Protein function prediction
198933920
DeepGOPlus: improved protein function prediction from sequence
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 http:/deepgoplus.bio2vec.net/ SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Bioinform.
Maxat Kulmanov R Hoehndorf
191188899
The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community driven effort to evaluate and improve the computational annotation of protein function. Here we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome wide experimental data for genes associated with biofilm formation and motility (P. aureginosa only) We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long term memory. We conclude that, while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. We finally report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bioontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.
Naihui Zhou Yuxiang Jiang Timothy Bergquist Alexandra J Lee Balint Z Kacsoh Alex W Crocker Kimberley A Lewis George E Georghiou Huy N Nguyen Nafiz Imtiaz Bin Hamid Larry Davis Tunca Dogan Volkan Atalay Ahmet Sureyya Rifaioglu Alperen Dalkiran Rengul Cetin-Atalay Chengxin Zhang Rebecca L Hurto Peter L Freddolino Yang Zhang Prajwal Bhat Fran Supek Jose Maria Fernandez Branislava Gemovic Vladimir Perovic Radoslav Davidovic Neven Sumonja Nevena Veljkovic Ehsaneddin Asgari Mohammad R K Mofrad Giuseppe Profiti Castrense Savojardo Pier Luigi Martelli Rita Casadio Florian Boecker Indika Kahanda Natalie Thurlby Alice Carolyn McHardy Alexandre Renaux Rabie Saidi Julian Gough Alex Alves Freitas Magdalena Antczak Fabio Fabris Mark N Wass Jie Hou Jianlin Cheng Zheng Wang Alfonso E Romero Alberto Paccanaro Haixuan Yang Tatyana Goldberg Chenguang Zhao Liisa Holm Petri Toronen Alan Medlar Elaine Zosa Itamar Borukhov Ilya B Novikov Angela D Wilkins Olivier Lichtarge Po-Han Chi Wei-Cheng Tseng Michal Linial Peter W Rose Christophe Dessimoz Vedrana Vidulin Saso Dzeroski Ian P W Sillitoe Sayoni Das Jonathan G Lees David T Jones Cen Wan Domenico Cozzetto Rui Fa Mateo Torres Alex Warwick Vesztrocy Jose Manuel Rodriguez Michael L Tress Marco Frasca Marco Notaro Giuliano Grossi Alessandro Petrini Matteo Re Giorgio Valentini Marco Mesiti Daniel B Roche Jonas Reeb David W Ritchie Sabeur Aridhi Seyed Ziaeddin Alborzi Marie-Dominique Devignes Da Chen Emily Koo Richard Bonneau Vladimir Gligorijevic Meet Barot Hai Fang Stefano Toppo Enrico Lavezzo Marco Falda Michele Berselli Silvio C E Tosatto Marco Carraro Damiano Piovesan Hafeez Ur Rehman Qizhong Mao Shanshan Zhang Slobodan Vucetic Gage S Black Dane Jo Dallas J Larsen Ashton Omdahl Luke W Sagers Erica Suh Jonathan B Dayton Liam James McGuffin Danielle Allison Brackenridge Patricia C Babbitt Jeffrey M Yunes Paolo Fontana Fuqing Zhang Shanfeng Zhu Ronghui You Zihan Zhang Suyang Dai Shuwei Yao Weidong Tian Renzhi Cao Caleb Chandler Miguel Amezola Devon Johnson Jia-Ming Chang Wen-Hung Liao Yi-Wei Liu Stefano Pascarelli Yotam Frank R Hoehndorf Maxat Kulmanov Imane Boudellioua Gianfranco Politano Stefano Di Carlo Alfredo Benso Kai Hakala Filip Ginter Farrokh Mehryary Suwisa Kaewphan Jari Bjorne Hans Moen Martti Tolvanen Tapio Salakoski Daisuke Kihara Aashish Jain Tomislav Smuc Adrian M Altenhoff Asa Ben-Hur Burkhard Rost Steven E Brenner Christine A Orengo Constance J Jeffery Giovanni Bosco Deborah A Hogan Maria Jesus Martin Claire O'Donovan Sean D Mooney Casey S Greene Predrag Radivojac Iddo Friedberg
225218236
Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks
Protein 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, 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 Gene Ontology through augmentation of the original training protein feature samples. Training machine learning models to predict the function of proteins is limited by the availability of only a small amount of labelled training data. Training can be improved by employing generative adversarial networks to generate additional synthetic protein samples.
Cen Wan David T Jones
210118451
A bacterial phyla dataset for protein function prediction
Protein function prediction has been the most worked upon and the most challenging problem for computational biologists. The vast majority of known proteins have yet not been characterised experimentally, and there is significant gap between their structures and functions. New un annotated sequences are being added to the public protein databases (e.g. UniprotKB) at an enormous pace [1] Such proteins with unknown functions might play key role in the metabolism, growth and development regulation. Thus, if functions of unknown proteins left undiscovered, researchers may skip important information(s) Based on their sequence, structure, evolutionary history, and their association with other proteins, tools of computational biology can provide insights into the function of proteins [2] For proteins with well characterised close relatives, it is trivial to infer function. Orphan proteins without discernible sequence relatives present a greater challenge [3] Here the task of experimental characterisation is blind and becomes unwieldy. It is highly unlikely that all known proteins will ever be completely experimentally characterised [4] Thus, there is an emergent need to develop fast and accurate computational approaches to fulfil this requirement. Towards this end, we prepared a dataset for protein function prediction by extracting protein sequences and annotations of reviewed prokaryotic proteins (total count 323,719 as accessed on date March 10, 2019) belonging to 9 bacterial phyla Actinobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Firmicutes, Fusobacteria, Proteobacteria, Spirochaetes and Tenericutes. Corresponding to the most frequent 1739 Gene Ontology (Molecular Function) terms, samples were filtered, and 171,212 proteins were retrieved for feature generation. The Dataset was generated by calculating the sequence, sub sequence, physiochemical, annotation based features for each 171,212 reviewed proteins using method in [10] These features constitute a total of 9890 attributes for each sequence of protein along with 1739 Gene Ontology terms. Each protein sequence is assigned one or more of 1739 Gene Ontology (Molecular Function) term as its target label. The Dataset contains the Entry and Entry name of each sequence corresponding to UniprotKB Database. This dataset being huge in size (171,212 samples X 9890 features, 1739 classes with multiple values) and equipped with enough number of positive and negative samples of each 1739 class, is good for testing efficiency of any upcoming deep learning models [5] We divided the full dataset of 171,212 reviewed proteins in the ratio 3:1 to form Train/Test dataset 1; train dataset with 128,409 samples and test dataset with 42,803 samples to facilitate training of a deep learning model. The train and test datasets are stratified to contain good proportion of each 1739 classes. We then prepared a dataset 2 of pathogenic unreviewed proteins of the 9 bacterial phyla each with 9890 features same as train/train dataset of reviewed proteins but without target labels in order to predict their functions using deep learning model proposed in [5]
Data in brief
Sarthak Mishra Yash Pratap Rastogi Suraiya Jabin Punit Kaur Mohammad Amir Shabanam Khatoon
146065857
DeepGOPlus: Improved protein function prediction from sequence
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. 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. We evaluate the performance of DeepGOPlus on the CAFA3 dataset and significantly improve the performance of predictions of biological processes and cellular components with Fmax of 0.47 and 0.70, respectively, using only the amino acid sequence of proteins as input. DeepGOPlus can annotate around 40 protein sequences per second, thereby making fast and accurate function predictions available for a wide range of proteins.
Maxat Kulmanov R Hoehndorf
125644756
Protein function prediction as a graph transduction game
Abstract Motivated by the observation that network based methods for the automatic prediction of protein functions can greatly benefit from exploiting both the similarity between proteins and the similarity between functional classes (as encoded, e.g. in the Gene Ontology) in this paper we propose a novel approach to the problem, based on the notion of a "graph transduction game." We envisage a (non cooperative) game, played over a graph, where the players (graph vertices) represent proteins, the functional classes correspond to the (pure) strategies, and protein and function level similarities are combined into a suitable payoff function. Within this formulation, Nash equilibria turn out to provide consistent functional labelings of proteins, and we use classical replicator dynamics from evolutionary game theory to find them. To test the effectiveness of our approach we conducted experiments on five different organisms and three ontologies, and the results obtained show that our method compares favorably with state of the art algorithms.
Pattern Recognit. Lett.
Sebastiano Vascon Marco Frasca Rocco Tripodi Giorgio Valentini Marcello Pelillo
186205066
Protein Function Prediction: From Traditional Classifier to Deep Learning
Deep learning demonstrates greater competence over traditional machine learning techniques for many tasks. In last several years, deep learning has been applied to protein function prediction and a series of good achievements has been obtained. These findings extensively advanced our understanding of protein function. However, the accuracy of protein function prediction based upon deep learning still has yet to be improved. In article number 1900019, Issue 12, Zhang et al. construct DeepFunc, a deep learning framework using derived feature information of protein sequence and protein interactions network. They find that implementing DeepFunc for protein function prediction is more accurate than using DeepGO, a similar method reported previously. Meanwhile, they find that the method of combining multiple derived feature information in DeepFunc is much better than the method of using only single derived feature information. Due to its fully exploiting feature representation learning ability, deep learning with more derived feature information will enable it to be a promising method for solving more complicated protein function prediction problems and other bioinformatics challenges. Recent researches have provided some major insights into the value for using deep learning to protein function prediction problem.
Proteomics
Zhibin Lv Chunyan Ao Quan Zou
221110988
NPF:network propagation for protein function prediction
Background The accurate annotation of protein functions is of great significance in elucidating the phenomena of life, treating disease and developing new medicines. Various methods have been developed to facilitate the prediction of these functions by combining protein interaction networks (PINs) with multi omics data. However, it is still challenging to make full use of multiple biological to improve the performance of functions annotation. 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. According to the comprehensive evaluation of NPF, it delivered a better performance than other competing methods in terms of leave one out cross validation and ten fold cross validation. Conclusions We demonstrated that network propagation, together with multi omics data, can both discover more partners with similar function, and is unconstricted by 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 information of similarity from protein correlations.
BMC Bioinformatics
Bi-hai Zhao Zhihong Zhang Meiping Jiang Sai Hu Yingchun Luo Lei Wang
153315064
DEEPred: Automated Protein Function Prediction with Multi task Feed forward Deep Neural Networks
Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required. Recently, deep learning based methods have outperformed conventional algorithms in computer vision and natural language processing due to the prevention of overfitting and efficient training. Here, we propose DEEPred, a hierarchical stack of multi task feed forward deep neural networks, as a solution to Gene Ontology (GO) based protein function prediction. DEEPred was optimized through rigorous hyper parameter tests, and benchmarked using three types of protein descriptors, training datasets with varying sizes and GO terms form different levels. Furthermore, in order to explore how training with larger but potentially noisy data would change the performance, electronically made GO annotations were also included in the training process. The overall predictive performance of DEEPred was assessed using CAFA2 and CAFA3 challenge datasets, in comparison with the state of the art protein function prediction methods. Finally, we evaluated selected novel annotations produced by DEEPred with a literature based case study considering the 'biofilm formation process' in Pseudomonas aeruginosa. This study reports that deep learning algorithms have significant potential in protein function prediction; particularly when the source data is large. The neural network architecture of DEEPred can also be applied to the prediction of the other types of ontological associations. The source code and all datasets used in this study are available at: https:/github.com/cansyl/DEEPred.
Scientific Reports
Ahmet Sureyya Rifaioglu Tunca Dogan Maria Jesus Martin Rengul Cetin-Atalay Volkan Atalay
52134676
Phylo PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences
Motivation: Function annotation of proteins is fundamental in contemporary biology across fields including genomics, molecular biology, biochemistry, systems biology and bioinformatics. Function prediction is indispensable in providing clues for interpreting omics scale data as well as in assisting biologists to build hypotheses for designing experiments. As sequencing genomes is now routine due to the rapid advancement of sequencing technologies, computational protein function prediction methods have become increasingly important. A conventional method of annotating a protein sequence is to transfer functions from top hits of a homology search; however, this approach has substantial short comings including a low coverage in genome annotation. Results: Here we have developed Phylo PFP, a new sequence based protein function prediction method, which mines functional information from a broad range of similar sequences, including those with a low sequence similarity identified by a PSI BLAST search. To evaluate functional similarity between identified sequences and the query protein more accurately, Phylo PFP reranks retrieved sequences by considering their phylogenetic distance. Compared to the Phylo PFP's predecessor, PFP, which was among the top ranked methods in the second round of the Critical Assessment of Functional Annotation (CAFA2) Phylo PFP demonstrated substantial improvement in prediction accuracy. Phylo PFP was further shown to outperform prediction programs to date that were ranked top in CAFA2. Availability and implementation: Phylo PFP web server is available for at http:/kiharalab.org/phylo_pfp.php. Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinform.
Aashish Jain Daisuke Kihara