Abstract
Many template-based modeling (TBM) methods have been developed over the recent years that allow for protein structure prediction and for the study of structure-function relationships for proteins. One major problem all TBM algorithms face, however, is their unsatisfactory performance when proteins under consideration are low-homology. To improve the performance of TBM methods for such targets, a novel model evaluation method was developed here, and named MEFTop. Our novel method focuses on evaluating the topology by using two novel groups of features. These novel features included secondary structure element (SSE) contact information and 3-dimensional topology information. By combining MEFTop algorithm with FR-t5, a threading program developed by our group, we found that this modified TBM program, which was named FR-t5-M, exhibited significant improvements in predictive abilities for low-homology protein targets. We further showed that the MEFTop could be a generalized method to improve threading programs for low-homology protein targets. The softwares (FR-t5-M and MEFTop) are available to non-commercial users at our website: http://jianglab.ibp.ac.cn/lims/FRt5M/FRt5M.html.
Highlights
Template-based modeling is defined as modeling of protein structures based on already determined structure templates, and it is currently the most powerful prediction method
Of 63 targets on the testing set, there were 33 Top1 models with better quality selected according to the P-score, while 22 Top1 models with better quality selected according to Z-score
In order to improve low-homology protein modeling, we have developed a useful model evaluation method (MEFTop) by focusing on evaluating the native-likeness of topology
Summary
Template-based modeling is defined as modeling of protein structures based on already determined structure templates, and it is currently the most powerful prediction method. A wide range of tools was developed for the last step, the model quality evaluation [13,14,15,16,17,18,19,20,21,22,23,24]. The underlying reasons behind the bottleneck can be complicated, and include issues like incorrect template selection and sequencetemplate alignment, modeling errors, or a biased scoring function, to name a few. All together, these errors result in the failure of generating high-quality models, even in the presence of good templates in the template library at use
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