TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPIs networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on similar sequence, structure 2D/3D, amino acid patterns or phylogenetic profiles. Results: In this work, we introduce a new approach, called TANA, for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. Doing so, we are able to discover the functions of proteins that could not to be easily described by sequence homology. We assess the performance of our approach using the standard metrics established by the CAFA challenge and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known beforehand.

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TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

10.21203/rs.2.21071/v3 ◽

2020 ◽

Author(s):

Warith Eddine DJEDDI ◽

Sadok BEN YAHIA ◽

Engelbert MEPHU NGUIFO

Keyword(s):

Protein Function ◽

Direct Interaction ◽

Function Prediction ◽

Multiple Alignment ◽

New Approach ◽

Ppi Networks ◽

Phylogenetic Profiles ◽

Protein Functions ◽

Cellular Components ◽

Combine Sequence

Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge PPI networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on a similar sequence, structure 2D/3D, amino acid patterns of phylogenetic profiles. Results: In this work, we introduce a new approach, called TANA, for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. In doing so, we are able to discover the functions of proteins that could not be easily described by sequence homology. We assess the performance of our approach using the standard metrics established by the CAFA challenge and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions and cellular components. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e. pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known beforehand

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TANA: efficient approach for predicting protein functions by transferring annotation via alignment networks

10.21203/rs.2.21071/v1 ◽

2020 ◽

Author(s):

Warith Eddine DJEDDI ◽

Sadok BEN YAHIA ◽

Engelbert MEPHU NGUIFO

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Direct Interaction ◽

Function Prediction ◽

Multiple Alignment ◽

Protein Protein Interactions ◽

New Approach ◽

Ppi Networks ◽

Phylogenetic Profiles ◽

Protein Functions

Abstract Background: One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactions networks for many model species, the computational methods revealed a great requirement to elucidate protein function based on many strategies. In this respect, most computational approaches integrate diverse kinds of functional interactions to unveil protein functions by transferring annotations across different species by relying on similar sequence, structure 2D/3D, amino acid motifs or phylogenetic profiles. Results: In this work, we introduce a new approach called TANA for inferring protein functions. The main originality of the introduced approach stands on the function prediction for the unannotated protein by transferring annotation via a network alignment as well as from the direct interaction neighborhood within their PPI networks. Doing so, we are able to discover the functions of proteins that could not to be easily described by sequence homology. We assess the performance of our method using the standard metrics established by the CAFA and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions. Conclusions: This research is one of the first attempts that combine sequence and networks-multiple-alignment-based function prediction approaches. We have been able to assess the accuracy of the prediction using pairwise and multiple alignment of the PPI networks for the compared species. Therefore, we recommend using different strategies (i.e pairwise, multiple, with/without neighborhood networks) especially in situations where the functions of the protein are not known in advance.

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ProCbA: Protein Function Prediction based on Clique Analysis

10.1101/2020.11.24.396432 ◽

2020 ◽

Author(s):

A. Khanteymoori ◽

M. B. Ghajehlo ◽

S. Behrouzinia ◽

M. H. Olyaee

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Protein Function Prediction ◽

Function Prediction ◽

Experimental Result ◽

Protein Protein Interactions ◽

Ppi Networks ◽

Protein Functions ◽

Important Challenge ◽

Result Analysis

AbstractProtein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods.To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis as can be seen in Section IV, the both ProCbA and ProNC-FA are generally able to outperform all the other methods.

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DeepGOZero: Improving protein function prediction from sequence and zero-shot learning based on ontology axioms

10.1101/2022.01.14.476325 ◽

2022 ◽

Author(s):

Maxat Kulmanov ◽

Robert Hoehndorf

Keyword(s):

Machine Learning ◽

Protein Function ◽

Protein Function Prediction ◽

Prediction Method ◽

Function Prediction ◽

Training Data ◽

Large Set ◽

Theoretic Approach ◽

Machine Learning Model ◽

Protein Functions

Motivation: Protein functions are often described using the Gene Ontology (GO) which is an ontology consisting of over 50,000 classes and a large set of formal axioms. Predicting the functions of proteins is one of the key challenges in computational biology and a variety of machine learning methods have been developed for this purpose. However, these methods usually require significant amount of training data and cannot make predictions for GO classes which have only few or no experimental annotations. Results: We developed DeepGOZero, a machine learning model which improves predictions for functions with no or only a small number of annotations. To achieve this goal, we rely on a model-theoretic approach for learning ontology embeddings and combine it with neural networks for protein function prediction. DeepGOZero can exploit formal axioms in the GO to make zero-shot predictions, i.e., predict protein functions even if not a single protein in the training phase was associated with that function. Furthermore, the zero-shot prediction method employed by DeepGOZero is generic and can be applied whenever associations with ontology classes need to be predicted. Availability: http://github.com/bio-ontology-research-group/deepgozero

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New advances in extracting and learning from protein–protein interactions within unstructured biomedical text data

Emerging Topics in Life Sciences ◽

10.1042/etls20190003 ◽

2019 ◽

Vol 3 (4) ◽

pp. 357-369

Author(s):

J. Harry Caufield ◽

Peipei Ping

Keyword(s):

Protein Interactions ◽

Protein Function ◽

Historical Context ◽

Specific Protein ◽

Basic Unit ◽

Biomedical Text ◽

Protein Protein Interactions ◽

Text Data ◽

Ppi Networks ◽

Protein Functions

Abstract Protein–protein interactions, or PPIs, constitute a basic unit of our understanding of protein function. Though substantial effort has been made to organize PPI knowledge into structured databases, maintenance of these resources requires careful manual curation. Even then, many PPIs remain uncurated within unstructured text data. Extracting PPIs from experimental research supports assembly of PPI networks and highlights relationships crucial to elucidating protein functions. Isolating specific protein–protein relationships from numerous documents is technically demanding by both manual and automated means. Recent advances in the design of these methods have leveraged emerging computational developments and have demonstrated impressive results on test datasets. In this review, we discuss recent developments in PPI extraction from unstructured biomedical text. We explore the historical context of these developments, recent strategies for integrating and comparing PPI data, and their application to advancing the understanding of protein function. Finally, we describe the challenges facing the application of PPI mining to the text concerning protein families, using the multifunctional 14-3-3 protein family as an example.

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NPF：Network propagation for protein function prediction

10.21203/rs.3.rs-16452/v2 ◽

2020 ◽

Author(s):

Bihai Zhao ◽

Zhihong Zhang ◽

Meiping Jiang ◽

Sai Hu ◽

Yingchun Luo ◽

...

Keyword(s):

Protein Interaction ◽

Protein Function ◽

Cross Validation ◽

Function Prediction ◽

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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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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Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens

BioMed Research International ◽

10.1155/2015/619438 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9

Author(s):

Jian-Sheng Wu ◽

Hai-Feng Hu ◽

Shan-Cheng Yan ◽

Li-Hua Tang

Keyword(s):

Protein Function ◽

Microbial Fuel Cells ◽

Learning Algorithm ◽

Protein Function Prediction ◽

Function Prediction ◽

Vast Number ◽

Learning Framework ◽

Learning Tasks ◽

Multilabel Learning ◽

Protein Functions

Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer fromweak-labelproblem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.

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