A “FRankenstein's monster” approach to comparative modeling: Merging the finest fragments of Fold‐Recognition models and iterative model refinement aided by 3D structure evaluation

We applied a new multi‐step protocol to predict the structures of all targets during CASP5, regardless of their potential category. 1) We used diverse fold‐recognition (FR) methods to generate initial target‐template alignments, which were converted into preliminary full‐atom models by comparative modeling. All preliminary models were evaluated (scored) by VERIFY3D to identify well‐ and poorly‐folded fragments. 2) Preliminary models with similar 3D folds were superimposed, poorly‐scoring regions were deleted and the “average model” structure was created by merging the remaining segments. All template structures reported by FR were superimposed and a composite multiple‐structure template was created from the most conserved fragments. 3). The average model was superimposed onto the composite template and the structure‐based target‐template alignment was inferred. This alignment was used to build a new (intermediate) comparative model of the target, again scored with VERIFY3D. 4) For all poorly scoring regions series of alternative alignments were generated by progressively shifting the “unfit” sequence fragment in either direction. Here, we considered additional information, such as secondary structure, placement of insertions and deletions in loops, conservation of putative catalytic residues, and the necessity to obtain a compact, well‐folded structure. For all alternative alignments, new models were built and evaluated. 5) All models were superimposed and the “FRankenstein's monster” (FR, fold recognition) model was built from best‐scoring segments. The final model was obtained after limited energy minimization to remove steric clashes between sidechains from different fragments. The novelty of this approach is in the focus on “vertical” recombination of structure fragments, typical for the ab initio field, rather than “horizontal” sequence alignment typical for comparative modeling. We tested the usefulness of the “FRankenstein” approach for non‐expert predictors: only the leader of our team had considerable experience in protein modeling ‐ he registered as a separate group (020) and submitted models built only by himself. At the onset of CASP5, the other five members of the team (students) had very little or no experience with modeling. They followed the same protocol in a deliberately naïve way. In the fourth step they used solely the VERIFY3D criterion to compare their models and the leader's model (the latter regarded only as one of the many alternatives) and generated the hybrid or selected only one model for submission (group 517). In order to compare our protocol with the traditional “one target‐one template‐one alignment” approach, we submitted (as a separate group 242) models selected from those automatically generated by all CAFASP servers (i.e. obtained without any human intervention). Here, we compare the results obtained by the three “groups”, describe successes and failures of the “FRankenstein” approach and discuss future developments of comparative modeling. The automatic version of our multi‐step protocol is being developed as a meta‐server; the prototype is freely available at http://genesilico.pl/meta/. Proteins 2003;53:369–379. © 2003 Wiley‐Liss, Inc.