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The prediction of protein–ligand interactions and their corresponding binding free energy is a challenging task in structure-based drug design and related applications. Docking and scoring is broadly used to propose the binding mode and underlying interactions as well as to provide a measure for ligand affinity or differentiate between active and inactive ligands. Various studies have revealed that most docking software packages reliably predict the binding mode, although scoring remains a challenge. Here, a diverse benchmark data set of 99 matched molecular pairs (3D-MMPs) with experimentally determined X-ray structures and corresponding binding affinities is introduced. This data set was used to study the predictive power of 13 commonly used scoring functions to demonstrate the applicability of the 3D-MMP data set as a valuable tool for benchmarking scoring functions.
The access to information on the dynamic behaviour of large proteins is usually hindered as spectroscopic methods require the site-specific attachment of biophysical probes. A powerful emerging tool to tackle this issue is amber codon suppression. Till date, its application on large and complex multidomain proteins of MDa size has not been reported. Herein, we systematically investigate the feasibility to introduce different non-canonical amino acids into a 540 kDa homodimeric fatty acid synthase type I by genetic code expansion with subsequent fluorescent labelling. Our approach relies on a microplate-based reporter assay of low complexity using a GFP fusion protein to quickly screen for sufficient suppression conditions. Once identified, these findings were successfully utilized to upscale both the expression scale and the protein size to full-length constructs. These fluorescently labelled samples of fatty acid synthase were subjected to initial biophysical experiments, including HPLC analysis, activity assays and fluorescence spectroscopy. Successful introduction of such probes into a molecular machine such as fatty acid synthases may pave the way to understand the conformational variability, which is a primary intrinsic property required for efficient interplay of all catalytic functionalities, and to engineer them.