scholarly journals Towards region-specific propagation of protein functions

2018 ◽  
Author(s):  
Da Chen Emily Koo ◽  
Richard Bonneau
Keyword(s):  
Protein Level ◽  
Protein Function ◽  
Predictive Performance ◽  
Function Prediction ◽  
Amino Acid Sequences ◽  
Prediction Methods ◽  
Functional Evaluation ◽  
Domain Family ◽  
Starting Point ◽  
Go Terms

AbstractMotivationDue to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.ResultsWe apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction by testing on both human and yeast proteomes. We compare region-level predictive performance of our method against that of a whole-protein baseline method using a held-out dataset of proteins with structurally-verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into site-specific and whole-protein terms and select prediction methods for different classes of GO terms.AvailabilityThe code is freely available at: https://github.com/ek1203/region_spec_func_pred

scholarly journals NNTox: Gene Ontology-Based Protein Toxicity Prediction Using Neural Network

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Aashish Jain ◽  
Daisuke Kihara
Keyword(s):  
Neural Network ◽  
Protein Function ◽  
Query Sequence ◽  
Function Prediction ◽  
Genetically Engineered ◽  
Prediction Methods ◽  
General Function ◽  
Research Activities ◽  
Protein Toxicity ◽  
Go Terms

AbstractWith advancements in synthetic biology, the cost and the time needed for designing and synthesizing customized gene products have been steadily decreasing. Many research laboratories in academia as well as industry routinely create genetically engineered proteins as a part of their research activities. However, manipulation of protein sequences could result in unintentional production of toxic proteins. Therefore, being able to identify the toxicity of a protein before the synthesis would reduce the risk of potential hazards. Existing methods are too specific, which limits their application. Here, we extended general function prediction methods for predicting the toxicity of proteins. Protein function prediction methods have been actively studied in the bioinformatics community and have shown significant improvement over the last decade. We have previously developed successful function prediction methods, which were shown to be among top-performing methods in the community-wide functional annotation experiment, CAFA. Based on our function prediction method, we developed a neural network model, named NNTox, which uses predicted GO terms for a target protein to further predict the possibility of the protein being toxic. We have also developed a multi-label model, which can predict the specific toxicity type of the query sequence. Together, this work analyses the relationship between GO terms and protein toxicity and builds predictor models of protein toxicity.

scholarly journals Protein function prediction with gene ontology: from traditional to deep learning models

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12019
Author(s):  
Thi Thuy Duong Vu ◽  
Jaehee Jung
Keyword(s):  
Gene Ontology ◽  
Deep Learning ◽  
Protein Function ◽  
High Throughput Sequencing ◽  
Protein Function Prediction ◽  
Rapid Development ◽  
Function Prediction ◽  
Amino Acid Sequences ◽  
Go Annotation ◽  
Go Terms

Protein function prediction is a crucial part of genome annotation. Prediction methods have recently witnessed rapid development, owing to the emergence of high-throughput sequencing technologies. Among the available databases for identifying protein function terms, Gene Ontology (GO) is an important resource that describes the functional properties of proteins. Researchers are employing various approaches to efficiently predict the GO terms. Meanwhile, deep learning, a fast-evolving discipline in data-driven approach, exhibits impressive potential with respect to assigning GO terms to amino acid sequences. Herein, we reviewed the currently available computational GO annotation methods for proteins, ranging from conventional to deep learning approach. Further, we selected some suitable predictors from among the reviewed tools and conducted a mini comparison of their performance using a worldwide challenge dataset. Finally, we discussed the remaining major challenges in the field, and emphasized the future directions for protein function prediction with GO.

scholarly journals deepNF: Deep network fusion for protein function prediction

2017 ◽  
Author(s):  
Vladimir Gligorijević ◽  
Meet Barot ◽  
Richard Bonneau
Keyword(s):  
Protein Function ◽  
Large Scale ◽  
Protein Function Prediction ◽  
Predictive Performance ◽  
Substantial Improvement ◽  
Function Prediction ◽  
Interaction Networks ◽  
Highly Nonlinear ◽  
High Level ◽  
String Networks

AbstractThe prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.AvailabilitydeepNF is freely available at: https://github.com/VGligorijevic/deepNF

scholarly journals Hierarchical Ensemble Methods for Protein Function Prediction

2014 ◽  
Vol 2014 ◽  
pp. 1-34 ◽  
Author(s):  
Giorgio Valentini
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Ensemble Methods ◽  
Function Prediction ◽  
Future Research ◽  
Prediction Methods ◽  
Multiple Sources ◽  
Open Problems ◽  
Functional Classes ◽  
Hierarchical Relationships

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research.

scholarly journals An expanded evaluation of protein function prediction methods shows an improvement in accuracy

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Yuxiang Jiang ◽  
Tal Ronnen Oron ◽  
Wyatt T. Clark ◽  
Asma R. Bankapur ◽  
Daniel D’Andrea ◽  
...  
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Prediction Methods

scholarly journals Prediction of post-translational modification sites using multiple kernel support vector machine

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e3261 ◽  
Author(s):  
BingHua Wang ◽  
Minghui Wang ◽  
Ao Li
Keyword(s):  
Protein Function ◽  
Predictive Performance ◽  
Computational Prediction ◽  
Computational Method ◽  
Support Vector ◽  
Prediction Methods ◽  
Sequence Information ◽  
Post Translational Modification ◽  
Multiple Kernel ◽  
Local Sequence

Protein post-translational modification (PTM) is an important mechanism that is involved in the regulation of protein function. Considering the high-cost and labor-intensive of experimental identification, many computational prediction methods are currently available for the prediction of PTM sites by using protein local sequence information in the context of conserved motif. Here we proposed a novel computational method by using the combination of multiple kernel support vector machines (SVM) for predicting PTM sites including phosphorylation, O-linked glycosylation, acetylation, sulfation and nitration. To largely make use of local sequence information and site-modification relationships, we developed a local sequence kernel and Gaussian interaction profile kernel, respectively. Multiple kernels were further combined to train SVM for efficiently leveraging kernel information to boost predictive performance. We compared the proposed method with existing PTM prediction methods. The experimental results revealed that the proposed method performed comparable or better performance than the existing prediction methods, suggesting the feasibility of the developed kernels and the usefulness of the proposed method in PTM sites prediction.

scholarly journals Improving protein function prediction with synthetic feature samples created by generative adversarial networks

2019 ◽  
Author(s):  
Cen Wan ◽  
David T. Jones
Keyword(s):  
Protein Function ◽  
Data Augmentation ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Generative Adversarial Networks ◽  
Prediction Methods ◽  
High Quality ◽  
Adversarial Networks ◽  
Synthetic Protein ◽  
Protein Feature

AbstractProtein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, namely FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of the Gene Ontology through augmentation of the original training protein feature samples.

scholarly journals Improving protein function prediction methods with integrated literature data

2008 ◽  
Vol 9 (1) ◽  
Author(s):  
Aaron P Gabow ◽  
Sonia M Leach ◽  
William A Baumgartner ◽  
Lawrence E Hunter ◽  
Debra S Goldberg
Keyword(s):  
Protein Function ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Prediction Methods

scholarly journals Predicting functions of maize proteins using graph convolutional network

2020 ◽  
Vol 21 (S16) ◽  
Author(s):  
Guangjie Zhou ◽  
Jun Wang ◽  
Xiangliang Zhang ◽  
Maozu Guo ◽  
Guoxian Yu
Keyword(s):  
Deep Learning ◽  
Amino Acid ◽  
Protein Function ◽  
Structural Information ◽  
Semantic Representation ◽  
Model Organism ◽  
Amino Acid Sequences ◽  
Feature Representation ◽  
Convolutional Network ◽  
Go Terms

Abstract Background Maize (Zea mays ssp. mays L.) is the most widely grown and yield crop in the world, as well as an important model organism for fundamental research of the function of genes. The functions of Maize proteins are annotated using the Gene Ontology (GO), which has more than 40000 terms and organizes GO terms in a direct acyclic graph (DAG). It is a huge challenge to accurately annotate relevant GO terms to a Maize protein from such a large number of candidate GO terms. Some deep learning models have been proposed to predict the protein function, but the effectiveness of these approaches is unsatisfactory. One major reason is that they inadequately utilize the GO hierarchy. Results To use the knowledge encoded in the GO hierarchy, we propose a deep Graph Convolutional Network (GCN) based model (DeepGOA) to predict GO annotations of proteins. DeepGOA firstly quantifies the correlations (or edges) between GO terms and updates the edge weights of the DAG by leveraging GO annotations and hierarchy, then learns the semantic representation and latent inter-relations of GO terms in the way by applying GCN on the updated DAG. Meanwhile, Convolutional Neural Network (CNN) is used to learn the feature representation of amino acid sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of the two representations, which enable to train the whole network end-to-end coherently. Extensive experiments show that DeepGOA can effectively integrate GO structural information and amino acid information, and then annotates proteins accurately. Conclusions Experiments on Maize PH207 inbred line and Human protein sequence dataset show that DeepGOA outperforms the state-of-the-art deep learning based methods. The ablation study proves that GCN can employ the knowledge of GO and boost the performance. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=DeepGOA.

Sign in / Sign up

Export Citation Format

Share Document