Improving the orientation-dependent statistical potential using a reference state

Liu, Yufeng; Zeng, Jianyang; Gong, H.

doi:10.1002/prot.24600

“…There have been multiple approaches developed over last 30 years. These include physics-based techniques [Randall andBaldi, 2008, Faraggi andKloczkowski, 2014], statistical and unsupervised methods, such as DFIRE [Zhou and Zhou, 2002], DOPE [Shen and Sali, 2006], GOAP [Zhou and Skolnick, 2011], RWplus [Zhang and Zhang, 2010], ORDER_AVE [Liu et al, 2014], VoroMQA [Olechnovič and Venclovas, 2014] and more, classical ML-approaches ModelEvaluator [Wang et al, 2009], ProQ2 [Ray et al, 2012], Wang_SVM [Liu et al, 2016], Qprob [Cao and Cheng, 2016], SBROD [Karasikov et al, 2019], a learning-to-rank technique [Jing et al, 2016], deep learning methods [Derevyanko et al, 2018, Conover et al, 2019, Sato and Ishida, 2019, Jing and Xu, 2020, Hiranuma et al, 2020, neural [Wallner and Elofsson, 2003], and graph neural networks [Baldassarre et al, 2020, Sanyal et al, 2020, Igashov et al, 2020.…”

Section: Related Workmentioning

confidence: 99%

Spherical convolutions on molecular graphs for protein model quality assessment

Igashov

¹

,

Pavlichenko

²

,

Grudinin

³

2021

Mach. Learn.: Sci. Technol.

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Processing information on 3D objects requires methods stable to rigid-body transformations, in particular rotations, of the input data. In image processing tasks, convolutional neural networks achieve this property using rotation-equivariant operations. However, contrary to images, graphs generally have irregular topology. This makes it challenging to define a rotation-equivariant convolution operation on these structures. In this work, we propose Spherical Graph Convolutional Network (S-GCN) that processes 3D models of proteins represented as molecular graphs. In a protein molecule, individual amino acids have common topological elements. This allows us to unambiguously associate each amino acid with a local coordinate system and construct rotation-equivariant spherical filters that operate on angular information between graph nodes. Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach. It is also comparable to state-of-the-art methods, as we demonstrate on Critical Assessment of Structure Prediction (CASP) benchmarks. The proposed technique operates only on geometric features of protein 3D models. This makes it universal and applicable to any other geometric-learning task where the graph structure allows constructing local coordinate systems. We will make the method available at https://team.inria.fr/nano-d/software/s-gcn/.

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“…There have been multiple approaches developed over last 30 years. These include physics-based techniques [22,23], statistical and unsupervised methods, such as DFIRE [24], DOPE [25], GOAP [26], RWplus [27], ORDER_AVE [28], VoroMQA [29] and more, classical ML-approaches ModelEvaluator [30], ProQ2 [13], Wang_SVM [31], Qprob [32], SBROD [21], a learning-to-rank technique [33], deep learning methods [15,16,17,34,35], neural [12], and graph neural networks [18,19,20].…”

Section: Related Workmentioning

confidence: 99%

Spherical convolutions on molecular graphs for protein model quality assessment

Igashov,

Pavlichenko,

Grudinin

2020

Preprint

0

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Processing information on 3D objects requires methods stable to rigid-body transformations, in particular rotations, of the input data. In image processing tasks, convolutional neural networks achieve this property using rotation-equivariant operations. However, contrary to images, graphs generally have irregular topology. This makes it challenging to define a rotation-equivariant convolution operation on these structures. In this work, we propose Spherical Graph Convolutional Network (S-GCN) that processes 3D models of proteins represented as molecular graphs. In a protein molecule, individual amino acids have common topological elements. This allows us to unambiguously associate each amino acid with a local coordinate system and construct rotation-equivariant spherical filters that operate on angular information between graph nodes. Within the framework of the protein model quality assessment problem, we demonstrate that the proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach. It is also comparable to state-of-the-art methods, as we demonstrate on Critical Assessment of Structure Prediction (CASP) benchmarks. The proposed technique operates only on geometric features of protein 3D models. This makes it universal and applicable to any other geometric-learning task where the graph structure allows constructing local coordinate systems.

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“…In addition to these two main types of QA methods, techniques combining both ideas have also been proposed (11,18), referred to as quasi-single model QA methods. Among recently proposed single-model QA methods, there are generally two main approaches to design a scoring function: physics-based and knowledge-based (data-driven) approaches (7,17). Physics-based scoring functions are constructed according to some physical knowledge of interactions in the system.…”

Section: Introductionmentioning

confidence: 99%

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

Karasikov

¹

,

Pagès

²

,

Grudinin

³

2018

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Motivation Protein quality assessment (QA) is a crucial element of protein structure prediction, a fundamental and yet open problem in structural bioinformatics. QA aims at ranking predicted protein models to select the best candidates. The assessment can be performed based either on a single model or on a consensus derived from an ensemble of models. The latter strategy can yield very high performance but substantially depends on the pool of available candidate models, which limits its applicability. Hence, single-model QA methods remain an important research target, also because they can assist the sampling of candidate models. Results We present a novel single-model QA method called SBROD. The SBROD (Smooth Backbone-Reliant Orientation-Dependent) method uses only the backbone protein conformation, and hence it can be applied to scoring coarse-grained protein models. The proposed method deduces its scoring function from a training set of protein models. The SBROD scoring function is composed of four terms related to different structural features: residue–residue orientations, contacts between backbone atoms, hydrogen bonding and solvent–solute interactions. It is smooth with respect to atomic coordinates and thus is potentially applicable to continuous gradient-based optimization of protein conformations. Furthermore, it can also be used for coarse-grained protein modeling and computational protein design. SBROD proved to achieve similar performance to state-of-the-art single-model QA methods on diverse datasets (CASP11, CASP12 and MOULDER). Availability and implementation The standalone application implemented in C++ and Python is freely available at https://gitlab.inria.fr/grudinin/sbrod and supported on Linux, MacOS and Windows. Supplementary information Supplementary data are available at Bioinformatics online.

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Improving the orientation-dependent statistical potential using a reference state

Cited by 10 publications

References 58 publications

Spherical convolutions on molecular graphs for protein model quality assessment

Spherical convolutions on molecular graphs for protein model quality assessment

Spherical convolutions on molecular graphs for protein model quality assessment

Smooth orientation-dependent scoring function for coarse-grained protein quality assessment

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