BOCS: Bottom-up Open-source Coarse-graining Software

Dunn, Nicholas J.H.; Lebold, Kathryn M.; DeLyser, Michael; Rudzinski, Joseph F.; Noid, W. G.

doi:10.1021/acs.jpcb.7b09993

Cited by 51 publications

(45 citation statements)

References 92 publications

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“…The most flexible among these approaches is to fit one-dimensional splines for each of the pairwise distance, angle, and dihedral terms to parametrize two-, three-, and four-body interactions. 75 To ensure a consistent comparison, we represent 1D splines with neural networks that map from a single input feature (pairwise distance, angle, or dihedral) to a single free energy term, resulting in the spline model network shown in Figure 2c. We use the same regularization and baseline energy for spline model networks and CGnets.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

et al. 2019

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Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.

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Section: Resultsmentioning

confidence: 99%

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

et al. 2019

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“…This protein corresponded to an engineered variant of the Green Fluorescent Protein (GFP), which preserved the folding without creating a chromophore inside 50 . It featured the typical central helix [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] surrounded by 11 antiparallel -strands, which was globally well maintained (Figure 3C). The best RMSD performing protein was Crambin, identified with the PDB id 1CRN.…”

Section: Validation Of Sirah 20mentioning

confidence: 99%

“…In the first case, frequently referred as systematic derivation, parameters are derived from higher-level simulations (fully atomistic or even quantum). Iterative Boltzmann Inversion, Force Matching or Inverse Montecarlo are among the most popular techniques for bottom-up parameterization 12,13 . In the second case, parameters are derived aiming to reproduce a selected set of experimental data relevant for the phenomenon under study.…”

Section: Introductionmentioning

confidence: 99%

The SIRAH 2.0 Force Field: Altius, Fortius, Citius

Machado

Barrera

Klein

et al. 2019

J. Chem. Theory Comput.

125

165

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A new version of the coarse-grained (CG) SIRAH force field for proteins has been developed. Modifications to bonded and non-bonded interactions on the existing molecular topologies significantly ameliorate the structural description and flexibility of a non-redundant set of proteins. The SIRAH 2.0 force field has also been ported to the popular simulation package AMBER, which along with the former implementation in GROMACS expands significantly the potential range of users and performance of this CG force field on CPU/GPU codes. As a non-trivial example of application, we undertook the structural and dynamical analysis of the most abundant and conserved calcium-binding protein, namely, Calmodulin (CaM). CaM is constituted by two calcium-binding motifs called EF-hands, which in presence of Calcium specifically recognize a cognate peptide by embracing it. CG simulations of CaM bound to four Calcium ions in the presence or absence of a binding peptide (holo and apo forms, respectively), resulted in good and stable ion coordination. The simulation of the holo form starting from an experimental structure sampled nearnative conformations, retrieving quasi-atomistic precision. Removing the binding peptide enabled the EF-hands to perform large reciprocal movements, comparable to those observed in NMR structures. On the other hand, the isolated peptide starting from the helical conformation experienced spontaneous unfolding, in agreement with previous experimental data. However, repositioning the peptide in the neighborhood of one EF-hand not only prevented the peptide unfolding but also drove CaM to a fully bound conformation with both EF-hands embracing the cognate peptide, resembling the experimental holo structure. Therefore, SIRAH 2.0 showed the capacity to handle a number of structurally and dynamically challenging situations including metal ion coordination, unbiased conformational sampling, and specific protein-peptide recognition.

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“…The method, however, has only been tested on a small number of systems and provides no theory or explanation of mapping operator choice. There are pipline softwares available, like BOCS [15], VOTCA [35] and Auto-Martini [3], to facilitate CG system preparation and subsequent simulation. However, these tools either require the user to select the mapping operator or create mapping based on established rules, like Martini CG mappings.…”

Section: Introductionmentioning

confidence: 99%