Prediction of DNA-binding residues from protein sequence information using random forests
Background Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for on...
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| Published in | BMC genomics Vol. 10; no. Suppl 1; p. S1 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
07.07.2009
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2164 1471-2164 |
| DOI | 10.1186/1471-2164-10-S1-S1 |
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| Abstract | Background
Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.
Results
A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures.
Conclusion
The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF
http://bioinfo.ggc.org/bindn-rf/
has thus been developed to make the RF classifier accessible to the biological research community. |
|---|---|
| AbstractList | Background Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data. Results A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures. Conclusion The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF has thus been developed to make the RF classifier accessible to the biological research community. Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data. A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures. The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF http://bioinfo.ggc.org/bindn-rf/ has thus been developed to make the RF classifier accessible to the biological research community. Background Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data. Results A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures. Conclusion The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF http://bioinfo.ggc.org/bindn-rf/ has thus been developed to make the RF classifier accessible to the biological research community. Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.BACKGROUNDProtein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA recognition, it is necessary to identify the DNA-binding residues in DNA-binding proteins. However, structural data are available for only a few hundreds of protein-DNA complexes. With the rapid accumulation of sequence data, it becomes an important but challenging task to accurately predict DNA-binding residues directly from amino acid sequence data.A new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures.RESULTSA new machine learning approach has been developed in this study for predicting DNA-binding residues from amino acid sequence data. The approach used both the labelled data instances collected from the available structures of protein-DNA complexes and the abundant unlabeled data found in protein sequence databases. The evolutionary information contained in the unlabeled sequence data was represented as position-specific scoring matrices (PSSMs) and several new descriptors. The sequence-derived features were then used to train random forests (RFs), which could handle a large number of input variables and avoid model overfitting. The use of evolutionary information was found to significantly improve classifier performance. The RF classifier was further evaluated using a separate test dataset, and the predicted DNA-binding residues were examined in the context of three-dimensional structures.The results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF http://bioinfo.ggc.org/bindn-rf/ has thus been developed to make the RF classifier accessible to the biological research community.CONCLUSIONThe results suggest that the RF-based approach gives rise to more accurate prediction of DNA-binding residues than previous studies. A new web server called BindN-RF http://bioinfo.ggc.org/bindn-rf/ has thus been developed to make the RF classifier accessible to the biological research community. |
| Author | Wang, Liangjiang Yang, Jack Y Yang, Mary Qu |
| AuthorAffiliation | 3 National Human Genome Research Institute, National Institutes of Health (NIH), U.S. Department of Health and Human Services, Bethesda, MD 20852, USA 4 Harvard Medical School, Harvard University, P.O. Box 400888, Cambridge, MA 02115, USA 2 J.C. Self Research Institute of Human Genetics, Greenwood Genetic Center, Greenwood, SC 29646, USA 1 Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA |
| AuthorAffiliation_xml | – name: 3 National Human Genome Research Institute, National Institutes of Health (NIH), U.S. Department of Health and Human Services, Bethesda, MD 20852, USA – name: 4 Harvard Medical School, Harvard University, P.O. Box 400888, Cambridge, MA 02115, USA – name: 1 Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA – name: 2 J.C. Self Research Institute of Human Genetics, Greenwood Genetic Center, Greenwood, SC 29646, USA |
| Author_xml | – sequence: 1 givenname: Liangjiang surname: Wang fullname: Wang, Liangjiang email: liangjw@clemson.edu organization: Department of Genetics and Biochemistry, Clemson University, J.C. Self Research Institute of Human Genetics, Greenwood Genetic Center – sequence: 2 givenname: Mary Qu surname: Yang fullname: Yang, Mary Qu organization: U.S. Department of Health and Human Services, National Human Genome Research Institute, National Institutes of Health (NIH) – sequence: 3 givenname: Jack Y surname: Yang fullname: Yang, Jack Y organization: Harvard Medical School, Harvard University |
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| Keywords | Classifier Performance Evolutionary Information Prediction Strength Receiver Operating Characteristic Curve Random Forest |
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| Snippet | Background
Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of... Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of protein-DNA... Background Protein-DNA interactions are involved in many biological processes essential for cellular function. To understand the molecular mechanism of... |
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| SubjectTerms | Algorithms Animal Genetics and Genomics Artificial Intelligence Binding Sites Biomedical and Life Sciences Computational Biology - methods DNA-Binding Proteins - metabolism Life Sciences Microarrays Microbial Genetics and Genomics Plant Genetics and Genomics Proteomics ROC Curve Sequence Analysis, Protein - methods Software |
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| Title | Prediction of DNA-binding residues from protein sequence information using random forests |
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