Limits...
Computational learning on specificity-determining residue-nucleotide interactions.

Wong KC, Li Y, Peng C, Moses AM, Zhang Z - Nucleic Acids Res. (2015)

Bottom Line: Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families.The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance.In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein-DNA interactions across different DNA binding families.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong kc.w@cityu.edu.hk.

Show MeSH
Receiver Operating Characteristic (ROC) curves for our proposed methods (in Blue and Black), BindN (in Green), BindN+(in Red) and DISIS (in Violet) on the entire DBD families.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4666365&req=5

Figure 2: Receiver Operating Characteristic (ROC) curves for our proposed methods (in Blue and Black), BindN (in Green), BindN+(in Red) and DISIS (in Violet) on the entire DBD families.

Mentions: For each DBD family, we have written network scripts to send the testing DBD sequences to the BindN web-server, BindN+ web-server and DISIS web-server for obtaining their predictions with the default settings suggested. Briefly, BindN is a support vector machine classifier using physicochemical sequence features (6). BindN+ is an extension of BindN which also takes in account the evolutionary information (29). DISIS is also a support vector machine classifier which considers evolutionary information, predicted secondary structural information and the neighboring residue information (28). The Receiver Operating Characteristic (ROC) and precision-recall (PRC) curves for the entire DBD families are plotted and shown in Figure 2 and Supplementary Figure S4. It can be observed that our proposed method using protein-only features (ours) and that using both-protein–DNA features (ours-both) have a competitive edge over the other sequence-based methods at low false positive rates.


Computational learning on specificity-determining residue-nucleotide interactions.

Wong KC, Li Y, Peng C, Moses AM, Zhang Z - Nucleic Acids Res. (2015)

Receiver Operating Characteristic (ROC) curves for our proposed methods (in Blue and Black), BindN (in Green), BindN+(in Red) and DISIS (in Violet) on the entire DBD families.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4666365&req=5

Figure 2: Receiver Operating Characteristic (ROC) curves for our proposed methods (in Blue and Black), BindN (in Green), BindN+(in Red) and DISIS (in Violet) on the entire DBD families.
Mentions: For each DBD family, we have written network scripts to send the testing DBD sequences to the BindN web-server, BindN+ web-server and DISIS web-server for obtaining their predictions with the default settings suggested. Briefly, BindN is a support vector machine classifier using physicochemical sequence features (6). BindN+ is an extension of BindN which also takes in account the evolutionary information (29). DISIS is also a support vector machine classifier which considers evolutionary information, predicted secondary structural information and the neighboring residue information (28). The Receiver Operating Characteristic (ROC) and precision-recall (PRC) curves for the entire DBD families are plotted and shown in Figure 2 and Supplementary Figure S4. It can be observed that our proposed method using protein-only features (ours) and that using both-protein–DNA features (ours-both) have a competitive edge over the other sequence-based methods at low false positive rates.

Bottom Line: Taking into account both sides (protein and DNA), we propose and describe a computational study for learning the specificity-determining residue-nucleotide interactions of different known DNA-binding domain families.The proposed learning models are compared to state-of-the-art models comprehensively, demonstrating its competitive learning performance.In addition, we describe and propose two applications which demonstrate how the learnt models can provide meaningful insights into protein-DNA interactions across different DNA binding families.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong kc.w@cityu.edu.hk.

Show MeSH