Specificity landscapes unmask submaximal binding site preferences of transcription factors

Significance Several experimental platforms and computational methods have been developed to identify DNA binding sites of over 1,000 transcription factors. Often, high-affinity (maximal) binding sites are reported as consensus motifs. Differences between experimental platforms contribute to uncertainty in ascribing binding to submaximal sites. However, biological studies emphasize the importance of submaximal binding sites in shaping regulatory functions of transcription factors. To bridge this gap, we developed Differential Specificity and Energy Landscapes to unmask differences between experimental and computational methods as well as capture distinct submaximal binding site preferences of transcription factors. Our results suggest that subtle variation in protein structure can allosterically confer homolog-specific differences in binding to submaximal affinity sites.

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