ArticleF1000Research2014
Ranking the quality of protein structure models using sidechain based network properties.
Article in F1000Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- An information gain-based approach for evaluating protein structure models.Computational and structural biotechnology journal · 2020Article
- In Silico evaluation and identification of fungi capable of producing endo-inulinase enzyme.PloS one · 2018Article
- MQAPRank: improved global protein model quality assessment by learning-to-rank.BMC bioinformatics · 2017Article
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Authors and funding
2 authors.
Funding
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Abstract
Determining the correct structure of a protein given its sequence still remains an arduous task with many researchers working towards this goal. Most structure prediction methodologies result in the generation of a large number of probable candidates with the final challenge being to select the best amongst these. In this work, we have used Protein Structure Networks of native and modeled proteins in combination with Support Vector Machines to estimate the quality of a protein structure model and finally to provide ranks for these models. Model ranking is performed using regression analysis and helps in model selection from a group of many similar and good quality structures. Our results show that structures with a rank greater than 16 exhibit native protein-like properties while those below 10 are non-native like. The tool is also made available as a web-server ( http://vishgraph.mbu.iisc.ernet.in/GraProStr/native_non_native_ranking.html), where, 5 modelled structures can be evaluated at a given time.
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Registered trials
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