Niki Messini1,2, Joel Roca-Martínez3, Nitin Kachariya1,2, Paola Redeghieri4, Anahí Higuera5,6, Francisco Morales4, Mireille Dumoulin4, Wolfgang Kaiser6, Wim Vranken3, Michael Sattler1,2 1 Bayerisches NMR Zentrum, Technical University of Munich, Garching, Germany 2 Institute of Structural Biology, Helmholtz Zentrum Munich, Neuherberg, Germany 4 Center of Protein Engineering, University of Liege, Belgium 3 Vrije University of Brussels, Belgium 5 Physics Department, Technical University of Munich, 85748 Garching, Germany 6 Dynamic Biosensors GmbH, Lochhamer Str. 15, 82152 Martinsried, Germany [1] J. Hennig, Nature 2014. [4] Y. Shamoo, Nucleic Acids Research 1995. Understanding and designing RNA binding specificity of the RRM domain The RNA recognition motif (RRM) is the largest family of RNA binding domains, and fulfills crucial roles in gene regulation. The canonical RRM fold has a β-α-β-β-α-β-β secondary structure, harboring two conserved ribonucleoprotein (RNP) sequence motifs, RNP1 and RNP2, in β3 and β1, respectively. Canonical RNA binding usually involves the RRM β-sheet, but many variations of RRM-RNA interactions are seen. Although numerous three-dimensional structures of RRM domains in complex with RNA are known, the RNA recognition code of RRM domains remains poorly understood. We combine computational approaches with experimental methods to identify key residues and their role for defining RNA binding specificity. The Sex-lethal protein (Sxl) from Drosophila Melanogaster is used as a model system, to redesign the RNA binding specificity of one of its two RRM domains. Binding kinetics and thermodynamic features are analyzed by NMR, ITC and switchSENSE® experiments, which show distinct on- and off-rate binding kinetics for the Sxl RRM domains, and significant binding preference for RNA over DNA. Computationally, a carefully curated multiple structure-based sequence alignment was analyzed to reveal residue-nucleotide preferences on a set of RRM-RNA complexes. This information is translated into a score to predict RNA binding preferences for millions of theoretical mutants. To enhance the scoring and improve its accuracy, phage display selection from a large combinatorial library of RRMs is used as an experimental validation of the approach, with the results validated by Bio-Layer Interferometry, NMR and further biophysical techniques. 1:00 1:0.25 1:0.5 1:0.75 1:1.0 1:2.0 Molar ratio Sxl RRMs show binding preference for RNA over DNA Distinct RNA binding preference and kinetics for RRM1 and RRM2 9 9 9 9 9 9 9 9 1 2 3 4 5 6 7 8 9aa mutations for 8 positions 89 mutants Designing novel RRMs by random mutagenesis and computational predictors Computational predictors • Expression of the synthetic DNA library with large number of RRM1 mutants at the surface of phages and selection of those specific of the target RNA by panning N Q D E R H K S T X01 (N) 7% 2% 27% 59% 2% 0% 0% 0% 2% X02 (N) 24% 17% 0% 5% 17% 2% 29% 5% 0% X03 (T) 2% 2% 2% 0% 7% 20% 51% 12% 2% X04 (R) 17% 2% 0% 2% 10% 27% 34% 0% 7% X05 (D) 54% 2% 12% 7% 17% 0% 5% 2% 0% X06 (K) 0% 15% 0% 0% 12% 20% 51% 0% 2% X07 (S) 7% 10% 22% 0% 15% 7% 17% 22% 0% X08 (R) 5% 37% 0% 10% 15% 7% 24% 2% 0% WT1: GUU GUU (4x) WT2: UUU UUU (4x) CCC CCC (4x) AAA AAA (4x) UUAGGA(4x) strong binder strong binder no/weak binder strong binder strong binder RRM1 +GUUGUU (4x) WT1: GUU GUU (4x) WT2: UUU UUU (4x) CCC CCC (4x) AAA AAA (4x) UUAGGA(4x) strong binder moderate binder no binder weak binder no/weak binder Computational predictors RRM2 RNA Anchor RRM1 Novel RRM • Design and modulate RRM-RNA binding preference • Sxl RRM1-RRM2 as a model system • RNA specificity of individual domains • Identify key positions in the RRM fold that mediate RNA binding specificity using 1) computational analysis and 2) random mutagenesis of key positions in RRM1, using RRM2 as “anchor” • Phage display selection of specific binders for RNA targets Novel RRMs • SXL RRM1,2 binds with higher affinity to RNA compared to