Biology subjects

Eisenhuth, P.

Publications and source records attributed to Eisenhuth, P..

3 recordsLinked to original sources

bioRxiv2026

Exploration of ligand (un)binding trajectories with Rosetta

Ligand transport through proteins underlies many aspects of molecular function, but its detailed mechanisms remain largely inaccessible to direct observation. Molecular dynamics simulations could close this gap, but are computationally too demanding for practical purposes, including filtering stages of enzyme design pipelines. Simplified docking-based approaches might be informative, but sometimes the oversimplifications required for high throughput applications might lead to unreliable predictions. Here we present RosettaLigand PathFinder (RLPF), a fast and practical tool explicitly built to ensure conformational continuity of the ligand trajectory, protein and ligand flexibility, and robustness of every run. Built using a flexible RosettaScripts XML interface, it enables fine-grained control over all simulation parameters and adaptability for a wide range of use cases. Applied to several representative systems, RLPF reproduced established transport features and mutation effects, highlighting its ability to produce biologically relevant insights and serve as a filter in enzyme design campaigns. Wide customizability and well-defined scope of RLPF make it a useful addition to the family of tools to study the dynamics of interactions between small molecules and proteins.

bioinformatics↗

bioRxiv2026

Identification of key Y4R residues enables the discovery of selective non-peptide small-molecule agonists

G protein-coupled receptors (GPCRs) are central regulators of human physiology and disease, classifying them as relevant targets for therapeutic interventions. As transmembrane proteins, they convert extracellular signals into intracellular responses through agonist-induced conformational changes. Understanding how agonists stabilize active receptor conformations is decisive for rational drug design. In this study, we used the endogenous ligand pancreatic polypeptide (PP) and the cyclic hexapeptides UR-AK95c and UR-AK86c as molecular tools to determine key interactions critical for Y4R activation, which plays a crucial role in metabolic diseases. Guided by molecular docking, we systematically replaced Y4R residues and assessed activation. The in vitro and in silico studies delineated a key Y4R activation interface centered around the conserved C-terminal RXRY-NH2 motif of the peptides and, separately, identified receptor residues with distinct peptide-specific functional effects. Next, we performed an ultra-large library screening (ULLS) and experimentally validated three predicted hits as selective Y4R agonists that engage in a substantial subset of the identified critical receptor contacts. This study demonstrates how GPCR activation interface knowledge can be translated into the discovery of novel small-molecule agonists and outlines a general strategy for advanced GPCR drug discovery.

biochemistry↗

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↗