JuliaCon 2026

Biological motivation

Protein recognition of DNA target sequences relies on two main mechanisms: direct and indirect readout. While direct readout arises from base-specific contacts between amino acids and DNA chemical groups, indirect readout depends on sequence-dependent DNA shape and deformability. This makes it subtler, but still highly influential in protein–DNA binding affinity.

Modeling DNA deformation

As a concrete example, we consider the Fis–DNA complex, whose binding affinity is sensitive to sequence changes even outside the protein–DNA contact region. Our work shows that a molecular dynamics (MD)-based protocol with modern force-field parameters can already capture these changes, with strong correlation to experiment. However, this approach is too computationally expensive for broad comparative studies or target-sequence optimization. To address this, we developed a Julia package to estimate DNA deformation free energies from simulation data using practical models with different accuracy tradeoffs.

These models build on well-established features of DNA mechanics: local interactions along the chain, rigid-base descriptions in helical coordinates, and the influence of discrete backbone states on conformational preferences. Together, these ingredients make it possible to represent DNA deformation energetics with a reduced and tractable set of parameters. Using MD simulations as a data source, we introduce maximum-likelihood and pseudo-maximum-likelihood approaches that learn these reduced models while retaining the essential physics of DNA deformation. The resulting analytical framework enables rapid estimation of thermodynamic quantities such as deformation free energies, opening the door to larger-scale studies and sequence screening.

Julia implementation

Julia is a natural fit for this problem because it brings together statistical modeling, optimization, and high-performance scientific computing in a single expressive environment. The package is designed with abstractions that separate model definitions from implementation details, making it easier to extend, test, and compare new approaches. This talk will show how biologically motivated assumptions and Julia-based software design come together in a practical tool for lower-cost estimation of sequence-dependent DNA deformation effects.