Darkness in the human gene and protein function space despite big omics data and decline in molecular mechanism discovery after 2000

Abstract Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, disease treatment and new drug development. Various methods have been developed to facilitate the prediction of functions by combining protein interaction networks (PINs) with multi-omics data. However, how to make full use of multiple biological data to improve the performance of functions annotation is still a dilemma. Results We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. Comprehensive evaluation of NPF indicates that NPF archived higher performance than competing methods in terms of leave-one-out cross-validation and ten-fold cross validation. Conclusions: We demonstrated that network propagation combined with multi-omics data can not only discover more partners with similar function, but also effectively free from the constraints of the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information from protein correlations.

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NPF：Network propagation for protein function prediction

10.21203/rs.3.rs-16452/v1 ◽

2020 ◽

Author(s):

bihai zhao ◽

Zhihong Zhang ◽

Meiping Jiang ◽

Sai Hu ◽

Yingchun Luo ◽

...

Keyword(s):

Protein Interaction ◽

Protein Function ◽

Function Prediction ◽

Biological Data ◽

Protein Interaction Networks ◽

Functional Similarity ◽

Interaction Networks ◽

Omics Data ◽

Protein Functions ◽

Network Propagation

Abstract Background: The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, disease treatment and new drug development. Various methods have been developed to facilitate the prediction of functions by combining protein interaction networks (PINs) with multi-omics data. However, how to make full use of multiple biological data to improve the performance of functions annotation is still a dilemma.Results: We presented NPF (Network Propagation for Functions prediction), an integrative protein function predicting framework assisted by network propagation and functional module detection, for discovering interacting partners with similar functions to target proteins. NPF leverages knowledge of the protein interaction network architecture and multi-omics data, such as domain annotation and protein complex information, to augment protein-protein functional similarity in a propagation manner. We have verified the great potential of NPF for accurately inferring protein functions. Comprehensive evaluation of NPF indicates that NPF archived higher performance than competing methods in terms of leave-one-out cross-validation and ten-fold cross validation.Conclusions: We demonstrated that network propagation combined with multi-omics data can not only discover more partners with similar function, but also effectively free from the constraints of the "small-world" feature of protein interaction networks. We conclude that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information from protein correlations.

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Protein function space: viewing the limits or limited by our view?

Current Opinion in Structural Biology ◽

10.1016/j.sbi.2007.05.010 ◽

2007 ◽

Vol 17 (3) ◽

pp. 362-369 ◽

Cited By ~ 24

Author(s):

Jeroen Raes ◽

Eoghan Donal Harrington ◽

Amoolya Hardev Singh ◽

Peer Bork

Keyword(s):

Function Space ◽

Protein Function

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Assessing the Role of Lipids in the Molecular Mechanism of Membrane Proteins

International Journal of Molecular Sciences ◽

10.3390/ijms22147267 ◽

2021 ◽

Vol 22 (14) ◽

pp. 7267

Author(s):

Léni Jodaitis ◽

Thomas van Oene ◽

Chloé Martens

Keyword(s):

Membrane Proteins ◽

Molecular Mechanism ◽

Protein Interactions ◽

Protein Function ◽

Biological Membrane ◽

Allosteric Coupling ◽

Lipid Protein Interactions ◽

Experimental Challenge ◽

Membrane Protein Function

Membrane proteins have evolved to work optimally within the complex environment of the biological membrane. Consequently, interactions with surrounding lipids are part of their molecular mechanism. Yet, the identification of lipid–protein interactions and the assessment of their molecular role is an experimental challenge. Recently, biophysical approaches have emerged that are compatible with the study of membrane proteins in an environment closer to the biological membrane. These novel approaches revealed specific mechanisms of regulation of membrane protein function. Lipids have been shown to play a role in oligomerization, conformational transitions or allosteric coupling. In this review, we summarize the recent biophysical approaches, or combination thereof, that allow to decipher the role of lipid–protein interactions in the mechanism of membrane proteins.

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Molecular Mechanism of HBx Protein Function in HBv Related Hepatocellular Carcinoma Carcinogenesis

Gastroenterology & Hepatology Open Access ◽

10.15406/ghoa.2016.05.00135 ◽

2016 ◽

Vol 5 (2) ◽

Author(s):

Xiao-Hong Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Molecular Mechanism ◽

Protein Function

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Optimizing network propagation for multi-omics data integration

PLoS Computational Biology ◽

10.1371/journal.pcbi.1009161 ◽

2021 ◽

Vol 17 (11) ◽

pp. e1009161

Author(s):

Konstantina Charmpi ◽

Manopriya Chokkalingam ◽

Ronja Johnen ◽

Andreas Beyer

Keyword(s):

Cancer Progression ◽

Protein Function ◽

Molecular Mechanisms ◽

Optimal Parameter ◽

Research Question ◽

Real Data ◽

Disease Genes ◽

Omics Data ◽

Optimal Parameters ◽

Network Propagation

Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a ‘topology bias’ caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

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Delayed Comparison and Apriori Algorithm (DCAA): A Tool for Discovering Protein–Protein Interactions From Time-Series Phosphoproteomic Data

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.606570 ◽

2020 ◽

Vol 7 ◽

Author(s):

Lianhong Ding ◽

Shaoshuai Xie ◽

Shucui Zhang ◽

Hangyu Shen ◽

Huaqiang Zhong ◽

...

Keyword(s):

Time Series ◽

Protein Synthesis ◽

Protein Interactions ◽

Protein Function ◽

A Priori ◽

Biological Macromolecules ◽

Apriori Algorithm ◽

Omics Data ◽

Previous Knowledge ◽

Protein Protein Interactions

Analysis of high-throughput omics data is one of the most important approaches for obtaining information regarding interactions between proteins/genes. Time-series omics data are a series of omics data points indexed in time order and normally contain more abundant information about the interactions between biological macromolecules than static omics data. In addition, phosphorylation is a key posttranslational modification (PTM) that is indicative of possible protein function changes in cellular processes. Analysis of time-series phosphoproteomic data should provide more meaningful information about protein interactions. However, although many algorithms, databases, and websites have been developed to analyze omics data, the tools dedicated to discovering molecular interactions from time-series omics data, especially from time-series phosphoproteomic data, are still scarce. Moreover, most reported tools ignore the lag between functional alterations and the corresponding changes in protein synthesis/PTM and are highly dependent on previous knowledge, resulting in high false-positive rates and difficulties in finding newly discovered protein–protein interactions (PPIs). Therefore, in the present study, we developed a new method to discover protein–protein interactions with the delayed comparison and Apriori algorithm (DCAA) to address the aforementioned problems. DCAA is based on the idea that there is a lag between functional alterations and the corresponding changes in protein synthesis/PTM. The Apriori algorithm was used to mine association rules from the relationships between items in a dataset and find PPIs based on time-series phosphoproteomic data. The advantage of DCAA is that it does not rely on previous knowledge and the PPI database. The analysis of actual time-series phosphoproteomic data showed that more than 68% of the protein interactions/regulatory relationships predicted by DCAA were accurate. As an analytical tool for PPIs that does not rely on a priori knowledge, DCAA should be useful to predict PPIs from time-series omics data, and this approach is not limited to phosphoproteomic data.

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