Misuse of the Michaelis-Menten rate law for protein interaction networks and its remedy.

For over a century, the Michaelis-Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.

IID 2018 update: context-specific physical protein-protein interactions in human, model organisms and domesticated species.

Knowing the set of physical protein-protein interactions (PPIs) that occur in a particular context-a tissue, disease, or other condition-can provide valuable insights into key research questions. However, while the number of identified human PPIs is expanding rapidly, context information remains limited, and for most non-human species context-specific networks are completely unavailable. The Integrated Interactions Database (IID) provides one of the most comprehensive sets of context-specific human PPI networks, including networks for 133 tissues, 91 disease conditions, and many other contexts. Importantly, it also provides context-specific networks for 17 non-human species including model organisms and domesticated animals. These species are vitally important for drug discovery and agriculture. IID integrates interactions from multiple databases and datasets. It comprises over 4.8 million PPIs annotated with several types of context: tissues, subcellular localizations, diseases, and druggability information (the latter three are new annotations not available in the previous version). This update increases the number of species from 6 to 18, the number of PPIs from ∼1.5 million to ∼4.8 million, and the number of tissues from 30 to 133. IID also now supports topology and enrichment analyses of returned networks. IID is available at http://ophid.utoronto.ca/iid.

Prediction of Protein-Protein Interaction Sites Using Convolutional Neural Network and Improved Data Sets.

Protein-protein interaction (PPI) sites play a key role in the formation of protein complexes, which is the basis of a variety of biological processes. Experimental methods to solve PPI sites are expensive and time-consuming, which has led to the development of different kinds of prediction algorithms. We propose a convolutional neural network for PPI site prediction and use residue binding propensity to improve the positive samples. Our method obtains a remarkable result of the area under the curve (AUC) = 0.912 on the improved data set. In addition, it yields much better results on samples with high binding propensity than on randomly selected samples. This suggests that there are considerable false-positive PPI sites in the positive samples defined by the distance between residue atoms.

Protein complex analysis: From raw protein lists to protein interaction networks.

The elucidation of molecular interaction networks is one of the pivotal challenges in the study of biology. Affinity purification-mass spectrometry and other co-complex methods have become widely employed experimental techniques to identify protein complexes. These techniques typically suffer from a high number of false negatives and false positive contaminants due to technical shortcomings and purification biases. To support a diverse range of experimental designs and approaches, a large number of computational methods have been proposed to filter, infer and validate protein interaction networks from experimental pull-down MS data. Nevertheless, this expansion of available methods complicates the selection of the most optimal ones to support systems biology-driven knowledge extraction. In this review, we give an overview of the most commonly used computational methods to process and interpret co-complex results, and we discuss the issues and unsolved problems that still exist within the field. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:600-614, 2017.

APID interactomes: providing proteome-based interactomes with controlled quality for multiple species and derived networks.

APID (Agile Protein Interactomes DataServer) is an interactive web server that provides unified generation and delivery of protein interactomes mapped to their respective proteomes. This resource is a new, fully redesigned server that includes a comprehensive collection of protein interactomes for more than 400 organisms (25 of which include more than 500 interactions) produced by the integration of only experimentally validated protein-protein physical interactions. For each protein-protein interaction (PPI) the server includes currently reported information about its experimental validation to allow selection and filtering at different quality levels. As a whole, it provides easy access to the interactomes from specific species and includes a global uniform compendium of 90,379 distinct proteins and 678,441 singular interactions. APID integrates and unifies PPIs from major primary databases of molecular interactions, from other specific repositories and also from experimentally resolved 3D structures of protein complexes where more than two proteins were identified. For this purpose, a collection of 8,388 structures were analyzed to identify specific PPIs. APID also includes a new graph tool (based on Cytoscape.js) for visualization and interactive analyses of PPI networks. The server does not require registration and it is freely available for use at http://apid.dep.usal.es.

RocSampler: regularizing overlapping protein complexes in protein-protein interaction networks.

BACKGROUND: In recent years, protein-protein interaction (PPI) networks have been well recognized as important resources to elucidate various biological processes and cellular mechanisms. In this paper, we address the problem of predicting protein complexes from a PPI network. This problem has two difficulties. One is related to small complexes, which contains two or three components. It is relatively difficult to identify them due to their simpler internal structure, but unfortunately complexes of such sizes are dominant in major protein complex databases, such as CYC2008. Another difficulty is how to model overlaps between predicted complexes, that is, how to evaluate different predicted complexes sharing common proteins because CYC2008 and other databases include such protein complexes. Thus, it is critical how to model overlaps between predicted complexes to identify them simultaneously. RESULTS: In this paper, we propose a sampling-based protein complex prediction method, RocSampler (Regularizing Overlapping Complexes), which exploits, as part of the whole scoring function, a regularization term for the overlaps of predicted complexes and that for the distribution of sizes of predicted complexes. We have implemented RocSampler in MATLAB and its executable file for Windows is available at the site, http://imi.kyushu-u.ac.jp/~om/software/RocSampler/ . CONCLUSIONS: We have applied RocSampler to five yeast PPI networks and shown that it is superior to other existing methods. This implies that the design of scoring functions including regularization terms is an effective approach for protein complex prediction.

Measuring protein interactions using Förster resonance energy transfer and fluorescence lifetime imaging microscopy.

The method of fluorescence lifetime imaging microscopy (FLIM) is a quantitative approach that can be used to detect Förster resonance energy transfer (FRET). The use of FLIM to measure the FRET that results from the interactions between proteins labeled with fluorescent proteins (FPs) inside living cells provides a non-invasive method for mapping interactomes. Here, the use of the phasor plot method to analyze frequency domain (FD) FLIM measurements is described, and measurements obtained from cells producing the 'FRET standard' fusion proteins are used to validate the FLIM system for FRET measurements. The FLIM FRET approach is then used to measure both homologous and heterologous protein-protein interactions (PPI) involving the CCAAT/enhancer-binding protein alpha (C/EBPα). C/EBPα is a transcription factor that controls cell differentiation, and localizes to heterochromatin where it interacts with the heterochromatin protein 1 alpha (HP1α). The FLIM-FRET method is used to quantify the homologous interactions between the FP-labeled basic leucine zipper (BZip) domain of C/EBPα. Then the heterologous interactions between the C/EBPa BZip domain and HP1a are quantified using the FRET-FLIM method. The results demonstrate that the basic region and leucine zipper (BZip) domain of C/EBPα is sufficient for the interaction with HP1α in regions of heterochromatin.

Bayesian integration of networks without gold standards.

MOTIVATION: Biological experiments give insight into networks of processes inside a cell, but are subject to error and uncertainty. However, due to the overlap between the large number of experiments reported in public databases it is possible to assess the chances of individual observations being correct. In order to do so, existing methods rely on high-quality 'gold standard' reference networks, but such reference networks are not always available. RESULTS: We present a novel algorithm for computing the probability of network interactions that operates without gold standard reference data. We show that our algorithm outperforms existing gold standard-based methods. Finally, we apply the new algorithm to a large collection of genetic interaction and protein-protein interaction experiments. AVAILABILITY: The integrated dataset and a reference implementation of the algorithm as a plug-in for the Ondex data integration framework are available for download at http://bio-nexus.ncl.ac.uk/projects/nogold/

Negative protein-protein interaction datasets derived from large-scale two-hybrid experiments.

Negative protein-protein interaction datasets are needed for training and evaluation of interaction prediction methods, as well as validation of high-throughput interaction discovery experiments. In large-scale two-hybrid assays, the direct interaction of a large number of protein pairs is systematically probed. We present a simple method to harness two-hybrid data to obtain negative protein-protein interaction datasets, which we validated using other available experimental data. The method identifies interactions that were likely tested but not observed in a two-hybrid screen. For each negative interaction, a confidence score is defined as the shortest-path length between the two proteins in the interaction network derived from the two-hybrid experiment. We show that these high-quality negative datasets are particularly important when a specific biological context is considered, such as in the study of protein interaction specificity. We also illustrate the use of a negative dataset in the evaluation of the InterPreTS interaction prediction method.

A comparison and optimization of yeast two-hybrid systems.

Two-hybrid (Y2H) assays are available in a variety of different versions, including bacterial, yeast, and mammalian systems. However, even when done exclusively in yeast, multiple different host strains, vectors, reporter genes, or protocols can be used. Here we systematically compare protein-protein interactions (PPIs) from several previously published Y2H datasets. PPIs of a human gold-standard dataset were generated by Y2H assays as well as other methods such as LUMIER or protein fragment complementation assays (PCAs). Different Y2H methods detect substantially different subsets of these PPIs, even when protocols are standardized. In order to maximize the number of interactions found and to minimize the number of false positive interactions we recommend to combine multiple vectors and protocols. While the combined results of all 18 methods detected about 92% of a gold-standard interaction set, a combination of just three Y2H assays detected up to 78% of these protein pairs, or up to 83% when a fourth assay was included. These findings indicate that three or four separate assays may be sufficient to detect the majority of protein-protein interactions in many systems.

Quantitative real-time PCR as a sensitive protein-protein interaction quantification method and a partial solution for non-accessible autoactivator and false-negative molecule analysis in the yeast two-hybrid system.

Many functional proteomic experiments make use of high-throughput technologies such as mass spectrometry combined with two-dimensional polyacrylamide gel electrophoresis and the yeast two-hybrid (Y2H) system. Currently there are even automated versions of the Y2H system available that can be used for proteome-wide research. The Y2H system has the capacity to deliver a profusion of Y2H positive colonies from a single library screen. However, subsequent analysis of these numerous primary candidates with complementary methods can be overwhelming. Therefore, a method to select the most promising candidates with strong interaction properties might be useful to reduce the number of candidates requiring further analysis. The method described here offers a new way of quantifying and rating the performance of positive Y2H candidates. The novelty lies in the detection and measurement of mRNA expression instead of proteins or conventional Y2H genetic reporters. This method correlates well with the direct genetic reporter readouts usually used in the Y2H system, and has greater sensitivity for detecting and quantifying protein-protein interactions (PPIs) than the conventional Y2H system, as demonstrated by detection of the Y2H false-negative PPI of RXR/PPARG. Approximately 20% of all proteins are not suitable for the Y2H system, the so-called autoactivators. A further advantage of this method is the possibility to evaluate molecules that usually cannot be analyzed in the Y2H system, exemplified by a VDR-LXXLL motif peptide interaction.

Fifteen years of the yeast three-hybrid system: RNA-protein interactions under investigation.

In 1996, the Wickens and the Kuhl labs developed the yeast three-hybrid system independently. By expressing two chimeric proteins and one chimeric RNA molecule in Saccharomyces cerevisiae, this method allows in vivo monitoring of RNA-protein interactions by measuring the expression levels of HIS3 and LacZ reporter genes. Specific RNA targets have been used to characterize unknown RNA binding proteins. Previously described RNA binding proteins have also been used as bait to select new RNA targets. Finally, this method has been widely used to investigate or confirm previously suspected RNA-protein interactions. However, this method falls short in some aspects, such as RNA display and selection of false positive molecules. This review will summarize the results obtained with this method from the past 15years, as well as on recent efforts to improve its specificity.

Multiplex IP-FCM (immunoprecipitation-flow cytometry): Principles and guidelines for assessing physiologic protein-protein interactions in multiprotein complexes.

There is significant interest in the development of methods with the potential to increase access to 'the interactome' for both experimental and clinical applications. Immunoprecipitation detected by flow cytometry (IP-FCM) is a robust, biochemical method that can be used for measuring physiologic protein-protein interactions (PPI) in multiprotein complexes (MPC) with high sensitivity. Because it is based on antibody-mediated capture of protein complexes onto microspheres, IP-FCM is potentially compatible with a multiplex platform that could allow simultaneous assessment of many physiologic PPI. Here, we consider the principles of ambient analyte conditions (AAC) and inter-bead independence, and provide a template set of experiments showing how to convert singleplex IP-FCM to multiplex IP-FCM, including assays to confirm the validity of the experimental conditions for data acquisition. We conclude that singleplex IP-FCM can be successfully upgraded to multiplex format, and propose that the unique strengths of multiplex IP-FCM make it a method that is likely to facilitate the acquisition of new PPI data from primary cell sources.

Bioinformatics analysis of protein interaction networks: statistics, topologies, and meeting the standards of experimental biologists.

In recent years, bioinformatics analyses of protein networks have allowed researchers to obtain exceptional theoretical predictions and subsequent experimental confirmations. The current view is that protein networks are scale-free networks and have a topology analogous to that of transport networks, the Internet, and social networks. However, an alternative hypothesis exists in which protein networks and scale-free networks possess significantly different properties. In this work, we show that existing information is insufficient to describe protein networks as scale-free networks.

HitPredict: a database of quality assessed protein-protein interactions in nine species.

Despite the availability of a large number of protein-protein interactions (PPIs) in several species, researchers are often limited to using very small subsets in a few organisms due to the high prevalence of spurious interactions. In spite of the importance of quality assessment of experimentally determined PPIs, a surprisingly small number of databases provide interactions with scores and confidence levels. We introduce HitPredict (http://hintdb.hgc.jp/htp/), a database with quality assessed PPIs in nine species. HitPredict assigns a confidence level to interactions based on a reliability score that is computed using evidence from sequence, structure and functional annotations of the interacting proteins. HitPredict was first released in 2005 and is updated annually. The current release contains 36,930 proteins with 176,983 non-redundant, physical interactions, of which 116,198 (66%) are predicted to be of high confidence.

Improved method for protein complex detection using bottleneck proteins.

BACKGROUND: Detecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable. METHODS: For more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other. RESULTS: We applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection. CONCLUSIONS: Our algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.

On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach.

BACKGROUND: Most previous Protein Protein Interaction (PPI) studies evaluated their algorithms' performance based on "per-instance" precision and recall, in which the instances of an interaction relation were evaluated independently. However, we argue that this standard evaluation method should be revisited. In a large corpus, the same relation can be described in various different forms and, in practice, correctly identifying not all but a small subset of them would often suffice to detect the given interaction. METHODS: In this regard, we propose a more pragmatic "per-relation" basis performance evaluation method instead of the conventional per-instance basis method. In the per-relation basis method, only a subset of a relation's instances needs to be correctly identified to make the relation positive. In this work, we also introduce a new high-precision rule-based PPI extraction algorithm. While virtually all current PPI extraction studies focus on improving F-score, aiming to balance the performance on both precision and recall, in many realistic scenarios involving large corpora, one can benefit more from a high-precision algorithm than a high-recall counterpart. RESULTS: We show that our algorithm not only achieves better per-relation performance than previous solutions but also serves as a good complement to the existing PPI extraction tools. Our algorithm improves the performance of the existing tools through simple pipelining. CONCLUSION: The significance of this research can be found in that this research brought new perspective to the performance evaluation of PPI extraction studies, which we believe is more important in practice than existing evaluation criteria. Given the new evaluation perspective, we also showed the importance of a high-precision extraction tool and validated the efficacy of our rule-based system as the high-precision tool candidate.

Up-to-date catalogues of yeast protein complexes.

Gold standard datasets on protein complexes are key to inferring and validating protein-protein interactions. Despite much progress in characterizing protein complexes in the yeast Saccharomyces cerevisiae, numerous researchers still use as reference the manually curated complexes catalogued by the Munich Information Center of Protein Sequences database. Although this catalogue has served the community extremely well, it no longer reflects the current state of knowledge. Here, we report two catalogues of yeast protein complexes as results of systematic curation efforts. The first one, denoted as CYC2008, is a comprehensive catalogue of 408 manually curated heteromeric protein complexes reliably backed by small-scale experiments reported in the current literature. This catalogue represents an up-to-date reference set for biologists interested in discovering protein interactions and protein complexes. The second catalogue, denoted as YHTP2008, comprises 400 high-throughput complexes annotated with current literature evidence. Among them, 262 correspond, at least partially, to CYC2008 complexes. Evidence for interacting subunits is collected for 68 complexes that have only partial or no overlap with CYC2008 complexes, whereas no literature evidence was found for 100 complexes. Some of these partially supported and as yet unsupported complexes may be interesting candidates for experimental follow up. Both catalogues are freely available at: http://wodaklab.org/cyc2008/.

Towards low false discovery rate estimation for protein-protein interactions detected by chemical cross-linking.

Chemical cross-linking (CX) of proteins in vivo or in cell free extracts followed by mass spectrometric (MS) identification of linked peptide pairs (CXMS) can reveal protein-protein interactions (PPIs) both at a proteome wide scale and the level of cross-linked amino acid residues. However, error estimation at the level of PPI remains challenging in large scale datasets. Here we discuss recent advances in the recognition of spurious inter-protein peptide pairs and in diminishing the FDR for these PPI-signaling cross-links, such as the use of chromatographic retention time prediction, in order to come to a more reliable reporting of PPIs.