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In this work, we combined Deep Docking and free energy MD simulations for the in silico screening and experimental validation for potential inhibitors of leucine rich repeat kinase 2 (LRRK2) targeting the WD40 repeat (WDR) domain. 

Abstract Environmental DNA studies have proliferated over the last decade, with promising data describing the diversity of organisms inhabiting aquatic and terrestrial ecosystems. The recovery of DNA present in the sediment of aquatic systems (sedDNA) has provided short‐ and long‐term data on a wide range of biological groups (e.g., photosynthetic organisms, zooplankton species) and has advanced our understanding of how environmental changes have affected aquatic communities. However, substantial challenges remain for recovering the genetic material of macro‐organisms (e.g., fish) from sediments, preventing complete reconstructions of past aquatic ecosystems, and limiting our understanding of historic, higher trophic level interactions. In this review, we outline the biotic and abiotic factors affecting the production, persistence, and transport of fish DNA from the water column to the sediments, and address questions regarding the preservation of fish DNA in sediment. We identify sources of uncertainties around the recovery of fish sedDNA arising during the sedDNA workflow. This includes methodological issues related to experimental design, DNA extraction procedures, and the selected molecular method (quantitative PCR, digital PCR, metabarcoding, metagenomics). By evaluating previous efforts (published and unpublished works) to recover fish sedDNA signals, we provide suggestions for future research and propose troubleshooting workflows for the effective detection and quantification of fish sedDNA. With further research, the use of sedDNA has the potential to be a powerful tool for inferring fish presence over time and reconstructing their population and community dynamics. 

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE 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 Parkinson’s 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. Of the 1955 molecules predicted by participants in Round 1 of the challenge, 73 were found to bind to LRRK2 in an SPR assay with a KD lower than 150 μM. These 73 molecules were advanced to the Round 2 hit expansion phase, where computational teams each selected up to 50 analogs. Binding was observed in two orthogonal assays for seven chemically diverse series, with affinities ranging from 18 to 140 μM. 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 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.