Elofsson, A. — Page 3 | bioRxiv Science

bioRxiv2017

Methods For Estimation Of Model Accuracy In CASP12

Methods for reliably estimating the quality of 3D models of proteins are essential drivers for the wide adoption and serious acceptance of protein structure predictions by life scientists. In this paper, the most successful groups in CASP12 describe their latest methods for Estimates of Model Accuracy (EMA). We show that pure single model accuracy estimation methods has shown clear progress since CASP11; the three top methods (MESHI, ProQ3, SVMQA) all perform better than the top method of CASP11 (ProQ2). The pure single model accuracy estimation methods outperform quasi-single (ModFOLD6 variations) and consensus methods (Pcons, ModFOLDclust2, Pcomb-domain and Wallner) in model selection, but are still not as good as those methods in absolute model quality estimation and predictions of local quality. Finally, we show that when using contact based model quality measures (CAD, 1DDT) the single model quality methods perform relatively better.

bioinformatics↗

bioRxiv2017

Large-Scale Structure Prediction By Improved Contact Predictions And Model Quality Assessment

MotivationAccurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. To what extent these predictions can be used to produce accurate models of the families is not known.\n\nResultsWe present the PconsFold2 pipeline that uses contact predictions from PconsC3, the CONFOLD folding algorithm and model quality estimations to predict the structure of a protein. We show that the model quality estimation significantly increases the number of models that reliably can be identified. Finally, we apply PconsFold2 to 6379 Pfam families of unknown structure and find that PconsFold2 can, with an estimated 90% specificity, predict the structure of up to 558 Pfam families of unknown structure. Out of these 415 have not been reported before.\n\nAvailabilityDatasets as well as models of all the 558 Pfam families are available at http://c3.pcons.net/. All programs used here are freely available.\n\nContactarne@bioinfo.se\n\nSupplementary informationNo supplementary data

bioinformatics↗

bioRxiv2017

High GC Content Causes De Novo Created Proteins to be Intrinsically Disordered

De novo creation of protein coding genes involves the formation of short ORFs from noncoding regions; some of these ORFs might then become fixed in the populationThese orphan proteins need to, at the bare minimum, not cause serious harm to the organism, meaning that they should for instance not aggregate. Therefore, although the creation of short ORFs could be truly random, the fixation should be subjected to some selective pressure. The selective forces acting on orphan proteins have been elusive, and contradictory results have been reported. In Drosophila young proteins are more disordered than ancient ones, while the opposite trend is present in yeast. To the best of our knowledge no valid explanation for this difference has been proposed.\n\nTo solve this riddle we studied structural properties and age of proteins in 187 eukaryotic organisms. We find that, with the exception of length, there are only small differences in the properties between proteins of different ages. However, when we take the GC content into account we noted that it could explain the opposite trends observed for orphans in yeast (low GC) and Drosophila (high GC). GC content is correlated with codons coding for disorder promoting amino acids. This leads us to propose that intrinsic disorder is not a strong determining factor for fixation of orphan proteins. Instead these proteins largely resemble random proteins given a particular GC level. During evolution the properties of a protein change faster than the GC level causing the relationship between disorder and GC to gradually weaken.\n\nAuthor SummaryWe show that the GC content of a genome is of great importance for the properties of an orphan protein. GC content affects the frequency of the codons and this affects the probability for each amino acid to be included in a de novo created protein. The codons encoding for Ala, Pro and Gly contain 80% GC, while codons for Lys, Phe, Asn, Tyr and Ile contain 20% or less. The three high GC amino acids are all disorder promoting, while Phe, Tyr and Ile are order promoting. Therefore, random protein sequences at a high GC will be more disordered than the ones created at a low GC. The structural properties of the youngest proteins match to a large degree the properties of random proteins when the GC content is taken into account. In contrast, structural properties of ancient proteins only show a weak correlation with GC content. This suggests that even after fixation in the population, proteins largely resemble random proteins given a certain GC content. Thereafter, during evolution the correlation between structural properties and GC weakens.

evolutionary biology↗