Biology subjects

Gkeka, P.

Publications and source records attributed to Gkeka, P..

5 recordsLinked to original sources

bioRxiv2026

HARIBOSS++: An Integrated Platform for RNA-Targeted Drug Design

Targeting RNA with small molecules is increasingly viewed as a pivotal strategy for future therapeutics. A major limitation in studying RNA-small molecule (RNA-SM) interactions is the lack of available experimental structures. HARIBOSS established a milestone as a curated database of RNA-SM complexes derived from the Protein Data Bank. Building on its community engagement, we present HARIBOSS++, an updated and next-generation platform designed to facilitate the study of future RNA-targeted drugs. Major updates include an extension of the pocketome to encompass binding sites where small molecules interact not only with RNA but also with other biomolecules, enhanced analysis of RNA molecules by family, detailed 3D structural characterization, improved analysis of ligands chemical space, addition of experimental binding affinity as well as molecular dynamics data, when available. HARIBOSS++ opens new avenues for biologists, bioinformaticians, and machine learning researchers to investigate RNA-SM interactions on a new scale. HARIBOSS++ can be explored using a web interface freely available at https://hariboss.pasteur.cloud.

bioinformatics↗

bioRxiv2026

Agent-driven Model Development for RNA 3D Structure Prediction

Large language model (LLM) agents have shown promise in driving scientific discovery, but their effectiveness in complex, real-world biological problems remains underexplored. We ask whether a general-purpose LLM agent can drive semi-autonomous development of a model for a genuinely hard biological problem, RNA 3D structure prediction. We designed a development loop where, under a fixed budget and with human supervision, the agent iteratively proposed, implemented, trained, and evaluated model changes. Over 297 iterations, the model evolved from a randomly-initialised baseline to QuickFold, an 8.9M-parameter folding trunk that matches the strongest open-source baselines (RhoFold+, NuFold) on lDDT and TM-score within noise on a held-out test set at a fraction of their inference cost. We frame this less as a new predictor than as a case study in feedback-driven, agent-led model development.

bioinformatics↗

bioRxiv2023

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

bioinformatics↗

bioRxiv2022

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases, and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 8undruggable9 protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that has hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule recognition. Here, we present HARIBOSS, a curated collection of RNA-small molecule structures determined by X-ray crystallography, Nuclear Magnetic Resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties, and ultimately the development of in silico strategies to identify RNA-targeting small molecules. HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.

bioinformatics↗

bioRxiv2018

Effect of 5-trans isomer of arachidonic acid on model liposomal membranes studied by a combined simulation and experimental approach

Unsaturated fatty acids are found in humans predominantly in the cis configuration. Fatty acids in the trans configuration are primarily the result of human processing (trans fats), but can also be formed endogenously by radical stress. The cis-trans isomerization of fatty acids by free radicals could be connected to several pathologies. Trans fats have been linked to an increased risk of coronary artery disease; however, the reasons for the resulting pathogenesis remain unclear. Here, we investigate the effect of a mono trans isomer of arachidonic acid (C20:4-5trans,8cis,11cis,14cis) produced by free radicals in physiological concentration on a model erythrocyte membrane using a combined experimental and theoretical approach. Molecular Dynamics (MD) simulations of two model lipid bilayers containing arachidonic acid and its 5-trans isomer in 3% mol. were carried out for this purpose. The 5-trans isomer formation in the phospholipids was catalyzed by HOCH2CH2S* radicals, generated from the corresponding thiol by {gamma}-irradiation, in multilamellar vesicles (MLVs) of SAPC. Large unilamellar vesicles were made by the extrusion method (LUVET) as a biomimetic model for cis-trans isomerization. Atomic Force Microscopy and Dynamic Light Scattering were used to measure the average size, morphology, and the z-potential of the liposomes. Both results from MD simulations and experiments are in agreement and indicate that the two model membranes display different physicochemical properties in that the bilayers containing the trans fatty acids were more ordered and more rigid than those containing solely the cis arachidonic acid. Correspondingly, the average size of the liposomes containing trans isomers was smaller than the ones without.

biophysics↗