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DNA methylation is a fundamental element of epigenetic regulation that is governed by the MBD protein superfamily, a group of "readers" that share a highly conserved methyl-CpG-binding domain (MBD) and mediate chromatin remodeler recruitment, transcription regulation, and coordination of DNA and histone modification. Previous work has characterized the binding affinity and sequence selectivity of MBD-containing proteins toward palindromes of 5-methylcytosine (5mC) containing 5mCpG dinucleotides, often referred to as single symmetrically methylated CpG sites. However, little is known about how MBD binding is influenced by the prototypical local clustering of methylated CpG sites and the presence of DNA structural motifs encountered, e.g., during DNA replication and transcription. Here, we use Single-Molecule Kinetics through Equilibrium Poisson Sampling (SiMKEPS) to measure precise binding and dissociation rate constants of the MBD of human protein MBD1 to DNAs with varying patterns of multiple methylated CpG sites and diverse structural motifs. MBD binding is promoted by two major properties of its DNA substrates: 1) tandem (consecutive) symmetrically methylated CpG sites in double-stranded DNA and secondary structures in single-stranded DNA; and 2) DNA forks. Based on our findings, we propose a mechanistic model for how MBD proteins contribute to epigenetic boundary maintenance between transcriptionally silenced and active genome regions., Competing Interests: Declaration of interests The authors declare the following competing financial interest(s): A.J.-B. and N.G.W. are inventors on multiple patent applications related to SiMREPS, and equity holders of aLight Sciences Inc., a startup company aiming to commercialize the presented technology.

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM) 1-5 . to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset 6 , we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data., Competing Interests: Competing financial interests The authors declare no competing interests.