Dataset Information

MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.

ABSTRACT:

Background

Protein model quality assessment (QA) is an essential procedure in protein structure prediction. QA methods can predict the qualities of protein models and identify good models from decoys. Clustering-based methods need a certain number of models as input. However, if a pool of models are not available, methods that only need a single model as input are indispensable.

Results

We developed MASS, a QA method to predict the global qualities of individual protein models using random forests and various novel energy functions. We designed six novel energy functions or statistical potentials that can capture the structural characteristics of a protein model, which can also be used in other protein-related bioinformatics research. MASS potentials demonstrated higher importance than the energy functions of RWplus, GOAP, DFIRE and Rosetta when the scores they generated are used as machine learning features. MASS outperforms almost all of the four CASP11 top-performing single-model methods for global quality assessment in terms of all of the four evaluation criteria officially used by CASP, which measure the abilities to assign relative and absolute scores, identify the best model from decoys, and distinguish between good and bad models. MASS has also achieved comparable performances with the leading QA methods in CASP12 and CASP13.

Conclusions

MASS and the source code for all MASS potentials are publicly available at http://dna.cs.miami.edu/MASS/ .

SUBMITTER: Liu T

PROVIDER: S-EPMC7336608 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.

Liu Tong T Wang Zheng Z

BMC bioinformatics 20200706 Suppl 4

<h4>Background</h4>Protein model quality assessment (QA) is an essential procedure in protein structure prediction. QA methods can predict the qualities of protein models and identify good models from decoys. Clustering-based methods need a certain number of models as input. However, if a pool of models are not available, methods that only need a single model as input are indispensable.<h4>Results</h4>We developed MASS, a QA method to predict the global qualities of individual protein models usi ...[more]

PMID: 32631256

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Dataset Information

MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.

Background

Results

Conclusions

Publications

MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.

Similar Datasets

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