A global machine learning based scoring function for protein structure prediction
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TL;DR: A knowledge-based function to score protein decoys based on their similarity to native structure, which was ranked third for all targets, and second for freely modeled hard targets among about 200 groups for top model prediction.
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About: This article is published in Biophysical Journal. The article was published on 01 May 2014. and is currently open access. The article focuses on the topics: CASP & Protein structure prediction.
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Spherical convolutions on molecular graphs for protein model quality assessment
Ilia Igashov,Ilia Igashov,Nikita Pavlichenko,Sergei Grudinin +3 more
- 06 Jan 2021
TL;DR: The proposed spherical convolution method significantly improves the quality of model assessment compared to the standard message-passing approach and is comparable to state-of-the-art methods on Critical Assessment of Structure Prediction (CASP) benchmarks.
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Spherical convolutions on molecular graphs for protein model quality assessment
TL;DR: Spherical Graph Convolutional Network (S-GCNets) as discussed by the authors constructs rotation-equivariant spherical filters that operate on angular information between graph nodes to process 3D models of proteins represented as molecular graphs.
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