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

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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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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DYNAMICALLY SEARCHING FOR A DOMAIN FOR PROTEIN FUNCTION PREDICTION

Journal of Bioinformatics and Computational Biology ◽

10.1142/s021972001350008x ◽

2013 ◽

Vol 11 (04) ◽

pp. 1350008 ◽

Cited By ~ 1

Author(s):

JINGYU HOU ◽

YONGQING JIANG

Keyword(s):

Protein Function ◽

Structural Information ◽

Protein Function Prediction ◽

Function Prediction ◽

Computational Approaches ◽

Protein Protein Interaction ◽

Protein Functions ◽

Novel Method ◽

Function Information ◽

The Relationship

The availability of large amounts of protein–protein interaction (PPI) data makes it feasible to use computational approaches to predict protein functions. The base of existing computational approaches is to exploit the known function information of annotated proteins in the PPI data to predict functions of un-annotated proteins. However, these approaches consider the prediction domain (i.e. the set of proteins from which the functions are predicted) as unchangeable during the prediction procedure. This may lead to valuable information being overwhelmed by the unavoidable noise information in the PPI data when predicting protein functions, and in turn, the prediction results will be distorted. In this paper, we propose a novel method to dynamically predict protein functions from the PPI data. Our method regards the function prediction as a dynamic process of finding a suitable prediction domain, from which representative functions of the domain are selected to predict functions of un-annotated proteins. Our method exploits the topological structural information of a PPI network and the semantic relationship between protein functions to measure the relationship between proteins, dynamically select a suitable prediction domain and predict functions. The evaluation on real PPI datasets demonstrated the effectiveness of our proposed method, and generated better prediction results.

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Structure-based protein function prediction using graph convolutional networks

Nature Communications ◽

10.1038/s41467-021-23303-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Vladimir Gligorijević ◽

P. Douglas Renfrew ◽

Tomasz Kosciolek ◽

Julia Koehler Leman ◽

Daniel Berenberg ◽

...

Keyword(s):

Protein Function ◽

High Performance ◽

Protein Function Prediction ◽

Language Model ◽

Protein Structures ◽

Function Prediction ◽

Convolutional Network ◽

Convolutional Networks ◽

Protein Functions ◽

Current Sequence

AbstractThe rapid increase in the number of proteins in sequence databases and the diversity of their functions challenge computational approaches for automated function prediction. Here, we introduce DeepFRI, a Graph Convolutional Network for predicting protein functions by leveraging sequence features extracted from a protein language model and protein structures. It outperforms current leading methods and sequence-based Convolutional Neural Networks and scales to the size of current sequence repositories. Augmenting the training set of experimental structures with homology models allows us to significantly expand the number of predictable functions. DeepFRI has significant de-noising capability, with only a minor drop in performance when experimental structures are replaced by protein models. Class activation mapping allows function predictions at an unprecedented resolution, allowing site-specific annotations at the residue-level in an automated manner. We show the utility and high performance of our method by annotating structures from the PDB and SWISS-MODEL, making several new confident function predictions. DeepFRI is available as a webserver at https://beta.deepfri.flatironinstitute.org/.

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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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Structure-Based Protein Function Prediction using Graph Convolutional Networks

10.1101/786236 ◽

2019 ◽

Cited By ~ 4

Author(s):

Vladimir Gligorijevic ◽

P. Douglas Renfrew ◽

Tomasz Kosciolek ◽

Julia Koehler Leman ◽

Daniel Berenberg ◽

...

Keyword(s):

Protein Function ◽

Sequence Data ◽

Protein Function Prediction ◽

General Structure ◽

Protein Structures ◽

Data Bank ◽

Function Prediction ◽

Convolutional Network ◽

Convolutional Networks ◽

Protein Functions

The large number of available sequences and the diversity of protein functions challenge current experimental and computational approaches to determining and predicting protein function. We present a deep learning Graph Convolutional Network (GCN) for predicting protein functions and concurrently identifying functionally important residues. This model is initially trained using experimentally determined structures from the Protein Data Bank (PDB) but has significant de-noising capability, with only a minor drop in performance observed when structure predictions are used. We take advantage of this denoising property to train the model on > 200,000 protein structures, including many homology-predicted structures, greatly expanding the reach and applications of the method. Our model learns general structure-function relationships by robustly predicting functions of proteins with ≤ 40% sequence identity to the training set. We show that our GCN architecture predicts functions more accurately than Convolutional Neural Networks trained on sequence data alone and previous competing methods. Using class activation mapping, we automatically identify structural regions at the residue-level that lead to each function prediction for every confidently predicted protein, advancing site-specific function prediction. We use our method to annotate PDB and SWISS-MODEL proteins, making several new confident function predictions spanning both fold and function classifications.

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The Applications of Clustering Methods in Predicting Protein Functions

Current Proteomics ◽

10.2174/1570164616666181212114612 ◽

2019 ◽

Vol 16 (5) ◽

pp. 354-358

Author(s):

Weiyang Chen ◽

Weiwei Li ◽

Guohua Huang ◽

Matthew Flavel

Keyword(s):

Computational Methods ◽

Protein Function ◽

Protein Function Prediction ◽

Function Prediction ◽

Biological Processes ◽

Clustering Methods ◽

Protein Functions

Background: The understanding of protein function is essential to the study of biological processes. However, the prediction of protein function has been a difficult task for bioinformatics to overcome. This has resulted in many scholars focusing on the development of computational methods to address this problem. Objective: In this review, we introduce the recently developed computational methods of protein function prediction and assess the validity of these methods. We then introduce the applications of clustering methods in predicting protein functions.

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DeepGOPlus: improved protein function prediction from sequence

Bioinformatics ◽

10.1093/bioinformatics/btz595 ◽

2019 ◽

Cited By ~ 17

Author(s):

Maxat Kulmanov ◽

Robert Hoehndorf

Keyword(s):

Protein Function ◽

Drug Targets ◽

Sequence Similarity ◽

Protein Function Prediction ◽

Function Prediction ◽

Supplementary Information ◽

Protein Protein Interaction ◽

Wide Range ◽

Protein Functions ◽

Novel Method

Abstract Motivation Protein function prediction is one of the major tasks of bioinformatics that can help in wide range of biological problems such as understanding disease mechanisms or finding drug targets. Many methods are available for predicting protein functions from sequence based features, protein–protein interaction networks, protein structure or literature. However, other than sequence, most of the features are difficult to obtain or not available for many proteins thereby limiting their scope. Furthermore, the performance of sequence-based function prediction methods is often lower than methods that incorporate multiple features and predicting protein functions may require a lot of time. Results We developed a novel method for predicting protein functions from sequence alone which combines deep convolutional neural network (CNN) model with sequence similarity based predictions. Our CNN model scans the sequence for motifs which are predictive for protein functions and combines this with functions of similar proteins (if available). We evaluate the performance of DeepGOPlus using the CAFA3 evaluation measures and achieve an Fmax of 0.390, 0.557 and 0.614 for BPO, MFO and CCO evaluations, respectively. These results would have made DeepGOPlus one of the three best predictors in CCO and the second best performing method in the BPO and MFO evaluations. We also compare DeepGOPlus with state-of-the-art methods such as DeepText2GO and GOLabeler on another dataset. DeepGOPlus can annotate around 40 protein sequences per second on common hardware, thereby making fast and accurate function predictions available for a wide range of proteins. Availability and implementation http://deepgoplus.bio2vec.net/. Supplementary information Supplementary data are available at Bioinformatics online.

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Predicting Protein Functions from Protein Interaction Networks

International Journal of Knowledge Discovery in Bioinformatics ◽

10.4018/ijkdb.2012100104 ◽

2012 ◽

Vol 3 (4) ◽

pp. 50-70 ◽

Cited By ~ 1

Author(s):

Hon Nian Chua ◽

Limsoon Wong

Keyword(s):

Protein Interaction ◽

Protein Function ◽

Protein Function Prediction ◽

Functional Characterization ◽

Function Prediction ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Protein Functions ◽

High Throughput Manner

Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of assigning functional annotations to proteins in a high-throughput manner. In this article, the authors provide an introduction to the task of automated protein function prediction. They discuss about the motivation for automated protein function prediction, the challenges faced in this task, as well as some approaches that are currently available. In particular, they take a closer look at methods that use protein-protein interaction for protein function prediction, elaborating on their underlying techniques and assumptions, as well as their strengths and limitations.

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Predicting Protein Functions from Protein Interaction Networks

Biological Data Mining in Protein Interaction Networks ◽

10.4018/978-1-60566-398-2.ch012 ◽

2009 ◽

pp. 203-222 ◽

Cited By ~ 3

Author(s):

Hon Nian Chua ◽

Limsoon Wong

Keyword(s):

Protein Interaction ◽

Protein Function ◽

Protein Function Prediction ◽

Functional Characterization ◽

Function Prediction ◽

Functional Annotations ◽

Protein Protein Interaction ◽

Protein Functions ◽

High Throughput Manner

Functional characterization of genes and their protein products is essential to biological and clinical research. Yet, there is still no reliable way of assigning functional annotations to proteins in a high-throughput manner. In this chapter, the authors provide an introduction to the task of automated protein function prediction. They discuss about the motivation for automated protein function prediction, the challenges faced in this task, as well as some approaches that are currently available. In particular, they take a closer look at methods that use protein-protein interaction for protein function prediction, elaborating on their underlying techniques and assumptions, as well as their strengths and limitations.

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Integrated Network Approach to Protein Function Prediction

Information Technology and Management Science ◽

10.7250/itms-2018-0016 ◽

2018 ◽

Vol 21 ◽

pp. 98-103

Author(s):

Natalia Novoselova ◽

Igar Tom

Keyword(s):

Protein Function ◽

Protein Function Prediction ◽

Function Prediction ◽

Biological Data ◽

Label Propagation ◽

Integrated Network ◽

Functional Annotations ◽

Additional Information ◽

Integration Schemes ◽

Protein Functions

One of the main problems in functional genomics is the prediction of the unknown gene/protein functions. With the rapid increase of high-throughput technologies, the vast amount of biological data describing different aspects of cellular functioning became available and made it possible to use them as the additional information sources for function prediction and to improve their accuracy.In our research, we have described an approach to protein function prediction on the basis of integration of several biological datasets. Initially, each dataset is presented in the form of a graph (or network), where the nodes represent genes or their products and the edges represent physical, functional or chemical relationships between nodes. The integration process makes it possible to estimate the network importance for the prediction of a particular function taking into account the imbalance between the functional annotations, notably the disproportion between positively and negatively annotated proteins. The protein function prediction consists in applying the label propagation algorithm to the integrated biological network in order to annotate the unknown proteins or determine the new function to already known proteins. The comparative analysis of the prediction efficiency with several integration schemes shows the positive effect in terms of several performance measures.

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