Filomeno Sánchez Rodríguez

Using deep learning predictions reveals a large number of register errors in PDB deposits

The paper identifies thousands of register errors in PDB structures using deep learning predictions and shows corrections improve refinement statistics.

Deep-learning protein structure predictions suggest likely molecular functions for three uncharacterised polytopic membrane proteins from the P. falciparum apicoplast

The paper uses AlphaFold2 to predict structures of three uncharacterized apicoplast proteins and suggests their potential molecular functions as transporters.

Using deep learning predictions of inter-residue distances for model validation

The paper introduces validation methods using deep learning-predicted inter-residue distances to detect and correct sequence register errors in protein models.

In silico prediction of structure and function for a large family of transmembrane proteins that includes human Tmem41b

The paper uses structural bioinformatics to predict the structure and function of Tmem41b and its homologues, identifying them as potential transporters with H+ antiporter activity.

Helical ensembles out-perform ideal helices in Molecular Replacement

This paper introduces helical ensembles as improved search models for molecular replacement, achieving a 30% increase in solutions and faster convergence compared to ideal helices.