ArticleNucleic acids research2005
Kernel-based machine learning protocol for predicting DNA-binding proteins.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers.
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Who cites it
60 citing papers in PubMed.
- Benchmarking recent computational tools for DNA-binding protein identification.Briefings in bioinformatics · 2024Article
- ProkDBP: Toward more precise identification of prokaryotic DNA binding proteins.Protein science : a publication of the Protein Society · 2024Article
- StackDPP: a stacking ensemble based DNA-binding protein prediction model.BMC bioinformatics · 2024Article
- PEP-Patch: Electrostatics in Protein-Protein Recognition, Specificity, and Antibody Developability.Journal of chemical information and modeling · 2023Article
- DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features.Applied bionics and biomechanics · 2022Article
- The Characterization of Structure and Prediction for Aquaporin in Tumour Progression by Machine Learning.Frontiers in cell and developmental biology · 2022Article
- UMAP-DBP: An Improved DNA-Binding Proteins Prediction Method Based on Uniform Manifold Approximation and Projection.The protein journal · 2021Article
- A sequence-based multiple kernel model for identifying DNA-binding proteins.BMC bioinformatics · 2021Article
- Article
- Promoter Architecture and Promoter Engineering inMetabolites · 2020Review
- Machine-Learning Classification Suggests That Many Alphaproteobacterial Prophages May Instead Be Gene Transfer Agents.Genome biology and evolution · 2019Article
- DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.Journal of computer-aided molecular design · 2019Article
- Gap Junction Channels of Innexins and Connexins: Relations and Computational Perspectives.International journal of molecular sciences · 2019Review
- An improved deep learning method for predicting DNA-binding proteins based on contextual features in amino acid sequences.PloS one · 2019Article
- Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.Frontiers in genetics · 2019Article
- Heterodimer Binding Scaffolds Recognition via the Analysis of Kinetically Hot Residues.Pharmaceuticals (Basel, Switzerland) · 2018Article
- A structural dissection of protein-RNA interactions based on different RNA base areas of interfaces.RSC advances · 2018Article
- Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.Molecules (Basel, Switzerland) · 2017Article
- PSFM-DBT: Identifying DNA-Binding Proteins by Combing Position Specific Frequency Matrix and Distance-Bigram Transformation.International journal of molecular sciences · 2017Article
- Improved detection of DNA-binding proteins via compression technology on PSSM information.PloS one · 2017Article
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Authors and funding
4 authors.
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Abstract
DNA-binding proteins (DNA-BPs) play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Attempts have been made to identify DNA-BPs based on their sequence and structural information with moderate accuracy. Here we develop a machine learning protocol for the prediction of DNA-BPs where the classifier is Support Vector Machines (SVMs). Information used for classification is derived from characteristics that include surface and overall composition, overall charge and positive potential patches on the protein surface. In total 121 DNA-BPs and 238 non-binding proteins are used to build and evaluate the protocol. In self-consistency, accuracy value of 100% has been achieved. For cross-validation (CV) optimization over entire dataset, we report an accuracy of 90%. Using leave 1-pair holdout evaluation, the accuracy of 86.3% has been achieved. When we restrict the dataset to less than 20% sequence identity amongst the proteins, the holdout accuracy is achieved at 85.8%. Furthermore, seven DNA-BPs with unbounded structures are all correctly predicted. The current performances are better than results published previously. The higher accuracy value achieved here originates from two factors: the ability of the SVM to handle features that demonstrate a wide range of discriminatory power and, a different definition of the positive patch. Since our protocol does not lean on sequence or structural homology, it can be used to identify or predict proteins with DNA-binding function(s) regardless of their homology to the known ones.
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Registered trials
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