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Improved protein model quality assessments by changing the target function
Author(s) -
Uziela Karolis,
Menéndez Hurtado David,
Shu Nanjiang,
Wallner Björn,
Elofsson Arne
Publication year - 2018
Publication title -
proteins: structure, function, and bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.699
H-Index - 191
eISSN - 1097-0134
pISSN - 0887-3585
DOI - 10.1002/prot.25492
Subject(s) - casp , superposition principle , computer science , quality (philosophy) , protein structure prediction , artificial intelligence , machine learning , function (biology) , set (abstract data type) , mathematics , protein structure , biology , physics , mathematical analysis , biochemistry , quantum mechanics , evolutionary biology , programming language
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.