Spectrophotometric Enzyme Assays for High-Throughput Screening

This paper reviews high-throughput screening enzyme assays developed in our laboratory over the last ten years. These enzyme assays were initially developed for the purpose of discovering catalytic antibodies by screening cell culture supernatants, but have proved generally useful for testing enzyme activities. Examples include TLC-based screening using acridone-labeled substrates, fluorogenic assays based on the β-elimination of umbelliferone or nitrophenol, and indirect assays such as the back-titration method with adrenaline and the copper-calcein fluorescence assay for aminoacids

Inverse Polyamidoamine (i‐PAMAM) Dendrimer Antimicrobials

Here we redesigned the branches of polyamidoamine (PAMAM) dendrimers by moving the amide carbonyl group on the other side of the amide nitrogen atom, transforming the β-alaninyl-amidoethylamine branch, which easily undergoes retro-Michael reactions and renders PAMAMs intrinsically unstable, into a more stable glycyl-amidopropylamine branch. The resulting inverse PAMAM (i-PAMAM) dendrimers have the same carbon framework as PAMAMs and only differ by the position of the carbonyl group. In contrast to PAMAMs which are prepared in solution and are difficult to purify, we synthesize i-PAMAMs using high-temperature solid-phase peptide synthesis by iterative coupling and deprotection of the commercially available N,N-bis(N′-Fmoc-3-aminopropyl)glycine and purify them preparative reverse phase HPLC. Our i-PAMAM dendrimers show no detectable degradation over time. We demonstrate this new class of dendrimers with the synthesis of antimicrobial dendrimers with potent yet non-membrane disruptive activities against both Gram-negative and Gram-positive bacteria

Expanding Bioactive Fragment Space with the Generated Database GDB-13s.

Identifying innovative fragments for drug design can help medicinal chemistry address new targets and overcome the limitations of the classical molecular series. By deconstructing molecules into ring fragments (RFs, consisting of ring atoms plus ring-adjacent atoms) and acyclic fragments (AFs, consisting of only acyclic atoms), we find that public databases of molecules (i.e., ZINC and PubChem) and natural products (i.e., COCONUT) contain mostly RFs and AFs of up to 13 atoms. We also find that many RFs and AFs are enriched in bioactive vs inactive compounds from ChEMBL. We then analyze the generated database GDB-13s, which enumerates 99 million possible molecules of up to 13 atoms, for RFs and AFs resembling ChEMBL bioactive RFs and AFs. This analysis reveals a large number of novel RFs and AFs that are structurally simple, have favorable synthetic accessibility scores, and represent opportunities for synthetic chemistry to contribute to drug innovation in the context of fragment-based drug discovery

Automatic Extraction of Reaction Templates for Synthesis Prediction

Several tools for the computational planning of synthetic routes have been developed over the last 60 years. Traditionally these have been built on manually or automatically extracted reaction rules or templates obtained from a deep knowledge of organic chemistry in the case of the former, and reaction databases for the latter. Herein we give an introductory overview to the process of automatically extracting reaction templates, starting from methods for reaction centre identification, through to their use in computer aided synthesis planning and the de novo design of compounds

ChEMBL-Likeness Score and Database GDBChEMBL

The generated database GDB17 enumerates 166.4 billion molecules up to 17 atoms of C, N, O, S and halogens following simple rules of chemical stability and synthetic feasibility. However, most molecules in GDB17 are too complex to be considered for chemical synthesis. To address this limitation, we report GDBChEMBL as a subset of GDB17 featuring 10 million molecules selected according to a ChEMBL-likeness score (CLscore) calculated from the frequency of occurrence of circular substructures in ChEMBL, followed by uniform sampling across molecular size, stereocenters and heteroatoms. Compared to the previously reported subsets FDB17 and GDBMedChem selected from GDB17 by fragment-likeness, respectively, medicinal chemistry criteria, our new subset features molecules with higher synthetic accessibility and possibly bioactivity yet retains a broad and continuous coverage of chemical space typical of the entire GDB17. GDBChEMBL is accessible at http://gdb.unibe.ch for download and for browsing using an interactive chemical space map at http://faerun.gdb.tools

Visualization of Very Large High-Dimensional Data Sets as Minimum Spanning Trees

The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation. Here, we propose a solution to this problem with a new data visualization method, TMAP, capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree (http://tmap.gdb.tools). Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL, FDB17, the Natural Products Atlas, DSSTox, as well as to the MoleculeNet benchmark collection of data sets. We also show its broad applicability with further examples from biology, particle physics, and literature.Comment: 33 pages, 14 figures, 1 table, supplementary information include

Can large language models predict antimicrobial peptide activity and toxicity?

Antimicrobial peptides (AMPs) are naturally occurring or designed peptides up to a few tens of amino acids which may help address the antimicrobial resistance crisis. However, their clinical development is limited by toxicity to human cells, a parameter which is very difficult to control. Given the similarity between peptide sequences and words, large language models (LLMs) might be able to predict AMP activity and toxicity. To test this hypothesis, we fine-tuned LLMs using data from the Database of Antimicrobial Activity and Structure of Peptides (DBAASP). GPT-3 performed well but not reproducibly for activity prediction and hemolysis, taken as a proxy for toxicity. The later GPT-3.5 performed more poorly and was surpassed by recurrent neural networks (RNN) trained on sequence-activity data or support vector machines (SVM) trained on MAP4C molecular fingerprint-activity data. These simpler models are therefore recommended, although the rapid evolution of LLMs warrants future re-evaluation of their prediction abilities

An efficient one-step site-directed and site-saturation mutagenesis protocol

We have developed a new primer design method based on the QuickChange™ site-directed mutagenesis protocol, which significantly improves the PCR amplification efficiency. This design method minimizes primer dimerization and ensures the priority of primer-template annealing over primer self-pairing during the PCR. Several different multiple mutations (up to 7 bases) were successfully performed with this partial overlapping primer design in a variety of vectors ranging from 4 to 12 kb in length. In comparison, all attempts failed when using complete-overlapping primer pairs as recommended in the standard QuickChange™ protocol. Our protocol was further extended to site-saturation mutagenesis by introducing randomized codons. Our data indicated no specific sequence selection during library construction, with the randomized positions resulting in average occurrence of each base in each position. This method should be useful to facilitate the preparation of high-quality site saturation librarie

Production of a Functional Catalytic Antibody ScFv-NusA Fusion Protein in Bacterial Cytoplasm

Functional expression of catalytic antibodies in the cytoplasm of E. coli is potentially of great interest in searching for new catalysts by genetic selection. Herein, a catalytic antibody single chain Fv (ScFv) 14D9, which catalyzes a highly enantioselective protonation, was expressed as a NusA fusion protein under the T7 promoter. A functional disulfide-containing ScFv fusion protein was obtained in the oxidizing environment of bacterial cytoplasm. The 14D9 ScFv could not be overexpressed alone without NusA fusion. The highly soluble NusA protein most likely retards aggregate formation of ScFv and indirectly supports correct folding and disulfide bridge formation in the fusion construct ScFv-NusA. The ScFv-NusA fusion product shows highly enantioselective, specific, hapten inhibited catalytic activity comparable to its parent monoclonal antibody, 14D9. The NusA fusion method might be generally helpful for functional antibody expression in vivo and for the new development of biocatalysts by genetic selectio