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Karczyń,ska, Agnieszka,Mozolewska, Magdalena A.,Krupa, Paweł,Giełdoń,, Artur,Bojarski, Krzysztof K.,Zaborowski, Bartłomiej,Liwo, Adam,Ś,lusarz, Rafał,Ś,lusarz, Magdalena,Lee, Jooyo Elsevier 2018 Journal of molecular graphics & modelling Vol.83 No.-
<P><B>Abstract</B></P> <P>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.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Hybrid (physics- and template-based) approach to protein structure prediction. </LI> <LI> Molecular dynamics with coarse-grained UNRES model, restraints from templates. </LI> <LI> Restraints derived from similar fragments of multiple templates. </LI> <LI> Method able filter out information from poor templates (70% confidence). </LI> <LI> Outstanding prediction obtained for some targets. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
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