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Varela, D.

Publications and source records attributed to Varela, D..

2 recordsLinked to original sources

Rescuing functional defects in a zebrafish model of CDKL5 deficiency disorder: Contribution to the identification of new therapeutic compounds

Mutations in the CDKL5 gene cause CDKL5 deficiency disorder (CDD), a severe neurodevelopmental encephalopathy characterized by a broad range of symptoms, including early-onset seizures, profound motor impairment and dysmorphic facial features. Current treatment options remain limited and largely focus on seizure management, which is often challenging to control, underscoring the critical need for new effective therapies. To identify potential novel candidate molecules for the treatment of CDD, we performed the first in vivo drug screening using a cdkl5 mutant zebrafish model. Recapitulating key features of the human disorder, cdkl5-/- larvae exhibit reduced locomotor behavior, providing a robust readout to assess therapeutic efficacy. By screening 170 compounds from MAPK Inhibitor and Histone Modification Libraries, both implicated in CDKL5 dysfunction, we identified 18 and 12 small molecules that partially or fully restored locomotor activity, respectively. Among these, fisetin, divalproex, resveratrol, and VX-702 were further evaluated for their capacity to rescue cdkl5-/- craniofacial defects and altered gene expression. Fisetin demonstrated the most consistent phenotypic improvement, including partial restoration of craniofacial abnormalities and normalization of gene expression levels. Future research aimed at elucidating the molecular mechanisms underlying the observed rescue effects will be critical to understand their mode of action. Overall, our study demonstrates the utility of this rapid and scalable zebrafish-based screening approach for therapeutic discovery in CDD and identifies promising therapeutic molecules that warrant further validation in complementary preclinical systems.

neuroscience↗

A memetic algorithm enables global all-atom protein-protein docking with sidechain flexibility

Protein complex formation is encoded by specific interactions at the atomic scale, but the computational cost of modeling proteins at this level often requires the use of simplified energy models and limited conformational flexibility. In particular, the use of all-atom energy functions, backbone and sidechain flexibility results in rugged energy landscapes that are difficult to explore. In this study we develop a protein-protein docking algorithm, EvoDOCK, that combine the strength of a differential evolution algorithm for efficient exploration of the global search space with the benefits of a local optimization method to refine detailed atomic interactions. EvoDOCK enabled accurate and fast local and global protein-protein docking using an all-atom energy function with side-chain flexibility. Comparison with a standard method built on Monte Carlo optimization demonstrated improved accuracy and with increases in computational speed of up to 35 times. The evolutionary algorithm also enabled efficient atomistic docking with backbone flexibility.

bioinformatics↗