A multi-template combination algorithm for protein comparative modeling

Here we develop an effective multi-template combination algorithm for protein comparative modeling. The algorithm selects templates according to the similarity significance of the alignments between template and target proteins. It combines the whole template-target alignments whose similarity significance score is close to that of the top template-target alignment within a threshold, whereas it only takes alignment fragments from a less similar template-target alignment that align with a sizable uncovered region of the target.We compare the algorithm with the traditional method of using a single top template on the 45 comparative modeling targets (i.e. easy template-based modeling targets) used in the seventh edition of Critical Assessment of Techniques for Protein Structure Prediction (CASP7). The multi-template combination algorithm improves the GDT-TS scores of predicted models by 6.8% on average. The statistical analysis shows that the improvement is significant (p-value < 10-4). Compared with the ideal approach that always uses the best template, the multi-template approach yields only slightly better performance. During the CASP7 experiment, the preliminary implementation of the multi-template combination algorithm (FOLDpro) was ranked second among 67 servers in the category of high-accuracy structure prediction in terms of GDT-TS measure.We have developed a novel multi-template algorithm to improve protein comparative modeling.Protein structure prediction is one of the most important problems in structural bioinformatics [1-3]. Comparative (or homology) modeling is currently the most accurate and practical structure prediction method [4-19].In general comparative modeling involves four steps [11,20,21]: (1) identify a homologous template protein for a target protein; (2) generate an alignment between the template and the target; (3) create a model based on the alignment and the template structure; (4) evaluate and refine the model. The two key factors determ