Biology subjects

Gapsys, V.

Publications and source records attributed to Gapsys, V..

7 recordsLinked to original sources

bioRxiv2026

Computational assay of hERG ion permeation and its modulation by small-molecule compounds

The human ether-a-go-go-related gene (hERG) potassium channel is an important off-target in drug discovery, as its inhibition can disrupt cardiac repolarisation and cause potentially fatal arrhythmias. Despite its importance, the molecular mechanisms underlying hERG ion permeation and its modulation by small molecules remain elusive. Here, we use molecular dynamics simulations to reproduce sustained K+ permeation and single-channel conductance comparable to experimental measurements. Our simulations reproduce the characteristic asymmetry in hERG single-channel conductance and reveal a structural mechanism underlying this behaviour. We identify the conformational dynamics of S624, located at the innermost ion-binding site of the selectivity filter, as a determinant of this conductance asymmetry. Substitution of S624 with the canonical threonine abolishes conductance asymmetry by facilitating K+ entry into the S4 binding site. Simulations of classical hERG inhibitors from cryo-EM-derived binding poses show that bound inhibitors sterically occlude K+ access to the selectivity filter, with binding stabilised by interactions with hydrophobic residues lining the central cavity. In contrast, simulations of hERG activators support a fenestration binding mode consistent with mutagenesis data, in which PD-118057 and PD-307243 increase cavity K+ occupancy and enhance conductance by two- to threefold relative to the apo channel. Together, these results establish a molecular framework connecting hERG structure, ion permeation, and small-molecule modulation, providing a basis for directly assessing the functional consequences of compound binding.

biophysics↗

bioRxiv2024

Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as {Delta}{Delta}G). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate {Delta}{Delta}G of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

biophysics↗

bioRxiv2022

Periodic boundaries in Molecular Dynamics simulations: why do we need salt?

Molecular dynamics (MD) simulations are usually performed by employing periodic boundary conditions (PBC). While this treatment of simulation system removes the necessity to treat the interactions with an otherwise artificial boundary, PBC also introduces additional constraints that need to be carefully considered for a robust and reliable simulation. Some of the issues pertaining to PBC are well explored and can be remedied by choosing a large enough unit cell, or by applying corrections to the generated trajectories. In current work, we study another artifact which cannot be alleviated by changing the box size. The artifact occurs due to the PBC imposed constraints affecting systems with permanent uncompensated dipoles, which is of particular relevance for lipid membrane simulations. Such dipoles often arise in many biologically-relevant setups, in particular those involving asymmetric lipid bilayers. The artifact manifests itself as an electric field formation in the simulation box which is counteracted by redistribution of mobile charge carriers (ions) and/or ordering of water dipoles. In the absence of ions, the artifact may cause strong water ordering, affecting thermodynamics of the studied system. This observation reveals a conceptually interesting effect of using explicit salt in MD simulations: ions help removing the unwanted periodicity-induced artifact occurring due to uncompensated electric dipoles. Therefore, we recommend adding mobile ions in molecular simulations whenever possible, and call for caution when simulating systems that require low salt concentration (or no salt at all), for example ion channel inactivation promoting conditions. In general, our findings are relevant for molecular simulations of any systems that contain uncompensated dipoles, that might occur more often than previously thought.

biophysics↗

bioRxiv2020

Computational Studies of Substrate Transport and Specificity in a Phospholipid Flippase

Type 4 P-type ATPases are lipid flippases which help maintain asymmetric phospholipid distribution in eukaryotic membranes by driving unidirectional translocation of phospholipid substrates. Recent cryo-EM and crystal structures have provided a detailed view of flippases, and we here use molecular dynamics simulations to study the first steps of phospholipid transport and lipid substrate specificity. Our simulations and new cryo-EM structure shows phospholipid binding to a groove and subsequent movement towards the centre of the membrane, and reveal a preference for phosphatidylserine lipids. We find that only the lipid head group stays in the groove while the lipid tails remain in the membrane, thus visualizing how flippases have evolved to transport large substrates. The flippase also induces deformation and thinning of the outer leaflet facilitating lipid recruitment. Our simulations provide insight into substrate binding to flippases and suggest that multiple sites and steps in the functional cycle contribute to substrate selectivity.

biophysics↗

bioRxiv2019

Comment on ‘Response to comment on ‘Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size’’

We recently expressed three major concerns about a 2018 article of El Hage et al. about a claimed effect of the box size in molecular dynamics simulations of hemoglobin. In the response of the authors to our comment, none of these concerns have been addressed, yet the authors maintain their original conclusions. Here, we challenge those conclusions and provide additional data that reestablish our original concerns. In addition, we identified six additional flaws in the response from El Hage et al. as well as a number of technical concerns about the presented simulations and analyses. Taken together, we conclude that there is no basis to support the hypothesis of significant box size effects in MD simulations for the studied systems in the examined range.

biophysics↗

bioRxiv2019

The SAMPL6 SAMPLing challenge: Assessing the reliability and efficiency of binding free energy calculations

Approaches for computing small molecule binding free energies based on molecular simulations are now regularly being employed by academic and industry practitioners to study receptor-ligand systems and prioritize the synthesis of small molecules for ligand design. Given the variety of methods and implementations available, it is natural to ask how the convergence rates and final predictions of these methods compare. In this study, we describe the concept and results for the SAMPL6 SAMPLing challenge, the first challenge from the SAMPL series focusing on the assessment of convergence properties and reproducibility of binding free energy methodologies. We provided parameter files, partial charges, and multiple initial geometries for two octa-acid (OA) and one cucurbit[8]uril (CB8) host-guest systems. Participants submitted binding free energy predictions as a function of the number of force and energy evaluations for seven different alchemical and physical-pathway (i.e., potential of mean force and weighted ensemble of trajectories) methodologies implemented with the GROMACS, AMBER, NAMD, or OpenMM simulation engines. To rank the methods, we developed an efficiency statistic based on bias and variance of the free energy estimates. For the two small OA binders, the free energy estimates computed with alchemical and potential of mean force approaches show relatively similar variance and bias as a function of the number of energy/force evaluations, with the attach-pull-release (APR), GROMACS expanded ensemble, and NAMD double decoupling submissions obtaining the greatest efficiency. The differences between the methods increase when analyzing the CB8-quinine system, where both the guest size and correlation times for system dynamics are greater. For this system, nonequilibrium switching (GROMACS/NS-DS/SB) obtained the overall highest efficiency. Surprisingly, the results suggest that specifying force field parameters and partial charges is insufficient to generally ensure reproducibility, and we observe differences between seemingly converged predictions ranging approximately from 0.3 to 1.0 kcal/mol, even with almost identical simulations parameters and system setup (e.g., Lennard-Jones cutoff, ionic composition). Further work will be required to completely identify the exact source of these discrepancies. Among the conclusions emerging from the data, we found that Hamiltonian replica exchange--while displaying very small variance--can be affected by a slowly-decaying bias that depends on the initial population of the replicas, that bidirectional estimators are significantly more efficient than unidirectional estimators for nonequilibrium free energy calculations for systems considered, and that the Berendsen barostat introduces non-negligible artifacts in expanded ensemble simulations.

biophysics↗

bioRxiv2019

Comment on 'Valid molecular dynamics simulations of human hemoglobin require a surprisingly large box size'

A recent molecular dynamics investigation into the stability of hemoglobin concluded that the unliganded protein is only stable in the T state when a solvent box is used in the simulations that is ten times larger than what is usually employed. Here, we express three main concerns about that study. In addition, we show that with an order of magnitude more statistics, the reported box size dependence is not reproducible. Overall, no significant effects on the kinetics or thermodynamics of conformational transitions were observed.

biophysics↗