Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: application to the human procarboxypeptidase A2.
作者： Patrick Aloy , José M. Mas , Marc A. Martí-Renom , Enrique Querol , Francesc X. Avilés
关键词: Set (abstract data type) 、 Procarboxypeptidase A2 、 Algorithm 、 Energy (signal processing) 、 Structure (category theory) 、 Template 、 Protein secondary structure 、 MODELLER 、 Work (thermodynamics) 、 Artificial intelligence 、 Computer science
摘要: Knowledge-based energy profiles combined with secondary structure prediction have been applied to molecular modelling refinement. To check the procedure, three different models of human procarboxypeptidase A2 (hPCPA2) built using 3D structures A1 (pPCPA1) and bovine A (bPCPA) as templates. The results refinement can be tested against X-ray hPCPA2 which has recently determined. Regions miss-modelled in activation segment were detected by means pseudo-energies Prosa II modified afterwards according prediction. Moreover, obtained automated methods COMPOSER, MODELLER distance restraints also compared, where it was found possible find out best model pseudo-energies. Two general conclusions elicited from this work: (1) on a given set putative is distinguish among them one closest crystallographic structure, (2) within those regions that defectively modelled.
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