Homology Modeling and Molecular Dynamics Simulations ofTrypanosoma cruziPhosphodiesterase b1

Abstract

Cyclic nucleotide phosphodiesterases have been implicated in the proliferation, differentiation and osmotic regulation of trypanosomatids; in some trypanosomatid species, they have been validated as molecular targets for the development of new therapeutic agents. Because the experimental structure of Trypanosoma cruzi PDEb1 (TcrPDEb1) has not been solved so far, an homology model of the target was created using the structure of Trypanosoma brucei PDEb1 (TbrPDEb1) as a template. The model was refined by extensive enhanced sampling molecular dynamics simulations, and representative snapshots were extracted from the trajectory by combined clustering analysis. This structural ensemble was used to develop a structure-based docking model of the target. The docking accuracy of the model was validated by redocking and cross-docking experiments using all available crystal structures of TbrPDEb1, whereas the scoring accuracy was validated through a retrospective screen, using a carefully curated dataset of compounds assayed against TbrPDEb1 and/or TcrPDEb1. Considering the results from in silico validations, the model may be applied in prospective virtual screening campaigns to identify novel hits, as well as to guide the rational design of potent and selective inhibitors targeting this enzyme.

Identification of Enolase 1 as a Potential Target for Magnaporthe oryzae: Integrated Proteomic and Molecular Dynamics Simulation

“…oryzae ENO1 structure prediction, with a sequence identity of 73.15% (Figure ). Based on matching results, we performed comparative modeling of protein structures using the SWISS-MODEL online server, which can automatically generate energy-minimized protein models and satisfy the spatial constraints of bond distances and dihedral angles . The established 3D structure was evaluated by the Ramachandran plot analysis and VERIFY 3D of the SAVES online server ().…”

A Combined Ligand- and Structure-Based Virtual Screening To Identify Novel NaV1.2 Blockers: In Vitro Patch Clamp Validation and In Vivo Anticonvulsant Activity

“…Preceding the rescoring, complexes were minimized considering protein side chains within 3 Å of the docked ligand as flexible. This postprocessing approach has been shown to improve the scoring power of docking models on other drug targets. , The performance of all docking models was evaluated in terms of Kendall’s Tau_B by comparing the experimental (pIC 50 ) and docking predicted affinity rankings . The uncertainty in each correlation coefficient was assessed by 1000 iterations of resampling with replacement (bootstrapping).…”

Descubrimiento de nuevos anticonvulsivos que actúan mediante interacciones con canales iónicos

“…Este re-scoring es precedido por una minimización de los complejos, considerando como flexibles las cadenas laterales del sitio activo que se encuentran a 3 Å o menos del ligando en cuestión. En todos los casos se encontró que este post-procesamiento mejora el scoring power de los modelos, algo que ya hemos observado en investigaciones previas (Castro et al 2020;Llanos et al , 2022a. Para el canal TRPV1, el re-scoring con la función de puntuación ad4 resultó ser el más apropiado, mientras que para el canal NaV1.2 fue el re-scoring con vinardo.…”