Property Map Collective Variable as a Useful Tool for a Force Field Correction.

The accuracy of biomolecular simulations depends on the accuracy of an empirical molecular mechanics potential known as a force field: a set of parameters and expressions to estimate the potential from atomic coordinates. Accurate parametrization of force fields for small organic molecules is a challenge due to their high diversity. One of the possible approaches is to apply a correction to the existing force fields. Here, we propose an approach to estimate the density functional theory (DFT)-derived force field correction which is calculated during the run of molecular dynamics without significantly affecting its speed. Using the formula known as a property map collective variable, we approximate the force field correction by a weighted average of this force field correction calculated only for a small series of reference structures. To validate this method, we used seven AMBER force fields, and we show how it is possible to convert one force field to behave like the other one. We also present the force field correction for the important anticancer drug Imatinib as a use case example. Our method appears to be suitable for adjusting the force field for general drug-like molecules. We provide a pipeline that generates the correction; this pipeline is available at https://pmcvff-correction.cerit-sc.cz/.

Atomistic simulation of carbohydrate-protein complex formation: Hevein-32 domain.

Interactions between proteins and their small molecule ligands are of great importance for the process of drug design. Here we report an unbiased molecular dynamics simulation of systems containing hevein domain (HEV32) with N-acetylglucosamine mono-, di- or trisaccharide. Carbohydrate molecules were placed outside the binding site. Three of six simulations (6 × 2 µs) led to binding of a carbohydrate ligand into the binding mode in agreement with the experimentally determined structure. Unbinding was observed in one simulation (monosaccharide). There were no remarkable intermediates of binding for mono and disaccharide. Trisaccharide binding was initiated by formation of carbohydrate-aromatic CH/π interactions. Our results indicate that binding of ligands followed the model of conformational selection because the conformation of the protein ready for ligand binding was observed before the binding. This study extends the concept of docking by dynamics on carbohydrate-protein interactions.

Anncolvar: Approximation of Complex Collective Variables by Artificial Neural Networks for Analysis and Biasing of Molecular Simulations.

The state of a molecular system can be described in terms of collective variables. These low-dimensional descriptors of molecular structure can be used to monitor the state of the simulation, to calculate free energy profiles or to accelerate rare events by a bias potential or a bias force. Frequent calculation of some complex collective variables may slow down the simulation or analysis of trajectories. Moreover, many collective variables cannot be explicitly calculated for newly sampled structures. In order to address this problem, we developed a new package called anncolvar. This package makes it possible to build and train an artificial neural network model that approximates a collective variable. It can be used to generate an input for the open-source enhanced sampling simulation PLUMED package, so the collective variable can be monitored and biased by methods available in this program. The computational efficiency and the accuracy of anncolvar are demonstrated on selected molecular systems (cyclooctane derivative, Trp-cage miniprotein) and selected collective variables (Isomap, molecular surface area).

Human histone deacetylase 6 shows strong preference for tubulin dimers over assembled microtubules.

Human histone deacetylase 6 (HDAC6) is the major deacetylase responsible for removing the acetyl group from Lys40 of α-tubulin (αK40), which is located lumenally in polymerized microtubules. Here, we provide a detailed kinetic analysis of tubulin deacetylation and HDAC6/microtubule interactions using individual purified components. Our data unequivocally show that free tubulin dimers represent the preferred HDAC6 substrate, with a K M value of 0.23 µM and a deacetylation rate over 1,500-fold higher than that of assembled microtubules. We attribute the lower deacetylation rate of microtubules to both longitudinal and lateral lattice interactions within tubulin polymers. Using TIRF microscopy, we directly visualized stochastic binding of HDAC6 to assembled microtubules without any detectable preferential binding to microtubule tips. Likewise, indirect immunofluorescence microscopy revealed that microtubule deacetylation by HDAC6 is carried out stochastically along the whole microtubule length, rather than from the open extremities. Our data thus complement prior studies on tubulin acetylation and further strengthen the rationale for the correlation between tubulin acetylation and microtubule age.