Graph neural network based coarse-grained mapping prediction

Li, Zhiheng; Wellawatte, Geemi P.; Chakraborty, Maghesree; Gandhi, Heta A.; Xu, Chenliang; White, Andrew D.

doi:10.1039/d0sc02458a

Cited by 42 publications

(45 citation statements)

References 24 publications

(31 reference statements)

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“…These steps are interconnected and are both important for the success of a CG model. 22 Although multiple algorithmic strategies have been proposed for the CG mapping, [23][24][25][26] they are most commonly based on physical and chemical intuition, and the optimization of the CG mapping is still an open area of research. 25 The definition of an effective Hamiltonian for a given CG mapping depends on the goal of the coarsegrained model.…”

Section: Introductionmentioning

confidence: 99%

“…22 Although multiple algorithmic strategies have been proposed for the CG mapping, [23][24][25][26] they are most commonly based on physical and chemical intuition, and the optimization of the CG mapping is still an open area of research. 25 The definition of an effective Hamiltonian for a given CG mapping depends on the goal of the coarsegrained model. As some of the information is necessarily lost upon coarse-graining, CG models must be designed such that certain targeted properties of the molecular system are retained and can be computed from both the all-atom and the CG ensembles.…”

Section: Introductionmentioning

confidence: 99%

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Multi-body effects in a coarse-grained protein force field

Wang

Charron

Husic

et al. 2021

The Journal of Chemical Physics

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The use of coarse-grained (CG) models is a popular approach to study complex biomolecular systems. By reducing the number of degrees of freedom, a CG model can explore long time-and length-scales inaccessible to computational models at higher resolution. If a CG model is designed by formally integrating out some of the system's degrees of freedom, one expects multi-body interactions to emerge in the effective CG model's energy function. In practice, it has been shown that the inclusion of multi-body terms indeed improves the accuracy of a CG model. However, no general approach has been proposed to systematically construct a CG effective energy that includes arbitrary orders of multi-body terms. In this work, we propose a neural network based approach to address this point and construct a CG model as a multibody expansion. By applying this approach to a small protein, we evaluate the relative importance of the different multi-body terms in the definition of an accurate model. We observe a slow convergence in the multi-body expansion, where up to five-body interactions are needed to reproduce the free energy of an atomistic model.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-body effects in a coarse-grained protein force field

Wang

Charron

Husic

et al. 2021

The Journal of Chemical Physics

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“…Li et al. 140 treated this problem as a graph segmentation problem and presented a GNN-based coarse-graining mapping predictor called Deep Supervised Graph Partitioning Model.…”

Section: Protein Representation and Function Predictionmentioning

confidence: 99%

Deep Learning in Protein Structural Modeling and Design

et al. 2020

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Summary Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields, including protein structural modeling. Protein structural modeling, such as predicting structure from amino acid sequence and evolutionary information, designing proteins toward desirable functionality, or predicting properties or behavior of a protein, is critical to understand and engineer biological systems at the molecular level. In this review, we summarize the recent advances in applying deep learning techniques to tackle problems in protein structural modeling and design. We dissect the emerging approaches using deep learning techniques for protein structural modeling and discuss advances and challenges that must be addressed. We argue for the central importance of structure, following the “sequence structure function” paradigm. This review is directed to help both computational biologists to gain familiarity with the deep learning methods applied in protein modeling, and computer scientists to gain perspective on the biologically meaningful problems that may benefit from deep learning techniques.

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“…Hence, a method would be required that enables the automated identification of which subset of retained degrees of freedom of a given system preserves the majority of important detail from the reference, while at the same time reducing the complexity of the problem. In the literature, this task has been addressed through several different techniques, such as graph-theoretical analyses ( Webb et al, 2019 ), geometric criteria ( Bereau and Kremer, 2015 ), and machine learning algorithms ( Murtola et al, 2007 ; Wang and Bombarelli, 2019 ; Li et al, 2020 ). These efforts are rooted in the assumption that the optimal CG representation of a system can be determined solely by exploiting a subset of features of the latter.…”

Section: Introductionmentioning

confidence: 99%

“…An essential element of the proposed method is thus a graph-based representation of our object of interest, namely a protein. With their long and successful story both in the field of coarse-graining ( Gfeller and Rios, 2007 ; Webb et al, 2019 ; Li et al, 2020 ) and in the prediction of protein properties ( Borgwardt et al, 2005 ; Ralaivola et al, 2005 ; Micheli et al, 2007 ; Fout et al, 2017 ; Gilmer et al, 2017 ; Torng and Altman, 2019 ), graph-based learning models represent a rather natural and common choice to encode the (static) features of a molecular structure; here, we show that a graph-based machine learning approach can reproduce the results of mapping entropy estimate obtained by means of a much more time-consuming algorithmic workflow. To this end, we rely on Deep Graph Networks (DGNs) ( Bacciu et al, 2020 ), a family of machine learning models that learn from graph-structured data, where the graph has a variable size and topology; by training the model on a set of tuples (protein, CG mapping, and S map ), we can infer the S map values of unseen mappings associated with the same protein making use of a tiny fraction of the extensive amount of information employed in the original method, i.e., the molecular structure viewed as a graph.…”

Section: Introductionmentioning

confidence: 99%

A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins

Errica

Giulini

Bacciu

et al. 2021

Front. Mol. Biosci.

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The limits of molecular dynamics (MD) simulations of macromolecules are steadily pushed forward by the relentless development of computer architectures and algorithms. The consequent explosion in the number and extent of MD trajectories induces the need for automated methods to rationalize the raw data and make quantitative sense of them. Recently, an algorithmic approach was introduced by some of us to identify the subset of a protein’s atoms, or mapping, that enables the most informative description of the system. This method relies on the computation, for a given reduced representation, of the associated mapping entropy, that is, a measure of the information loss due to such simplification; albeit relatively straightforward, this calculation can be time-consuming. Here, we describe the implementation of a deep learning approach aimed at accelerating the calculation of the mapping entropy. We rely on Deep Graph Networks, which provide extreme flexibility in handling structured input data and whose predictions prove to be accurate and-remarkably efficient. The trained network produces a speedup factor as large as 105 with respect to the algorithmic computation of the mapping entropy, enabling the reconstruction of its landscape by means of the Wang–Landau sampling scheme. Applications of this method reach much further than this, as the proposed pipeline is easily transferable to the computation of arbitrary properties of a molecular structure.

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Graph neural network based coarse-grained mapping prediction

Abstract: The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice...

Cited by 42 publications

References 24 publications

Multi-body effects in a coarse-grained protein force field

Multi-body effects in a coarse-grained protein force field

Deep Learning in Protein Structural Modeling and Design

A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins

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