8 research outputs found
Simulated tempering with irreversible Gibbs sampling techniques
We present here two novel algorithms for simulated tempering simulations,
which break detailed balance condition (DBC) but satisfy the skewed detailed
balance to ensure invariance of the target distribution. The irreversible
methods we present here are based on Gibbs sampling and concern breaking DBC at
the update scheme of the temperature swaps. We utilise three systems as a test
bed for our methods: an MCMC simulation on a simple system described by a 1D
double well potential, the Ising model and MD simulations on Alanine
pentapeptide (ALA5). The relaxation times of inverse temperature, magnetic
susceptibility and energy density for the Ising model indicate clear gains in
sampling efficiency over conventional Gibbs sampling techniques with DBC and
also over the conventionally used simulated tempering with Metropolis-Hastings
(MH) scheme. Simulations on ALA5 with large number of temperatures indicate
distinct gains in mixing times for inverse temperature and consequently the
energy of the system compared to conventional MH. With no additional
computational overhead, our methods were found to be more efficient
alternatives to conventionally used simulated tempering methods with DBC. Our
algorithms should be particularly advantageous in simulations of large systems
with many temperature ladders, as our algorithms showed a more favorable
constant scaling in Ising spin systems as compared with both reversible and
irreversible MH algorithms. In future applications, our irreversible methods
can also be easily tailored to utilize a given dynamical variable other than
temperature to flatten rugged free energy landscapes
Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor
Patient symptom relief is often heavily influenced by the residence time of the inhibitor-target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3's activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization
Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor
Patient symptom relief is often heavily influenced by the residence time of the inhibitor–target complex. For the human muscarinic receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used in conditions such as asthma or chronic obstructive pulmonary disease (COPD). The mechanistic insights into this inhibitor remain unclear; specifically, the elucidation of the main factors determining the unbinding rates could help develop the next generation of antimuscarinic agents. Using our novel unbinding algorithm, we were able to investigate ligand dissociation from hMR3. The unbinding paths of tiotropium and two of its analogues, N-methylscopolamin and homatropine methylbromide, show a consistent qualitative mechanism and allow us to identify the structural bottleneck of the process. Furthermore, our machine learning-based analysis identified key roles of the ECL2/TM5 junction involved in the transition state. Additionally, our results point to relevant changes at the intracellular end of the TM6 helix leading to the ICL3 kinase domain, highlighting the closest residue L482. This residue is located right between two main protein binding sites involved in signal transduction for hMR3′s activation and regulation. We also highlight key pharmacophores of tiotropium that play determining roles in the unbinding kinetics and could aid toward drug design and lead optimization
Bayesian-Maximum-Entropy Reweighting of IDP Ensembles Based on NMR Chemical Shifts
Bayesian and Maximum Entropy approaches allow for a statistically sound and systematic fitting of experimental and computational data. Unfortunately, assessing the relative confidence in these two types of data remains difficult as several steps add unknown error. Here we propose the use of a validation-set method to determine the balance, and thus the amount of fitting. We apply the method to synthetic NMR chemical shift data of an intrinsically disordered protein. We show that the method gives consistent results even when other methods to assess the amount of fitting cannot be applied. Finally, we also describe how the errors in the chemical shift predictor can lead to an incorrect fitting and how using secondary chemical shifts could alleviate this problem.R.C. acknowledges funding from the MINECO (CTQ2016-78636-P) and to AGAUR, together with X.S. (2017 SGR 324). X.S. acknowledges funding from MINECO (BIO2015-70092-R), and the European Research Council (CONCERT, contract number 648201). K.L.-L. acknowledges funding from the Lundbeck Foundation BRAINSTRUC initiative.Peer reviewe
Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics
The determination of drug residence times, which define the time an inhibitor is in complex with its target, is a fundamental part of the drug discovery process. Synthesis and experimental measurements of kinetic rate constants are, however, expensive and time consuming. In this work, we aimed to obtain drug residence times computationally. Furthermore, we propose a novel algorithm to identify molecular design objectives based on ligand unbinding kinetics. We designed an enhanced sampling technique to accurately predict the free-energy profiles of the ligand unbinding process, focusing on the free-energy barrier for unbinding. Our method first identifies unbinding paths determining a corresponding set of internal coordinates (ICs) that form contacts between the protein and the ligand; it then iteratively updates these interactions during a series of biased molecular dynamics (MD) simulations to reveal the ICs that are important for the whole of the unbinding process. Subsequently, we performed finite-temperature string simulations to obtain the free-energy barrier for unbinding using the set of ICs as a complex reaction coordinate. Importantly, we also aimed to enable the further design of drugs focusing on improved residence times. To this end, we developed a supervised machine learning (ML) approach with inputs from unbiased "downhill" trajectories initiated near the transition state (TS) ensemble of the string unbinding path. We demonstrate that our ML method can identify key ligand-protein interactions driving the system through the TS. Some of the most important drugs for cancer treatment are kinase inhibitors. One of these kinase targets is cyclin-dependent kinase 2 (CDK2), an appealing target for anticancer drug development. Here, we tested our method using two different CDK2 inhibitors for the potential further development of these compounds. We compared the free-energy barriers obtained from our calculations with those observed in available experimental data. We highlighted important interactions at the distal ends of the ligands that can be targeted for improved residence times. Our method provides a new tool to determine unbinding rates and to identify key structural features of the inhibitors that can be used as starting points for novel design strategies in drug discovery
Investigating the Unbinding of Muscarinic Antagonists from the Muscarinic 3 Receptor
Patient symptom relief is often heavily influenced by
the residence
time of the inhibitor–target complex. For the human muscarinic
receptor 3 (hMR3), tiotropium is a long-acting bronchodilator used
in conditions such as asthma or chronic obstructive pulmonary disease
(COPD). The mechanistic insights into this inhibitor remain unclear;
specifically, the elucidation of the main factors determining the
unbinding rates could help develop the next generation of antimuscarinic
agents. Using our novel unbinding algorithm, we were able to investigate
ligand dissociation from hMR3. The unbinding paths of tiotropium and
two of its analogues, N-methylscopolamin and homatropine
methylbromide, show a consistent qualitative mechanism and allow us
to identify the structural bottleneck of the process. Furthermore,
our machine learning-based analysis identified key roles of the ECL2/TM5
junction involved in the transition state. Additionally, our results
point to relevant changes at the intracellular end of the TM6 helix
leading to the ICL3 kinase domain, highlighting the closest residue
L482. This residue is located right between two main protein binding
sites involved in signal transduction for hMR3′s activation
and regulation. We also highlight key pharmacophores of tiotropium
that play determining roles in the unbinding kinetics and could aid
toward drug design and lead optimization
Modelling the active SARS-CoV-2 helicase complex as a basis for structure-based inhibitor design
The RNA helicase (non-structural protein 13, NSP13) of SARS-CoV-2 is essential for viral replication, and it is highly conserved among the coronaviridae family, thus a prominent drug target to treat COVID-19. We present here structural models and dynamics of the helicase in complex with its native substrates based on thorough analysis of homologous sequences and existing experimental structures. We performed and analysed microseconds of molecular dynamics (MD) simulations, and our model provides valuable insights to the binding of the ATP and ssRNA at the atomic level. We identify the principal motions characterising the enzyme and highlight the effect of the natural substrates on this dynamics. Furthermore, allosteric binding sites are suggested by our pocket analysis. Our obtained structural and dynamical insights are important for subsequent studies of the catalytic function and for the development of specific inhibitors at our characterised binding pockets for this promising COVID-19 drug target