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Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: Application to the human procarboxypeptidase A2

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Abstract

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) have been built using the 3D structures of procarboxypeptidase A1 (pPCPA1) and bovine procarboxypeptidase A (bPCPA) as templates. The results of the refinement can be tested against the X-ray structure of hPCPA2 which has been recently determined. Regions miss-modelled in the activation segment of hPCPA2 were detected by means of pseudo-energies using Prosa II and modified afterwards according to the secondary structure prediction. Moreover, models obtained by automated methods as COMPOSER, MODELLER and distance restraints have also been compared, where it was found possible to find out the best model 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 distinguish 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.

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Authors and Affiliations

  1. Institut de Biologia Fonamental and Departament de Bioquímica, Universitat Autònoma de Barcelona, Spain

    Patrick Aloy, José M. Mas, Marc A. Martí-Renom, Enrique Querol, Francesc X. Avilés & Baldomero Oliva

  2. 08193 Bellaterra, Barcelona, Spain

    Patrick Aloy, José M. Mas, Marc A. Martí-Renom, Enrique Querol, Francesc X. Avilés & Baldomero Oliva

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  1. Patrick Aloy
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  2. José M. Mas
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  3. Marc A. Martí-Renom
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  4. Enrique Querol
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  5. Francesc X. Avilés
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  6. Baldomero Oliva
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Aloy, P., Mas, J.M., Martí-Renom, M.A. et al. Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: Application to the human procarboxypeptidase A2. J Comput Aided Mol Des 14, 83–92 (2000). https://doi.org/10.1023/A:1008197831529

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