scholarly journals Un-complicating protein complex prediction.

2015 ◽  
Author(s):  
Konstantinos Koutroumpas ◽  
François Képès
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Parametric Method ◽  
Training Data ◽  
Parametric Methods ◽  
Protein Protein Interaction ◽  
Protein Complex Prediction ◽  
Parameter Values ◽  
Non Parametric

Identification of protein complexes from proteomic experiments is crucial to understand not only their function but also the principles of cellular organization. Advances in experimental techniques have enabled the construction of large-scale protein-protein interaction networks, and computational methods have been developed to analyze high-throughput data. In most cases several parameters are introduced that have to be trained before application. But how do we select the parameter values when there are no training data available? How many data do we need to properly train a method. How is the performance of a method affected when we incorrectly select the parameter values? The above questions, although important to determine the applicability of a method, are most of the time overlooked. We highlight the importance of such an analysis by investigating how limited knowledge, in the form of incomplete training data, affects the performance of parametric protein-complex prediction algorithms. Furthermore, we develop a simple non-parametric method that does not rely on the existence of training data and we compare it with the parametric alternatives. Using datasets from yeast and fly we demonstrate that parametric methods trained with limited data provide sub-optimal predictions, while our non-parametric method performs better or is on par with the parametric alternatives. Overall, our analysis questions, at least for the specific problem, whether parametric methods provide significantly better results than non-parametric ones to justify the additional effort for applying them.

scholarly journals Protein complex similarity based on Weisfeiler-Lehman labeling

2018 ◽  
Author(s):  
Bianca K Stöcker ◽  
Till Schäfer ◽  
Petra Mutzel ◽  
Johannes Köster ◽  
Nils Kriege ◽  
...  
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Similarity Measures ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Complex Prediction ◽  
Protein Protein Interaction Networks ◽  
Human Integrin ◽  
Good Agreement

Being able to quantify the similarity between two protein complexes is essential for numerous applications. Prominent examples are database searches for known complexes with a given query complex, comparison of the output of different protein complex prediction algorithms, or summarizing and clustering protein complexes, e.g., for visualization. While the corresponding problems have received much attention on single proteins and protein families, the question about how to model and compute similarity between protein complexes has not yet been systematically studied. Because protein complexes can be naturally modeled as graphs, in principle general graph similarity measures may be used, but these are often computationally hard to obtain and do not take typical properties of protein complexes into account. Here we propose a parametric family of similarity measures based on Weisfeiler-Lehman labeling. We evaluate it on simulated complexes of the extended human integrin adhesome network. Because the connectivity (graph topology) of real complexes is often unknown and hard to obtain experimentally, we use both known protein-protein interaction networks and known interdependencies (constraints) between interactions to simulate more realistic complexes than from interaction networks alone. We empirically show that the defined family of similarity measures is in good agreement with edit similarity, a similarity measure derived from graph edit distance, but can be much more efficiently computed. It can therefore be used in large-scale studies and simulations and serve as a basis for further refinements of modeling protein complex similarity.

scholarly journals Protein complex similarity based on Weisfeiler-Lehman labeling

2018 ◽  
Author(s):  
Bianca K Stöcker ◽  
Till Schäfer ◽  
Petra Mutzel ◽  
Johannes Köster ◽  
Nils Kriege ◽  
...  
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Similarity Measures ◽  
Interaction Networks ◽  
Protein Protein Interaction ◽  
Protein Complex Prediction ◽  
Protein Protein Interaction Networks ◽  
Human Integrin ◽  
Good Agreement

Being able to quantify the similarity between two protein complexes is essential for numerous applications. Prominent examples are database searches for known complexes with a given query complex, comparison of the output of different protein complex prediction algorithms, or summarizing and clustering protein complexes, e.g., for visualization. While the corresponding problems have received much attention on single proteins and protein families, the question about how to model and compute similarity between protein complexes has not yet been systematically studied. Because protein complexes can be naturally modeled as graphs, in principle general graph similarity measures may be used, but these are often computationally hard to obtain and do not take typical properties of protein complexes into account. Here we propose a parametric family of similarity measures based on Weisfeiler-Lehman labeling. We evaluate it on simulated complexes of the extended human integrin adhesome network. Because the connectivity (graph topology) of real complexes is often unknown and hard to obtain experimentally, we use both known protein-protein interaction networks and known interdependencies (constraints) between interactions to simulate more realistic complexes than from interaction networks alone. We empirically show that the defined family of similarity measures is in good agreement with edit similarity, a similarity measure derived from graph edit distance, but can be much more efficiently computed. It can therefore be used in large-scale studies and simulations and serve as a basis for further refinements of modeling protein complex similarity.

scholarly journals ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

2019 ◽  
Author(s):  
Wojciech Michalak ◽  
Vasileios Tsiamis ◽  
Veit Schwämmle ◽  
Adelina Rogowska-Wrzesińska
Keyword(s):  
T Cell Activation ◽  
Protein Complex ◽  
Quantitative Proteomics ◽  
Large Scale ◽  
Protein Complexes ◽  
Quantitative Measure ◽  
Exploratory Analysis ◽  
List Type ◽  
Link Type ◽  
Complex Components

AbstractWe have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.A fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/The source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowserHighlightsAutomated analysis of protein complexes in proteomics experimentsQuantitative measure of the coordinated changes in protein complex componentsInteractive visualisations for exploratory analysis of proteomics resultsIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

ReSAPP: Predicting overlapping protein complexes by merging multiple-sampled partitions of proteins

2014 ◽  
Vol 12 (06) ◽  
pp. 1442004 ◽  
Author(s):  
So Kobiki ◽  
Osamu Maruyama
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Protein Complexes ◽  
Performance Comparison ◽  
Challenging Problem ◽  
Protein Protein Interaction ◽  
Second Stage ◽  
Novel Method ◽  
Previous Algorithm ◽  
F Measure

Many proteins are known to perform their own functions when they form particular groups of proteins, called protein complexes. With the advent of large-scale protein–protein interaction (PPI) studies, it has been a challenging problem in systems biology to predict protein complexes from PPIs. In this paper, we propose a novel method, called Repeated Simulated Annealing of Partitions of Proteins (ReSAPP), which predicts protein complexes from weighted PPIs. ReSAPP, in the first stage, generates multiple (possibly different) partitions of all proteins of given PPIs by repeatedly applying a simulated annealing based optimization algorithm to the PPIs. In the second stage, all different clusters of size two or more in those multiple partitions are merged into a collection of those clusters, which are outputted as predicted protein complexes. In performance comparison of ReSAPP with our previous algorithm, PPSampler2, as well as other various tools, MCL, MCODE, DPClus, CMC, COACH, RRW, NWE, and PPSampler1, ReSAPP is shown to outperform the other methods. Furthermore, the value of F-measure of ReSAPP is higher than that of the variant of ReSAPP without merging partitions. Thus, we empirically conclude that the combination of sampling multiple partitions and merging them is effective to predict protein complexes.

scholarly journals PERBANDINGAN ANTARA METODE CART (CLASSIFICATION AND EGRESSION TREE) DAN REGRESI LOGISTIK (LOGISTIC REGRESSION) DALAM MENGKLASIFIKASIKAN PASIEN PENDERITA DBD (DEMAM BERDARAH DENGUE)

2018 ◽  
Vol 15 (1) ◽  
pp. 98-107
Author(s):  
R Lestawati ◽  
Rais Rais ◽  
I T Utami
Keyword(s):  
Logistic Regression ◽  
Statistical Methods ◽  
Parametric Method ◽  
Parametric Methods ◽  
Classification Methods ◽  
Classification Result ◽  
The Status ◽  
Classification Table ◽  
Non Parametric

Classification is one of statistical methods in grouping the data compiled systematically. The classification of an object can be done by two approaches, namely classification methods parametric and non-parametric methods. Non-parametric methods is used in this study is the method of CART to be compared to the classification result of the logistic regression as one of a parametric method. From accuracy classification table of CART method to classify the status of DHF patient into category of severe and non-severe exactly 76.3%, whereas the percentage of truth logistic regression was 76.7%, CART method to classify the status of DHF patient into categories of severe and non-severe exactly 76.3%, CART method yielded 4 significant variables that hepatomegaly, epitaksis, melena and diarrhea as well as the classification is divided into several segmens into a more accurate whereas the logistic regression produces only 1 significant variables that hepatomegaly

Denoising Protein–Protein interaction network via variational graph auto-encoder for protein complex detection

2020 ◽  
Vol 18 (03) ◽  
pp. 2040010 ◽  
Author(s):  
Heng Yao ◽  
Jihong Guan ◽  
Tianying Liu
Keyword(s):  
Protein Interaction ◽  
Protein Complex ◽  
Protein Complexes ◽  
Empirical Evaluation ◽  
High Rate ◽  
Detection Methods ◽  
Convolutional Network ◽  
Protein Protein Interaction ◽  
Protein Complex Detection ◽  
Complex Detection

Identifying protein complexes is an important issue in computational biology, as it benefits the understanding of cellular functions and the design of drugs. In the past decades, many computational methods have been proposed by mining dense subgraphs in Protein–Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents accurately detecting complexes directly from the raw PINs. In this paper, we propose a denoising approach for protein complex detection by using variational graph auto-encoder. First, we embed a PIN to vector space by a stacked graph convolutional network (GCN), then decide which interactions in the PIN are credible. If the probability of an interaction being credible is less than a threshold, we delete the interaction. In such a way, we reconstruct a reliable PIN. Following that, we detect protein complexes in the reconstructed PIN by using several typical detection methods, including CPM, Coach, DPClus, GraphEntropy, IPCA and MCODE, and compare the results with those obtained directly from the original PIN. We conduct the empirical evaluation on four yeast PPI datasets (Gavin, Krogan, DIP and Wiphi) and two human PPI datasets (Reactome and Reactomekb), against two yeast complex benchmarks (CYC2008 and MIPS) and three human complex benchmarks (REACT, REACT_uniprotkb and CORE_COMPLEX_human), respectively. Experimental results show that with the reconstructed PINs obtained by our denoising approach, complex detection performance can get obviously boosted, in most cases by over 5%, sometimes even by 200%. Furthermore, we compare our approach with two existing denoising methods (RWS and RedNemo) while varying different matching rates on separate complex distributions. Our results show that in most cases (over 2/3), the proposed approach outperforms the existing methods.

scholarly journals Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association

Biomolecules ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. 1056 ◽  
Author(s):  
Kalyani Dhusia ◽  
Zhaoqian Su ◽  
Yinghao Wu
Keyword(s):  
Protein Complex ◽  
Large Scale ◽  
Protein Complexes ◽  
Kinetic Property ◽  
Simulation Method ◽  
Coarse Grained ◽  
Structural Level ◽  
Simulation Approach ◽  
Protein Association ◽  
Order Of Magnitude

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.

scholarly journals Comparison of Three Statistical Classification Techniques for Maser Identification

2016 ◽  
Vol 33 ◽  
Author(s):  
Ellen M. Manning ◽  
Barbara R. Holland ◽  
Simon P. Ellingsen ◽  
Shari L. Breen ◽  
Xi Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forests ◽  
Parametric Method ◽  
Parametric Models ◽  
Critical Parameters ◽  
Parametric Methods ◽  
Statistical Classification ◽  
Classification Techniques ◽  
Linear Discriminant ◽  
Non Parametric

AbstractWe applied three statistical classification techniques—linear discriminant analysis (LDA), logistic regression, and random forests—to three astronomical datasets associated with searches for interstellar masers. We compared the performance of these methods in identifying whether specific mid-infrared or millimetre continuum sources are likely to have associated interstellar masers. We also discuss the interpretability of the results of each classification technique. Non-parametric methods have the potential to make accurate predictions when there are complex relationships between critical parameters. We found that for the small datasets the parametric methods logistic regression and LDA performed best, for the largest dataset the non-parametric method of random forests performed with comparable accuracy to parametric techniques, rather than any significant improvement. This suggests that at least for the specific examples investigated here accuracy of the predictions obtained is not being limited by the use of parametric models. We also found that for LDA, transformation of the data to match a normal distribution led to a significant improvement in accuracy. The different classification techniques had significant overlap in their predictions; further astronomical observations will enable the accuracy of these predictions to be tested.

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