ENCORE: Software for Quantitative Ensemble Comparison

Abstract

There is increasing evidence that protein dynamics and conformational changes can play an important role in modulating biological function. As a result, experimental and computational methods are being developed, often synergistically, to study the dynamical heterogeneity of a protein or other macromolecules in solution. Thus, methods such as molecular dynamics simulations or ensemble refinement approaches have provided conformational ensembles that can be used to understand protein function and biophysics. These developments have in turn created a need for algorithms and software that can be used to compare structural ensembles in the same way as the root-mean-square-deviation is often used to compare static structures. Although a few such approaches have been proposed, these can be difficult to implement efficiently, hindering a broader applications and further developments. Here, we present an easily accessible software toolkit, called ENCORE, which can be used to compare conformational ensembles generated either from simulations alone or synergistically with experiments. ENCORE implements three previously described methods for ensemble comparison, that each can be used to quantify the similarity between conformational ensembles by estimating the overlap between the probability distributions that underlie them. We demonstrate the kinds of insights that can be obtained by providing examples of three typical use-cases: comparing ensembles generated with different molecular force fields, assessing convergence in molecular simulations, and calculating differences and similarities in structural ensembles refined with various sources of experimental data. We also demonstrate efficient computational scaling for typical analyses, and robustness against both the size and sampling of the ensembles. ENCORE is freely available and extendable, integrates with the established MDAnalysis software package, reads ensemble data in many common formats, and can work with large trajectory files.

Conformational Rigidity and Protein Dynamics at Distinct Timescales Regulate PTP1B Activity and Allostery

“…To improve accuracy and sampling, we performed experimentally-guided simulations, where the NMR chemical shifts were used as a system-specific force field correction (Camilloni et al, 2012; Tiberti et al, 2015). Importantly, because the NMR chemical shifts are time- and ensemble-averaged over multiple, interconverting conformations, the experimental restraints were enforced such that individual conformations do not have to satisfy all data, as long as the entire ensemble of conformations is in agreement with experiment.…”

“…In the former, dynamics is governed by a physics-based molecular mechanics force field, whereas the biased simulations also integrate information from experimental data. In particular, we used the NMR chemical shifts as a “system-specific force field correction” (Tiberti et al, 2015), so that the resulting conformational ensemble simultaneously integrates information from the most accurate force fields (Lindorff-Larsen et al, 2012) and the experimental chemical shifts (Camilloni et al, 2012) following the maximum entropy principle (Boomsma et al, 2014). All simulations were performed using the Amber ff99sb*-ILDN force field for the protein (Lindorff-Larsen et al, 2012) and TIP3P model for water.…”

The Mutational Landscape of the Oncogenic MZF1 SCAN Domain in Cancer

“…We accounted for differences in the formulation of the physical models employed in MD on the simulated dynamics, using five different force fields, i.e., CHARMM22 * , CHARMM27, Amber99-SB * -ILDN, Amber99-SB-NMR-ILDN, and RSFF1. Even minor changes in the torsional potential of protein backbone and side chains in the force fields have a major impact on the dynamics and structure described by MD simulations (Guvench and MacKerell, 2008; Lange et al, 2010; Lindorff-Larsen et al, 2012; Martín-García et al, 2015; Tiberti et al, 2015b; Unan et al, 2015) and the structural consequences upon selecting a certain set of force field parameters are hard to predict.…”

“…These data are a valuable resource for experimental validation of the conformational ensembles collected in our MD simulations as well as for comparison of MD force fields (Guvench and MacKerell, 2008; Lange et al, 2010; Beauchamp et al, 2012; Best et al, 2012; Dror et al, 2012; Lindorff-Larsen et al, 2012; Piana et al, 2014; Papaleo et al, 2014b; Henriques et al, 2015; Martín-García et al, 2015; Papaleo, 2015; Unan et al, 2015; Tiberti et al, 2015b). Thus, we calculated these NMR parameters from each MD ensemble and compared them to the experimental values.…”

Unrolr: Structural analysis of protein conformations using stochastic proximity embedding

“…SPE has been mostly used for conformational organization of chemical compound librairies, although applications to protein conformations from MD simulations have been reported . The main drawback of current implementations of SPE was linked to the use of the full distance matrix between conformations, limiting its application to small conformational ensembles.…”