iNClusive: a database collecting useful information on non-canonical amino acids and their incorporation into proteins for easier genetic code expansion implementation.

The incorporation of non-canonical amino acids (ncAAs) into proteins is a powerful technique used in various research fields. Genetic code expansion (GCE) is the most common way to achieve this: a specific codon is selected to be decoded by a dedicated tRNA orthogonal to the endogenous ones. In the past 30 years, great progress has been made to obtain novel tRNA synthetases (aaRSs) accepting a variety of ncAAs with distinct physicochemical properties, to develop robust in vitro assays or approaches for codon reassignment. This sparked the use of the technique, leading to the accumulation of publications, from which gathering all relevant information can appear daunting. Here we present iNClusive (https://non-canonical-aas.biologie.uni-freiburg.de/), a manually curated, extensive repository using standardized nomenclature that provides organized information on ncAAs successfully incorporated into target proteins as verified by mass spectrometry. Since we focused on tRNA synthetase-based tRNA loading, we provide the sequence of the tRNA and aaRS used for the incorporation. Derived from more than 687 peer-reviewed publications, it currently contains 2432 entries about 466 ncAAs, 569 protein targets, 500 aaRSs and 144 tRNAs. We foresee iNClusive will encourage more researchers to experiment with ncAA incorporation thus contributing to the further development of this exciting technique.

finDr: A web server for in silico D-peptide ligand identification.

In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.