ProFold: Protein Fold Classification with Additional Structural Features and a Novel Ensemble Classifier

Chen, Daozheng; Tian, Xiaoyu; Zhou, Bo; Gao, Jun

doi:10.1155/2016/6802832

Cited by 21 publications

(16 citation statements)

References 89 publications

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“…Moreover, Chen et al [35] recently proposed a recognition method called ProFold. In ProFold, information on protein tertiary structures is first considered in its feature extraction framework in addition to other commonly used features, such as global features of amino acid sequence, PSSM features, functional domain features, and physiochemical features.…”

Section: Recent Representative Methods For Protein Fold Recognitionmentioning

confidence: 99%

“…The significant performance improvement of ProFold contributes to the first use of the DSSP feature in the field of protein fold recognition. Their research results indicate that integrating the DSSP features into feature representations remarkably enhanced the overall accuracy from 71.2% to 76.2% [35]. This provides an alternative way to further improve predictive performance by integrating some unexplored but informative features.…”

Section: Comparisons With Different Methods On Benchmark Datasetmentioning

confidence: 99%

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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: Recent Representative Methods For Protein Fold Recognitionmentioning

confidence: 99%

Section: Comparisons With Different Methods On Benchmark Datasetmentioning

confidence: 99%

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

Wei

Zou

2016

IJMS

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“…For feature construction, a sparse encoding scheme named CKSAAP is employed which has been used by researchers in various protein prediction problems [13] [15], [21]. CKSAAP stands for Compostion of k-spaced Amino Acid Pairs in which the frequency of the amino acid pair is calculated that are separated by k-residues (k = 0,1,2,3,4,5...).…”

Section: B Featuresmentioning

confidence: 99%

“…CKSAAP stands for Compostion of k-spaced Amino Acid Pairs in which the frequency of the amino acid pair is calculated that are separated by k-residues (k = 0,1,2,3,4,5...). This scheme basically represents the short and long range interactions amongst the residues along the sequence resulting in competitive performance in protein prediction problems [13], [22]. Selecting the value of k = 0, CKSAAP encoding becomes identical to the di-peptide composition.…”

Section: B Featuresmentioning

confidence: 99%

“…In this study we propose to use composition of k spaced arXiv:1910.06392v1 [q-bio.QM] 11 Sep 2019 amino acid pairs (CKSAAP) to construct the feature vector for the prediction of AFPs. Application of CKSAAP can be found in variety of bioinformatics [13], [14], [15]. The encoding in this scheme is based on the frequency of amino acid pairs that are separated by k number of residues.…”

Section: Introductionmentioning

confidence: 99%

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AFP-CKSAAP: Prediction of Antifreeze Proteins Using Composition of k-Spaced Amino Acid Pairs with Deep Neural Network

Usman

Lee

2019

2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE)

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Antifreeze proteins (AFPs) are the sub-set of ice binding proteins indispensable for the species living in extreme cold weather. These proteins bind to the ice crystals, hindering their growth into large ice lattice that could cause physical damage. There are variety of AFPs found in numerous organisms and due to the heterogeneous sequence characteristics, AFPs are found to demonstrate a high degree of diversity, which makes their prediction a challenging task. Herein, we propose a machine learning framework to deal with this vigorous and diverse prediction problem using the manifolding learning through composition of k-spaced amino acid pairs. We propose to use the deep neural network with skipped connection and ReLU non-linearity to learn the non-linear mapping of protein sequence descriptor and class label. The proposed antifreeze protein prediction method called AFP-CKSAAP has shown to outperform the contemporary methods, achieving excellent prediction scores on standard dataset. The main evaluater for the performance of the proposed method in this study is Youden's index whose high value is dependent on both sensitivity and specificity. In particular, AFP-CKSAAP yields a Youden's index value of 0.82 on the independent dataset, which is better than previous methods.

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