Dataset Information

Fast and effective protein model refinement using deep graph neural networks.

ABSTRACT: Protein model refinement is the last step applied to improve the quality of a predicted protein model. Currently the most successful refinement methods rely on extensive conformational sampling and thus, take hours or days to refine even a single protein model. Here we propose a fast and effective model refinement method that applies GNN (graph neural networks) to predict refined inter-atom distance probability distribution from an initial model and then rebuilds 3D models from the predicted distance distribution. Tested on the CASP (Critical Assessment of Structure Prediction) refinement targets, our method has comparable accuracy as two leading human groups Feig and Baker, but runs substantially faster. Our method may refine one protein model within ~11 minutes on 1 CPU while Baker needs ~30 hours on 60 CPUs and Feig needs ~16 hours on 1 GPU. Finally, our study shows that GNN outperforms ResNet (convolutional residual neural networks) for model refinement when very limited conformational sampling is allowed.

SUBMITTER: Jing X

PROVIDER: S-EPMC8939834 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

Fast and effective protein model refinement using deep graph neural networks.

Jing Xiaoyang X Xu Jinbo J

Nature computational science 20210715 7

Protein model refinement is the last step applied to improve the quality of a predicted protein model. Currently the most successful refinement methods rely on extensive conformational sampling and thus, take hours or days to refine even a single protein model. Here we propose a fast and effective model refinement method that applies GNN (graph neural networks) to predict refined inter-atom distance probability distribution from an initial model and then rebuilds 3D models from the predicted dis ...[more]

PMID: 35321360

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Project description:BackgroundIn recent years, there has been an increase of interest in plant behaviour as represented by growth-driven responses. These are generally classified into nastic (internally driven) and tropic (environmentally driven) movements. Nastic movements include circumnutations, a circular movement of plant organs commonly associated with search and exploration, while tropisms refer to the directed growth of plant organs toward or away from environmental stimuli, such as light and gravity. Tracking these movements is therefore fundamental for the study of plant behaviour. Convolutional neural networks, as used for human and animal pose estimation, offer an interesting avenue for plant tracking. Here we adopted the Social LEAP Estimates Animal Poses (SLEAP) framework for plant tracking. We evaluated it on time-lapse videos of cases spanning a variety of parameters, such as: (i) organ types and imaging angles (e.g., top-view crown leaves vs. side-view shoots and roots), (ii) lighting conditions (full spectrum vs. IR), (iii) plant morphologies and scales (100 μm-scale Arabidopsis seedlings vs. cm-scale sunflowers and beans), and (iv) movement types (circumnutations, tropisms and twining).ResultsOverall, we found SLEAP to be accurate in tracking side views of shoots and roots, requiring only a low number of user-labelled frames for training. Top views of plant crowns made up of multiple leaves were found to be more challenging, due to the changing 2D morphology of leaves, and the occlusions of overlapping leaves. This required a larger number of labelled frames, and the choice of labelling "skeleton" had great impact on prediction accuracy, i.e., a more complex skeleton with fewer individuals (tracking individual plants) provided better results than a simpler skeleton with more individuals (tracking individual leaves).ConclusionsIn all, these results suggest SLEAP is a robust and versatile tool for high-throughput automated tracking of plants, presenting a new avenue for research focusing on plant dynamics.

Dataset Information

Fast and effective protein model refinement using deep graph neural networks.

Publications

Fast and effective protein model refinement using deep graph neural networks.

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