Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure

Li, Tao; Li, Qian‐Zhong

doi:10.1016/j.jtbi.2012.07.020

Cited by 5 publications

(2 citation statements)

References 90 publications

(118 reference statements)

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“…The role of DBP in biological activities has gained a lot of attention in recent years, as various large genome projects and research on DBP identification have rapidly progressed. However, identifying DBP using traditional biochemical analyses is inefficient and expensive ( Li and Li, 2012 ; Xu et al, 2021b ). In recent years, machine learning methods have been widely used in the field of bioinformatics ( Jiang et al, 2013 ; Geete and Pandey, 2020 ; Tao et al, 2020 ; Wang et al, 2021a ; Long et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

2021

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DNA-binding protein (DBP) is a protein with a special DNA binding domain that is associated with many important molecular biological mechanisms. Rapid development of computational methods has made it possible to predict DBP on a large scale; however, existing methods do not fully integrate DBP-related features, resulting in rough prediction results. In this article, we develop a DNA-binding protein identification method called KK-DBP. To improve prediction accuracy, we propose a feature extraction method that fuses multiple PSSM features. The experimental results show a prediction accuracy on the independent test dataset PDB186 of 81.22%, which is the highest of all existing methods.

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Section: Introductionmentioning

confidence: 99%

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

2021

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“…However, one research difficulty exists in the current research of bioinformatics, that is, how to predict proteins effectively by its sequence information. Although, whether structure or function, the recognition of traditional proteins via physical, chemical, and biological experiments (such as filtration-binding analysis and genetic analysis) [3] can predict effectively, these methods need high actual cost and consume much time.…”

Section: Introductionmentioning

confidence: 99%

Use Chou’s 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information

Song

Ding

et al. 2020

BioMed Research International

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The knowledge of DNA-binding proteins would help to understand the functions of proteins better in cellular biological processes. Research on the prediction of DNA-binding proteins can promote the research of drug proteins and computer acidified drugs. In recent years, methods based on machine learning are usually used to predict proteins. Although great predicted performance can be achieved via current methods, researchers still need to invest more research in terms of the improvement of predicted performance. In this study, the prediction of DNA-binding proteins is studied from the perspective of evolutionary information and the support vector machine method. One machine learning model for predicting DNA-binding proteins based on evolutionary features by using Chou’s 5-step rule is put forward. The results show that great predicted performance is obtained on benchmark dataset PDB1075 and independent dataset PDB186, achieving the accuracy of 86.05% and 75.30%, respectively. Thus, the method proposed is comparable to a certain degree, and it may work even better than other methods to some extent.

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PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information

Liu

et al. 2013

Bioinformatics

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Evaluated on a new non-redundant protein set with 224 chains, the method has 80.7% sensitivity and 82.9% specificity in the 5-fold cross-validation test. In addition, it predicts DNA-binding sites with 85.1% sensitivity and 85.3% specificity when tested on a dataset with 62 protein-DNA complexes. Compared with a recently published method, BindN+, our method predicts DNA-binding sites with a 7% better area under the receiver operating characteristic curve value when tested on the same dataset. Many important problems in cell biology require the dense non-linear interactions between functional modules be considered. Thus, our prediction method will be useful in detecting such complex interactions.

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Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure

Cited by 5 publications

References 90 publications

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

KK-DBP: A Multi-Feature Fusion Method for DNA-Binding Protein Identification Based on Random Forest

Use Chou’s 5-Step Rule to Predict DNA-Binding Proteins with Evolutionary Information

PreDNA: accurate prediction of DNA-binding sites in proteins by integrating sequence and geometric structure information

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