Enhancing sampling and analyzing simulations are central issues in molecular simulation. Recently, we introduced PLUMED, an open-source plug-in that provides some of the most popular molecular dynamics (MD) codes with implementations of a variety of different enhanced sampling algorithms and collective variables (CVs). The rapid changes in this field, in particular new directions in enhanced sampling and dimensionality reduction together with new hardwares, require a code that is more flexible and more efficient. We therefore present PLUMED 2 here -a complete rewrite of the code in an object-oriented programming language (C++). This new version introduces greater flexibility and greater modularity, which both extends its core capabilities and makes it far easier to add new methods and CVs. It also has a simpler interface with the MD engines and provides a single software library containing both tools and core facilities. Ultimately, the new code better serves the ever-growing community of users and contributors in coping with the new challenges arising in the field.

Here we present a program aimed at free-energy calculations in molecular systems. It consists of a series of routines that can be interfaced with the most popular classical molecular dynamics (MD) codes through a simple patching procedure. This leaves the possibility for the user to exploit many different MD engines depending on the system simulated and on the computational resources available. Free-energy calculations can be performed as a function of many collective variables, with a particular focus on biological problems, and using state-of-the-art methods such as metadynamics, umbrella sampling and Jarzynski-equation based steered MD. The present software, written in ANSI-C language, can be easily interfaced with both fortran and C/C++ codes.

The PLUMED consortium (2019). Promoting transparency and reproducibility in enhanced molecular simulations. Nature Methods, 16(8), 670-673. https://doi.

One of the major open challenges in structural biology is to achieve effective descriptions of disordered states of proteins. This problem is difficult because these states are conformationally highly heterogeneous and cannot be represented as single structures, and therefore it is necessary to characterize their conformational properties in terms of probability distributions. Here we show that it is possible to obtain highly quantitative information about particularly important types of probability distributions, the populations of secondary structure elements (α-helix, β-strand, random coil, and polyproline II), by using the information provided by backbone chemical shifts. The application of this approach to mammalian prions indicates that for these proteins a key role in molecular recognition is played by disordered regions characterized by highly conserved polyproline II populations. We also determine the secondary structure populations of a range of other disordered proteins that are medically relevant, including p53, α-synuclein, and the Aβ peptide, as well as an oligomeric form of αB-crystallin. Because chemical shifts are the nuclear magnetic resonance parameters that can be measured under the widest variety of conditions, our approach can be used to obtain detailed information about secondary structure populations for a vast range of different protein states.

The biological functions of protein molecules are intimately dependent on their conformational dynamics. This aspect is particularly evident for disordered proteins, which constitute perhaps one-third of the human proteome. Therefore, structural ensembles often offer more useful representations of proteins than individual conformations. Here, we describe how the well-established principles of protein structure determination should be extended to the case of protein structural ensembles determination. These principles concern primarily how to deal with conformationally heterogeneous states, and with experimental measurements that are averaged over such states and affected by a variety of errors. We first review the growing literature of recent methods that combine experimental and computational information to model structural ensembles, highlighting their similarities and differences. We then address some conceptual problems in the determination of structural ensembles and define future goals towards the establishment of objective criteria for the comparison, validation, visualization and dissemination of such ensembles.