Improving the quality of protein structure models by selecting from alignment alternatives

Background: In the area of protein structure prediction, recently a lot of effort has gone into the development of Model Quality Assessment Programs (MQAPs). MQAPs distinguish high quality protein structure models from inferior models. Here, we propose a new method to use an MQAP to improve the quality of models. With a given target sequence and template structure, we construct a number of different alignments and corresponding models for the sequence. The quality of these models is scored with an MQAP and used to choose the most promising model. An SVMbased selection scheme is suggested for combining MQAP partial potentials, in order to optimize for improved model selection. Results: The approach has been tested on a representative set of proteins. The ability of the method to improve models was validated by comparing the MQAP-selected structures to the native structures with the model quality evaluation program TM-score. Using the SVM-based model selection, a significant increase in...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Ingolf Sommer, Stefano Toppo, Oliver Sander, Thomas Lengauer, Silvio C. E. Tosatto
• Keywords: BMCBI 2006, Model, Protein Structure, Protein Structure Prediction