scholarly journals Phylo-PFP: improved automated protein function prediction using phylogenetic distance of distantly related sequences

2018 ◽  
Vol 35 (5) ◽  
pp. 753-759 ◽  
Author(s):  
Aashish Jain ◽  
Daisuke Kihara
Keyword(s):  
Protein Function ◽  
Transfer Functions ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Query Protein ◽  
Function Prediction ◽  
Homology Search ◽  
Supplementary Information ◽  
Phylogenetic Distance

Abstract 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.

scholarly journals DeepGOPlus: improved protein function prediction from sequence

2019 ◽  
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.

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

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

scholarly journals Unsupervised protein embeddings outperform hand-crafted sequence and structure features at predicting molecular function

2020 ◽  
Author(s):  
Amelia Villegas-Morcillo ◽  
Stavros Makrodimitris ◽  
Roeland C H J van Ham ◽  
Angel M Gomez ◽  
Victoria Sanchez ◽  
...  
Keyword(s):  
Protein Function ◽  
Prediction Models ◽  
Protein Function Prediction ◽  
3D Structure ◽  
Function Prediction ◽  
Feature Representation ◽  
Training Data ◽  
Supplementary Information ◽  
Molecular Function ◽  
Structure Information

Abstract Motivation Protein function prediction is a difficult bioinformatics problem. Many recent methods use deep neural networks to learn complex sequence representations and predict function from these. Deep supervised models require a lot of labeled training data which are not available for this task. However, a very large amount of protein sequences without functional labels is available. Results We applied an existing deep sequence model that had been pretrained in an unsupervised setting on the supervised task of protein molecular function prediction. We found that this complex feature representation is effective for this task, outperforming hand-crafted features such as one-hot encoding of amino acids, k-mer counts, secondary structure and backbone angles. Also, it partly negates the need for complex prediction models, as a two-layer perceptron was enough to achieve competitive performance in the third Critical Assessment of Functional Annotation benchmark. We also show that combining this sequence representation with protein 3D structure information does not lead to performance improvement, hinting that 3D structure is also potentially learned during the unsupervised pretraining. Availability and implementation Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals 2P303 FCANAL : A structure based protein function prediction method. Application to enzymes and binding proteins

2005 ◽  
Vol 45 (supplement) ◽  
pp. S195
Author(s):  
A. Suzuki ◽  
T. Ando ◽  
A. Matsumura ◽  
H. Sakao ◽  
I. Yamato ◽  
...  
Keyword(s):  
Protein Function ◽  
Binding Proteins ◽  
Protein Function Prediction ◽  
Prediction Method ◽  
Function Prediction

scholarly journals INGA: protein function prediction combining interaction networks, domain assignments and sequence similarity

2015 ◽  
Vol 43 (W1) ◽  
pp. W134-W140 ◽  
Author(s):  
Damiano Piovesan ◽  
Manuel Giollo ◽  
Emanuela Leonardi ◽  
Carlo Ferrari ◽  
Silvio C.E. Tosatto
Keyword(s):  
Protein Function ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Interaction Networks

FUNCTION PREDICTION OF UNCHARACTERIZED PROTEINS

2007 ◽  
Vol 05 (01) ◽  
pp. 1-30 ◽  
Author(s):  
TROY HAWKINS ◽  
DAISUKE KIHARA
Keyword(s):  
Protein Function ◽  
Sequence Similarity ◽  
Protein Function Prediction ◽  
Function Prediction ◽  
Biological Research ◽  
Special Focus ◽  
Genomic Context ◽  
Uncharacterized Protein ◽  
Genomics And Proteomics ◽  
Proteomics Experiment

Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.

scholarly journals NetQuilt: Deep Multispecies Network-based Protein Function Prediction using Homology-informed Network Similarity

2021 ◽  
Author(s):  
Meet Barot ◽  
Vladimir Gligorijević ◽  
Kyunghyun Cho ◽  
Richard Bonneau
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Functional Annotation ◽  
Sequence Similarity ◽  
Function Prediction ◽  
Supplementary Information ◽  
Learning Sequence ◽  
Network Information ◽  
Ppi Networks ◽  
Multiple Species

Abstract Motivation Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to protein functional annotation use sequence similarity to transfer knowledge between species. These approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular context for meaningful prediction. To supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, most of these methods are tied to a network for a single species, and many species lack biological networks. Results In this work, we integrate sequence and network information across multiple species by computing IsoRank similarity scores to create a meta-network profile of the proteins of multiple species. We use this integrated multispecies meta-network as input to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and consequently leads to significant improvements in function prediction performance compared to two network-based methods, a deep learning sequence-based method, and the BLAST annotation method used in the Critial Assessment of Functional Annotation. We are able to demonstrate that our approach performs well even in cases where a species has no network information available: when an organism’s PPI network is left out we can use our multi-species method to make predictions for the left-out organism with good performance. Availability The code is freely available at https://github.com/nowittynamesleft/NetQuilt Supplementary information Supplementary data are available at Bioinformatics online.

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