Biology subjects

Piasentin, N.

Publications and source records attributed to Piasentin, N..

8 recordsLinked to original sources

bioRxiv2026

Disrupting aberrant EGFR catalytic trimers reverses T790M gefitinib resistance

Epidermal growth factor receptor (EGFR) mutations drive up to 50% of non-small-cell lung cancers (NSCLC). Although tyrosine kinase inhibitors provide substantial clinical benefit, remissions are prematurely terminated by the inevitable acquisition of on-target resistance. Beyond structural changes that alter ATP-pocket affinity, EGFR oligomerization drives this resistance, though the underlying mechanisms remain unclear. Here we show that progressive secondary and tertiary resistant NSCLC EGFR-mutants form ligand-free cell surface oligomers that contain catalytic trimers instead of the canonical dimers found within these oligomers in wild-type and gefitinib-sensitive EGFR-mutants. Genetically disrupting these pathological trimers into dimers via a single-point mutation rewires downstream signaling, decelerates tumor progression, and reverses gefitinib resistance in vivo. Conversely, genetic engineering of dimers into trimers reinstates normal tumor growth. These findings reveal a structural vulnerability specific to these refractory variants, demonstrating that targeting intra-oligomer interactions can overcome resistance, and providing a blueprint for protein-protein interface modulation strategies in NSCLC.

cancer biology↗

bioRxiv2026

Buprenorphine Restricts the Conformational Landscape of the μ-Opioid Receptor

Biased signaling and partial agonism in GPCRs are considered promising strategies to develop safer and more effective drugs. Various mechanisms have been proposed to explain these phenomena, including the ability of biased and partial ligands to promote specific receptor conformations able to engage distinct intracellular partners. Recent cryo-electron microscopy structures of the {micro}-opioid receptor (MOR) bound to both G-proteins and {beta}-arrestins have shown clear structural differences shedding light into the putative signaling states. However, the accessibility of such conformations in the absence of the intracellular partner and the mechanisms by which ligands modulate their populations remain poorly understood. In the present manuscript, we employ extensive enhanced-sampling simulations to explore the free-energy landscapes of MOR bound to a biased partial agonist (buprenorphine) and a full unbiased agonist (endomorphin-1) showing that the ligands indeed stabilize distinct ensembles of active-like states. In particular, we observe that endomorphin-1, the natural ligand, explores two different ensembles of active-like conformations, including states compatible with either G-protein or {beta}-arrestin1 recruitment, whereas buprenorphine restricts access to these conformations and favors a narrower subset of active states more prone to G-protein binding. Moreover, we analyze the allosteric pathways connecting the ligands to the different active-like states. Together, our findings provide a thermodynamic framework for understanding biased signaling at MOR and confirm that in MOR ligand efficacy and signaling preference emerge from the selective redistribution of receptor conformational populations even in the absence of the effector proteins. This perspective may guide the rational design of opioid therapeutics with improved signaling profiles and reduced adverse effects.

biophysics↗

bioRxiv2026

Molecular basis of mitogen-activated protein kinase ERK2 activation by its upstream kinase MEK1

The RAS-RAF-MEK-ERK mitogen-activated protein kinase (MAPK) pathway relays extracellular signals into a cellular response and its dysregulation leads to many pathologies, particularly cancer. Here, we determined cryo-EM structures of the MAP2K MEK1 activating its substrate MAPK ERK2, the final event in the cascade. We define the molecular details of specificity and phosphoryl transfer to the tyrosine of the ERK2 activation loop and examine the mechanism of substrate recognition using solution techniques and molecular dynamics. Binding of the substrate MAPK leads to release of the MAP2K catalytic machinery, a mechanism enhanced in many gain of function disease mutations. Further, we observe that ERK2 release is not required for nucleotide exchange, suggesting how a processive mechanism could proceed. Our data advance the understanding of MAPK signalling and provide a starting point for drug development. One-Sentence SummaryCryo-EM structures of the MEK1-ERK2 complex reveal details of cellular signal transmission.

molecular biology↗

bioRxiv2025

A Transferable and Robust Computational Framework for Class A GPCR Activation Free Energies

The activation of G-protein coupled receptors is involved in many bio-medically important cellular pathways. However, capturing it with molecular simulations is far from trivial as it requires capturing both local and global motions. We recently achieved this goal in a specific receptor (the {beta}1-adrenergic receptor, or ADRB1) by combining a multiple replica enhanced sampling approach with tailored collective variables. While that approach can be applied to other receptors, it would require a tedious and error-prone choice and refinement of the collective variables, and in particular of the main path-like variable. Herein, we introduce an effective and stream-lined evolved strategy for defining the CVs that reduces user intervention while still achieving a robust free energy convergence. We apply it to two apo-GPCRs of pharmacological relevance, ADRB1 and the {micro}-opioid receptor. In the first case we show that the reconstructed free energies agree with those obtained with the previous tailored approach, while for the {micro}-opioid receptor activation we gain novel biological insights. The proposed method can be easily applied to other class A GPCRs, paving the avenue to the systematic elucidation of the activation mechanisms of many crucial drug targets. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=199 HEIGHT=200 SRC="FIGDIR/small/692536v1_ufig1.gif" ALT="Figure 1"> View larger version (6K): org.highwire.dtl.DTLVardef@9fae49org.highwire.dtl.DTLVardef@16b5288org.highwire.dtl.DTLVardef@f79af1org.highwire.dtl.DTLVardef@1dd71d8_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

All You Need Is Water: Converging Ligand Binding Simulations with Hydration Collective Variables

Selecting appropriate collective variables (CVs) is a crucial bottleneck in enhanced sampling molecular dynamics (MD) simulations. Although progress has been made with data-driven and intuition-based approaches, optimal CVs remain system-specific. Meanwhile, simple geometric descriptors are still widely used due to their transferability. A promising, yet under-explored, candidate for a more efficient CV is solvation. Indeed, despite its central role in ligand binding and folding, the complexity of solvent behavior has hindered its widespread use. Here, we introduce a data-driven and automatic strategy to construct robust solvation-based CVs. Our method identifies critical hydration sites by analyzing the radial distribution function of water around a ligand. Remarkably, using only these hydration CVs within on-the-fly probability enhanced sampling (OPES) simulations, we successfully converge the binding free energy landscapes for a series of host-guest systems. These landscapes show excellent agreement with those from more computationally expensive benchmark methods. We further demonstrate that the choice of where to bias water is key to efficient convergence, providing clear guidelines for implementation. This work not only underscores the central role of water in molecular recognition but also offers a powerful and generalizable framework for enhancing the sampling of complex biomolecular events.

biophysics↗

bioRxiv2025

The Arch from the Stones: Understanding Protein Folding Energy Landscapes via Bio-inspired Collective Variables

Protein folding remains a formidable challenge despite significant advances, particularly in sequence-to-structure prediction. Accurately capturing thermodynamics and intermediates via simulations demands overcoming timescale limitations, making effective collective variable (CV) design for enhanced sampling crucial. Here, we introduce a strategy to automatically construct complementary, bio-inspired CVs. These uniquely capture local hydrogen bonding--explicitly distinguishing protein-protein from protein-water interactions--and side-chain packing, taking into account both native and non-native contacts to enhance state resolution. Using these CVs in combination with advanced enhanced sampling methods, we simulate the folding of Chignolin and TRP-cage, validating our approach against extensive unbiased simulations. Our results accurately resolve complex free energy landscapes, reveal critical intermediates such as the dry molten globule and demonstrate agreement with reference data. This interpretable and portable strategy underscores the critical role of microscopic details in protein folding, opening up a promising avenue for studying larger, more complex biomolecular systems.

biophysics↗

bioRxiv2025

Revealing Water-Mediated Activation Mechanisms in the Beta 1-Adrenergic Receptor via OneOPES-Enhanced Free Energy Landscapes

The beta-1 adrenergic receptor (ADRB1) is a prominent pharmacological target due to its critical role in regulating cardiovascular function and is therefore at the forefront of therapeutic interventions in heart diseases. Here we explore the activation mechanism of ADRB1 in both apo (unbound) and holo (adrenaline-bound) forms with OneOPES, a novel multi-replica enhanced sampling simulation algorithm. Our approach leads to converged and reproducible free energy landscapes as shown by independent simulations and identifies key water-mediated interactions that ease structural rearrangements crucial for the activation of ADRB1. The detailed computational analysis provides a comprehensive understanding of the effects of adrenaline on ADRB1s activation mechanism as well as the role of sodium ions, protonation states and microswitches. Our methodology can be adapted to other ligands and receptors and serves as a blueprint for computational exploration of agonist-induced activation of ADRB1 and other class A GPCRs, paving the way for the development of drugs with fine-tuned modulatory effects.

biochemistry↗

bioRxiv2020

The control of acidity in tumor cells: a biophysical model

Acidosis of the tumor microenvironment leads to cancer invasion, progression and resistance to therapies. We present a biophysical model that describes how tumor cells regulate intracellular and extracellular acidity while they grow in a microenvironment characterized by increasing acidity and hypoxia. The model takes into account the dynamic interplay between glucose and O2 consumption with lactate and CO2 production and connects these processes to H+ and [Formula] fluxes inside and outside cells. We have validated the model with independent experimental data and used it to investigate how and to which extent tumor cells can survive in adverse micro-environments characterized by acidity and hypoxia. The simulations show a dominance of the H+ exchanges in well-oxygenated regions, and of [Formula] exchanges in the inner hypoxic regions where tumor cells are known to acquire malignant phenotypes. The model also includes the activity of the enzyme Carbonic Anhydrase 9 (CA9), a known marker of tumor aggressiveness, and the simulations demonstrate that CA9 acts as a nonlinear pHi equalizer at any O2 level in cells that grow in acidic extracellular environments. SIGNIFICANCEThe activity of cancer cells in solid tumors affects the surrounding environment in many ways, and an elevated acidity is a common feature of the tumor microenvironment. In this paper we propose a model of intracellular/extracellular acidity that is linked to cellular metabolism and includes all the main molecular players. The model is reliable, robust and validated with experimental data and can be used as an essential building block of more comprehensive in silico research on solid tumors.

biophysics↗