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Structure sampling for computational estimation of localized DNA interaction rates.

ABSTRACT: Molecular circuits implemented using molecular components tethered to a DNA tile nanostructure have certain advantages over solution-phase circuits. Tethering components in close proximity increases the speed of reactions by reducing diffusion and improves scalability by enabling reuse of identical DNA sequences at different locations in the circuit. These systems show great potential for practical applications including delivery of diagnostic and therapeutic molecular circuits to cells. When modeling such systems, molecular geometry plays an important role in determining whether the two species interact and at what rate. In this paper, we present an automated method for estimating reaction rates in tethered molecular circuits that takes the geometry of the tethered species into account. We probabilistically generate samples of structure distributions based on simple biophysical models and use these to estimate important parameters for kinetic models. This work provides a basis for subsequent enhanced modeling and design tools for localized molecular circuits.

SUBMITTER: Kumar S

PROVIDER: S-EPMC8209221 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Structure sampling for computational estimation of localized DNA interaction rates.

Kumar Sarika S Weisburd Julian M JM Lakin Matthew R MR

Scientific reports 20210616 1

Molecular circuits implemented using molecular components tethered to a DNA tile nanostructure have certain advantages over solution-phase circuits. Tethering components in close proximity increases the speed of reactions by reducing diffusion and improves scalability by enabling reuse of identical DNA sequences at different locations in the circuit. These systems show great potential for practical applications including delivery of diagnostic and therapeutic molecular circuits to cells. When mo ...[more]

PMID: 34135406

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Project description:BackgroundCharacterizing transcription factor binding motifs is a common bioinformatics task. For transcription factors with variable binding sites, we need to get many suboptimal binding sites in our training dataset to get accurate estimates of free energy penalties for deviating from the consensus DNA sequence. One procedure to do that involves a modified SELEX (Systematic Evolution of Ligands by Exponential Enrichment) method designed to produce many such sequences.ResultsWe analyzed low stringency SELEX data for E. coli Catabolic Activator Protein (CAP), and we show here that appropriate quantitative analysis improves our ability to predict in vitro affinity. To obtain large number of sequences required for this analysis we used a SELEX SAGE protocol developed by Roulet et al. The sequences obtained from here were subjected to bioinformatic analysis. The resulting bioinformatic model characterizes the sequence specificity of the protein more accurately than those sequence specificities predicted from previous analysis just by using a few known binding sites available in the literature. The consequences of this increase in accuracy for prediction of in vivo binding sites (and especially functional ones) in the E. coli genome are also discussed. We measured the dissociation constants of several putative CAP binding sites by EMSA (Electrophoretic Mobility Shift Assay) and compared the affinities to the bioinformatics scores provided by methods like the weight matrix method and QPMEME (Quadratic Programming Method of Energy Matrix Estimation) trained on known binding sites as well as on the new sites from SELEX SAGE data. We also checked predicted genome sites for conservation in the related species S. typhimurium. We found that bioinformatics scores based on SELEX SAGE data does better in terms of prediction of physical binding energies as well as in detecting functional sites.ConclusionWe think that training binding site detection algorithms on datasets from binding assays lead to better prediction. The improvements in accuracy came from the unbiased nature of the SELEX dataset rather than from the number of sites available. We believe that with progress in short-read sequencing technology, one could use SELEX methods to characterize binding affinities of many low specificity transcription factors.

Dataset Information

Structure sampling for computational estimation of localized DNA interaction rates.

Publications

Structure sampling for computational estimation of localized DNA interaction rates.

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