Rationalizing target recognition of site-specific DNA recombinases through structure- and MD-derived energy-based functional descriptors.

• Applicant: Professorin Dr. María Teresa Pisabarro
• Subject Area: Bioinformatics and Theoretical Biology
• Term: since 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 408106607

Tyrosine-type site-specific recombinases (SSRs) represent a very promising asset for human genome engineering, offering controlled excision, inversion, insertion or exchange of target DNA sequences without relying on DNA repair mechanisms, as seen with CRISPR-Cas9. However, achieving efficient and safe reprograming of SSRs’ DNA specificity to a target site of interest is hindered by an incomplete understanding of the specificity determinants governing protein-DNA interactions in SSRs’ biological function. This understanding is crucial for designing customized and safe SSRs for human therapeutic applications. Our prior work demonstrated the power of integrating experimentally-obtained in vitro evolution data with structural information, computer-aided modelling and molecular dynamics (MD) simulations. This approach elucidates structure-function relationships, providing insights into the activity and specificity of both native and evolved SSRs. While these studies have led to the rational engineering of SSRs variants with improved or newly customized properties, they underscore the complexity of protein-DNA recognition controlling SSRs activity and target specificity. Achieving a comprehensive and more in-depth analysis of the physicochemical and dynamic characteristics of these systems is essential to fully grasp the underlying molecular mechanisms and design active and highly specific SRRs. Our objective is to integrate deep-sequencing and activity data from substrate-linked in vitro evolved SSRs, along with structural and conformational dynamic information from MD simulations to quantify the plasticity in the protein-DNA recognition profiles, providing a more accurate and reliable means of predicting activity and target selectivity of SSR systems. We plan a comprehensive analysis of MD-based energetic descriptors, and we aim to explore the applicability of these "structural and energetic descriptors" as "functional markers" in learning-based prediction. The ultimate goal is to discern sensitive structure- and energy-based descriptors and establish a robust ML-based rationale for an enhanced design of activity and target selectivity in SSRs.

• DFG Programme: Research Grants