Improving protein fold recognition by extracting fold-specific features from predicted residue–residue contacts

Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

doi:10.1093/bioinformatics/btx514

Cited by 48 publications

(50 citation statements)

References 59 publications

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“…Alternatively, it can be interpreted as the adjacency matrix of a graph, where each amino acid is a node and edges represent amino acids that are in contact with each other. In order to extract meaningful information from contact maps, both two-dimensional CNNs (Zhu et al, 2017;Zheng et al, 2019) and graph convolutional networks (GCNs) (Fout et al, 2017;Zamora-Resendiz and Crivelli, 2019) have been proposed.…”

Section: Introductionmentioning

confidence: 99%

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

Villegas-Morcillo¹,

Makrodimitris²,

Ham³

et al. 2020

Preprint

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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 pre-trained in an unsupervised setting on the supervised task of protein 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 deep prediction models, as a two-layer perceptron was enough to achieve state-of-the-art 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 three-dimensional structure is also potentially learned during the unsupervised pre-training. Availability: Implementations of all used models can be found at https://github.com/stamakro/GCN-for-Structure-and-Function.

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Section: Introductionmentioning

confidence: 99%

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

Villegas-Morcillo¹,

Makrodimitris²,

Ham³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…TBM is based upon the observation that many proteins share similar structures even if their sequences diverge ( Kinch and Grishin, 2002 ; Zhang and Skolnick, 2005 ). The quality of TBM critically depends on accurate sequence-template alignment and correct template recognition, both of which are challenging when only distantly-related templates are available for a protein sequence under prediction ( Cozzetto and Tramontano, 2004 ; Hou, et al , 2018 ; Jo, et al , 2015 ; Jones, 1997 ; Peng and Xu, 2011a ; Peng and Xu, 2011b ; Zhu, et al , 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Protein threading using residue co-variation and deep learning

Zhu

Wang

et al. 2018

Bioinformatics

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MotivationTemplate-based modeling, including homology modeling and protein threading, is a popular method for protein 3D structure prediction. However, alignment generation and template selection for protein sequences without close templates remain very challenging.ResultsWe present a new method called DeepThreader to improve protein threading, including both alignment generation and template selection, by making use of deep learning (DL) and residue co-variation information. Our method first employs DL to predict inter-residue distance distribution from residue co-variation and sequential information (e.g. sequence profile and predicted secondary structure), and then builds sequence-template alignment by integrating predicted distance information and sequential features through an ADMM algorithm. Experimental results suggest that predicted inter-residue distance is helpful to both protein alignment and template selection especially for protein sequences without very close templates, and that our method outperforms currently popular homology modeling method HHpred and threading method CNFpred by a large margin and greatly outperforms the latest contact-assisted protein threading method EigenTHREADER.Availability and implementation http://raptorx.uchicago.edu/ Supplementary information Supplementary data are available at Bioinformatics online.

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“…Constructing a proper feature vector from a protein sequence is a critical step for protein fragment prediction [ 7 ]. Using multiple features extraction strategy, representing sequence, evolutionary, physicochemical information of a sequence fragment, maximizes the discriminative capability of the fold recognizer [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

“…Later, ensemble classifiers and kernel-based methods are introduced to discover correlations between sequence features to overcome the weakness of the early machine learning methods and improve the discriminability of the fold recognizers [ 5 ]. Recently, deep learning techniques have been applied to extract effective features, such as secondary structures [ 4 ] and inter-residue contacts [ 7 ], to further improve fold recognition.…”

Section: Introductionmentioning

confidence: 99%

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

Elhefnawy

Wang

et al. 2020

BMC Bioinformatics

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Background One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold. Results Our results show that DeepFrag-k yields 92.98% accuracy in predicting the top-100 most popular fragments, which can be used to generate discriminative fragment feature vectors to improve protein fold recognition. Conclusions There is a set of fragments that can serve as structural “keywords” distinguishing between major protein folds. The deep learning architecture in DeepFrag-k is able to accurately identify these fragments as structure features to improve protein fold recognition.

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Improving protein fold recognition by extracting fold-specific features from predicted residue–residue contacts

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 48 publications

References 59 publications

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

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

Protein threading using residue co-variation and deep learning

DeepFrag-k: a fragment-based deep learning approach for protein fold recognition

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