Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: Application to the human procarboxypeptidaseA2

Knowledge-based energy profiles combined with secondary structure predictio n have been applied to molecular modelling refinement. To check the procedu re, three different models of human procarboxypeptidase A2 (hPCPA2) have be en built using the 3D structures of procarboxypeptidase A1 (pPCPA1) and bov ine procarboxypeptidase A (bPCPA) as templates. The results of the refineme nt can be tested against the X-ray structure of hPCPA2 which has been recen tly determined. Regions miss-modelled in the activation segment of hPCPA2 w ere detected by means of pseudo-energies using Prosa II and modified afterw ards according to the secondary structure prediction. Moreover, models obta ined by automated methods as COMPOSER, MODELLER and distance restraints hav e also been compared, where it was found possible to find out the best mode l by means of pseudo-energies. Two general conclusions can be elicited from this work: (1) on a given set of putative models it is possible to disting uish among them the one closest to the crystallographic structure, and (2) within a given structure it is possible to find by means of pseudo-energies those regions that have been defectively modelled.