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How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry.

ABSTRACT: Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without a priori knowledge on chemical reactions. This is demonstrated by training machine learning models to predict directly a specific outcome quantity of ab initio molecular dynamics simulations - the timescale of the decomposition of 1,2-dioxetane. The machine learning models accurately reproduce the dissociation time of the compound. Keeping the aim of gaining physical insight, it is demonstrated that, in order to make accurate predictions, the models evidence empirical rules that are, today, part of the common chemical knowledge. This opens the way for conceptual breakthroughs in chemistry where machine analysis would provide a source of inspiration to humans.

SUBMITTER: Hase F

PROVIDER: S-EPMC6385677 | biostudies-other | 2019 Feb

REPOSITORIES: biostudies-other

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How machine learning can assist the interpretation of <i>ab initio</i> molecular dynamics simulations and conceptual understanding of chemistry.

Häse Florian F Fdez Galván Ignacio I Aspuru-Guzik Alán A Lindh Roland R Vacher Morgane M

Chemical science 20181221 8

Molecular dynamics simulations are often key to the understanding of the mechanism, rate and yield of chemical reactions. One current challenge is the in-depth analysis of the large amount of data produced by the simulations, in order to produce valuable insight and general trends. In the present study, we propose to employ recent machine learning analysis tools to extract relevant information from simulation data without <i>a priori</i> knowledge on chemical reactions. This is demonstrated by t ...[more]

PMID: 30881655

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Project description:BackgroundPredicting 3-dimensional protein structures from amino-acid sequences is an important unsolved problem in computational structural biology. The problem becomes relatively easier if close homologous proteins have been solved, as high-resolution models can be built by aligning target sequences to the solved homologous structures. However, for sequences without similar folds in the Protein Data Bank (PDB) library, the models have to be predicted from scratch. Progress in the ab initio structure modeling is slow. The aim of this study was to extend the TASSER (threading/assembly/refinement) method for the ab initio modeling and examine systemically its ability to fold small single-domain proteins.ResultsWe developed I-TASSER by iteratively implementing the TASSER method, which is used in the folding test of three benchmarks of small proteins. First, data on 16 small proteins (< 90 residues) were used to generate I-TASSER models, which had an average Calpha-root mean square deviation (RMSD) of 3.8A, with 6 of them having a Calpha-RMSD < 2.5A. The overall result was comparable with the all-atomic ROSETTA simulation, but the central processing unit (CPU) time by I-TASSER was much shorter (150 CPU days vs. 5 CPU hours). Second, data on 20 small proteins (< 120 residues) were used. I-TASSER folded four of them with a Calpha-RMSD < 2.5A. The average Calpha-RMSD of the I-TASSER models was 3.9A, whereas it was 5.9A using TOUCHSTONE-II software. Finally, 20 non-homologous small proteins (< 120 residues) were taken from the PDB library. An average Calpha-RMSD of 3.9A was obtained for the third benchmark, with seven cases having a Calpha-RMSD < 2.5A.ConclusionOur simulation results show that I-TASSER can consistently predict the correct folds and sometimes high-resolution models for small single-domain proteins. Compared with other ab initio modeling methods such as ROSETTA and TOUCHSTONE II, the average performance of I-TASSER is either much better or is similar within a lower computational time. These data, together with the significant performance of automated I-TASSER server (the Zhang-Server) in the 'free modeling' section of the recent Critical Assessment of Structure Prediction (CASP)7 experiment, demonstrate new progresses in automated ab initio model generation. The I-TASSER server is freely available for academic users http://zhang.bioinformatics.ku.edu/I-TASSER.

Project description:We investigated excess electron solvation dynamics in (NH3)n- ammonia clusters in the n = 8-32 size range by performing finite temperature molecular dynamics simulations. In particular, we focused on three possible scenarios. The first case is designed to model electron attachment to small neutral ammonia clusters (n ≤ ∼10) that form hydrogen-bonded chains. The excess electron is bound to the clusters via dipole bound states, and persists with a VDE of ∼160 meV at 100 K for the n = 8 cluster. The coupled nuclear and electronic relaxation is fast (within ∼100 fs) and takes place predominantly by intermolecular librational motions and the intramolecular umbrella mode. The second scenario illustrates the mechanism of excess electron attachment to cold compact neutral clusters of medium size (8 ≤ n ≤ 32). The neutral clusters show increasing tendency with size to bind the excess electron on the surface of the clusters in weakly bound, diffuse, and highly delocalized states. Anionic relaxation trajectories launched from these initial states provide no indication for excess electron stabilization for sizes n < 24. Excess electrons are likely to autodetach from these clusters. The two largest investigated clusters (n = 24 and 32) can accommodate the excess electron in electronic states that are mainly localized on the surface, but they may be partly embedded in the cluster. In the last 500 fs of the simulated trajectories, the VDE of the solvated electron fluctuates around ∼200 meV for n = 24 and ∼500 meV for n = 32, consistent with the values extrapolated from the experimentally observed linear VDE-n-1/3 trend. In the third case, we prepared neutral ammonia cluster configurations, including an n = 48 cluster, that contain possible electron localization sites within the interior of the cluster. Excess electrons added to these clusters localize in cavities with high VDEs up to 1.9 eV for n = 48. The computed VDE values for larger clusters are considerably higher than the experimentally observed photoelectric threshold energy for the solvated electron in bulk ammonia (∼1.4 eV). Molecular dynamics simulations launched from these initial cavity states strongly indicate, however, that these cavity structures exist only for ∼200 fs. During the relaxation the electron leaves the cavity and becomes delocalized, while the cluster loses its initial compactness.

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How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry.

Publications

How machine learning can assist the interpretation of <i>ab initio</i> molecular dynamics simulations and conceptual understanding of chemistry.

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