A deep learning framework for improving long-range residue–residue contact prediction using a hierarchical strategy

Xiong, Daxi; Zeng, Jianyang; Gong, H.

doi:10.1093/bioinformatics/btx296

Cited by 41 publications

(31 citation statements)

References 52 publications

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“…Practically speaking, contact identification methods and evolutionary couplings are increasingly important for a variety of applications. Frequently, evolutionary couplings are combined with a variety of other features and used as input for machine learning and associated neural network-based algorithms to predict structural and functional properties of proteins (Cheng and Baldi, 2007;Tegge et al, 2009;Lena et al, 2012;Jones et al, 2015;Michel et al, 2017;Xiong et al, 2017;Stahl et al, 2017;He et al, 2017;Riesselman et al, 2018;Liu et al, 2018;Wozniak et al, 2018;Jones and Kandathil, 2018;Adhikari et al, 2018;Hanson et al, 2018). Our analysis suggests that there may be biases in the training data-essential to supervised learning techniques-owing to the method used to define true positive contacts.…”

Section: /16mentioning

confidence: 99%

“…Manuscript to be reviewed (Burger and Van Nimwegen, 2008;Hopf et al, 2014;Ovchinnikov et al, 2014), as well as to predict the effect of mutations on protein stability and function (Hopf et al, 2017). Many of these approaches have been further improved through the use of machine learning (Cheng and Baldi, 2007;Jones et al, 2015;Michel et al, 2017), and specifically deep neural networks that leverage evolutionary couplings along-side numerous other protein features (Tegge et al, 2009;Lena et al, 2012;Xiong et al, 2017;Stahl et al, 2017;He et al, 2017;Riesselman et al, 2018;Liu et al, 2018;Wozniak et al, 2018;Jones and Kandathil, 2018;Adhikari et al, 2018;Hanson et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Peer Review #1 of "Evolutionary couplings detect side-chain interactions (v0.1)"

2019

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Patterns of amino acid covariation in large protein sequence alignments can inform the prediction of de novo protein structures, binding interfaces, and mutational effects. While algorithms that detect these so-called evolutionary couplings between residues have proven useful for practical applications, less is known about how and why these methods perform so well, and what insights into biological processes can be gained from their application. Evolutionary coupling algorithms are commonly benchmarked by comparison to true structural contacts derived from solved protein structures. However, the methods used to determine true structural contacts are not standardized and different definitions of structural contacts may have important consequences for interpreting the results from evolutionary coupling analyses and understanding their overall utility. Here, we show that evolutionary coupling analyses are significantly more likely to identify structural contacts between side-chain atoms than between backbone atoms. We use both simulations and empirical analyses to highlight that purely backbone-based definitions of true residue-residue contacts (i.e., based on the distance between Cα atoms) may underestimate the accuracy of evolutionary coupling algorithms by as much as 40% and that a commonly used reference point (Cβ atoms) underestimates the accuracy by 10-15%. These findings show that co-evolutionary outcomes differ according to which atoms participate in residue-residue interactions and suggest that accounting for different interaction types may lead to further improvements to contact-prediction methods.

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Section: /16mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Evolutionary couplings detect side-chain interactions (v0.1)"

2019

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“…These methods effectively reduce false positive predictions by globally considering all inter-residue correlations. More recently, methods like MetaPSICOV [19], SAE-DNN [20], DeepConPred [21], NeBcon [22] and RaptorX-Contact [23] integrated sophisticated machine-learning techniques to further enhance the prediction accuracy. In the latest CASP12 competition, RaptorX-Contact achieved the best performance in the category of template-free modeling targets.…”

Section: Author Summarymentioning

confidence: 99%

“…Indeed, many protein families lack enough homologous sequences for reliable inference of residue contacts [21], and the predicted residue contact maps of these targets may be dominated by false positives, which hinders the subsequent protein structure prediction/modeling. However, even in the highly noisy residue contact maps for these small-family protein targets, characteristic patterns of specific structural motifs could be identified, because a collective pattern of multiple residue contacts is less likely to be perturbed by individual prediction errors and therefore could be more reliably identified than a single residue contact.…”

Section: Author Summarymentioning

confidence: 99%

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Mao

Wang

Zhang

et al. 2017

Preprint

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Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β -β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we introduce a novel ridge-detection-based β -β contact predictor, RDb 2 C, to identify residue pairing in β strands from any predicted residue contact map. The algorithm adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb 2 C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~62% and ~76%at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb 2 C achieves impressively higher performance, with F1-scores reaching ~76% and ~86% at the residue level and strand level, respectively. According to our tests on 61 mainly β proteins, improvement in the β -β contact prediction can further ameliorate the structural prediction. Availability:All source data and codes are available at http://166.111.152.91/Downloads.html or at the GitHub address of https://github.com/wzmao/RDb2C. Author summaryDue to the topological complexity, mainly β proteins are challenging targets in protein structure prediction. Knowledge of the pairing between β strands, especially the residue pairing pattern, can greatly facilitate the tertiary structure prediction of mainly β proteins. In this work, we developed a novel algorithm to identify the residue pairing in β strands from a predicted residue contact map.This method adopts the ridge detection technique to capture the characteristic pattern of β -β interactions from the map and then utilizes a multi-stage random forest framework to predict β -β contacts at the residue level. According to our tests, our method could effectively improve the prediction of β -β contacts even from a highly noisy contact map. Moreover, the refined β -β contact information could effectively improve the structural modeling of mainly β proteins.

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“…There are also a number of breakthroughs in using deep learning to perform biomedical image processing and biomedical diagnosis. For example, [35] proposes a method based on deep neural networks, which can reach dermatologist-level performance in classifying skin cancer; [66] uses transfer learning to solve the data-hungry problem to promote the automatic medical diagnosis; [22] proposes a deep learning method to automatically predict fluorescent labels from transmitted-light images of unlabeled biological samples; [41,160] also propose deep learning methods to analyze 1D data CNN, RNN [198,3,25,156,87,6,80,157,158,175,169] Structure prediction and reconstruction MRI images, Cryo-EM images, fluorescence microscopy images, protein contact map 2D data CNN, GAN, VAE [167,90,38,168,180,196,170] Biomolecular property and function prediction Sequencing data, PSSM, structure properties, microarray gene expression 1D data, 2D data, structured data DNN, CNN, RNN [85,204,75,4] Biomedical image processing and diagnosis CT images, PET images, MRI images 2D data CNN, GAN [35,66,41,22,160] Biomolecule interaction prediction and systems biology Microarray gene expression, PPI, gene-disease interaction, diseasedisease similarity network, diseasevariant network 1D data, 2D data, structured data, graph data CNN, GCN [95,201,203,165,71,…”

Section: Introductionmentioning

confidence: 99%

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

Liu

Huang

Ding

et al. 2019

Methods

273

107

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Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities of analysis pipelines. In this review, we provide both the exoteric introduction of deep learning, and concrete examples and implementations of its representative applications in bioinformatics. We start from the recent achievements of deep learning in the bioinformatics field, pointing out the problems which are suitable to use deep learning. After that, we introduce deep learning in an easy-to-understand fashion, from shallow neural networks to legendary convolutional neural networks, legendary recurrent neural networks, graph neural networks, generative adversarial networks, variational autoencoder, and the most recent state-of-the-art architectures. After that, we provide eight examples, covering five bioinformatics research directions and all the four kinds of data type, with the implementation written in Tensorflow and Keras. Finally, we discuss the common issues, such as overfitting and interpretability, that users will encounter when adopting deep learning methods and provide corresponding suggestions. The implementations are freely available at https://github.com/lykaust15/Deep_learning_examples.

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A deep learning framework for improving long-range residue–residue contact prediction using a hierarchical strategy

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 41 publications

References 52 publications

Peer Review #1 of "Evolutionary couplings detect side-chain interactions (v0.1)"

Peer Review #1 of "Evolutionary couplings detect side-chain interactions (v0.1)"

Identification of residue pairing in interacting β-strands from a predicted residue contact map

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

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