Exquisite binding specificity is essential for many protein functions but is difficult to engineer. Many biotechnological or biomedical applications require the discrimination of very similar ligands, which poses the challenge of designing protein sequences with highly specific binding profiles. Experimental methods for generating specific binders rely on in vitro selection, which is limited in terms of library size and control over specificity profiles. Additional control was recently demonstrated through high-throughput sequencing and downstream computational analysis. Here we follow such an approach to demonstrate the design of specific antibodies beyond those probed experimentally. We do so in a context where very similar epitopes need to be discriminated, and where these epitopes cannot be experimentally dissociated from other epitopes present in the selection. Our approach involves the identification of different binding modes, each associated with a particular ligand against which the antibodies are either selected or not. Using data from phage display experiments, we show that the model successfully disentangles these modes, even when they are associated with chemically very similar ligands. Additionally, we demonstrate and validate experimentally the computational design of antibodies with customized specificity profiles, either with specific high affinity for a particular target ligand, or with cross-specificity for multiple target ligands. Overall, our results showcase the potential of leveraging a biophysical model learned from selections against multiple ligands to design proteins with tailored specificity, with applications to protein engineering extending beyond the design of antibodies.

SUMMARY: The rapidly increasing number of discovered non-coding RNAs makes the understanding of their structure a key feature toward a deeper comprehension of gene expression regulation. Various enzymatic- and chemically- based approaches have been recently developed to allow whole-genome studies of RNA secondary structures. Several methods have been recently presented that allow high-throughput RNA structure probing (CIRS-seq, Structure-seq, SHAPE-seq, PARS, etc.) and unbiased structural inference of residues within RNAs in their native conformation. We here present an analysis toolkit, named RNA Structure Framework (RSF), which allows fast and fully-automated analysis of high-throughput structure probing data, from data pre-processing to whole-transcriptome RNA structure inference. AVAILABILITY AND IMPLEMENTATION: RSF is written in Perl and is freely available under the GPLv3 license from http://rsf.hugef-research.org. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.