DEEPre: sequence-based enzyme EC number prediction by deep learning

Abstract

MotivationAnnotation of enzyme function has a broad range of applications, such as metagenomics, industrial biotechnology, and diagnosis of enzyme deficiency-caused diseases. However, the time and resource required make it prohibitively expensive to experimentally determine the function of every enzyme. Therefore, computational enzyme function prediction has become increasingly important. In this paper, we develop such an approach, determining the enzyme function by predicting the Enzyme Commission number.ResultsWe propose an end-to-end feature selection and classification model training approach, as well as an automatic and robust feature dimensionality uniformization method, DEEPre, in the field of enzyme function prediction. Instead of extracting manually crafted features from enzyme sequences, our model takes the raw sequence encoding as inputs, extracting convolutional and sequential features from the raw encoding based on the classification result to directly improve the prediction performance. The thorough cross-fold validation experiments conducted on two large-scale datasets show that DEEPre improves the prediction performance over the previous state-of-the-art methods. In addition, our server outperforms five other servers in determining the main class of enzymes on a separate low-homology dataset. Two case studies demonstrate DEEPre’s ability to capture the functional difference of enzyme isoforms.Availability and implementationThe server could be accessed freely at http://www.cbrc.kaust.edu.sa/DEEPre.Supplementary information Supplementary data are available at Bioinformatics online.

A deep dense inception network for protein beta‐turn prediction

“…There are many successful deep learning applications in protein bioinformatics, [53][54][55] including applications in modeling protein sequences to predict hierarchical labels 56 There are several innovations in this work.…”

UDSMProt: Universal Deep Sequence Models for Protein Classification

“…If not noted otherwise, CNN models are trained on representative sequences as this considerably reduces the computational burden for determining PSSM features and is in line with the literature, see e.g. [Li et al, 2018, Dalkiran et al, 2018, whereas UDSMProt is conventionally trained using the full training set including redundant sequences, whereas the corresponding test and validation sets always contain only non-redundant sequences. For the EC50 dataset non-redundant sequences enlarge the size of the training set from 45k to 114k and from 86k to 170k sequences for level 1 and level 0 respectively.…”

“…This process is repeated to a desired number of iterations. In our experiments we used the same parameters as reported in the literature [Li et al, 2018, Dalkiran et al, 2018, Shen and Chou, 2007, namely three iterations with e_value = 0.001, where e_value relates to the threshold for which an alignment is considered as significant. While the raw sequences from Swiss-Prot contained 26 unique amino acids (20 standard and 6 non-standard amino acids), PSSM features are computed only for the 20 standard amino acids.…”

“…For hierarchical classification (level 0 to level 2), a simple k-nearest-neighbor classifier (KNN) was trained in order to achieve convincing results. EzyPred was superseded by DEEPre [Li et al, 2018] where deep learning was applied to raw sequence and homology data as input. Instead of training simple classifiers on highly engineered features, they trained feature representation and classification in an end-to-end fashion with a hybrid CNN-LSTM-approach.…”

The renaissance of yeasts as microbial factories in the modern age of biomanufacturing

“…The first step in synthetic pathway design is the identification of relevant genes in existing biosynthetic pathways. Increasingly, sophisticated structure–function prediction algorithms (Li et al, ) and metabolic models (Heavner & Price, ) are used to “bioprospect” in sequence databases to find genes that might be used to construct a desired pathway (Figure ). Once an appropriate set of genes is assembled, transcriptional networks are reconstructed, rewired, or even created de novo to precisely control the expression of the genes and engineer a genetic circuit to produce a specific product (Figure ).…”