||Knowledge-based methods are, at present, the most effective ones for the prediction of protein structures; however, their results heavily depend on the similarity of a target sequence to those of proteins with known structures. On the other hand, the physics-based methods, although still less accurate and more expensive to execute, are independent of databases and give reasonable results where the knowledge-based methods fail because of weak sequence similarity. Therefore, a plausible approach seems to be the use of knowledge-based methods to determine the sections of the structures that correspond to sufficient sequence similarity and physics-based methods to determine the remaining structure. By participating in the 12th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP12) as the KIAS-Gdansk group, we tested our recently developed hybrid approach, in which protein-structure prediction is carried out by using the physics based UNRES coarse-grained energy function, with restraints derived from the server models. Best predictions among all groups were obtained for 2 targets and 80% of our models were in the upper 50% of the models submitted to CASP. Our method was also able to exclude, with about 70% confidence, the information from the servers that performed poorly on a given target. Moreover, the method resulted in the best models of 2 refinement targets and performed remarkably well on oligomeric targets. (C) 2018 Elsevier Inc. All rights reserved.