Atomic-level reconstruction of biomolecules by a rigid-fragment- and local-frame-based (RF-LF) strategy

Li, Min; Teng, Bing; Lü, Wei; Zhang, John Z. H.

doi:10.1007/s00894-020-4298-7

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…Instead they typically seek a representative AA configuration, r R * = M + ( R ), that (ideally) maximizes p r ( r | R ) for the given R . In practice, back-mapping schemes often first introduce atomic detail by geometrically interpolating atoms between CG sites or by inserting fragments from libraries of atomistic structures. − The resulting AA configuration is then typically relaxed via energy minimization and possibly short MD simulations that are often restrained by the initial CG configuration.…”

Section: Coarse-grained Representationmentioning

confidence: 99%

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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By averaging over atomic details, coarse-grained (CG) models provide profound computational and conceptual advantages for studying soft materials. In particular, bottom-up approaches develop CG models based upon information obtained from atomically detailed models. At least in principle, a bottom-up model can reproduce all the properties of an atomically detailed model that are observable at the resolution of the CG model. Historically, bottom-up approaches have accurately modeled the structure of liquids, polymers, and other amorphous soft materials, but have provided lower structural fidelity for more complex biomolecular systems. Moreover, they have also been plagued by unpredictable transferability and a poor description of thermodynamic properties. Fortunately, recent studies have reported dramatic advances in addressing these prior limitations. This Perspective reviews this remarkable progress, while focusing on its foundation in the basic theory of coarse-graining. In particular, we describe recent insights and advances for treating the CG mapping, for modeling many-body interactions, for addressing the state-point dependence of effective potentials, and even for reproducing atomic observables that are beyond the resolution of the CG model. We also outline outstanding challenges and promising directions in the field. We anticipate that the synthesis of rigorous theory and modern computational tools will result in practical bottom-up methods that not only are accurate and transferable but also provide predictive insight for complex systems.

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Section: Coarse-grained Representationmentioning

confidence: 99%

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Noid

2023

J. Phys. Chem. B

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“…State-of-the-art backmapping algorithms can roughly be categorized into geometric, [34][35][36][37] fragment-based, 38,39 and recently emerging machine learning (ML) [40][41][42][43][44][45] approaches. Geometrybased methods build an initial atomistic struc-ture from a set of rules and aim to recover the Boltzmann distribution with a subsequent relaxation step, which typically consists of energy minimizations (EMs) and short MD simulations.…”

Section: Introductionmentioning

confidence: 99%

“…This database can either be predefined, as in the case of CG2AT2, 39 or directly extracted from a reference structure. 38 Ideally, each fragment represents a minimum energy structure to obtain a reliable approximation of the overall energy minimum of every molecule and to reduce the runtime of the subsequent relaxation step compared to geometric algorithms. However, only one rigid conformation per fragment is usually available in the database.…”

Section: Introductionmentioning

confidence: 99%

ART-SM: Boosting Fragment-based Backmapping by Machine Learning

Pfaendner,

Korn,

Gogoi

et al. 2024

Preprint

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In sequential multiscale molecular dynamics simulations, which advantageously combine the increased sampling and dynamics at coarse-grained resolution with the higher accuracy ofatomistic simulations, the resolution is altered over time. While coarse-graining is straightforward, the reintroduction of the atomistic detail is a non-trivial process called backmapping. Here, we present ART-SM, a fragment-based machine learning backmapping framework that learns the Boltzmann distribution from atomistic data to switch from coarse-grained to atomistic resolution seamlessly. ART-SM requires minimal user input and goes beyond state-of-the-art fragment-based approaches by selecting from multiple conformations per fragment to simultaneously reflect the coarse-grained structure and the Boltzmann distribution. Additionally, we introduce a novel refinement step to connect individual fragments via optimization of specific bonds, angles, and dihedral angles in the backmapping process. We demonstrate that our algorithm accurately restores the atomistic bond length, angle, and dihedral angle distributions for various small molecules of up to three Martini coarse-grained beads and that the resulting high-resolution structures are representative of the original coarse-grained conformations. Moreover, the reconstruction of the TIP3P water model is fast and robust, and we illustrate that ART-SM can be, in principle, applied to larger molecules as well, indicating its potential extension to more complex molecules like lipids, proteins, and macromolecules in the future.

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Back and forth modeling through biological scales

Pantano

2022

Biochemical and Biophysical Research Communications

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Atomic-level reconstruction of biomolecules by a rigid-fragment- and local-frame-based (RF-LF) strategy

Cited by 4 publications

References 53 publications

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models

ART-SM: Boosting Fragment-based Backmapping by Machine Learning

Back and forth modeling through biological scales

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