Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs

Abstract

Protein-DNA interactions are ubiquitous in a wide variety of biological processes. Correctly locating DNA-binding residues solely from protein sequences is an important but challenging task for protein function annotations and drug discovery, especially in the post-genomic era where large volumes of protein sequences have quickly accumulated. In this study, we report a new predictor, named TargetDNA, for targeting protein-DNA binding residues from primary sequences. TargetDNA uses a protein's evolutionary information and its predicted solvent accessibility as two base features and employs a centered linear kernel alignment algorithm to learn the weights for weightedly combining the two features. Based on the weightedly combined feature, multiple initial predictors with SVM as classifiers are trained by applying a random under-sampling technique to the original dataset, the purpose of which is to cope with the severe imbalance phenomenon that exists between the number of DNA-binding and non-binding residues. The final ensembled predictor is obtained by boosting the multiple initially trained predictors. Experimental simulation results demonstrate that the proposed TargetDNA achieves a high prediction performance and outperforms many existing sequence-based protein-DNA binding residue predictors. The TargetDNA web server and datasets are freely available at http://csbio.njust.edu.cn/bioinf/TargetDNA/ for academic use.

Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information

“…PDNA-543 and PDNA-41 are independent test datasets that have been constructed by Hu et al [ 13 ]. They collect a dataset that contains DNA-binding protein chains and has clear target annotations in PDB (Protein Data Bank) [ 55 ].…”

“…The concomitant information of sequence-based tactics [ 1 , 2 ] usually comprises physical and chemical properties of amino acids, evolutionary and other sequence information, such as BindN [ 3 ], BindN Random Forest (BindN-RF) [ 4 ], BindN+ [ 5 ], DNABindR [ 6 ], DNA Binding Sites Prediction (DBS-PRED) [ 7 ], DNA Binding Sites based on Position Specific Scoring Matrix (DBS-PSSM) [ 8 ], ProteDNA [ 9 ], DNA Protein-Binding (DP-Bind) [ 10 ], DNA Interaction Sites Identified from Sequence (DISIS) [ 11 ], Meta DNA Binding Site (MetaDBSite) [ 12 ], TargetDNA [ 13 ], etc.…”

“…Si et al [ 12 ] consolidate the prediction results from six available online web servers: DISIS [ 11 ], DNABindR [ 6 ], BindN [ 3 ], BindN-RF [ 4 ], DP-Bind [ 10 ] and DBS-PRED [ 7 ], which only employ sequence information of proteins. Hu et al [ 13 ] deploy PSSM and PSA to build a multiple SVMs model with weighted features. Georgiou et al [ 14 ] use metric spaces and fuzzy sets to study entropy/clarity of genetic sequences.…”

SXGBsite: Prediction of Protein–Ligand Binding Sites Using Sequence Information and Extreme Gradient Boosting

“…Due to having the same features, the results in Table 9 also show the running time comparison of SMOTE + XGBoost and RUS + ensemble classifiers, which means that two schemes for the class imbalance problem. Different from the previous protein-DNA binding site dataset, PDNA-543 (9549 binding residues and 134,995 non-binding residues) and PDNA-41 (734 binding residues and 14,021 non-binding residues) are datasets constructed by Hu et al [61]. SXGBsite constructed the prediction model by the PDNA-543 training set, obtained prediction results on the PDNA-41 independent test set, and the comparison of SXGBsite with BindN [37], ProteDNA [62], BindN+ [63], MetaDBSite [32], DP-Bind [39], DNABind [64], TargetDNA [61], and EC-RUS(DNA) [44] is provided in Table 10.…”

Optimasi Support Vector Machine untuk Memprediksi Adanya Mutasi pada DNA Hepatitis C Virus

“…I. PENDAHULUAN Pengolahan data Dioxyribo Nucleic Acid (DNA) untuk mencari suatu pola dalam sequence sudah sering dilakukan [1]- [3]. Tujuan dari pencarian pola ini beragam, ada yang dipakai sebagai forensik, mutasi genetik, atau adanya suatu penyakit di dalam suatu DNA.…”