The very small fraction of putative binding sites (BSs) that are occupied by transcription factors (TFs) in vivo can be highly variable across different cell types. This observation has been partly attributed to changes in chromatin accessibility and histone modification (HM) patterns surrounding BSs. Previous studies focusing on BSs within DNA regulatory regions found correlations between HM patterns and TF binding specificities. However, a mechanistic understanding of TF–DNA binding specificity determinants is still not available. The ability to predict in vivo TF binding on a genome-wide scale requires the identification of features that determine TF binding based on evolutionary relationships of DNA binding proteins. To reveal protein family–dependent mechanisms of TF binding, we conducted comprehensive comparisons of HM patterns surrounding BSs and non-BSs with exactly matched core motifs for TFs in three cell lines: 33 TFs in GM12878, 37 TFs in K562, and 18 TFs in H1-hESC. These TFs displayed protein family–specific preferences for HM patterns surrounding BSs, with high agreement among cell lines. Moreover, compared to models based on DNA sequence and shape at flanking regions of BSs, HM-augmented quantitative machine-learning methods resulted in increased performance in a TF family–specific manner. Analysis of the relative importance of features in these models indicated that TFs, displaying larger HM pattern differences between BSs and non-BSs, bound DNA in an HM-specific manner on a protein family–specific basis. We propose that TF family–specific HM preferences reveal distinct mechanisms that assist in guiding TFs to their cognate BSs by altering chromatin structure and accessibility.

在体内，转录因子（TFs）占据的一小部分推定的结合位点（BSs）在不同的细胞类型之间可能高度可变。该观察结果部分归因于BS周围染色质可及性的改变和组蛋白修饰（HM）模式。先前针对DNA调节区内的BS的研究发现，HM模式与TF结合特异性之间存在相关性。然而，对TF-DNA结合特异性决定因素的机械理解仍然不可用。在全基因组范围内预测体内TF结合的能力需要基于DNA结合蛋白的进化关系来鉴定确定TF结合的特征。为了揭示TF结合的蛋白质家族依赖性机制，我们对三种细胞系中TF的核心图案完全匹配的BS和非BS周围的HM模式进行了全面比较：GM12878中的33 TF，K562中的37 TF和H1-hESC中的18 TF。这些TF对BS周围的HM模式表现出蛋白质家族特有的偏好，在细胞系间高度一致。此外，与基于BS侧翼区域的DNA序列和形状的模型相比，HM增强的定量机器学习方法以TF家族特有的方式提高了性能。对这些模型中特征相对重要性的分析表明，显示BS和非BS之间较大HM模式差异的TF以蛋白家族特定的方式以HM特异性方式结合DNA。