Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network.
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ABSTRACT:
SUBMITTER: He X
PROVIDER: S-EPMC8276041 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.
| S-EPMC4987049 | biostudies-literature
Project description: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.
| S-EPMC8010314 | biostudies-literature
Project description:Considering that traditional biological experiments are expensive and time consuming, it is important to develop effective computational models to infer potential essential proteins. In this manuscript, a novel collaborative filtering model-based method called CFMM was proposed, in which, an updated protein-domain interaction (PDI) network was constructed first by applying collaborative filtering algorithm on the original PDI network, and then, through integrating topological features of PDI networks with biological features of proteins, a calculative method was designed to infer potential essential proteins based on an improved PageRank algorithm. The novelties of CFMM lie in construction of an updated PDI network, application of the commodity-customer-based collaborative filtering algorithm, and introduction of the calculation method based on an improved PageRank algorithm, which ensured that CFMM can be applied to predict essential proteins without relying entirely on known protein-domain associations. Simulation results showed that CFMM can achieve reliable prediction accuracies of 92.16, 83.14, 71.37, 63.87, 55.84, and 52.43% in the top 1, 5, 10, 15, 20, and 25% predicted candidate key proteins based on the DIP database, which are remarkably higher than 14 competitive state-of-the-art predictive models as a whole, and in addition, CFMM can achieve satisfactory predictive performances based on different databases with various evaluation measurements, which further indicated that CFMM may be a useful tool for the identification of essential proteins in the future.
| S-EPMC8566338 | biostudies-literature
Project description: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.
| S-EPMC8358660 | biostudies-literature
Project description:Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be obtained from git.io/diffslcpy. The R implementation and code to reproduce the analysis is available via git.io/diffslc.
| S-EPMC5679606 | biostudies-literature
Project description:The SARS-CoV-2 spike protein is the primary antigenic determinant of the virus and has been studied extensively, yet the process of membrane fusion remains poorly understood. The fusion domain (FD) of viral glycoproteins is well established as facilitating the initiation of membrane fusion. An improved understanding of the structural plasticity associated with these highly conserved regions aids in our knowledge of the molecular mechanisms that drive viral fusion. Within the spike protein, the FD of SARS-CoV-2 exists immediately following S2' cleavage at the N-terminus of the S2 domain. Here we have shown that following the introduction of a membrane at pH 7.4, the FD undergoes a transition from a random coil to a more structurally well-defined postfusion state. Furthermore, we have classified the domain into two distinct regions, a fusion peptide (FP, S816-G838) and a fusion loop (FL, D839-F855). The FP forms a helix-turn-helix motif upon association with a membrane, and the favorable entropy gained during this transition from a random coil is likely the driving force behind membrane insertion. Membrane depth experiments then revealed the FP is found inserted within the membrane below the lipid headgroups, while the interaction of the FL with the membrane is shallower in nature. Thus, we propose a structural model relevant to fusion at the plasma membrane in which the FP inserts itself just below the phospholipid headgroups and the FL lays upon the lipid membrane surface.
| S-EPMC8491435 | biostudies-literature
Project description:Growing evidence suggests that DNA methylation plays a role in tissue-specific differentiation. Current approaches to methylome analysis using MBD-enrichment are restricted to large (M-bM-^IM-%1 M-BM-5g) DNA samples, limiting the analysis of small tissue samples. Here we present a technique which enables characterization of genome wide tissue-specific methylation patterns from nanogram quantities of DNA. We have developed a methodology utilizing MBD2b/MBD3L1-enrichment for methylated DNA, kinase pre-treated ligation-mediated PCR amplification (MeKL) and hybridization to the comprehensive high-throughput array for relative methylation (CHARM) customized NimbleGen 2.1M mouse tiling arrays (Feinberg_MM8_Me_HX1), which we termed MeKL-chip. The kinase-modification in combination with the addition of PEG have increased the ligation-mediated PCR amplification over 20-fold, enabling >400-fold amplification of starting DNA. We have shown that MeKL-chip can be applied to as little as 20 ng DNA, enabling comprehensive analysis of small DNA samples. Applying MeKL-chip to the mouse retina (a limited tissue source) and brain, 2498 tissue-specific differentially methylated regions (T-DMRs) were characterized. The top five T-DMRs (Rgs20, Hes2, Nfic, Cckbr and Six3os1) were validated by pyrosequencing. MeKL-chip enables genome-wide methylation analysis of nanogram quantities of DNA with a wide range of observed-to-expected CpG ratios due to the binding properties of the MBD2b/MBD3L1 protein complex. This methodology enabled the first analysis of genome-wide methylation in the mouse retina, characterizing novel T-DMRs. MBD-enrichment was performed on 3 x retina and 3 x brain DNA samples from adult C57BL/6J mice on varied DNA quantities (250, 50, 25 and 10 ng). Samples were amplified using a modified ligation-mediated PCR and hybridized to custom NimbleGen 2.1M mouse arrays (Feinberg_MM8_Me_HX1)
2013-06-10 | E-GEOD-46683 | biostudies-arrayexpress
Project description:Growing evidence suggests that DNA methylation plays a role in tissue-specific differentiation. Current approaches to methylome analysis using MBD-enrichment are restricted to large (≥1 µg) DNA samples, limiting the analysis of small tissue samples. Here we present a technique which enables characterization of genome wide tissue-specific methylation patterns from nanogram quantities of DNA. We have developed a methodology utilizing MBD2b/MBD3L1-enrichment for methylated DNA, kinase pre-treated ligation-mediated PCR amplification (MeKL) and hybridization to the comprehensive high-throughput array for relative methylation (CHARM) customized NimbleGen 2.1M mouse tiling arrays (Feinberg_MM8_Me_HX1), which we termed MeKL-chip. The kinase-modification in combination with the addition of PEG have increased the ligation-mediated PCR amplification over 20-fold, enabling >400-fold amplification of starting DNA. We have shown that MeKL-chip can be applied to as little as 20 ng DNA, enabling comprehensive analysis of small DNA samples. Applying MeKL-chip to the mouse retina (a limited tissue source) and brain, 2498 tissue-specific differentially methylated regions (T-DMRs) were characterized. The top five T-DMRs (Rgs20, Hes2, Nfic, Cckbr and Six3os1) were validated by pyrosequencing. MeKL-chip enables genome-wide methylation analysis of nanogram quantities of DNA with a wide range of observed-to-expected CpG ratios due to the binding properties of the MBD2b/MBD3L1 protein complex. This methodology enabled the first analysis of genome-wide methylation in the mouse retina, characterizing novel T-DMRs.
2013-06-10 | GSE46683 | GEO
Project description:Rheumatoid arthritis (RA) is a chronic inflammatory disease of the synovial joints. Though the current RA therapeutics such as disease-modifying antirheumatic drugs (DMARDs), nonsteroidal anti-inflammatory drugs (NSAIDs) and biologics can halt the progression of the disease, none of these would either dramatically reduce or cure RA. So, the identification of potential therapeutic targets and new therapies for RA are active areas of research. Several studies have discovered the involvement of cytokines in the pathogenesis of this disease. These cytokines induce signal transduction pathways in RA synovial fibroblasts (RASF). These pathways share many signal transducers and their interacting proteins, resulting in the formation of a signaling network. In order to understand the involvement of this network in RA pathogenesis, it is essential to identify the key transducers and their interacting proteins that are part of this network. In this study, based on a detailed literature survey, we have identified a list of 12 cytokines that induce signal transduction pathways in RASF. For these cytokines, we have built a signaling network using the protein-protein interaction (PPI) data that was obtained from public repositories such as HPRD, BioGRID, MINT, IntAct and STRING. By combining the network centrality measures with the gene expression data from the RA related microarrays that are available in the open source Gene Expression Omnibus (GEO) database, we have identified 24 key proteins of this signaling network. Two of these 24 are already drug targets for RA, and of the remaining, 12 have direct PPI links to some of the current drug targets of RA. Therefore, these key proteins seem to be crucial in the pathogenesis of RA and hence might be treated as potential drug targets.
| S-EPMC6013252 | biostudies-literature
Project description: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.
| S-EPMC7186452 | biostudies-literature