Dataset Information

Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function.

ABSTRACT: Template-based prediction of DNA binding proteins requires not only structural similarity between target and template structures but also prediction of binding affinity between the target and DNA to ensure binding. Here, we propose to predict protein-DNA binding affinity by introducing a new volume-fraction correction to a statistical energy function based on a distance-scaled, finite, ideal-gas reference (DFIRE) state.We showed that this energy function together with the structural alignment program TM-align achieves the Matthews correlation coefficient (MCC) of 0.76 with an accuracy of 98%, a precision of 93% and a sensitivity of 64%, for predicting DNA binding proteins in a benchmark of 179 DNA binding proteins and 3797 non-binding proteins. The MCC value is substantially higher than the best MCC value of 0.69 given by previous methods. Application of this method to 2235 structural genomics targets uncovered 37 as DNA binding proteins, 27 (73%) of which are putatively DNA binding and only 1 protein whose annotated functions do not contain DNA binding, while the remaining proteins have unknown function. The method provides a highly accurate and sensitive technique for structure-based prediction of DNA binding proteins.The method is implemented as a part of the Structure-based function-Prediction On-line Tools (SPOT) package available at http://sparks.informatics.iupui.edu/spot

SUBMITTER: Zhao H

PROVIDER: S-EPMC2905551 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function.

Zhao Huiying H Yang Yuedong Y Zhou Yaoqi Y

Bioinformatics (Oxford, England) 20100604 15

<h4>Motivation</h4>Template-based prediction of DNA binding proteins requires not only structural similarity between target and template structures but also prediction of binding affinity between the target and DNA to ensure binding. Here, we propose to predict protein-DNA binding affinity by introducing a new volume-fraction correction to a statistical energy function based on a distance-scaled, finite, ideal-gas reference (DFIRE) state.<h4>Results</h4>We showed that this energy function togeth ...[more]

PMID: 20525822

Similar Datasets

Project description:MotivationProtein-binding sites prediction lays a foundation for functional annotation of protein and structure-based drug design. As the number of available protein structures increases, structural alignment based algorithm becomes the dominant approach for protein-binding sites prediction. However, the present algorithms underutilize the ever increasing numbers of three-dimensional protein-ligand complex structures (bound protein), and it could be improved on the process of alignment, selection of templates and clustering of template. Herein, we built so far the largest database of bound templates with stringent quality control. And on this basis, bSiteFinder as a protein-binding sites prediction server was developed.ResultsBy introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server has been significantly improved. Further, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset. For 210 bound dataset, bSiteFinder achieved high accuracies up to 94.8 % (MCC 0.95). For another 48 bound/unbound dataset, bSiteFinder achieved high accuracies up to 93.8 % for bound proteins (MCC 0.95) and 85.4 % for unbound proteins (MCC 0.72). Our bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/, and the source code is provided at the methods page.ConclusionAn online bSiteFinder server is freely available at http://binfo.shmtu.edu.cn/bsitefinder/. Our work lays a foundation for functional annotation of protein and structure-based drug design. With ever increasing numbers of three-dimensional protein-ligand complex structures, our server should be more accurate and less time-consuming.Graphical Abstract bSiteFinder (http://binfo.shmtu.edu.cn/bsitefinder/) as a protein-binding sites prediction server was developed based on the largest database of bound templates so far with stringent quality control. By introducing Homology Indexing, Chain Length Indexing, Stability of Complex and Optimized Multiple-Templates Clustering into our algorithm, the efficiency of our server have been significantly improved. What's more, the accuracy was approximately 2-10 % higher than that of other algorithms for the test with either bound dataset or unbound dataset.

Dataset Information

Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function.

Publications

Structure-based prediction of DNA-binding proteins by structural alignment and a volume-fraction corrected DFIRE-based energy function.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets