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Improved protein model quality assessments by changing the target function
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
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Number of Authors: 52018 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 86, no 6, p. 654-663Article in journal (Refereed) Published
Abstract [en]

Protein modeling quality is an important part of protein structure prediction. We have for more than a decade developed a set of methods for this problem. We have used various types of description of the protein and different machine learning methodologies. However, common to all these methods has been the target function used for training. The target function in ProQ describes the local quality of a residue in a protein model. In all versions of ProQ the target function has been the S-score. However, other quality estimation functions also exist, which can be divided into superposition- and contact-based methods. The superposition-based methods, such as S-score, are based on a rigid body superposition of a protein model and the native structure, while the contact-based methods compare the local environment of each residue. Here, we examine the effects of retraining our latest predictor, ProQ3D, using identical inputs but different target functions. We find that the contact-based methods are easier to predict and that predictors trained on these measures provide some advantages when it comes to identifying the best model. One possible reason for this is that contact based methods are better at estimating the quality of multi-domain targets. However, training on the S-score gives the best correlation with the GDT_TS score, which is commonly used in CASP to score the global model quality. To take the advantage of both of these features we provide an updated version of ProQ3D that predicts local and global model quality estimates based on different quality estimates.

Place, publisher, year, edition, pages
2018. Vol. 86, no 6, p. 654-663
Keywords [en]
CASP, deep learning, estimation of model accuracy, model quality assessments, protein structure prediction
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:su:diva-156779DOI: 10.1002/prot.25492ISI: 000431734800006PubMedID: 29524250OAI: oai:DiVA.org:su-156779DiVA, id: diva2:1213127
Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-06-04Bibliographically approved

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Uziela, KarolisMenéndez Hurtado, DavidShu, NanjiangWallner, BjörnElofsson, Ame
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Department of Biochemistry and BiophysicsScience for Life Laboratory (SciLifeLab)
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Proteins: Structure, Function, and Bioinformatics
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