Search

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software's functionalities. To address these issues, here we introduce ``PLUMED Tutorials'', a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources., Comment: 26 pages, 5 figures

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOM) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit. The SOM approach offers a data-driven strategy to create a map of the interaction process and pathways based on unbiased MD. Furthermore, we show how the preliminary SOM mapping is complementary to kinetic analysis, both with the employment of network-based approaches and Markov State Models (MSM). We demonstrate the method by comprehensively mapping a large dataset of 640 {\mu}s of unbiased trajectories sampling the recognition process between the phosphorylated YEEI peptide and its high-specificity target Lck-SH2. The integration of SOM into unbiased simulation protocols significantly advances our understanding of the ligand binding mechanism. This approach serves as a potent tool for mapping intricate ligand-target interactions with unprecedented detail, thereby enhancing the characterization of kinetic properties crucial to drug design., Comment: The following article has been accepted by JCP. After it is published, it will be found at publishing.aip.org

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science.

Steered molecular dynamics (SMD) simulations for the calculation of free energies are well suited for high-throughput molecular simulations on a distributed infrastructure due to the simplicity of the setup and parallel granularity of the runs. However, so far, the computational cost limited the estimation of the free energy typically over just a few pullings, thus impeding the evaluation of statistical uncertainties involved. In this work, we performed two thousand pulls for the permeation of a potassium ion in the gramicidin A pore by all-atom molecular dynamics in order to assess the bidirectional SMD protocol with a proper amount of sampling. The estimated free energy profile still shows a statistical error of several kcal/mol, while the work distributions are estimated to be non-Gaussian at pulling speeds of 10 {\AA}/ns. We discuss the methodology and the confidence intervals in relation to increasing amounts of computed trajectories and how different permeation pathways for the potassium ion, knock-on and sideways, affect the sampling and the free energy estimation., Comment: This document is the Accepted Manuscript version of a Published Work that appeared in final form in J. Chem. Theory Comput., copyright 2011 after peer review and technical editing by the publisher. To access the final edited and published work see doi:10.1021/ct100707s

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

The multidomain protein gelsolin (GSN) is composed of six homologous modules, sequentially named G1 to G6. Single point substitutions in this protein are responsible for AGel amyloidosis, a hereditary disease characterized by progressive corneal lattice dystrophy, cutis laxa, and polyneuropathy. Several different amyloidogenic variants of GSN have been identified over the years, but only the most common D187N/Y mutants, in G2, have been thoroughly characterized, and the underlying functional mechanistic link between mutation, altered protein structure, susceptibility to aberrant furin cleavage and aggregative potential resolved. Little is known about the recently identified mutations A551P, E553K and M517R hosted at the interface between G4 and G5, whose aggregation process likely follows an alternative pathway. We demonstrate that these three substitutions impair temperature and pressure stability of GSN but do not increase its susceptibility to furin cleavage, the first event of the canonical aggregation pathway. The variants are also characterized by a higher tendency to aggregate in the unproteolysed forms and show a higher proteotoxicity in a C. elegans-based assay. Structural studies point to a destabilization of the interface between G4 and G5 due to three different structural determinants: beta-strand breaking, steric hindrance and/or charge repulsion, all implying the impairment of interdomain contacts. All available evidence suggests that the rearrangement of the protein global architecture triggers a furin-independent aggregation of the protein, supporting the existence of a non-canonical pathway of gelsolin amyloidosis pathogenesis., Comment: main: 28 pages, 7 figures; supplementary: 11 pages, 6 figures

Protein-protein interactions are the basis of many important physiological processes and are currently promising, yet difficult, targets for drug discovery. In this context, inhibitor of apoptosis proteins (IAPs)-mediated interactions are pivotal for cancer cell survival; the interaction of the BIR1 domain of cIAP2 with TRAF2 was shown to lead the recruitment of cIAPs to the TNF receptor, promoting the activation of the NF-\kappa B survival pathway. In this work, using a combined in silico-in vitro approach, we identified a drug-like molecule, NF023, able to disrupt cIAP2 interaction with TRAF2. We demonstrated in vitro its ability to interfere with the assembly of the cIAP2-BIR1/TRAF2 complex and performed a thorough characterization of the compound's mode of action through 248 parallel unbiased molecular dynamics simulations of 300 ns (totaling almost 75 {\mu}s of all-atom sampling), which identified multiple binding modes to the BIR1 domain of cIAP2 via clustering and ensemble docking. NF023 is, thus, a promising protein-protein interaction disruptor, representing a starting point to develop modulators of NF-\kappa B-mediated cell survival in cancer. This study represents a model procedure that shows the use of large-scale molecular dynamics methods to typify promiscuous interactors.

Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived with machine learning approaches. Here, we present TorchMD, a framework for molecular simulations with mixed classical and machine learning potentials. All of force computations including bond, angle, dihedral, Lennard-Jones and Coulomb interactions are expressed as PyTorch arrays and operations. Moreover, TorchMD enables learning and simulating neural network potentials. We validate it using standard Amber all-atom simulations, learning an ab-initio potential, performing an end-to-end training and finally learning and simulating a coarse-grained model for protein folding. We believe that TorchMD provides a useful tool-set to support molecular simulations of machine learning potentials. Code and data are freely available at \url{github.com/torchmd}.

The second domain of gelsolin (G2) hosts mutations responsible for a hereditary form of amyloidosis. The active form of gelsolin is Ca2+-bound; it is also a dynamic protein, hence structural biologists often rely on the study of the isolated G2. However, the wild type G2 structure that have been used so far in comparative studies is bound to a crystallographic Cd2+, in lieu of the physiological calcium. Here, we report the wild type structure of G2 in complex with Ca2+ highlighting subtle ion-dependent differences. Previous findings on different G2 mutations are also briefly revised in light of these results.

Mutations in the gelsolin protein are responsible for a rare conformational disease known as AGel amyloidosis. Four of these mutations are hosted by the second domain of the protein (G2): D187N/Y, G167R and N184K. The impact of the latter has been so far evaluated only by studies on the isolated G2. Here we report the characterization of full-length gelsolin carrying the N184K mutation and compare the findings with those obtained on the wild type and the other variants. The crystallographic structure of the N184K variant in the Ca2+-free conformation shows remarkable similarities with the wild type protein. Only minimal local rearrangements can be observed and the mutant is as efficient as the wild type in severing filamentous actin. However, thermal stability of the pathological variant is compromised in the Ca2+-free conditions. These data suggest that the N to K substitution causes a local disruption of the H-bond network in the core of the G2 domain. Such a subtle rearrangement of the connections does not lead to significant conformational changes but severely affects the stability of the protein.

AGel amyloidosis, formerly known as familial amyloidosis of the Finnish-type, is caused by pathological aggregation of proteolytic fragments of plasma gelsolin. So far, four mutations in the gelsolin gene have been reported as responsible for the disease. Although D187N is the first identified variant and the best characterized, its structure has been hitherto elusive. Exploiting a recently-developed nanobody targeting gelsolin, we were able to stabilize the G2 domain of the D187N protein and obtained, for the first time, its high-resolution crystal structure. In the nanobody-stabilized conformation, the main effect of the D187N substitution is the impairment of the calcium binding capability, leading to a destabilization of the C-terminal tail of G2. However, molecular dynamics simulations show that in the absence of the nanobody, D187N-mutated G2 further misfolds, ultimately exposing its hydrophobic core and the furin cleavage site. The nanobody's protective effect is based on the enhancement of the thermodynamic stability of different G2 mutants (D187N, G167R and N184K). In particular, the nanobody reduces the flexibility of dynamic stretches, and most notably decreases the conformational entropy of the C-terminal tail, otherwise stabilized by the presence of the Ca2+ ion. A Caenorhabditis elegans-based assay was also applied to quantify the proteotoxic potential of the mutants and determine whether nanobody stabilization translates into a biologically relevant effect. Successful protection from G2 toxicity in vivo points to the use of C. elegans as a tool for investigating the mechanisms underlying AGel amyloidosis and rapidly screen new therapeutics.

Although interdisciplinarity is often touted as a necessity for modern research, the evidence on the relative impact of sectorial versus to interdisciplinary science is qualitative at best. In this paper we leverage the bibliographic data set of the American Physical Society to quantify the role of interdisciplinarity in physics, and that of talent and luck in achieving success in scientific careers. We analyze a period of 30 years (1980-2009) tagging papers and their authors by means of the Physics and Astronomy Classification Scheme (PACS), to show that some degree of interdisciplinarity is quite helpful to reach success, measured as a proxy of either the number of articles or the citations score. We also propose an agent-based model of the publication-reputation-citation dynamics reproduces the trends observed in the APS data set. On the one hand, the results highlight the crucial role of randomness and serendipity in real scientific research; on the other, they shed light on a counter-intuitive effect indicating that the most talented authors are not necessarily the most successful ones., Comment: 21 pages, 19 figures

The proper choice of collective variables (CVs) is central to biased-sampling free energy reconstruction methods in molecular dynamics simulations. The PLUMED 2 library, for instance, provides several sophisticated CV choices, implemented in a C++ framework; however, developing new CVs is still time consuming due to the need to provide code for the analytical derivatives of all functions with respect to atomic coordinates. We present two solutions to this problem, namely (a) symbolic differentiation and code generation, and (b) automatic code differentiation, in both cases leveraging open-source libraries (SymPy and Stan Math respectively). The two approaches are demonstrated and discussed in detail implementing a realistic example CV, the local radius of curvature of a polymer. Users may use the code as a template to streamline the implementation of their own CVs using high-level constructs and automatic gradient computation., Comment: Title changed with respect to the initial submission

Molecular dynamics (MD) simulations allow the exploration of the phase space of biopolymers through the integration of equations of motion of their constituent atoms. The analysis of MD trajectories often relies on the choice of collective variables (CVs) along which the dynamics of the system is projected. We developed a graphical user interface (GUI) for facilitating the interactive choice of the appropriate CVs. The GUI allows: defining interactively new CVs; partitioning the configurations into microstates characterized by similar values of the CVs; calculating the free energies of the microstates for both unbiased and biased (metadynamics) simulations; clustering the microstates in kinetic basins; visualizing the free energy landscape as a function of a subset of the CVs used for the analysis. A simple mouse click allows one to quickly inspect structures corresponding to specific points in the landscape., Comment: Published in Computer Physics Communications

PLUMED-GUI is an interactive environment to develop and test complex PLUMED scripts within the Visual Molecular Dynamics (VMD) environment. Computational biophysicists can take advantage of both PLUMED's rich syntax to define collective variables (CVs) and VMD's chemically-aware atom selection language, while working within a natural point-and-click interface. Pre-defined templates and syntax mnemonics facilitate the definition of well-known reaction coordinates. Complex CVs, e.g. involving reference snapshots used for RMSD or native contacts calculations, can be built through dialogs that provide a synoptic view of the available options. Scripts can be either exported for use in simulation programs, or evaluated on the currently loaded molecular trajectories. Development of scripts takes place without leaving VMD, thus enabling an incremental try-see-modify development model for molecular metrics.

Molecular dynamics simulations have a prominent role in biophysics and drug discovery due to the atomistic information they provide on the structure, energetics and dynamics of biomolecules. Specialized software packages are required to analyze simulated trajectories, either interactively or via scripts, to derive quantities of interest and provide insight for further experiments. This paper presents the Density Profile Tool, a package that enhances the Visual Molecular Dynamics environment with the ability to interactively compute and visualize 1-D projections of various density functions of molecular models. We describe how the plugin is used to perform computations both via a graphical interface and programmatically. Results are presented for realistic examples, all-atom bilayer models, showing how mass and electron densities readily provide measurements such as membrane thickness, location of structural elements, and how they compare to X-ray diffraction experiments.

A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes, involving (i) identification of the structural changes involved in these processes, and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, Master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the "slow subspace" of a large set of prior order parameters - either generic internal coordinates (distances and dihedral angles), or a user-defined set of parameters. It is shown that a method to identify this slow subspace exists in statistics: the time-lagged independent component analysis (TICA). Furthermore, optimal indicators-order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates-are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue natively disordered peptide KID. The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis.