Biology subjects

Furini, S.

Publications and source records attributed to Furini, S..

8 recordsLinked to original sources

bioRxiv2026

The wild-type hERG cryo-EM structure fails to support conduction and evolve toward an inactivated-like selectivity filter conformation in molecular dynamics simulations

The atomic structure of the human Ether-a-go-go-Related Gene (hERG) K+ channel has recently been resolved by cryo-electron microscopy (cryo-EM) under both high- and low-K+ conditions, in order to obtain information on the mechanism of the K+-sensitive, very rapid C-type inactivation typical of this channel. Although the currently available high-K+ structures have been widely interpreted as representing the conductive, active state, whether they correspond to a dynamically stable conductive conformation remains unresolved. Here, we used extensive all-atom molecular dynamics (MD) simulations, with and without Electronic Continuum Correction (ECC), to investigate selectivity filter (SF) dynamics and ion permeation in wild-type (WT) hERG and the non-inactivating N629D mutant. Across all membrane potentials tested, WT hERG failed to support K+ permeation and instead spontaneously evolved towards a non-conductive SF conformation characterized by extracellular dilation, localized inner constriction, depletion of the outer ion-binding sites, and persistent trapping of K+ ions within the central binding sites of the filter, closely resembling the inactivated SF of Shaker channels recently resolved by cryo-EM. By contrast, N629D maintained a stable conductive SF architecture, analogous to the conductive filters of canonical K+ channels such as KcsA, while exhibiting robust voltage-dependent K+ permeation. ECC enhanced ion permeation in N629D mutant, but failed to support conduction in WT hERG, which remained structurally and functionally non-conductive. Together, these findings challenge the prevailing interpretation of the high-K+ WT cryo-EM structure as a stable conductive state and identify SF remodeling as the structural mechanism underlying hERG C-type inactivation.

biophysics↗

bioRxiv2026

Current-voltage characteristics of K+ channels estimated by MD simulations and Markov State Models

Molecular dynamics (MD) simulations have yielded important insights into ion conduction in potassium channels, but quantitative comparison with electrophysiological experiments remains challenging. Due to their high computational cost, MD simulations are typically performed at membrane potentials well above physiological values, and at only a limited number of voltages. Since current-voltage relationships are not necessarily linear, this limits direct comparison between simulations and experiments. Here, we introduce a method to estimate the current-voltage characteristics of ion channels from Markov state models (MSMs) constructed from MD simulations performed at only a few membrane potentials. Time-discrete MSMs of ion conduction are converted into continuous-time rate matrices, whose voltage dependence is modelled using a rate theory formulation with free energy barriers depending on membrane potential. This approach enables the prediction of channel currents over a wide voltage range without additional simulations. We validated the method using MD simulations of the potassium channels KcsA and MthK. In both cases, the currents predicted at low membrane potentials are in good agreement with those obtained directly from MD simulations, demonstrating the robustness and efficiency of the approach.

biophysics↗

bioRxiv2025

Role of Water Models in Simulations of Ion Conduction in Potassium Channels

Potassium channels exhibit high selectivity and conductance, yet the atomic details of ion permeation, particularly the involvement of water molecules, remain debated. Two main conduction mechanisms have been proposed: the hard knock-on, in which ions traverse the selectivity filter in direct contact, and the soft knock-on, which involves co-permeation of water molecules. Using microsecond molecular dynamics simulations with the OPC water model, the AMBER19SB protein force field, and the 12-6-4 Sengupta et al. ion model, we observed that both hard and soft knock-on mechanisms are accessible and, notably, can reversibly transition in the MthK and KcsA channels across all simulated membrane potentials. These reversible transitions contrast with previous observations using the TIP3P water model, where water entry either disrupted conduction or was expelled, favoring exclusive hard knock-on events. Our results suggest that the choice of the water model, force field, and ion parameters significantly influences the observed conduction mechanism. Importantly, the coexistence of hard and soft knock-on in these simulations provides a potential reconciliation between structural data supporting hard knock-on and streaming potential measurements demonstrating water co-permeation. These findings reintroduce soft knock-on as a viable conduction mechanism and highlight the critical role of simulation parameters in reproducing potassium channel permeation behavior.

biophysics↗

bioRxiv2024

Building predictive Markov State Models of ion channel permeation from Molecular Dynamics

Molecular dynamics (MD) simulation of biological processes has always been a very challenging task due to the long timescales of the processes involved and the challenges associated with handling the large amount of output data. Markov State Models (MSMs) have been recently introduced as a powerful tool in this area of research, as they provide a mechanistically comprehensible synthesis of the large amount of MD data and, at the same time, can be used to estimate experimental properties of biological processes. Of the many studies on protein simulation and the MSM-assisted approach, only a few have addressed ion channel permeation and, more importantly, none of these have tried to build a model capable to predict the currents passing through the channels, which are ultimately crucial for comparing simulations with experimental results. Herein, we propose a method for building an MSM of ion channel permeation that correctly evaluates the current flowing through the channel. This was done by including in the model the definition of a flux matrix carrying information on the charge moving across the channel, suitably built to be used in conjunction with the transition matrix to predict the ion current. The proposed method is also able to drastically reduce the number of states so to obtain an MSM simple enough to be easily understood. Finally, we applied the method to the KcsA channel, obtaining a four-state MSM capable of accurately reproducing the single channel ion current from microseconds MD trajectories.

biophysics↗

bioRxiv2023

Host genetics and COVID-19 severity: increasing the accuracy of latest severity scores by Boolean quantum features

The impact of common and rare variants in COVID-19 host genetics is widely studied in [16]. Here, common and rare variants were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. After that, the Boolean features, selected by these logistic models, were combined into an Integrated PolyGenic Score, the so called IPGS, which offers a very simple description of the contribution of host genetics in COVID-19 severity. IPGS leads to an accuracy of 55-60% on different cohorts and, after a logistic regression with in input both IPGS and the age, it leads to an accuracy of 75%. The goal of this paper is to improve the previous results, using the information on the host organs involved in the disease. We generalized the IPGS adding a statistical weight for each organ, through the transformation of Boolean features into "Boolean quantum features", inspired by the Quantum Mechanics. The organs coefficients were set via the application of the genetic algorithm Pygad and, after that, we defined two new Integrated PolyGenic Score ([Formula] and [Formula]). By applying a logistic regression with both [Formula] (or indifferently [Formula]) and age as input, we reach an accuracy of 84-86%, thus improving the results previously shown in [16] by a factor of 10%.

genomics↗

bioRxiv2022

Resource-aware construct design in mammalian cells

Resource competition can be the cause of unintended coupling between co-expressed genetic constructs. Here we report quantification of the resource load imposed by different mammalian genetic components and identify construct designs with increased performance and reduced resource footprint. We use these to generate improved synthetic circuits and optimise the co-expression of co-transfected cassettes, shedding light on how this can be useful for bioproduction and biotherapeutic applications. This work provides the scientific community with a framework to take resource demand into consideration when designing mammalian constructs to achieve robust and optimised gene expression.

synthetic biology↗

bioRxiv2021

Cryptic SARS-CoV2-spike-with-sugar interactions revealed by 'universal' saturation transfer analysis

Many host pathogen interactions such as human viruses (including non-SARS-coronaviruses) rely on attachment to host cell-surface glycans. There are conflicting reports about whether the Spike protein of SARS-CoV-2 binds to sialic acid commonly found on host cell-surface N-linked glycans. In the absence of a biochemical assay, the ability to analyze the binding of glycans to heavily- modified proteins and resolve this issue is limited. Classical Saturation Transfer Difference (STD) NMR can be confounded by overlapping sugar resonances that compound with known experimental constraints. Here we present universal saturation transfer analysis (uSTA), an NMR method that builds on existing approaches to provide a general and automated workflow for studying protein-ligand interactions. uSTA reveals that B-origin-lineage-SARS-CoV-2 spike trimer binds sialoside sugars in an end on manner and modelling guided by uSTA localises binding to the spike N-terminal domain (NTD). The sialylated-polylactosamine motif is found on tetraantennary human N-linked-glycoproteins in deeper lung and may have played a role in zoonosis. Provocatively, sialic acid binding is abolished by mutations in some subsequent SARS- CoV-2 variants-of-concern. A very high resolution cryo-EM structure confirms the NTD location and end on mode; it rationalises the effect of NTD mutations and the structure-activity relationship of sialic acid analogues. uSTA is demonstrated to be a robust, rapid and quantitative tool for analysis of binding, even in the most demanding systems. Extended AbstractThe surface proteins found on both pathogens and host cells mediate entry (and exit) and influence disease progression and transmission. Both types can bear host-generated post- translational modifications such as glycosylation that are essential for function but can confound biophysical methods used for dissecting key interactions. Several human viruses (including non- SARS-coronaviruses) attach to host cell-surface N-linked glycans that include forms of sialic acid (sialosides). There remains, however, conflicting evidence as to if or how SARS-associated coronaviruses might use such a mechanism. Here, we demonstrate quantitative extension of saturation transfer protein NMR methods to a complete mathematical model of the magnetization transfer caused by interactions between protein and ligand. The method couples objective resonance-identification via a deconvolution algorithm with Bloch-McConnell analysis to enable a structural, kinetic and thermodynamic analysis of ligand binding beyond previously-perceived limits of exchange rates, concentration or system. Using an automated and openly available workflow this universal saturation transfer analysis (uSTA) can be readily-applied in a range of even heavily-modified systems in a general manner to now obtain quantitative binding interaction parameters (KD, kEx). uSTA proved critical in mapping direct interactions between natural sialoside sugar ligands and relevant virus-surface attachment glycoproteins - SARS-CoV-2-spike and influenza-H1N1-haemagglutinin variants - by quantitating ligand signal in spectral regions otherwise occluded by resonances from mobile protein glycans (that also include sialosides). In B- origin-lineage-SARS-CoV-2 spike trimer end on-binding to sialoside sugars was revealed contrasting with extended surface-binding for heparin sugar ligands; uSTA-derived constraints used in structural modelling suggested sialoside-glycan binding sites in a beta-sheet-rich region of spike N-terminal domain (NTD). Consistent with this, uSTA-glycan binding was minimally- perturbed by antibodies that neutralize the ACE2-binding domain (RBD) but strongly disrupted in spike from the B1.1.7/alpha and B1.351/beta variants-of-concern, which possess hotspot mutations in the NTD. Sialoside binding in B-origin-lineage-NTD was unequivocally pinpointed by cryo-EM to a site that is created from residues that are notably deleted in variants (e.g. H69,V70,Y145 in alpha). An analysis of beneficial genetic variances in cohorts of patients from early 2020 suggests a model in which this site in the NTD of B-origin-lineage-SARS-CoV-2 (but not in alpha/beta-variants) may have exploited a specific sialylated-polylactosamine motif found on tetraantennary human N-linked-glycoproteins in deeper lung. Together these confirm a novel binding mode mediated by the unusual NTD of SARS-CoV-2 and suggest how it may drive virulence and/or zoonosis via modulation of glycan attachment. Since cell-surface glycans are widely relevant to biology and pathology, uSTA can now provide ready, quantitative, widespread analysis of complex, host-derived and post-translationally modified proteins with putative ligands relevant to disease even in previously confounding complex systems.

biophysics↗

bioRxiv2017

Burden-driven feedback control of gene expression

Cells use feedback regulation to ensure robust growth despite fluctuating demands on resources and different environmental conditions. Yet the expression of foreign proteins from engineered constructs is an unnatural burden on resources that cells are not adapted for. Here we combined multiplex RNAseq with an in vivo assay to reveal the major transcriptional changes in two E. coli strains when a set of inducible synthetic constructs are expressed. We identified that native promoters related to the heat-shock response activate expression rapidly in response to synthetic expression, regardless of the construct. Using these promoters, we built a CRISPR/dCas9-based feedback regulation system that automatically adjusts synthetic construct expression in response to burden. Cells equipped with this general-use controller maintain capacity for native gene expression to ensure robust growth and as such outperform unregulated cells at protein yields in batch production. This engineered feedback is the first example of a universal, burden-based biomolecular control system and is modular, tuneable and portable.

synthetic biology↗