Poly(ADP-ribose)polymerase 1 (PARP1) is a key target for treatment of cancer-related diseases. Detailed structural changes DBD in PARP1 during the binding process with DNA were investigated and the dynamic conformational differences of DBD caused by zinc ions were revealed.

Accurately predicting the interaction modes for metalloproteins remains extremely challenging in structure-based drug design and mechanism analysis of enzymatic catalysis due to the complexity of metal coordination in metalloproteins. Here, we report a docking method for metalloproteins based on geometric probability (GPDOCK) with unprecedented accuracy. The docking tests of 10 common metal ions with 9360 metalloprotein–ligand complexes demonstrate that GPDOCK has an accuracy of 94.3% in predicting binding pose. What is more, it can accurately realize the docking of metalloproteins with ligand when one or two water molecules are engaged in the metal ion coordination. Since GPDOCK only depends on the three-dimensional structure of metalloprotein and ligand, structure-based machine learning model is employed for the scoring of binding poses, which significantly improves computational efficiency. The proposed docking strategy can be an effective and efficient tool for drug design and further study of binding mechanism of metalloproteins. The manual of GPDOCK and the code for the logistical regression model used to re-rank the docking results are available at https://github.com/wangkai-zhku/GPDOCK.git.

The catalytic (CAT) domain is a key region of poly (ADP-ribose) polymerase 1 (PARP1), which has crucial interactions with inhibitors, DNA, and other domains of PARP1. To facilitate the development of potential inhibitors of PARP1, it is of great significance to clarify the differences in structural dynamics and key residues between CAT/inhibitors and DNA/PARP1/inhibitors through structure-based computational design. In this paper, conformational changes in PAPR1 and differences in key residue interactions induced by inhibitors were revealed at the molecular level by comparative molecular dynamics (MD) simulations and energy decomposition. On one hand, PARP1 inhibitors indirectly change some residues of the CAT domain which interact with DNA and other domains. Furthermore, the interaction between ligands and catalytic binding sites can be transferred to the DNA recognition domain of PARP1 by a strong negative correlation movement among multi-domains of PARP1. On the other hand, it is not reliable to use the binding energy of CAT/ligand as a measure of ligand activity, because it may in some cases differs greatly from the that of PARP1/DNA/ligand. For PARP1/DNA/ligand, the stronger the binding stability between the ligand and PARP1, the stronger the binding stability between PARP1 and DNA. The findings of this work can guide further novel inhibitor design and the structural modification of PARP1 through structure-based computational design.