ArticleComputational and structural biotechnology journal2021
Representations of protein structure for exploring the conformational space: A speed-accuracy trade-off.
Article in Computational and structural biotechnology journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Deep learning-enhanced clustering and classification of protein molecule tertiary structures using weighted distance matrices.Briefings in bioinformatics · 2025Article
- An integrated protein structure fitness scoring approach for identifying native-like model structures.Computational and structural biotechnology journal · 2022Article
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3 authors.
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Abstract
The recent breakthrough in the field of protein structure prediction shows the relevance of using knowledge-based based scoring functions in combination with a low-resolution 3D representation of protein macromolecules. The choice of not using all atoms is barely supported by any data in the literature, and is mostly motivated by empirical and practical reasons, such as the computational cost of assessing the numerous folds of the protein conformational space. Here, we present a comprehensive study, carried on a large and balanced benchmark of predicted protein structures, to see how different types of structural representations rank in either accuracy or calculation speed, and which ones offer the best compromise between these two criteria. We tested ten representations, including low-resolution, high-resolution, and coarse-grained approaches. We also investigated the generalization of the findings to other formalisms than the widely-used "potential of mean force" (PMF) method. Thus, we observed that representing protein structures by their β carbons-combined or not with Cα-provides the best speed
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