Template-based protein structure modeling using TASSER

Despite considerable efforts to develop accurate template free approaches, template-based protein structure modeling is still the only reliable protein structure modeling method.1 Templatebased modeling involves (1) template identification, usually by threading; (2) construction of the target-template alignment; (3) model generation based on this alignment; and (4) refinement of the template models with the goal of generating closer structures to the target native state than that provided by the template. The accuracy of the final model depends on the alignment accuracy between the template and target as well as the quality of the subsequent refinement. An ideal threading method should identify the best templates, namely, those with the best structural similarity to the native structure. In practice, this is not always the case.2 Furthermore, the target-template alignment may not be optimal, especially for hard targets.3 Hard targets are those with poor quality threading alignments and good/poor quality structure alignments, respectively. Advances have been made in increasing the accuracy of template identification and threading alignment by going from single sequence alignments4 to profileprofile alignments,5–12 to machine learning,13,14 and metathreading.6,15,16 Recent studies show that using multiple templates yields better models than those obtained from a single best template approach.17–22 Results from the latest critical assessment of protein structure prediction (CASP9)23 show that the top ranked automatic servers all used multiple template information in template-based modeling.23 The Zhang-Server,24 which performed well across all levels of target difficulty, employed a locally installed metaserver, LOMETS,25 that uses more than eight individual threading methods for template identification/target alignment and TASSER17 for refinement. HHpredA,26,27 which performed well for easy targets, but moderately well for hard targets, used an updated method for hidden Markov profile generation and a neural network trained to filter distance restraints from multiple templates.23 The final models are built by MODELLER28 using filtered distance restraints. RaptorX,29,30 however, performed