Creating PWMs of transcription factors using 3D structure-based computation of protein-DNA free binding energies

Abstract

BackgroundKnowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.ResultsThe position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.ConclusionsA novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties.

An Improved SELEX-Seq Strategy for Characterizing DNA-Binding Specificity of Transcription Factor: NF-κB as an Example

“…With this cytosine, the 11-mer motifs contained two symmetrically opposed 5-bp half-sites [5′-GGG(G/A)A-3′]. This was in agreement with the motifs of p50 homodimer obtained with universal PBM (uPBM) [41] (Figure 7) and SELEX-cloning sequencing [40]. The final 10-mer motif also resembled the NF-κB motif constructed by TRANSFAC.…”

“…Moreover, almost identical motifs were obtained for human and mouse p50 homodimers with uPBM (Figure 7). In comparison, the 11-mer motif obtained by this study was almost identical to the motifs obtained by uPBM [41]. Therefore, this study firstly characterized the DNA-binding specificity of p50 homodimer with all possible 16-mer sequences by using an improved SELEX-Seq strategy.…”

“…To evaluate the SELEX-Seq data, the reads contained the known κB motifs (from TRANSFAC entry V$NFKAPPAB_01 and reference [41]; JASPAR entry MA0105) and the final motifs obtained in this study were counted. It was found that the reads containing these motifs gradually increased along with the rounds of SELEX selection (Figure 8A).…”

Atomistic modeling of protein–DNA interaction specificity: progress and applications

“…Alamanova et al . [22] derived a distance-dependent all-atom statistical potential from protein-DNA crystal structures and successfully applied it to predict PWMs for transcription factors (TFs) in the p53 and NF-κB families. Compressed sensing methods were used by AlQuraishi and McAdams [23] to learn a “de novo” interatomic energy potential from protein-DNA interface structures and binding energies.…”

Using potential master regulator sites and paralogous expansion to construct tissue-specific transcriptional networks

“…Unfortunately, we are far from this ideal situation, so that we can do such predictions only for a subset of, e.g., human TFs. Although promising methods have been reported for inferring DNA-binding specificities by homology modeling [2,3], the required 3D structures of TF-DNA complexes are known for only a minority of factors.…”