Gunnarsson, A. | bioRxiv Science

bioRxiv2025

A molecular stabiliser of an inhibitory eIF2B-eIF2(αP) complex activates the Integrated Stress Response

Eukaryotic initiation factor 2B (eIF2B), a guanine nucleotide exchange factor (GEF), promotes protein synthesis by charging translation initiation factor 2 (eIF2) with GTP. Stress-induced phosphorylation of eIF2 on its -subunit [eIF2(P)] inhibits this reaction triggering a protective Integrated Stress Response (ISR). A DNA-encoded chemical library (DEL) screen for modulators of eIF2B, led to the identification of a chemical series that inactivates eIF2B, stimulating the ISR. Cryo-EM of compound-bound eIF2B revealed a conformational switch to the inactive state engaged by eIF2(P). In cells, compound activity was sensitive to eIF2s phosphorylation state and to a competing eIF2B ligand (ISRIB) that activates the GEF allosterically. These findings mark the discovery of a first-in-class drug-like allosteric inhibitor of eIF2B, an ISR activator (ISRAC), paving the way to explore the therapeutic potential of eIF2B-directed ISR activation.

molecular biology↗

bioRxiv2024

CACHE Challenge #1: targeting the WDR domain of LRRK2, a Parkinson's Disease associated protein.

The CACHE challenges are a series of prospective benchmarking exercises meant to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE #1 challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinsons disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods. Seventy-three of the 1955 procured molecules bound LRRK2 in an SPR assay with KD lower than 150 M and were advanced to a hit expansion phase where computational teams each selected up to 50 analogs each. Binding was observed in two orthogonal assays with affinities ranging from 18 to 140 M for seven chemically diverse series. The seven successful computational workflows varied in their screening strategies and techniques. Three used molecular dynamics to produce a conformational ensemble of the targeted site, three included a fragment docking step, three implemented a generative design strategy and five used one or more deep learning steps. CACHE #1 reflects a highly exploratory phase in computational drug design where participants sometimes adopted strikingly diverging screening strategies. Machine-learning accelerated methods achieved similar results to brute force (e.g. exhaustive) docking. First-in-class, experimentally confirmed compounds were rare and weakly potent, indicating that recent advances are not sufficient to effectively address challenging targets.

biochemistry↗