Binding Energy Topography by Sequencing
Summary
Binding Energy Topography by sequencing (BET-seq) is an in vitro method for measuring relative protein–DNA binding energies across a pooled DNA library. It combines equilibrium binding on a Mechanically Induced Trapping of Molecular Interactions (MITOMI) microfluidic device with high-throughput sequencing. The relative abundance of each DNA species in a protein-bound fraction is compared with its abundance in the input library. Under stated equilibrium and recovery assumptions, ratios of these enrichments are related logarithmically to relative binding free energies [1,2].
Scope
Covered
- The physical BET-seq workflow.
- Input and bound-library sequencing counts and normalized fractions.
- The relationship between enrichment ratios and relative binding energy.
- Sampling depth, zero counts, replicates, and major assay limitations.
Not covered
- Values from a particular BET-seq dataset or replicate.
- A particular sequence library layout.
- A prescribed statistical or machine-learning model.
- Cellular transcriptional activity.
Key concepts and notation
| Term or symbol | Meaning |
|---|---|
| MITOMI | Microfluidic platform that mechanically traps molecular interactions |
| Input library | Pooled DNA library before affinity-dependent recovery |
| Bound library | DNA recovered while associated with immobilized protein |
| (c_i^{\mathrm{in}}) | Sequencing count for species (i) in the input library |
| (c_i^{\mathrm{bound}}) | Sequencing count for species (i) in the bound library |
| (f_i^{\mathrm{in}}) | Input count divided by total input-library counts |
| (f_i^{\mathrm{bound}}) | Bound count divided by total bound-library counts |
| Enrichment | Relative abundance in the bound library divided by relative abundance in the input library |
| (\Delta\Delta G) | Binding free-energy difference between two DNA species |
Core knowledge
Experimental workflow
BET-seq expresses a fluorescently labeled transcription factor in vitro and captures it on a MITOMI microfluidic device. A pooled double-stranded DNA library is introduced and allowed to interact with the surface-immobilized protein. Pneumatic “button” valves are then actuated to trap equilibrium protein–DNA complexes while unbound DNA is washed away. The trapped DNA is recovered and quantified by high-throughput sequencing. An aliquot of the input DNA pool is also sequenced to measure the starting abundance of each library member [1,2].
This design measures many sequences in the same physical experiment. Input sequencing is necessary because synthesis and amplification do not generally place every sequence into the pool at exactly the same abundance.
From counts to enrichment
For sequencing depths
[ C^{\mathrm{in}}=\sum_i c_i^{\mathrm{in}} \quad\text{and}\quad C^{\mathrm{bound}}=\sum_i c_i^{\mathrm{bound}}, ]
normalized fractions can be written as
[ f_i^{\mathrm{in}}=\frac{c_i^{\mathrm{in}}}{C^{\mathrm{in}}}, \qquad f_i^{\mathrm{bound}}= \frac{c_i^{\mathrm{bound}}}{C^{\mathrm{bound}}}. ]
A relative enrichment is
[ E_i=\frac{f_i^{\mathrm{bound}}}{f_i^{\mathrm{in}}}. ]
Comparing two DNA species (i) and (j) cancels shared normalization and recovery factors. In the ideal equilibrium regime used to motivate BET-seq,
[ \Delta\Delta G_{i,j} \approx -RT\ln\left(\frac{E_i}{E_j}\right), ]
where (\Delta\Delta G_{i,j}=G_i-G_j) under this sign convention. Greater relative enrichment corresponds to more favorable, lower binding free energy. The approximation relies on the assay model: the measured bound fraction must track equilibrium occupancy, and shared experimental factors must cancel in the comparison [1,2].
An unreferenced quantity such as (-RT\ln E_i) contains an arbitrary common offset set by library normalization, protein concentration, recovery, and other experiment-wide terms. Obtaining absolute (\Delta G) or (K_d) requires additional calibration rather than only a bound/input sequencing ratio [1,2].
Sequencing noise and depth
Sequencing counts are finite samples. The relative uncertainty of a rare species is larger than that of a highly counted species, and a large library requires greater total sequencing depth to achieve the same typical count per species. BET-seq simulation and experimental analyses show that energetic resolution depends on library size, sequencing depth, and the distribution of binding energies [1,2].
A zero observed count means that no read for that species was sampled in that library. It does not establish a physically infinite binding energy. Direct log ratios involving a zero count are undefined or infinite, so censoring, pseudocounts, likelihood-based treatment, or other uncertainty-aware analysis may be used. The choice is an analysis convention and should be reported.
Independent experimental replicates can differ because of stochastic sequencing, library preparation, protein preparation, device operation, and other sources of variation. Combining evidence across replicates can improve precision, but a composite estimate should preserve which uncertainty comes from counting and which comes from between-replicate variation [1,2].
Unique molecular identifiers
The detailed BET-seq protocol incorporates unique molecular identifiers (UMIs) during library construction. UMIs label original molecules before some amplification steps and can help identify polymerase-chain-reaction duplicates. Their effectiveness depends on UMI complexity, assignment quality, and the deduplication procedure [2].
Conditions, limitations, and uncertainty
- The relation between enrichment and equilibrium energy assumes adequate equilibration and faithful trapping and recovery of bound molecules.
- Immobilization, fluorescent tags, protein truncations, buffer composition, temperature, ionic strength, and DNA construct design can alter binding.
- A pooled library introduces competition and possible ligand-depletion effects; assay design must keep these within the regime assumed by the analysis [1,2].
- PCR and sequencing can introduce sequence-dependent sampling biases. Input normalization corrects starting abundance but does not guarantee removal of every downstream bias.
- Low or zero counts carry asymmetric and sometimes censored uncertainty.
- BET-seq is an in vitro affinity assay. Chromatin accessibility, cofactors, competitors, localization, and gene-regulatory context are not measured.
Related knowledge resources
binding_affinity_and_thermodynamics: (K_d), occupancy, and Gibbs energy.transcription_factor_dna_binding: molecular recognition and the distinction between affinity and cellular function.binding_sites_motifs_and_sequence_context: sequence context around motif cores.
References
- Le DD, Shimko TC, Aditham AK, Keys AM, Longwell SA, Orenstein Y, Fordyce PM. Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(16):E3702–E3711. https://doi.org/10.1073/pnas.1715888115. [Primary research]
- Aditham AK, Shimko TC, Fordyce PM. BET-seq: Binding energy topographies revealed by microfluidics and high-throughput sequencing. Methods in Cell Biology. 2018;148:229–250. https://doi.org/10.1016/bs.mcb.2018.09.011. [Methods protocol]