NPF:network propagation for protein function prediction

Background The accurate characterization of protein functions is critical to understanding life at the molecular level and has a huge impact on biomedicine and pharmaceuticals. Computationally predicting protein function has been studied in the past decades. Plagued by noise and errors in protein–protein interaction (PPI) networks, researchers have undertaken to focus on the fusion of multi-omics data in recent years. A data model that appropriately integrates network topologies with biological data and preserves their intrinsic characteristics is still a bottleneck and an aspirational goal for protein function prediction. Results In this paper, we propose the RWRT (Random Walks with Restart on Tensor) method to accomplish protein function prediction by applying bi-random walks on the tensor. RWRT firstly constructs a functional similarity tensor by combining protein interaction networks with multi-omics data derived from domain annotation and protein complex information. After this, RWRT extends the bi-random walks algorithm from a two-dimensional matrix to the tensor for scoring functional similarity between proteins. Finally, RWRT filters out possible pretenders based on the concept of cohesiveness coefficient and annotates target proteins with functions of the remaining functional partners. Experimental results indicate that RWRT performs significantly better than the state-of-the-art methods and improves the area under the receiver-operating curve (AUROC) by no less than 18%. Conclusions The functional similarity tensor offers us an alternative, in that it is a collection of networks sharing the same nodes; however, the edges belong to different categories or represent interactions of different nature. We demonstrate that the tensor-based random walk model can not only discover more partners with similar functions but also free from the constraints of errors in protein interaction networks effectively. We believe that the performance of function prediction depends greatly on whether we can extract and exploit proper functional similarity information on protein correlations.

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“…The proof of convergence for the random walk on tensor algorithm is related to our previous work [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

A tensor-based bi-random walks model for protein function prediction

Xiong

Jiang

et al. 2022

BMC Bioinformatics

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“…Protein functions have been determined naturally from sequence similarity to known proteins 6 and other characteristics of proteins that can trace functional relevance. Such information includes structural configuration 7 – 9 , phylogenetic information 10 , 11 , domain distribution 12 – 14 , protein networks 3 , 15 , and combinations of multiple sources 16 , 17 . Recently, various deep learning-based methods were proposed to learn a functional representation of proteins 8 , 16 , 18 – 22 .…”

Section: Introductionmentioning

confidence: 99%

Domain-PFP allows protein function prediction using function-aware domain embedding representations

Ibtehaz,

Kagaya,

Kihara

2023

Commun Biol

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Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, substantially outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.

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“…Protein functions have been determined naturally from sequence similarity to known proteins 6 and other characteristics of proteins that can trace functional relevance. Such information includes structural configuration [7][8][9] , phylogenetic information 10,11 , domain distribution [12][13][14] , protein networks 3,15 , and combinations of multiple sources 16,17 . Recently, various deep learning-based methods were proposed to learn a functional representation of proteins 8,16,[18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations

Ibtehaz,

Kagaya,

Kihara

2023

Preprint

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Domains are functional and structural units of proteins that govern various biological functions performed by the proteins. Therefore, the characterization of domains in a protein can serve as a proper functional representation of proteins. Here, we employ a self-supervised protocol to derive functionally consistent representations for domains by learning domain-Gene Ontology (GO) co-occurrences and associations. The domain embeddings we constructed turned out to be effective in performing actual function prediction tasks. Extensive evaluations showed that protein representations using the domain embeddings are superior to those of large-scale protein language models in GO prediction tasks. Moreover, the new function prediction method built on the domain embeddings, named Domain-PFP, significantly outperformed the state-of-the-art function predictors. Additionally, Domain-PFP demonstrated competitive performance in the CAFA3 evaluation, achieving overall the best performance among the top teams that participated in the assessment.

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NPF:network propagation for protein function prediction

Cited by 12 publications

References 50 publications

A tensor-based bi-random walks model for protein function prediction

A tensor-based bi-random walks model for protein function prediction

Domain-PFP allows protein function prediction using function-aware domain embedding representations

Domain-PFP: Protein Function Prediction Using Function-Aware Domain Embedding Representations

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