MIPS: analysis and annotation of proteins from whole genomes in 2005

A protein network refinement method based on module discovery and biological information

BACKGROUND

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development to discover drug targets and prevent disease. Nowadays, node ranking methods are a common way to identify essential proteins, but the poor data quality of the underlying PIN has somewhat hindered the identification accuracy of essential proteins for these methods in the PIN. Therefore, researchers constructed refinement networks by considering certain biological properties of interacting protein pairs to improve the performance of node ranking methods in the PIN. Studies show that proteins in a complex are more likely to be essential than proteins not present in the complex. However, the modularity is usually ignored for the refinement methods of the PINs.

METHODS

Based on this, we proposed a network refinement method based on module discovery and biological information. The idea is, first, to extract the maximal connected subgraph in the PIN, and to divide it into different modules by using Fast-unfolding algorithm; then, to detect critical modules according to the orthologous information, subcellular localization information and topology information within each module; finally, to construct a more refined network (CM-PIN) by using the identified critical modules.

RESULTS

To evaluate the effectiveness of the proposed method, we used 12 typical node ranking methods (LAC, DC, DMNC, NC, TP, LID, CC, BC, PR, LR, PeC, WDC) to compare the overall performance of the CM-PIN with those on the S-PIN, D-PIN and RD-PIN. The experimental results showed that the CM-PIN was optimal in terms of the identification number of essential proteins, precision-recall curve, Jackknifing method and other criteria, and can help to identify essential proteins more accurately.

EPI-SF: essential protein identification in protein interaction networks using sequence features

Proteins are considered indispensable for facilitating an organism's viability, reproductive capabilities, and other fundamental physiological functions. Conventional biological assays are characterized by prolonged duration, extensive labor requirements, and financial expenses in order to identify essential proteins. Therefore, it is widely accepted that employing computational methods is the most expeditious and effective approach to successfully discerning essential proteins. Despite being a popular choice in machine learning (ML) applications, the deep learning (DL) method is not suggested for this specific research work based on sequence features due to the restricted availability of high-quality training sets of positive and negative samples. However, some DL works on limited availability of data are also executed at recent times which will be our future scope of work. Conventional ML techniques are thus utilized in this work due to their superior performance compared to DL methodologies. In consideration of the aforementioned, a technique called EPI-SF is proposed here, which employs ML to identify essential proteins within the protein-protein interaction network (PPIN). The protein sequence is the primary determinant of protein structure and function. So, initially, relevant protein sequence features are extracted from the proteins within the PPIN. These features are subsequently utilized as input for various machine learning models, including XGB Boost Classifier, AdaBoost Classifier, logistic regression (LR), support vector classification (SVM), Decision Tree model (DT), Random Forest model (RF), and Naïve Bayes model (NB). The objective is to detect the essential proteins within the PPIN. The primary investigation conducted on yeast examined the performance of various ML models for yeast PPIN. Among these models, the RF model technique had the highest level of effectiveness, as indicated by its precision, recall, F1-score, and AUC values of 0.703, 0.720, 0.711, and 0.745, respectively. It is also found to be better in performance when compared to the other state-of-arts based on traditional centrality like betweenness centrality (BC), closeness centrality (CC), etc. and deep learning methods as well like DeepEP, as emphasized in the result section. As a result of its favorable performance, EPI-SF is later employed for the prediction of novel essential proteins inside the human PPIN. Due to the tendency of viruses to selectively target essential proteins involved in the transmission of diseases within human PPIN, investigations are conducted to assess the probable involvement of these proteins in COVID-19 and other related severe diseases.

MEM-FET: Essential protein prediction using membership feature and machine learning approach

Proteins are played key roles in different functionalities in our daily life. All functional roles of a protein are a bit enhanced in interaction compared to individuals. Identification of essential proteins of an organism is a time consume and costly task during observation in the wet lab. The results of observation in wet lab always ensure high reliability and accuracy in the biological ground. Essential protein prediction using computational approaches is an alternative choice in research. It proves its significance rapidly in day-to-day life as well as reduces the experimental cost of wet lab effectively. Existing computational methods were implemented using Protein interaction networks (PPIN), Sequence, Gene Expression Dataset (GED), Gene Ontology (GO), Orthologous groups, and Subcellular localized datasets. Machine learning has diverse categories of features that enable to model and predict essential macromolecules of understudied organisms. A novel methodology MEM-FET (membership feature) is predicted based on features, that is, edge clustering coefficient, Average clustering coefficient, subcellular localization, and Gene Ontology within a compartment of common neighbors. The accuracy (ACC) values of the predicted true positive (TP) essential proteins are 0.79, 0.74, 0.78, and 0.71 for YHQ, YMIPS, YDIP, and YMBD datasets. An enriched set of essential proteins are also predicted using the MEM-FET algorithm. Ensemble ML also validated the proposed model with an accuracy of 60%. It has been predicted that MEM-FET algorithms outperform other existing algorithms with an ACC value of 80% for the yeast dataset.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein-protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

PCGAN: a generative approach for protein complex identification from protein interaction networks

MOTIVATION

Protein complexes are groups of polypeptide chains linked by non-covalent protein-protein interactions, which play important roles in biological systems and perform numerous functions, including DNA transcription, mRNA translation, and signal transduction. In the past decade, a number of computational methods have been developed to identify protein complexes from protein interaction networks by mining dense subnetworks or subgraphs.

RESULTS

In this article, different from the existing works, we propose a novel approach for this task based on generative adversarial networks, which is called PCGAN, meaning identifying Protein Complexes by GAN. With the help of some real complexes as training samples, our method can learn a model to generate new complexes from a protein interaction network. To effectively support model training and testing, we construct two more comprehensive and reliable protein interaction networks and a larger gold standard complex set by merging existing ones of the same organism (including human and yeast). Extensive comparison studies indicate that our method is superior to existing protein complex identification methods in terms of various performance metrics. Furthermore, functional enrichment analysis shows that the identified complexes are of high biological significance, which indicates that these generated protein complexes are very possibly real complexes.

AVAILABILITY AND IMPLEMENTATION

https://github.com/yul-pan/PCGAN.

A disease-related essential protein prediction model based on the transfer neural network

Essential proteins play important roles in the development and survival of organisms whose mutations are proven to be the drivers of common internal diseases having higher prevalence rates. Due to high costs of traditional biological experiments, an improved Transfer Neural Network (TNN) was designed to extract raw features from multiple biological information of proteins first, and then, based on the newly-constructed Transfer Neural Network, a novel computational model called TNNM was designed to infer essential proteins in this paper. Different from traditional Markov chain, since Transfer Neural Network adopted the gradient descent algorithm to automatically obtain the transition probability matrix, the prediction accuracy of TNNM was greatly improved. Moreover, additional antecedent memory coefficient and bias term were introduced in Transfer Neural Network, which further enhanced both the robustness and the non-linear expression ability of TNNM as well. Finally, in order to evaluate the identification performance of TNNM, intensive experiments have been executed based on two well-known public databases separately, and experimental results show that TNNM can achieve better performance than representative state-of-the-art prediction models in terms of both predictive accuracies and decline rate of accuracies. Therefore, TNNM may play an important role in key protein prediction in the future.

Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins

Identifying key proteins from protein-protein interaction (PPI) networks is one of the most fundamental and important tasks for computational biologists. However, the protein interactions obtained by high-throughput technology are characterized by a high false positive rate, which severely hinders the prediction accuracy of the current computational methods. In this paper, we propose a novel strategy to identify key proteins by constructing reliable PPI networks. Five Gene Ontology (GO)-based semantic similarity measurements (Jiang, Lin, Rel, Resnik, and Wang) are used to calculate the confidence scores for protein pairs under three annotation terms (Molecular function (MF), Biological process (BP), and Cellular component (CC)). The protein pairs with low similarity values are assumed to be low-confidence links, and the refined PPI networks are constructed by filtering the low-confidence links. Six topology-based centrality methods (the BC, DC, EC, NC, SC, and aveNC) are applied to test the performance of the measurements under the original network and refined network. We systematically compare the performance of the five semantic similarity metrics with the three GO annotation terms on four benchmark datasets, and the simulation results show that the performance of these centrality methods under refined PPI networks is relatively better than that under the original networks. Resnik with a BP annotation term performs best among all five metrics with the three annotation terms. These findings suggest the importance of semantic similarity metrics in measuring the reliability of the links between proteins and highlight the Resnik metric with the BP annotation term as a favourable choice.

Identification of Essential Proteins Based on Local Random Walk and Adaptive Multi-View Multi-Label Learning

Accumulating evidences have indicated that essential proteins play vital roles in human physiological process. In recent years, although researches on prediction of essential proteins have been developing rapidly, there are as well various limitations such as unsatisfactory data suitability, low accuracy of predictive results and so on. In this manuscript, a novel method called RWAMVL was proposed to predict essential proteins based on the Random Walk and the Adaptive Multi-View multi-label Learning. In RWAMVL, considering that the inherent noise is ubiquitous in existing datasets of known protein-protein interactions (PPIs), a variety of different features including biological features of proteins and topological features of PPI networks were obtained by adopting adaptive multi-view multi-label learning first. And then, an improved random walk method was designed to detect essential proteins based on these different features. Finally, in order to verify the predictive performance of RWAMVL, intensive experiments were done to compare it with multiple state-of-the-art predictive methods under different expeditionary frameworks. And as a result, RWAMVL was proven that it can achieve better prediction accuracy than all those competitive methods, which demonstrated as well that RWAMVL may be a potential tool for prediction of key proteins in the future.

Inference of pan-cancer related genes by orthologs matching based on enhanced LSTM model

Many disease-related genes have been found to be associated with cancer diagnosis, which is useful for understanding the pathophysiology of cancer, generating targeted drugs, and developing new diagnostic and treatment techniques. With the development of the pan-cancer project and the ongoing expansion of sequencing technology, many scientists are focusing on mining common genes from The Cancer Genome Atlas (TCGA) across various cancer types. In this study, we attempted to infer pan-cancer associated genes by examining the microbial model organism Saccharomyces Cerevisiae (Yeast) by homology matching, which was motivated by the benefits of reverse genetics. First, a background network of protein-protein interactions and a pathogenic gene set involving several cancer types in humans and yeast were created. The homology between the human gene and yeast gene was then discovered by homology matching, and its interaction sub-network was obtained. This was undertaken following the principle that the homologous genes of the common ancestor may have similarities in expression. Then, using bidirectional long short-term memory (BiLSTM) in combination with adaptive integration of heterogeneous information, we further explored the topological characteristics of the yeast protein interaction network and presented a node representation score to evaluate the node ability in graphs. Finally, homologous mapping for human genes matched the important genes identified by ensemble classifiers for yeast, which may be thought of as genes connected to all types of cancer. One way to assess the performance of the BiLSTM model is through experiments on the database. On the other hand, enrichment analysis, survival analysis, and other outcomes can be used to confirm the biological importance of the prediction results. You may access the whole experimental protocols and programs at https://github.com/zhuyuan-cug/AI-BiLSTM/tree/master.

Rule-Based Pruning and In Silico Identification of Essential Proteins in Yeast PPIN

Proteins are vital for the significant cellular activities of living organisms. However, not all of them are essential. Identifying essential proteins through different biological experiments is relatively more laborious and time-consuming than the computational approaches used in recent times. However, practical implementation of conventional scientific methods sometimes becomes challenging due to poor performance impact in specific scenarios. Thus, more developed and efficient computational prediction models are required for essential protein identification. An effective methodology is proposed in this research, capable of predicting essential proteins in a refined yeast protein–protein interaction network (PPIN). The rule-based refinement is done using protein complex and local interaction density information derived from the neighborhood properties of proteins in the network. Identification and pruning of non-essential proteins are equally crucial here. In the initial phase, careful assessment is performed by applying node and edge weights to identify and discard the non-essential proteins from the interaction network. Three cut-off levels are considered for each node and edge weight for pruning the non-essential proteins. Once the PPIN has been filtered out, the second phase starts with two centralities-based approaches: (1) local interaction density (LID) and (2) local interaction density with protein complex (LIDC), which are successively implemented to identify the essential proteins in the yeast PPIN. Our proposed methodology achieves better performance in comparison to the existing state-of-the-art techniques.

Star topology convolution for graph representation learning

We present a novel graph convolutional method called star topology convolution (STC). This method makes graph convolution more similar to conventional convolutional neural networks (CNNs) in Euclidean feature spaces. STC learns subgraphs which have a star topology rather than learning a fixed graph like most spectral methods. Due to the properties of a star topology, STC is graph-scale free (without a fixed graph size constraint). It has fewer parameters in its convolutional filter and is inductive, so it is more flexible and can be applied to large and evolving graphs. The convolutional filter is learnable and localized, similar to CNNs in Euclidean feature spaces, and can share weights across graphs. To test the method, STC was compared with the state-of-the-art graph convolutional methods in a supervised learning setting on nine node properties prediction benchmark datasets: Cora, Citeseer, Pubmed, PPI, Arxiv, MAG, ACM, DBLP, and IMDB. The experimental results showed that STC achieved the state-of-the-art performance on all these datasets and maintained good robustness. In an essential protein identification task, STC outperformed the state-of-the-art essential protein identification methods. An application of using pretrained STC as the embedding for feature extraction of some downstream classification tasks was introduced. The experimental results showed that STC can share weights across different graphs and be used as the embedding to improve the performance of downstream tasks.

DPCMNE: Detecting Protein Complexes From Protein-Protein Interaction Networks Via Multi-Level Network Embedding

Biological functions of a cell are typically carried out through protein complexes. The detection of protein complexes is therefore of great significance for understanding the cellular organizations and protein functions. In the past decades, many computational methods have been proposed to detect protein complexes. However, most of the existing methods just search the local topological information to mine dense subgraphs as protein complexes, ignoring the global topological information. To tackle this issue, we propose the DPCMNE method to detect protein complexes via multi-level network embedding. It can preserve both the local and global topological information of biological networks. First, DPCMNE employs a hierarchical compressing strategy to recursively compress the input protein-protein interaction (PPI) network into multi-level smaller PPI networks. Then, a network embedding method is applied on these smaller PPI networks to learn protein embeddings of different levels of granularity. The embeddings learned from all the compressed PPI networks are concatenated to represent the final protein embeddings of the original input PPI network. Finally, a core-attachment based strategy is adopted to detect protein complexes in the weighted PPI network constructed by the pairwise similarity of protein embeddings. To assess the efficiency of our proposed method, DPCMNE is compared with other eight clustering algorithms on two yeast datasets. The experimental results show that the performance of DPCMNE outperforms those state-of-the-art complex detection methods in terms of F1 and F1+Acc. Furthermore, the results of functional enrichment analysis indicate that protein complexes detected by DPCMNE are more biologically significant in terms of P-score.

An efficient strategy for identifying essential proteins based on homology, subcellular location and protein-protein interaction information

High throughput biological experiments are expensive and time consuming. For the past few years, many computational methods based on biological information have been proposed and widely used to understand the biological background. However, the processing of biological information data inevitably produces false positive and false negative data, such as the noise in the Protein-Protein Interaction (PPI) networks and the noise generated by the integration of a variety of biological information. How to solve these noise problems is the key role in essential protein predictions. An Identifying Essential Proteins model based on non-negative Matrix Symmetric tri-Factorization and multiple biological information (IEPMSF) is proposed in this paper, which utilizes only the PPI network proteins common neighbor characters to develop a weighted network, and uses the non-negative matrix symmetric tri-factorization method to find more potential interactions between proteins in the network so as to optimize the weighted network. Then, using the subcellular location and lineal homology information, the starting score of proteins is determined, and the random walk algorithm with restart mode is applied to the optimized network to mark and rank each protein. We tested the suggested forecasting model against current representative approaches using a public database. Experiment shows high efficiency of new method in essential proteins identification. The effectiveness of this method shows that it can dramatically solve the noise problems that existing in the multi-source biological information itself and cased by integrating them.

Topological ranks reveal functional knowledge encoded in biological networks: a comparative analysis

MOTIVATION

Biological networks topology yields important insights into biological function, occurrence of diseases and drug design. In the last few years, different types of topological measures have been introduced and applied to infer the biological relevance of network components/interactions, according to their position within the network structure. Although comparisons of such measures have been previously proposed, to what extent the topology per se may lead to the extraction of novel biological knowledge has never been critically examined nor formalized in the literature.

RESULTS

We present a comparative analysis of nine outstanding topological measures, based on compact views obtained from the rank they induce on a given input biological network. The goal is to understand their ability in correctly positioning nodes/edges in the rank, according to the functional knowledge implicitly encoded in biological networks. To this aim, both internal and external (gold standard) validation criteria are taken into account, and six networks involving three different organisms (yeast, worm and human) are included in the comparison. The results show that a distinct handful of best-performing measures can be identified for each of the considered organisms, independently from the reference gold standard.

AVAILABILITY

Input files and code for the computation of the considered topological measures and K-haus distance are available at https://gitlab.com/MaryBonomo/ranking.

CONTACT

simona.rombo@unipa.it.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Briefings in Bioinformatics online.

Predicting Essential Proteins Based on Integration of Local Fuzzy Fractal Dimension and Subcellular Location Information

Essential proteins are indispensable to cells’ survival and development. Prediction and analysis of essential proteins are crucial for uncovering the mechanisms of cells. With the help of computer science and high-throughput technologies, forecasting essential proteins by protein–protein interaction (PPI) networks has become more efficient than traditional approaches (expensive experimental methods are generally used). Many computational algorithms were employed to predict the essential proteins; however, they have various restrictions. To improve the prediction accuracy, by introducing the Local Fuzzy Fractal Dimension (LFFD) of complex networks into the analysis of the PPI network, we propose a novel algorithm named LDS, which combines the LFFD of the PPI network with the protein subcellular location information. By testing the proposed LDS algorithm on three different yeast PPI networks, the experimental results show that LDS outperforms some state-of-the-art essential protein-prediction techniques.

PROTREC: A probability-based approach for recovering missing proteins based on biological networks

A novel network-based approach for predicting missing proteins (MPs) is proposed here. This approach, PROTREC (short for PROtein RECovery), dominates existing network-based methods - such as Functional Class Scoring (FCS), Hypergeometric Enrichment (HE), and Gene Set Enrichment Analysis (GSEA) - across a variety of proteomics datasets derived from different proteomics data acquisition paradigms: Higher PROTREC scores are much more closely correlated with higher recovery rates of MPs across sample replicates. The PROTREC score, unlike methods reporting p-values, can be directly interpreted as the probability that an unreported protein in a proteomic screen is actually present in the sample being screened. SIGNIFICANCE: Mass spectrometry (MS) has developed rapidly in recent years; however, an obvious proportion of proteins is still undetected, leading to missing protein problems. A few existing protein recovery methods are based on biological networks, but the performance is not satisfactory. We propose a new protein recovery method, PROTREC, a Bayesian-inspired approach based on biological networks, which shows exceptional performance across multiple validation strategies. It does not rely on peptide information, so it avoids the ambiguity issue that most protein assembly methods face.

Significance-Based Essential Protein Discovery

The identification of essential proteins is an important problem in bioinformatics. During the past decades, many centrality measures and algorithms have been proposed to address this issue. However, existing methods still deserve the following drawbacks: (1) the lack of a context-free and readily interpretable quantification of their centrality values; (2) the difficulty of specifying a proper threshold for their centrality values; (3) the incapability of controlling the quality of reported essential proteins in a statistically sound manner. To overcome the limitations of existing solutions, we tackle the essential protein discovery problem from a significance testing perspective. More precisely, the essential protein discovery problem is formulated as a multiple hypothesis testing problem, where the null hypothesis is that each protein is not an essential protein. To quantify the statistical significance of each protein, we present a p-value calculation method in which both the degree and the local clustering coefficient are used as the test statistic and the Erdös-Rényi model is employed as the random graph model. After calculating the p-value for each protein, the false discovery rate is used as the error rate in the multiple testing correction procedure. Our significance-based essential protein discovery method is named as SigEP, which is tested on both simulated networks and real PPI networks. The experimental results show that our method is able to achieve better performance than those competing algorithms.

Comparative Genomics across Three Ensifer Species Using a New Complete Genome Sequence of the Medicago Symbiont Sinorhizobium (Ensifer) meliloti WSM1022

Here, we report an improved and complete genome sequence of Sinorhizobium (Ensifer) meliloti strain WSM1022, a microsymbiont of Medicago species, revealing its tripartite structure. This improved genome sequence was generated combining Illumina and Oxford nanopore sequencing technologies to better understand the symbiotic properties of the bacterium. The 6.75 Mb WSM1022 genome consists of three scaffolds, corresponding to a chromosome (3.70 Mb) and the pSymA (1.38 Mb) and pSymB (1.66 Mb) megaplasmids. The assembly has an average GC content of 62.2% and a mean coverage of 77X. Genome annotation of WSM1022 predicted 6058 protein coding sequences (CDSs), 202 pseudogenes, 9 rRNAs (3 each of 5S, 16S, and 23S), 55 tRNAs, and 4 ncRNAs. We compared the genome of WSM1022 to two other rhizobial strains, closely related Sinorhizobium (Ensifer) meliloti Sm1021 and Sinorhizobium (Ensifer) medicae WSM419. Both WSM1022 and WSM419 species are high-efficiency rhizobial strains when in symbiosis with Medicago truncatula, whereas Sm1021 is ineffective. Our findings report significant genomic differences across the three strains with some similarities between the meliloti strains and some others between the high efficiency strains WSM1022 and WSM419. The addition of this high-quality rhizobial genome sequence in conjunction with comparative analyses will help to unravel the features that make a rhizobial symbiont highly efficient for nitrogen fixation.

Essential Protein Recognition via Community Significance

Essential protein plays a vital role in understanding the cellular life. With the advance in high-throughput technologies, a number of protein-protein interaction (PPI) networks have been constructed such that essential proteins can be identified from a system biology perspective. Although a series of network-based essential protein discovery methods have been proposed, these existing methods still have some drawbacks. Recently, it has been shown that the significance-based method SigEP is promising on overcoming the defects that are inherent in currently available essential protein identification methods. However, the SigEP method is developed under the unrealistic Erdös-Rényi (E-R) model and its time complexity is very high. Hence, we propose a new significance-based essential protein recognition method named EPCS in which the essential protein discovery problem is formulated as a community significance testing problem. Experimental results on four PPI networks show that EPCS performs better than nine state-of-the-art essential protein identification methods and the only significance-based essential protein identification method SigEP.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information

Background

Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.

Results

In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein–protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models.

Conclusions

We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization

Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, there has been an increasing interest in using computational methods to predict essential proteins based on protein–protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false-negative and false-positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this article, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of the network, so as to avoid more noise. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, the NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network with more potential protein–protein interactions. Then, we use the PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicate that our NTMEP model has better performance in predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of the protein–protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein–protein interaction networks.

A Novel Model for Identifying Essential Proteins Based on Key Target Convergence Sets

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the prediction accuracy of these models is still not satisfactory. In this manuscript, a novel key target convergence sets based prediction model (KTCSPM) is proposed to identify essential proteins. In KTCSPM, a weighted PPI network and a weighted (Domain-Domain Interaction) network are constructed first based on known PPIs and PDIs downloaded from benchmark databases. And then, by integrating these two kinds of networks, a novel weighted PDI network is built. Next, through assigning a unique key target convergence set (KTCS) for each node in the weighted PDI network, an improved method based on the random walk with restart is designed to identify essential proteins. Finally, in order to evaluate the predictive effects of KTCSPM, it is compared with 12 competitive state-of-the-art models, and experimental results show that KTCSPM can achieve better prediction accuracy. Considering the satisfactory predictive performance achieved by KTCSPM, it indicates that KTCSPM might be a good supplement to the future research on prediction of essential proteins.

Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network

In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.

A novel essential protein identification method based on PPI networks and gene expression data

Background

Some proposed methods for identifying essential proteins have better results by using biological information. Gene expression data is generally used to identify essential proteins. However, gene expression data is prone to fluctuations, which may affect the accuracy of essential protein identification. Therefore, we propose an essential protein identification method based on gene expression and the PPI network data to calculate the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network. Our experiments show that the method can improve the accuracy in predicting essential proteins.

Results

In this paper, we propose a new measure named JDC, which is based on the PPI network data and gene expression data. The JDC method offers a dynamic threshold method to binarize gene expression data. After that, it combines the degree centrality and Jaccard similarity index to calculate the JDC score for each protein in the PPI network. We benchmark the JDC method on four organisms respectively, and evaluate our method by using ROC analysis, modular analysis, jackknife analysis, overlapping analysis, top analysis, and accuracy analysis. The results show that the performance of JDC is better than DC, IC, EC, SC, BC, CC, NC, PeC, and WDC. We compare JDC with both NF-PIN and TS-PIN methods, which predict essential proteins through active PPI networks constructed from dynamic gene expression.

Conclusions

We demonstrate that the new centrality measure, JDC, is more efficient than state-of-the-art prediction methods with same input. The main ideas behind JDC are as follows: (1) Essential proteins are generally densely connected clusters in the PPI network. (2) Binarizing gene expression data can screen out fluctuations in gene expression profiles. (3) The essentiality of the protein depends on the similarity of "active" and "inactive" state of gene expression in a cluster of the PPI network.

Analysing the yeast complexome-the Complex Portal rising to the challenge

The EMBL-EBI Complex Portal is a knowledgebase of macromolecular complexes providing persistent stable identifiers. Entries are linked to literature evidence and provide details of complex membership, function, structure and complex-specific Gene Ontology annotations. Data are freely available and downloadable in HUPO-PSI community standards and missing entries can be requested for curation. In collaboration with Saccharomyces Genome Database and UniProt, the yeast complexome, a compendium of all known heteromeric assemblies from the model organism Saccharomyces cerevisiae, was curated. This expansion of knowledge and scope has led to a 50% increase in curated complexes compared to the previously published dataset, CYC2008. The yeast complexome is used as a reference resource for the analysis of complexes from large-scale experiments. Our analysis showed that genes coding for proteins in complexes tend to have more genetic interactions, are co-expressed with more genes, are more multifunctional, localize more often in the nucleus, and are more often involved in nucleic acid-related metabolic processes and processes where large machineries are the predominant functional drivers. A comparison to genetic interactions showed that about 40% of expanded co-complex pairs also have genetic interactions, suggesting strong functional links between complex members.

Ortho_Sim_Loc: Essential protein prediction using orthology and priority-based similarity approach

Proteins are the essential macro-molecules of living organism. But all proteins cannot be considered as essential in different relevant studies. Essentiality of a protein is thus computed by computation methods rather than biological experiments which in turn save both time and effort. Different computational approaches are already predicted to select essential proteins successfully with different biological significances by researchers. Most of the experimental approaches return higher false negative outcomes with respect to others. In order to retain the prediction accuracy level, a novel methodology "Ortho_Sim_Loc"has been proposed which is a combined approach of Orthology, Similarity (using clustering and priority based GO-Annotation) and Subcellular localization. Ortho_Sim_Loc can predict enriched functional set essential proteins. The predicted results are validated with other existing methods like different centrality measures, LIDC. The validation results exhibits better performance of Ortho_Sim_Loc in compare to other existing computational approaches.

Predicting Protein-Protein Interactions Using Symmetric Logistic Matrix Factorization

Accurate assessment of protein-protein interactions (PPIs) is critical to deciphering disease mechanisms and developing novel drugs, and with rapidly growing PPI data, the need for more efficient predictive methods is emerging. We propose here a symmetric logistic matrix factorization (symLMF)-based approach to predict PPIs, especially useful for large PPI networks. Benchmarked against two widely used datasets (Saccharomyces cerevisiae and Homo sapiens benchmarks) and their extended versions, the symLMF-based method proves to outperform most of the state-of-the-art data-driven methods applied to human PPIs, and it shows a performance comparable to those of deep learning methods despite its conceptual and technical simplicity and efficiency. Tests performed on humans, yeast, and tissue (brain and liver)- and disease (neurodegenerative and metabolic disorders)-specific datasets further demonstrate the high capability to capture the hidden interactions. Notably, many "de novo predictions" made by symLMF are verified to exist in PPI databases other than those used for training/testing the method, indicating that the method could be of broad utility as a simple, yet efficient and accurate, tool applicable to PPI datasets.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

A Novel Multi-Ensemble Method for Identifying Essential Proteins

Essential proteins possess critical functions for cell survival. Identifying essential proteins improves our understanding of how a cell works and also plays a vital role in the research fields of disease treatment and drug development. Recently, some machine-learning methods and ensemble learning methods have been proposed to identify essential proteins by introducing effective protein features. However, the ensemble learning method only used to focus on the choice of base classifiers. In this article, we propose a novel ensemble learning framework called multi-ensemble to integrate different base classifiers. The multi-ensemble method adopts the idea of multi-view learning and selects multiple base classifiers and trains those classifiers by continually adding the samples that are predicted correctly by the other base classifiers. We applied multi-ensemble to Yeast data and Escherichia coli data. The results show that our approach achieved better performance than both individual classifiers and the other ensemble learning methods.

A novel scheme for essential protein discovery based on multi-source biological information

Mining essential protein is crucial for discovering the process of cellular organization and viability. At present, there are many computational methods for essential proteins detecting. However, these existing methods only focus on the topological information of the networks and ignore the biological information of proteins, which lead to low accuracy of essential protein identification. Therefore, this paper presents a new essential proteins prediction strategy, called DEP-MSB which integrates a variety of biological information including gene expression profiles, GO annotations, and Domain interaction strength. In order to evaluate the performance of DEP-MSB, we conduct a series of experiments on the yeast PPI network and the experimental results have shown that the proposed algorithm DEP-MSB is more superior to the other existing traditional methods and has obviously improvement in prediction accuracy.

Predicting Essential Proteins by Integrating Network Topology, Subcellular Localization Information, Gene Expression Profile and GO Annotation Data

Essential proteins are indispensable for maintaining normal cellular functions. Identification of essential proteins from Protein-protein interaction (PPI) networks has become a hot topic in recent years. Traditionally biological experimental based approaches are time-consuming and expensive, although lots of computational based methods have been developed in the past years; however, the prediction accuracy is still unsatisfied. In this research, by introducing the protein sub-cellular localization information, we define a new measurement for characterizing the protein's subcellular localization essentiality, and a new data fusion based method is developed for identifying essential proteins, named TEGS, based on integrating network topology, gene expression profile, GO annotation information, and protein subcellular localization information. To demonstrate the efficiency of the proposed method TEGS, we evaluate its performance on two Saccharomyces cerevisiae datasets and compare with other seven state-of-the-art methods (DC, BC, NC, PeC, WDC, SON, and TEO) in terms of true predicted number, jackknife curve, and precision-recall curve. Simulation results show that the TEGS outperforms the other compared methods in identifying essential proteins. The source code of TEGS is freely available at https://github.com/wzhangwhu/TEGS.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Identification of Protein Complexes Based on Core-Attachment Structure and Combination of Centrality Measures and Biological Properties in PPI Weighted Networks

In protein interaction networks, a complex is a group of proteins that causes a biological process to take place. The correct identification of complexes can help to better understand function of cells used for therapeutic purposes, such as drug discoveries. This paper uses core-attachment structure, centrality measures, and biological properties of proteins to identify protein complex with the aim of enhancing prediction accuracy compared to related work. We used the inherent organization of complex to the identification in this article, while most methods have not considered such properties. On the other hand, clustering methods, as the common method for identifying complexes in protein interaction networks have been applied. However, we want to propose a method for more accurate identification of complexes in this article. Using this method, we determined the core center of each complex and its attachment proteins using the centrality measures, biological properties and weight density, whereby the weight of each interaction was calculated using the protein information in the gene ontology. In the proposed approach to weighting the network and measuring the importance of proteins, we used our previous work. To compare with other methods, we used datasets DIP, Collins, Krogan, and Human. The results show that the performance of our method was significantly improved, compared to other methods, in terms of detecting the protein complex. Using the p-value concept, we show the biological significance of our predicted complexes. The proposed method could identify an acceptable number of protein complexes, with the highest proportion of biological significance in collaborating on the functional annotation of proteins.

United Neighborhood Closeness Centrality and Orthology for Predicting Essential Proteins

Identifying essential proteins plays an important role in disease study, drug design, and understanding the minimal requirement for cellular life. Computational methods for essential proteins discovery overcome the disadvantages of biological experimental methods that are often time-consuming, expensive, and inefficient. The topological features of protein-protein interaction (PPI) networks are often used to design computational prediction methods, such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), and Neighborhood Centrality (NC). However, the prediction accuracies of these individual methods still have space to be improved. Studies show that additional information, such as orthologous relations, helps discover essential proteins. Many researchers have proposed different methods by combining multiple information sources to gain improvement of prediction accuracy. In this study, we find that essential proteins appear in triangular structure in PPI network significantly more often than nonessential ones. Based on this phenomenon, we propose a novel pure centrality measure, so-called Neighborhood Closeness Centrality (NCC). Accordingly, we develop a new combination model, Extended Pareto Optimality Consensus model, named EPOC, to fuse NCC and Orthology information and a novel essential proteins identification method, NCCO, is fully proposed. Compared with seven existing classic centrality methods (DC, BC, IC, CC, SC, EC, and NC) and three consensus methods (PeC, ION, and CSC), our results on S.cerevisiae and E.coli datasets show that NCCO has clear advantages. As a consensus method, EPOC also yields better performance than the random walk model.

A Novel Computational Approach for Identifying Essential Proteins From Multiplex Biological Networks

The identification of essential proteins can help in understanding the minimum requirements for cell survival and development. Ever-increasing amounts of high-throughput data provide us with opportunities to detect essential proteins from protein interaction networks (PINs). Existing network-based approaches are limited by the poor quality of the underlying PIN data, which exhibits high rates of false positive and false negative results. To overcome this problem, researchers have focused on the prediction of essential proteins by combining PINs with other biological data, which has led to the emergence of various interactions between proteins. It remains challenging, however, to use aggregated multiplex interactions within a single analysis framework to identify essential proteins. In this study, we created a multiplex biological network (MON) by initially integrating PINs, protein domains, and gene expression profiles. Next, we proposed a new approach to discover essential proteins by extending the random walk with restart algorithm to the tensor, which provides a data model representation of the MON. In contrast to existing approaches, the proposed MON approach considers for the importance of nodes and the different types of interactions between proteins during the iteration. MON was implemented to identify essential proteins within two yeast PINs. Our comprehensive experimental results demonstrated that MON outperformed 11 other state-of-the-art approaches in terms of precision-recall curve, jackknife curve, and other criteria.

A novel method to predict essential proteins based on tensor and HITS algorithm

BACKGROUND

Essential proteins are an important part of the cell and closely related to the life activities of the cell. Hitherto, Protein-Protein Interaction (PPI) networks have been adopted by many computational methods to predict essential proteins. Most of the current approaches focus mainly on the topological structure of PPI networks. However, those methods relying solely on the PPI network have low detection accuracy for essential proteins. Therefore, it is necessary to integrate the PPI network with other biological information to identify essential proteins.

RESULTS

In this paper, we proposed a novel random walk method for identifying essential proteins, called HEPT. A three-dimensional tensor is constructed first by combining the PPI network of Saccharomyces cerevisiae with multiple biological data such as gene ontology annotations and protein domains. Then, based on the newly constructed tensor, we extended the Hyperlink-Induced Topic Search (HITS) algorithm from a two-dimensional to a three-dimensional tensor model that can be utilized to infer essential proteins. Different from existing state-of-the-art methods, the importance of proteins and the types of interactions will both contribute to the essential protein prediction. To evaluate the performance of our newly proposed HEPT method, proteins are ranked in the descending order based on their ranking scores computed by our method and other competitive methods. After that, a certain number of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. Experimental results show that our method can achieve better prediction performance in comparison with other nine state-of-the-art methods in identifying essential proteins.

CONCLUSIONS

Through analysis and experimental results, it is obvious that HEPT can be used to effectively improve the prediction accuracy of essential proteins by the use of HITS algorithm and the combination of network topology with gene ontology annotations and protein domains, which provides a new insight into multi-data source fusion.

Artificial Fish Swarm Optimization Based Method to Identify Essential Proteins

It is well known that essential proteins play an extremely important role in controlling cellular activities in living organisms. Identifying essential proteins from protein protein interaction (PPI) networks is conducive to the understanding of cellular functions and molecular mechanisms. Hitherto, many essential proteins detection methods have been proposed. Nevertheless, those existing identification methods are not satisfactory because of low efficiency and low sensitivity to noisy data. This paper presents a novel computational approach based on artificial fish swarm optimization for essential proteins prediction in PPI networks (called AFSO_EP). In AFSO_EP, first, a part of known essential proteins are randomly chosen as artificial fishes of priori knowledge. Then, detecting essential proteins by imitating four principal biological behaviors of artificial fishes when searching for food or companions, including foraging behavior, following behavior, swarming behavior, and random behavior, in which process, the network topology, gene expression, gene ontology (GO) annotation, and subcellular localization information are utilized. To evaluate the performance of AFSO_EP, we conduct experiments on two species (Saccharomyces cerevisiae and Drosophila melanogaster), the experimental results show that our method AFSO_EP achieves a better performance for identifying essential proteins in comparison with several other well-known identification methods, which confirms the effectiveness of AFSO_EP.

Isolation and Characterization of a Novel Bacteriophage Infecting Carbapenem-Resistant Klebsiella pneumoniae

A novel virulent phage, vB_KpnP_IME337, isolated from a hospital sewage in Beijing, China, that infects carbapenem-resistant Klebsiella pneumoniae KN2 capsular type was identified and characterized. Next-generation sequencing and genome analysis revealed that vB_KpnP_IME337 had a linear double-stranded genome with a length of 44,266 base pairs and G+C content of 53.7%. Fifty-two putative open reading frames were identified, and no transfer RNA-encoding genes were detected. BLASTn analysis revealed that phage vB_KpnP_IME337 had the highest sequence similarity with Klebsiella phage phiBO1E, with genome coverage of 79%. Based on morphology, phage vB_KpnP_IME337 was determined to belong to the family Podoviridae of the order Caudovirales. It was shown that phage vB_KpnP_IME337 had an infection duration of ~ 90 min and 10 min latent period, and a highly specific to host strain. In conclusion, phage vB_KpnP_IME337 may be a promising alternative candidate to antibiotic treatment for controlling diseases caused by drug-resistant K. pneumoniae.

A novel extended Pareto Optimality Consensus model for predicting essential proteins

Essential proteins have vital functions, when they are destroyed in cells, the cells will die or stop reproducing. Therefore, it is very important to identify essential proteins from a large number of other proteins. Due to the time-consuming, expensive, and inefficient process in biological experimental methods, computational methods become more and more popular to recognize them. In the early stages, these methods mainly rely on protein-protein interaction (PPI) information, which limits their discovery capacities. Researchers find novel methods by fusing multi-information to improve prediction accuracy. According to these features, essential proteins are more important and conservative in the evolution process, their neighbors in PPI networks are usually likely to be essential, there are many false positives in PPI data, whether a protein is essential can be assessed by the importance of a protein itself, the relevance of neighbors and the reliability of PPIs. The importance of neighbors and the reliability of PPIs can be further integrated into neighborhood feature. In the study, orthologous information, edge-clustering coefficient and gene expression information are used to measure the importance of a protein itself, the importance of the neighbors and the reliability of PPIs, respectively. Then, a novel expanded POC model, E_POC, is proposed to fuse the above information to discover essential proteins, a weighted PPI network is constructed. The proteins ranked high according to their weights are treated as candidate essential proteins. This novel method is named as E_POC. E_POC outperforms the existing classical methods on S. cerevisiae and E. coli data.

Identifying protein complexes based on an edge weight algorithm and core-attachment structure

Background

Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.

Results

In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy.

Conclusions

In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA.

Construction of Refined Protein Interaction Network for Predicting Essential Proteins

Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. Up to now, a number of network-based essential protein discovery methods have been proposed. Generally, a static protein interaction network was constructed by using the protein-protein interactions obtained from different experiments or databases. Unfortunately, most of the network-based essential protein discovery methods are sensitive to the reliability of the constructed PIN. In this paper, we propose a new method for constructing refined PIN by using gene expression profiles and subcellular location information. The basic idea behind refining the PIN is that two proteins should have higher possibility to physically interact with each other if they appear together at the same subcellular location and are active together at least at a time point in the cell cycle. The original static PIN is denoted by S-PIN while the final PIN refined by our method is denoted by TS-PIN. To evaluate whether the constructed TS-PIN is more suitable to be used in the identification of essential proteins, 10 network-based essential protein discovery methods (DC, EC, SC, BC, CC, IC, LAC, NC, BN, and DMNC) are applied on it to identify essential proteins. A comparison of TS-PIN and two other networks: S-PIN and NF-APIN (a noise-filtered active PIN constructed by using gene expression data and S-PIN) is implemented on the prediction of essential proteins by using these ten network-based methods. The comparison results show that all of the 10 network-based methods achieve better results when being applied on TS-PIN than that being applied on S-PIN and NF-APIN.

An iteration method for identifying yeast essential proteins from heterogeneous network

BACKGROUND

Essential proteins are distinctly important for an organism's survival and development and crucial to disease analysis and drug design as well. Large-scale protein-protein interaction (PPI) data sets exist in Saccharomyces cerevisiae, which provides us with a valuable opportunity to predict identify essential proteins from PPI networks. Many network topology-based computational methods have been designed to detect essential proteins. However, these methods are limited by the completeness of available PPI data. To break out of these restraints, some computational methods have been proposed by integrating PPI networks and multi-source biological data. Despite the progress in the research of multiple data fusion, it is still challenging to improve the prediction accuracy of the computational methods.

RESULTS

In this paper, we design a novel iterative model for essential proteins prediction, named Randomly Walking in the Heterogeneous Network (RWHN). In RWHN, a weighted protein-protein interaction network and a domain-domain association network are constructed according to the original PPI network and the known protein-domain association network, firstly. And then, we establish a new heterogeneous matrix by combining the two constructed networks with the protein-domain association network. Based on the heterogeneous matrix, a transition probability matrix is established by normalized operation. Finally, an improved PageRank algorithm is adopted on the heterogeneous network for essential proteins prediction. In order to eliminate the influence of the false negative, information on orthologous proteins and the subcellular localization information of proteins are integrated to initialize the score vector of proteins. In RWHN, the topology, conservative and functional features of essential proteins are all taken into account in the prediction process. The experimental results show that RWHN obviously exceeds in predicting essential proteins ten other competing methods.

CONCLUSIONS

We demonstrated that integrating multi-source data into a heterogeneous network can preserve the complex relationship among multiple biological data and improve the prediction accuracy of essential proteins. RWHN, our proposed method, is effective for the prediction of essential proteins.

Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks

Background

Detecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.

Methods

In this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores.

Results

The experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes.

Conclusions

Our proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.

A Survey on Computational Methods for Essential Proteins and Genes Prediction

Background:

Essential proteins play important roles in the survival or reproduction of an organism and support the stability of the system. Essential proteins are the minimum set of proteins absolutely required to maintain a living cell. The identification of essential proteins is a very important topic not only for a better comprehension of the minimal requirements for cellular life, but also for a more efficient discovery of the human disease genes and drug targets. Traditionally, as the experimental identification of essential proteins is complex, it usually requires great time and expense. With the cumulation of high-throughput experimental data, many computational methods that make useful complements to experimental methods have been proposed to identify essential proteins. In addition, the ability to rapidly and precisely identify essential proteins is of great significance for discovering disease genes and drug design, and has great potential for applications in basic and synthetic biology research.

Objective:

The aim of this paper is to provide a review on the identification of essential proteins and genes focusing on the current developments of different types of computational methods, point out some progress and limitations of existing methods, and the challenges and directions for further research are discussed.

A Novel Method for Identifying Essential Genes by Fusing Dynamic Protein⁻Protein Interactive Networks

Essential genes play an indispensable role in supporting the life of an organism. Identification of essential genes helps us to understand the underlying mechanism of cell life. The essential genes of bacteria are potential drug targets of some diseases genes. Recently, several computational methods have been proposed to detect essential genes based on the static protein⁻protein interactive (PPI) networks. However, these methods have ignored the fact that essential genes play essential roles under certain conditions. In this work, a novel method was proposed for the identification of essential proteins by fusing the dynamic PPI networks of different time points (called by FDP). Firstly, the active PPI networks of each time point were constructed and then they were fused into a final network according to the networks' similarities. Finally, a novel centrality method was designed to assign each gene in the final network a ranking score, whilst considering its orthologous property and its global and local topological properties in the network. This model was applied on two different yeast data sets. The results showed that the FDP achieved a better performance in essential gene prediction as compared to other existing methods that are based on the static PPI network or that are based on dynamic networks.

The underlying pathophysiology association between the Type 2-diabetic and hepatocellular carcinoma

Type 2-diabetic (T2D) disease has been reported to increase the incidence of liver cancer, however, the underlying pathophysiology is still not fully understood. Here, we aimed to reveal the underlying pathophysiology association between the T2D and hepatocellular carcinoma (HCC) and, therefore, to find the possible therapeutic targets in the occurrence and development of HCC. The methylation microarray data of T2D and HCC were extracted from the Gene Expression Omnibus and The Cancer Genome Atlas. A total of 504 differentially methylated genes (DMGs) between T2D samples and the controls were identified, whereas 6269 DMGs were identified between HCC samples and the control groups. There were 336 DMGs coexisting in diabetes and HCC, among which 86 genes were comethylated genes. These genes were mostly enriched in pathways as glycosaminoglycan biosynthesis, fatty acid, and metabolic pathway as glycosaminoglycan degradation and thiamine, fructose and mannose. There were 250 DMGs that had differential methylation direction between T2D DMGs and HCC DMGs, and these genes were enriched in the Sphingolipid metabolism pathway and immune pathways through natural killer cell-mediated cytotoxicity and ak-STAT signaling pathway. Eight genes were found related to the occurrence and development of diabetes and HCC. Moreover, the result of protein-protein interaction network showed that CDKN1A gene was related to the prognosis of HCC. In summary, eight genes were found to be associated with the development of HCC and CDKN1A may serve as the potential prognostic gene for HCC.

Identification of essential proteins based on a new combination of topological and biological features in weighted protein-protein interaction networks

The identification of essential proteins in protein-protein interaction (PPI) networks is not only important in understanding the process of cellular life but also useful in diagnosis and drug design. The network topology-based centrality measures are sensitive to noise of network. Moreover, these measures cannot detect low-connectivity essential proteins. The authors have proposed a new method using a combination of topological centrality measures and biological features based on statistical analyses of essential proteins and protein complexes. With incomplete PPI networks, they face the challenge of false-positive interactions. To remove these interactions, the PPI networks are weighted by gene ontology. Furthermore, they use a combination of classifiers, including the newly proposed measures and traditional weighted centrality measures, to improve the precision of identification. This combination is evaluated using the logistic regression model in terms of significance levels. The proposed method has been implemented and compared to both previous and more recent efficient computational methods using six statistical standards. The results show that the proposed method is more precise in identifying essential proteins than the previous methods. This level of precision was obtained through the use of four different data sets: YHQ-W, YMBD-W, YDIP-W and YMIPS-W.