AI Article Synopsis

  • DNA-binding proteins are essential for key cellular processes like DNA replication and gene expression, but identifying them is a significant challenge in genome annotation.
  • A new method, PSSM Distance Transformation, is introduced to encode protein sequences and is combined with support vector machine (SVM) techniques to create a tool (SVM-PSSM-DT) for identifying these proteins.
  • The SVM-PSSM-DT method demonstrated high accuracy (ACC of 79.96% on benchmark tests) and outperformed existing methods when tested on an independent dataset, with further development of a user-friendly web-server for accessibility.

Article Abstract

Background: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions.

Results: We firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods.

Conclusions: The experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331676PMC
http://dx.doi.org/10.1186/1752-0509-9-S1-S10DOI Listing

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