interference screens in the nematode Caenorhabditis elegans and mouse macrophages. These screens have uncovered many candidate regulators of the response to lipopolysaccharide (LPS), several of which interact physically in multiple species to form an innate immunity protein interaction network. This protein interaction network contains several proteins in the canonical LPS-responsive TLR4 pathway as well as many novel interacting proteins. Using RNAi and overexpression studies, we show that almost every gene in this network can modulate the innate immune response in mouse cell lines. We validate the importance of this network in innate immunity regulation in vivo using available mutants in C. elegans and mice. PMID:23209288

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.

PubMed

Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ohue, Masahito; Akiyama, Yutaka

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

From pull-down data to protein interaction networks and complexes with biological relevance.

PubMed

Zhang, Bing; Park, Byung-Hoon; Karpinets, Tatiana; Samatova, Nagiza F

2008-04-01

Recent improvements in high-throughput Mass Spectrometry (MS) technology have expedited genome-wide discovery of protein-protein interactions by providing a capability of detecting protein complexes in a physiological setting. Computational inference of protein interaction networks and protein complexes from MS data are challenging. Advances are required in developing robust and seamlessly integrated procedures for assessment of protein-protein interaction affinities, mathematical representation of protein interaction networks, discovery of protein complexes and evaluation of their biological relevance. A multi-step but easy-to-follow framework for identifying protein complexes from MS pull-down data is introduced. It assesses interaction affinity between two proteins based on similarity of their co-purification patterns derived from MS data. It constructs a protein interaction network by adopting a knowledge-guided threshold selection method. Based on the network, it identifies protein complexes and infers their core components using a graph-theoretical approach. It deploys a statistical evaluation procedure to assess biological relevance of each found complex. On Saccharomyces cerevisiae pull-down data, the framework outperformed other more complicated schemes by at least 10% in F(1)-measure and identified 610 protein complexes with high-functional homogeneity based on the enrichment in Gene Ontology (GO) annotation. Manual examination of the complexes brought forward the hypotheses on cause of false identifications. Namely, co-purification of different protein complexes as mediated by a common non-protein molecule, such as DNA, might be a source of false positives. Protein identification bias in pull-down technology, such as the hydrophilic bias could result in false negatives.

Global investigation of protein-protein interactions in yeast Saccharomyces cerevisiae using re-occurring short polypeptide sequences.

PubMed

Pitre, S; North, C; Alamgir, M; Jessulat, M; Chan, A; Luo, X; Green, J R; Dumontier, M; Dehne, F; Golshani, A

2008-08-01

Protein-protein interaction (PPI) maps provide insight into cellular biology and have received considerable attention in the post-genomic era. While large-scale experimental approaches have generated large collections of experimentally determined PPIs, technical limitations preclude certain PPIs from detection. Recently, we demonstrated that yeast PPIs can be computationally predicted using re-occurring short polypeptide sequences between known interacting protein pairs. However, the computational requirements and low specificity made this method unsuitable for large-scale investigations. Here, we report an improved approach, which exhibits a specificity of approximately 99.95% and executes 16,000 times faster. Importantly, we report the first all-to-all sequence-based computational screen of PPIs in yeast, Saccharomyces cerevisiae in which we identify 29,589 high confidence interactions of approximately 2 x 10(7) possible pairs. Of these, 14,438 PPIs have not been previously reported and may represent novel interactions. In particular, these results reveal a richer set of membrane protein interactions, not readily amenable to experimental investigations. From the novel PPIs, a novel putative protein complex comprised largely of membrane proteins was revealed. In addition, two novel gene functions were predicted and experimentally confirmed to affect the efficiency of non-homologous end-joining, providing further support for the usefulness of the identified PPIs in biological investigations.

Improvements in the Protein Identifier Cross-Reference service.

PubMed

Wein, Samuel P; Côté, Richard G; Dumousseau, Marine; Reisinger, Florian; Hermjakob, Henning; Vizcaíno, Juan A

2012-07-01

The Protein Identifier Cross-Reference (PICR) service is a tool that allows users to map protein identifiers, protein sequences and gene identifiers across over 100 different source databases. PICR takes input through an interactive website as well as Representational State Transfer (REST) and Simple Object Access Protocol (SOAP) services. It returns the results as HTML pages, XLS and CSV files. It has been in production since 2007 and has been recently enhanced to add new functionality and increase the number of databases it covers. Protein subsequences can be Basic Local Alignment Search Tool (BLAST) against the UniProt Knowledgebase (UniProtKB) to provide an entry point to the standard PICR mapping algorithm. In addition, gene identifiers from UniProtKB and Ensembl can now be submitted as input or mapped to as output from PICR. We have also implemented a 'best-guess' mapping algorithm for UniProt. In this article, we describe the usefulness of PICR, how these changes have been implemented, and the corresponding additions to the web services. Finally, we explain that the number of source databases covered by PICR has increased from the initial 73 to the current 102. New resources include several new species-specific Ensembl databases as well as the Ensembl Genome ones. PICR can be accessed at http://www.ebi.ac.uk/Tools/picr/.

Identification of proteins interacting with Toxoplasma SRCAP by yeast two-hybrid screening.

PubMed

Nallani, Karuna C; Sullivan, William J

2005-03-01

Toxoplasma gondii is an opportunistic protozoan parasite that differentiates into latent cysts (bradyzoite) that can be reactivated during immunosuppression. TgSRCAP (Toxoplasma gondii Snf2-related CBP activator protein) is a SWI2/SNF2 family chromatin remodeler whose expression increases during cyst development. Identifying the proteins associating with TgSRCAP during the pre-cyst stage (tachyzoite) will increase our understanding of how parasite differentiation is initiated. We employed the yeast two-hybrid system to identify proteins that may interact directly with TgSRCAP. A stretch of 1,060 amino acids between ATPase subdomains IV and V of TgSRCAP was chosen as "bait" since the corresponding region in human SRCAP interacts with other proteins, including CREB binding protein. We have identified several novel parasite-specific transcription factors predicted to be in the T. gondii genome. Metabolic enzymes that may participate in cyst development were also identified as interacting with TgSRCAP.

New partner proteins containing novel internal recognition motif for human Glutaminase Interacting Protein (hGIP)

PubMed Central

Zencir, Sevil; Banerjee, Monimoy; Dobson, Melanie J.; Ayaydin, Ferhan; Fodor, Elfrieda Ayaydin; Topcu, Zeki; Mohanty, Smita

2013-01-01

Regulation of gene expression in cells is mediated by protein-protein, DNA-protein and receptor-ligand interactions. PDZ (PSD-95/Discs-large/ZO-1) domains are protein–protein interaction modules. PDZ-containing proteins function in the organization of multi-protein complexes controlling spatial and temporal fidelity of intracellular signaling pathways. In general, PDZ proteins possess multiple domains facilitating distinct interactions. The human Glutaminase Interacting Protein (hGIP) is an unusual PDZ protein comprising entirely of a single PDZ domain and plays pivotal roles in many cellular processes through its interaction with the C-terminus of partner proteins. Here, we report the identification by yeast two-hybrid screening of two new hGIP-interacting partners, DTX1 and STAU1. Both proteins lack the typical C-terminal PDZ recognition motif but contain a novel internal hGIP recognition motif recently identified in a phage display library screen. Fluorescence resonance energy transfer and confocal microscopy analysis confirmed the in vivo association of hGIP with DTX1 and STAU1 in mammalian cells validating the previous discovery of S/T-X-V/L-D as a consensus internal motif for hGIP recognition. Similar to hGIP, DTX1 and STAU1 have been implicated in neuronal function. Identification of these new interacting partners furthers our understanding of GIP-regulated signaling cascades and these interactions may represent potential new drug targets in humans. PMID:23395680

DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

PubMed

Mistry, Divya; Wise, Roger P; Dickerson, Julie A

2017-01-01

Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

Unique secreted–surface protein complex of Lactobacillus rhamnosus, identified by phage display

PubMed Central

Gagic, Dragana; Wen, Wesley; Collett, Michael A; Rakonjac, Jasna

2013-01-01

Proteins are the most diverse structures on bacterial surfaces; hence, they are candidates for species- and strain-specific interactions of bacteria with the host, environment, and other microorganisms. Genomics has decoded thousands of bacterial surface and secreted proteins, yet the function of most cannot be predicted because of the enormous variability and a lack of experimental data that would allow deduction of function through homology. Here, we used phage display to identify a pair of interacting extracellular proteins in the probiotic bacterium Lactobacillus rhamnosus HN001. A secreted protein, SpcA, containing two bacterial immunoglobulin-like domains type 3 (Big-3) and a domain distantly related to plant pathogen response domain 1 (PR-1-like) was identified by screening of an L. rhamnosus HN001 library using HN001 cells as bait. The SpcA-“docking” protein, SpcB, was in turn detected by another phage display library screening, using purified SpcA as bait. SpcB is a 3275-residue cell-surface protein that contains general features of large glycosylated Serine-rich adhesins/fibrils from gram-positive bacteria, including the hallmark signal sequence motif KxYKxGKxW. Both proteins are encoded by genes within a L. rhamnosus-unique gene cluster that distinguishes this species from other lactobacilli. To our knowledge, this is the first example of a secreted-docking protein pair identified in lactobacilli. PMID:23233310

The essential and downstream common proteins of amyotrophic lateral sclerosis: A protein-protein interaction network analysis.

PubMed

Mao, Yimin; Kuo, Su-Wei; Chen, Le; Heckman, C J; Jiang, M C

2017-01-01

Amyotrophic Lateral Sclerosis (ALS) is a devastative neurodegenerative disease characterized by selective loss of motoneurons. While several breakthroughs have been made in identifying ALS genetic defects, the detailed molecular mechanisms are still unclear. These genetic defects involve in numerous biological processes, which converge to a common destiny: motoneuron degeneration. In addition, the common comorbid Frontotemporal Dementia (FTD) further complicates the investigation of ALS etiology. In this study, we aimed to explore the protein-protein interaction network built on known ALS-causative genes to identify essential proteins and common downstream proteins between classical ALS and ALS+FTD (classical ALS + ALS/FTD) groups. The results suggest that classical ALS and ALS+FTD share similar essential protein set (VCP, FUS, TDP-43 and hnRNPA1) but have distinctive functional enrichment profiles. Thus, disruptions to these essential proteins might cause motoneuron susceptible to cellular stresses and eventually vulnerable to proteinopathies. Moreover, we identified a common downstream protein, ubiquitin-C, extensively interconnected with ALS-causative proteins (22 out of 24) which was not linked to ALS previously. Our in silico approach provides the computational background for identifying ALS therapeutic targets, and points out the potential downstream common ground of ALS-causative mutations.

Coarse-Grained MD Simulations and Protein-Protein Interactions: The Cohesin-Dockerin System.

PubMed

Hall, Benjamin A; Sansom, Mark S P

2009-09-08

Coarse-grained molecular dynamics (CG-MD) may be applied as part of a multiscale modeling approach to protein-protein interactions. The cohesin-dockerin interaction provides a valuable test system for evaluation of the use of CG-MD, as structural (X-ray) data indicate a dual binding mode for the cohesin-dockerin pair. CG-MD simulations (of 5 μs duration) of the association of cohesin and dockerin identify two distinct binding modes, which resemble those observed in X-ray structures. For each binding mode, ca. 80% of interfacial residues are predicted correctly. Furthermore, each of the binding modes identified by CG-MD is conformationally stable when converted to an atomistic model and used as the basis of a conventional atomistic MD simulation of duration 20 ns.

Predicting protein-protein interactions from protein domains using a set cover approach.

PubMed

Huang, Chengbang; Morcos, Faruck; Kanaan, Simon P; Wuchty, Stefan; Chen, Danny Z; Izaguirre, Jesús A

2007-01-01

One goal of contemporary proteome research is the elucidation of cellular protein interactions. Based on currently available protein-protein interaction and domain data, we introduce a novel method, Maximum Specificity Set Cover (MSSC), for the prediction of protein-protein interactions. In our approach, we map the relationship between interactions of proteins and their corresponding domain architectures to a generalized weighted set cover problem. The application of a greedy algorithm provides sets of domain interactions which explain the presence of protein interactions to the largest degree of specificity. Utilizing domain and protein interaction data of S. cerevisiae, MSSC enables prediction of previously unknown protein interactions, links that are well supported by a high tendency of coexpression and functional homogeneity of the corresponding proteins. Focusing on concrete examples, we show that MSSC reliably predicts protein interactions in well-studied molecular systems, such as the 26S proteasome and RNA polymerase II of S. cerevisiae. We also show that the quality of the predictions is comparable to the Maximum Likelihood Estimation while MSSC is faster. This new algorithm and all data sets used are accessible through a Web portal at http://ppi.cse.nd.edu.

Interaction of structural core protein of Classical Swine Fever Virus with endoplasmic reticulum-associated degradation pathway protein OS9

USDA-ARS?s Scientific Manuscript database

Classical Swine Fever Virus (CSFV) Core protein is involved in virus RNA protection, transcription regulation and virus virulence. To discover additional Core protein functions a yeast two-hybrid system was used to identify host proteins that interact with Core. Among the identified host proteins, t...

The identification of protein domains that mediate functional interactions between Rab-GTPases and RabGAPs using 3D protein modeling.

PubMed

Davie, Jeremiah J; Faitar, Silviu L

2017-01-01

Currently, time-consuming serial in vitro experimentation involving immunocytochemistry or radiolabeled materials is required to identify which of the numerous Rab-GTPases (Rab) and Rab-GTPase activating proteins (RabGAP) are capable of functional interactions. These interactions are essential for numerous cellular functions, and in silico methods of reducing in vitro trial and error would accelerate the pace of research in cell biology. We have utilized a combination of three-dimensional protein modeling and protein bioinformatics to identify domains present in Rab proteins that are predictive of their functional interaction with a specific RabGAP. The RabF2 and RabSF1 domains appear to play functional roles in mediating the interaction between Rabs and RabGAPs. Moreover, the RabSF1 domain can be used to make in silico predictions of functional Rab/RabGAP pairs. This method is expected to be a broadly applicable tool for predicting protein-protein interactions where existing crystal structures for homologs of the proteins of interest are available.

Identification of New Protein Interactions between Dengue Fever Virus and Its Hosts, Human and Mosquito

PubMed Central

Mairiang, Dumrong; Zhang, Huamei; Sodja, Ann; Murali, Thilakam; Suriyaphol, Prapat; Malasit, Prida; Limjindaporn, Thawornchai; Finley, Russell L.

2013-01-01

The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host – dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies. PMID:23326450

Identification of new protein interactions between dengue fever virus and its hosts, human and mosquito.

PubMed

Mairiang, Dumrong; Zhang, Huamei; Sodja, Ann; Murali, Thilakam; Suriyaphol, Prapat; Malasit, Prida; Limjindaporn, Thawornchai; Finley, Russell L

2013-01-01

The four divergent serotypes of dengue virus are the causative agents of dengue fever, dengue hemorrhagic fever and dengue shock syndrome. About two-fifths of the world's population live in areas where dengue is prevalent, and thousands of deaths are caused by the viruses every year. Dengue virus is transmitted from one person to another primarily by the yellow fever mosquito, Aedes aegypti. Recent studies have begun to define how the dengue viral proteins interact with host proteins to mediate viral replication and pathogenesis. A combined analysis of these studies, however, suggests that many virus-host protein interactions remain to be identified, especially for the mosquito host. In this study, we used high-throughput yeast two-hybrid screening to identify mosquito and human proteins that physically interact with dengue proteins. We tested each identified host protein against the proteins from all four serotypes of dengue to identify interactions that are conserved across serotypes. We further confirmed many of the interactions using co-affinity purification assays. As in other large-scale screens, we identified some previously detected interactions and many new ones, moving us closer to a complete host - dengue protein interactome. To help summarize and prioritize the data for further study, we combined our interactions with other published data and identified a subset of the host-dengue interactions that are now supported by multiple forms of evidence. These data should be useful for understanding the interplay between dengue and its hosts and may provide candidates for drug targets and vector control strategies.

Genome-wide protein-protein interactions and protein function exploration in cyanobacteria

PubMed Central

Lv, Qi; Ma, Weimin; Liu, Hui; Li, Jiang; Wang, Huan; Lu, Fang; Zhao, Chen; Shi, Tieliu

2015-01-01

Genome-wide network analysis is well implemented to study proteins of unknown function. Here, we effectively explored protein functions and the biological mechanism based on inferred high confident protein-protein interaction (PPI) network in cyanobacteria. We integrated data from seven different sources and predicted 1,997 PPIs, which were evaluated by experiments in molecular mechanism, text mining of literatures in proved direct/indirect evidences, and “interologs” in conservation. Combined the predicted PPIs with known PPIs, we obtained 4,715 no-redundant PPIs (involving 3,231 proteins covering over 90% of genome) to generate the PPI network. Based on the PPI network, terms in Gene ontology (GO) were assigned to function-unknown proteins. Functional modules were identified by dissecting the PPI network into sub-networks and analyzing pathway enrichment, with which we investigated novel function of underlying proteins in protein complexes and pathways. Examples of photosynthesis and DNA repair indicate that the network approach is a powerful tool in protein function analysis. Overall, this systems biology approach provides a new insight into posterior functional analysis of PPIs in cyanobacteria. PMID:26490033

A novel assay to identify the trafficking proteins that bind to specific vesicle populations

PubMed Central

Bentley, Marvin; Banker, Gary

2016-01-01

Here we describe a method capable of identifying interactions between candidate trafficking proteins and a defined vesicle population in intact cells. The assay involves the expression of an FKBP12-rapamycin–binding domain (FRB)–tagged candidate vesicle-binding protein that can be inducibly linked to an FKBP-tagged molecular motor. If the FRB-tagged candidate protein binds the labeled vesicles, then linking the FRB and FKBP domains recruits motors to the vesicles and causes a predictable, highly distinctive change in vesicle trafficking. We describe two versions of the assay: a general protocol for use in cells with a typical microtubule-organizing center and a specialized protocol designed to detect protein-vesicle interactions in cultured neurons. We have successfully used this assay to identify kinesins and Rabs that bind to a variety of different vesicle populations. In principle, this assay could be used to investigate interactions between any category of vesicle trafficking proteins and any vesicle population that can be specifically labeled. PMID:26621371

The ChIP-exo Method: Identifying Protein-DNA Interactions with Near Base Pair Precision.

PubMed

Perreault, Andrea A; Venters, Bryan J

2016-12-23

Chromatin immunoprecipitation (ChIP) is an indispensable tool in the fields of epigenetics and gene regulation that isolates specific protein-DNA interactions. ChIP coupled to high throughput sequencing (ChIP-seq) is commonly used to determine the genomic location of proteins that interact with chromatin. However, ChIP-seq is hampered by relatively low mapping resolution of several hundred base pairs and high background signal. The ChIP-exo method is a refined version of ChIP-seq that substantially improves upon both resolution and noise. The key distinction of the ChIP-exo methodology is the incorporation of lambda exonuclease digestion in the library preparation workflow to effectively footprint the left and right 5' DNA borders of the protein-DNA crosslink site. The ChIP-exo libraries are then subjected to high throughput sequencing. The resulting data can be leveraged to provide unique and ultra-high resolution insights into the functional organization of the genome. Here, we describe the ChIP-exo method that we have optimized and streamlined for mammalian systems and next-generation sequencing-by-synthesis platform.

Protein- protein interaction detection system using fluorescent protein microdomains

DOEpatents

Waldo, Geoffrey S.; Cabantous, Stephanie

2010-02-23

The invention provides a protein labeling and interaction detection system based on engineered fragments of fluorescent and chromophoric proteins that require fused interacting polypeptides to drive the association of the fragments, and further are soluble and stable, and do not change the solubility of polypeptides to which they are fused. In one embodiment, a test protein X is fused to a sixteen amino acid fragment of GFP (.beta.-strand 10, amino acids 198-214), engineered to not perturb fusion protein solubility. A second test protein Y is fused to a sixteen amino acid fragment of GFP (.beta.-strand 11, amino acids 215-230), engineered to not perturb fusion protein solubility. When X and Y interact, they bring the GFP strands into proximity, and are detected by complementation with a third GFP fragment consisting of GFP amino acids 1-198 (strands 1-9). When GFP strands 10 and 11 are held together by interaction of protein X and Y, they spontaneous association with GFP strands 1-9, resulting in structural complementation, folding, and concomitant GFP fluorescence.

Amyloid precursor protein interaction network in human testis: sentinel proteins for male reproduction.

PubMed

Silva, Joana Vieira; Yoon, Sooyeon; Domingues, Sara; Guimarães, Sofia; Goltsev, Alexander V; da Cruz E Silva, Edgar Figueiredo; Mendes, José Fernando F; da Cruz E Silva, Odete Abreu Beirão; Fardilha, Margarida

2015-01-16

Amyloid precursor protein (APP) is widely recognized for playing a central role in Alzheimer's disease pathogenesis. Although APP is expressed in several tissues outside the human central nervous system, the functions of APP and its family members in other tissues are still poorly understood. APP is involved in several biological functions which might be potentially important for male fertility, such as cell adhesion, cell motility, signaling, and apoptosis. Furthermore, APP superfamily members are known to be associated with fertility. Knowledge on the protein networks of APP in human testis and spermatozoa will shed light on the function of APP in the male reproductive system. We performed a Yeast Two-Hybrid screen and a database search to study the interaction network of APP in human testis and sperm. To gain insights into the role of APP superfamily members in fertility, the study was extended to APP-like protein 2 (APLP2). We analyzed several topological properties of the APP interaction network and the biological and physiological properties of the proteins in the APP interaction network were also specified by gene ontologyand pathways analyses. We classified significant features related to the human male reproduction for the APP interacting proteins and identified modules of proteins with similar functional roles which may show cooperative behavior for male fertility. The present work provides the first report on the APP interactome in human testis. Our approach allowed the identification of novel interactions and recognition of key APP interacting proteins for male reproduction, particularly in sperm-oocyte interaction.

Identification of host cellular proteins that interact with the M protein of a highly pathogenic porcine reproductive and respiratory syndrome virus vaccine strain.

PubMed

Wang, Qian; Li, Yanwei; Dong, Hong; Wang, Li; Peng, Jinmei; An, Tongqing; Yang, Xufu; Tian, Zhijun; Cai, Xuehui

2017-02-22

The highly pathogenic porcine reproductive and respiratory syndrome virus (HP-PRRSV) continues to pose one of the greatest threats to the swine industry. M protein is the most conserved and important structural protein of PRRSV. However, information about the host cellular proteins that interact with M protein remains limited. Host cellular proteins that interact with the M protein of HP-PRRSV were immunoprecipitated from MARC-145 cells infected with PRRSV HuN4-F112 using the M monoclonal antibody (mAb). The differentially expressed proteins were identified by LC-MS/MS. The screened proteins were used for bioinformatics analysis including Gene Ontology, the interaction network, and the enriched KEGG pathways. Some interested cellular proteins were validated to interact with M protein by CO-IP. The PRRSV HuN4-F112 infection group had 10 bands compared with the control group. The bands included 219 non-redundant cellular proteins that interact with M protein, which were identified by LC-MS/MS with high confidence. The gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway bioinformatic analyses indicated that the identified proteins could be assigned to several different subcellular locations and functional classes. Functional analysis of the interactome profile highlighted cellular pathways associated with protein translation, infectious disease, and signal transduction. Two interested cellular proteins-nuclear factor of activated T cells 45 kDa (NF45) and proliferating cell nuclear antigen (PCNA)-that could interact with M protein were validated by Co-IP and confocal analyses. The interactome data between PRRSV M protein and cellular proteins were identified and contribute to the understanding of the roles of M protein in the replication and pathogenesis of PRRSV. The interactome of M protein will aid studies of virus/host interactions and provide means to decrease the threat of PRRSV to the swine industry in the future.

Integrative Analysis of GWASs, Human Protein Interaction, and Gene Expression Identified Gene Modules Associated With BMDs

PubMed Central

He, Hao; Zhang, Lei; Li, Jian; Wang, Yu-Ping; Zhang, Ji-Gang; Shen, Jie; Guo, Yan-Fang

2014-01-01

Context: To date, few systems genetics studies in the bone field have been performed. We designed our study from a systems-level perspective by integrating genome-wide association studies (GWASs), human protein-protein interaction (PPI) network, and gene expression to identify gene modules contributing to osteoporosis risk. Methods: First we searched for modules significantly enriched with bone mineral density (BMD)-associated genes in human PPI network by using 2 large meta-analysis GWAS datasets through a dense module search algorithm. One included 7 individual GWAS samples (Meta7). The other was from the Genetic Factors for Osteoporosis Consortium (GEFOS2). One was assigned as a discovery dataset and the other as an evaluation dataset, and vice versa. Results: In total, 42 modules and 129 modules were identified significantly in both Meta7 and GEFOS2 datasets for femoral neck and spine BMD, respectively. There were 3340 modules identified for hip BMD only in Meta7. As candidate modules, they were assessed for the biological relevance to BMD by gene set enrichment analysis in 2 expression profiles generated from circulating monocytes in subjects with low versus high BMD values. Interestingly, there were 2 modules significantly enriched in monocytes from the low BMD group in both gene expression datasets (nominal P value <.05). Two modules had 16 nonredundant genes. Functional enrichment analysis revealed that both modules were enriched for genes involved in Wnt receptor signaling and osteoblast differentiation. Conclusion: We highlighted 2 modules and novel genes playing important roles in the regulation of bone mass, providing important clues for therapeutic approaches for osteoporosis. PMID:25119315

Molecular modelling of protein-protein/protein-solvent interactions

NASA Astrophysics Data System (ADS)

Luchko, Tyler

The inner workings of individual cells are based on intricate networks of protein-protein interactions. However, each of these individual protein interactions requires a complex physical interaction between proteins and their aqueous environment at the atomic scale. In this thesis, molecular dynamics simulations are used in three theoretical studies to gain insight at the atomic scale about protein hydration, protein structure and tubulin-tubulin (protein-protein) interactions, as found in microtubules. Also presented, in a fourth project, is a molecular model of solvation coupled with the Amber molecular modelling package, to facilitate further studies without the need of explicitly modelled water. Basic properties of a minimally solvated protein were calculated through an extended study of myoglobin hydration with explicit solvent, directly investigating water and protein polarization. Results indicate a close correlation between polarization of both water and protein and the onset of protein function. The methodology of explicit solvent molecular dynamics was further used to study tubulin and microtubules. Extensive conformational sampling of the carboxy-terminal tails of 8-tubulin was performed via replica exchange molecular dynamics, allowing the characterisation of the flexibility, secondary structure and binding domains of the C-terminal tails through statistical analysis methods. Mechanical properties of tubulin and microtubules were calculated with adaptive biasing force molecular dynamics. The function of the M-loop in microtubule stability was demonstrated in these simulations. The flexibility of this loop allowed constant contacts between the protofilaments to be maintained during simulations while the smooth deformation provided a spring-like restoring force. Additionally, calculating the free energy profile between the straight and bent tubulin configurations was used to test the proposed conformational change in tubulin, thought to cause microtubule

A protein interaction map for cell polarity development

PubMed Central

Drees, Becky L.; Sundin, Bryan; Brazeau, Elizabeth; Caviston, Juliane P.; Chen, Guang-Chao; Guo, Wei; Kozminski, Keith G.; Lau, Michelle W.; Moskow, John J.; Tong, Amy; Schenkman, Laura R.; McKenzie, Amos; Brennwald, Patrick; Longtine, Mark; Bi, Erfei; Chan, Clarence; Novick, Peter; Boone, Charles; Pringle, John R.; Davis, Trisha N.; Fields, Stanley; Drubin, David G.

2001-01-01

Many genes required for cell polarity development in budding yeast have been identified and arranged into a functional hierarchy. Core elements of the hierarchy are widely conserved, underlying cell polarity development in diverse eukaryotes. To enumerate more fully the protein–protein interactions that mediate cell polarity development, and to uncover novel mechanisms that coordinate the numerous events involved, we carried out a large-scale two-hybrid experiment. 68 Gal4 DNA binding domain fusions of yeast proteins associated with the actin cytoskeleton, septins, the secretory apparatus, and Rho-type GTPases were used to screen an array of yeast transformants that express ∼90% of the predicted Saccharomyces cerevisiae open reading frames as Gal4 activation domain fusions. 191 protein–protein interactions were detected, of which 128 had not been described previously. 44 interactions implicated 20 previously uncharacterized proteins in cell polarity development. Further insights into possible roles of 13 of these proteins were revealed by their multiple two-hybrid interactions and by subcellular localization. Included in the interaction network were associations of Cdc42 and Rho1 pathways with proteins involved in exocytosis, septin organization, actin assembly, microtubule organization, autophagy, cytokinesis, and cell wall synthesis. Other interactions suggested direct connections between Rho1- and Cdc42-regulated pathways; the secretory apparatus and regulators of polarity establishment; actin assembly and the morphogenesis checkpoint; and the exocytic and endocytic machinery. In total, a network of interactions that provide an integrated response of signaling proteins, the cytoskeleton, and organelles to the spatial cues that direct polarity development was revealed. PMID:11489916

A Computational Investigation of Small-Molecule Engagement of Hot Spots at Protein-Protein Interaction Interfaces.

PubMed

Xu, David; Si, Yubing; Meroueh, Samy O

2017-09-25

The binding affinity of a protein-protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein-protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein-protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein-protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2Rα. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein-protein interactions do not optimally mimic protein-ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials.

Identification of proteins that may directly interact with human RPA.

PubMed

Nakaya, Ryou; Takaya, Junichiro; Onuki, Takeshi; Moritani, Mariko; Nozaki, Naohito; Ishimi, Yukio

2010-11-01

RPA, which consisted of three subunits (RPA1, 2 and 3), plays essential roles in DNA transactions. At the DNA replication forks, RPA binds to single-stranded DNA region to stabilize the structure and to assemble other replication proteins. Interactions between RPA and several replication proteins have been reported but the analysis is not comprehensive. We systematically performed the qualitative analysis to identify RPA interaction partners to understand the protein-protein interaction at the replication forks. We expressed in insect cells the three subunits of human RPA, together with one replication protein, which is present at the forks under normal conditions and/or under the replication stress conditions, to examine the interaction. Among 30 proteins examined in total, it was found that at least 14 proteins interacted with RPA. RPA interacted with MCM3-7, MCM-BP and CDC45 proteins among the proteins that play roles in the initiation and the elongation of the DNA replication. RPA bound with TIPIN, CLASPIN and RAD17, which are involved in the DNA replication checkpoint functions. RPA also bound with cyclin-dependent kinases and an amino-terminal fragment of Rb protein that negatively regulates DNA replication. These results suggest that RPA interacts with the specific proteins among those that play roles in the regulation of the replication fork progression.

Noninvasive imaging of protein-protein interactions in living animals

NASA Astrophysics Data System (ADS)

Luker, Gary D.; Sharma, Vijay; Pica, Christina M.; Dahlheimer, Julie L.; Li, Wei; Ochesky, Joseph; Ryan, Christine E.; Piwnica-Worms, Helen; Piwnica-Worms, David

2002-05-01

Protein-protein interactions control transcription, cell division, and cell proliferation as well as mediate signal transduction, oncogenic transformation, and regulation of cell death. Although a variety of methods have been used to investigate protein interactions in vitro and in cultured cells, none can analyze these interactions in intact, living animals. To enable noninvasive molecular imaging of protein-protein interactions in vivo by positron-emission tomography and fluorescence imaging, we engineered a fusion reporter gene comprising a mutant herpes simplex virus 1 thymidine kinase and green fluorescent protein for readout of a tetracycline-inducible, two-hybrid system in vivo. By using micro-positron-emission tomography, interactions between p53 tumor suppressor and the large T antigen of simian virus 40 were visualized in tumor xenografts of HeLa cells stably transfected with the imaging constructs. Imaging protein-binding partners in vivo will enable functional proteomics in whole animals and provide a tool for screening compounds targeted to specific protein-protein interactions in living animals.

Topology and weights in a protein domain interaction network--a novel way to predict protein interactions.

PubMed

Wuchty, Stefan

2006-05-23

While the analysis of unweighted biological webs as diverse as genetic, protein and metabolic networks allowed spectacular insights in the inner workings of a cell, biological networks are not only determined by their static grid of links. In fact, we expect that the heterogeneity in the utilization of connections has a major impact on the organization of cellular activities as well. We consider a web of interactions between protein domains of the Protein Family database (PFAM), which are weighted by a probability score. We apply metrics that combine the static layout and the weights of the underlying interactions. We observe that unweighted measures as well as their weighted counterparts largely share the same trends in the underlying domain interaction network. However, we only find weak signals that weights and the static grid of interactions are connected entities. Therefore assuming that a protein interaction is governed by a single domain interaction, we observe strong and significant correlations of the highest scoring domain interaction and the confidence of protein interactions in the underlying interactions of yeast and fly. Modeling an interaction between proteins if we find a high scoring protein domain interaction we obtain 1, 428 protein interactions among 361 proteins in the human malaria parasite Plasmodium falciparum. Assessing their quality by a logistic regression method we observe that increasing confidence of predicted interactions is accompanied by high scoring domain interactions and elevated levels of functional similarity and evolutionary conservation. Our results indicate that probability scores are randomly distributed, allowing to treat static grid and weights of domain interactions as separate entities. In particular, these finding confirms earlier observations that a protein interaction is a matter of a single interaction event on domain level. As an immediate application, we show a simple way to predict potential protein interactions

Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases.

PubMed

Berger, Seth I; Posner, Jeremy M; Ma'ayan, Avi

2007-10-04

In recent years, mammalian protein-protein interaction network databases have been developed. The interactions in these databases are either extracted manually from low-throughput experimental biomedical research literature, extracted automatically from literature using techniques such as natural language processing (NLP), generated experimentally using high-throughput methods such as yeast-2-hybrid screens, or interactions are predicted using an assortment of computational approaches. Genes or proteins identified as significantly changing in proteomic experiments, or identified as susceptibility disease genes in genomic studies, can be placed in the context of protein interaction networks in order to assign these genes and proteins to pathways and protein complexes. Genes2Networks is a software system that integrates the content of ten mammalian interaction network datasets. Filtering techniques to prune low-confidence interactions were implemented. Genes2Networks is delivered as a web-based service using AJAX. The system can be used to extract relevant subnetworks created from "seed" lists of human Entrez gene symbols. The output includes a dynamic linkable three color web-based network map, with a statistical analysis report that identifies significant intermediate nodes used to connect the seed list. Genes2Networks is powerful web-based software that can help experimental biologists to interpret lists of genes and proteins such as those commonly produced through genomic and proteomic experiments, as well as lists of genes and proteins associated with disease processes. This system can be used to find relationships between genes and proteins from seed lists, and predict additional genes or proteins that may play key roles in common pathways or protein complexes.

An ontology-based search engine for protein-protein interactions.

PubMed

Park, Byungkyu; Han, Kyungsook

2010-01-18

Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology.

A Global Protein Kinase and Phosphatase Interaction Network in Yeast

PubMed Central

Breitkreutz, Ashton; Choi, Hyungwon; Sharom, Jeffrey R.; Boucher, Lorrie; Neduva, Victor; Larsen, Brett; Lin, Zhen-Yuan; Breitkreutz, Bobby-Joe; Stark, Chris; Liu, Guomin; Ahn, Jessica; Dewar-Darch, Danielle; Reguly, Teresa; Tang, Xiaojing; Almeida, Ricardo; Qin, Zhaohui Steve; Pawson, Tony; Gingras, Anne-Claude; Nesvizhskii, Alexey I.; Tyers, Mike

2011-01-01

The interactions of protein kinases and phosphatases with their regulatory subunits and substrates underpin cellular regulation. We identified a kinase and phosphatase interaction (KPI) network of 1844 interactions in budding yeast by mass spectrometric analysis of protein complexes. The KPI network contained many dense local regions of interactions that suggested new functions. Notably, the cell cycle phosphatase Cdc14 associated with multiple kinases that revealed roles for Cdc14 in mitogen-activated protein kinase signaling, the DNA damage response, and metabolism, whereas interactions of the target of rapamycin complex 1 (TORC1) uncovered new effector kinases in nitrogen and carbon metabolism. An extensive backbone of kinase-kinase interactions cross-connects the proteome and may serve to coordinate diverse cellular responses. PMID:20489023

Building protein-protein interaction networks for Leishmania species through protein structural information.

PubMed

Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

2018-03-06

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

Protein interactions in 3D: from interface evolution to drug discovery.

PubMed

Winter, Christof; Henschel, Andreas; Tuukkanen, Anne; Schroeder, Michael

2012-09-01

Over the past 10years, much research has been dedicated to the understanding of protein interactions. Large-scale experiments to elucidate the global structure of protein interaction networks have been complemented by detailed studies of protein interaction interfaces. Understanding the evolution of interfaces allows one to identify convergently evolved interfaces which are evolutionary unrelated but share a few key residues and hence have common binding partners. Understanding interaction interfaces and their evolution is an important basis for pharmaceutical applications in drug discovery. Here, we review the algorithms and databases on 3D protein interactions and discuss in detail applications in interface evolution, drug discovery, and interface prediction. Copyright © 2012 Elsevier Inc. All rights reserved.

Characterization of essential proteins based on network topology in proteins interaction networks

NASA Astrophysics Data System (ADS)

Bakar, Sakhinah Abu; Taheri, Javid; Zomaya, Albert Y.

2014-06-01

The identification of essential proteins is theoretically and practically important as (1) it is essential to understand the minimal surviving requirements for cellular lives, and (2) it provides fundamental for development of drug. As conducting experimental studies to identify essential proteins are both time and resource consuming, here we present a computational approach in predicting them based on network topology properties from protein-protein interaction networks of Saccharomyces cerevisiae. The proposed method, namely EP3NN (Essential Proteins Prediction using Probabilistic Neural Network) employed a machine learning algorithm called Probabilistic Neural Network as a classifier to identify essential proteins of the organism of interest; it uses degree centrality, closeness centrality, local assortativity and local clustering coefficient of each protein in the network for such predictions. Results show that EP3NN managed to successfully predict essential proteins with an accuracy of 95% for our studied organism. Results also show that most of the essential proteins are close to other proteins, have assortativity behavior and form clusters/sub-graph in the network.

Identification of Nuclear Phosphatidylinositol 4,5-Bisphosphate-Interacting Proteins by Neomycin Extraction*

PubMed Central

Lewis, Aurélia E.; Sommer, Lilly; Arntzen, Magnus Ø.; Strahm, Yvan; Morrice, Nicholas A.; Divecha, Nullin; D'Santos, Clive S.

2011-01-01

Considerable insight into phosphoinositide-regulated cytoplasmic functions has been gained by identifying phosphoinositide-effector proteins. Phosphoinositide-regulated nuclear functions however are fewer and less clear. To address this, we established a proteomic method based on neomycin extraction of intact nuclei to enrich for nuclear phosphoinositide-effector proteins. We identified 168 proteins harboring phosphoinositide-binding domains. Although the vast majority of these contained lysine/arginine-rich patches with the following motif, K/R-(Xn = 3–7)-K-X-K/R-K/R, we also identified a smaller subset of known phosphoinositide-binding proteins containing pleckstrin homology or plant homeodomain modules. Proteins with no prior history of phosphoinositide interaction were identified, some of which have functional roles in RNA splicing and processing and chromatin assembly. The remaining proteins represent potentially other novel nuclear phosphoinositide-effector proteins and as such strengthen our appreciation of phosphoinositide-regulated nuclear functions. DNA topology was exemplar among these: Biochemical assays validated our proteomic data supporting a direct interaction between phosphatidylinositol 4,5-bisphosphate and DNA Topoisomerase IIα. In addition, a subset of neomycin extracted proteins were further validated as phosphatidyl 4,5-bisphosphate-interacting proteins by quantitative lipid pull downs. In summary, data sets such as this serve as a resource for a global view of phosphoinositide-regulated nuclear functions. PMID:21048195

A feature-based approach to modeling protein-protein interaction hot spots.

PubMed

Cho, Kyu-il; Kim, Dongsup; Lee, Doheon

2009-05-01

Identifying features that effectively represent the energetic contribution of an individual interface residue to the interactions between proteins remains problematic. Here, we present several new features and show that they are more effective than conventional features. By combining the proposed features with conventional features, we develop a predictive model for interaction hot spots. Initially, 54 multifaceted features, composed of different levels of information including structure, sequence and molecular interaction information, are quantified. Then, to identify the best subset of features for predicting hot spots, feature selection is performed using a decision tree. Based on the selected features, a predictive model for hot spots is created using support vector machine (SVM) and tested on an independent test set. Our model shows better overall predictive accuracy than previous methods such as the alanine scanning methods Robetta and FOLDEF, and the knowledge-based method KFC. Subsequent analysis yields several findings about hot spots. As expected, hot spots have a larger relative surface area burial and are more hydrophobic than other residues. Unexpectedly, however, residue conservation displays a rather complicated tendency depending on the types of protein complexes, indicating that this feature is not good for identifying hot spots. Of the selected features, the weighted atomic packing density, relative surface area burial and weighted hydrophobicity are the top 3, with the weighted atomic packing density proving to be the most effective feature for predicting hot spots. Notably, we find that hot spots are closely related to pi-related interactions, especially pi . . . pi interactions.

Bacteriophage Protein–Protein Interactions

PubMed Central

Häuser, Roman; Blasche, Sonja; Dokland, Terje; Haggård-Ljungquist, Elisabeth; von Brunn, Albrecht; Salas, Margarita; Casjens, Sherwood; Molineux, Ian

2012-01-01

Bacteriophages T7, λ, P22, and P2/P4 (from Escherichia coli), as well as ϕ29 (from Bacillus subtilis), are among the best-studied bacterial viruses. This chapter summarizes published protein interaction data of intraviral protein interactions, as well as known phage–host protein interactions of these phages retrieved from the literature. We also review the published results of comprehensive protein interaction analyses of Pneumococcus phages Dp-1 and Cp-1, as well as coliphages λ and T7. For example, the ≈55 proteins encoded by the T7 genome are connected by ≈43 interactions with another ≈15 between the phage and its host. The chapter compiles published interactions for the well-studied phages λ (33 intra-phage/22 phage-host), P22 (38/9), P2/P4 (14/3), and ϕ29 (20/2). We discuss whether different interaction patterns reflect different phage lifestyles or whether they may be artifacts of sampling. Phages that infect the same host can interact with different host target proteins, as exemplified by E. coli phage λ and T7. Despite decades of intensive investigation, only a fraction of these phage interactomes are known. Technical limitations and a lack of depth in many studies explain the gaps in our knowledge. Strategies to complete current interactome maps are described. Although limited space precludes detailed overviews of phage molecular biology, this compilation will allow future studies to put interaction data into the context of phage biology. PMID:22748812

Protein-protein interactions and metabolite channelling in the plant tricarboxylic acid cycle

PubMed Central

Zhang, Youjun; Beard, Katherine F. M.; Swart, Corné; Bergmann, Susan; Krahnert, Ina; Nikoloski, Zoran; Graf, Alexander; Ratcliffe, R. George; Sweetlove, Lee J.; Fernie, Alisdair R.; Obata, Toshihiro

2017-01-01

Protein complexes of sequential metabolic enzymes, often termed metabolons, may permit direct channelling of metabolites between the enzymes, providing increased control over metabolic pathway fluxes. Experimental evidence supporting their existence in vivo remains fragmentary. In the present study, we test binary interactions of the proteins constituting the plant tricarboxylic acid (TCA) cycle. We integrate (semi-)quantitative results from affinity purification-mass spectrometry, split-luciferase and yeast-two-hybrid assays to generate a single reliability score for assessing protein–protein interactions. By this approach, we identify 158 interactions including those between catalytic subunits of sequential enzymes and between subunits of enzymes mediating non-adjacent reactions. We reveal channelling of citrate and fumarate in isolated potato mitochondria by isotope dilution experiments. These results provide evidence for a functional TCA cycle metabolon in plants, which we discuss in the context of contemporary understanding of this pathway in other kingdoms. PMID:28508886

Specific RNA-protein interactions detected with saturation transfer difference NMR.

PubMed

Harris, Kimberly A; Shekhtman, Alexander; Agris, Paul F

2013-08-01

RNA, at the forefront of biochemical research due to its central role in biology, is recognized by proteins through various mechanisms. Analysis of the RNA-protein interface provides insight into the recognition determinants and function. As such, there is a demand for developing new methods to characterize RNA-protein interactions. Saturation transfer difference (STD) NMR can identify binding ligands for proteins in a rather short period of time, with data acquisitions of just a few hours. Two RNA-protein systems involved in RNA modification were studied using STD NMR. The N (6)-threonylcarbamoyltransferase, YrdC, with nucleoside-specific recognition, was shown to bind the anticodon stem-loop of tRNA(Lys)UUU. The points of contact on the RNA were assigned and a binding interface was identified. STD NMR was also applied to the interaction of the archaeal ribosomal protein, L7Ae, with the box C/D K-turn RNA. The distinctiveness of the two RNA-protein interfaces was evident. Both RNAs exhibited strong STD signals indicative of direct contact with the respective protein, but reflected the nature of recognition. Characterization of nucleic acid recognition determinants traditionally involves cost and time prohibitive methods. This approach offers significant insight into interaction interfaces fairly rapidly, and complements existing structural methods.

Bifunctional fusion proteins of calmodulin and protein A as affinity ligands in protein purification and in the study of protein-protein interactions.

PubMed

Hentz, N G; Daunert, S

1996-11-15

An affinity chromatography system is described that incorporates a genetically designed bifunctional affinity ligand. The utility of the system in protein purification and in the study of protein-protein interactions is demonstrated by using the interaction between protein A and the heat shock protein DnaK as a model system. The bifunctional affinity ligand was developed by genetically fusing calmodulin (CaM) to protein A (ProtA). The dual functionality of protein A-calmodulin (ProtA-CaM) stems from the molecular recognition properties of the two components of the fusion protein. In particular, CaM serves as the anchoring component by virtue of its binding properties toward phenothiazine. Thus, the ProtA-CaM can be immobilized on a solid support containing phenothiazine from the C-terminal domain of the fusion protein. Protein A is at the N-terminal domain of the fusion protein and serves as the affinity site for DnaK. While DnaK binds specifically to the protein A domain of the bifunctional ligand, it is released upon addition of ATP and under very mild conditions (pH 7.0). In addition to obtaining highly purified DnaK, this system is very rugged in terms of its performance. The proteinaceous bifunctional affinity ligand can be easily removed by addition of EGTA, and fresh ProtA-CaM can be easily reloaded onto the column. This allows for a facile regeneration of the affinity column because the phenothiazine-silica support matrix is stable for long periods of time under a variety of conditions. This study also demonstrates that calmodulin fusions can provide a new approach to study protein-protein interactions. Indeed, the ProtA-CaM fusion protein identified DnaK as a cellular component that interacts with protein A from among the thousands of proteins present in Escherichia coli.

Identifying protein complexes in PPI network using non-cooperative sequential game.

PubMed

Maulik, Ujjwal; Basu, Srinka; Ray, Sumanta

2017-08-21

Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.

Interactions between the Nse3 and Nse4 Components of the SMC5-6 Complex Identify Evolutionarily Conserved Interactions between MAGE and EID Families

PubMed Central

Kozakova, Lucie; Liao, Chunyan; Guerineau, Marc; Colnaghi, Rita; Vidot, Susanne; Marek, Jaromir; Bathula, Sreenivas R.; Lehmann, Alan R.; Palecek, Jan

2011-01-01

Background The SMC5-6 protein complex is involved in the cellular response to DNA damage. It is composed of 6–8 polypeptides, of which Nse1, Nse3 and Nse4 form a tight sub-complex. MAGEG1, the mammalian ortholog of Nse3, is the founding member of the MAGE (melanoma-associated antigen) protein family and Nse4 is related to the EID (E1A-like inhibitor of differentiation) family of transcriptional repressors. Methodology/Principal Findings Using site-directed mutagenesis, protein-protein interaction analyses and molecular modelling, we have identified a conserved hydrophobic surface on the C-terminal domain of Nse3 that interacts with Nse4 and identified residues in its N-terminal domain that are essential for interaction with Nse1. We show that these interactions are conserved in the human orthologs. Furthermore, interaction of MAGEG1, the mammalian ortholog of Nse3, with NSE4b, one of the mammalian orthologs of Nse4, results in transcriptional co-activation of the nuclear receptor, steroidogenic factor 1 (SF1). In an examination of the evolutionary conservation of the Nse3-Nse4 interactions, we find that several MAGE proteins can interact with at least one of the NSE4/EID proteins. Conclusions/Significance We have found that, despite the evolutionary diversification of the MAGE family, the characteristic hydrophobic surface shared by all MAGE proteins from yeast to humans mediates its binding to NSE4/EID proteins. Our work provides new insights into the interactions, evolution and functions of the enigmatic MAGE proteins. PMID:21364888

An ontology-based search engine for protein-protein interactions

PubMed Central

2010-01-01

Background Keyword matching or ID matching is the most common searching method in a large database of protein-protein interactions. They are purely syntactic methods, and retrieve the records in the database that contain a keyword or ID specified in a query. Such syntactic search methods often retrieve too few search results or no results despite many potential matches present in the database. Results We have developed a new method for representing protein-protein interactions and the Gene Ontology (GO) using modified Gödel numbers. This representation is hidden from users but enables a search engine using the representation to efficiently search protein-protein interactions in a biologically meaningful way. Given a query protein with optional search conditions expressed in one or more GO terms, the search engine finds all the interaction partners of the query protein by unique prime factorization of the modified Gödel numbers representing the query protein and the search conditions. Conclusion Representing the biological relations of proteins and their GO annotations by modified Gödel numbers makes a search engine efficiently find all protein-protein interactions by prime factorization of the numbers. Keyword matching or ID matching search methods often miss the interactions involving a protein that has no explicit annotations matching the search condition, but our search engine retrieves such interactions as well if they satisfy the search condition with a more specific term in the ontology. PMID:20122195

Comprehensive peptidomimetic libraries targeting protein-protein interactions.

PubMed

Whitby, Landon R; Boger, Dale L

2012-10-16

Transient protein-protein interactions (PPIs) are essential components in cellular signaling pathways as well as in important processes such as viral infection, replication, and immune suppression. The unknown or uncharacterized PPIs involved in such interaction networks often represent compelling therapeutic targets for drug discovery. To date, however, the main strategies for discovery of small molecule modulators of PPIs are typically limited to structurally characterized targets. Recent developments in molecular scaffolds that mimic the side chain display of peptide secondary structures have yielded effective designs, but few screening libraries of such mimetics are available to interrogate PPI targets. We initiated a program to prepare a comprehensive small molecule library designed to mimic the three major recognition motifs that mediate PPIs (α-helix, β-turn, and β-strand). Three libraries would be built around templates designed to mimic each such secondary structure and substituted with all triplet combinations of groups representing the 20 natural amino acid side chains. When combined, the three libraries would contain a member capable of mimicking the key interaction and recognition residues of most targetable PPIs. In this Account, we summarize the results of the design, synthesis, and validation of an 8000 member α-helix mimetic library and a 4200 member β-turn mimetic library. We expect that the screening of these libraries will not only provide lead structures against α-helix- or β-turn-mediated protein-protein or peptide-receptor interactions, even if the nature of the interaction is unknown, but also yield key insights into the recognition motif (α-helix or β-turn) and identify the key residues mediating the interaction. Consistent with this expectation, the screening of the libraries against p53/MDM2 and HIV-1 gp41 (α-helix mimetic library) or the opioid receptors (β-turn mimetic library) led to the discovery of library members expected

Structure and expression of a novel compact myelin protein – Small VCP-interacting protein (SVIP)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Jiawen; Peng, Dungeng; Voehler, Markus

2013-10-11

Highlights: •SVIP (small p97/VCP-interacting protein) co-localizes with myelin basic protein (MBP) in compact myelin. •We determined that SVIP is an intrinsically disordered protein (IDP). •The helical content of SVIP increases dramatically during its interaction with negatively charged lipid membrane. •This study provides structural insight into interactions between SVIP and myelin membranes. -- Abstract: SVIP (small p97/VCP-interacting protein) was initially identified as one of many cofactors regulating the valosin containing protein (VCP), an AAA+ ATPase involved in endoplasmic-reticulum-associated protein degradation (ERAD). Our previous study showed that SVIP is expressed exclusively in the nervous system. In the present study, SVIP and VCPmore » were seen to be co-localized in neuronal cell bodies. Interestingly, we also observed that SVIP co-localizes with myelin basic protein (MBP) in compact myelin, where VCP was absent. Furthermore, using nuclear magnetic resonance (NMR) and circular dichroism (CD) spectroscopic measurements, we determined that SVIP is an intrinsically disordered protein (IDP). However, upon binding to the surface of membranes containing a net negative charge, the helical content of SVIP increases dramatically. These findings provide structural insight into interactions between SVIP and myelin membranes.« less

Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

PubMed Central

Li, Bi-Qing; Feng, Kai-Yan; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2012-01-01

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. PMID:22937126

Interaction study of rice stripe virus proteins reveals a region of the nucleocapsid protein (NP) required for NP self-interaction and nuclear localization.

PubMed

Lian, Sen; Cho, Won Kyong; Jo, Yeonhwa; Kim, Sang-Min; Kim, Kook-Hyung

2014-04-01

Rice stripe virus (RSV), which belongs to the genus Tenuivirus, is an emergent virus problem. The RSV genome is composed of four single-strand RNAs (RNA1-RNA4) and encodes seven proteins. We investigated interactions between six of the RSV proteins by yeast-two hybrid (Y2H) assay in vitro and by bimolecular fluorescence complementation (BiFC) in planta. Y2H identified self-interaction of the nucleocapsid protein (NP) and NS3, while BiFC revealed self-interaction of NP, NS3, and NCP. To identify regions(s) and/or crucial amino acid (aa) residues required for NP self-interaction, we generated various truncated and aa substitution mutants. Y2H assay showed that the N-terminal region of NP (aa 1-56) is necessary for NP self-interaction. Further analysis with substitution mutants demonstrated that additional aa residues located at 42-47 affected their interaction with full-length NP. These results indicate that the N-terminal region (aa 1-36 and 42-47) is required for NP self-interaction. BiFC and co-localization studies showed that the region required for NP self-interaction is also required for NP localization at the nucleus. Overall, our results indicate that the N-terminal region (aa 1-47) of the NP is important for NP self-interaction and that six aa residues (42-47) are essential for both NP self-interaction and nuclear localization. Copyright © 2014 Elsevier B.V. All rights reserved.

Differential Occurrence of Interactions and Interaction Domains in Proteins Containing Homopolymeric Amino Acid Repeats

PubMed Central

Pelassa, Ilaria; Fiumara, Ferdinando

2015-01-01

Homopolymeric amino acids repeats (AARs), which are widespread in proteomes, have often been viewed simply as spacers between protein domains, or even as “junk” sequences with no obvious function but with a potential to cause harm upon expansion as in genetic diseases associated with polyglutamine or polyalanine expansions, including Huntington disease and cleidocranial dysplasia. A growing body of evidence indicates however that at least some AARs can form organized, functional protein structures, and can regulate protein function. In particular, certain AARs can mediate protein-protein interactions, either through homotypic AAR-AAR contacts or through heterotypic contacts with other protein domains. It is still unclear however, whether AARs may have a generalized, proteome-wide role in shaping protein-protein interaction networks. Therefore, we have undertaken here a bioinformatics screening of the human proteome and interactome in search of quantitative evidence of such a role. We first identified the sets of proteins that contain repeats of any one of the 20 amino acids, as well as control sets of proteins chosen at random in the proteome. We then analyzed the connectivity between the proteins of the AAR-containing protein sets and we compared it with that observed in the corresponding control networks. We find evidence for different degrees of connectivity in the different AAR-containing protein networks. Indeed, networks of proteins containing polyglutamine, polyglutamate, polyproline, and other AARs show significantly increased levels of connectivity, whereas networks containing polyleucine and other hydrophobic repeats show lower degrees of connectivity. Furthermore, we observed that numerous protein-protein, -nucleic acid, and -lipid interaction domains are significantly enriched in specific AAR protein groups. These findings support the notion of a generalized, combinatorial role of AARs, together with conventional protein interaction domains, in

A mass spectrometry-based proteomic analysis of Homer2-interacting proteins in the mouse brain.

PubMed

Goulding, Scott P; Szumlinski, Karen K; Contet, Candice; MacCoss, Michael J; Wu, Christine C

2017-08-23

In the brain, the Homer protein family modulates excitatory signal transduction and receptor plasticity through interactions with other proteins in dendritic spines. Homer proteins are implicated in a variety of psychiatric disorders such as schizophrenia and addiction. Since long Homers serve as scaffolding proteins, identifying their interacting partners is an important first step in understanding their biological function and could help to guide the design of new therapeutic strategies. The present study set out to document Homer2-interacting proteins in the mouse brain using a co-immunoprecipitation-based mass spectrometry approach where Homer2 knockout samples were used to filter out non-specific interactors. We found that in the mouse brain, Homer2 interacts with a limited subset of its previously reported interacting partners (3 out of 31). Importantly, we detected an additional 15 novel Homer2-interacting proteins, most of which are part of the N-methyl-D-aspartate receptor signaling pathway. These results corroborate the central role Homer2 plays in glutamatergic transmission and expand the network of proteins potentially contributing to the behavioral abnormalities associated with altered Homer2 expression. Long Homer proteins are scaffolding proteins that regulate signal transduction in neurons. Identifying their interacting partners is key to understanding their function. We used co-immunoprecipitation in combination with mass spectrometry to establish the first comprehensive list of Homer2-interacting partners in the mouse brain. The specificity of interactions was evaluated using Homer2 knockout brain tissue as a negative control. The set of proteins that we identified minimally overlaps with previously reported interacting partners of Homer2; however, we identified novel interactors that are part of a signaling cascade activated by glutamatergic transmission, which improves our mechanistic understanding of the role of Homer2 in behavior. Copyright �

Improving protein-protein interaction prediction using evolutionary information from low-quality MSAs.

PubMed

Várnai, Csilla; Burkoff, Nikolas S; Wild, David L

2017-01-01

Evolutionary information stored in multiple sequence alignments (MSAs) has been used to identify the interaction interface of protein complexes, by measuring either co-conservation or co-mutation of amino acid residues across the interface. Recently, maximum entropy related correlated mutation measures (CMMs) such as direct information, decoupling direct from indirect interactions, have been developed to identify residue pairs interacting across the protein complex interface. These studies have focussed on carefully selected protein complexes with large, good-quality MSAs. In this work, we study protein complexes with a more typical MSA consisting of fewer than 400 sequences, using a set of 79 intramolecular protein complexes. Using a maximum entropy based CMM at the residue level, we develop an interface level CMM score to be used in re-ranking docking decoys. We demonstrate that our interface level CMM score compares favourably to the complementarity trace score, an evolutionary information-based score measuring co-conservation, when combined with the number of interface residues, a knowledge-based potential and the variability score of individual amino acid sites. We also demonstrate, that, since co-mutation and co-complementarity in the MSA contain orthogonal information, the best prediction performance using evolutionary information can be achieved by combining the co-mutation information of the CMM with co-conservation information of a complementarity trace score, predicting a near-native structure as the top prediction for 41% of the dataset. The method presented is not restricted to small MSAs, and will likely improve interface prediction also for complexes with large and good-quality MSAs.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses.

PubMed

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL ( MeCBL ) and 26 CIPK ( MeCIPK ) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBL s and CIPK s. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10 , and Na + /H + antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses.

Expression Patterns and Identified Protein-Protein Interactions Suggest That Cassava CBL-CIPK Signal Networks Function in Responses to Abiotic Stresses

PubMed Central

Mo, Chunyan; Wan, Shumin; Xia, Youquan; Ren, Ning; Zhou, Yang; Jiang, Xingyu

2018-01-01

Cassava is an energy crop that is tolerant of multiple abiotic stresses. It has been reported that the interaction between Calcineurin B-like (CBL) protein and CBL-interacting protein kinase (CIPK) is implicated in plant development and responses to various stresses. However, little is known about their functions in cassava. Herein, 8 CBL (MeCBL) and 26 CIPK (MeCIPK) genes were isolated from cassava by genome searching and cloning of cDNA sequences of Arabidopsis CBLs and CIPKs. Reverse-transcriptase polymerase chain reaction (RT-PCR) analysis showed that the expression levels of MeCBL and MeCIPK genes were different in different tissues throughout the life cycle. The expression patterns of 7 CBL and 26 CIPK genes in response to NaCl, PEG, heat and cold stresses were analyzed by quantitative real-time PCR (qRT-PCR), and it was found that the expression of each was induced by multiple stimuli. Furthermore, we found that many pairs of CBLs and CIPKs could interact with each other via investigating the interactions between 8 CBL and 25 CIPK proteins using a yeast two-hybrid system. Yeast cells co-transformed with cassava MeCIPK24, MeCBL10, and Na+/H+ antiporter MeSOS1 genes exhibited higher salt tolerance compared to those with one or two genes. These results suggest that the cassava CBL-CIPK signal network might play key roles in response to abiotic stresses. PMID:29552024

Characterization of the interactions between protein and carbon black.

PubMed

Chen, Tzu-Tao; Chuang, Kai-Jen; Chiang, Ling-Ling; Chen, Chun-Chao; Yeh, Chi-Tai; Wang, Liang-Shun; Gregory, Clive; Jones, Tim; BéruBé, Kelly; Lee, Chun-Nin; Chuang, Hsiao-Chi; Cheng, Tsun-Jen

2014-01-15

A considerable amount of studies have been conducted to investigate the interactions of biological fluids with nanoparticle surfaces, which exhibit a high affinity for proteins and particles. However, the mechanisms underlying these interactions have not been elucidated, particularly as they relate to human health. Using bovine serum albumin (BSA) and mice bronchoalveolar lavage fluid (BALF) as models for protein-particle conjugates, we characterized the physicochemical modifications of carbon blacks (CB) with 23nm or 65nm in diameter after protein treatment. Adsorbed BALF-containing proteins were quantified and identified by pathways, biological analyses and protein classification. Significant modifications of the physicochemistry of CB were induced by the addition of BSA. Enzyme modulators and hydrolase predominately interacted with CB, with protein-to-CB interactions that were associated with the coagulation pathways. Additionally, our results revealed that an acute-phase response could be activated by these proteins. With regard to human health, the present study revealed that the CB can react with proteins (∼55kDa and 70kDa) after inhalation and may modify the functional structures of lung proteins, leading to the activation of acute-inflammatory responses in the lungs. Copyright © 2013 Elsevier B.V. All rights reserved.

Predicting Pharmacodynamic Drug-Drug Interactions through Signaling Propagation Interference on Protein-Protein Interaction Networks.

PubMed

Park, Kyunghyun; Kim, Docyong; Ha, Suhyun; Lee, Doheon

2015-01-01

As pharmacodynamic drug-drug interactions (PD DDIs) could lead to severe adverse effects in patients, it is important to identify potential PD DDIs in drug development. The signaling starting from drug targets is propagated through protein-protein interaction (PPI) networks. PD DDIs could occur by close interference on the same targets or within the same pathways as well as distant interference through cross-talking pathways. However, most of the previous approaches have considered only close interference by measuring distances between drug targets or comparing target neighbors. We have applied a random walk with restart algorithm to simulate signaling propagation from drug targets in order to capture the possibility of their distant interference. Cross validation with DrugBank and Kyoto Encyclopedia of Genes and Genomes DRUG shows that the proposed method outperforms the previous methods significantly. We also provide a web service with which PD DDIs for drug pairs can be analyzed at http://biosoft.kaist.ac.kr/targetrw.

The split Renilla luciferase complementation assay is useful for identifying the interaction of Epstein-Barr virus protein kinase BGLF4 and a heat shock protein Hsp90.

PubMed

Wang, J; Guo, W; Long, C; Zhou, H; Wang, H; Sun, X

2016-03-01

Protein-protein interactions can regulate different cellular processes, such as transcription, translation, and oncogenic transformation. The split Renilla luciferase complementation assay (SRLCA) is one of the techniques that detect protein-protein interactions. The SRLCA is based on the complementation of the LN and LC non-functional halves of Renilla luciferase fused to possibly interacting proteins which after interaction form a functional enzyme and emit luminescence. The BGLF4 of Epstein-Barr virus (EBV) is a viral protein kinase that is expressed during the early and late stages of lytic cycles, which can regulate multiple cellular and viral substrates to optimize the DNA replication environment. The heat shock protein Hsp90 is a molecular chaperone that maintains the integrity of structure and function of various interacting proteins, which can form a complex with BGLF4 and stabilize its expression in cells. The interaction between BGLF4 and Hsp90 could be specifically detected through the SRLCA. The region of aa 250-295 of BGLF4 is essential for the BGLF4/Hsp90 interaction and the mutation of Phe-254, Leu-266, and Leu-267 can disrupt this interaction. These results suggest that the SRLCA can specifically detect the BGLF4/Hsp90 interaction and provide a reference to develop inhibitors that disrupt the BGLF4/Hsp90 interaction.

Evolutionary diversification of protein–protein interactions by interface add-ons

PubMed Central

Plach, Maximilian G.; Semmelmann, Florian; Busch, Florian; Busch, Markus; Heizinger, Leonhard; Wysocki, Vicki H.; Sterner, Reinhard

2017-01-01

Cells contain a multitude of protein complexes whose subunits interact with high specificity. However, the number of different protein folds and interface geometries found in nature is limited. This raises the question of how protein–protein interaction specificity is achieved on the structural level and how the formation of nonphysiological complexes is avoided. Here, we describe structural elements called interface add-ons that fulfill this function and elucidate their role for the diversification of protein–protein interactions during evolution. We identified interface add-ons in 10% of a representative set of bacterial, heteromeric protein complexes. The importance of interface add-ons for protein–protein interaction specificity is demonstrated by an exemplary experimental characterization of over 30 cognate and hybrid glutamine amidotransferase complexes in combination with comprehensive genetic profiling and protein design. Moreover, growth experiments showed that the lack of interface add-ons can lead to physiologically harmful cross-talk between essential biosynthetic pathways. In sum, our complementary in silico, in vitro, and in vivo analysis argues that interface add-ons are a practical and widespread evolutionary strategy to prevent the formation of nonphysiological complexes by specializing protein–protein interactions. PMID:28923934

Identifying proteins that bind to specific RNAs - focus on simple repeat expansion diseases

PubMed Central

Jazurek, Magdalena; Ciesiolka, Adam; Starega-Roslan, Julia; Bilinska, Katarzyna; Krzyzosiak, Wlodzimierz J.

2016-01-01

RNA–protein complexes play a central role in the regulation of fundamental cellular processes, such as mRNA splicing, localization, translation and degradation. The misregulation of these interactions can cause a variety of human diseases, including cancer and neurodegenerative disorders. Recently, many strategies have been developed to comprehensively analyze these complex and highly dynamic RNA–protein networks. Extensive efforts have been made to purify in vivo-assembled RNA–protein complexes. In this review, we focused on commonly used RNA-centric approaches that involve mass spectrometry, which are powerful tools for identifying proteins bound to a given RNA. We present various RNA capture strategies that primarily depend on whether the RNA of interest is modified. Moreover, we briefly discuss the advantages and limitations of in vitro and in vivo approaches. Furthermore, we describe recent advances in quantitative proteomics as well as the methods that are most commonly used to validate robust mass spectrometry data. Finally, we present approaches that have successfully identified expanded repeat-binding proteins, which present abnormal RNA–protein interactions that result in the development of many neurological diseases. PMID:27625393

Biospecific protein immobilization for rapid analysis of weak protein interactions using self-interaction nanoparticle spectroscopy.

PubMed

Bengali, Aditya N; Tessier, Peter M

2009-10-01

"Reversible" protein interactions govern diverse biological behavior ranging from intracellular transport and toxic protein aggregation to protein crystallization and inactivation of protein therapeutics. Much less is known about weak protein interactions than their stronger counterparts since they are difficult to characterize, especially in a parallel format (in contrast to a sequential format) necessary for high-throughput screening. We have recently introduced a highly efficient approach of characterizing protein self-association, namely self-interaction nanoparticle spectroscopy (SINS; Tessier et al., 2008; J Am Chem Soc 130:3106-3112). This approach exploits the separation-dependent optical properties of gold nanoparticles to detect weak self-interactions between proteins immobilized on nanoparticles. A limitation of our previous work is that differences in the sequence and structure of proteins can lead to significant differences in their affinity to adsorb to nanoparticle surfaces, which complicates analysis of the corresponding protein self-association behavior. In this work we demonstrate a highly specific approach for coating nanoparticles with proteins using biotin-avidin interactions to generate protein-nanoparticle conjugates that report protein self-interactions through changes in their optical properties. Using lysozyme as a model protein that is refractory to characterization by conventional SINS, we demonstrate that surface Plasmon wavelengths for gold-avidin-lysozyme conjugates over a range of solution conditions (i.e., pH and ionic strength) are well correlated with lysozyme osmotic second virial coefficient measurements. Since SINS requires orders of magnitude less protein and time than conventional methods (e.g., static light scattering), we envision this approach will find application in large screens of protein self-association aimed at either preventing (e.g., protein aggregation) or promoting (e.g., protein crystallization) these

Selection of peptides interfering with protein-protein interaction.

PubMed

Gaida, Annette; Hagemann, Urs B; Mattay, Dinah; Räuber, Christina; Müller, Kristian M; Arndt, Katja M

2009-01-01

Cell physiology depends on a fine-tuned network of protein-protein interactions, and misguided interactions are often associated with various diseases. Consequently, peptides, which are able to specifically interfere with such adventitious interactions, are of high interest for analytical as well as medical purposes. One of the most abundant protein interaction domains is the coiled-coil motif, and thus provides a premier target. Coiled coils, which consist of two or more alpha-helices wrapped around each other, have one of the simplest interaction interfaces, yet they are able to confer highly specific homo- and heterotypic interactions involved in virtually any cellular process. While there are several ways to generate interfering peptides, the combination of library design with a powerful selection system seems to be one of the most effective and promising approaches. This chapter guides through all steps of such a process, starting with library options and cloning, detailing suitable selection techniques and ending with purification for further down-stream characterization. Such generated peptides will function as versatile tools to interfere with the natural function of their targets thereby illuminating their down-stream signaling and, in general, promoting understanding of factors leading to specificity and stability in protein-protein interactions. Furthermore, peptides interfering with medically relevant proteins might become important diagnostics and therapeutics.

Research Techniques Made Simple: Emerging Methods to Elucidate Protein Interactions through Spatial Proximity.

PubMed

Che, Yonglu; Khavari, Paul A

2017-12-01

Interactions between proteins are essential for fundamental cellular processes, and the diversity of such interactions enables the vast variety of functions essential for life. A persistent goal in biological research is to develop assays that can faithfully capture different types of protein interactions to allow their study. A major step forward in this direction came with a family of methods that delineates spatial proximity of proteins as an indirect measure of protein-protein interaction. A variety of enzyme- and DNA ligation-based methods measure protein co-localization in space, capturing novel interactions that were previously too transient or low affinity to be identified. Here we review some of the methods that have been successfully used to measure spatially proximal protein-protein interactions. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Modulators of 14-3-3 Protein–Protein Interactions

PubMed Central

2017-01-01

Direct interactions between proteins are essential for the regulation of their functions in biological pathways. Targeting the complex network of protein–protein interactions (PPIs) has now been widely recognized as an attractive means to therapeutically intervene in disease states. Even though this is a challenging endeavor and PPIs have long been regarded as “undruggable” targets, the last two decades have seen an increasing number of successful examples of PPI modulators, resulting in growing interest in this field. PPI modulation requires novel approaches and the integrated efforts of multiple disciplines to be a fruitful strategy. This perspective focuses on the hub-protein 14-3-3, which has several hundred identified protein interaction partners, and is therefore involved in a wide range of cellular processes and diseases. Here, we aim to provide an integrated overview of the approaches explored for the modulation of 14-3-3 PPIs and review the examples resulting from these efforts in both inhibiting and stabilizing specific 14-3-3 protein complexes by small molecules, peptide mimetics, and natural products. PMID:28968506

Boosting compound-protein interaction prediction by deep learning.

PubMed

Tian, Kai; Shao, Mingyu; Wang, Yang; Guan, Jihong; Zhou, Shuigeng

2016-11-01

The identification of interactions between compounds and proteins plays an important role in network pharmacology and drug discovery. However, experimentally identifying compound-protein interactions (CPIs) is generally expensive and time-consuming, computational approaches are thus introduced. Among these, machine-learning based methods have achieved a considerable success. However, due to the nonlinear and imbalanced nature of biological data, many machine learning approaches have their own limitations. Recently, deep learning techniques show advantages over many state-of-the-art machine learning methods in some applications. In this study, we aim at improving the performance of CPI prediction based on deep learning, and propose a method called DL-CPI (the abbreviation of Deep Learning for Compound-Protein Interactions prediction), which employs deep neural network (DNN) to effectively learn the representations of compound-protein pairs. Extensive experiments show that DL-CPI can learn useful features of compound-protein pairs by a layerwise abstraction, and thus achieves better prediction performance than existing methods on both balanced and imbalanced datasets. Copyright © 2016 Elsevier Inc. All rights reserved.

Examining post-translational modification-mediated protein–protein interactions using a chemical proteomics approach

PubMed Central

Li, Xiang; Foley, Emily A; Kawashima, Shigehiro A; Molloy, Kelly R; Li, Yinyin; Chait, Brian T; Kapoor, Tarun M

2013-01-01

Post-translational modifications (PTM) of proteins can control complex and dynamic cellular processes via regulating interactions between key proteins. To understand these regulatory mechanisms, it is critical that we can profile the PTM-dependent protein–protein interactions. However, identifying these interactions can be very difficult using available approaches, as PTMs can be dynamic and often mediate relatively weak protein–protein interactions. We have recently developed CLASPI (cross-linking-assisted and stable isotope labeling in cell culture-based protein identification), a chemical proteomics approach to examine protein–protein interactions mediated by methylation in human cell lysates. Here, we report three extensions of the CLASPI approach. First, we show that CLASPI can be used to analyze methylation-dependent protein–protein interactions in lysates of fission yeast, a genetically tractable model organism. For these studies, we examined trimethylated histone H3 lysine-9 (H3K9Me3)-dependent protein–protein interactions. Second, we demonstrate that CLASPI can be used to examine phosphorylation-dependent protein–protein interactions. In particular, we profile proteins recognizing phosphorylated histone H3 threonine-3 (H3T3-Phos), a mitotic histone “mark” appearing exclusively during cell division. Our approach identified survivin, the only known H3T3-Phos-binding protein, as well as other proteins, such as MCAK and KIF2A, that are likely to be involved in weak but selective interactions with this histone phosphorylation “mark”. Finally, we demonstrate that the CLASPI approach can be used to study the interplay between histone H3T3-Phos and trimethylation on the adjacent residue lysine 4 (H3K4Me3). Together, our findings indicate the CLASPI approach can be broadly applied to profile protein–protein interactions mediated by PTMs. PMID:23281010

A predicted protein interactome identifies conserved global networks and disease resistance subnetworks in maize

PubMed Central

Musungu, Bryan; Bhatnagar, Deepak; Brown, Robert L.; Fakhoury, Ahmad M.; Geisler, Matt

2015-01-01

Interactomes are genome-wide roadmaps of protein-protein interactions. They have been produced for humans, yeast, the fruit fly, and Arabidopsis thaliana and have become invaluable tools for generating and testing hypotheses. A predicted interactome for Zea mays (PiZeaM) is presented here as an aid to the research community for this valuable crop species. PiZeaM was built using a proven method of interologs (interacting orthologs) that were identified using both one-to-one and many-to-many orthology between genomes of maize and reference species. Where both maize orthologs occurred for an experimentally determined interaction in the reference species, we predicted a likely interaction in maize. A total of 49,026 unique interactions for 6004 maize proteins were predicted. These interactions are enriched for processes that are evolutionarily conserved, but include many otherwise poorly annotated proteins in maize. The predicted maize interactions were further analyzed by comparing annotation of interacting proteins, including different layers of ontology. A map of pairwise gene co-expression was also generated and compared to predicted interactions. Two global subnetworks were constructed for highly conserved interactions. These subnetworks showed clear clustering of proteins by function. Another subnetwork was created for disease response using a bait and prey strategy to capture interacting partners for proteins that respond to other organisms. Closer examination of this subnetwork revealed the connectivity between biotic and abiotic hormone stress pathways. We believe PiZeaM will provide a useful tool for the prediction of protein function and analysis of pathways for Z. mays researchers and is presented in this paper as a reference tool for the exploration of protein interactions in maize. PMID:26089837

A Discontinuous Potential Model for Protein-Protein Interactions.

PubMed

Shao, Qing; Hall, Carol K

2016-01-01

Protein-protein interactions play an important role in many biologic and industrial processes. In this work, we develop a two-bead-per-residue model that enables us to account for protein-protein interactions in a multi-protein system using discontinuous molecular dynamics simulations. This model deploys discontinuous potentials to describe the non-bonded interactions and virtual bonds to keep proteins in their native state. The geometric and energetic parameters are derived from the potentials of mean force between sidechain-sidechain, sidechain-backbone, and backbone-backbone pairs. The energetic parameters are scaled with the aim of matching the second virial coefficient of lysozyme reported in experiment. We also investigate the performance of several bond-building strategies.

Assessment of the reliability of protein-protein interactions and protein function prediction.

PubMed

Deng, Minghua; Sun, Fengzhu; Chen, Ting

2003-01-01

As more and more high-throughput protein-protein interaction data are collected, the task of estimating the reliability of different data sets becomes increasingly important. In this paper, we present our study of two groups of protein-protein interaction data, the physical interaction data and the protein complex data, and estimate the reliability of these data sets using three different measurements: (1) the distribution of gene expression correlation coefficients, (2) the reliability based on gene expression correlation coefficients, and (3) the accuracy of protein function predictions. We develop a maximum likelihood method to estimate the reliability of protein interaction data sets according to the distribution of correlation coefficients of gene expression profiles of putative interacting protein pairs. The results of the three measurements are consistent with each other. The MIPS protein complex data have the highest mean gene expression correlation coefficients (0.256) and the highest accuracy in predicting protein functions (70% sensitivity and specificity), while Ito's Yeast two-hybrid data have the lowest mean (0.041) and the lowest accuracy (15% sensitivity and specificity). Uetz's data are more reliable than Ito's data in all three measurements, and the TAP protein complex data are more reliable than the HMS-PCI data in all three measurements as well. The complex data sets generally perform better in function predictions than do the physical interaction data sets. Proteins in complexes are shown to be more highly correlated in gene expression. The results confirm that the components of a protein complex can be assigned to functions that the complex carries out within a cell. There are three interaction data sets different from the above two groups: the genetic interaction data, the in-silico data and the syn-express data. Their capability of predicting protein functions generally falls between that of the Y2H data and that of the MIPS protein complex

Systematic identification of phosphorylation-mediated protein interaction switches

PubMed Central

Wichmann, Oliver; Utz, Mathias; Andre, Timon; Minguez, Pablo; Parca, Luca; Roth, Frederick P.; Gavin, Anne-Claude; Bork, Peer; Russell, Robert B.

2017-01-01

Proteomics techniques can identify thousands of phosphorylation sites in a single experiment, the majority of which are new and lack precise information about function or molecular mechanism. Here we present a fast method to predict potential phosphorylation switches by mapping phosphorylation sites to protein-protein interactions of known structure and analysing the properties of the protein interface. We predict 1024 sites that could potentially enable or disable particular interactions. We tested a selection of these switches and showed that phosphomimetic mutations indeed affect interactions. We estimate that there are likely thousands of phosphorylation mediated switches yet to be uncovered, even among existing phosphorylation datasets. The results suggest that phosphorylation sites on globular, as distinct from disordered, parts of the proteome frequently function as switches, which might be one of the ancient roles for kinase phosphorylation. PMID:28346509

Coevolution study of mitochondria respiratory chain proteins: toward the understanding of protein--protein interaction.

PubMed

Yang, Ming; Ge, Yan; Wu, Jiayan; Xiao, Jingfa; Yu, Jun

2011-05-20

Coevolution can be seen as the interdependency between evolutionary histories. In the context of protein evolution, functional correlation proteins are ever-present coordinated evolutionary characters without disruption of organismal integrity. As to complex system, there are two forms of protein--protein interactions in vivo, which refer to inter-complex interaction and intra-complex interaction. In this paper, we studied the difference of coevolution characters between inter-complex interaction and intra-complex interaction using "Mirror tree" method on the respiratory chain (RC) proteins. We divided the correlation coefficients of every pairwise RC proteins into two groups corresponding to the binary protein--protein interaction in intra-complex and the binary protein--protein interaction in inter-complex, respectively. A dramatical discrepancy is detected between the coevolution characters of the two sets of protein interactions (Wilcoxon test, p-value = 4.4 × 10(-6)). Our finding reveals some critical information on coevolutionary study and assists the mechanical investigation of protein--protein interaction. Furthermore, the results also provide some unique clue for supramolecular organization of protein complexes in the mitochondrial inner membrane. More detailed binding sites map and genome information of nuclear encoded RC proteins will be extraordinary valuable for the further mitochondria dynamics study. Copyright © 2011. Published by Elsevier Ltd.

Protein-protein interactions and cancer: targeting the central dogma.

PubMed

Garner, Amanda L; Janda, Kim D

2011-01-01

Between 40,000 and 200,000 protein-protein interactions have been predicted to exist within the human interactome. As these interactions are of a critical nature in many important cellular functions and their dysregulation is causal of disease, the modulation of these binding events has emerged as a leading, yet difficult therapeutic arena. In particular, the targeting of protein-protein interactions relevant to cancer is of fundamental importance as the tumor-promoting function of several aberrantly expressed proteins in the cancerous state is directly resultant of its ability to interact with a protein-binding partner. Of significance, these protein complexes play a crucial role in each of the steps of the central dogma of molecular biology, the fundamental processes of genetic transmission. With the many important discoveries being made regarding the mechanisms of these genetic process, the identification of new chemical probes are needed to better understand and validate the druggability of protein-protein interactions related to the central dogma. In this review, we provide an overview of current small molecule-based protein-protein interaction inhibitors for each stage of the central dogma: transcription, mRNA splicing and translation. Importantly, through our analysis we have uncovered a lack of necessary probes targeting mRNA splicing and translation, thus, opening up the possibility for expansion of these fields.

NMR identification of the binding surfaces involved in the Salmonella and Shigella Type III secretion tip-translocon protein-protein interactions.

PubMed

McShan, Andrew C; Kaur, Kawaljit; Chatterjee, Srirupa; Knight, Kevin M; De Guzman, Roberto N

2016-08-01

The type III secretion system (T3SS) is essential for the pathogenesis of many bacteria including Salmonella and Shigella, which together are responsible for millions of deaths worldwide each year. The structural component of the T3SS consists of the needle apparatus, which is assembled in part by the protein-protein interaction between the tip and the translocon. The atomic detail of the interaction between the tip and the translocon proteins is currently unknown. Here, we used NMR methods to identify that the N-terminal domain of the Salmonella SipB translocon protein interacts with the SipD tip protein at a surface at the distal region of the tip formed by the mixed α/β domain and a portion of its coiled-coil domain. Likewise, the Shigella IpaB translocon protein and the IpaD tip protein interact with each other using similar surfaces identified for the Salmonella homologs. Furthermore, removal of the extreme N-terminal residues of the translocon protein, previously thought to be important for the interaction, had little change on the binding surface. Finally, mutations at the binding surface of SipD reduced invasion of Salmonella into human intestinal epithelial cells. Together, these results reveal the binding surfaces involved in the tip-translocon protein-protein interaction and advance our understanding of the assembly of the T3SS needle apparatus. Proteins 2016; 84:1097-1107. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Dynamic Fluctuations of Protein-Carbohydrate Interactions Promote Protein Aggregation

PubMed Central

Voynov, Vladimir; Chennamsetty, Naresh; Kayser, Veysel; Helk, Bernhard; Forrer, Kurt; Zhang, Heidi; Fritsch, Cornelius; Heine, Holger; Trout, Bernhardt L.

2009-01-01

Protein-carbohydrate interactions are important for glycoprotein structure and function. Antibodies of the IgG class, with increasing significance as therapeutics, are glycosylated at a conserved site in the constant Fc region. We hypothesized that disruption of protein-carbohydrate interactions in the glycosylated domain of antibodies leads to the exposure of aggregation-prone motifs. Aggregation is one of the main problems in protein-based therapeutics because of immunogenicity concerns and decreased efficacy. To explore the significance of intramolecular interactions between aromatic amino acids and carbohydrates in the IgG glycosylated domain, we utilized computer simulations, fluorescence analysis, and site-directed mutagenesis. We find that the surface exposure of one aromatic amino acid increases due to dynamic fluctuations. Moreover, protein-carbohydrate interactions decrease upon stress, while protein-protein and carbohydrate-carbohydrate interactions increase. Substitution of the carbohydrate-interacting aromatic amino acids with non-aromatic residues leads to a significantly lower stability than wild type, and to compromised binding to Fc receptors. Our results support a mechanism for antibody aggregation via decreased protein-carbohydrate interactions, leading to the exposure of aggregation-prone regions, and to aggregation. PMID:20037630

Template-Based Modeling of Protein-RNA Interactions.

PubMed

Zheng, Jinfang; Kundrotas, Petras J; Vakser, Ilya A; Liu, Shiyong

2016-09-01

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

Template-Based Modeling of Protein-RNA Interactions

PubMed Central

Zheng, Jinfang; Kundrotas, Petras J.; Vakser, Ilya A.

2016-01-01

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. PMID:27662342

Quality control methodology for high-throughput protein-protein interaction screening.

PubMed

Vazquez, Alexei; Rual, Jean-François; Venkatesan, Kavitha

2011-01-01

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

Small Molecules Engage Hot Spots through Cooperative Binding To Inhibit a Tight Protein-Protein Interaction.

PubMed

Liu, Degang; Xu, David; Liu, Min; Knabe, William Eric; Yuan, Cai; Zhou, Donghui; Huang, Mingdong; Meroueh, Samy O

2017-03-28

Protein-protein interactions drive every aspect of cell signaling, yet only a few small-molecule inhibitors of these interactions exist. Despite our ability to identify critical residues known as hot spots, little is known about how to effectively engage them to disrupt protein-protein interactions. Here, we take advantage of the ease of preparation and stability of pyrrolinone 1, a small-molecule inhibitor of the tight interaction between the urokinase receptor (uPAR) and its binding partner, the urokinase-type plasminogen activator uPA, to synthesize more than 40 derivatives and explore their effect on the protein-protein interaction. We report the crystal structure of uPAR bound to previously discovered pyrazole 3 and to pyrrolinone 12. While both 3 and 12 bind to uPAR and compete with a fluorescently labeled peptide probe, only 12 and its derivatives inhibit the full uPAR·uPA interaction. Compounds 3 and 12 mimic and engage different hot-spot residues on uPA and uPAR, respectively. Interestingly, 12 is involved in a π-cation interaction with Arg-53, which is not considered a hot spot. Explicit-solvent molecular dynamics simulations reveal that 3 and 12 exhibit dramatically different correlations of motion with residues on uPAR. Free energy calculations for the wild-type and mutant uPAR bound to uPA or 12 show that Arg-53 interacts with uPA or with 12 in a highly cooperative manner, thereby altering the contributions of hot spots to uPAR binding. The direct engagement of peripheral residues not considered hot spots through π-cation or salt-bridge interactions could provide new opportunities for enhanced small-molecule engagement of hot spots to disrupt challenging protein-protein interactions.

Hot-spot analysis for drug discovery targeting protein-protein interactions.

PubMed

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Coarse-Grained Model for Colloidal Protein Interactions, B22, and Protein Cluster Formation

PubMed Central

Blanco, Marco A.; Sahin, Eric; Robinson, Anne S.; Roberts, Christopher J.

2014-01-01

Reversible protein cluster formation is an important initial step in the processes of native and non-native protein aggregation, but involves relatively long time and length scales for detailed atomistic simulations and extensive mapping of free energy landscapes. A coarse-grained (CG) model is presented to semi-quantitatively characterize the thermodynamics and key configurations involved in the landscape for protein oligomerization, as well as experimental measures of interactions such as the osmotic second virial coefficient (B22). Based on earlier work, this CG model treats proteins as rigid bodies composed of one bead per amino acid, with each amino acid having specific parameters for its size, hydrophobicity, and charge. The net interactions are a combination of steric repulsions, short-range attractions, and screened long-range charge-charge interactions. Model parametrization was done by fitting simulation results against experimental values of the B22 as a function of solution ionic strength for α-chymotrypsinogen A and γD-crystallin (gD-Crys). The CG model is applied to characterize the pairwise interactions and dimerization of gD-Crys and the dependance on temperature, protein concentration, and ionic strength. The results illustrate that at experimentally relevant conditions where stable dimers do not form, the entropic contributions are predominant in the free-energy of protein cluster formation and colloidal protein interactions, arguing against interpretations that treat B22 primarily from energetic considerations alone. Additionally, the results suggest that electrostatic interactions help to modulate the population of the different stable configurations for protein nearest-neighbor pairs, while short-range attractions determine the relative orientations of proteins within these configurations. Finally, simulation results are combined with Principal Component Analysis to identify those amino-acids / surface patches that form inter-protein contacts

Protein-protein interaction predictions using text mining methods.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Iliopoulos, Ioannis

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

Quantifying the Molecular Origins of Opposite Solvent Effects on Protein-Protein Interactions

PubMed Central

Vagenende, Vincent; Han, Alvin X.; Pek, Han B.; Loo, Bernard L. W.

2013-01-01

Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments. PMID:23696727

Quantifying the molecular origins of opposite solvent effects on protein-protein interactions.

PubMed

Vagenende, Vincent; Han, Alvin X; Pek, Han B; Loo, Bernard L W

2013-01-01

Although the nature of solvent-protein interactions is generally weak and non-specific, addition of cosolvents such as denaturants and osmolytes strengthens protein-protein interactions for some proteins, whereas it weakens protein-protein interactions for others. This is exemplified by the puzzling observation that addition of glycerol oppositely affects the association constants of two antibodies, D1.3 and D44.1, with lysozyme. To resolve this conundrum, we develop a methodology based on the thermodynamic principles of preferential interaction theory and the quantitative characterization of local protein solvation from molecular dynamics simulations. We find that changes of preferential solvent interactions at the protein-protein interface quantitatively account for the opposite effects of glycerol on the antibody-antigen association constants. Detailed characterization of local protein solvation in the free and associated protein states reveals how opposite solvent effects on protein-protein interactions depend on the extent of dewetting of the protein-protein contact region and on structural changes that alter cooperative solvent-protein interactions at the periphery of the protein-protein interface. These results demonstrate the direct relationship between macroscopic solvent effects on protein-protein interactions and atom-scale solvent-protein interactions, and establish a general methodology for predicting and understanding solvent effects on protein-protein interactions in diverse biological environments.

A credit-card library approach for disrupting protein-protein interactions.

PubMed

Xu, Yang; Shi, Jin; Yamamoto, Noboru; Moss, Jason A; Vogt, Peter K; Janda, Kim D

2006-04-15

Protein-protein interfaces are prominent in many therapeutically important targets. Using small organic molecules to disrupt protein-protein interactions is a current challenge in chemical biology. An important example of protein-protein interactions is provided by the Myc protein, which is frequently deregulated in human cancers. Myc belongs to the family of basic helix-loop-helix leucine zipper (bHLH-ZIP) transcription factors. It is biologically active only as heterodimer with the bHLH-ZIP protein Max. Herein, we report a new strategy for the disruption of protein-protein interactions that has been corroborated through the design and synthesis of a small parallel library composed of 'credit-card' compounds. These compounds are derived from a planar, aromatic scaffold and functionalized with four points of diversity. From a 285 membered library, several hits were obtained that disrupted the c-Myc-Max interaction and cellular functions of c-Myc. The IC50 values determined for this small focused library for the disruption of Myc-Max dimerization are quite potent, especially since small molecule antagonists of protein-protein interactions are notoriously difficult to find. Furthermore, several of the compounds were active at the cellular level as shown by their biological effects on Myc action in chicken embryo fibroblast assays. In light of our findings, this approach is considered a valuable addition to the armamentarium of new molecules being developed to interact with protein-protein interfaces. Finally, this strategy for disrupting protein-protein interactions should prove applicable to other families of proteins.

Identification of lipopolysaccharide-interacting plasma membrane-type proteins in Arabidopsis thaliana.

PubMed

Vilakazi, Cornelius S; Dubery, Ian A; Piater, Lizelle A

2017-02-01

Lipopolysaccharide (LPS) is an amphiphatic bacterial glycoconjugate found on the external membrane of Gram-negative bacteria. This endotoxin is considered as a microbe-associated molecular pattern (MAMP) molecule and has been shown to elicit defense responses in plants. Here, LPS-interacting proteins from Arabidopsis thaliana plasma membrane (PM)-type fractions were captured and identified in order to investigate those involved in LPS perception and linked to triggering of innate immune responses. A novel proteomics-based affinity-capture strategy coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for the enrichment and identification of LPS-interacting proteins. As such, LPS isolated from Burkholderia cepacia (LPS B.cep. ) was immobilized on three independent and distinct affinity-based matrices to serve as bait for interacting proteins from A. thaliana leaf and callus tissue. These were resolved by 1D electrophoresis and identified by mass spectrometry. Proteins specifically bound to LPS B.cep. have been implicated in membrane structure (e.g. COBRA-like and tubulin proteins), membrane trafficking and/or transport (e.g. soluble NSF attachment protein receptor (SNARE) proteins, patellin, aquaporin, PM instrinsic proteins (PIP) and H + -ATPase), signal transduction (receptor-like kinases and calcium-dependent protein kinases) as well as defense/stress responses (e.g. hypersensitive-induced response (HIR) proteins, jacalin-like lectin domain-containing protein and myrosinase-binding proteins). The novel affinity-capture strategy for the enrichment of LPS-interacting proteins proved to be effective, especially in the binding of proteins involved in plant defense responses, and can thus be used to elucidate LPS-mediated molecular recognition and disease mechanism(s). Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Molecular tweezers modulate 14-3-3 protein-protein interactions

NASA Astrophysics Data System (ADS)

Bier, David; Rose, Rolf; Bravo-Rodriguez, Kenny; Bartel, Maria; Ramirez-Anguita, Juan Manuel; Dutt, Som; Wilch, Constanze; Klärner, Frank-Gerrit; Sanchez-Garcia, Elsa; Schrader, Thomas; Ottmann, Christian

2013-03-01

Supramolecular chemistry has recently emerged as a promising way to modulate protein functions, but devising molecules that will interact with a protein in the desired manner is difficult as many competing interactions exist in a biological environment (with solvents, salts or different sites for the target biomolecule). We now show that lysine-specific molecular tweezers bind to a 14-3-3 adapter protein and modulate its interaction with partner proteins. The tweezers inhibit binding between the 14-3-3 protein and two partner proteins—a phosphorylated (C-Raf) protein and an unphosphorylated one (ExoS)—in a concentration-dependent manner. Protein crystallography shows that this effect arises from the binding of the tweezers to a single surface-exposed lysine (Lys214) of the 14-3-3 protein in the proximity of its central channel, which normally binds the partner proteins. A combination of structural analysis and computer simulations provides rules for the tweezers' binding preferences, thus allowing us to predict their influence on this type of protein-protein interactions.

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

DTIC Science & Technology

2009-06-05

the interaction data sets we determined, via comparisons with strict randomized simulations , the propensity for essential proteins to selectively...and analysis of high- quality PPI data sets. Materials and Methods We analyzed protein interaction networks for yeast and E. coli determined from Y2H...we reinvestigated the centrality-lethality rule, which implies that proteins having more interactions are more likely to be essential. From analysis

ChIP-PaM: an algorithm to identify protein-DNA interaction using ChIP-Seq data.

PubMed

Wu, Song; Wang, Jianmin; Zhao, Wei; Pounds, Stanley; Cheng, Cheng

2010-06-03

ChIP-Seq is a powerful tool for identifying the interaction between genomic regulators and their bound DNAs, especially for locating transcription factor binding sites. However, high cost and high rate of false discovery of transcription factor binding sites identified from ChIP-Seq data significantly limit its application. Here we report a new algorithm, ChIP-PaM, for identifying transcription factor target regions in ChIP-Seq datasets. This algorithm makes full use of a protein-DNA binding pattern by capitalizing on three lines of evidence: 1) the tag count modelling at the peak position, 2) pattern matching of a specific tag count distribution, and 3) motif searching along the genome. A novel data-based two-step eFDR procedure is proposed to integrate the three lines of evidence to determine significantly enriched regions. Our algorithm requires no technical controls and efficiently discriminates falsely enriched regions from regions enriched by true transcription factor (TF) binding on the basis of ChIP-Seq data only. An analysis of real genomic data is presented to demonstrate our method. In a comparison with other existing methods, we found that our algorithm provides more accurate binding site discovery while maintaining comparable statistical power.

A cysteine-rich plant protein potentiates Potyvirus movement through an interaction with the virus genome-linked protein VPg.

PubMed

Dunoyer, P; Thomas, C; Harrison, S; Revers, F; Maule, A

2004-03-01

We have identified a cellular factor that interacts with the virus genome-linked proteins (VPgs) of a diverse range of potyviruses. The factor, called Potyvirus VPg-interacting protein (PVIP), is a plant-specific protein with homologues in all the species examined, i.e., pea, Arabidopsis thaliana, and Nicotiana benthamiana. The sequence of PVIP does not identify a specific function, although the existence of a "PHD finger" domain may implicate the protein in transcriptional control through chromatin remodeling. Deletion analysis using the yeast two-hybrid system showed that the determinants of the interaction lay close to the N terminus of VPg; indeed, the N-terminal 16 amino acids were shown to be both necessary and sufficient for the interaction with at least one PVIP protein. From a sequence comparison of different potyvirus VPg proteins, a specific amino acid at position 12 was directly implicated in the interaction. This part of VPg is distinct from regions associated with other functional roles of VPg. Through mutation of Turnip mosaic virus (TuMV) at VPg position 12, we showed that the interaction with PVIP affected systemic symptoms in infected plants. This resulted from reduced cell-to-cell and systemic movement more than reduced virus replication, as visualized by comparing green fluorescent protein-tagged wild-type and mutant viruses. Furthermore, by using RNA interference of PVIP in Arabidopsis, we showed that reduced expression of PVIP genes reduced susceptibility to TuMV infection. We conclude that PVIP functions as an ancillary factor to support potyvirus movement in plants.

Coevolution analysis of Hepatitis C virus genome to identify the structural and functional dependency network of viral proteins

NASA Astrophysics Data System (ADS)

Champeimont, Raphaël; Laine, Elodie; Hu, Shuang-Wei; Penin, Francois; Carbone, Alessandra

2016-05-01

A novel computational approach of coevolution analysis allowed us to reconstruct the protein-protein interaction network of the Hepatitis C Virus (HCV) at the residue resolution. For the first time, coevolution analysis of an entire viral genome was realized, based on a limited set of protein sequences with high sequence identity within genotypes. The identified coevolving residues constitute highly relevant predictions of protein-protein interactions for further experimental identification of HCV protein complexes. The method can be used to analyse other viral genomes and to predict the associated protein interaction networks.

[Detection of protein-protein interactions by FRET and BRET methods].

PubMed

Matoulková, E; Vojtěšek, B

2014-01-01

Nowadays, in vivo protein-protein interaction studies have become preferable detecting meth-ods that enable to show or specify (already known) protein interactions and discover their inhibitors. They also facilitate detection of protein conformational changes and discovery or specification of signaling pathways in living cells. One group of in vivo methods enabling these findings is based on fluorescent resonance energy transfer (FRET) and its bio-luminescent modification (BRET). They are based on visualization of protein-protein interactions via light or enzymatic excitation of fluorescent or bio-luminescent proteins. These methods allow not only protein localization within the cell or its organelles (or small animals) but they also allow us to quantify fluorescent signals and to discover weak or strong interaction partners. In this review, we explain the principles of FRET and BRET, their applications in the characterization of protein-protein interactions and we describe several findings using these two methods that clarify molecular and cellular mechanisms and signals related to cancer biology.

The protein-protein interaction network of eyestalk, Y-organ and hepatopancreas in Chinese mitten crab Eriocheir sinensis.

PubMed

Hao, Tong; Zeng, Zheng; Wang, Bin; Zhang, Yichen; Liu, Yichen; Geng, Xuyun; Sun, Jinsheng

2014-03-27

The protein-protein interaction network (PIN) is an effective information tool for understanding the complex biological processes inside the cell and solving many biological problems such as signaling pathway identification and prediction of protein functions. Eriocheir sinensis is a highly-commercial aquaculture species with an unclear proteome background which hinders the construction and development of PIN for E. sinensis. However, in recent years, the development of next-generation deep-sequencing techniques makes it possible to get high throughput data of E. sinensis tanscriptome and subsequently obtain a systematic overview of the protein-protein interaction system. In this work we sequenced the transcriptional RNA of eyestalk, Y-organ and hepatopancreas in E. sinensis and generated a PIN of E. sinensis which included 3,223 proteins and 35,787 interactions. Each protein-protein interaction in the network was scored according to the homology and genetic relationship. The signaling sub-network, representing the signal transduction pathways in E. sinensis, was extracted from the global network, which depicted a global view of the signaling systems in E. sinensis. Seven basic signal transduction pathways were identified in E. sinensis. By investigating the evolution paths of the seven pathways, we found that these pathways got mature in different evolutionary stages. Moreover, the functions of unclassified proteins and unigenes in the PIN of E. sinensis were predicted. Specifically, the functions of 549 unclassified proteins related to 864 unclassified unigenes were assigned, which respectively covered 76% and 73% of all the unclassified proteins and unigenes in the network. The PIN generated in this work is the first large-scale PIN of aquatic crustacean, thereby providing a paradigmatic blueprint of the aquatic crustacean interactome. Signaling sub-network extracted from the global PIN depicts the interaction of different signaling proteins and the evolutionary

Ultra-High-Throughput Structure-Based Virtual Screening for Small-Molecule Inhibitors of Protein-Protein Interactions

PubMed Central

Johnson, David K.; Karanicolas, John

2016-01-01

Protein-protein interactions play important roles in virtually all cellular processes, making them enticing targets for modulation by small-molecule therapeutics: specific examples have been well validated in diseases ranging from cancer and autoimmune disorders, to bacterial and viral infections. Despite several notable successes, however, overall these remain a very challenging target class. Protein interaction sites are especially challenging for computational approaches, because the target protein surface often undergoes a conformational change to enable ligand binding: this confounds traditional approaches for virtual screening. Through previous studies, we demonstrated that biased “pocket optimization” simulations could be used to build collections of low-energy pocket-containing conformations, starting from an unbound protein structure. Here, we demonstrate that these pockets can further be used to identify ligands that complement the protein surface. To do so, we first build from a given pocket its “exemplar”: a perfect, but non-physical, pseudo-ligand that would optimally match the shape and chemical features of the pocket. In our previous studies, we used these exemplars to quantitatively compare protein surface pockets to one another. Here, we now introduce this exemplar as a template for pharmacophore-based screening of chemical libraries. Through a series of benchmark experiments, we demonstrate that this approach exhibits comparable performance as traditional docking methods for identifying known inhibitors acting at protein interaction sites. However, because this approach is predicated on ligand/exemplar overlays, and thus does not require explicit calculation of protein-ligand interactions, exemplar screening provides a tremendous speed advantage over docking: 6 million compounds can be screened in about 15 minutes on a single 16-core, dual-GPU computer. The extreme speed at which large compound libraries can be traversed easily enables

Experimental evolution of protein–protein interaction networks

PubMed Central

Kaçar, Betül; Gaucher, Eric A.

2013-01-01

The modern synthesis of evolutionary theory and genetics has enabled us to discover underlying molecular mechanisms of organismal evolution. We know that in order to maximize an organism's fitness in a particular environment, individual interactions among components of protein and nucleic acid networks need to be optimized by natural selection, or sometimes through random processes, as the organism responds to changes and/or challenges in the environment. Despite the significant role of molecular networks in determining an organism's adaptation to its environment, we still do not know how such inter- and intra-molecular interactions within networks change over time and contribute to an organism's evolvability while maintaining overall network functions. One way to address this challenge is to identify connections between molecular networks and their host organisms, to manipulate these connections, and then attempt to understand how such perturbations influence molecular dynamics of the network and thus influence evolutionary paths and organismal fitness. In the present review, we discuss how integrating evolutionary history with experimental systems that combine tools drawn from molecular evolution, synthetic biology and biochemistry allow us to identify the underlying mechanisms of organismal evolution, particularly from the perspective of protein interaction networks. PMID:23849056

Identification of HYPK-Interacting Proteins Reveals Involvement of HYPK in Regulating Cell Growth, Cell Cycle, Unfolded Protein Response and Cell Death

PubMed Central

Choudhury, Kamalika Roy; Raychaudhuri, Swasti; Bhattacharyya, Nitai P.

2012-01-01

Huntingtin Yeast Two-Hybrid Protein K (HYPK) is an intrinsically unstructured huntingtin (HTT)-interacting protein with chaperone-like activity. To obtain more information about the function(s) of the protein, we identified 27 novel interacting partners of HYPK by pull-down assay coupled with mass spectrometry and, further, 9 proteins were identified by co-localization and co-immunoprecipitation (co-IP) assays. In neuronal cells, (EEF1A1 and HSPA1A), (HTT and LMNB2) and (TP53 and RELA) were identified in complex with HYPK in different experiments. Various Gene Ontology (GO) terms for biological processes, like protein folding (GO: 0006457), response to unfolded protein (GO: 0006986), cell cycle arrest (GO: 0007050), anti-apoptosis (GO: 0006916) and regulation of transcription (GO: 0006355) were significantly enriched with the HYPK-interacting proteins. Cell growth and the ability to refold heat-denatured reporter luciferase were decreased, but cytotoxicity was increased in neuronal cells where HYPK was knocked-down using HYPK antisense DNA construct. The proportion of cells in different phases of cell cycle was also altered in cells with reduced levels of HYPK. These results show that HYPK is involved in several biological processes, possibly through interaction with its partners. PMID:23272104

Coarse-grained model for colloidal protein interactions, B(22), and protein cluster formation.

PubMed

Blanco, Marco A; Sahin, Erinc; Robinson, Anne S; Roberts, Christopher J

2013-12-19

Reversible protein cluster formation is an important initial step in the processes of native and non-native protein aggregation, but involves relatively long time and length scales for detailed atomistic simulations and extensive mapping of free energy landscapes. A coarse-grained (CG) model is presented to semiquantitatively characterize the thermodynamics and key configurations involved in the landscape for protein oligomerization, as well as experimental measures of interactions such as the osmotic second virial coefficient (B22). Based on earlier work (Grüenberger et al., J. Phys. Chem. B 2013, 117, 763), this CG model treats proteins as rigid bodies composed of one bead per amino acid, with each amino acid having specific parameters for its size, hydrophobicity, and charge. The net interactions are a combination of steric repulsions, short-range attractions, and screened long-range charge-charge interactions. Model parametrization was done by fitting simulation results against experimental value of B22 as a function of solution ionic strength for α-chymotrypsinogen A and γD-Crystallin (gD-Crys). The CG model is applied to characterize the pairwise interactions and dimerization of gD-Crys and the dependence on temperature, protein concentration, and ionic strength. The results illustrate that at experimentally relevant conditions where stable dimers do not form, the entropic contributions are predominant in the free-energy of protein cluster formation and colloidal protein interactions, arguing against interpretations that treat B22 primarily from energetic considerations alone. Additionally, the results suggest that electrostatic interactions help to modulate the population of the different stable configurations for protein nearest-neighbor pairs, while short-range attractions determine the relative orientations of proteins within these configurations. Finally, simulation results are combined with Principal Component Analysis to identify those amino

A computational tool to predict the evolutionarily conserved protein-protein interaction hot-spot residues from the structure of the unbound protein.

PubMed

Agrawal, Neeraj J; Helk, Bernhard; Trout, Bernhardt L

2014-01-21

Identifying hot-spot residues - residues that are critical to protein-protein binding - can help to elucidate a protein's function and assist in designing therapeutic molecules to target those residues. We present a novel computational tool, termed spatial-interaction-map (SIM), to predict the hot-spot residues of an evolutionarily conserved protein-protein interaction from the structure of an unbound protein alone. SIM can predict the protein hot-spot residues with an accuracy of 36-57%. Thus, the SIM tool can be used to predict the yet unknown hot-spot residues for many proteins for which the structure of the protein-protein complexes are not available, thereby providing a clue to their functions and an opportunity to design therapeutic molecules to target these proteins. Copyright © 2013 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

InterProSurf: a web server for predicting interacting sites on protein surfaces

PubMed Central

Negi, Surendra S.; Schein, Catherine H.; Oezguen, Numan; Power, Trevor D.; Braun, Werner

2009-01-01

Summary A new web server, InterProSurf, predicts interacting amino acid residues in proteins that are most likely to interact with other proteins, given the 3D structures of subunits of a protein complex. The prediction method is based on solvent accessible surface area of residues in the isolated subunits, a propensity scale for interface residues and a clustering algorithm to identify surface regions with residues of high interface propensities. Here we illustrate the application of InterProSurf to determine which areas of Bacillus anthracis toxins and measles virus hemagglutinin protein interact with their respective cell surface receptors. The computationally predicted regions overlap with those regions previously identified as interface regions by sequence analysis and mutagenesis experiments. PMID:17933856

Protein function prediction using neighbor relativity in protein-protein interaction network.

PubMed

Moosavi, Sobhan; Rahgozar, Masoud; Rahimi, Amir

2013-04-01

There is a large gap between the number of discovered proteins and the number of functionally annotated ones. Due to the high cost of determining protein function by wet-lab research, function prediction has become a major task for computational biology and bioinformatics. Some researches utilize the proteins interaction information to predict function for un-annotated proteins. In this paper, we propose a novel approach called "Neighbor Relativity Coefficient" (NRC) based on interaction network topology which estimates the functional similarity between two proteins. NRC is calculated for each pair of proteins based on their graph-based features including distance, common neighbors and the number of paths between them. In order to ascribe function to an un-annotated protein, NRC estimates a weight for each neighbor to transfer its annotation to the unknown protein. Finally, the unknown protein will be annotated by the top score transferred functions. We also investigate the effect of using different coefficients for various types of functions. The proposed method has been evaluated on Saccharomyces cerevisiae and Homo sapiens interaction networks. The performance analysis demonstrates that NRC yields better results in comparison with previous protein function prediction approaches that utilize interaction network. Copyright © 2012 Elsevier Ltd. All rights reserved.

Quantification of the Influence of Protein-Protein Interactions on Adsorbed Protein Structure and Bioactivity

PubMed Central

Wei, Yang; Thyparambil, Aby A.; Latour, Robert A.

2013-01-01

While protein-surface interactions have been widely studied, relatively little is understood at this time regarding how protein-surface interaction effects are influenced by protein-protein interactions and how these effects combine with the internal stability of a protein to influence its adsorbed-state structure and bioactivity. The objectives of this study were to develop a method to study these combined effects under widely varying protein-protein interaction conditions using hen egg-white lysozyme (HEWL) adsorbed on silica glass, poly(methyl methacrylate), and polyethylene as our model systems. In order to vary protein-protein interaction effects over a wide range, HEWL was first adsorbed to each surface type under widely varying protein solution concentrations for 2 h to saturate the surface, followed by immersion in pure buffer solution for 15 h to equilibrate the adsorbed protein layers in the absence of additionally adsorbing protein. Periodic measurements were made at selected time points of the areal density of the adsorbed protein layer as an indicator of the level of protein-protein interaction effects within the layer, and these values were then correlated with measurements of the adsorbed protein’s secondary structure and bioactivity. The results from these studies indicate that protein-protein interaction effects help stabilize the structure of HEWL adsorbed on silica glass, have little influence on the structural behavior of HEWL on HDPE, and actually serve to destabilize HEWL’s structure on PMMA. The bioactivity of HEWL on silica glass and HDPE was found to decrease in direct proportion to the degree of adsorption-induce protein unfolding. A direct correlation between bioactivity and the conformational state of adsorbed HEWL was less apparent on PMMA, thus suggesting that other factors influenced HEWL’s bioactivity on this surface, such as the accessibility of HEWL’s bioactive site being blocked by neighboring proteins or the surface

Dynamic interactions of proteins in complex networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Appella, E.; Anderson, C.

2009-10-01

Recent advances in techniques such as NMR and EPR spectroscopy have enabled the elucidation of how proteins undergo structural changes to act in concert in complex networks. The three minireviews in this series highlight current findings and the capabilities of new methodologies for unraveling the dynamic changes controlling diverse cellular functions. They represent a sampling of the cutting-edge research presented at the 17th Meeting of Methods in Protein Structure Analysis, MPSA2008, in Sapporo, Japan, 26-29 August, 2008 (http://www.iapsap.bnl.gov). The first minireview, by Christensen and Klevit, reports on a structure-based yeast two-hybrid method for identifying E2 ubiquitin-conjugating enzymes that interact withmore » the E3 BRCA1/BARD1 heterodimer ligase to generate either mono- or polyubiquitinated products. This method demonstrated for the first time that the BRCA1/BARD1 E3 can interact with 10 different E2 enzymes. Interestingly, the interaction with multiple E2 enzymes displayed unique ubiquitin-transfer properties, a feature expected to be common among other RING and U-box E3s. Further characterization of new E3 ligases and the E2 enzymes that interact with them will greatly enhance our understanding of ubiquitin transfer and facilitate studies of roles of ubiquitin and ubiquitin-like proteins in protein processing and trafficking. Stein et al., in the second minireview, describe recent progress in defining the binding specificity of different peptide-binding domains. The authors clearly point out that transient peptide interactions mediated by both post-translational modifications and disordered regions ensure a high level of specificity. They postulate that a regulatory code may dictate the number of combinations of domains and post-translational modifications needed to achieve the required level of interaction specificity. Moreover, recognition alone is not enough to obtain a stable complex, especially in a complex cellular environment. Increasing

Analysis of In Vivo Chromatin and Protein Interactions of Arabidopsis Transcript Elongation Factors.

PubMed

Pfab, Alexander; Antosz, Wojciech; Holzinger, Philipp; Bruckmann, Astrid; Griesenbeck, Joachim; Grasser, Klaus D

2017-01-01

A central step to elucidate the function of proteins commonly comprises the analysis of their molecular interactions in vivo. For nuclear regulatory proteins this involves determining protein-protein interactions as well as mapping of chromatin binding sites. Here, we present two protocols to identify protein-protein and chromatin interactions of transcript elongation factors (TEFs) in Arabidopsis. The first protocol (Subheading 3.1) describes protein affinity-purification coupled to mass spectrometry (AP-MS) that utilizes suspension cultured cells as experimental system. This approach provides an unbiased view of proteins interacting with epitope-tagged TEFs. The second protocol (Subheading 3.2) depicts details about a chromatin immunoprecipitation (ChIP) procedure to characterize genomic binding sites of TEFs. These methods should be valuable tools for the analysis of a broad variety of nuclear proteins.

A facile method to screen inhibitors of protein-protein interactions including MDM2-p53 displayed on T7 phage.

PubMed

Ishi, Kazutomo; Sugawara, Fumio

2008-05-01

Protein-protein interactions are essential in many biological processes including cell cycle and apoptosis. It is currently of great medical interest to inhibit specific protein-protein interactions in order to treat a variety of disease states. Here, we describe a facile multiwell plate assay method using T7 phage display to screen for candidate inhibitors of protein-protein interactions. Because T7 phage display is an effective method for detecting protein-protein interactions, we aimed to utilize this technique to screen for small-molecule inhibitors that disrupt these types of interaction. We used the well-characterized interaction between p53 and MDM2 and an inhibitor of this interaction, nutlin 3, as a model system to establish a new screening method. Phage particles displaying p53 interacted with GST-MDM2 immobilized on 96-well plates, and the interaction was inhibited by nutlin 3. Multiwell plate assay was then performed using a natural product library, which identified dehydroaltenusin as a candidate inhibitor of the p53-MDM2 interaction. We discuss the potential applications of this novel T7 phage display methodology, which we propose to call 'reverse phage display'.

Characterization of Protein-Carbohydrate Interactions by NMR Spectroscopy.

PubMed

Grondin, Julie M; Langelaan, David N; Smith, Steven P

2017-01-01

Solution-state nuclear magnetic resonance (NMR) spectroscopy can be used to monitor protein-carbohydrate interactions. Two-dimensional 1 H- 15 N heteronuclear single quantum coherence (HSQC)-based techniques described in this chapter can be used quickly and effectively to screen a set of possible carbohydrate binding partners, to quantify the dissociation constant (K d ) of any identified interactions, and to map the carbohydrate binding site on the structure of the protein. Here, we describe the titration of a family 32 carbohydrate binding module from Clostridium perfringens (CpCBM32) with the monosaccharide N-acetylgalactosamine (GalNAc), in which we calculate the apparent dissociation of the interaction, and map the GalNAc binding site onto the structure of CpCBM32.

PREFACE: Physics approaches to protein interactions and gene regulation Physics approaches to protein interactions and gene regulation

NASA Astrophysics Data System (ADS)

Nussinov, Ruth; Panchenko, Anna R.; Przytycka, Teresa

2011-06-01

networks have been identified, including scale free distribution of the vertex degree, network motifs, and modularity, to name a few. These studies of network organization require the network to be as complete as possible, which given the limitations of experimental techniques is not currently the case. Therefore, experimental procedures for detecting biomolecular interactions should be complemented by computational approaches. The paper by Lees et al provides a review of computational methods, integrating multiple independent sources of data to infer physical and functional protein-protein interaction networks. One of the important aspects of protein interactions that should be accounted for in the prediction of protein interaction networks is that many proteins are composed of distinct domains. Protein domains may mediate protein interactions while proteins and their interaction networks may gain complexity through gene duplication and expansion of existing domain architectures via domain rearrangements. The latter mechanisms have been explored in detail in the paper by Cohen-Gihon et al. Protein-protein interactions are not the only component of the cell's interactome. Regulation of cell activity can be achieved at the level of transcription and involve a transcription factor—DNA binding which typically requires recognition of a specific DNA sequence motif. Chip-Chip and the more recent Chip-Seq technologies allow in vivo identification of DNA binding sites and, together with novel in vitro approaches, provide data necessary for deciphering the corresponding binding motifs. Such information, complemented by structures of protein-DNA complexes and knowledge of the differences in binding sites among homologs, opens the door to constructing predictive binding models. The paper by Persikov and Singh provides an example of such a model in the Cys2His2 zinc finger family. Recent studies have indicated that the presence of such binding motifs is, however, neither necessary

Dynamic imaging of protein-protein interactions by MP-FLIM

NASA Astrophysics Data System (ADS)

Ameer-Beg, Simon M.; Peter, Marion; Keppler, Melanie D.; Prag, Soren; Barber, Paul R.; Ng, Tony C.; Vojnovic, Borivoj

2005-03-01

The spatio-temporal localization of molecular interactions within cells in situ is of great importance in elucidating the key mechanisms in regulation of fundamental process within the cell. Measurements of such near-field localization of protein complexes may be achieved by the detection of fluorescence (or Forster) resonance energy transfer (FRET) between protein-conjugated fluorophores. We demonstrate the applicability of time-correlated single photon counting multiphoton microscopy to the spatio-temporal localization of protein-protein interactions in live and fixed cell populations. Intramolecular interactions between protein hetero-dimers are investigated using green fluorescent protein variants. We present an improved monomeric form of the red fluorescent protein, mRFP1, as the acceptor in biological fluorescence resonance energy transfer (FRET) experiments using the enhanced green fluorescent protein as donor. We find particular advantage in using this fluorophore pair for quantitative measurements of FRET. The technique was exploited to demonstrate a novel receptor-kinase interaction between the chemokine receptor (CXCR4) and protein kinase C (PKC) α in carcinoma cells for both live and fixed cell experiments.

Proteins that interact with calgranulin B in the human colon cancer cell line HCT-116.

PubMed

Myung, Jae Kyung; Yeo, Seung-Gu; Kim, Kyung Hee; Baek, Kwang-Soo; Shin, Daye; Kim, Jong Heon; Cho, Jae Youl; Yoo, Byong Chul

2017-01-24

Calgranulin B is released from immune cells and can be internalized into colon cancer cells to prevent proliferation. The present study aimed to identify proteins that interact with calgranulin B to suppress the proliferation of colon cancer cells, and to obtain information on the underlying anti-tumor mechanism(s) of calgranulin B. Calgranulin B expression was induced in colon cancer cell line HCT-116 by infection with calgranulin B-FLAG expressing lentivirus, and it led to a significant suppression of cell proliferation. Proteins that interacted with calgranulin B were obtained by immunoprecipitation using whole homogenate of lentivirus-infected HCT-116 cells which expressing calgranulin B-FLAG, and identified using liquid chromatography-mass spectrometry/mass spectrometry analysis. A total of 454 proteins were identified that potentially interact with calgranulin B, and most identified proteins were associated with RNA processing, post-transcriptional modifications and the EIF2 signaling pathway. Direct interaction of calgranulin B with flotillin-1, dynein intermediate chain 1, and CD59 glycoprotein has been confirmed, and the molecules N-myc proto-oncogene protein, rapamycin-insensitive companion of mTOR, and myc proto-oncogene protein were shown to regulate calgranulin B-interacting proteins. Our results provide new insight and useful information to explain the possible mechanism(s) underlying the role of calgranulin B as an anti-tumor effector in colon cancer cells.

Protein-protein interaction networks (PPI) and complex diseases

PubMed Central

Safari-Alighiarloo, Nahid; Taghizadeh, Mohammad; Rezaei-Tavirani, Mostafa; Goliaei, Bahram

2014-01-01

The physical interaction of proteins which lead to compiling them into large densely connected networks is a noticeable subject to investigation. Protein interaction networks are useful because of making basic scientific abstraction and improving biological and biomedical applications. Based on principle roles of proteins in biological function, their interactions determine molecular and cellular mechanisms, which control healthy and diseased states in organisms. Therefore, such networks facilitate the understanding of pathogenic (and physiologic) mechanisms that trigger the onset and progression of diseases. Consequently, this knowledge can be translated into effective diagnostic and therapeutic strategies. Furthermore, the results of several studies have proved that the structure and dynamics of protein networks are disturbed in complex diseases such as cancer and autoimmune disorders. Based on such relationship, a novel paradigm is suggested in order to confirm that the protein interaction networks can be the target of therapy for treatment of complex multi-genic diseases rather than individual molecules with disrespect the network. PMID:25436094

In silico prediction of protein-protein interactions in human macrophages

PubMed Central

2014-01-01

Background Protein-protein interaction (PPI) network analyses are highly valuable in deciphering and understanding the intricate organisation of cellular functions. Nevertheless, the majority of available protein-protein interaction networks are context-less, i.e. without any reference to the spatial, temporal or physiological conditions in which the interactions may occur. In this work, we are proposing a protocol to infer the most likely protein-protein interaction (PPI) network in human macrophages. Results We integrated the PPI dataset from the Agile Protein Interaction DataAnalyzer (APID) with different meta-data to infer a contextualized macrophage-specific interactome using a combination of statistical methods. The obtained interactome is enriched in experimentally verified interactions and in proteins involved in macrophage-related biological processes (i.e. immune response activation, regulation of apoptosis). As a case study, we used the contextualized interactome to highlight the cellular processes induced upon Mycobacterium tuberculosis infection. Conclusion Our work confirms that contextualizing interactomes improves the biological significance of bioinformatic analyses. More specifically, studying such inferred network rather than focusing at the gene expression level only, is informative on the processes involved in the host response. Indeed, important immune features such as apoptosis are solely highlighted when the spotlight is on the protein interaction level. PMID:24636261

Detection of protein-protein interactions by ribosome display and protein in situ immobilisation.

PubMed

He, Mingyue; Liu, Hong; Turner, Martin; Taussig, Michael J

2009-12-31

We describe a method for identification of protein-protein interactions by combining two cell-free protein technologies, namely ribosome display and protein in situ immobilisation. The method requires only PCR fragments as the starting material, the target proteins being made through cell-free protein synthesis, either associated with their encoding mRNA as ribosome complexes or immobilised on a solid surface. The use of ribosome complexes allows identification of interacting protein partners from their attached coding mRNA. To demonstrate the procedures, we have employed the lymphocyte signalling proteins Vav1 and Grb2 and confirmed the interaction between Grb2 and the N-terminal SH3 domain of Vav1. The method has promise for library screening of pairwise protein interactions, down to the analytical level of individual domain or motif mapping.

ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.

PubMed

Frenkel-Morgenstern, Milana; Gorohovski, Alessandro; Tagore, Somnath; Sekar, Vaishnovi; Vazquez, Miguel; Valencia, Alfonso

2017-07-07

Fusion proteins, comprising peptides deriving from the translation of two parental genes, are produced in cancer by chromosomal aberrations. The expressed fusion protein incorporates domains of both parental proteins. Using a methodology that treats discrete protein domains as binding sites for specific domains of interacting proteins, we have cataloged the protein interaction networks for 11 528 cancer fusions (ChiTaRS-3.1). Here, we present our novel method, chimeric protein-protein interactions (ChiPPI) that uses the domain-domain co-occurrence scores in order to identify preserved interactors of chimeric proteins. Mapping the influence of fusion proteins on cell metabolism and pathways reveals that ChiPPI networks often lose tumor suppressor proteins and gain oncoproteins. Furthermore, fusions often induce novel connections between non-interactors skewing interaction networks and signaling pathways. We compared fusion protein PPI networks in leukemia/lymphoma, sarcoma and solid tumors finding distinct enrichment patterns for each disease type. While certain pathways are enriched in all three diseases (Wnt, Notch and TGF β), there are distinct patterns for leukemia (EGFR signaling, DNA replication and CCKR signaling), for sarcoma (p53 pathway and CCKR signaling) and solid tumors (FGFR and EGFR signaling). Thus, the ChiPPI method represents a comprehensive tool for studying the anomaly of skewed cellular networks produced by fusion proteins in cancer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Polyamines: naturally occurring small molecule modulators of electrostatic protein-protein interactions.

PubMed

Berwanger, Anja; Eyrisch, Susanne; Schuster, Inge; Helms, Volkhard; Bernhardt, Rita

2010-02-01

Modulations of protein-protein interactions are a key step in regulating protein function, especially in networks. Modulators of these interactions are supposed to be candidates for the development of novel drugs. Here, we describe the role of the small, polycationic and highly abundant natural polyamines that could efficiently bind to charged spots at protein interfaces as modulators of such protein-protein interactions. Using the mitochondrial cytochrome P45011A1 (CYP11A1) electron transfer system as a model, we have analyzed the capability of putrescine, spermidine, and spermine at physiologically relevant concentrations to affect the protein-protein interactions between adrenodoxin reductase (AdR), adrenodoxin (Adx), and CYP11A1. The actions of polyamines on the individual components, on their association/dissociation, on electron transfer, and on substrate conversion were examined. These studies revealed modulating effects of polyamines on distinct interactions and on the entire system in a complex way. Modulation via changed protein-protein interactions appeared plausible from docking experiments that suggested favourable high-affinity binding sites of polyamines (spermine>spermidine>putrescine) at the AdR-Adx interface. Our findings imply for the first time that small endogenous compounds are capable of interfering with distinct components of transient protein complexes and might control protein functions by modulating electrostatic protein-protein interactions.

Bioinformatic prediction and in vivo validation of residue-residue interactions in human proteins

NASA Astrophysics Data System (ADS)

Jordan, Daniel; Davis, Erica; Katsanis, Nicholas; Sunyaev, Shamil

2014-03-01

Identifying residue-residue interactions in protein molecules is important for understanding both protein structure and function in the context of evolutionary dynamics and medical genetics. Such interactions can be difficult to predict using existing empirical or physical potentials, especially when residues are far from each other in sequence space. Using a multiple sequence alignment of 46 diverse vertebrate species we explore the space of allowed sequences for orthologous protein families. Amino acid changes that are known to damage protein function allow us to identify specific changes that are likely to have interacting partners. We fit the parameters of the continuous-time Markov process used in the alignment to conclude that these interactions are primarily pairwise, rather than higher order. Candidates for sites under pairwise epistasis are predicted, which can then be tested by experiment. We report the results of an initial round of in vivo experiments in a zebrafish model that verify the presence of multiple pairwise interactions predicted by our model. These experimentally validated interactions are novel, distant in sequence, and are not readily explained by known biochemical or biophysical features.

Prediction and redesign of protein–protein interactions

PubMed Central

Lua, Rhonald C.; Marciano, David C.; Katsonis, Panagiotis; Adikesavan, Anbu K.; Wilkins, Angela D.; Lichtarge, Olivier

2014-01-01

Understanding the molecular basis of protein function remains a central goal of biology, with the hope to elucidate the role of human genes in health and in disease, and to rationally design therapies through targeted molecular perturbations. We review here some of the computational techniques and resources available for characterizing a critical aspect of protein function – those mediated by protein–protein interactions (PPI). We describe several applications and recent successes of the Evolutionary Trace (ET) in identifying molecular events and shapes that underlie protein function and specificity in both eukaryotes and prokaryotes. ET is a part of analytical approaches based on the successes and failures of evolution that enable the rational control of PPI. PMID:24878423

Computational design of protein interactions: designing proteins that neutralize influenza by inhibiting its hemagglutinin surface protein

NASA Astrophysics Data System (ADS)

Fleishman, Sarel

2012-02-01

Molecular recognition underlies all life processes. Design of interactions not seen in nature is a test of our understanding of molecular recognition and could unlock the vast potential of subtle control over molecular interaction networks, allowing the design of novel diagnostics and therapeutics for basic and applied research. We developed the first general method for designing protein interactions. The method starts by computing a region of high affinity interactions between dismembered amino acid residues and the target surface and then identifying proteins that can harbor these residues. Designs are tested experimentally for binding the target surface and successful ones are affinity matured using yeast cell surface display. Applied to the conserved stem region of influenza hemagglutinin we designed two unrelated proteins that, following affinity maturation, bound hemagglutinin at subnanomolar dissociation constants. Co-crystal structures of hemagglutinin bound to the two designed binders were within 1Angstrom RMSd of their models, validating the accuracy of the design strategy. One of the designed proteins inhibits the conformational changes that underlie hemagglutinin's cell-invasion functions and blocks virus infectivity in cell culture, suggesting that such proteins may in future serve as diagnostics and antivirals against a wide range of pathogenic influenza strains. We have used this method to obtain experimentally validated binders of several other target proteins, demonstrating the generality of the approach. We discuss the combination of modeling and high-throughput characterization of design variants which has been key to the success of this approach, as well as how we have used the data obtained in this project to enhance our understanding of molecular recognition. References: Science 332:816 JMB, in press Protein Sci 20:753

HyCCAPP as a tool to characterize promoter DNA-protein interactions in Saccharomyces cerevisiae.

PubMed

Guillen-Ahlers, Hector; Rao, Prahlad K; Levenstein, Mark E; Kennedy-Darling, Julia; Perumalla, Danu S; Jadhav, Avinash Y L; Glenn, Jeremy P; Ludwig-Kubinski, Amy; Drigalenko, Eugene; Montoya, Maria J; Göring, Harald H; Anderson, Corianna D; Scalf, Mark; Gildersleeve, Heidi I S; Cole, Regina; Greene, Alexandra M; Oduro, Akua K; Lazarova, Katarina; Cesnik, Anthony J; Barfknecht, Jared; Cirillo, Lisa A; Gasch, Audrey P; Shortreed, Michael R; Smith, Lloyd M; Olivier, Michael

2016-06-01

Currently available methods for interrogating DNA-protein interactions at individual genomic loci have significant limitations, and make it difficult to work with unmodified cells or examine single-copy regions without specific antibodies. In this study, we describe a physiological application of the Hybridization Capture of Chromatin-Associated Proteins for Proteomics (HyCCAPP) methodology we have developed. Both novel and known locus-specific DNA-protein interactions were identified at the ENO2 and GAL1 promoter regions of Saccharomyces cerevisiae, and revealed subgroups of proteins present in significantly different levels at the loci in cells grown on glucose versus galactose as the carbon source. Results were validated using chromatin immunoprecipitation. Overall, our analysis demonstrates that HyCCAPP is an effective and flexible technology that does not require specific antibodies nor prior knowledge of locally occurring DNA-protein interactions and can now be used to identify changes in protein interactions at target regions in the genome in response to physiological challenges. Copyright © 2016 Elsevier Inc. All rights reserved.

A novel method for identifying disease associated protein complexes based on functional similarity protein complex networks.

PubMed

Le, Duc-Hau

2015-01-01

Protein complexes formed by non-covalent interaction among proteins play important roles in cellular functions. Computational and purification methods have been used to identify many protein complexes and their cellular functions. However, their roles in terms of causing disease have not been well discovered yet. There exist only a few studies for the identification of disease-associated protein complexes. However, they mostly utilize complicated heterogeneous networks which are constructed based on an out-of-date database of phenotype similarity network collected from literature. In addition, they only apply for diseases for which tissue-specific data exist. In this study, we propose a method to identify novel disease-protein complex associations. First, we introduce a framework to construct functional similarity protein complex networks where two protein complexes are functionally connected by either shared protein elements, shared annotating GO terms or based on protein interactions between elements in each protein complex. Second, we propose a simple but effective neighborhood-based algorithm, which yields a local similarity measure, to rank disease candidate protein complexes. Comparing the predictive performance of our proposed algorithm with that of two state-of-the-art network propagation algorithms including one we used in our previous study, we found that it performed statistically significantly better than that of these two algorithms for all the constructed functional similarity protein complex networks. In addition, it ran about 32 times faster than these two algorithms. Moreover, our proposed method always achieved high performance in terms of AUC values irrespective of the ways to construct the functional similarity protein complex networks and the used algorithms. The performance of our method was also higher than that reported in some existing methods which were based on complicated heterogeneous networks. Finally, we also tested our method with

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs.

PubMed

Huo, Tong; Liu, Wei; Guo, Yu; Yang, Cheng; Lin, Jianping; Rao, Zihe

2015-03-26

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease. We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by 'interolog' method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins. This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.

PubMed

You, Zhu-Hong; Li, Shuai; Gao, Xin; Luo, Xin; Ji, Zhen

2014-01-01

Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell. During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions. However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive. On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates. Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model. This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor. When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%. We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine. The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.

Functional structural motifs for protein-ligand, protein-protein, and protein-nucleic acid interactions and their connection to supersecondary structures.

PubMed

Kinjo, Akira R; Nakamura, Haruki

2013-01-01

Protein functions are mediated by interactions between proteins and other molecules. One useful approach to analyze protein functions is to compare and classify the structures of interaction interfaces of proteins. Here, we describe the procedures for compiling a database of interface structures and efficiently comparing the interface structures. To do so requires a good understanding of the data structures of the Protein Data Bank (PDB). Therefore, we also provide a detailed account of the PDB exchange dictionary necessary for extracting data that are relevant for analyzing interaction interfaces and secondary structures. We identify recurring structural motifs by classifying similar interface structures, and we define a coarse-grained representation of supersecondary structures (SSS) which represents a sequence of two or three secondary structure elements including their relative orientations as a string of four to seven letters. By examining the correspondence between structural motifs and SSS strings, we show that no SSS string has particularly high propensity to be found interaction interfaces in general, indicating any SSS can be used as a binding interface. When individual structural motifs are examined, there are some SSS strings that have high propensity for particular groups of structural motifs. In addition, it is shown that while the SSS strings found in particular structural motifs for nonpolymer and protein interfaces are as abundant as in other structural motifs that belong to the same subunit, structural motifs for nucleic acid interfaces exhibit somewhat stronger preference for SSS strings. In regard to protein folds, many motif-specific SSS strings were found across many folds, suggesting that SSS may be a useful description to investigate the universality of ligand binding modes.

Effects of surface compositional and structural heterogeneity on nanoparticle-protein interactions: different protein configurations.

PubMed

Huang, Rixiang; Carney, Randy P; Ikuma, Kaoru; Stellacci, Francesco; Lau, Boris L T

2014-06-24

As nanoparticles (NPs) enter into biological systems, they are immediately exposed to a variety and concentration of proteins. The physicochemical interactions between proteins and NPs are influenced by the surface properties of the NPs. To identify the effects of NP surface heterogeneity, the interactions between bovine serum albumin (BSA) and gold NPs (AuNPs) with similar chemical composition but different surface structures were investigated. Different interaction modes and BSA conformations were studied by dynamic light scattering, circular dichroism spectroscopy, fluorescence quenching and isothermal titration calorimetry (ITC). Depending on the surface structure of AuNPs, BSA seems to adopt either a "side-on" or an "end-on" conformation on AuNPs. ITC demonstrated that the adsorption of BSA onto AuNPs with randomly distributed polar and nonpolar groups was primarily driven by electrostatic interaction, and all BSA were adsorbed in the same process. The adsorption of BSA onto AuNPs covered with alternating domains of polar and nonpolar groups was a combination of different interactions. Overall, the results of this study point to the potential for utilizing nanoscale manipulation of NP surfaces to control the resulting NP-protein interactions.

Membrane proteins from the cyanobacterium Synechocystis sp. PCC 6803 interacting with thioredoxin.

PubMed

Mata-Cabana, Alejandro; Florencio, Francisco J; Lindahl, Marika

2007-11-01

Cysteine dithiol/disulphide exchange forms the molecular basis for regulation of a wide variety of enzymatic activities and for transduction of cellular signals. Thus, the search for proteins with reactive, accessible cysteines is expected to contribute to the unravelling of new molecular mechanisms for enzyme regulation and signal transduction. Several methods have been designed for this purpose taking advantage of the interactions between thioredoxins and their protein substrates. Thioredoxins comprise a family of redox-active enzymes, which catalyse reduction of protein disulphides and sulphenic acids. Due to the inherent practical difficulties associated with studies of membrane proteins these have been largely overlooked in the many proteomic studies of thioredoxin-interacting proteins. In the present work, we have developed a procedure to isolate membrane proteins interacting with thioredoxin by binding in situ to a monocysteinic His-tagged thioredoxin added directly to the intact membranes. Following fractionation and solubilisation of the membranes, thioredoxin target proteins were isolated by Ni-affinity chromatography and 2-DE SDS-PAGE under nonreducing/reducing conditions. Applying this method to total membranes, including thylakoid and plasma membranes, from the cyanobacterium Synechocystis sp. PCC 6803 we have identified 50 thioredoxin-interacting proteins. Among the 38 newly identified thioredoxin targets are the ATP-binding subunits of several transporters and members of the AAA-family of ATPases.

Druggable orthosteric and allosteric hot spots to target protein-protein interactions.

PubMed

Ma, Buyong; Nussinov, Ruth

2014-01-01

Drug designing targeting protein-protein interactions is challenging. Because structural elucidation and computational analysis have revealed the importance of hot spot residues in stabilizing these interactions, there have been on-going efforts to develop drugs which bind the hot spots and out-compete the native protein partners. The question arises as to what are the key 'druggable' properties of hot spots in protein-protein interactions and whether these mimic the general hot spot definition. Identification of orthosteric (at the protein- protein interaction site) and allosteric (elsewhere) druggable hot spots is expected to help in discovering compounds that can more effectively modulate protein-protein interactions. For example, are there any other significant features beyond their location in pockets in the interface? The interactions of protein-protein hot spots are coupled with conformational dynamics of protein complexes. Currently increasing efforts focus on the allosteric drug discovery. Allosteric drugs bind away from the native binding site and can modulate the native interactions. We propose that identification of allosteric hot spots could similarly help in more effective allosteric drug discovery. While detection of allosteric hot spots is challenging, targeting drugs to these residues has the potential of greatly increasing the hot spot and protein druggability.

Directed Evolution of a Cyclized Peptoid-Peptide Chimera against a Cell-Free Expressed Protein and Proteomic Profiling of the Interacting Proteins to Create a Protein-Protein Interaction Inhibitor.

PubMed

Kawakami, Takashi; Ogawa, Koji; Hatta, Tomohisa; Goshima, Naoki; Natsume, Tohru

2016-06-17

N-alkyl amino acids are useful building blocks for the in vitro display evolution of ribosomally synthesized peptides because they can increase the proteolytic stability and cell permeability of these peptides. However, the translation initiation substrate specificity of nonproteinogenic N-alkyl amino acids has not been investigated. In this study, we screened various N-alkyl amino acids and nonamino carboxylic acids for translation initiation with an Escherichia coli reconstituted cell-free translation system (PURE system) and identified those that efficiently initiated translation. Using seven of these efficiently initiating acids, we next performed in vitro display evolution of cyclized peptidomimetics against an arbitrarily chosen model human protein (β-catenin) cell-free expressed from its cloned cDNA (HUPEX) and identified a novel β-catenin-binding cyclized peptoid-peptide chimera. Furthermore, by a proteomic approach using direct nanoflow liquid chromatography-tandem mass spectrometry (DNLC-MS/MS), we successfully identified which protein-β-catenin interaction is inhibited by the chimera. The combination of in vitro display evolution of cyclized N-alkyl peptidomimetics and in vitro expression of human proteins would be a powerful approach for the high-speed discovery of diverse human protein-targeted cyclized N-alkyl peptidomimetics.

Analysis of Protein-RNA and Protein-Peptide Interactions in Equine Infectious Anemia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Jae-Hyung

2007-01-01

Macromolecular interactions are essential for virtually all cellular functions including signal transduction processes, metabolic processes, regulation of gene expression and immune responses. This dissertation focuses on the characterization of two important macromolecular interactions involved in the relationship between Equine Infectious Anemia Virus (EIAV) and its host cell in horse: (1) the interaction between the EIAV Rev protein and its binding site, the Rev-responsive element (RRE) and (2) interactions between equine MHC class I molecules and epitope peptides derived from EIAV proteins. EIAV, one of the most divergent members of the lentivirus family, has a single-stranded RNA genome and carries severalmore » regulatory and structural proteins within its viral particle. Rev is an essential EIAV regulatory encoded protein that interacts with the viral RRE, a specific binding site in the viral mRNA. Using a combination of experimental and computational methods, the interactions between EIAV Rev and RRE were characterized in detail. EIAV Rev was shown to have a bipartite RNA binding domain contain two arginine rich motifs (ARMs). The RRE secondary structure was determined and specific structural motifs that act as cis-regulatory elements for EIAV Rev-RRE interaction were identified. Interestingly, a structural motif located in the high affinity Rev binding site is well conserved in several diverse lentiviral genoes, including HIV-1. Macromolecular interactions involved in the immune response of the horse to EIAV infection were investigated by analyzing complexes between MHC class I proteins and epitope peptides derived from EIAV Rev, Env and Gag proteins. Computational modeling results provided a mechanistic explanation for the experimental finding that a single amino acid change in the peptide binding domain of the quine MHC class I molecule differentially affectes the recognitino of specific epitopes by EIAV-specific CTL. Together, the findings in this

Categorizing Biases in High-Confidence High-Throughput Protein-Protein Interaction Data Sets*

PubMed Central

Yu, Xueping; Ivanic, Joseph; Memišević, Vesna; Wallqvist, Anders; Reifman, Jaques

2011-01-01

We characterized and evaluated the functional attributes of three yeast high-confidence protein-protein interaction data sets derived from affinity purification/mass spectrometry, protein-fragment complementation assay, and yeast two-hybrid experiments. The interacting proteins retrieved from these data sets formed distinct, partially overlapping sets with different protein-protein interaction characteristics. These differences were primarily a function of the deployed experimental technologies used to recover these interactions. This affected the total coverage of interactions and was especially evident in the recovery of interactions among different functional classes of proteins. We found that the interaction data obtained by the yeast two-hybrid method was the least biased toward any particular functional characterization. In contrast, interacting proteins in the affinity purification/mass spectrometry and protein-fragment complementation assay data sets were over- and under-represented among distinct and different functional categories. We delineated how these differences affected protein complex organization in the network of interactions, in particular for strongly interacting complexes (e.g. RNA and protein synthesis) versus weak and transient interacting complexes (e.g. protein transport). We quantified methodological differences in detecting protein interactions from larger protein complexes, in the correlation of protein abundance among interacting proteins, and in their connectivity of essential proteins. In the latter case, we showed that minimizing inherent methodology biases removed many of the ambiguous conclusions about protein essentiality and protein connectivity. We used these findings to rationalize how biological insights obtained by analyzing data sets originating from different sources sometimes do not agree or may even contradict each other. An important corollary of this work was that discrepancies in biological insights did not

Identification of Modules in Protein-Protein Interaction Networks

NASA Astrophysics Data System (ADS)

Erten, Sinan; Koyutürk, Mehmet

In biological systems, most processes are carried out through orchestration of multiple interacting molecules. These interactions are often abstracted using network models. A key feature of cellular networks is their modularity, which contributes significantly to the robustness, as well as adaptability of biological systems. Therefore, modularization of cellular networks is likely to be useful in obtaining insights into the working principles of cellular systems, as well as building tractable models of cellular organization and dynamics. A common, high-throughput source of data on molecular interactions is in the form of physical interactions between proteins, which are organized into protein-protein interaction (PPI) networks. This chapter provides an overview on identification and analysis of functional modules in PPI networks, which has been an active area of research in the last decade.

Revealing protein functions based on relationships of interacting proteins and GO terms.

PubMed

Teng, Zhixia; Guo, Maozu; Liu, Xiaoyan; Tian, Zhen; Che, Kai

2017-09-20

In recent years, numerous computational methods predicted protein function based on the protein-protein interaction (PPI) network. These methods supposed that two proteins share the same function if they interact with each other. However, it is reported by recent studies that the functions of two interacting proteins may be just related. It will mislead the prediction of protein function. Therefore, there is a need for investigating the functional relationship between interacting proteins. In this paper, the functional relationship between interacting proteins is studied and a novel method, called as GoDIN, is advanced to annotate functions of interacting proteins in Gene Ontology (GO) context. It is assumed that the functional difference between interacting proteins can be expressed by semantic difference between GO term and its relatives. Thus, the method uses GO term and its relatives to annotate the interacting proteins separately according to their functional roles in the PPI network. The method is validated by a series of experiments and compared with the concerned method. The experimental results confirm the assumption and suggest that GoDIN is effective on predicting functions of protein. This study demonstrates that: (1) interacting proteins are not equal in the PPI network, and their function may be same or similar, or just related; (2) functional difference between interacting proteins can be measured by their degrees in the PPI network; (3) functional relationship between interacting proteins can be expressed by relationship between GO term and its relatives.

Structural principles within the human-virus protein-protein interaction network

PubMed Central

Franzosa, Eric A.; Xia, Yu

2011-01-01

General properties of the antagonistic biomolecular interactions between viruses and their hosts (exogenous interactions) remain poorly understood, and may differ significantly from known principles governing the cooperative interactions within the host (endogenous interactions). Systems biology approaches have been applied to study the combined interaction networks of virus and human proteins, but such efforts have so far revealed only low-resolution patterns of host-virus interaction. Here, we layer curated and predicted 3D structural models of human-virus and human-human protein complexes on top of traditional interaction networks to reconstruct the human-virus structural interaction network. This approach reveals atomic resolution, mechanistic patterns of host-virus interaction, and facilitates systematic comparison with the host’s endogenous interactions. We find that exogenous interfaces tend to overlap with and mimic endogenous interfaces, thereby competing with endogenous binding partners. The endogenous interfaces mimicked by viral proteins tend to participate in multiple endogenous interactions which are transient and regulatory in nature. While interface overlap in the endogenous network results largely from gene duplication followed by divergent evolution, viral proteins frequently achieve interface mimicry without any sequence or structural similarity to an endogenous binding partner. Finally, while endogenous interfaces tend to evolve more slowly than the rest of the protein surface, exogenous interfaces—including many sites of endogenous-exogenous overlap—tend to evolve faster, consistent with an evolutionary “arms race” between host and pathogen. These significant biophysical, functional, and evolutionary differences between host-pathogen and within-host protein-protein interactions highlight the distinct consequences of antagonism versus cooperation in biological networks. PMID:21680884

Towards Inferring Protein Interactions: Challenges and Solutions

NASA Astrophysics Data System (ADS)

Zhang, Ya; Zha, Hongyuan; Chu, Chao-Hsien; Ji, Xiang

2006-12-01

Discovering interacting proteins has been an essential part of functional genomics. However, existing experimental techniques only uncover a small portion of any interactome. Furthermore, these data often have a very high false rate. By conceptualizing the interactions at domain level, we provide a more abstract representation of interactome, which also facilitates the discovery of unobserved protein-protein interactions. Although several domain-based approaches have been proposed to predict protein-protein interactions, they usually assume that domain interactions are independent on each other for the convenience of computational modeling. A new framework to predict protein interactions is proposed in this paper, where no assumption is made about domain interactions. Protein interactions may be the result of multiple domain interactions which are dependent on each other. A conjunctive norm form representation is used to capture the relationships between protein interactions and domain interactions. The problem of interaction inference is then modeled as a constraint satisfiability problem and solved via linear programing. Experimental results on a combined yeast data set have demonstrated the robustness and the accuracy of the proposed algorithm. Moreover, we also map some predicted interacting domains to three-dimensional structures of protein complexes to show the validity of our predictions.

Molecular interactions between chondroitin-dermatan sulfate and growth factors/receptors/matrix proteins.

PubMed

Mizumoto, Shuji; Yamada, Shuhei; Sugahara, Kazuyuki

2015-10-01

Recent functional studies on chondroitin sulfate-dermatan sulfate (CS-DS) demonstrated its indispensable roles in various biological events including brain development and cancer. CS-DS proteoglycans exert their physiological activity through interactions with specific proteins including growth factors, cell surface receptors, and matrix proteins. The characterization of these interactions is essential for regulating the biological functions of CS-DS proteoglycans. Although amino acid sequences on the bioactive proteins required for these interactions have already been elucidated, the specific saccharide sequences involved in the binding of CS-DS to target proteins have not yet been sufficiently identified. In this review, recent findings are described on the interaction between CS-DS and some proteins which are especially involved in the central nervous system and cancer development/metastasis. Copyright © 2015. Published by Elsevier Ltd.

DARC: Mapping Surface Topography by Ray-Casting for Effective Virtual Screening at Protein Interaction Sites.

PubMed

Gowthaman, Ragul; Miller, Sven A; Rogers, Steven; Khowsathit, Jittasak; Lan, Lan; Bai, Nan; Johnson, David K; Liu, Chunjing; Xu, Liang; Anbanandam, Asokan; Aubé, Jeffrey; Roy, Anuradha; Karanicolas, John

2016-05-12

Protein-protein interactions represent an exciting and challenging target class for therapeutic intervention using small molecules. Protein interaction sites are often devoid of the deep surface pockets presented by "traditional" drug targets, and crystal structures reveal that inhibitors typically engage these sites using very shallow binding modes. As a consequence, modern virtual screening tools developed to identify inhibitors of traditional drug targets do not perform as well when they are instead deployed at protein interaction sites. To address the need for novel inhibitors of important protein interactions, here we introduce an alternate docking strategy specifically designed for this regime. Our method, termed DARC (Docking Approach using Ray-Casting), matches the topography of a surface pocket "observed" from within the protein to the topography "observed" when viewing a potential ligand from the same vantage point. We applied DARC to carry out a virtual screen against the protein interaction site of human antiapoptotic protein Mcl-1 and found that four of the top-scoring 21 compounds showed clear inhibition in a biochemical assay. The Ki values for these compounds ranged from 1.2 to 21 μM, and each had ligand efficiency comparable to promising small-molecule inhibitors of other protein-protein interactions. These hit compounds do not resemble the natural (protein) binding partner of Mcl-1, nor do they resemble any known inhibitors of Mcl-1. Our results thus demonstrate the utility of DARC for identifying novel inhibitors of protein-protein interactions.

Comprehensive Characterization of Minichromosome Maintenance Complex (MCM) Protein Interactions Using Affinity and Proximity Purifications Coupled to Mass Spectrometry.

PubMed

Dubois, Marie-Line; Bastin, Charlotte; Lévesque, Dominique; Boisvert, François-Michel

2016-09-02

The extensive identification of protein-protein interactions under different conditions is an important challenge to understand the cellular functions of proteins. Here we use and compare different approaches including affinity purification and purification by proximity coupled to mass spectrometry to identify protein complexes. We explore the complete interactome of the minichromosome maintenance (MCM) complex by using both approaches for all of the different MCM proteins. Overall, our analysis identified unique and shared interaction partners and proteins enriched for distinct biological processes including DNA replication, DNA repair, and cell cycle regulation. Furthermore, we mapped the changes in protein interactions of the MCM complex in response to DNA damage, identifying a new role for this complex in DNA repair. In summary, we demonstrate the complementarity of these approaches for the characterization of protein interactions within the MCM complex.

Keep your fingers off my DNA: protein-protein interactions mediated by C2H2 zinc finger domains.

PubMed

Brayer, Kathryn J; Segal, David J

2008-01-01

Cys2-His2 (C2H2) zinc finger domains (ZFs) were originally identified as DNA-binding domains, and uncharacterized domains are typically assumed to function in DNA binding. However, a growing body of evidence suggests an important and widespread role for these domains in protein binding. There are even examples of zinc fingers that support both DNA and protein interactions, which can be found in well-known DNA-binding proteins such as Sp1, Zif268, and Ying Yang 1 (YY1). C2H2 protein-protein interactions (PPIs) are proving to be more abundant than previously appreciated, more plastic than their DNA-binding counterparts, and more variable and complex in their interactions surfaces. Here we review the current knowledge of over 100 C2H2 zinc finger-mediated PPIs, focusing on what is known about the binding surface, contributions of individual fingers to the interaction, and function. An accurate understanding of zinc finger biology will likely require greater insights into the potential protein interaction capabilities of C2H2 ZFs.

Topological, functional, and dynamic properties of the protein interaction networks rewired by benzo(a)pyrene

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ba, Qian; Key Laboratory of Food Safety Risk Assessment, Ministry of Health, Beijing; Li, Junyang

2015-03-01

Benzo(a)pyrene is a common environmental and foodborne pollutant that has been identified as a human carcinogen. Although the carcinogenicity of benzo(a)pyrene has been extensively reported, its precise molecular mechanisms and the influence on system-level protein networks are not well understood. To investigate the system-level influence of benzo(a)pyrene on protein interactions and regulatory networks, a benzo(a)pyrene-rewired protein interaction network was constructed based on 769 key proteins derived from more than 500 literature reports. The protein interaction network rewired by benzo(a)pyrene was a scale-free, highly-connected biological system. Ten modules were identified, and 25 signaling pathways were enriched, most of which belong tomore » the human diseases category, especially cancer and infectious disease. In addition, two lung-specific and two liver-specific pathways were identified. Three pathways were specific in short and medium-term networks (< 48 h), and five pathways were enriched only in the medium-term network (6 h–48 h). Finally, the expression of linker genes in the network was validated by Western blotting. These findings establish the overall, tissue- and time-specific benzo(a)pyrene-rewired protein interaction networks and provide insights into the biological effects and molecular mechanisms of action of benzo(a)pyrene. - Highlights: • Benzo(a)pyrene induced scale-free, highly-connected protein interaction networks. • 25 signaling pathways were enriched through modular analysis. • Tissue- and time-specific pathways were identified.« less

Protein-protein interactions in the regulation of WRKY transcription factors.

PubMed

Chi, Yingjun; Yang, Yan; Zhou, Yuan; Zhou, Jie; Fan, Baofang; Yu, Jing-Quan; Chen, Zhixiang

2013-03-01

It has been almost 20 years since the first report of a WRKY transcription factor, SPF1, from sweet potato. Great progress has been made since then in establishing the diverse biological roles of WRKY transcription factors in plant growth, development, and responses to biotic and abiotic stress. Despite the functional diversity, almost all analyzed WRKY proteins recognize the TTGACC/T W-box sequences and, therefore, mechanisms other than mere recognition of the core W-box promoter elements are necessary to achieve the regulatory specificity of WRKY transcription factors. Research over the past several years has revealed that WRKY transcription factors physically interact with a wide range of proteins with roles in signaling, transcription, and chromatin remodeling. Studies of WRKY-interacting proteins have provided important insights into the regulation and mode of action of members of the important family of transcription factors. It has also emerged that the slightly varied WRKY domains and other protein motifs conserved within each of the seven WRKY subfamilies participate in protein-protein interactions and mediate complex functional interactions between WRKY proteins and between WRKY and other regulatory proteins in the modulation of important biological processes. In this review, we summarize studies of protein-protein interactions for WRKY transcription factors and discuss how the interacting partners contribute, at different levels, to the establishment of the complex regulatory and functional network of WRKY transcription factors.

Probabilistic analysis for identifying the driving force of protein folding

NASA Astrophysics Data System (ADS)

Tokunaga, Yoshihiko; Yamamori, Yu; Matubayasi, Nobuyuki

2018-03-01

Toward identifying the driving force of protein folding, energetics was analyzed in water for Trp-cage (20 residues), protein G (56 residues), and ubiquitin (76 residues) at their native (folded) and heat-denatured (unfolded) states. All-atom molecular dynamics simulation was conducted, and the hydration effect was quantified by the solvation free energy. The free-energy calculation was done by employing the solution theory in the energy representation, and it was seen that the sum of the protein intramolecular (structural) energy and the solvation free energy is more favorable for a folded structure than for an unfolded one generated by heat. Probabilistic arguments were then developed to determine which of the electrostatic, van der Waals, and excluded-volume components of the interactions in the protein-water system governs the relative stabilities between the folded and unfolded structures. It was found that the electrostatic interaction does not correspond to the preference order of the two structures. The van der Waals and excluded-volume components were shown, on the other hand, to provide the right order of preference at probabilities of almost unity, and it is argued that a useful modeling of protein folding is possible on the basis of the excluded-volume effect.

Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords.

PubMed

Koyabu, Shun; Phan, Thi Thanh Thuy; Ohkawa, Takenao

2015-01-01

For the automatic extraction of protein-protein interaction information from scientific articles, a machine learning approach is useful. The classifier is generated from training data represented using several features to decide whether a protein pair in each sentence has an interaction. Such a specific keyword that is directly related to interaction as "bind" or "interact" plays an important role for training classifiers. We call it a dominant keyword that affects the capability of the classifier. Although it is important to identify the dominant keywords, whether a keyword is dominant depends on the context in which it occurs. Therefore, we propose a method for predicting whether a keyword is dominant for each instance. In this method, a keyword that derives imbalanced classification results is tentatively assumed to be a dominant keyword initially. Then the classifiers are separately trained from the instance with and without the assumed dominant keywords. The validity of the assumed dominant keyword is evaluated based on the classification results of the generated classifiers. The assumption is updated by the evaluation result. Repeating this process increases the prediction accuracy of the dominant keyword. Our experimental results using five corpora show the effectiveness of our proposed method with dominant keyword prediction.

The Role of Shape Complementarity in the Protein-Protein Interactions

NASA Astrophysics Data System (ADS)

Li, Ye; Zhang, Xianren; Cao, Dapeng

2013-11-01

We use a dissipative particle dynamic simulation to investigate the effects of shape complementarity on the protein-protein interactions. By monitoring different kinds of protein shape-complementarity modes, we gave a clear mechanism to reveal the role of the shape complementarity in the protein-protein interactions, i.e., when the two proteins with shape complementarity approach each other, the conformation of lipid chains between two proteins would be restricted significantly. The lipid molecules tend to leave the gap formed by two proteins to maximize the configuration entropy, and therefore yield an effective entropy-induced protein-protein attraction, which enhances the protein aggregation. In short, this work provides an insight into understanding the importance of the shape complementarity in the protein-protein interactions especially for protein aggregation and antibody-antigen complexes. Definitely, the shape complementarity is the third key factor affecting protein aggregation and complex, besides the electrostatic-complementarity and hydrophobic complementarity.

Computational Framework for Prediction of Peptide Sequences That May Mediate Multiple Protein Interactions in Cancer-Associated Hub Proteins.

PubMed

Sarkar, Debasree; Patra, Piya; Ghosh, Abhirupa; Saha, Sudipto

2016-01-01

A considerable proportion of protein-protein interactions (PPIs) in the cell are estimated to be mediated by very short peptide segments that approximately conform to specific sequence patterns known as linear motifs (LMs), often present in the disordered regions in the eukaryotic proteins. These peptides have been found to interact with low affinity and are able bind to multiple interactors, thus playing an important role in the PPI networks involving date hubs. In this work, PPI data and de novo motif identification based method (MEME) were used to identify such peptides in three cancer-associated hub proteins-MYC, APC and MDM2. The peptides corresponding to the significant LMs identified for each hub protein were aligned, the overlapping regions across these peptides being termed as overlapping linear peptides (OLPs). These OLPs were thus predicted to be responsible for multiple PPIs of the corresponding hub proteins and a scoring system was developed to rank them. We predicted six OLPs in MYC and five OLPs in MDM2 that scored higher than OLP predictions from randomly generated protein sets. Two OLP sequences from the C-terminal of MYC were predicted to bind with FBXW7, component of an E3 ubiquitin-protein ligase complex involved in proteasomal degradation of MYC. Similarly, we identified peptides in the C-terminal of MDM2 interacting with FKBP3, which has a specific role in auto-ubiquitinylation of MDM2. The peptide sequences predicted in MYC and MDM2 look promising for designing orthosteric inhibitors against possible disease-associated PPIs. Since these OLPs can interact with other proteins as well, these inhibitors should be specific to the targeted interactor to prevent undesired side-effects. This computational framework has been designed to predict and rank the peptide regions that may mediate multiple PPIs and can be applied to other disease-associated date hub proteins for prediction of novel therapeutic targets of small molecule PPI modulators.

NMR Studies of Protein Hydration and Protein-Ligand Interactions

NASA Astrophysics Data System (ADS)

Chong, Yuan

Water on the surface of a protein is called hydration water. Hydration water is known to play a crucial role in a variety of biological processes including protein folding, enzymatic activation, and drug binding. Although the significance of hydration water has been recognized, the underlying mechanism remains far from being understood. This dissertation employs a unique in-situ nuclear magnetic resonance (NMR) technique to study the mechanism of protein hydration and the role of hydration in alcohol-protein interactions. Water isotherms in proteins are measured at different temperatures via the in-situ NMR technique. Water is found to interact differently with hydrophilic and hydrophobic groups on the protein. Water adsorption on hydrophilic groups is hardly affected by the temperature, while water adsorption on hydrophobic groups strongly depends on the temperature around 10 C, below which the adsorption is substantially reduced. This effect is induced by the dramatic decrease in the protein flexibility below 10 C. Furthermore, nanosecond to microsecond protein dynamics and the free energy, enthalpy, and entropy of protein hydration are studied as a function of hydration level and temperature. A crossover at 10 C in protein dynamics and thermodynamics is revealed. The effect of water at hydrophilic groups on protein dynamics and thermodynamics shows little temperature dependence, whereas water at hydrophobic groups has stronger effect above 10 C. In addition, I investigate the role of water in alcohol binding to the protein using the in-situ NMR detection. The isotherms of alcohols are first measured on dry proteins, then on proteins with a series of controlled hydration levels. The free energy, enthalpy, and entropy of alcohol binding are also determined. Two distinct types of alcohol binding are identified. On the one hand, alcohols can directly bind to a few specific sites on the protein. This type of binding is independent of temperature and can be

[Identification of proteins interacting with the circadian clock protein PER1 in tumors using bacterial two-hybrid system technique].

PubMed

Zhang, Yu; Yao, Youlin; Jiang, Siyuan; Lu, Yilu; Liu, Yunqiang; Tao, Dachang; Zhang, Sizhong; Ma, Yongxin

2015-04-01

To identify protein-protein interaction partners of PER1 (period circadian protein homolog 1), key component of the molecular oscillation system of the circadian rhythm in tumors using bacterial two-hybrid system technique. Human cervical carcinoma cell Hela library was adopted. Recombinant bait plasmid pBT-PER1 and pTRG cDNA plasmid library were cotransformed into the two-hybrid system reporter strain cultured in a special selective medium. Target clones were screened. After isolating the positive clones, the target clones were sequenced and analyzed. Fourteen protein coding genes were identified, 4 of which were found to contain whole coding regions of genes, which included optic atrophy 3 protein (OPA3) associated with mitochondrial dynamics and homo sapiens cutA divalent cation tolerance homolog of E. coli (CUTA) associated with copper metabolism. There were also cellular events related proteins and proteins which are involved in biochemical reaction and signal transduction-related proteins. Identification of potential interacting proteins with PER1 in tumors may provide us new insights into the functions of the circadian clock protein PER1 during tumorigenesis.

Linear motif-mediated interactions have contributed to the evolution of modularity in complex protein interaction networks.

PubMed

Kim, Inhae; Lee, Heetak; Han, Seong Kyu; Kim, Sanguk

2014-10-01

The modular architecture of protein-protein interaction (PPI) networks is evident in diverse species with a wide range of complexity. However, the molecular components that lead to the evolution of modularity in PPI networks have not been clearly identified. Here, we show that weak domain-linear motif interactions (DLIs) are more likely to connect different biological modules than strong domain-domain interactions (DDIs). This molecular division of labor is essential for the evolution of modularity in the complex PPI networks of diverse eukaryotic species. In particular, DLIs may compensate for the reduction in module boundaries that originate from increased connections between different modules in complex PPI networks. In addition, we show that the identification of biological modules can be greatly improved by including molecular characteristics of protein interactions. Our findings suggest that transient interactions have played a unique role in shaping the architecture and modularity of biological networks over the course of evolution.

Potential Interference of Protein-Protein Interactions by Graphyne.

PubMed

Luan, Binquan; Huynh, Tien; Zhou, Ruhong

2016-03-10

Graphyne has attracted tremendous attention recently due to its many potentially superior properties relative to those of graphene. Although extensive efforts have been devoted to explore the applicability of graphyne as an alternative nanomaterial for state-of-the-art nanotechnology (including biomedical applications), knowledge regarding its possible adverse effects to biological cells is still lacking. Here, using large-scale all-atom molecular dynamics simulations, we investigate the potential toxicity of graphyne by interfering a protein-protein interaction (ppI). We found that graphyne could indeed disrupt the ppIs by cutting through the protein-protein interface and separating the protein complex into noncontacting ones, due to graphyne's dispersive and hydrophobic interaction with the hydrophobic residues residing at the dimer interface. Our results help to elucidate the mechanism of interaction between graphyne and ppI networks within a biological cell and provide insights for its hazard reduction.

Identification of proteins interacting with lactate dehydrogenase in claw muscle of the porcelain crab Petrolisthes cinctipes

PubMed Central

Cayenne, Andrea P.; Gabert, Beverly; Stillman, Jonathon H.

2011-01-01

Biochemical adaptation of enzymes involves conservation of activity, stability and affinity across a wide range of intracellular and environmental conditions. Enzyme adaptation by alteration of primary structure is well known, but the roles of protein-protein interactions in enzyme adaptation are less well understood. Interspecific differences in thermal stability of lactate dehydrogenase (LDH) in porcelain crabs (genus Petrolisthes) are related to intrinsic differences among LDH molecules and by interactions with other stabilizing proteins. Here, we identified proteins that interact with LDH in porcelain crab claw muscle tissue using co-immunoprecipitation, and showed LDH exists in high molecular weight complexes using size exclusion chromatography and Western blot analyses. Co-immunoprecipitated proteins were separated using 2D SDS PAGE and analyzed by LC/ESI using peptide MS/MS. Peptide MS/MS ions were compared to an EST database for Petrolisthes cinctipes to identify proteins. Identified proteins included cytoskeletal elements, glycolytic enzymes, a phosphagen kinase, and the respiratory protein hemocyanin. Our results support the hypothesis that LDH interacts with glycolytic enzymes in a metabolon structured by cytoskeletal elements that may also include the enzyme for transfer of the adenylate charge in glycolytically produced ATP. Those interactions may play specific roles in biochemical adaptation of glycolytic enzymes. PMID:21968246

Expressed proteins of Herbaspirillum seropedicae in maize (DKB240) roots-bacteria interaction revealed using proteomics.

PubMed

Ferrari, Cibele Santos; Amaral, Fernanda Plucani; Bueno, Jessica Cavalheiro Ferreira; Scariot, Mirella Christine; Valentim-Neto, Pedro Alexandre; Arisi, Ana Carolina Maisonnave

2014-11-01

Several molecular tools have been used to clarify the basis of plant-bacteria interaction; however, the mechanism behind the association is still unclear. In this study, we used a proteomic approach to investigate the root proteome of Zea mays (cv. DKB240) inoculated with Herbaspirillum seropedicae strain SmR1 grown in vitro and harvested 7 days after inoculation. Eighteen differentially accumulated proteins were observed in root samples, ten of which were identified by MALDI-TOF mass spectrometry peptide mass fingerprint. Among the identified proteins, we observed three proteins present exclusively in inoculated root samples and six upregulated proteins and one downregulated protein relative to control. Differentially expressed maize proteins were identified as hypothetical protein ZEAMMB73_483204, hypothetical protein ZEAMMB73_269466, and tubulin beta-7 chain. The following were identified as H. seropedicae proteins: peroxiredoxin protein, EF-Tu elongation factor protein, cation transport ATPase, NADPH:quinone oxidoreductase, dinitrogenase reductase, and type III secretion ATP synthase. Our results presented the first evidence of type III secretion ATP synthase expression during H. seropedicae-maize root interaction.

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting.

PubMed

Radifar, Muhammad; Yuniarti, Nunung; Istyastono, Enade Perdana

2013-01-01

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα). PyPLIF is freely available at http://code.google.com/p/pyplif.

Split Renilla Luciferase Protein Fragment-assisted Complementation (SRL-PFAC) to Characterize Hsp90-Cdc37 Complex and Identify Critical Residues in Protein/Protein Interactions*

PubMed Central

Jiang, Yiqun; Bernard, Denzil; Yu, Yanke; Xie, Yehua; Zhang, Tao; Li, Yanyan; Burnett, Joseph P.; Fu, Xueqi; Wang, Shaomeng; Sun, Duxin

2010-01-01

Hsp90 requires cochaperone Cdc37 to load its clients to the Hsp90 superchaperone complex. The purpose of this study was to utilize split Renilla luciferase protein fragment-assisted complementation (SRL-PFAC) bioluminescence to study the full-length human Hsp90-Cdc37 complex and to identity critical residues and their contributions for Hsp90/Cdc37 interaction in living cells. SRL-PFAC showed that full-length human Hsp90/Cdc37 interaction restored dramatically high luciferase activity through Hsp90-Cdc37-assisted complementation of the N and C termini of luciferase (compared with the set of controls). Immunoprecipitation confirmed that the expressed fusion proteins (NRL-Hsp90 and Cdc37-CRL) preserved their ability to interact with each other and also with native Hsp90 or Cdc37. Molecular dynamic simulation revealed several critical residues in the two interaction patches (hydrophobic and polar) at the interface of Hsp90/Cdc37. Mutagenesis confirmed the critical residues for Hsp90-Cdc37 complex formation. SRL-PFAC bioluminescence evaluated the contributions of these critical residues in Hsp90/Cdc37 interaction. The results showed that mutations in Hsp90 (Q133A, F134A, and A121N) and mutations in Cdc37 (M164A, R167A, L205A, and Q208A) reduced the Hsp90/Cdc37 interaction by 70–95% as measured by the resorted luciferase activity through Hsp90-Cdc37-assisted complementation. In comparison, mutations in Hsp90 (E47A and S113A) and a mutation in Cdc37 (A204E) decreased the Hsp90/Cdc37 interaction by 50%. In contrast, mutations of Hsp90 (R46A, S50A, C481A, and C598A) and mutations in Cdc37 (C54S, C57S, and C64S) did not change Hsp90/Cdc37 interactions. The data suggest that single amino acid mutation in the interface of Hsp90/Cdc37 is sufficient to disrupt its interaction, although Hsp90/Cdc37 interactions are through large regions of hydrophobic and polar interactions. These findings provides a rationale to develop inhibitors for disruption of the Hsp90/Cdc37 interaction

Split Renilla luciferase protein fragment-assisted complementation (SRL-PFAC) to characterize Hsp90-Cdc37 complex and identify critical residues in protein/protein interactions.

PubMed

Jiang, Yiqun; Bernard, Denzil; Yu, Yanke; Xie, Yehua; Zhang, Tao; Li, Yanyan; Burnett, Joseph P; Fu, Xueqi; Wang, Shaomeng; Sun, Duxin

2010-07-02

Hsp90 requires cochaperone Cdc37 to load its clients to the Hsp90 superchaperone complex. The purpose of this study was to utilize split Renilla luciferase protein fragment-assisted complementation (SRL-PFAC) bioluminescence to study the full-length human Hsp90-Cdc37 complex and to identity critical residues and their contributions for Hsp90/Cdc37 interaction in living cells. SRL-PFAC showed that full-length human Hsp90/Cdc37 interaction restored dramatically high luciferase activity through Hsp90-Cdc37-assisted complementation of the N and C termini of luciferase (compared with the set of controls). Immunoprecipitation confirmed that the expressed fusion proteins (NRL-Hsp90 and Cdc37-CRL) preserved their ability to interact with each other and also with native Hsp90 or Cdc37. Molecular dynamic simulation revealed several critical residues in the two interaction patches (hydrophobic and polar) at the interface of Hsp90/Cdc37. Mutagenesis confirmed the critical residues for Hsp90-Cdc37 complex formation. SRL-PFAC bioluminescence evaluated the contributions of these critical residues in Hsp90/Cdc37 interaction. The results showed that mutations in Hsp90 (Q133A, F134A, and A121N) and mutations in Cdc37 (M164A, R167A, L205A, and Q208A) reduced the Hsp90/Cdc37 interaction by 70-95% as measured by the resorted luciferase activity through Hsp90-Cdc37-assisted complementation. In comparison, mutations in Hsp90 (E47A and S113A) and a mutation in Cdc37 (A204E) decreased the Hsp90/Cdc37 interaction by 50%. In contrast, mutations of Hsp90 (R46A, S50A, C481A, and C598A) and mutations in Cdc37 (C54S, C57S, and C64S) did not change Hsp90/Cdc37 interactions. The data suggest that single amino acid mutation in the interface of Hsp90/Cdc37 is sufficient to disrupt its interaction, although Hsp90/Cdc37 interactions are through large regions of hydrophobic and polar interactions. These findings provides a rationale to develop inhibitors for disruption of the Hsp90/Cdc37 interaction.

The Role of Shape Complementarity in the Protein-Protein Interactions

PubMed Central

Li, Ye; Zhang, Xianren; Cao, Dapeng

2013-01-01

We use a dissipative particle dynamic simulation to investigate the effects of shape complementarity on the protein-protein interactions. By monitoring different kinds of protein shape-complementarity modes, we gave a clear mechanism to reveal the role of the shape complementarity in the protein-protein interactions, i.e., when the two proteins with shape complementarity approach each other, the conformation of lipid chains between two proteins would be restricted significantly. The lipid molecules tend to leave the gap formed by two proteins to maximize the configuration entropy, and therefore yield an effective entropy-induced protein-protein attraction, which enhances the protein aggregation. In short, this work provides an insight into understanding the importance of the shape complementarity in the protein-protein interactions especially for protein aggregation and antibody–antigen complexes. Definitely, the shape complementarity is the third key factor affecting protein aggregation and complex, besides the electrostatic-complementarity and hydrophobic complementarity. PMID:24253561

Context-based retrieval of functional modules in protein-protein interaction networks.

PubMed

Dobay, Maria Pamela; Stertz, Silke; Delorenzi, Mauro

2017-03-27

Various techniques have been developed for identifying the most probable interactants of a protein under a given biological context. In this article, we dissect the effects of the choice of the protein-protein interaction network (PPI) and the manipulation of PPI settings on the network neighborhood of the influenza A virus (IAV) network, as well as hits in genome-wide small interfering RNA screen results for IAV host factors. We investigate the potential of context filtering, which uses text mining evidence linked to PPI edges, as a complement to the edge confidence scores typically provided in PPIs for filtering, for obtaining more biologically relevant network neighborhoods. Here, we estimate the maximum performance of context filtering to isolate a Kyoto Encyclopedia of Genes and Genomes (KEGG) network Ki from a union of KEGG networks and its network neighborhood. The work gives insights on the use of human PPIs in network neighborhood approaches for functional inference. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Computational prediction of protein interactions related to the invasion of erythrocytes by malarial parasites.

PubMed

Liu, Xuewu; Huang, Yuxiao; Liang, Jiao; Zhang, Shuai; Li, Yinghui; Wang, Jun; Shen, Yan; Xu, Zhikai; Zhao, Ya

2014-11-30

The invasion of red blood cells (RBCs) by malarial parasites is an essential step in the life cycle of Plasmodium falciparum. Human-parasite surface protein interactions play a critical role in this process. Although several interactions between human and parasite proteins have been discovered, the mechanism related to invasion remains poorly understood because numerous human-parasite protein interactions have not yet been identified. High-throughput screening experiments are not feasible for malarial parasites due to difficulty in expressing the parasite proteins. Here, we performed computational prediction of the PPIs involved in malaria parasite invasion to elucidate the mechanism by which invasion occurs. In this study, an expectation maximization algorithm was used to estimate the probabilities of domain-domain interactions (DDIs). Estimates of DDI probabilities were then used to infer PPI probabilities. We found that our prediction performance was better than that based on the information of D. melanogaster alone when information related to the six species was used. Prediction performance was assessed using protein interaction data from S. cerevisiae, indicating that the predicted results were reliable. We then used the estimates of DDI probabilities to infer interactions between 490 parasite and 3,787 human membrane proteins. A small-scale dataset was used to illustrate the usability of our method in predicting interactions between human and parasite proteins. The positive predictive value (PPV) was lower than that observed in S. cerevisiae. We integrated gene expression data to improve prediction accuracy and to reduce false positives. We identified 80 membrane proteins highly expressed in the schizont stage by fast Fourier transform method. Approximately 221 erythrocyte membrane proteins were identified using published mass spectral datasets. A network consisting of 205 interactions was predicted. Results of network analysis suggest that SNARE proteins of

[Identification and analysis of the proteins interacted with Prestin in cochlear outer hair cells of guinea pig].

PubMed

Luo, X; Wang, J Y; Zhang, F L; Xia, Y

2018-01-07

Objective: To explore the regulation and mechanism of Prestin protein by identifying the proteins interacted with Prestin in cochlear outer hair cell(OHC) and analyzing their biological function. Methods: Co-immunoprecipitation combined mass spectrometry technology was used to isolate and identify the proteins interacted with Prestin protein of OHC, bioinformatics was used to construct Prestin protein interaction network. The proteins interacted with Prestin in OHC of guinea pig were determined by matching primary interaction mass spectrometry with protein interaction network, and annotated their functions. Results: The results of co-immunoprecipitation combined with mass spectrometry showed that 116 kinds of credible proteins could interact with Prestin. By constructing Prestin protein interaction network, matching the results of mass spectrometry and analyzing of sub-cellular localization, eight kinds of proteins were confirmed that they interacted with Prestin directly, namely EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1, and ERH, respectively, which were mainly involved in the synthesis and transportation, transmembrane folding and localization, structural stability and signal transduction of Prestin protein. Conclusion: EEF2, HSP90AB1, FN1, FLNA, EEF1A1, HSP90B1, ATP5A1 and ERH provide molecular basis for sensory amplification function of OHCs by participating in biotransformation, transmembrane folding and localization, signal transduction and other biological processes of Prestin protein.

Elucidating the druggable interface of protein-protein interactions using fragment docking and coevolutionary analysis.

PubMed

Bai, Fang; Morcos, Faruck; Cheng, Ryan R; Jiang, Hualiang; Onuchic, José N

2016-12-13

Protein-protein interactions play a central role in cellular function. Improving the understanding of complex formation has many practical applications, including the rational design of new therapeutic agents and the mechanisms governing signal transduction networks. The generally large, flat, and relatively featureless binding sites of protein complexes pose many challenges for drug design. Fragment docking and direct coupling analysis are used in an integrated computational method to estimate druggable protein-protein interfaces. (i) This method explores the binding of fragment-sized molecular probes on the protein surface using a molecular docking-based screen. (ii) The energetically favorable binding sites of the probes, called hot spots, are spatially clustered to map out candidate binding sites on the protein surface. (iii) A coevolution-based interface interaction score is used to discriminate between different candidate binding sites, yielding potential interfacial targets for therapeutic drug design. This approach is validated for important, well-studied disease-related proteins with known pharmaceutical targets, and also identifies targets that have yet to be studied. Moreover, therapeutic agents are proposed by chemically connecting the fragments that are strongly bound to the hot spots.

Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.

PubMed

Engin, H Billur; Kreisberg, Jason F; Carter, Hannah

2016-01-01

Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10(-4)) and oncogenes (Odds Ratio 1.17, P-value < 10(-3)). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10(-8)). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer

Molecular simulation of the effect of cholesterol on lipid-mediated protein-protein interactions.

PubMed

de Meyer, Frédérick J-M; Rodgers, Jocelyn M; Willems, Thomas F; Smit, Berend

2010-12-01

Experiments and molecular simulations have shown that the hydrophobic mismatch between proteins and membranes contributes significantly to lipid-mediated protein-protein interactions. In this article, we discuss the effect of cholesterol on lipid-mediated protein-protein interactions as function of hydrophobic mismatch, protein diameter and protein cluster size, lipid tail length, and temperature. To do so, we study a mesoscopic model of a hydrated bilayer containing lipids and cholesterol in which proteins are embedded, with a hybrid dissipative particle dynamics-Monte Carlo method. We propose a mechanism by which cholesterol affects protein interactions: protein-induced, cholesterol-enriched, or cholesterol-depleted lipid shells surrounding the proteins affect the lipid-mediated protein-protein interactions. Our calculations of the potential of mean force between proteins and protein clusters show that the addition of cholesterol dramatically reduces repulsive lipid-mediated interactions between proteins (protein clusters) with positive mismatch, but does not affect attractive interactions between proteins with negative mismatch. Cholesterol has only a modest effect on the repulsive interactions between proteins with different mismatch. Copyright © 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.