Biology subjects

Rizzi, V.

Publications and source records attributed to Rizzi, V..

10 recordsLinked to original sources

bioRxiv2026

Energetic cross-talk of filter gate and lower helices drives polymodal regulation and disease in TREK K2P channels

The TREK subfamily of two-pore domain potassium (K2P) channels are essential regulators of membrane excitability, and their activity is modulated by a wide range of physiological stimuli, including phosphorylation and membrane stretch. Single-site mutations in this subfamily have been identified in patients with FHEIG symptom (facial dysmorphism, hypertrichosis, epilepsy, intellectual disability/developmental delay, and gingival overgrowth), where they cause pathological channel hyperactivation. Using OneOPES framework, which unifies multiple enhanced-sampling molecular dynamics strategies, we provide a detailed energetic characterization of the TREK-2 conformational landscape. Our simulations uncover a unifying mechanism in which coupling between the lower transmembrane helices, the proximal C-terminal domain, and selectivity filter stability governs channel gating under different physiological stimuli, as well as disease-mimicking conditions. The predicted conformational effects of an FHEIG syndrome-associated mutation were further validated by electrophysiological measurements using a conformation-sensitive TREK-2 inhibitor. Together, these results establish an energetic framework for TREK-2 regulation and dysfunction and provide a foundation for structure-based drug discovery targeting the K2P TREK channel family.

biophysics↗

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↗

bioRxiv2024

Host-Guest binding free energies a la carte: an automated OneOPES protocol

Estimating absolute binding free energies from molecular simulations is a key step in computer-aided drug design pipelines, but agreement between computational results and experiments is still very inconsistent. Both the accuracy of the computational model and the quality of the statistical sampling contribute to this discrepancy, yet disentangling the two remains a challenge. In this study, we present an automated protocol based on OneOPES, an enhanced sampling method that exploits replica exchange and can accelerate several collective variables, to address the sampling problem. We apply this protocol to 37 host-guest systems. The simplicity of setting up the simulations and of producing well-converged binding free energy estimates without the need to optimize simulation parameters provides a reliable solution to the sampling problem. This, in turn, allows for a systematic force field comparison and ranking according to the correlation between simulations and experiments, which can inform the selection of an appropriate model. The protocol can be readily adapted to test more force field combinations and study more complex protein-ligand systems, where the choice of an appropriate physical model is often based on heuristic considerations rather than a systematic optimization.

biochemistry↗

bioRxiv2024

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study

MicroRNAs (miRNAs) are short, non-coding RNA strands that regulate the activity of messenger RNAs (mRNAs) by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumourigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for an effective structure-based drug design, with potentially broad applications in drug discovery. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/601024v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@188857borg.highwire.dtl.DTLVardef@1f37d2forg.highwire.dtl.DTLVardef@9fc9dforg.highwire.dtl.DTLVardef@7e8629_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

SWISH-X, an expanded approach to detect cryptic pockets inproteins and at protein-protein interfaces

Protein-protein interactions mediate most molecular processes in the cell, offering a significant opportunity to expand the set of known druggable targets. Unfortunately, targeting these interactions can be challenging due to their typically flat and featureless interaction surfaces, which often change as the complex forms. Such surface changes may reveal hidden (cryptic) druggable pockets. Here, we analyse a set of well-characterised protein-protein interactions harbouring cryptic pockets and investigate the predictive power of current computational methods. Based on our observations, we develop a new computational strategy, SWISH-X (SWISH Expanded), which combines the established cryptic pocket identification capabilities of SWISH with the rapid temperature range exploration of OPES MultiThermal. SWISH-X is able to reliably identify cryptic pockets at protein-protein interfaces while retaining its predictive power for revealing cryptic pockets in isolated proteins, such as TEM-1 {beta}-lactamase.

biophysics↗

bioRxiv2023

OneOPES, a combined enhanced sampling method to rule them all

Enhanced sampling techniques have revolutionised molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a systems dynamics and recover the original systems statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, with-out requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multi-replica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems. Table of Content Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/531337v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@88b5ceorg.highwire.dtl.DTLVardef@15817deorg.highwire.dtl.DTLVardef@8721bforg.highwire.dtl.DTLVardef@1b8b314_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Water-triggered, irreversible conformational change of SARS-CoV-2 main protease on passing from the solid state to aqueous solution

The main protease from SARS-CoV-2 is a homodimer. Yet, a recent 0.1 ms long molecular dynamics simulation shows that it readily undergoes a symmetry breaking event on passing from the solid state to the aqueous solution. As a result, the subunits present distinct conformations of the binding pocket. By analysing this long time simulation, here we uncover a previously unrecognised role of water molecules in triggering the transition. Interestingly, each subunit presents a different collection of long-lived water molecules. Enhanced sampling methods performed here, along with machine learning approaches, further establish that the transition to the asymmetric state is essentially irreversible.

biophysics↗