Abstract
Methods that reliably estimate the likely similarity between the predicted and native structures of proteins have become essential for driving the acceptance and adoption of three-dimensional protein models by life scientists. ModFOLD6 is the latest version of our leading resource for Estimates of Model Accuracy (EMA), which uses a pioneering hybrid quasi-single model approach. The ModFOLD6 server integrates scores from three pure-single model methods and three quasi-single model methods using a neural network to estimate local quality scores. Additionally, the server provides three options for producing global score estimates, depending on the requirements of the user: (i) ModFOLD6_rank, which is optimized for ranking/selection, (ii) ModFOLD6_cor, which is optimized for correlations of predicted and observed scores and (iii) ModFOLD6 global for balanced performance. The ModFOLD6 methods rank among the top few for EMA, according to independent blind testing by the CASP12 assessors. The ModFOLD6 server is also continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers. The ModFOLD6 server is freely available at: http://www.reading.ac.uk/bioinf/ModFOLD/.
Highlights
Predicted three-dimensional (3D) models of proteins are routinely relied upon to drive research across the life sciences, mainly due to the expense and time limitations of determining structures experimentally. 3D models are comparatively quick to produce and can often be of sufficiently high quality
The ModFOLD6 server is continuously automatically evaluated as part of the CAMEO project, where significant performance gains have been observed compared to our previous server and other publicly available servers
With all predictions there is some level of uncertainty, and accurate methods for model quality assessment have become necessary for driving the acceptance of structure prediction methods
Summary
Predicted three-dimensional (3D) models of proteins are routinely relied upon to drive research across the life sciences, mainly due to the expense and time limitations of determining structures experimentally. 3D models are comparatively quick to produce and can often be of sufficiently high quality. The model quality assessment field has its roots in early structure validation tools [1,2,3]. Such tools can be used to perform basic stereochemical checks, and they are very useful in identifying unusual geometric features in a model. Such methods are not able to produce a single global score that can be used for ranking alternative models or discriminating good models from bad (often bad models will still have good stereochemistry). Pure-single model methods are less accurate overall, but they are more rapid, they produce consistent scores for single or few models at a time and they often perform better at model ranking and selection
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
