Improving Protein Fold Recognition by Deep Learning Networks

Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

doi:10.1038/srep17573

Cited by 116 publications

(69 citation statements)

References 32 publications

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“…The use of deep learning algorithms for classification tasks has been a recent research hotspot in the machine learning field. Deep learning networks have been successfully applied in protein fold recognition [57]. Combining deep learning networks with well-established ensemble classifiers is probably an alternative means to improve the efficiency of protein fold recognition.…”

Section: Discussionmentioning

confidence: 99%

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

Wei

Zou

2016

IJMS

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Knowledge on protein folding has a profound impact on understanding the heterogeneity and molecular function of proteins, further facilitating drug design. Predicting the 3D structure (fold) of a protein is a key problem in molecular biology. Determination of the fold of a protein mainly relies on molecular experimental methods. With the development of next-generation sequencing techniques, the discovery of new protein sequences has been rapidly increasing. With such a great number of proteins, the use of experimental techniques to determine protein folding is extremely difficult because these techniques are time consuming and expensive. Thus, developing computational prediction methods that can automatically, rapidly, and accurately classify unknown protein sequences into specific fold categories is urgently needed. Computational recognition of protein folds has been a recent research hotspot in bioinformatics and computational biology. Many computational efforts have been made, generating a variety of computational prediction methods. In this review, we conduct a comprehensive survey of recent computational methods, especially machine learning-based methods, for protein fold recognition. This review is anticipated to assist researchers in their pursuit to systematically understand the computational recognition of protein folds.

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Section: Discussionmentioning

confidence: 99%

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

Wei

Zou

2016

IJMS

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“…DN-Fold is another fold recognition technique, but it uses a deep learning neural network as a basis for learning [36]. A deep learning network has many more layers than a typical neural network.…”

Section: Protein Fold Recognitionmentioning

confidence: 99%

Adaptive local learning in sampling based motion planning for protein folding

Ekenna

Thomas

Amato

2015

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Background: Simulating protein folding motions is an important problem in computational biology. Motion planning algorithms, such as Probabilistic Roadmap Methods, have been successful in modeling the folding landscape. Probabilistic Roadmap Methods and variants contain several phases (i.e., sampling, connection, and path extraction). Most of the time is spent in the connection phase and selecting which variant to employ is a difficult task. Global machine learning has been applied to the connection phase but is inefficient in situations with varying topology, such as those typical of folding landscapes. Results: We develop a local learning algorithm that exploits the past performance of methods within the neighborhood of the current connection attempts as a basis for learning. It is sensitive not only to different types of landscapes but also to differing regions in the landscape itself, removing the need to explicitly partition the landscape. We perform experiments on 23 proteins of varying secondary structure makeup with 52-114 residues. We compare the success rate when using our methods and other methods. We demonstrate a clear need for learning (i.e., only learning methods were able to validate against all available experimental data) and show that local learning is superior to global learning producing, in many cases, significantly higher quality results than the other methods. Conclusions:We present an algorithm that uses local learning to select appropriate connection methods in the context of roadmap construction for protein folding. Our method removes the burden of deciding which method to use, leverages the strengths of the individual input methods, and it is extendable to include other future connection methods.

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“…5, 11 and 21 residues) that is centered on a target residue are combined by machine learning approaches to predict the local quality of the residue. Recently, deep learning techniques that can handle input of varied size have achieved significant success in the bioinformatics field 14,34,35 . Especially, the application of deep convolutional neural network (CNN) 7,36,37 (e.g.…”

Section: Introductionmentioning

confidence: 99%

Deep convolutional neural networks for predicting the quality of single protein structural models

Hou

Cao

Cheng

2019

Preprint

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Predicting the global quality and local (residual-specific) quality of a single protein structural model is important for protein structure prediction and application. In this work, we developed a deep one-dimensional convolutional neural network (1DCNN) that predicts the absolute local quality of a single protein model as well as two 1DCNNs to predict both local and global quality simultaneously through a novel multi-task learning framework. The networks accept sequential and structural features (i.e. amino acid sequence, agreement of secondary structure and solvent accessibilities, residual disorder properties and Rosetta energies) of a protein model of any size as input to predict its quality, which is different from existing methods using a fixed number of handcrafted features as input. Our three methods (InteractQA-net, JointQA-net and LocalQA-net) were trained on the structural models of the single-domain protein targets of CASP8, 9, 10 and evaluated on the models of CASP11 and CASP12 targets. The results show that the performance of our deep learning methods is comparable to the state-of-the-art quality assessment methods. Our study also demonstrates that combining local and global quality predictions together improves the global 2 quality prediction accuracy. The source code and executable of our methods are available at: https://github.com/multicom-toolbox/DeepCovQA Affiliations

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Improving Protein Fold Recognition by Deep Learning Networks

Cited by 116 publications

References 32 publications

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition

Adaptive local learning in sampling based motion planning for protein folding

Deep convolutional neural networks for predicting the quality of single protein structural models

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