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RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold.
PLoS One 2019; 14(10):e0218149Plos

Abstract

While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources.

Authors+Show Affiliations

Department of Statistics, University of Oxford, Oxford, England, United Kingdom.SLAC National Accelerator Laboratory, Stanford University, Menlo Park, California, United States of America. Bioengineering, Stanford University, Stanford, California, United States of America.Department of Statistics, University of Oxford, Oxford, England, United Kingdom.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31634369

Citation

West, Clare E., et al. "RFQAmodel: Random Forest Quality Assessment to Identify a Predicted Protein Structure in the Correct Fold." PloS One, vol. 14, no. 10, 2019, pp. e0218149.
West CE, de Oliveira SHP, Deane CM. RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold. PLoS ONE. 2019;14(10):e0218149.
West, C. E., de Oliveira, S. H. P., & Deane, C. M. (2019). RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold. PloS One, 14(10), pp. e0218149. doi:10.1371/journal.pone.0218149.
West CE, de Oliveira SHP, Deane CM. RFQAmodel: Random Forest Quality Assessment to Identify a Predicted Protein Structure in the Correct Fold. PLoS ONE. 2019;14(10):e0218149. PubMed PMID: 31634369.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - RFQAmodel: Random Forest Quality Assessment to identify a predicted protein structure in the correct fold. AU - West,Clare E, AU - de Oliveira,Saulo H P, AU - Deane,Charlotte M, Y1 - 2019/10/21/ PY - 2019/05/23/received PY - 2019/10/02/accepted PY - 2019/10/22/entrez PY - 2019/10/22/pubmed PY - 2019/10/22/medline SP - e0218149 EP - e0218149 JF - PloS one JO - PLoS ONE VL - 14 IS - 10 N2 - While template-free protein structure prediction protocols now produce good quality models for many targets, modelling failure remains common. For these methods to be useful it is important that users can both choose the best model from the hundreds to thousands of models that are commonly generated for a target, and determine whether this model is likely to be correct. We have developed Random Forest Quality Assessment (RFQAmodel), which assesses whether models produced by a protein structure prediction pipeline have the correct fold. RFQAmodel uses a combination of existing quality assessment scores with two predicted contact map alignment scores. These alignment scores are able to identify correct models for targets that are not otherwise captured. Our classifier was trained on a large set of protein domains that are structurally diverse and evenly balanced in terms of protein features known to have an effect on modelling success, and then tested on a second set of 244 protein domains with a similar spread of properties. When models for each target in this second set were ranked according to the RFQAmodel score, the highest-ranking model had a high-confidence RFQAmodel score for 67 modelling targets, of which 52 had the correct fold. At the other end of the scale RFQAmodel correctly predicted that for 59 targets the highest-ranked model was incorrect. In comparisons to other methods we found that RFQAmodel is better able to identify correct models for targets where only a few of the models are correct. We found that RFQAmodel achieved a similar performance on the model sets for CASP12 and CASP13 free-modelling targets. Finally, by iteratively generating models and running RFQAmodel until a model is produced that is predicted to be correct with high confidence, we demonstrate how such a protocol can be used to focus computational efforts on difficult modelling targets. RFQAmodel and the accompanying data can be downloaded from http://opig.stats.ox.ac.uk/resources. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/31634369/RFQAmodel:_Random_Forest_Quality_Assessment_to_identify_a_predicted_protein_structure_in_the_correct_fold L2 - http://dx.plos.org/10.1371/journal.pone.0218149 DB - PRIME DP - Unbound Medicine ER -