AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Crystal Structure of Bovine Alpha-Chymotrypsin in Space Group P6₅

Chymotrypsin is a protease that is commonly used as a standard for protein crystallization and as a model system for studying serine proteases. Unliganded bovine α-chymotrypsin was crystallized at neutral pH using ammonium sulphate as the precipitant, resulting in crystals that conform to P65 symmetry with unit cell parameters that have not been reported previously. Inspection of crystallographic interfaces revealed that the major interface between any two molecules in the crystal lattice represents the interface of the biological dimer, as previously observed for crystals of unliganded α-chymotrypsin grown at low pH in space group P21

Towards a High-Affinity Peptidomimetic Targeting Proliferating Cell Nuclear Antigen from <i>Aspergillus fumigatus</i>

Invasive fungal infections (IFIs) are prevalent in immunocompromised patients. Due to alarming levels of increasing resistance in clinical settings, new drugs targeting the major fungal pathogen Aspergillus fumigatus are required. Attractive drug targets are those involved in essential processes like DNA replication, such as proliferating cell nuclear antigens (PCNAs). PCNA has been previously studied in cancer research and presents a viable target for antifungals. Human PCNA interacts with the p21 protein, outcompeting binding proteins to halt DNA replication. The affinity of p21 for hPCNA has been shown to outcompete other associating proteins, presenting an attractive scaffold for peptidomimetic design. p21 has no A. fumigatus homolog to our knowledge, yet our group has previously demonstrated that human p21 can interact with A. fumigatus PCNA (afumPCNA). This suggests that a p21-based inhibitor could be designed to outcompete the native binding partners of afumPCNA to inhibit fungal growth. Here, we present an investigation of extensive structure–activity relationships between designed p21-based peptides and afumPCNA and the first crystal structure of a p21 peptide bound to afumPCNA, demonstrating that the A. fumigatus replication model uses a PIP-box sequence as the method for binding to afumPCNA. These results inform the new optimized secondary structure design of a potential peptidomimetic inhibitor of afumPCNA