Dataset Information

Single-nucleotide mutation matrix: a new model for predicting the NF-?B DNA binding sites.

ABSTRACT: In this study, we established a single nucleotide mutation matrix (SNMM) model based on the relative binding affinities of NF-?B p50 homodimer to a wild-type binding site (GGGACTTTCC) and its all single-nucleotide mutants detected with the double-stranded DNA microarray. We evaluated this model by scoring different groups of 10-bp DNA sequences with this model and analyzing the correlations between the scores and the relative binding affinities detected with three wet experiments, including the electrophoresis mobility shift assay (EMSA), the protein-binding microarray (PBM) and the systematic evolution of ligands by exponential enrichment-sequencing (SELEX-Seq). The results revealed that the SNMM scores were strongly correlated with the detected binding affinities. We also scored the DNA sequences with other three models, including the principal coordinate (PC) model, the position weight matrix scoring algorithm (PWMSA) model and the Match model, and analyzed the correlations between the scores and the detected binding affinities. In comparison with these models, the SNMM model achieved reliable results. We finally determined 0.747 as the optimal threshold for predicting the NF-?B DNA-binding sites with the SNMM model. The SNMM model thus provides a new alternative model for scoring the relative binding affinities of NF-?B to the 10-bp DNA sequences and predicting the NF-?B DNA-binding sites.

SUBMITTER: Du W

PROVIDER: S-EPMC4081663 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Single-nucleotide mutation matrix: a new model for predicting the NF-κB DNA binding sites.

Du Wenxin W Gao Jing J Wang Tingting T Wang Jinke J

PloS one 20140703 7

In this study, we established a single nucleotide mutation matrix (SNMM) model based on the relative binding affinities of NF-κB p50 homodimer to a wild-type binding site (GGGACTTTCC) and its all single-nucleotide mutants detected with the double-stranded DNA microarray. We evaluated this model by scoring different groups of 10-bp DNA sequences with this model and analyzing the correlations between the scores and the relative binding affinities detected with three wet experiments, including the ...[more]

PMID: 24992458

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Project description:Understanding the molecular details of protein-DNA interactions is critical for deciphering the mechanisms of gene regulation. We present a machine learning approach for the identification of amino acid residues involved in protein-DNA interactions.We start with a Naïve Bayes classifier trained to predict whether a given amino acid residue is a DNA-binding residue based on its identity and the identities of its sequence neighbors. The input to the classifier consists of the identities of the target residue and 4 sequence neighbors on each side of the target residue. The classifier is trained and evaluated (using leave-one-out cross-validation) on a non-redundant set of 171 proteins. Our results indicate the feasibility of identifying interface residues based on local sequence information. The classifier achieves 71% overall accuracy with a correlation coefficient of 0.24, 35% specificity and 53% sensitivity in identifying interface residues as evaluated by leave-one-out cross-validation. We show that the performance of the classifier is improved by using sequence entropy of the target residue (the entropy of the corresponding column in multiple alignment obtained by aligning the target sequence with its sequence homologs) as additional input. The classifier achieves 78% overall accuracy with a correlation coefficient of 0.28, 44% specificity and 41% sensitivity in identifying interface residues. Examination of the predictions in the context of 3-dimensional structures of proteins demonstrates the effectiveness of this method in identifying DNA-binding sites from sequence information. In 33% (56 out of 171) of the proteins, the classifier identifies the interaction sites by correctly recognizing at least half of the interface residues. In 87% (149 out of 171) of the proteins, the classifier correctly identifies at least 20% of the interface residues. This suggests the possibility of using such classifiers to identify potential DNA-binding motifs and to gain potentially useful insights into sequence correlates of protein-DNA interactions.Naïve Bayes classifiers trained to identify DNA-binding residues using sequence information offer a computationally efficient approach to identifying putative DNA-binding sites in DNA-binding proteins and recognizing potential DNA-binding motifs.

Dataset Information

Single-nucleotide mutation matrix: a new model for predicting the NF-?B DNA binding sites.

Publications

Single-nucleotide mutation matrix: a new model for predicting the NF-κB DNA binding sites.

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