PDE3s belong to the phosphodiesterases family, where the PDE3A isoform is the major subtype in platelets involved in the cAMP regulation pathway of platelet aggregation. PDE3A inhibitors have been designed as potential antiplatelet agents. In this work, a homology model of PDE3A was developed and used to obtain the binding modes of bicyclic heteroaromatic pyridazinones and pyrazolones. Most of the studied compounds adopted similar orientations within the PDE3A active site, establishing hydrogen bonds with catalytic amino acids. Besides, the structure-activity relationship of the studied inhibitors was described by using a field-based 3D-QSAR method. Different structure alignment strategies were employed, including template-based and docking-based alignments. Adequate correlation models were obtained according to internal and external validations. In general, QSAR models revealed that steric and hydrophobic fields describe the different inhibitory activities of the compounds, where the hydrogen bond donor and acceptor fields have minor contributions. It should be stressed that structural elements of PDE3A inhibitors are reported here, through descriptions of their binding interactions and their differential affinities. In this sense, the present results could be useful in the future design of more specific and potent PDE3A inhibitors that may be used for the treatment of cardiovascular diseases.
<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>
<p>Although molecular dynamics simulations allow for the study of interactions among virtually all biomolecular entities, metal ions still pose significant challenges to achieve an accurate structural and dynamical description of many biological assemblies. This is particularly the case for coarse-grained (CG) models. Although the reduced computational cost of CG methods often makes them the technique of choice for the study of large biomolecular systems, the parameterization of metal ions is still very crude or simply not available for the vast majority of CG- force fields. Here, we show that incorporating statistical data retrieved from the Protein Data Bank (PDB) to set specific Lennard-Jones interactions can produce structurally accurate CG molecular dynamics simulations. Using this simple approach, we provide a set of interaction parameters for Calcium, Magnesium, and Zinc ions, which cover more than 80% of the metal-bound structures reported on the PDB. Simulations performed using the SIRAH force field on several proteins and DNA systems show that using the present approach it is possible to obtain non-bonded interaction parameters that obviate the use of topological constraints. </p>
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