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Abstract <p>A full-length tertiary structure of the poly(ADP-ribose)-polymerase <b>1</b> (PARP1) enzyme is dynamically predicted by molecular modeling methods. The prediction is performed using known tools as well as machine learning and homology construction methods. Positions of the C<sub>α</sub> atoms of amino acid residues in the predicted structures are compared with experimentally determined geometric parameters of enzyme domains reported earlier in scientific literature and deposited to the Protein Data Bank. The obtained results can be used to make a rational choice of an appropriate prediction tool and to apply the PARP1 geometric parameters to construct dimeric forms of this enzyme and develop novel PARP1 inhibitors.</p>

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Predicting the PARP1 Tertiary Structure by Molecular Modeling Methods

  • 期刊论文

  • 发表日期：

    2025年5月1日

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  • E. A. Mustaev,
  • E. M. Khamitov

摘要

Abstract

A full-length tertiary structure of the poly(ADP-ribose)-polymerase 1 (PARP1) enzyme is dynamically predicted by molecular modeling methods. The prediction is performed using known tools as well as machine learning and homology construction methods. Positions of the Cα atoms of amino acid residues in the predicted structures are compared with experimentally determined geometric parameters of enzyme domains reported earlier in scientific literature and deposited to the Protein Data Bank. The obtained results can be used to make a rational choice of an appropriate prediction tool and to apply the PARP1 geometric parameters to construct dimeric forms of this enzyme and develop novel PARP1 inhibitors.