Biology subjects

Parrinello, M.

Publications and source records attributed to Parrinello, M..

6 recordsLinked to original sources

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↗

bioRxiv2020

On the accuracy of molecular simulation-based predictions of koff values: a Metadynamics study

Molecular simulations have made great progresses in predicting koff values--the kinetic constant of drug unbinding, a key parameter for modern pharmacology--yet computed values under- or over-estimate experimental data in a system- and/or technique-dependent way. In an effort at gaining insights on this issue, here we used an established method to calculate koff values--frequency-adaptive metadynamics with force field-- and a subsequent QM/MM descriptions of the interactions. First, using force field-based metadynamics, we calculate koff of the Positron Emission Tomography (PET) ligand iperoxo targeting the human muscarinic acetylcholine receptor M2. In line with previously performed in silico studies, the prediction (3.7 {+/-} 0.7 * 10-4 s-1) turned out to differ significantly from the experimentally measured value (1.0 {+/-} 0.2 * 10-2 s-1). Next, we use DFT-based QM/MM simulations to show that this discrepancy arises from erroneous force field energetics at the transition state. It turns out that this discrepancy is partly caused by lack of electronic polarization and/or charge transfer in commonly employed force field. We expect these issues to arise also in other systems where charged portions of the system play a pivotal role, such as protein- or DNA-protein complexes. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/015396v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1ee4e1aorg.highwire.dtl.DTLVardef@49ba0forg.highwire.dtl.DTLVardef@565f25org.highwire.dtl.DTLVardef@80940e_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2019

Molecular Dynamics Simulations,Exact NOE Measurements, and Machine Learning Reveal a Low-populated State of the UUCG RNA Tetraloop

We provide an atomic-level description of the structure and dynamics of the UUCG RNA stem-loop by combining molecular dynamics simulations with experimental data. The integration of simulations with exact nuclear Overhauser enhancements data allowed us to characterize two distinct states of this molecule. The most stable conformation corresponds to the consensus three-dimensional structure. The second state is characterized by the absence of the peculiar non-Watson-Crick interactions in the loop region. By using machine learning techniques we identify a set of experimental measurements that are most sensitive to the presence of non-native states. We find that although our MD ensemble, as well as the consensus UUCG tetraloop structures, are in good agreement with experiments, there are remaining discrepancies. Together, our results show that i) the structural interpretation of experimental data for dynamic RNAs is highly complex, even for a simple model system such as the UUCG tetraloop ii) the MD simulation overstabilize a non-native loop conformation, and iii) eNOE data support its presence with a population of {approx}10%.

biophysics↗

bioRxiv2019

Chasing the full free energy landscape of neuroreceptor/ligand unbinding by metadynamics simulations

Predicting the complete free energy landscape associated with protein-ligand un-binding would greatly help designing drugs with highly optimized pharmacokinetics. Here we investigate the unbinding of the iperoxo agonist to its target human neuroreceptor M2, embedded in a neuronal membrane. By feeding out-of-equilibrium molecular simulations data in a classification analysis, we identify the few essential reaction coordinates of the process. The full landscape is then reconstructed using an exact enhanced sampling method, well-tempered metadynamics in its funnel variant. The calculations reproduce well the measured affinity, provide a rationale for mutagenesis data and show that the ligand can escape via two different routes. The allosteric modulator LY2119620 turns out to hamper both escapes routes, thus slowing down the unbinding process, as experimentally observed. This computationally affordable protocol is totally general and it can be easily applied to determine the full free energy landscape of membrane receptors/drug interactions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/544577v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@cfc6b2org.highwire.dtl.DTLVardef@fde50corg.highwire.dtl.DTLVardef@c8a893org.highwire.dtl.DTLVardef@1b36a12_HPS_FORMAT_FIGEXP M_FIG Graphical TOC Entry C_FIG

biophysics↗

bioRxiv2018

Accelerating the Calculation of Protein-Ligand Binding Free Energy and residence Times using Dynamically Optimized Collective Variables

Elucidation of the ligand/protein binding interaction is of paramount relevance in pharmacology to increase the success rate of drug design. To this end a number of computational methods have been proposed, however all of them suffer from limitations since the ligand binding/unbinding transitions to the molecular target involve many slow degrees of freedom that hamper a full characterization of the binding process. Being able to express this transition in simple and general slow degrees of freedom, would give a distinctive advantage, since it would require minimal knowledge of the system under study, while in turn it would elucidate its physics and accelerate the convergence speed of enhanced sampling methods relying on collective variables. In this study we pursuit this goal by combining for the first time Variation Approach to Conformational dynamics with Funnel-Metadynamics. In so doing, we predict for the benzamidine/trypsin system the ligand binding mode, and we accurately compute the absolute protein-ligand binding free energy and unbinding rate at unprecedented low computational cost. Finally, our simulation protocol reveals the energetics and structural details of the ligand binding mechanism and shows that water and binding pocket solvation/desolvation are the dominant slow degrees of freedom.

biophysics↗

bioRxiv2018

Enhanced Sampling of Protein Conformational Transitions via Dynamically Optimized Collective Variables

Protein conformational transitions often involve many slow degrees of freedom. Their knowledge would give distinctive advantages since it provides chemical and mechanistic insight and accelerates the convergence of enhanced sampling techniques that rely on collective variables. In this study, we implemented a recently developed variational approach to conformational dynamics metadynamics to the conformational transition of the moderate size protein, L99A T4 Lysozyme. In order to find the slow modes of the system we combined data coming from NMR experiments as well as short MD simulations. A Metadynamics simulation based on these information reveals the presence of two intermediate states, at an affordable computational cost.

biophysics↗