Are Deep Learning Structural Models Sufficiently Accurate for Virtual Screening? Application of Docking Algorithms to AlphaFold2 Predicted Structures.

Machine learning-based protein structure prediction algorithms, such as RosettaFold and AlphaFold2, have greatly impacted the structural biology field, arousing a fair amount of discussion around their potential role in drug discovery. While there are few preliminary studies addressing the usage of these models in virtual screening, none of them focus on the prospect of hit-finding in a real-world virtual screen with a model based on low prior structural information. In order to address this, we have developed an AlphaFold2 version where we exclude all structural templates with more than 30% sequence identity from the model-building process.

Structure-Activity Relationship Studies of Antimalarial Proteasome Inhibitors─Part II.

With increasing reports of resistance to artemisinins and artemisinin-combination therapies, targeting the proteasome is a promising strategy for antimalarial development. We recently reported a highly selective proteasome inhibitor with anti-malarial activity in the humanized mouse model. To balance the permeability of the series of macrocycles with other drug-like properties, we conducted further structure-activity relationship studies on a biphenyl ether-tethered macrocyclic scaffold.

Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures.

The availability of AlphaFold2 has led to great excitement in the scientific community─particularly among drug hunters─due to the ability of the algorithm to predict protein structures with high accuracy. However, beyond globally accurate protein structure prediction, it remains to be determined whether ligand binding sites are predicted with sufficient accuracy in these structures to be useful in supporting computationally driven drug discovery programs. We explored this question by performing free-energy perturbation (FEP) calculations on a set of well-studied protein-ligand complexes, where AlphaFold2 predictions were performed by removing all templates with >30% identity to the target protein from the training set.

Design, Synthesis, and Optimization of Macrocyclic Peptides as Species-Selective Antimalaria Proteasome Inhibitors.

With over 200 million cases and close to half a million deaths each year, malaria is a threat to global health, particularly in developing countries. , the parasite that causes the most severe form of the disease, has developed resistance to all antimalarial drugs. Resistance to the first-line antimalarial artemisinin and to artemisinin combination therapies is widespread in Southeast Asia and is emerging in sub-Saharan Africa.

Development of a Highly Selective Plasmodium falciparum Proteasome Inhibitor with Anti-malaria Activity in Humanized Mice.

Plasmodium falciparum proteasome (Pf20S) inhibitors are active against Plasmodium at multiple stages-erythrocytic, gametocyte, liver, and gamete activation stages-indicating that selective Pf20S inhibitors possess the potential to be therapeutic, prophylactic, and transmission-blocking antimalarials. Starting from a reported compound, we developed a noncovalent, macrocyclic peptide inhibitor of the malarial proteasome with high species selectivity and improved pharmacokinetic properties. The compound demonstrates specific, time-dependent inhibition of the β5 subunit of the Pf20S, kills artemisinin-sensitive and artemisinin-resistant P.